From 3f844cac42c7481cffd8ce0b061a1443909dc073 Mon Sep 17 00:00:00 2001
From: City <125218114+city96@users.noreply.github.com>
Date: Tue, 10 Dec 2024 22:19:02 +0100
Subject: [PATCH 01/15] PixArt initial rewrite

---
 PixArt/LICENSE-PixArt                         | 661 -----------------
 PixArt/conf.py                                | 140 ----
 PixArt/config.py                              | 137 ++++
 PixArt/diffusers_convert.py                   | 308 +++-----
 PixArt/loader.py                              | 238 ++-----
 PixArt/lora.py                                | 146 ----
 T5/LICENSE-T5 => PixArt/model/LICENSE         |   2 +-
 PixArt/model/__init__.py                      |   0
 .../PixArt_blocks.py => model/blocks.py}      |   0
 PixArt/{models/PixArt.py => model/pixart.py}  |   2 +-
 .../{models/PixArtMS.py => model/pixartms.py} |   4 +-
 PixArt/{models => model}/utils.py             |   0
 PixArt/models/pixart_controlnet.py            | 312 --------
 PixArt/nodes.py                               | 353 +++------
 T5/LICENSE-ComfyUI                            | 674 ------------------
 T5/loader.py                                  | 118 ---
 T5/nodes.py                                   |  95 ---
 T5/t5v11-xxl_config.json                      |  31 -
 T5/t5v11.py                                   | 227 ------
 __init__.py                                   |  71 +-
 nodes.py                                      |  34 +
 nodes/pixart.py                               |   0
 text_encoders/nodes.py                        |  37 +
 text_encoders/pixart/tenc.py                  |  41 ++
 text_encoders/tenc.py                         |  72 ++
 .../t5_tokenizer/special_tokens_map.json      |   0
 .../tokenizers}/t5_tokenizer/spiece.model     | Bin
 .../t5_tokenizer/tokenizer_config.json        |   0
 28 files changed, 610 insertions(+), 3093 deletions(-)
 delete mode 100644 PixArt/LICENSE-PixArt
 delete mode 100644 PixArt/conf.py
 create mode 100644 PixArt/config.py
 delete mode 100644 PixArt/lora.py
 rename T5/LICENSE-T5 => PixArt/model/LICENSE (99%)
 create mode 100644 PixArt/model/__init__.py
 rename PixArt/{models/PixArt_blocks.py => model/blocks.py} (100%)
 rename PixArt/{models/PixArt.py => model/pixart.py} (98%)
 rename PixArt/{models/PixArtMS.py => model/pixartms.py} (95%)
 rename PixArt/{models => model}/utils.py (100%)
 delete mode 100644 PixArt/models/pixart_controlnet.py
 delete mode 100644 T5/LICENSE-ComfyUI
 delete mode 100644 T5/loader.py
 delete mode 100644 T5/nodes.py
 delete mode 100644 T5/t5v11-xxl_config.json
 delete mode 100644 T5/t5v11.py
 create mode 100644 nodes.py
 create mode 100644 nodes/pixart.py
 create mode 100644 text_encoders/nodes.py
 create mode 100644 text_encoders/pixart/tenc.py
 create mode 100644 text_encoders/tenc.py
 rename {T5 => text_encoders/tokenizers}/t5_tokenizer/special_tokens_map.json (100%)
 rename {T5 => text_encoders/tokenizers}/t5_tokenizer/spiece.model (100%)
 rename {T5 => text_encoders/tokenizers}/t5_tokenizer/tokenizer_config.json (100%)

diff --git a/PixArt/LICENSE-PixArt b/PixArt/LICENSE-PixArt
deleted file mode 100644
index 0ad25db..0000000
--- a/PixArt/LICENSE-PixArt
+++ /dev/null
@@ -1,661 +0,0 @@
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diff --git a/PixArt/conf.py b/PixArt/conf.py
deleted file mode 100644
index 128146f..0000000
--- a/PixArt/conf.py
+++ /dev/null
@@ -1,140 +0,0 @@
-"""
-List of all PixArt model types / settings
-"""
-
-sampling_settings = {
-	"beta_schedule" : "sqrt_linear",
-	"linear_start"  : 0.0001,
-	"linear_end"    : 0.02,
-	"timesteps"     : 1000,
-}
-
-pixart_conf = {
-	"PixArtMS_XL_2": { # models/PixArtMS
-		"target": "PixArtMS",
-		"unet_config": {
-			"input_size"  : 1024//8,
-			"depth"       :   28,
-			"num_heads"   :   16,
-			"patch_size"  :    2,
-			"hidden_size" : 1152,
-			"pe_interpolation": 2,
-		},
-		"sampling_settings" : sampling_settings,
-	},
-	"PixArtMS_Sigma_XL_2": {
-		"target": "PixArtMSSigma",
-		"unet_config": {
-			"input_size"  : 1024//8,
-			"token_num"   :  300,
-			"depth"       :   28,
-			"num_heads"   :   16,
-			"patch_size"  :    2,
-			"hidden_size" : 1152,
-			"micro_condition": False,
-			"pe_interpolation": 2,
-			"model_max_length": 300,
-		},
-		"sampling_settings" : sampling_settings,
-	},
-	"PixArtMS_Sigma_XL_2_900M": {
-		"target": "PixArtMSSigma",
-		"unet_config": {
-			"input_size": 1024 // 8,
-			"token_num": 300,
-			"depth": 42,
-			"num_heads": 16,
-			"patch_size": 2,
-			"hidden_size": 1152,
-			"micro_condition": False,
-			"pe_interpolation": 2,
-			"model_max_length": 300,
-		},
-		"sampling_settings": sampling_settings,
-	},
-	"PixArtMS_Sigma_XL_2_2K": {
-		"target": "PixArtMSSigma",
-		"unet_config": {
-			"input_size"  : 2048//8,
-			"token_num"   :  300,
-			"depth"       :   28,
-			"num_heads"   :   16,
-			"patch_size"  :    2,
-			"hidden_size" : 1152,
-			"micro_condition": False,
-			"pe_interpolation": 4,
-			"model_max_length": 300,
-		},
-		"sampling_settings" : sampling_settings,
-	},
-	"PixArt_XL_2": { # models/PixArt
-		"target": "PixArt",
-		"unet_config": {
-			"input_size"  :  512//8,
-			"token_num"   :  120,
-			"depth"       :   28,
-			"num_heads"   :   16,
-			"patch_size"  :    2,
-			"hidden_size" : 1152,
-			"pe_interpolation": 1,
-		},
-		"sampling_settings" : sampling_settings,
-	},
-}
-
-pixart_conf.update({ # controlnet models
-	"ControlPixArtHalf": {
-		"target": "ControlPixArtHalf",
-		"unet_config": pixart_conf["PixArt_XL_2"]["unet_config"],
-		"sampling_settings": pixart_conf["PixArt_XL_2"]["sampling_settings"],
-	},
-	"ControlPixArtMSHalf": {
-		"target": "ControlPixArtMSHalf",
-		"unet_config": pixart_conf["PixArtMS_XL_2"]["unet_config"],
-		"sampling_settings": pixart_conf["PixArtMS_XL_2"]["sampling_settings"],
-	}
-})
-
-pixart_res = {
-	"PixArtMS_XL_2": { # models/PixArtMS 1024x1024
-		'0.25': [512, 2048], '0.26': [512, 1984], '0.27': [512, 1920], '0.28': [512, 1856],
-		'0.32': [576, 1792], '0.33': [576, 1728], '0.35': [576, 1664], '0.40': [640, 1600],
-		'0.42': [640, 1536], '0.48': [704, 1472], '0.50': [704, 1408], '0.52': [704, 1344],
-		'0.57': [768, 1344], '0.60': [768, 1280], '0.68': [832, 1216], '0.72': [832, 1152],
-		'0.78': [896, 1152], '0.82': [896, 1088], '0.88': [960, 1088], '0.94': [960, 1024],
-		'1.00': [1024,1024], '1.07': [1024, 960], '1.13': [1088, 960], '1.21': [1088, 896],
-		'1.29': [1152, 896], '1.38': [1152, 832], '1.46': [1216, 832], '1.67': [1280, 768],
-		'1.75': [1344, 768], '2.00': [1408, 704], '2.09': [1472, 704], '2.40': [1536, 640],
-		'2.50': [1600, 640], '2.89': [1664, 576], '3.00': [1728, 576], '3.11': [1792, 576],
-		'3.62': [1856, 512], '3.75': [1920, 512], '3.88': [1984, 512], '4.00': [2048, 512],
-	},
-	"PixArt_XL_2": { # models/PixArt 512x512
-		'0.25': [256,1024], '0.26': [256, 992], '0.27': [256, 960], '0.28': [256, 928],
-		'0.32': [288, 896], '0.33': [288, 864], '0.35': [288, 832], '0.40': [320, 800],
-		'0.42': [320, 768], '0.48': [352, 736], '0.50': [352, 704], '0.52': [352, 672],
-		'0.57': [384, 672], '0.60': [384, 640], '0.68': [416, 608], '0.72': [416, 576],
-		'0.78': [448, 576], '0.82': [448, 544], '0.88': [480, 544], '0.94': [480, 512],
-		'1.00': [512, 512], '1.07': [512, 480], '1.13': [544, 480], '1.21': [544, 448],
-		'1.29': [576, 448], '1.38': [576, 416], '1.46': [608, 416], '1.67': [640, 384],
-		'1.75': [672, 384], '2.00': [704, 352], '2.09': [736, 352], '2.40': [768, 320],
-		'2.50': [800, 320], '2.89': [832, 288], '3.00': [864, 288], '3.11': [896, 288],
-		'3.62': [928, 256], '3.75': [960, 256], '3.88': [992, 256], '4.00': [1024,256]
-	},
-	"PixArtMS_Sigma_XL_2_2K": {
-		'0.25': [1024, 4096], '0.26': [1024, 3968], '0.27': [1024, 3840], '0.28': [1024, 3712],
-		'0.32': [1152, 3584], '0.33': [1152, 3456], '0.35': [1152, 3328], '0.40': [1280, 3200],
-		'0.42': [1280, 3072], '0.48': [1408, 2944], '0.50': [1408, 2816], '0.52': [1408, 2688],
-		'0.57': [1536, 2688], '0.60': [1536, 2560], '0.68': [1664, 2432], '0.72': [1664, 2304],
-		'0.78': [1792, 2304], '0.82': [1792, 2176], '0.88': [1920, 2176], '0.94': [1920, 2048],
-		'1.00': [2048, 2048], '1.07': [2048, 1920], '1.13': [2176, 1920], '1.21': [2176, 1792],
-		'1.29': [2304, 1792], '1.38': [2304, 1664], '1.46': [2432, 1664], '1.67': [2560, 1536],
-		'1.75': [2688, 1536], '2.00': [2816, 1408], '2.09': [2944, 1408], '2.40': [3072, 1280],
-		'2.50': [3200, 1280], '2.89': [3328, 1152], '3.00': [3456, 1152], '3.11': [3584, 1152],
-		'3.62': [3712, 1024], '3.75': [3840, 1024], '3.88': [3968, 1024], '4.00': [4096, 1024]
-	}
-}
-# These should be the same
-pixart_res.update({
-	"PixArtMS_Sigma_XL_2": pixart_res["PixArtMS_XL_2"],
-	"PixArtMS_Sigma_XL_2_512": pixart_res["PixArt_XL_2"],
-})
diff --git a/PixArt/config.py b/PixArt/config.py
new file mode 100644
index 0000000..14975f7
--- /dev/null
+++ b/PixArt/config.py
@@ -0,0 +1,137 @@
+"""
+Model config and setting logic
+"""
+import math
+import logging
+
+import comfy.supported_models_base
+import comfy.supported_models
+import comfy.latent_formats
+
+from .model.pixart import PixArt
+from .model.pixartms import PixArtMS
+from ..text_encoders.pixart.tenc import PixArtTokenizer, PixArtT5XXL
+
+class PixArtConfig(comfy.supported_models_base.BASE):
+    unet_class = PixArtMS
+    unet_config = {}
+    unet_extra_config = {}
+
+    latent_format = comfy.latent_formats.SD15
+    sampling_settings = {
+        "beta_schedule" : "sqrt_linear",
+        "linear_start"  : 0.0001,
+        "linear_end"    : 0.02,
+        "timesteps"     : 1000,
+    }
+
+    def model_type(self, state_dict, prefix=""):
+        return comfy.model_base.ModelType.EPS
+
+    def get_model(self, state_dict, prefix="", device=None):
+        return PixArtModel(model_config=self, unet_model=self.unet_class, device=device)
+
+    def clip_target(self, state_dict={}):
+        return comfy.supported_models_base.ClipTarget(PixArtTokenizer, PixArtT5XXL)
+
+class PixArtModel(comfy.model_base.BaseModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        return out
+
+def model_config_from_unet(sd):
+    """
+    Guess config based on (converted) state dict.
+    """
+    # Shared settings based on DiT_XL_2 - could be enumerated
+    config = {
+        "num_heads"   :   16, # get from attention
+        "patch_size"  :    2, # final layer I guess?
+        "hidden_size" : 1152, # pos_embed.shape[2]
+    }
+    config["depth"] = sum([key.endswith(".attn.proj.weight") for key in sd.keys()]) or 28
+
+    try:
+        # this is not present in the diffusers version for sigma?
+        config["model_max_length"] = sd["y_embedder.y_embedding"].shape[0]
+    except KeyError:
+        # need better logic to guess this
+        config["model_max_length"] = 300
+
+    if "pos_embed" in sd:
+        config["input_size"] = int(math.sqrt(sd["pos_embed"].shape[1])) * config["patch_size"]
+        config["pe_interpolation"] = config["input_size"] // (512//8) # dumb guess
+
+    model_config = PixArtModel
+    if config["model_max_length"] == 300:
+        # Sigma
+        model_class = PixArtMS
+        model_config.latent_format = comfy.latent_formats.SDXL
+        config["micro_condition"] = False
+        if "input_size" not in config:
+            # The diffusers weights for 1K/2K are exactly the same...?
+            # replace patch embed logic with HyDiT?
+            logging.warn(f"PixArt: diffusers weights - 2K model will be broken, use manual loading!")
+            config["input_size"] = 1024//8
+    else:
+        # Alpha
+        if "csize_embedder.mlp.0.weight" in sd:
+            # MS (microconds)
+            model_class = PixArtMS
+            config["micro_condition"] = True
+            if "input_size" not in config:
+                config["input_size"] = 1024//8
+                config["pe_interpolation"] = 2
+        else:
+            # PixArt
+            model_class = PixArt
+            if "input_size" not in config:
+                config["input_size"] = 512//8
+                config["pe_interpolation"] = 1
+    model_config = PixArtConfig(config)
+    model_config.unet_class = model_class
+    logging.info(f"Detected PixArt model as [{model_class}]")
+    logging.info(f"PixArt config:\n{config}")
+    return model_config
+
+resolutions = {
+    "PixArt 512": {
+        0.25: [256,1024], 0.26: [256, 992], 0.27: [256, 960], 0.28: [256, 928],
+        0.32: [288, 896], 0.33: [288, 864], 0.35: [288, 832], 0.40: [320, 800],
+        0.42: [320, 768], 0.48: [352, 736], 0.50: [352, 704], 0.52: [352, 672],
+        0.57: [384, 672], 0.60: [384, 640], 0.68: [416, 608], 0.72: [416, 576],
+        0.78: [448, 576], 0.82: [448, 544], 0.88: [480, 544], 0.94: [480, 512],
+        1.00: [512, 512], 1.07: [512, 480], 1.13: [544, 480], 1.21: [544, 448],
+        1.29: [576, 448], 1.38: [576, 416], 1.46: [608, 416], 1.67: [640, 384],
+        1.75: [672, 384], 2.00: [704, 352], 2.09: [736, 352], 2.40: [768, 320],
+        2.50: [800, 320], 2.89: [832, 288], 3.00: [864, 288], 3.11: [896, 288],
+        3.62: [928, 256], 3.75: [960, 256], 3.88: [992, 256], 4.00: [1024,256]
+    },
+    "PixArt 1024": {
+        0.25: [512, 2048], 0.26: [512, 1984], 0.27: [512, 1920], 0.28: [512, 1856],
+        0.32: [576, 1792], 0.33: [576, 1728], 0.35: [576, 1664], 0.40: [640, 1600],
+        0.42: [640, 1536], 0.48: [704, 1472], 0.50: [704, 1408], 0.52: [704, 1344],
+        0.57: [768, 1344], 0.60: [768, 1280], 0.68: [832, 1216], 0.72: [832, 1152],
+        0.78: [896, 1152], 0.82: [896, 1088], 0.88: [960, 1088], 0.94: [960, 1024],
+        1.00: [1024,1024], 1.07: [1024, 960], 1.13: [1088, 960], 1.21: [1088, 896],
+        1.29: [1152, 896], 1.38: [1152, 832], 1.46: [1216, 832], 1.67: [1280, 768],
+        1.75: [1344, 768], 2.00: [1408, 704], 2.09: [1472, 704], 2.40: [1536, 640],
+        2.50: [1600, 640], 2.89: [1664, 576], 3.00: [1728, 576], 3.11: [1792, 576],
+        3.62: [1856, 512], 3.75: [1920, 512], 3.88: [1984, 512], 4.00: [2048, 512],
+    },
+    "PixArt 2K": {
+        0.25: [1024, 4096], 0.26: [1024, 3968], 0.27: [1024, 3840], 0.28: [1024, 3712],
+        0.32: [1152, 3584], 0.33: [1152, 3456], 0.35: [1152, 3328], 0.40: [1280, 3200],
+        0.42: [1280, 3072], 0.48: [1408, 2944], 0.50: [1408, 2816], 0.52: [1408, 2688],
+        0.57: [1536, 2688], 0.60: [1536, 2560], 0.68: [1664, 2432], 0.72: [1664, 2304],
+        0.78: [1792, 2304], 0.82: [1792, 2176], 0.88: [1920, 2176], 0.94: [1920, 2048],
+        1.00: [2048, 2048], 1.07: [2048, 1920], 1.13: [2176, 1920], 1.21: [2176, 1792],
+        1.29: [2304, 1792], 1.38: [2304, 1664], 1.46: [2432, 1664], 1.67: [2560, 1536],
+        1.75: [2688, 1536], 2.00: [2816, 1408], 2.09: [2944, 1408], 2.40: [3072, 1280],
+        2.50: [3200, 1280], 2.89: [3328, 1152], 3.00: [3456, 1152], 3.11: [3584, 1152],
+        3.62: [3712, 1024], 3.75: [3840, 1024], 3.88: [3968, 1024], 4.00: [4096, 1024]
+    }
+}
diff --git a/PixArt/diffusers_convert.py b/PixArt/diffusers_convert.py
index 312ea9d..6013d17 100644
--- a/PixArt/diffusers_convert.py
+++ b/PixArt/diffusers_convert.py
@@ -4,220 +4,116 @@
 import torch
 
 conversion_map_ms = [ # for multi_scale_train (MS)
-	# Resolution
-	("csize_embedder.mlp.0.weight", "adaln_single.emb.resolution_embedder.linear_1.weight"),
-	("csize_embedder.mlp.0.bias",   "adaln_single.emb.resolution_embedder.linear_1.bias"),
-	("csize_embedder.mlp.2.weight", "adaln_single.emb.resolution_embedder.linear_2.weight"),
-	("csize_embedder.mlp.2.bias",   "adaln_single.emb.resolution_embedder.linear_2.bias"),
-	# Aspect ratio
-	("ar_embedder.mlp.0.weight", "adaln_single.emb.aspect_ratio_embedder.linear_1.weight"),
-	("ar_embedder.mlp.0.bias",   "adaln_single.emb.aspect_ratio_embedder.linear_1.bias"),
-	("ar_embedder.mlp.2.weight", "adaln_single.emb.aspect_ratio_embedder.linear_2.weight"),
-	("ar_embedder.mlp.2.bias",   "adaln_single.emb.aspect_ratio_embedder.linear_2.bias"),
+    # Resolution
+    ("csize_embedder.mlp.0.weight", "adaln_single.emb.resolution_embedder.linear_1.weight"),
+    ("csize_embedder.mlp.0.bias",   "adaln_single.emb.resolution_embedder.linear_1.bias"),
+    ("csize_embedder.mlp.2.weight", "adaln_single.emb.resolution_embedder.linear_2.weight"),
+    ("csize_embedder.mlp.2.bias",   "adaln_single.emb.resolution_embedder.linear_2.bias"),
+    # Aspect ratio
+    ("ar_embedder.mlp.0.weight", "adaln_single.emb.aspect_ratio_embedder.linear_1.weight"),
+    ("ar_embedder.mlp.0.bias",   "adaln_single.emb.aspect_ratio_embedder.linear_1.bias"),
+    ("ar_embedder.mlp.2.weight", "adaln_single.emb.aspect_ratio_embedder.linear_2.weight"),
+    ("ar_embedder.mlp.2.bias",   "adaln_single.emb.aspect_ratio_embedder.linear_2.bias"),
 ]
 
 def get_depth(state_dict):
-	return sum(key.endswith('.attn1.to_k.bias') for key in state_dict.keys())
+    return sum(key.endswith('.attn1.to_k.bias') for key in state_dict.keys())
 
 def get_lora_depth(state_dict):
-	cnt = max([
-		sum(key.endswith('.attn1.to_k.lora_A.weight') for key in state_dict.keys()),
-		sum(key.endswith('_attn1_to_k.lora_A.weight') for key in state_dict.keys()),
-		sum(key.endswith('.attn1.to_k.lora_up.weight') for key in state_dict.keys()),
-		sum(key.endswith('_attn1_to_k.lora_up.weight') for key in state_dict.keys()),
-	])
-	assert cnt > 0, "Unable to detect model depth!"
-	return cnt
+    cnt = max([
+        sum(key.endswith('.attn1.to_k.lora_A.weight') for key in state_dict.keys()),
+        sum(key.endswith('_attn1_to_k.lora_A.weight') for key in state_dict.keys()),
+        sum(key.endswith('.attn1.to_k.lora_up.weight') for key in state_dict.keys()),
+        sum(key.endswith('_attn1_to_k.lora_up.weight') for key in state_dict.keys()),
+    ])
+    assert cnt > 0, "Unable to detect model depth!"
+    return cnt
 
 def get_conversion_map(state_dict):
-	conversion_map = [  # main SD conversion map (PixArt reference, HF Diffusers)
-		# Patch embeddings
-		("x_embedder.proj.weight", "pos_embed.proj.weight"),
-		("x_embedder.proj.bias", "pos_embed.proj.bias"),
-		# Caption projection
-		("y_embedder.y_embedding", "caption_projection.y_embedding"),
-		("y_embedder.y_proj.fc1.weight", "caption_projection.linear_1.weight"),
-		("y_embedder.y_proj.fc1.bias", "caption_projection.linear_1.bias"),
-		("y_embedder.y_proj.fc2.weight", "caption_projection.linear_2.weight"),
-		("y_embedder.y_proj.fc2.bias", "caption_projection.linear_2.bias"),
-		# AdaLN-single LN
-		("t_embedder.mlp.0.weight", "adaln_single.emb.timestep_embedder.linear_1.weight"),
-		("t_embedder.mlp.0.bias", "adaln_single.emb.timestep_embedder.linear_1.bias"),
-		("t_embedder.mlp.2.weight", "adaln_single.emb.timestep_embedder.linear_2.weight"),
-		("t_embedder.mlp.2.bias", "adaln_single.emb.timestep_embedder.linear_2.bias"),
-		# Shared norm
-		("t_block.1.weight", "adaln_single.linear.weight"),
-		("t_block.1.bias", "adaln_single.linear.bias"),
-		# Final block
-		("final_layer.linear.weight", "proj_out.weight"),
-		("final_layer.linear.bias", "proj_out.bias"),
-		("final_layer.scale_shift_table", "scale_shift_table"),
-	]
-
-	# Add actual transformer blocks
-	for depth in range(get_depth(state_dict)):
-		# Transformer blocks
-		conversion_map += [
-			(f"blocks.{depth}.scale_shift_table", f"transformer_blocks.{depth}.scale_shift_table"),
-			# Projection
-			(f"blocks.{depth}.attn.proj.weight", f"transformer_blocks.{depth}.attn1.to_out.0.weight"),
-			(f"blocks.{depth}.attn.proj.bias",   f"transformer_blocks.{depth}.attn1.to_out.0.bias"),
-			# Feed-forward
-			(f"blocks.{depth}.mlp.fc1.weight", f"transformer_blocks.{depth}.ff.net.0.proj.weight"),
-			(f"blocks.{depth}.mlp.fc1.bias",   f"transformer_blocks.{depth}.ff.net.0.proj.bias"),
-			(f"blocks.{depth}.mlp.fc2.weight", f"transformer_blocks.{depth}.ff.net.2.weight"),
-			(f"blocks.{depth}.mlp.fc2.bias",   f"transformer_blocks.{depth}.ff.net.2.bias"),
-			# Cross-attention (proj)
-			(f"blocks.{depth}.cross_attn.proj.weight" ,f"transformer_blocks.{depth}.attn2.to_out.0.weight"),
-			(f"blocks.{depth}.cross_attn.proj.bias"   ,f"transformer_blocks.{depth}.attn2.to_out.0.bias"),
-		]
-	return conversion_map
+    conversion_map = [  # main SD conversion map (PixArt reference, HF Diffusers)
+        # Patch embeddings
+        ("x_embedder.proj.weight", "pos_embed.proj.weight"),
+        ("x_embedder.proj.bias", "pos_embed.proj.bias"),
+        # Caption projection
+        ("y_embedder.y_embedding", "caption_projection.y_embedding"),
+        ("y_embedder.y_proj.fc1.weight", "caption_projection.linear_1.weight"),
+        ("y_embedder.y_proj.fc1.bias", "caption_projection.linear_1.bias"),
+        ("y_embedder.y_proj.fc2.weight", "caption_projection.linear_2.weight"),
+        ("y_embedder.y_proj.fc2.bias", "caption_projection.linear_2.bias"),
+        # AdaLN-single LN
+        ("t_embedder.mlp.0.weight", "adaln_single.emb.timestep_embedder.linear_1.weight"),
+        ("t_embedder.mlp.0.bias", "adaln_single.emb.timestep_embedder.linear_1.bias"),
+        ("t_embedder.mlp.2.weight", "adaln_single.emb.timestep_embedder.linear_2.weight"),
+        ("t_embedder.mlp.2.bias", "adaln_single.emb.timestep_embedder.linear_2.bias"),
+        # Shared norm
+        ("t_block.1.weight", "adaln_single.linear.weight"),
+        ("t_block.1.bias", "adaln_single.linear.bias"),
+        # Final block
+        ("final_layer.linear.weight", "proj_out.weight"),
+        ("final_layer.linear.bias", "proj_out.bias"),
+        ("final_layer.scale_shift_table", "scale_shift_table"),
+    ]
+
+    # Add actual transformer blocks
+    for depth in range(get_depth(state_dict)):
+        # Transformer blocks
+        conversion_map += [
+            (f"blocks.{depth}.scale_shift_table", f"transformer_blocks.{depth}.scale_shift_table"),
+            # Projection
+            (f"blocks.{depth}.attn.proj.weight", f"transformer_blocks.{depth}.attn1.to_out.0.weight"),
+            (f"blocks.{depth}.attn.proj.bias",   f"transformer_blocks.{depth}.attn1.to_out.0.bias"),
+            # Feed-forward
+            (f"blocks.{depth}.mlp.fc1.weight", f"transformer_blocks.{depth}.ff.net.0.proj.weight"),
+            (f"blocks.{depth}.mlp.fc1.bias",   f"transformer_blocks.{depth}.ff.net.0.proj.bias"),
+            (f"blocks.{depth}.mlp.fc2.weight", f"transformer_blocks.{depth}.ff.net.2.weight"),
+            (f"blocks.{depth}.mlp.fc2.bias",   f"transformer_blocks.{depth}.ff.net.2.bias"),
+            # Cross-attention (proj)
+            (f"blocks.{depth}.cross_attn.proj.weight" ,f"transformer_blocks.{depth}.attn2.to_out.0.weight"),
+            (f"blocks.{depth}.cross_attn.proj.bias"   ,f"transformer_blocks.{depth}.attn2.to_out.0.bias"),
+        ]
+    return conversion_map
 
 def find_prefix(state_dict, target_key):
-	prefix = ""
-	for k in state_dict.keys():
-		if k.endswith(target_key):
-			prefix = k.split(target_key)[0]
-			break
-	return prefix
+    prefix = ""
+    for k in state_dict.keys():
+        if k.endswith(target_key):
+            prefix = k.split(target_key)[0]
+            break
+    return prefix
 
 def convert_state_dict(state_dict):
-	if "adaln_single.emb.resolution_embedder.linear_1.weight" in state_dict.keys():
-		cmap = get_conversion_map(state_dict) + conversion_map_ms
-	else:
-		cmap = get_conversion_map(state_dict)
-
-	missing = [k for k,v in cmap if v not in state_dict]
-	new_state_dict = {k: state_dict[v] for k,v in cmap if k not in missing}
-	matched = list(v for k,v in cmap if v in state_dict.keys())
-	
-	for depth in range(get_depth(state_dict)):
-		for wb in ["weight", "bias"]:
-			# Self Attention
-			key = lambda a: f"transformer_blocks.{depth}.attn1.to_{a}.{wb}"
-			new_state_dict[f"blocks.{depth}.attn.qkv.{wb}"] = torch.cat((
-				state_dict[key('q')], state_dict[key('k')], state_dict[key('v')]
-			), dim=0)
-			matched += [key('q'), key('k'), key('v')]
-
-			# Cross-attention (linear)
-			key = lambda a: f"transformer_blocks.{depth}.attn2.to_{a}.{wb}"
-			new_state_dict[f"blocks.{depth}.cross_attn.q_linear.{wb}"] = state_dict[key('q')]
-			new_state_dict[f"blocks.{depth}.cross_attn.kv_linear.{wb}"] = torch.cat((
-				state_dict[key('k')], state_dict[key('v')]
-			), dim=0)
-			matched += [key('q'), key('k'), key('v')]
-
-	if len(matched) < len(state_dict):
-		print(f"PixArt: UNET conversion has leftover keys! ({len(matched)} vs {len(state_dict)})")
-		print(list( set(state_dict.keys()) - set(matched) ))
-
-	if len(missing) > 0:
-		print(f"PixArt: UNET conversion has missing keys!")
-		print(missing)
-
-	return new_state_dict
-
-# Same as above but for LoRA weights:
-def convert_lora_state_dict(state_dict, peft=True):
-	# koyha
-	rep_ak = lambda x: x.replace(".weight", ".lora_down.weight")
-	rep_bk = lambda x: x.replace(".weight", ".lora_up.weight")
-	rep_pk = lambda x: x.replace(".weight", ".alpha")
-	if peft: # peft
-		rep_ap = lambda x: x.replace(".weight", ".lora_A.weight")
-		rep_bp = lambda x: x.replace(".weight", ".lora_B.weight")
-		rep_pp = lambda x: x.replace(".weight", ".alpha")
-		
-		prefix = find_prefix(state_dict, "adaln_single.linear.lora_A.weight")
-		state_dict = {k[len(prefix):]:v for k,v in state_dict.items()}
-	else: # OneTrainer
-		rep_ap = lambda x: x.replace(".", "_")[:-7] + ".lora_down.weight"
-		rep_bp = lambda x: x.replace(".", "_")[:-7] + ".lora_up.weight"
-		rep_pp = lambda x: x.replace(".", "_")[:-7] + ".alpha"
-
-		prefix = "lora_transformer_"
-		t5_marker = "lora_te_encoder"
-		t5_keys = []
-		for key in list(state_dict.keys()):
-			if key.startswith(prefix):
-				state_dict[key[len(prefix):]] = state_dict.pop(key)
-			elif t5_marker in key:
-				t5_keys.append(state_dict.pop(key))
-		if len(t5_keys) > 0:
-			print(f"Text Encoder not supported for PixArt LoRA, ignoring {len(t5_keys)} keys")
-
-	cmap = []
-	cmap_unet = get_conversion_map(state_dict) + conversion_map_ms # todo: 512 model
-	for k, v in cmap_unet:
-		if v.endswith(".weight"):
-			cmap.append((rep_ak(k), rep_ap(v)))
-			cmap.append((rep_bk(k), rep_bp(v)))
-			if not peft:
-				cmap.append((rep_pk(k), rep_pp(v)))
-
-	missing = [k for k,v in cmap if v not in state_dict]
-	new_state_dict = {k: state_dict[v] for k,v in cmap if k not in missing}
-	matched = list(v for k,v in cmap if v in state_dict.keys())
-
-	lora_depth = get_lora_depth(state_dict)
-	for fp, fk in ((rep_ap, rep_ak),(rep_bp, rep_bk)):
-		for depth in range(lora_depth):
-			# Self Attention
-			key = lambda a: fp(f"transformer_blocks.{depth}.attn1.to_{a}.weight")
-			new_state_dict[fk(f"blocks.{depth}.attn.qkv.weight")] = torch.cat((
-				state_dict[key('q')], state_dict[key('k')], state_dict[key('v')]
-			), dim=0)
-
-			matched += [key('q'), key('k'), key('v')]
-			if not peft:
-				akey = lambda a: rep_pp(f"transformer_blocks.{depth}.attn1.to_{a}.weight")
-				new_state_dict[rep_pk((f"blocks.{depth}.attn.qkv.weight"))] = state_dict[akey("q")]
-				matched += [akey('q'), akey('k'), akey('v')]
-
-			# Self Attention projection?
-			key = lambda a: fp(f"transformer_blocks.{depth}.attn1.to_{a}.weight")
-			new_state_dict[fk(f"blocks.{depth}.attn.proj.weight")] = state_dict[key('out.0')]
-			matched += [key('out.0')]
-
-			# Cross-attention (linear)
-			key = lambda a: fp(f"transformer_blocks.{depth}.attn2.to_{a}.weight")
-			new_state_dict[fk(f"blocks.{depth}.cross_attn.q_linear.weight")]  = state_dict[key('q')]
-			new_state_dict[fk(f"blocks.{depth}.cross_attn.kv_linear.weight")] = torch.cat((
-				state_dict[key('k')], state_dict[key('v')]
-			), dim=0)
-			matched += [key('q'), key('k'), key('v')]
-			if not peft:
-				akey = lambda a: rep_pp(f"transformer_blocks.{depth}.attn2.to_{a}.weight")
-				new_state_dict[rep_pk((f"blocks.{depth}.cross_attn.q_linear.weight"))]  = state_dict[akey("q")]
-				new_state_dict[rep_pk((f"blocks.{depth}.cross_attn.kv_linear.weight"))] = state_dict[akey("k")]
-				matched += [akey('q'), akey('k'), akey('v')]
-			
-			# Cross Attention projection?
-			key = lambda a: fp(f"transformer_blocks.{depth}.attn2.to_{a}.weight")
-			new_state_dict[fk(f"blocks.{depth}.cross_attn.proj.weight")] = state_dict[key('out.0')]
-			matched += [key('out.0')]
-			
-			try:
-				key = fp(f"transformer_blocks.{depth}.ff.net.0.proj.weight")
-				new_state_dict[fk(f"blocks.{depth}.mlp.fc1.weight")]  = state_dict[key]
-				matched += [key]
-			except KeyError:
-				pass
-
-			try:
-				key = fp(f"transformer_blocks.{depth}.ff.net.2.weight")
-				new_state_dict[fk(f"blocks.{depth}.mlp.fc2.weight")]  = state_dict[key]
-				matched += [key]
-			except KeyError:
-				pass
-
-	if len(matched) < len(state_dict):
-		print(f"PixArt: LoRA conversion has leftover keys! ({len(matched)} vs {len(state_dict)})")
-		print(list( set(state_dict.keys()) - set(matched) ))
-
-	if len(missing) > 0:
-		print(f"PixArt: LoRA conversion has missing keys! (probably)")
-		print(missing)
-
-	return new_state_dict
+    if "adaln_single.emb.resolution_embedder.linear_1.weight" in state_dict.keys():
+        cmap = get_conversion_map(state_dict) + conversion_map_ms
+    else:
+        cmap = get_conversion_map(state_dict)
+
+    missing = [k for k,v in cmap if v not in state_dict]
+    new_state_dict = {k: state_dict[v] for k,v in cmap if k not in missing}
+    matched = list(v for k,v in cmap if v in state_dict.keys())
+    
+    for depth in range(get_depth(state_dict)):
+        for wb in ["weight", "bias"]:
+            # Self Attention
+            key = lambda a: f"transformer_blocks.{depth}.attn1.to_{a}.{wb}"
+            new_state_dict[f"blocks.{depth}.attn.qkv.{wb}"] = torch.cat((
+                state_dict[key('q')], state_dict[key('k')], state_dict[key('v')]
+            ), dim=0)
+            matched += [key('q'), key('k'), key('v')]
+
+            # Cross-attention (linear)
+            key = lambda a: f"transformer_blocks.{depth}.attn2.to_{a}.{wb}"
+            new_state_dict[f"blocks.{depth}.cross_attn.q_linear.{wb}"] = state_dict[key('q')]
+            new_state_dict[f"blocks.{depth}.cross_attn.kv_linear.{wb}"] = torch.cat((
+                state_dict[key('k')], state_dict[key('v')]
+            ), dim=0)
+            matched += [key('q'), key('k'), key('v')]
+
+    if len(matched) < len(state_dict):
+        print(f"PixArt: UNET conversion has leftover keys! ({len(matched)} vs {len(state_dict)})")
+        print(list( set(state_dict.keys()) - set(matched) ))
+
+    if len(missing) > 0:
+        print(f"PixArt: UNET conversion has missing keys!")
+        print(missing)
+
+    return new_state_dict
diff --git a/PixArt/loader.py b/PixArt/loader.py
index cedb5fd..b3627d6 100644
--- a/PixArt/loader.py
+++ b/PixArt/loader.py
@@ -1,180 +1,76 @@
 import comfy.supported_models_base
 import comfy.latent_formats
+import comfy.model_detection
 import comfy.model_patcher
 import comfy.model_base
 import comfy.utils
 import comfy.conds
+import logging
 import torch
-import math 
+
 from comfy import model_management
 from .diffusers_convert import convert_state_dict
-
-class EXM_PixArt(comfy.supported_models_base.BASE):
-	unet_config = {}
-	unet_extra_config = {}
-	latent_format = comfy.latent_formats.SD15
-
-	def __init__(self, model_conf):
-		self.model_target = model_conf.get("target")
-		self.unet_config = model_conf.get("unet_config", {})
-		self.sampling_settings = model_conf.get("sampling_settings", {})
-		self.latent_format = self.latent_format()
-		# UNET is handled by extension
-		self.unet_config["disable_unet_model_creation"] = True
-
-	def model_type(self, state_dict, prefix=""):
-		return comfy.model_base.ModelType.EPS
-
-class EXM_PixArt_Model(comfy.model_base.BaseModel):
-	def __init__(self, *args, **kwargs):
-		super().__init__(*args, **kwargs)
-	
-	def extra_conds(self, **kwargs):
-		out = super().extra_conds(**kwargs)
-
-		img_hw = kwargs.get("img_hw", None)
-		if img_hw is not None:
-			out["img_hw"] = comfy.conds.CONDRegular(torch.tensor(img_hw))
-		
-		aspect_ratio = kwargs.get("aspect_ratio", None)
-		if aspect_ratio is not None:
-			out["aspect_ratio"] = comfy.conds.CONDRegular(torch.tensor(aspect_ratio))
-
-		cn_hint = kwargs.get("cn_hint", None)
-		if cn_hint is not None:
-			out["cn_hint"] = comfy.conds.CONDRegular(cn_hint)
-
-		return out
-
-def load_pixart(model_path, model_conf=None):
-	state_dict = comfy.utils.load_torch_file(model_path)
-	state_dict = state_dict.get("model", state_dict)
-
-	# prefix
-	for prefix in ["model.diffusion_model.",]:
-		if any(True for x in state_dict if x.startswith(prefix)):
-			state_dict = {k[len(prefix):]:v for k,v in state_dict.items()}
-
-	# diffusers
-	if "adaln_single.linear.weight" in state_dict:
-		state_dict = convert_state_dict(state_dict) # Diffusers
-
-	# guess auto config
-	if model_conf is None:
-		model_conf = guess_pixart_config(state_dict)
-
-	parameters = comfy.utils.calculate_parameters(state_dict)
-	unet_dtype = model_management.unet_dtype(model_params=parameters)
-	load_device = comfy.model_management.get_torch_device()
-	offload_device = comfy.model_management.unet_offload_device()
-
-	# ignore fp8/etc and use directly for now
-	manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device)
-	if manual_cast_dtype:
-		print(f"PixArt: falling back to {manual_cast_dtype}")
-		unet_dtype = manual_cast_dtype
-
-	model_conf = EXM_PixArt(model_conf) # convert to object
-	model = EXM_PixArt_Model( # same as comfy.model_base.BaseModel
-		model_conf,
-		model_type=comfy.model_base.ModelType.EPS,
-		device=model_management.get_torch_device()
-	)
-
-	if model_conf.model_target == "PixArtMS":
-		from .models.PixArtMS import PixArtMS
-		model.diffusion_model = PixArtMS(**model_conf.unet_config)
-	elif model_conf.model_target == "PixArt":
-		from .models.PixArt import PixArt
-		model.diffusion_model = PixArt(**model_conf.unet_config)
-	elif model_conf.model_target == "PixArtMSSigma":
-		from .models.PixArtMS import PixArtMS
-		model.diffusion_model = PixArtMS(**model_conf.unet_config)
-		model.latent_format = comfy.latent_formats.SDXL()
-	elif model_conf.model_target == "ControlPixArtMSHalf":
-		from .models.PixArtMS import PixArtMS
-		from .models.pixart_controlnet import ControlPixArtMSHalf
-		model.diffusion_model = PixArtMS(**model_conf.unet_config)
-		model.diffusion_model = ControlPixArtMSHalf(model.diffusion_model)
-	elif model_conf.model_target == "ControlPixArtHalf":
-		from .models.PixArt import PixArt
-		from .models.pixart_controlnet import ControlPixArtHalf
-		model.diffusion_model = PixArt(**model_conf.unet_config)
-		model.diffusion_model = ControlPixArtHalf(model.diffusion_model)
-	else:
-		raise NotImplementedError(f"Unknown model target '{model_conf.model_target}'")
-
-	m, u = model.diffusion_model.load_state_dict(state_dict, strict=False)
-	if len(m) > 0: print("Missing UNET keys", m)
-	if len(u) > 0: print("Leftover UNET keys", u)
-	model.diffusion_model.dtype = unet_dtype
-	model.diffusion_model.eval()
-	model.diffusion_model.to(unet_dtype)
-
-	model_patcher = comfy.model_patcher.ModelPatcher(
-		model,
-		load_device = load_device,
-		offload_device = offload_device,
-	)
-	return model_patcher
-
-def guess_pixart_config(sd):
-	"""
-	Guess config based on converted state dict.
-	"""
-	# Shared settings based on DiT_XL_2 - could be enumerated
-	config = {
-		"num_heads"   :   16, # get from attention
-		"patch_size"  :    2, # final layer I guess?
-		"hidden_size" : 1152, # pos_embed.shape[2]
-	}
-	config["depth"] = sum([key.endswith(".attn.proj.weight") for key in sd.keys()]) or 28
-
-	try:
-		# this is not present in the diffusers version for sigma?
-		config["model_max_length"] = sd["y_embedder.y_embedding"].shape[0]
-	except KeyError:
-		# need better logic to guess this
-		config["model_max_length"] = 300
-
-	if "pos_embed" in sd:
-		config["input_size"] = int(math.sqrt(sd["pos_embed"].shape[1])) * config["patch_size"]
-		config["pe_interpolation"] = config["input_size"] // (512//8) # dumb guess
-
-	target_arch = "PixArtMS"
-	if config["model_max_length"] == 300:
-		# Sigma
-		target_arch = "PixArtMSSigma"
-		config["micro_condition"] = False
-		if "input_size" not in config:
-			# The diffusers weights for 1K/2K are exactly the same...?
-			# replace patch embed logic with HyDiT?
-			print(f"PixArt: diffusers weights - 2K model will be broken, use manual loading!")
-			config["input_size"] = 1024//8
-	else:
-		# Alpha
-		if "csize_embedder.mlp.0.weight" in sd:
-			# MS (microconds)
-			target_arch = "PixArtMS"
-			config["micro_condition"] = True
-			if "input_size" not in config:
-				config["input_size"] = 1024//8
-				config["pe_interpolation"] = 2
-		else:
-			# PixArt
-			target_arch = "PixArt"
-			if "input_size" not in config:
-				config["input_size"] = 512//8
-				config["pe_interpolation"] = 1
-
-	print("PixArt guessed config:", target_arch, config)
-	return {
-		"target": target_arch,
-		"unet_config": config,
-		"sampling_settings": {
-			"beta_schedule" : "sqrt_linear",
-			"linear_start"  : 0.0001,
-			"linear_end"    : 0.02,
-			"timesteps"     : 1000,
-		}	
-	}
+from .config import model_config_from_unet
+
+class PixArtModel(comfy.model_base.BaseModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+    
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+
+        img_hw = kwargs.get("img_hw", None)
+        if img_hw is not None:
+            out["img_hw"] = comfy.conds.CONDRegular(torch.tensor(img_hw))
+
+        aspect_ratio = kwargs.get("aspect_ratio", None)
+        if aspect_ratio is not None:
+            out["aspect_ratio"] = comfy.conds.CONDRegular(torch.tensor(aspect_ratio))
+
+        return out
+
+def load_pixart_state_dict(sd, model_options={}):
+    # prefix / format
+    sd = sd.get("model", sd) # ref ckpt
+    diffusion_model_prefix = comfy.model_detection.unet_prefix_from_state_dict(sd)
+    temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True)
+    if len(temp_sd) > 0:
+        sd = temp_sd
+
+    # diffusers convert
+    if "adaln_single.linear.weight" in sd:
+        sd = convert_state_dict(sd)
+
+    # model config
+    model_config = model_config_from_unet(sd)
+
+    # TODO: move lines below to utils
+    parameters = comfy.utils.calculate_parameters(sd)
+    load_device = model_management.get_torch_device()
+    offload_device = comfy.model_management.unet_offload_device()
+
+    dtype = model_options.get("dtype", torch.float16) # TODO: fix this
+    weight_dtype = comfy.utils.weight_dtype(sd)
+    unet_weight_dtype = list(model_config.supported_inference_dtypes)
+
+    if weight_dtype is not None and model_config.scaled_fp8 is None:
+        unet_weight_dtype.append(weight_dtype)
+
+    if dtype is None:
+        unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype)
+    else:
+        unet_dtype = dtype
+
+    manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
+    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
+    model_config.custom_operations = model_options.get("custom_operations", model_config.custom_operations)
+    if model_options.get("fp8_optimizations", False):
+        model_config.optimizations["fp8"] = True
+
+    model = model_config.get_model(sd, "")
+    model = model.to(offload_device).eval()
+    model.load_model_weights(sd, "")
+    left_over = sd.keys()
+    if len(left_over) > 0:
+        logging.info("left over keys in unet: {}".format(left_over))
+    return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)
diff --git a/PixArt/lora.py b/PixArt/lora.py
deleted file mode 100644
index fca5931..0000000
--- a/PixArt/lora.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import os
-import copy
-import json
-import torch
-import comfy.lora
-import comfy.model_management
-from comfy.model_patcher import ModelPatcher
-from .diffusers_convert import convert_lora_state_dict
-
-class EXM_PixArt_ModelPatcher(ModelPatcher):
-	def calculate_weight(self, patches, weight, key):
-		"""
-		This is almost the same as the comfy function, but stripped down to just the LoRA patch code.
-		The problem with the original code is the q/k/v keys being combined into one for the attention.
-		In the diffusers code, they're treated as separate keys, but in the reference code they're recombined (q+kv|qkv).
-		This means, for example, that the [1152,1152] weights become [3456,1152] in the state dict.
-		The issue with this is that the LoRA weights are [128,1152],[1152,128] and become [384,1162],[3456,128] instead.
-		
-		This is the best thing I could think of that would fix that, but it's very fragile.
-		 - Check key shape to determine if it needs the fallback logic
-		 - Cut the input into parts based on the shape (undoing the torch.cat)
-		 - Do the matrix multiplication logic
-		 - Recombine them to match the expected shape
-		"""
-		for p in patches:
-			alpha = p[0]
-			v = p[1]
-			strength_model = p[2]
-			if strength_model != 1.0:
-				weight *= strength_model
-			
-			if isinstance(v, list):
-				v = (self.calculate_weight(v[1:], v[0].clone(), key), )
-
-			if len(v) == 2:
-				patch_type = v[0]
-				v = v[1]
-
-			if patch_type == "lora":
-				mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32)
-				mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32)
-				if v[2] is not None:
-					alpha *= v[2] / mat2.shape[0]
-				try:
-					mat1 = mat1.flatten(start_dim=1)
-					mat2 = mat2.flatten(start_dim=1)
-
-					ch1 = mat1.shape[0] // mat2.shape[1]
-					ch2 = mat2.shape[0] // mat1.shape[1]
-					### Fallback logic for shape mismatch ###
-					if mat1.shape[0] != mat2.shape[1] and ch1 == ch2 and (mat1.shape[0]/mat2.shape[1])%1 == 0:
-						mat1 = mat1.chunk(ch1, dim=0)
-						mat2 = mat2.chunk(ch1, dim=0)
-						weight += torch.cat(
-							[alpha * torch.mm(mat1[x], mat2[x]) for x in range(ch1)],
-							dim=0,
-						).reshape(weight.shape).type(weight.dtype)
-					else:
-						weight += (alpha * torch.mm(mat1, mat2)).reshape(weight.shape).type(weight.dtype)
-				except Exception as e:
-					print("ERROR", key, e)
-		return weight
-
-	def clone(self):
-		n = EXM_PixArt_ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device, weight_inplace_update=self.weight_inplace_update)
-		n.patches = {}
-		for k in self.patches:
-			n.patches[k] = self.patches[k][:]
-
-		n.object_patches = self.object_patches.copy()
-		n.model_options = copy.deepcopy(self.model_options)
-		n.model_keys = self.model_keys
-		return n
-
-def replace_model_patcher(model):
-	n = EXM_PixArt_ModelPatcher(
-		model                 = model.model,
-		size                  = model.size,
-		load_device           = model.load_device,
-		offload_device        = model.offload_device,
-		weight_inplace_update = model.weight_inplace_update,
-	)
-	n.patches = {}
-	for k in model.patches:
-		n.patches[k] = model.patches[k][:]
-
-	n.object_patches = model.object_patches.copy()
-	n.model_options = copy.deepcopy(model.model_options)
-	return n
-
-def find_peft_alpha(path):
-	def load_json(json_path):
-		with open(json_path) as f:
-			data = json.load(f)
-		alpha = data.get("lora_alpha")
-		alpha = alpha or data.get("alpha")
-		if not alpha:
-			print(" Found config but `lora_alpha` is missing!")
-		else:
-			print(f" Found config at {json_path} [alpha:{alpha}]")
-		return alpha
-
-	# For some weird reason peft doesn't include the alpha in the actual model
-	print("PixArt: Warning! This is a PEFT LoRA. Trying to find config...")
-	files = [
-		f"{os.path.splitext(path)[0]}.json",
-		f"{os.path.splitext(path)[0]}.config.json",
-		os.path.join(os.path.dirname(path),"adapter_config.json"),
-	]
-	for file in files:
-		if os.path.isfile(file):
-			return load_json(file)
-
-	print(" Missing config/alpha! assuming alpha of 8. Consider converting it/adding a config json to it.")
-	return 8.0
-
-def load_pixart_lora(model, lora, lora_path, strength):
-	k_back = lambda x: x.replace(".lora_up.weight", "")
-	# need to convert the actual weights for this to work.
-	if any(True for x in lora.keys() if x.endswith("adaln_single.linear.lora_A.weight")):
-		lora = convert_lora_state_dict(lora, peft=True)
-		alpha = find_peft_alpha(lora_path)
-		lora.update({f"{k_back(x)}.alpha":torch.tensor(alpha) for x in lora.keys() if "lora_up" in x})
-	else: # OneTrainer
-		lora = convert_lora_state_dict(lora, peft=False)
-
-	key_map = {k_back(x):f"diffusion_model.{k_back(x)}.weight" for x in lora.keys() if "lora_up" in x} # fake
-
-	loaded = comfy.lora.load_lora(lora, key_map)
-	if model is not None:
-		# switch to custom model patcher when using LoRAs
-		if isinstance(model, EXM_PixArt_ModelPatcher):
-			new_modelpatcher = model.clone()
-		else:
-			new_modelpatcher = replace_model_patcher(model)
-		k = new_modelpatcher.add_patches(loaded, strength)
-	else:
-		k = ()
-		new_modelpatcher = None
-
-	k = set(k)
-	for x in loaded:
-		if (x not in k):
-			print("NOT LOADED", x)
-
-	return new_modelpatcher
diff --git a/T5/LICENSE-T5 b/PixArt/model/LICENSE
similarity index 99%
rename from T5/LICENSE-T5
rename to PixArt/model/LICENSE
index 261eeb9..fb524b1 100644
--- a/T5/LICENSE-T5
+++ b/PixArt/model/LICENSE
@@ -186,7 +186,7 @@
       same "printed page" as the copyright notice for easier
       identification within third-party archives.
 
-   Copyright [yyyy] [name of copyright owner]
+   Copyright 2024 Junsong Chen, Jincheng Yu, Enze Xie
 
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.
diff --git a/PixArt/model/__init__.py b/PixArt/model/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/PixArt/models/PixArt_blocks.py b/PixArt/model/blocks.py
similarity index 100%
rename from PixArt/models/PixArt_blocks.py
rename to PixArt/model/blocks.py
diff --git a/PixArt/models/PixArt.py b/PixArt/model/pixart.py
similarity index 98%
rename from PixArt/models/PixArt.py
rename to PixArt/model/pixart.py
index 4d6cf93..f200177 100644
--- a/PixArt/models/PixArt.py
+++ b/PixArt/model/pixart.py
@@ -18,7 +18,7 @@
 
 
 from .utils import auto_grad_checkpoint, to_2tuple
-from .PixArt_blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, LabelEmbedder, FinalLayer
+from .blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, LabelEmbedder, FinalLayer
 
 
 class PixArtBlock(nn.Module):
diff --git a/PixArt/models/PixArtMS.py b/PixArt/model/pixartms.py
similarity index 95%
rename from PixArt/models/PixArtMS.py
rename to PixArt/model/pixartms.py
index 908589c..0bb5b36 100644
--- a/PixArt/models/PixArtMS.py
+++ b/PixArt/model/pixartms.py
@@ -15,8 +15,8 @@
 from timm.models.vision_transformer import Mlp
 
 from .utils import auto_grad_checkpoint, to_2tuple
-from .PixArt_blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, SizeEmbedder
-from .PixArt import PixArt, get_2d_sincos_pos_embed
+from .blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, SizeEmbedder
+from .pixart import PixArt, get_2d_sincos_pos_embed
 
 
 class PatchEmbed(nn.Module):
diff --git a/PixArt/models/utils.py b/PixArt/model/utils.py
similarity index 100%
rename from PixArt/models/utils.py
rename to PixArt/model/utils.py
diff --git a/PixArt/models/pixart_controlnet.py b/PixArt/models/pixart_controlnet.py
deleted file mode 100644
index 37fa4c1..0000000
--- a/PixArt/models/pixart_controlnet.py
+++ /dev/null
@@ -1,312 +0,0 @@
-import re
-import torch
-import torch.nn as nn
-
-from copy import deepcopy
-from torch import Tensor
-from torch.nn import Module, Linear, init
-from typing import Any, Mapping
-
-from .PixArt import PixArt, get_2d_sincos_pos_embed
-from .PixArtMS import PixArtMSBlock, PixArtMS
-from .utils import auto_grad_checkpoint
-
-# The implementation of ControlNet-Half architrecture
-# https://github.com/lllyasviel/ControlNet/discussions/188
-class ControlT2IDitBlockHalf(Module):
-    def __init__(self, base_block: PixArtMSBlock, block_index: 0) -> None:
-        super().__init__()
-        self.copied_block = deepcopy(base_block)
-        self.block_index = block_index
-
-        for p in self.copied_block.parameters():
-            p.requires_grad_(True)
-
-        self.copied_block.load_state_dict(base_block.state_dict())
-        self.copied_block.train()
-        
-        self.hidden_size = hidden_size = base_block.hidden_size
-        if self.block_index == 0:
-            self.before_proj = Linear(hidden_size, hidden_size)
-            init.zeros_(self.before_proj.weight)
-            init.zeros_(self.before_proj.bias)
-        self.after_proj = Linear(hidden_size, hidden_size) 
-        init.zeros_(self.after_proj.weight)
-        init.zeros_(self.after_proj.bias)
-
-    def forward(self, x, y, t, mask=None, c=None):
-        
-        if self.block_index == 0:
-            # the first block
-            c = self.before_proj(c)
-            c = self.copied_block(x + c, y, t, mask)
-            c_skip = self.after_proj(c)
-        else:
-            # load from previous c and produce the c for skip connection
-            c = self.copied_block(c, y, t, mask)
-            c_skip = self.after_proj(c)
-        
-        return c, c_skip
-        
-
-# The implementation of ControlPixArtHalf net
-class ControlPixArtHalf(Module):
-    # only support single res model
-    def __init__(self, base_model: PixArt, copy_blocks_num: int = 13) -> None:
-        super().__init__()
-        self.dtype = torch.get_default_dtype()
-        self.base_model = base_model.eval()
-        self.controlnet = []
-        self.copy_blocks_num = copy_blocks_num
-        self.total_blocks_num = len(base_model.blocks)
-        for p in self.base_model.parameters():
-            p.requires_grad_(False)
-
-        # Copy first copy_blocks_num block
-        for i in range(copy_blocks_num):
-            self.controlnet.append(ControlT2IDitBlockHalf(base_model.blocks[i], i))
-        self.controlnet = nn.ModuleList(self.controlnet)
-    
-    def __getattr__(self, name: str) -> Tensor or Module:
-        if name in ['forward', 'forward_with_dpmsolver', 'forward_with_cfg', 'forward_c', 'load_state_dict']:
-            return self.__dict__[name]
-        elif name in ['base_model', 'controlnet']:
-            return super().__getattr__(name)
-        else:
-            return getattr(self.base_model, name)
-
-    def forward_c(self, c):
-        self.h, self.w = c.shape[-2]//self.patch_size, c.shape[-1]//self.patch_size
-        pos_embed = torch.from_numpy(get_2d_sincos_pos_embed(self.pos_embed.shape[-1], (self.h, self.w), lewei_scale=self.lewei_scale, base_size=self.base_size)).unsqueeze(0).to(c.device).to(self.dtype)
-        return self.x_embedder(c) + pos_embed if c is not None else c
-
-    # def forward(self, x, t, c, **kwargs):
-    #     return self.base_model(x, t, c=self.forward_c(c), **kwargs)
-    def forward_raw(self, x, timestep, y, mask=None, data_info=None, c=None, **kwargs):
-        # modify the original PixArtMS forward function
-        if c is not None:
-            c = c.to(self.dtype)
-            c = self.forward_c(c)
-        """
-        Forward pass of PixArt.
-        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
-        t: (N,) tensor of diffusion timesteps
-        y: (N, 1, 120, C) tensor of class labels
-        """
-        x = x.to(self.dtype)
-        timestep = timestep.to(self.dtype)
-        y = y.to(self.dtype)
-        pos_embed = self.pos_embed.to(self.dtype)
-        self.h, self.w = x.shape[-2]//self.patch_size, x.shape[-1]//self.patch_size
-        x = self.x_embedder(x) + pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
-        t = self.t_embedder(timestep.to(x.dtype))  # (N, D)
-        t0 = self.t_block(t)
-        y = self.y_embedder(y, self.training)  # (N, 1, L, D)
-        if mask is not None:
-            if mask.shape[0] != y.shape[0]:
-                mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
-            mask = mask.squeeze(1).squeeze(1)
-            y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
-            y_lens = mask.sum(dim=1).tolist()
-        else:
-            y_lens = [y.shape[2]] * y.shape[0]
-            y = y.squeeze(1).view(1, -1, x.shape[-1])
-
-        # define the first layer
-        x = auto_grad_checkpoint(self.base_model.blocks[0], x, y, t0, y_lens, **kwargs)  # (N, T, D) #support grad checkpoint
-
-        if c is not None:
-            # update c
-            for index in range(1, self.copy_blocks_num + 1):
-                c, c_skip = auto_grad_checkpoint(self.controlnet[index - 1], x, y, t0, y_lens, c, **kwargs)
-                x = auto_grad_checkpoint(self.base_model.blocks[index], x + c_skip, y, t0, y_lens, **kwargs)
-        
-            # update x
-            for index in range(self.copy_blocks_num + 1, self.total_blocks_num):
-                x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs)
-        else:
-            for index in range(1, self.total_blocks_num):
-                x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs)
-
-        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
-        x = self.unpatchify(x)  # (N, out_channels, H, W)
-        return x
-
-    def forward(self, x, timesteps, context, cn_hint=None, **kwargs):
-        """
-        Forward pass that adapts comfy input to original forward function
-        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
-        timesteps: (N,) tensor of diffusion timesteps
-        context: (N, 1, 120, C) conditioning
-        cn_hint: controlnet hint
-        """
-        ## Still accepts the input w/o that dim but returns garbage
-        if len(context.shape) == 3:
-            context = context.unsqueeze(1)
-
-        ## run original forward pass
-        out = self.forward_raw(
-            x = x.to(self.dtype),
-            timestep = timesteps.to(self.dtype),
-            y = context.to(self.dtype),
-            c = cn_hint,
-        )
-
-        ## only return EPS
-        out = out.to(torch.float)
-        eps, rest = out[:, :self.in_channels], out[:, self.in_channels:]
-        return eps
-
-    def forward_with_dpmsolver(self, x, t, y, data_info, c, **kwargs):
-        model_out = self.forward_raw(x, t, y, data_info=data_info, c=c, **kwargs)
-        return model_out.chunk(2, dim=1)[0]
-
-    # def forward_with_dpmsolver(self, x, t, y, data_info, c, **kwargs):
-    #     return self.base_model.forward_with_dpmsolver(x, t, y, data_info=data_info, c=self.forward_c(c), **kwargs)
-
-    def forward_with_cfg(self, x, t, y, cfg_scale, data_info, c, **kwargs):
-        return self.base_model.forward_with_cfg(x, t, y, cfg_scale, data_info, c=self.forward_c(c), **kwargs)
-
-    def load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True):
-        if all((k.startswith('base_model') or k.startswith('controlnet')) for k in state_dict.keys()):
-            return super().load_state_dict(state_dict, strict)
-        else:
-            new_key = {}
-            for k in state_dict.keys():
-                new_key[k] = re.sub(r"(blocks\.\d+)(.*)", r"\1.base_block\2", k)
-            for k, v in new_key.items():
-                if k != v:
-                    print(f"replace {k} to {v}")
-                    state_dict[v] = state_dict.pop(k)
-
-            return self.base_model.load_state_dict(state_dict, strict)
-    
-    def unpatchify(self, x):
-        """
-        x: (N, T, patch_size**2 * C)
-        imgs: (N, H, W, C)
-        """
-        c = self.out_channels
-        p = self.x_embedder.patch_size[0]
-        assert self.h * self.w == x.shape[1]
-
-        x = x.reshape(shape=(x.shape[0], self.h, self.w, p, p, c))
-        x = torch.einsum('nhwpqc->nchpwq', x)
-        imgs = x.reshape(shape=(x.shape[0], c, self.h * p, self.w * p))
-        return imgs
-
-    # @property
-    # def dtype(self):
-        ## 返回模型参数的数据类型
-        # return next(self.parameters()).dtype
-
-
-# The implementation for PixArtMS_Half + 1024 resolution
-class ControlPixArtMSHalf(ControlPixArtHalf):
-    # support multi-scale res model (multi-scale model can also be applied to single reso training & inference)
-    def __init__(self, base_model: PixArtMS, copy_blocks_num: int = 13) -> None:
-        super().__init__(base_model=base_model, copy_blocks_num=copy_blocks_num)
-
-    def forward_raw(self, x, timestep, y, mask=None, data_info=None, c=None, **kwargs):
-        # modify the original PixArtMS forward function
-        """
-        Forward pass of PixArt.
-        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
-        t: (N,) tensor of diffusion timesteps
-        y: (N, 1, 120, C) tensor of class labels
-        """
-        if c is not None:
-            c = c.to(self.dtype)
-            c = self.forward_c(c)
-        bs = x.shape[0]
-        x = x.to(self.dtype)
-        timestep = timestep.to(self.dtype)
-        y = y.to(self.dtype)
-        c_size, ar = data_info['img_hw'].to(self.dtype), data_info['aspect_ratio'].to(self.dtype)
-        self.h, self.w = x.shape[-2]//self.patch_size, x.shape[-1]//self.patch_size
-
-        pos_embed = torch.from_numpy(get_2d_sincos_pos_embed(self.pos_embed.shape[-1], (self.h, self.w), lewei_scale=self.lewei_scale, base_size=self.base_size)).unsqueeze(0).to(x.device).to(self.dtype)
-        x = self.x_embedder(x) + pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
-        t = self.t_embedder(timestep)  # (N, D)
-        csize = self.csize_embedder(c_size, bs)  # (N, D)
-        ar = self.ar_embedder(ar, bs)  # (N, D)
-        t = t + torch.cat([csize, ar], dim=1)
-        t0 = self.t_block(t)
-        y = self.y_embedder(y, self.training)  # (N, D)
-        if mask is not None:
-            if mask.shape[0] != y.shape[0]:
-                mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
-            mask = mask.squeeze(1).squeeze(1)
-            y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
-            y_lens = mask.sum(dim=1).tolist()
-        else:
-            y_lens = [y.shape[2]] * y.shape[0]
-            y = y.squeeze(1).view(1, -1, x.shape[-1])
-
-        # define the first layer
-        x = auto_grad_checkpoint(self.base_model.blocks[0], x, y, t0, y_lens, **kwargs)  # (N, T, D) #support grad checkpoint
-
-        if c is not None:
-            # update c
-            for index in range(1, self.copy_blocks_num + 1):
-                c, c_skip = auto_grad_checkpoint(self.controlnet[index - 1], x, y, t0, y_lens, c, **kwargs)
-                x = auto_grad_checkpoint(self.base_model.blocks[index], x + c_skip, y, t0, y_lens, **kwargs)
-        
-            # update x
-            for index in range(self.copy_blocks_num + 1, self.total_blocks_num):
-                x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs)
-        else:
-            for index in range(1, self.total_blocks_num):
-                x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs)
-
-        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
-        x = self.unpatchify(x)  # (N, out_channels, H, W)
-        return x
-
-    def forward(self, x, timesteps, context, img_hw=None, aspect_ratio=None, cn_hint=None, **kwargs):
-        """
-        Forward pass that adapts comfy input to original forward function
-        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
-        timesteps: (N,) tensor of diffusion timesteps
-        context: (N, 1, 120, C) conditioning
-        img_hw: height|width conditioning
-        aspect_ratio: aspect ratio conditioning
-        cn_hint: controlnet hint
-        """
-        ## size/ar from cond with fallback based on the latent image shape.
-        bs = x.shape[0]
-        data_info = {}
-        if img_hw is None:
-            data_info["img_hw"] = torch.tensor(
-                [[x.shape[2]*8, x.shape[3]*8]],
-                dtype=self.dtype,
-                device=x.device
-            ).repeat(bs, 1)
-        else:
-            data_info["img_hw"] = img_hw.to(x.dtype)
-        if aspect_ratio is None or True:
-            data_info["aspect_ratio"] = torch.tensor(
-                [[x.shape[2]/x.shape[3]]],
-                dtype=self.dtype,
-                device=x.device
-            ).repeat(bs, 1)
-        else:
-            data_info["aspect_ratio"] = aspect_ratio.to(x.dtype)
-
-        ## Still accepts the input w/o that dim but returns garbage
-        if len(context.shape) == 3:
-            context = context.unsqueeze(1)
-
-        ## run original forward pass
-        out = self.forward_raw(
-            x = x.to(self.dtype),
-            timestep = timesteps.to(self.dtype),
-            y = context.to(self.dtype),
-            c = cn_hint,
-            data_info=data_info,
-        )
-
-        ## only return EPS
-        out = out.to(torch.float)
-        eps, rest = out[:, :self.in_channels], out[:, self.in_channels:]
-        return eps
diff --git a/PixArt/nodes.py b/PixArt/nodes.py
index f027d89..6a1f2dd 100644
--- a/PixArt/nodes.py
+++ b/PixArt/nodes.py
@@ -1,278 +1,87 @@
-import os
-import json
-import torch
-import folder_paths
-
-from comfy import utils
-from .conf import pixart_conf, pixart_res
-from .lora import load_pixart_lora
-from .loader import load_pixart
-
-class PixArtCheckpointLoader:
-	@classmethod
-	def INPUT_TYPES(s):
-		return {
-			"required": {
-				"ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
-				"model": (list(pixart_conf.keys()),),
-			}
-		}
-	RETURN_TYPES = ("MODEL",)
-	RETURN_NAMES = ("model",)
-	FUNCTION = "load_checkpoint"
-	CATEGORY = "ExtraModels/PixArt"
-	TITLE = "PixArt Checkpoint Loader"
-
-	def load_checkpoint(self, ckpt_name, model):
-		ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
-		model_conf = pixart_conf[model]
-		model = load_pixart(
-			model_path = ckpt_path,
-			model_conf = model_conf,
-		)
-		return (model,)
-
-class PixArtCheckpointLoaderSimple(PixArtCheckpointLoader):
-	@classmethod
-	def INPUT_TYPES(s):
-		return {
-			"required": {
-				"ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
-			}
-		}
-	TITLE = "PixArt Checkpoint Loader (auto)"
-
-	def load_checkpoint(self, ckpt_name):
-		ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
-		model = load_pixart(model_path=ckpt_path)
-		return (model,)
-
-class PixArtResolutionSelect():
-	@classmethod
-	def INPUT_TYPES(s):
-		return {
-			"required": {
-				"model": (list(pixart_res.keys()),),
-				# keys are the same for both
-				"ratio": (list(pixart_res["PixArtMS_XL_2"].keys()),{"default":"1.00"}),
-			}
-		}
-	RETURN_TYPES = ("INT","INT")
-	RETURN_NAMES = ("width","height")
-	FUNCTION = "get_res"
-	CATEGORY = "ExtraModels/PixArt"
-	TITLE = "PixArt Resolution Select"
-
-	def get_res(self, model, ratio):
-		width, height = pixart_res[model][ratio]
-		return (width,height)
-
-class PixArtLoraLoader:
-	def __init__(self):
-		self.loaded_lora = None
-
-	@classmethod
-	def INPUT_TYPES(s):
-		return {
-			"required": {
-				"model": ("MODEL",),
-				"lora_name": (folder_paths.get_filename_list("loras"), ),
-				"strength": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
-			}
-		}
-	RETURN_TYPES = ("MODEL",)
-	FUNCTION = "load_lora"
-	CATEGORY = "ExtraModels/PixArt"
-	TITLE = "PixArt Load LoRA"
-
-	def load_lora(self, model, lora_name, strength,):
-		if strength == 0:
-			return (model)
-
-		lora_path = folder_paths.get_full_path("loras", lora_name)
-		lora = None
-		if self.loaded_lora is not None:
-			if self.loaded_lora[0] == lora_path:
-				lora = self.loaded_lora[1]
-			else:
-				temp = self.loaded_lora
-				self.loaded_lora = None
-				del temp
-
-		if lora is None:
-			lora = utils.load_torch_file(lora_path, safe_load=True)
-			self.loaded_lora = (lora_path, lora)
-
-		model_lora = load_pixart_lora(model, lora, lora_path, strength,)
-		return (model_lora,)
-
 class PixArtResolutionCond:
-	@classmethod
-	def INPUT_TYPES(s):
-		return {
-			"required": {
-				"cond": ("CONDITIONING", ),
-				"width": ("INT", {"default": 1024.0, "min": 0, "max": 8192}),
-				"height": ("INT", {"default": 1024.0, "min": 0, "max": 8192}),
-			}
-		}
-
-	RETURN_TYPES = ("CONDITIONING",)
-	RETURN_NAMES = ("cond",)
-	FUNCTION = "add_cond"
-	CATEGORY = "ExtraModels/PixArt"
-	TITLE = "PixArt Resolution Conditioning"
-	
-	def add_cond(self, cond, width, height):
-		for c in range(len(cond)):
-			cond[c][1].update({
-				"img_hw": [[height, width]],
-				"aspect_ratio": [[height/width]],
-			})
-		return (cond,)
-
-class PixArtControlNetCond:
-	@classmethod
-	def INPUT_TYPES(s):
-		return {
-			"required": {
-				"cond": ("CONDITIONING",),
-				"latent": ("LATENT",),
-				# "image": ("IMAGE",),
-				# "vae": ("VAE",),
-				# "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01})
-			}
-		}
-
-	RETURN_TYPES = ("CONDITIONING",)
-	RETURN_NAMES = ("cond",)
-	FUNCTION = "add_cond"
-	CATEGORY = "ExtraModels/PixArt"
-	TITLE = "PixArt ControlNet Conditioning"
-
-	def add_cond(self, cond, latent):
-		for c in range(len(cond)):
-			cond[c][1]["cn_hint"] = latent["samples"] * 0.18215
-		return (cond,)
-
-class PixArtT5TextEncode:
-	"""
-	Reference code, mostly to verify compatibility.
-	 Once everything works, this should instead inherit from the
-	 T5 text encode node and simply add the extra conds (res/ar).
-	"""
-	@classmethod
-	def INPUT_TYPES(s):
-		return {
-			"required": {
-				"text": ("STRING", {"multiline": True}),
-				"T5": ("T5",),
-			}
-		}
-
-	RETURN_TYPES = ("CONDITIONING",)
-	FUNCTION = "encode"
-	CATEGORY = "ExtraModels/PixArt"
-	TITLE = "PixArt T5 Text Encode [Reference]"
-
-	def mask_feature(self, emb, mask):
-		if emb.shape[0] == 1:
-			keep_index = mask.sum().item()
-			return emb[:, :, :keep_index, :], keep_index
-		else:
-			masked_feature = emb * mask[:, None, :, None]
-			return masked_feature, emb.shape[2]
-
-	def encode(self, text, T5):
-		text = text.lower().strip()
-		tokenizer_out = T5.tokenizer.tokenizer(
-			text,
-			max_length            = 120,
-			padding               = 'max_length',
-			truncation            = True,
-			return_attention_mask = True,
-			add_special_tokens    = True,
-			return_tensors        = 'pt'
-		)
-		tokens = tokenizer_out["input_ids"]
-		mask = tokenizer_out["attention_mask"]
-		embs = T5.cond_stage_model.transformer(
-			input_ids      = tokens.to(T5.load_device),
-			attention_mask = mask.to(T5.load_device),
-		)['last_hidden_state'].float()[:, None]
-		masked_embs, keep_index = self.mask_feature(
-			embs.detach().to("cpu"),
-			mask.detach().to("cpu")
-		)
-		masked_embs = masked_embs.squeeze(0) # match CLIP/internal
-		print("Encoded T5:", masked_embs.shape)
-		return ([[masked_embs, {}]], )
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "cond": ("CONDITIONING", ),
+                "width": ("INT", {"default": 1024.0, "min": 0, "max": 8192}),
+                "height": ("INT", {"default": 1024.0, "min": 0, "max": 8192}),
+            }
+        }
+
+    RETURN_TYPES = ("CONDITIONING",)
+    RETURN_NAMES = ("cond",)
+    FUNCTION = "add_cond"
+    CATEGORY = "ExtraModels/PixArt"
+    TITLE = "PixArt Resolution Conditioning"
+    
+    def add_cond(self, cond, width, height):
+        for c in range(len(cond)):
+            cond[c][1].update({
+                "img_hw": [[height, width]],
+                "aspect_ratio": [[height/width]],
+            })
+        return (cond,)
 
 class PixArtT5FromSD3CLIP:
-	"""
-	Split the T5 text encoder away from SD3
-	"""
-	@classmethod
-	def INPUT_TYPES(s):
-		return {
-			"required": {
-				"sd3_clip": ("CLIP",),
-				"padding": ("INT", {"default": 1, "min": 1, "max": 300}),
-			}
-		}
-
-	RETURN_TYPES = ("CLIP",)
-	RETURN_NAMES = ("t5",)
-	FUNCTION = "split"
-	CATEGORY = "ExtraModels/PixArt"
-	TITLE = "PixArt T5 from SD3 CLIP"
-
-	def split(self, sd3_clip, padding):
-		try:
-			from comfy.text_encoders.sd3_clip import SD3Tokenizer, SD3ClipModel
-		except ImportError:
-			# fallback for older ComfyUI versions
-			from comfy.sd3_clip import SD3Tokenizer, SD3ClipModel
-		import copy
-	
-		clip = sd3_clip.clone()
-		assert clip.cond_stage_model.t5xxl is not None, "CLIP must have T5 loaded!"
-
-		# remove transformer
-		transformer = clip.cond_stage_model.t5xxl.transformer
-		clip.cond_stage_model.t5xxl.transformer = None
-
-		# clone object
-		tmp = SD3ClipModel(clip_l=False, clip_g=False, t5=False)
-		tmp.t5xxl = copy.deepcopy(clip.cond_stage_model.t5xxl)
-		# put transformer back
-		clip.cond_stage_model.t5xxl.transformer = transformer
-		tmp.t5xxl.transformer = transformer
-
-		# override special tokens
-		tmp.t5xxl.special_tokens = copy.deepcopy(clip.cond_stage_model.t5xxl.special_tokens)
-		tmp.t5xxl.special_tokens.pop("end") # make sure empty tokens match
-		
-		# add attn mask opt if present in original
-		if hasattr(sd3_clip.cond_stage_model, "t5_attention_mask"):
-			tmp.t5_attention_mask = False
-
-		# tokenizer
-		tok = SD3Tokenizer()
-		tok.t5xxl.min_length = padding
-		
-		clip.cond_stage_model = tmp
-		clip.tokenizer = tok
-
-		return (clip, )
+    """
+    Split the T5 text encoder away from SD3
+    """
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sd3_clip": ("CLIP",),
+                "padding": ("INT", {"default": 1, "min": 1, "max": 300}),
+            }
+        }
+
+    RETURN_TYPES = ("CLIP",)
+    RETURN_NAMES = ("t5",)
+    FUNCTION = "split"
+    CATEGORY = "ExtraModels/PixArt"
+    TITLE = "PixArt T5 from SD3 CLIP"
+
+    def split(self, sd3_clip, padding):
+        try:
+            from comfy.text_encoders.sd3_clip import SD3Tokenizer, SD3ClipModel
+        except ImportError:
+            # fallback for older ComfyUI versions
+            from comfy.sd3_clip import SD3Tokenizer, SD3ClipModel # type: ignore
+        import copy
+    
+        clip = sd3_clip.clone()
+        assert clip.cond_stage_model.t5xxl is not None, "CLIP must have T5 loaded!"
+
+        # remove transformer
+        transformer = clip.cond_stage_model.t5xxl.transformer
+        clip.cond_stage_model.t5xxl.transformer = None
+
+        # clone object
+        tmp = SD3ClipModel(clip_l=False, clip_g=False, t5=False)
+        tmp.t5xxl = copy.deepcopy(clip.cond_stage_model.t5xxl)
+        # put transformer back
+        clip.cond_stage_model.t5xxl.transformer = transformer
+        tmp.t5xxl.transformer = transformer
+
+        # override special tokens
+        tmp.t5xxl.special_tokens = copy.deepcopy(clip.cond_stage_model.t5xxl.special_tokens)
+        tmp.t5xxl.special_tokens.pop("end") # make sure empty tokens match
+        
+        # add attn mask opt if present in original
+        if hasattr(sd3_clip.cond_stage_model, "t5_attention_mask"):
+            tmp.t5_attention_mask = False
+
+        # tokenizer
+        tok = SD3Tokenizer()
+        tok.t5xxl.min_length = padding
+        
+        clip.cond_stage_model = tmp
+        clip.tokenizer = tok
+
+        return (clip, )
 
 NODE_CLASS_MAPPINGS = {
-	"PixArtCheckpointLoader" : PixArtCheckpointLoader,
-	"PixArtCheckpointLoaderSimple" : PixArtCheckpointLoaderSimple,
-	"PixArtResolutionSelect" : PixArtResolutionSelect,
-	"PixArtLoraLoader" : PixArtLoraLoader,
-	"PixArtT5TextEncode" : PixArtT5TextEncode,
-	"PixArtResolutionCond" : PixArtResolutionCond,
-	"PixArtControlNetCond" : PixArtControlNetCond,
-	"PixArtT5FromSD3CLIP": PixArtT5FromSD3CLIP,
+    "PixArtResolutionCond" : PixArtResolutionCond,
+    "PixArtT5FromSD3CLIP": PixArtT5FromSD3CLIP,
 }
diff --git a/T5/LICENSE-ComfyUI b/T5/LICENSE-ComfyUI
deleted file mode 100644
index f288702..0000000
--- a/T5/LICENSE-ComfyUI
+++ /dev/null
@@ -1,674 +0,0 @@
-                    GNU GENERAL PUBLIC LICENSE
-                       Version 3, 29 June 2007
-
- Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
- Everyone is permitted to copy and distribute verbatim copies
- of this license document, but changing it is not allowed.
-
-                            Preamble
-
-  The GNU General Public License is a free, copyleft license for
-software and other kinds of works.
-
-  The licenses for most software and other practical works are designed
-to take away your freedom to share and change the works.  By contrast,
-the GNU General Public License is intended to guarantee your freedom to
-share and change all versions of a program--to make sure it remains free
-software for all its users.  We, the Free Software Foundation, use the
-GNU General Public License for most of our software; it applies also to
-any other work released this way by its authors.  You can apply it to
-your programs, too.
-
-  When we speak of free software, we are referring to freedom, not
-price.  Our General Public Licenses are designed to make sure that you
-have the freedom to distribute copies of free software (and charge for
-them if you wish), that you receive source code or can get it if you
-want it, that you can change the software or use pieces of it in new
-free programs, and that you know you can do these things.
-
-  To protect your rights, we need to prevent others from denying you
-these rights or asking you to surrender the rights.  Therefore, you have
-certain responsibilities if you distribute copies of the software, or if
-you modify it: responsibilities to respect the freedom of others.
-
-  For example, if you distribute copies of such a program, whether
-gratis or for a fee, you must pass on to the recipients the same
-freedoms that you received.  You must make sure that they, too, receive
-or can get the source code.  And you must show them these terms so they
-know their rights.
-
-  Developers that use the GNU GPL protect your rights with two steps:
-(1) assert copyright on the software, and (2) offer you this License
-giving you legal permission to copy, distribute and/or modify it.
-
-  For the developers' and authors' protection, the GPL clearly explains
-that there is no warranty for this free software.  For both users' and
-authors' sake, the GPL requires that modified versions be marked as
-changed, so that their problems will not be attributed erroneously to
-authors of previous versions.
-
-  Some devices are designed to deny users access to install or run
-modified versions of the software inside them, although the manufacturer
-can do so.  This is fundamentally incompatible with the aim of
-protecting users' freedom to change the software.  The systematic
-pattern of such abuse occurs in the area of products for individuals to
-use, which is precisely where it is most unacceptable.  Therefore, we
-have designed this version of the GPL to prohibit the practice for those
-products.  If such problems arise substantially in other domains, we
-stand ready to extend this provision to those domains in future versions
-of the GPL, as needed to protect the freedom of users.
-
-  Finally, every program is threatened constantly by software patents.
-States should not allow patents to restrict development and use of
-software on general-purpose computers, but in those that do, we wish to
-avoid the special danger that patents applied to a free program could
-make it effectively proprietary.  To prevent this, the GPL assures that
-patents cannot be used to render the program non-free.
-
-  The precise terms and conditions for copying, distribution and
-modification follow.
-
-                       TERMS AND CONDITIONS
-
-  0. Definitions.
-
-  "This License" refers to version 3 of the GNU General Public License.
-
-  "Copyright" also means copyright-like laws that apply to other kinds of
-works, such as semiconductor masks.
-
-  "The Program" refers to any copyrightable work licensed under this
-License.  Each licensee is addressed as "you".  "Licensees" and
-"recipients" may be individuals or organizations.
-
-  To "modify" a work means to copy from or adapt all or part of the work
-in a fashion requiring copyright permission, other than the making of an
-exact copy.  The resulting work is called a "modified version" of the
-earlier work or a work "based on" the earlier work.
-
-  A "covered work" means either the unmodified Program or a work based
-on the Program.
-
-  To "propagate" a work means to do anything with it that, without
-permission, would make you directly or secondarily liable for
-infringement under applicable copyright law, except executing it on a
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-public, and in some countries other activities as well.
-
-  To "convey" a work means any kind of propagation that enables other
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-a computer network, with no transfer of a copy, is not conveying.
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-  An interactive user interface displays "Appropriate Legal Notices"
-to the extent that it includes a convenient and prominently visible
-feature that (1) displays an appropriate copyright notice, and (2)
-tells the user that there is no warranty for the work (except to the
-extent that warranties are provided), that licensees may convey the
-work under this License, and how to view a copy of this License.  If
-the interface presents a list of user commands or options, such as a
-menu, a prominent item in the list meets this criterion.
-
-  1. Source Code.
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diff --git a/T5/loader.py b/T5/loader.py
deleted file mode 100644
index db321ab..0000000
--- a/T5/loader.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import os
-import torch
-import comfy.utils
-import comfy.model_patcher
-from comfy import model_management
-import folder_paths
-
-from .t5v11 import T5v11Model, T5v11Tokenizer
-
-class EXM_T5v11:
-	def __init__(self, textmodel_ver="xxl", embedding_directory=None, textmodel_path=None, no_init=False, device="cpu", dtype=None):
-		if no_init:
-			return
-
-		if device == "auto":
-			size = 0
-			self.load_device = model_management.text_encoder_device()
-			self.offload_device = model_management.text_encoder_offload_device()
-			self.init_device = "cpu"
-		elif dtype == "bnb8bit":
-			# BNB doesn't support size enum
-			size = 12.4 * (1024**3)
-			# Or moving between devices
-			self.load_device = model_management.get_torch_device()
-			self.offload_device = self.load_device
-			self.init_device = self.load_device
-		elif dtype == "bnb4bit":
-			# This seems to use the same VRAM as 8bit on Pascal?
-			size = 6.2 * (1024**3)
-			self.load_device = model_management.get_torch_device()
-			self.offload_device = self.load_device
-			self.init_device = self.load_device
-		elif device == "cpu":
-			size = 0
-			self.load_device = "cpu"
-			self.offload_device = "cpu"
-			self.init_device="cpu"
-		elif device.startswith("cuda"):
-			print("Direct CUDA device override!\nVRAM will not be freed by default.")
-			size = 0
-			self.load_device = device
-			self.offload_device = device
-			self.init_device = device
-		else:
-			size = 0
-			self.load_device = model_management.get_torch_device()
-			self.offload_device = "cpu"
-			self.init_device="cpu"
-
-		self.cond_stage_model = T5v11Model(
-			textmodel_ver  = textmodel_ver,
-			textmodel_path = textmodel_path,
-			device         = device,
-			dtype          = dtype,
-		)
-		self.tokenizer = T5v11Tokenizer(embedding_directory=embedding_directory)
-		self.patcher = comfy.model_patcher.ModelPatcher(
-			self.cond_stage_model,
-			load_device    = self.load_device,
-			offload_device = self.offload_device,
-			size           = size,
-		)
-
-	def clone(self):
-		n = T5(no_init=True)
-		n.patcher = self.patcher.clone()
-		n.cond_stage_model = self.cond_stage_model
-		n.tokenizer = self.tokenizer
-		return n
-
-	def tokenize(self, text, return_word_ids=False):
-		return self.tokenizer.tokenize_with_weights(text, return_word_ids)
-
-	def encode_from_tokens(self, tokens):
-		self.load_model()
-		return self.cond_stage_model.encode_token_weights(tokens)
-
-	def encode(self, text):
-		tokens = self.tokenize(text)
-		return self.encode_from_tokens(tokens)
-
-	def load_sd(self, sd):
-		return self.cond_stage_model.load_sd(sd)
-
-	def get_sd(self):
-		return self.cond_stage_model.state_dict()
-
-	def load_model(self):
-		if self.load_device != "cpu":
-			model_management.load_model_gpu(self.patcher)
-		return self.patcher
-
-	def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
-		return self.patcher.add_patches(patches, strength_patch, strength_model)
-
-	def get_key_patches(self):
-		return self.patcher.get_key_patches()
-
-
-def load_t5(model_type, model_ver, model_path, path_type="file", device="cpu", dtype=None):
-	assert model_type in ["t5v11"] # Only supported model for now
-	model_args = {
-		"textmodel_ver" : model_ver,
-		"device" : device,
-		"dtype"  : dtype,
-	}
-
-	if path_type == "folder":
-		# pass directly to transformers and initialize there
-		# this is to avoid having to handle multi-file state dict loading for now.
-		model_args["textmodel_path"] = os.path.dirname(model_path)
-		return EXM_T5v11(**model_args)
-	else:
-		# for some reason this returns garbage with torch.int8 weights, or just OOMs
-		model = EXM_T5v11(**model_args)
-		sd = comfy.utils.load_torch_file(model_path)
-		model.load_sd(sd)
-		return model
diff --git a/T5/nodes.py b/T5/nodes.py
deleted file mode 100644
index 021386e..0000000
--- a/T5/nodes.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import os
-import json
-import torch
-import folder_paths
-
-from .loader import load_t5
-from ..utils.dtype import string_to_dtype
-
-# initialize custom folder path
-os.makedirs(
-	os.path.join(folder_paths.models_dir,"t5"),
-	exist_ok = True,
-)
-folder_paths.folder_names_and_paths["t5"] = (
-	[
-		os.path.join(folder_paths.models_dir,"t5"),
-		*folder_paths.folder_names_and_paths.get("t5", [[],set()])[0]
-	],
-	folder_paths.supported_pt_extensions
-)
-
-dtypes = [
-	"default",
-	"auto (comfy)",
-	"FP32",
-	"FP16",
-	# Note: remove these at some point
-	"bnb8bit",
-	"bnb4bit",
-]
-try: torch.float8_e5m2
-except AttributeError: print("Torch version too old for FP8")
-else: dtypes += ["FP8 E4M3", "FP8 E5M2"]
-
-class T5v11Loader:
-	@classmethod
-	def INPUT_TYPES(s):
-		devices = ["auto", "cpu", "gpu"]
-		# hack for using second GPU as offload
-		for k in range(1, torch.cuda.device_count()):
-			devices.append(f"cuda:{k}")
-		return {
-			"required": {
-				"t5v11_name": (folder_paths.get_filename_list("t5"),),
-				"t5v11_ver": (["xxl"],),
-				"path_type": (["folder", "file"],),
-				"device": (devices, {"default":"cpu"}),
-				"dtype": (dtypes,),
-			}
-		}
-	RETURN_TYPES = ("T5",)
-	FUNCTION = "load_model"
-	CATEGORY = "ExtraModels/T5"
-	TITLE = "T5v1.1 Loader"
-
-	def load_model(self, t5v11_name, t5v11_ver, path_type, device, dtype):
-		if "bnb" in dtype:
-			assert device == "gpu" or device.startswith("cuda"), "BitsAndBytes only works on CUDA! Set device to 'gpu'."
-		dtype = string_to_dtype(dtype, "text_encoder")
-		if device == "cpu":
-			assert dtype in [None, torch.float32], f"Can't use dtype '{dtype}' with CPU! Set dtype to 'default'."
-
-		return (load_t5(
-			model_type = "t5v11",
-			model_ver  = t5v11_ver,
-			model_path = folder_paths.get_full_path("t5", t5v11_name),
-			path_type  = path_type,
-			device     = device,
-			dtype      = dtype,
-		),)
-
-class T5TextEncode:
-	@classmethod
-	def INPUT_TYPES(s):
-		return {
-			"required": {
-				"text": ("STRING", {"multiline": True}),
-				"T5": ("T5",),
-			}
-		}
-
-	RETURN_TYPES = ("CONDITIONING",)
-	FUNCTION = "encode"
-	CATEGORY = "ExtraModels/T5"
-	TITLE = "T5 Text Encode"
-
-	def encode(self, text, T5=None):
-		tokens = T5.tokenize(text)
-		cond = T5.encode_from_tokens(tokens)
-		return ([[cond, {}]], )
-
-NODE_CLASS_MAPPINGS = {
-	"T5v11Loader"  : T5v11Loader,
-	"T5TextEncode" : T5TextEncode,
-}
diff --git a/T5/t5v11-xxl_config.json b/T5/t5v11-xxl_config.json
deleted file mode 100644
index d133daa..0000000
--- a/T5/t5v11-xxl_config.json
+++ /dev/null
@@ -1,31 +0,0 @@
-{
-  "_name_or_path": "google/t5-v1_1-xxl",
-  "architectures": [
-    "T5EncoderModel"
-  ],
-  "d_ff": 10240,
-  "d_kv": 64,
-  "d_model": 4096,
-  "decoder_start_token_id": 0,
-  "dense_act_fn": "gelu_new",
-  "dropout_rate": 0.1,
-  "eos_token_id": 1,
-  "feed_forward_proj": "gated-gelu",
-  "initializer_factor": 1.0,
-  "is_encoder_decoder": true,
-  "is_gated_act": true,
-  "layer_norm_epsilon": 1e-06,
-  "model_type": "t5",
-  "num_decoder_layers": 24,
-  "num_heads": 64,
-  "num_layers": 24,
-  "output_past": true,
-  "pad_token_id": 0,
-  "relative_attention_max_distance": 128,
-  "relative_attention_num_buckets": 32,
-  "tie_word_embeddings": false,
-  "torch_dtype": "float32",
-  "transformers_version": "4.21.1",
-  "use_cache": true,
-  "vocab_size": 32128
-}
diff --git a/T5/t5v11.py b/T5/t5v11.py
deleted file mode 100644
index 76cec9c..0000000
--- a/T5/t5v11.py
+++ /dev/null
@@ -1,227 +0,0 @@
-"""
-Adapted from comfyui CLIP code.
-https://github.com/comfyanonymous/ComfyUI/blob/master/comfy/sd1_clip.py
-"""
-
-import os
-
-from transformers import T5Tokenizer, T5EncoderModel, T5Config, modeling_utils
-import torch
-import traceback
-import zipfile
-from comfy import model_management
-
-from comfy.sd1_clip import parse_parentheses, token_weights, escape_important, unescape_important, safe_load_embed_zip, expand_directory_list, load_embed
-
-class T5v11Model(torch.nn.Module):
-    def __init__(self, textmodel_ver="xxl", textmodel_json_config=None, textmodel_path=None, device="cpu", max_length=120, freeze=True, dtype=None):
-        super().__init__()
-
-        self.num_layers = 24
-        self.max_length = max_length
-        self.bnb = False
-
-        if textmodel_path is not None:
-            model_args = {}
-            model_args["low_cpu_mem_usage"] = True # Don't take 2x system ram on cpu
-            if dtype == "bnb8bit":
-                self.bnb = True
-                model_args["load_in_8bit"] = True
-            elif dtype == "bnb4bit":
-                self.bnb = True
-                model_args["load_in_4bit"] = True
-            else:
-                if dtype: model_args["torch_dtype"] = dtype
-                self.bnb = False
-            # second GPU offload hack part 2
-            if device.startswith("cuda"):
-                model_args["device_map"] = device
-            print(f"Loading T5 from '{textmodel_path}'")
-            self.transformer = T5EncoderModel.from_pretrained(textmodel_path, **model_args)
-        else:
-            if textmodel_json_config is None:
-                textmodel_json_config = os.path.join(
-                    os.path.dirname(os.path.realpath(__file__)),
-                    f"t5v11-{textmodel_ver}_config.json"
-                )
-            config = T5Config.from_json_file(textmodel_json_config)
-            self.num_layers = config.num_hidden_layers
-            with modeling_utils.no_init_weights():
-                self.transformer = T5EncoderModel(config)
-
-        if freeze:
-            self.freeze()
-        self.empty_tokens = [[0] * self.max_length] # <pad> token
-
-    def freeze(self):
-        self.transformer = self.transformer.eval()
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, tokens):
-        device = self.transformer.get_input_embeddings().weight.device
-        tokens = torch.LongTensor(tokens).to(device)
-        attention_mask = torch.zeros_like(tokens)
-        max_token = 1 # </s> token
-        for x in range(attention_mask.shape[0]):
-            for y in range(attention_mask.shape[1]):
-                attention_mask[x, y] = 1
-                if tokens[x, y] == max_token:
-                    break
-
-        outputs = self.transformer(input_ids=tokens, attention_mask=attention_mask)
-
-        z = outputs['last_hidden_state']
-        z.detach().cpu().float()
-        return z
-
-    def encode(self, tokens):
-        return self(tokens)
-
-    def load_sd(self, sd):
-        return self.transformer.load_state_dict(sd, strict=False)
-
-    def to(self, *args, **kwargs):
-        """BNB complains if you try to change the device or dtype"""
-        if self.bnb:
-            print("Thanks to BitsAndBytes, T5 becomes an immovable rock.", args, kwargs)
-        else:
-            self.transformer.to(*args, **kwargs)
-
-    def encode_token_weights(self, token_weight_pairs, return_padded=False):
-        to_encode = list(self.empty_tokens)
-        for x in token_weight_pairs:
-            tokens = list(map(lambda a: a[0], x))
-            to_encode.append(tokens)
-
-        out = self.encode(to_encode)
-        z_empty = out[0:1]
-
-        output = []
-        for k in range(1, out.shape[0]):
-            z = out[k:k+1]
-            for i in range(len(z)):
-                for j in range(len(z[i])):
-                    weight = token_weight_pairs[k - 1][j][1]
-                    z[i][j] = (z[i][j] - z_empty[0][j]) * weight + z_empty[0][j]
-            output.append(z)
-
-        if (len(output) == 0):
-            return z_empty.cpu()
-
-        out = torch.cat(output, dim=-2)
-        if not return_padded:
-            # Count number of tokens that aren't <pad>, then use that number as an index.
-            keep_index = sum([sum([1 for y in x if y[0] != 0]) for x in token_weight_pairs])
-            out = out[:, :keep_index, :]
-        return out
-
-
-class T5v11Tokenizer:
-    """
-    This is largely just based on the ComfyUI CLIP code.
-    """
-    def __init__(self, tokenizer_path=None, max_length=120, embedding_directory=None, embedding_size=4096, embedding_key='t5'):
-        if tokenizer_path is None:
-            tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
-        self.tokenizer = T5Tokenizer.from_pretrained(tokenizer_path)
-        self.max_length = max_length
-        self.max_tokens_per_section = self.max_length - 1 # </s> but no <BOS>
-
-        self.pad_token = self.tokenizer("<pad>", add_special_tokens=False)["input_ids"][0]
-        self.end_token = self.tokenizer("</s>", add_special_tokens=False)["input_ids"][0]
-        vocab = self.tokenizer.get_vocab()
-        self.inv_vocab = {v: k for k, v in vocab.items()}
-        self.embedding_directory = embedding_directory
-        self.max_word_length = 8 # haven't verified this
-        self.embedding_identifier = "embedding:"
-        self.embedding_size = embedding_size
-        self.embedding_key = embedding_key
-
-    def _try_get_embedding(self, embedding_name:str):
-        '''
-        Takes a potential embedding name and tries to retrieve it.
-        Returns a Tuple consisting of the embedding and any leftover string, embedding can be None.
-        '''
-        embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key)
-        if embed is None:
-            stripped = embedding_name.strip(',')
-            if len(stripped) < len(embedding_name):
-                embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key)
-                return (embed, embedding_name[len(stripped):])
-        return (embed, "")
-
-    def tokenize_with_weights(self, text:str, return_word_ids=False):
-        '''
-        Takes a prompt and converts it to a list of (token, weight, word id) elements.
-        Tokens can both be integer tokens and pre computed T5 tensors.
-        Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens.
-        Returned list has the dimensions NxM where M is the input size of T5
-        '''
-        pad_token = self.pad_token
-        text = escape_important(text)
-        parsed_weights = token_weights(text, 1.0)
-
-        #tokenize words
-        tokens = []
-        for weighted_segment, weight in parsed_weights:
-            to_tokenize = unescape_important(weighted_segment).replace("\n", " ").split(' ')
-            to_tokenize = [x for x in to_tokenize if x != ""]
-            for word in to_tokenize:
-                #if we find an embedding, deal with the embedding
-                if word.startswith(self.embedding_identifier) and self.embedding_directory is not None:
-                    embedding_name = word[len(self.embedding_identifier):].strip('\n')
-                    embed, leftover = self._try_get_embedding(embedding_name)
-                    if embed is None:
-                        print(f"warning, embedding:{embedding_name} does not exist, ignoring")
-                    else:
-                        if len(embed.shape) == 1:
-                            tokens.append([(embed, weight)])
-                        else:
-                            tokens.append([(embed[x], weight) for x in range(embed.shape[0])])
-                    #if we accidentally have leftover text, continue parsing using leftover, else move on to next word
-                    if leftover != "":
-                        word = leftover
-                    else:
-                        continue
-                #parse word
-                tokens.append([(t, weight) for t in self.tokenizer(word, add_special_tokens=False)["input_ids"]])
-
-        #reshape token array to T5 input size
-        batched_tokens = []
-        batch = []
-        batched_tokens.append(batch)
-        for i, t_group in enumerate(tokens):
-            #determine if we're going to try and keep the tokens in a single batch
-            is_large = len(t_group) >= self.max_word_length
-
-            while len(t_group) > 0:
-                if len(t_group) + len(batch) > self.max_length - 1:
-                    remaining_length = self.max_length - len(batch) - 1
-                    #break word in two and add end token
-                    if is_large:
-                        batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]])
-                        batch.append((self.end_token, 1.0, 0))
-                        t_group = t_group[remaining_length:]
-                    #add end token and pad
-                    else:
-                        batch.append((self.end_token, 1.0, 0))
-                        batch.extend([(self.pad_token, 1.0, 0)] * (remaining_length))
-                    #start new batch
-                    batch = []
-                    batched_tokens.append(batch)
-                else:
-                    batch.extend([(t,w,i+1) for t,w in t_group])
-                    t_group = []
-
-        # fill last batch
-        batch.extend([(self.end_token, 1.0, 0)] + [(self.pad_token, 1.0, 0)] * (self.max_length - len(batch) - 1))
-        # instead of filling, just add EOS (DEBUG)
-        # batch.extend([(self.end_token, 1.0, 0)])
-
-        if not return_word_ids:
-            batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens]
-        return batched_tokens
-
-    def untokenize(self, token_weight_pair):
-        return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair))
diff --git a/__init__.py b/__init__.py
index 1fff84c..5dc119f 100644
--- a/__init__.py
+++ b/__init__.py
@@ -1,51 +1,50 @@
 # only import if running as a custom node
 try:
-	import comfy.utils
+    import comfy.utils
 except ImportError:
-	pass
+    pass
 else:
-	NODE_CLASS_MAPPINGS = {}
+    NODE_CLASS_MAPPINGS = {}
 
-	# Deci Diffusion
-	# from .DeciDiffusion.nodes import NODE_CLASS_MAPPINGS as DeciDiffusion_Nodes
-	# NODE_CLASS_MAPPINGS.update(DeciDiffusion_Nodes)
+    # All text encoders
+    from .text_encoders.nodes import NODE_CLASS_MAPPINGS as Tenc_Nodes
+    NODE_CLASS_MAPPINGS.update(Tenc_Nodes)
 
-	# DiT
-	from .DiT.nodes import NODE_CLASS_MAPPINGS as DiT_Nodes
-	NODE_CLASS_MAPPINGS.update(DiT_Nodes)
+    # Generic nodes
+    from .nodes import NODE_CLASS_MAPPINGS as Base_Nodes
+    NODE_CLASS_MAPPINGS.update(Base_Nodes)
 
-	# PixArt
-	from .PixArt.nodes import NODE_CLASS_MAPPINGS as PixArt_Nodes
-	NODE_CLASS_MAPPINGS.update(PixArt_Nodes)
+    # DiT
+    from .DiT.nodes import NODE_CLASS_MAPPINGS as DiT_Nodes
+    NODE_CLASS_MAPPINGS.update(DiT_Nodes)
 
-	# T5
-	from .T5.nodes import NODE_CLASS_MAPPINGS as T5_Nodes
-	NODE_CLASS_MAPPINGS.update(T5_Nodes)
+    # PixArt
+    from .PixArt.nodes import NODE_CLASS_MAPPINGS as PixArt_Nodes
+    NODE_CLASS_MAPPINGS.update(PixArt_Nodes)
 
-	# HYDiT
-	from .HunYuanDiT.nodes import NODE_CLASS_MAPPINGS as HunYuanDiT_Nodes
-	NODE_CLASS_MAPPINGS.update(HunYuanDiT_Nodes)
+    # HYDiT
+    from .HunYuanDiT.nodes import NODE_CLASS_MAPPINGS as HunYuanDiT_Nodes
+    NODE_CLASS_MAPPINGS.update(HunYuanDiT_Nodes)
 
-	# VAE
-	from .VAE.nodes import NODE_CLASS_MAPPINGS as VAE_Nodes
-	NODE_CLASS_MAPPINGS.update(VAE_Nodes)
+    # VAE
+    from .VAE.nodes import NODE_CLASS_MAPPINGS as VAE_Nodes
+    NODE_CLASS_MAPPINGS.update(VAE_Nodes)
  
- 	# MiaoBi
-	from .MiaoBi.nodes import NODE_CLASS_MAPPINGS as MiaoBi_Nodes
-	NODE_CLASS_MAPPINGS.update(MiaoBi_Nodes)
+    # MiaoBi
+    from .MiaoBi.nodes import NODE_CLASS_MAPPINGS as MiaoBi_Nodes
+    NODE_CLASS_MAPPINGS.update(MiaoBi_Nodes)
 
-	# Extra
-	from .utils.nodes import NODE_CLASS_MAPPINGS as Extra_Nodes
-	NODE_CLASS_MAPPINGS.update(Extra_Nodes)
+    # Extra
+    from .utils.nodes import NODE_CLASS_MAPPINGS as Extra_Nodes
+    NODE_CLASS_MAPPINGS.update(Extra_Nodes)
 
-	# Sana
-	from .Sana.nodes import NODE_CLASS_MAPPINGS as Sana_Nodes
-	NODE_CLASS_MAPPINGS.update(Sana_Nodes)
+    # Sana
+    from .Sana.nodes import NODE_CLASS_MAPPINGS as Sana_Nodes
+    NODE_CLASS_MAPPINGS.update(Sana_Nodes)
 
-	# Gemma
-	from .Gemma.nodes import NODE_CLASS_MAPPINGS as Gemma_Nodes
-	NODE_CLASS_MAPPINGS.update(Gemma_Nodes)
-
-	NODE_DISPLAY_NAME_MAPPINGS = {k:v.TITLE for k,v in NODE_CLASS_MAPPINGS.items()}
-	__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
+    # Gemma
+    from .Gemma.nodes import NODE_CLASS_MAPPINGS as Gemma_Nodes
+    NODE_CLASS_MAPPINGS.update(Gemma_Nodes)
 
+    NODE_DISPLAY_NAME_MAPPINGS = {k:v.TITLE for k,v in NODE_CLASS_MAPPINGS.items()}
+    __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
diff --git a/nodes.py b/nodes.py
new file mode 100644
index 0000000..eab0d3d
--- /dev/null
+++ b/nodes.py
@@ -0,0 +1,34 @@
+import folder_paths
+import comfy.utils
+
+from .PixArt.loader import load_pixart_state_dict
+
+loaders = {
+    "PixArt": load_pixart_state_dict,
+}
+
+class EXMUnetLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "unet_name": (folder_paths.get_filename_list("unet"),),
+                "model_type": (list(loaders.keys()),)
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "load_unet"
+    CATEGORY = "ExtraModels"
+    TITLE = "Load Diffusion Model (ExtraModels)"
+
+    def load_unet(self, unet_name, model_type):
+        model_options = {}
+        unet_path = folder_paths.get_full_path("diffusion_models", unet_name)
+        loader_fn = loaders[model_type]
+        sd = comfy.utils.load_torch_file(unet_path)
+        return (loader_fn(sd),)
+
+NODE_CLASS_MAPPINGS = {
+    "EXMUnetLoader": EXMUnetLoader,
+}
diff --git a/nodes/pixart.py b/nodes/pixart.py
new file mode 100644
index 0000000..e69de29
diff --git a/text_encoders/nodes.py b/text_encoders/nodes.py
new file mode 100644
index 0000000..5bda333
--- /dev/null
+++ b/text_encoders/nodes.py
@@ -0,0 +1,37 @@
+import folder_paths
+
+from .tenc import load_text_encoder, tenc_names
+
+class EXMCLIPLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        files = []
+        files += folder_paths.get_filename_list("clip")
+        # if "clip_gguf" in folder_paths.folder_names_and_paths:
+        #     files += folder_paths.get_filename_list("clip_gguf")
+        return {
+            "required": {
+                "clip_name": (files, ),
+                "type": (["PixArt", "MiaoBi", "Sana"],),
+            }
+        }
+    
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "load_clip"
+    CATEGORY = "ExtraModels"
+    TITLE = "CLIPLoader (ExtraModels)"
+
+    def load_clip(self, clip_name, type):
+        clip_path = folder_paths.get_full_path("clip", clip_name)
+        clip_type = tenc_names.get(type, None)
+
+        clip = load_text_encoder(
+            ckpt_paths =[clip_path],
+            embedding_directory = folder_paths.get_folder_paths("embeddings"),
+            clip_type = clip_type
+        )
+        return (clip,)
+
+NODE_CLASS_MAPPINGS = {
+    "EXMCLIPLoader": EXMCLIPLoader,
+}
diff --git a/text_encoders/pixart/tenc.py b/text_encoders/pixart/tenc.py
new file mode 100644
index 0000000..456cd92
--- /dev/null
+++ b/text_encoders/pixart/tenc.py
@@ -0,0 +1,41 @@
+from comfy import sd1_clip
+import comfy.text_encoders.t5
+import comfy.text_encoders.sd3_clip
+import comfy.model_management
+from transformers import T5TokenizerFast
+import torch
+import os
+
+class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        # make sure empty tokens match
+        self.special_tokens.pop("end")
+
+class PixArtT5XXL(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options)
+
+class T5XXLTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer_path = os.path.join(
+            os.path.dirname(os.path.dirname(os.path.realpath(__file__))),
+            "tokenizers", "t5_tokenizer",
+        )
+        super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1)
+
+class PixArtTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
+
+# TODO: don't duplicate this?
+def pixart_te(dtype_t5=None, t5xxl_scaled_fp8=None):
+    class PixArtTEModel_(PixArtT5XXL):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+                model_options = model_options.copy()
+                model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+            if dtype is None:
+                dtype = dtype_t5
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return PixArtTEModel_
diff --git a/text_encoders/tenc.py b/text_encoders/tenc.py
new file mode 100644
index 0000000..239d97d
--- /dev/null
+++ b/text_encoders/tenc.py
@@ -0,0 +1,72 @@
+import logging
+from enum import Enum
+
+import comfy.sd
+import comfy.utils
+import comfy.text_encoders
+
+from .pixart.tenc import pixart_te, PixArtTokenizer
+
+class TencType(Enum):
+    # offset in case we ever integrate w/ original
+    PixArt = 1001
+    MiaoBi = 1002
+    # HunYuan = 1003 # deprecated
+    Sana = 1004
+
+tenc_names = {
+    # for node readout
+    "PixArt": TencType.PixArt,
+    "MiaoBi": TencType.MiaoBi,
+    # "HunYuan": TencType.HunYuan,
+    "Sana": TencType.Sana,
+}
+
+
+def load_text_encoder(ckpt_paths, embedding_directory=None, clip_type=TencType.PixArt, model_options={}):
+    # Partial duplicate of ComfyUI/comfy/sd:load_clip
+    clip_data = []
+    for p in ckpt_paths:
+        if p.lower().endswith(".gguf"):
+            # TODO: cross-node call w/o code duplication
+            raise NotImplementedError("Planned!")
+        else:
+            clip_data.append(comfy.utils.load_torch_file(p, safe_load=True))
+    return load_text_encoder_state_dicts(clip_data, embedding_directory=embedding_directory, clip_type=clip_type, model_options=model_options)
+
+def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip_type=TencType.PixArt, model_options={}):
+    # Partial duplicate of ComfyUI/comfy/sd:load_text_encoder_state_dicts
+    clip_data = state_dicts
+
+    class EmptyClass:
+        pass
+
+    for i in range(len(clip_data)):
+        if "transformer.resblocks.0.ln_1.weight" in clip_data[i]:
+            clip_data[i] = comfy.utils.clip_text_transformers_convert(clip_data[i], "", "")
+        else:
+            if "text_projection" in clip_data[i]:
+                clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node
+    
+    clip_target = EmptyClass()
+    clip_target.params = {}
+
+    if clip_type == TencType.PixArt:
+        clip_target.clip = pixart_te(**comfy.sd.t5xxl_detect(clip_data))
+        clip_target.tokenizer = PixArtTokenizer
+
+    parameters = 0
+    tokenizer_data = {}
+    for c in clip_data:
+        parameters += comfy.utils.calculate_parameters(c)
+        tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options)
+
+    clip = comfy.sd.CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, model_options=model_options)
+    for c in clip_data:
+        m, u = clip.load_sd(c)
+        if len(m) > 0:
+            logging.warning("clip missing: {}".format(m))
+
+        if len(u) > 0:
+            logging.debug("clip unexpected: {}".format(u))
+    return clip
diff --git a/T5/t5_tokenizer/special_tokens_map.json b/text_encoders/tokenizers/t5_tokenizer/special_tokens_map.json
similarity index 100%
rename from T5/t5_tokenizer/special_tokens_map.json
rename to text_encoders/tokenizers/t5_tokenizer/special_tokens_map.json
diff --git a/T5/t5_tokenizer/spiece.model b/text_encoders/tokenizers/t5_tokenizer/spiece.model
similarity index 100%
rename from T5/t5_tokenizer/spiece.model
rename to text_encoders/tokenizers/t5_tokenizer/spiece.model
diff --git a/T5/t5_tokenizer/tokenizer_config.json b/text_encoders/tokenizers/t5_tokenizer/tokenizer_config.json
similarity index 100%
rename from T5/t5_tokenizer/tokenizer_config.json
rename to text_encoders/tokenizers/t5_tokenizer/tokenizer_config.json

From 45423d96734b88fe40e1f5cee9389945fbaa2b20 Mon Sep 17 00:00:00 2001
From: City <125218114+city96@users.noreply.github.com>
Date: Tue, 10 Dec 2024 22:24:37 +0100
Subject: [PATCH 02/15] Remove HyDiT - supported in main

---
 HunYuanDiT/LICENSE-HYDiT                      |    74 -
 HunYuanDiT/conf.py                            |    61 -
 HunYuanDiT/config_clip.json                   |    34 -
 HunYuanDiT/config_mt5.json                    |    33 -
 HunYuanDiT/loader.py                          |    80 -
 HunYuanDiT/models/attn_layers.py              |   374 -
 HunYuanDiT/models/embedders.py                |   111 -
 HunYuanDiT/models/models.py                   |   439 -
 HunYuanDiT/models/norm_layers.py              |    68 -
 HunYuanDiT/models/poolers.py                  |    39 -
 HunYuanDiT/models/posemb_layers.py            |   225 -
 HunYuanDiT/mt5_tokenizer/config.json          |    33 -
 .../mt5_tokenizer/special_tokens_map.json     |     1 -
 HunYuanDiT/mt5_tokenizer/spiece.model         |   Bin 4309802 -> 0 bytes
 .../mt5_tokenizer/tokenizer_config.json       |     1 -
 HunYuanDiT/nodes.py                           |   198 -
 HunYuanDiT/tenc.py                            |   180 -
 HunYuanDiT/tokenizer/config.json              |    34 -
 HunYuanDiT/tokenizer/special_tokens_map.json  |     7 -
 HunYuanDiT/tokenizer/tokenizer_config.json    |    16 -
 HunYuanDiT/tokenizer/vocab.txt                | 47020 ----------------
 __init__.py                                   |     4 -
 22 files changed, 49032 deletions(-)
 delete mode 100644 HunYuanDiT/LICENSE-HYDiT
 delete mode 100644 HunYuanDiT/conf.py
 delete mode 100644 HunYuanDiT/config_clip.json
 delete mode 100644 HunYuanDiT/config_mt5.json
 delete mode 100644 HunYuanDiT/loader.py
 delete mode 100644 HunYuanDiT/models/attn_layers.py
 delete mode 100644 HunYuanDiT/models/embedders.py
 delete mode 100644 HunYuanDiT/models/models.py
 delete mode 100644 HunYuanDiT/models/norm_layers.py
 delete mode 100644 HunYuanDiT/models/poolers.py
 delete mode 100644 HunYuanDiT/models/posemb_layers.py
 delete mode 100644 HunYuanDiT/mt5_tokenizer/config.json
 delete mode 100644 HunYuanDiT/mt5_tokenizer/special_tokens_map.json
 delete mode 100644 HunYuanDiT/mt5_tokenizer/spiece.model
 delete mode 100644 HunYuanDiT/mt5_tokenizer/tokenizer_config.json
 delete mode 100644 HunYuanDiT/nodes.py
 delete mode 100644 HunYuanDiT/tenc.py
 delete mode 100644 HunYuanDiT/tokenizer/config.json
 delete mode 100644 HunYuanDiT/tokenizer/special_tokens_map.json
 delete mode 100644 HunYuanDiT/tokenizer/tokenizer_config.json
 delete mode 100644 HunYuanDiT/tokenizer/vocab.txt

diff --git a/HunYuanDiT/LICENSE-HYDiT b/HunYuanDiT/LICENSE-HYDiT
deleted file mode 100644
index 61ea65d..0000000
--- a/HunYuanDiT/LICENSE-HYDiT
+++ /dev/null
@@ -1,74 +0,0 @@
-TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT
-Tencent Hunyuan Release Date: 2024/5/14
-By clicking to agree or by using, reproducing, modifying, distributing, performing or displaying any portion or element of the Tencent Hunyuan Works, including via any Hosted Service, You will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.
-1.	DEFINITIONS.
-a.	“Acceptable Use Policy” shall mean the policy made available by Tencent as set forth in the Exhibit A.
-b.	“Agreement” shall mean the terms and conditions for use, reproduction, distribution, modification, performance and displaying of the Hunyuan Works or any portion or element thereof set forth herein.
-c.	“Documentation” shall mean the specifications, manuals and documentation for Tencent Hunyuan made publicly available by Tencent.
-d.	“Hosted Service” shall mean a hosted service offered via an application programming interface (API), web access, or any other electronic or remote means.
-e.	“Licensee,” “You” or “Your” shall mean a natural person or legal entity exercising the rights granted by this Agreement and/or using the Tencent Hunyuan Works for any purpose and in any field of use.
-f.	“Materials” shall mean, collectively, Tencent’s proprietary Tencent Hunyuan and Documentation (and any portion thereof) as made available by Tencent under this Agreement.
-g.	“Model Derivatives” shall mean all: (i) modifications to Tencent Hunyuan or any Model Derivative of Tencent Hunyuan; (ii) works based on Tencent Hunyuan or any Model Derivative of Tencent Hunyuan; or (iii) any other machine learning model which is created by transfer of patterns of the weights, parameters, operations, or Output of Tencent Hunyuan or any Model Derivative of Tencent Hunyuan, to that model in order to cause that model to perform similarly to Tencent Hunyuan or a Model Derivative of Tencent Hunyuan, including distillation methods, methods that use intermediate data representations, or methods based on the generation of synthetic data Outputs by Tencent Hunyuan or a Model Derivative of Tencent Hunyuan for training that model. For clarity, Outputs by themselves are not deemed Model Derivatives.
-h.	“Output” shall mean the information and/or content output of Tencent Hunyuan or a Model Derivative that results from operating or otherwise using Tencent Hunyuan or a Model Derivative, including via a Hosted Service.
-i.	“Tencent,” “We” or “Us” shall mean THL A29 Limited.
-j.	“Tencent Hunyuan” shall mean the large language models, image/video/audio/3D generation models, and multimodal large language models and their software and algorithms, including trained model weights, parameters (including optimizer states), machine-learning model code, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing made publicly available by Us at  https://huggingface.co/Tencent-Hunyuan/HunyuanDiT and https://github.com/Tencent/HunyuanDiT .
-k.	“Tencent Hunyuan Works” shall mean: (i) the Materials; (ii) Model Derivatives; and (iii) all derivative works thereof.
-l.	“Third Party” or “Third Parties” shall mean individuals or legal entities that are not under common control with Us or You.
-m.	“including” shall mean including but not limited to.
-2.	GRANT OF RIGHTS.
-We grant You a non-exclusive, worldwide, non-transferable and royalty-free limited license under Tencent’s intellectual property or other rights owned by Us embodied in or utilized by the Materials to use, reproduce, distribute, create derivative works of (including Model Derivatives), and make modifications to the Materials, only in accordance with the terms of this Agreement and the Acceptable Use Policy, and You must not violate (or encourage or permit anyone else to violate) any term of this Agreement or the Acceptable Use Policy.
-3.	DISTRIBUTION.
-You may, subject to Your compliance with this Agreement, distribute or make available to Third Parties the Tencent Hunyuan Works, provided that You meet all of the following conditions:
-a.	You must provide all such Third Party recipients of the Tencent Hunyuan Works or products or services using them a copy of this Agreement;
-b.	You must cause any modified files to carry prominent notices stating that You changed the files;
-c.	You are encouraged to: (i) publish at least one technology introduction blogpost or one public statement expressing Your experience of using the Tencent Hunyuan Works; and (ii) mark the products or services developed by using the Tencent Hunyuan Works to indicate that the product/service is “Powered by Tencent Hunyuan”; and
-d.	All distributions to Third Parties (other than through a Hosted Service) must be accompanied by a “Notice” text file that contains the following notice: “Tencent Hunyuan is licensed under the Tencent Hunyuan Community License Agreement, Copyright © 2024 Tencent. All Rights Reserved. The trademark rights of “Tencent Hunyuan” are owned by Tencent or its affiliate.”
-You may add Your own copyright statement to Your modifications and, except as set forth in this Section and in Section 5, may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Model Derivatives as a whole, provided Your use, reproduction, modification, distribution, performance and display of the work otherwise complies with the terms and conditions of this Agreement. If You receive Tencent Hunyuan Works from a Licensee as part of an integrated end user product, then this Section 3 of this Agreement will not apply to You.
-4.	ADDITIONAL COMMERCIAL TERMS.
-If, on the Tencent Hunyuan version release date, the monthly active users of all products or services made available by or for Licensee is greater than 100 million monthly active users in the preceding calendar month, You must request a license from Tencent, which Tencent may grant to You in its sole discretion, and You are not authorized to exercise any of the rights under this Agreement unless or until Tencent otherwise expressly grants You such rights.
-5.	RULES OF USE.
-a.	Your use of the Tencent Hunyuan Works must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Tencent Hunyuan Works, which is hereby incorporated by reference into this Agreement. You must include the use restrictions referenced in these Sections 5(a) and 5(b) as an enforceable provision in any agreement (e.g., license agreement, terms of use, etc.) governing the use and/or distribution of Tencent Hunyuan Works and You must provide notice to subsequent users to whom You distribute that Tencent Hunyuan Works are subject to the use restrictions in these Sections 5(a) and 5(b).
-b.	You must not use the Tencent Hunyuan Works or any Output or results of the Tencent Hunyuan Works to improve any other large language model (other than Tencent Hunyuan or Model Derivatives thereof).
-6.	INTELLECTUAL PROPERTY.
-a.	Subject to Tencent’s ownership of Tencent Hunyuan Works made by or for Tencent and intellectual property rights therein, conditioned upon Your compliance with the terms and conditions of this Agreement, as between You and Tencent, You will be the owner of any derivative works and modifications of the Materials and any Model Derivatives that are made by or for You.
-b.	No trademark licenses are granted under this Agreement, and in connection with the Tencent Hunyuan Works, Licensee may not use any name or mark owned by or associated with Tencent or any of its affiliates, except as required for reasonable and customary use in describing and distributing the Tencent Hunyuan Works. Tencent hereby grants You a license to use “Tencent Hunyuan” (the “Mark”) solely as required to comply with the provisions of Section 3(c), provided that You comply with any applicable laws related to trademark protection. All goodwill arising out of Your use of the Mark will inure to the benefit of Tencent.
-c.	If You commence a lawsuit or other proceedings (including a cross-claim or counterclaim in a lawsuit) against Us or any person or entity alleging that the Materials or any Output, or any portion of any of the foregoing, infringe any intellectual property or other right owned or licensable by You, then all licenses granted to You under this Agreement shall terminate as of the date such lawsuit or other proceeding is filed. You will defend, indemnify and hold harmless Us from and against any claim by any Third Party arising out of or related to Your or the Third Party’s use or distribution of the Tencent Hunyuan Works.
-d.	Tencent claims no rights in Outputs You generate. You and Your users are solely responsible for Outputs and their subsequent uses.
-7.	DISCLAIMERS OF WARRANTY AND LIMITATIONS OF LIABILITY.
-a.	We are not obligated to support, update, provide training for, or develop any further version of the Tencent Hunyuan Works or to grant any license thereto.
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-8.	SURVIVAL AND TERMINATION.
-a.	The term of this Agreement shall commence upon Your acceptance of this Agreement or access to the Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein.
-b.	We may terminate this Agreement if You breach any of the terms or conditions of this Agreement. Upon termination of this Agreement, You must promptly delete and cease use of the Tencent Hunyuan Works. Sections 6(a), 6(c), 7 and 9 shall survive the termination of this Agreement.
-9.	GOVERNING LAW AND JURISDICTION.
-a.	This Agreement and any dispute arising out of or relating to it will be governed by the laws of the Hong Kong Special Administrative Region of the People’s Republic of China, without regard to conflict of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement.
-b.	Exclusive jurisdiction and venue for any dispute arising out of or relating to this Agreement will be a court of competent jurisdiction in the Hong Kong Special Administrative Region of the People’s Republic of China, and Tencent and Licensee consent to the exclusive jurisdiction of such court with respect to any such dispute.
- 
-
-EXHIBIT A
-ACCEPTABLE USE POLICY
-
-Tencent reserves the right to update this Acceptable Use Policy from time to time.
-Last modified: 2024/5/14
-
-Tencent endeavors to promote safe and fair use of its tools and features, including Tencent Hunyuan. You agree not to use Tencent Hunyuan or Model Derivatives:
-1.	In any way that violates any applicable national, federal, state, local, international or any other law or regulation;
-2.	To harm Yourself or others;
-3.	To repurpose or distribute output from Tencent Hunyuan or any Model Derivatives to harm Yourself or others; 
-4.	To override or circumvent the safety guardrails and safeguards We have put in place;
-5.	For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
-6.	To generate or disseminate verifiably false information and/or content with the purpose of harming others or influencing elections;
-7.	To generate or facilitate false online engagement, including fake reviews and other means of fake online engagement;
-8.	To intentionally defame, disparage or otherwise harass others;
-9.	To generate and/or disseminate malware (including ransomware) or any other content to be used for the purpose of harming electronic systems;
-10.	To generate or disseminate personal identifiable information with the purpose of harming others;
-11.	To generate or disseminate information (including images, code, posts, articles), and place the information in any public context (including –through the use of bot generated tweets), without expressly and conspicuously identifying that the information and/or content is machine generated;
-12.	To impersonate another individual without consent, authorization, or legal right;
-13.	To make high-stakes automated decisions in domains that affect an individual’s safety, rights or wellbeing (e.g., law enforcement, migration, medicine/health, management of critical infrastructure, safety components of products, essential services, credit, employment, housing, education, social scoring, or insurance);
-14.	In a manner that violates or disrespects the social ethics and moral standards of other countries or regions;
-15.	To perform, facilitate, threaten, incite, plan, promote or encourage violent extremism or terrorism;
-16.	For any use intended to discriminate against or harm individuals or groups based on protected characteristics or categories, online or offline social behavior or known or predicted personal or personality characteristics;
-17.	To intentionally exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
-18.	For military purposes;
-19.	To engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or other professional practices.
diff --git a/HunYuanDiT/conf.py b/HunYuanDiT/conf.py
deleted file mode 100644
index 4322753..0000000
--- a/HunYuanDiT/conf.py
+++ /dev/null
@@ -1,61 +0,0 @@
-"""
-List of all HYDiT model types / settings
-"""
-from argparse import Namespace
-hydit_args = Namespace(**{ # normally from argparse
-	"infer_mode": "torch",
-	"norm": "layer",
-	"learn_sigma": True,
-	"text_states_dim": 1024,
-	"text_states_dim_t5": 2048,
-	"text_len": 77,
-	"text_len_t5": 256,
-})
-
-hydit_conf = {
-	"G/2": { # Seems to be the main one
-		"unet_config": {
-			"depth"       :   40,
-			"num_heads"   :   16,
-			"patch_size"  :    2,
-			"hidden_size" : 1408,
-			"mlp_ratio" : 4.3637,
-			"input_size": (1024//8, 1024//8),
-			"args": hydit_args,
-		},
-		"sampling_settings" : {
-			"beta_schedule" : "linear",
-			"linear_start"  : 0.00085,
-			"linear_end"    : 0.03,
-			"timesteps"     : 1000,
-		},
-	},
-	"G/2-1.2": {
-		"unet_config": {
-			"depth"       :   40,
-			"num_heads"   :   16,
-			"patch_size"  :    2,
-			"hidden_size" : 1408,
-			"mlp_ratio" : 4.3637,
-			"input_size": (1024//8, 1024//8),
-			"cond_style": False,
-            "cond_res"  : False,
-			"args": hydit_args,
-		},
-		"sampling_settings" : {
-			"beta_schedule" : "linear",
-			"linear_start"  : 0.00085,
-			"linear_end"    : 0.018,
-			"timesteps"     : 1000,
-		},
-	}
-}
-
-# these are the same as regular DiT, I think
-from ..DiT.conf import dit_conf
-for name in ["XL/2", "L/2", "B/2"]:
-	hydit_conf[name] = {
-		"unet_config": dit_conf[name]["unet_config"].copy(),
-		"sampling_settings": hydit_conf["G/2"]["sampling_settings"],
-	}
-	hydit_conf[name]["unet_config"]["args"] = hydit_args
diff --git a/HunYuanDiT/config_clip.json b/HunYuanDiT/config_clip.json
deleted file mode 100644
index f629874..0000000
--- a/HunYuanDiT/config_clip.json
+++ /dev/null
@@ -1,34 +0,0 @@
-{
-  "_name_or_path": "hfl/chinese-roberta-wwm-ext-large",
-  "architectures": [
-    "BertModel"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "bos_token_id": 0,
-  "classifier_dropout": null,
-  "directionality": "bidi",
-  "eos_token_id": 2,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 1024,
-  "initializer_range": 0.02,
-  "intermediate_size": 4096,
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 16,
-  "num_hidden_layers": 24,
-  "output_past": true,
-  "pad_token_id": 0,
-  "pooler_fc_size": 768,
-  "pooler_num_attention_heads": 12,
-  "pooler_num_fc_layers": 3,
-  "pooler_size_per_head": 128,
-  "pooler_type": "first_token_transform",
-  "position_embedding_type": "absolute",
-  "torch_dtype": "float32",
-  "transformers_version": "4.22.1",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 47020
-}
diff --git a/HunYuanDiT/config_mt5.json b/HunYuanDiT/config_mt5.json
deleted file mode 100644
index 825cb35..0000000
--- a/HunYuanDiT/config_mt5.json
+++ /dev/null
@@ -1,33 +0,0 @@
-{
-  "_name_or_path": "mt5",
-  "architectures": [
-    "MT5EncoderModel"
-  ],
-  "classifier_dropout": 0.0,
-  "d_ff": 5120,
-  "d_kv": 64,
-  "d_model": 2048,
-  "decoder_start_token_id": 0,
-  "dense_act_fn": "gelu_new",
-  "dropout_rate": 0.1,
-  "eos_token_id": 1,
-  "feed_forward_proj": "gated-gelu",
-  "initializer_factor": 1.0,
-  "is_encoder_decoder": true,
-  "is_gated_act": true,
-  "layer_norm_epsilon": 1e-06,
-  "model_type": "mt5",
-  "num_decoder_layers": 24,
-  "num_heads": 32,
-  "num_layers": 24,
-  "output_past": true,
-  "pad_token_id": 0,
-  "relative_attention_max_distance": 128,
-  "relative_attention_num_buckets": 32,
-  "tie_word_embeddings": false,
-  "tokenizer_class": "T5Tokenizer",
-  "torch_dtype": "float16",
-  "transformers_version": "4.40.2",
-  "use_cache": true,
-  "vocab_size": 250112
-}
diff --git a/HunYuanDiT/loader.py b/HunYuanDiT/loader.py
deleted file mode 100644
index 23be529..0000000
--- a/HunYuanDiT/loader.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import comfy.supported_models_base
-import comfy.latent_formats
-import comfy.model_patcher
-import comfy.model_base
-import comfy.utils
-import comfy.conds
-import torch
-from comfy import model_management
-from tqdm import tqdm
-
-class EXM_HYDiT(comfy.supported_models_base.BASE):
-	unet_config = {}
-	unet_extra_config = {}
-	latent_format = comfy.latent_formats.SDXL
-
-	def __init__(self, model_conf):
-		self.unet_config = model_conf.get("unet_config", {})
-		self.sampling_settings = model_conf.get("sampling_settings", {})
-		self.latent_format = self.latent_format()
-		# UNET is handled by extension
-		self.unet_config["disable_unet_model_creation"] = True
-
-	def model_type(self, state_dict, prefix=""):
-		return comfy.model_base.ModelType.V_PREDICTION
-
-class EXM_HYDiT_Model(comfy.model_base.BaseModel):
-	def __init__(self, *args, **kwargs):
-		super().__init__(*args, **kwargs)
-	
-	def extra_conds(self, **kwargs):
-		out = super().extra_conds(**kwargs)
-
-		for name in ["context_t5", "context_mask", "context_t5_mask"]:
-			out[name] = comfy.conds.CONDRegular(kwargs[name])
-
-		src_size_cond = kwargs.get("src_size_cond", None)
-		if src_size_cond is not None:
-			out["src_size_cond"] = comfy.conds.CONDRegular(torch.tensor(src_size_cond))
-
-		return out
-
-def load_hydit(model_path, model_conf):
-	state_dict = comfy.utils.load_torch_file(model_path)
-	state_dict = state_dict.get("model", state_dict)
-
-	parameters = comfy.utils.calculate_parameters(state_dict)
-	unet_dtype = model_management.unet_dtype(model_params=parameters)
-	load_device = comfy.model_management.get_torch_device()
-	offload_device = comfy.model_management.unet_offload_device()
-
-	# ignore fp8/etc and use directly for now
-	manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device)
-	if manual_cast_dtype:
-		print(f"HunYuanDiT: falling back to {manual_cast_dtype}")
-		unet_dtype = manual_cast_dtype
-
-	model_conf = EXM_HYDiT(model_conf)
-	model = EXM_HYDiT_Model(
-		model_conf,
-		model_type=comfy.model_base.ModelType.V_PREDICTION,
-		device=model_management.get_torch_device()
-	)
-
-	from .models.models import HunYuanDiT
-	model.diffusion_model = HunYuanDiT(
-		**model_conf.unet_config,
-		log_fn=tqdm.write,
-	)
-
-	model.diffusion_model.load_state_dict(state_dict)
-	model.diffusion_model.dtype = unet_dtype
-	model.diffusion_model.eval()
-	model.diffusion_model.to(unet_dtype)
-
-	model_patcher = comfy.model_patcher.ModelPatcher(
-		model,
-		load_device = load_device,
-		offload_device = offload_device,
-	)
-	return model_patcher
diff --git a/HunYuanDiT/models/attn_layers.py b/HunYuanDiT/models/attn_layers.py
deleted file mode 100644
index b767d83..0000000
--- a/HunYuanDiT/models/attn_layers.py
+++ /dev/null
@@ -1,374 +0,0 @@
-import torch
-import torch.nn as nn
-from typing import Tuple, Union, Optional
-
-try:
-    import flash_attn
-    if hasattr(flash_attn, '__version__') and int(flash_attn.__version__[0]) == 2:
-        from flash_attn.flash_attn_interface import flash_attn_kvpacked_func
-        from flash_attn.modules.mha import FlashSelfAttention, FlashCrossAttention
-    else:
-        from flash_attn.flash_attn_interface import flash_attn_unpadded_kvpacked_func
-        from flash_attn.modules.mha import FlashSelfAttention, FlashCrossAttention
-except Exception as e:
-    print(f'flash_attn import failed: {e}')
-
-
-def reshape_for_broadcast(freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], x: torch.Tensor, head_first=False):
-    """
-    Reshape frequency tensor for broadcasting it with another tensor.
-
-    This function reshapes the frequency tensor to have the same shape as the target tensor 'x'
-    for the purpose of broadcasting the frequency tensor during element-wise operations.
-
-    Args:
-        freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Frequency tensor to be reshaped.
-        x (torch.Tensor): Target tensor for broadcasting compatibility.
-        head_first (bool): head dimension first (except batch dim) or not.
-
-    Returns:
-        torch.Tensor: Reshaped frequency tensor.
-
-    Raises:
-        AssertionError: If the frequency tensor doesn't match the expected shape.
-        AssertionError: If the target tensor 'x' doesn't have the expected number of dimensions.
-    """
-    ndim = x.ndim
-    assert 0 <= 1 < ndim
-
-    if isinstance(freqs_cis, tuple):
-        # freqs_cis: (cos, sin) in real space
-        if head_first:
-            assert freqs_cis[0].shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}'
-            shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
-        else:
-            assert freqs_cis[0].shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}'
-            shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
-        return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape)
-    else:
-        # freqs_cis: values in complex space
-        if head_first:
-            assert freqs_cis.shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}'
-            shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
-        else:
-            assert freqs_cis.shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}'
-            shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
-        return freqs_cis.view(*shape)
-
-
-def rotate_half(x):
-    x_real, x_imag = x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1)  # [B, S, H, D//2]
-    return torch.stack([-x_imag, x_real], dim=-1).flatten(3)
-
-
-def apply_rotary_emb(
-        xq: torch.Tensor,
-        xk: Optional[torch.Tensor],
-        freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]],
-        head_first: bool = False,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    """
-    Apply rotary embeddings to input tensors using the given frequency tensor.
-
-    This function applies rotary embeddings to the given query 'xq' and key 'xk' tensors using the provided
-    frequency tensor 'freqs_cis'. The input tensors are reshaped as complex numbers, and the frequency tensor
-    is reshaped for broadcasting compatibility. The resulting tensors contain rotary embeddings and are
-    returned as real tensors.
-
-    Args:
-        xq (torch.Tensor): Query tensor to apply rotary embeddings. [B, S, H, D]
-        xk (torch.Tensor): Key tensor to apply rotary embeddings.   [B, S, H, D]
-        freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Precomputed frequency tensor for complex exponentials.
-        head_first (bool): head dimension first (except batch dim) or not.
-
-    Returns:
-        Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.
-
-    """
-    xk_out = None
-    if isinstance(freqs_cis, tuple):
-        cos, sin = reshape_for_broadcast(freqs_cis, xq, head_first)    # [S, D]
-        cos, sin = cos.to(xq.device), sin.to(xq.device)
-        xq_out = (xq.float() * cos + rotate_half(xq.float()) * sin).type_as(xq)
-        if xk is not None:
-            xk_out = (xk.float() * cos + rotate_half(xk.float()) * sin).type_as(xk)
-    else:
-        xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))  # [B, S, H, D//2]
-        freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to(xq.device)   # [S, D//2] --> [1, S, 1, D//2]
-        xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq)
-        if xk is not None:
-            xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))  # [B, S, H, D//2]
-            xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk)
-
-    return xq_out, xk_out
-
-
-class FlashSelfMHAModified(nn.Module):
-    """
-    Use QK Normalization.
-    """
-    def __init__(self,
-                 dim,
-                 num_heads,
-                 qkv_bias=True,
-                 qk_norm=False,
-                 attn_drop=0.0,
-                 proj_drop=0.0,
-                 device=None,
-                 dtype=None,
-                 norm_layer=nn.LayerNorm,
-                 ):
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        super().__init__()
-        self.dim = dim
-        self.num_heads = num_heads
-        assert self.dim % num_heads == 0, "self.kdim must be divisible by num_heads"
-        self.head_dim = self.dim // num_heads
-        assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8"
-
-        self.Wqkv = nn.Linear(dim, 3 * dim, bias=qkv_bias, **factory_kwargs)
-        # TODO: eps should be 1 / 65530 if using fp16
-        self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
-        self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
-        self.inner_attn = FlashSelfAttention(attention_dropout=attn_drop)
-        self.out_proj = nn.Linear(dim, dim, bias=qkv_bias, **factory_kwargs)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x, freqs_cis_img=None):
-        """
-        Parameters
-        ----------
-        x: torch.Tensor
-            (batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim)
-        freqs_cis_img: torch.Tensor
-            (batch, hidden_dim // 2), RoPE for image
-        """
-        b, s, d = x.shape
-
-        qkv = self.Wqkv(x)
-        qkv = qkv.view(b, s, 3, self.num_heads, self.head_dim)  # [b, s, 3, h, d]
-        q, k, v = qkv.unbind(dim=2) # [b, s, h, d]
-        q = self.q_norm(q).half()   # [b, s, h, d]
-        k = self.k_norm(k).half()
-
-        # Apply RoPE if needed
-        if freqs_cis_img is not None:
-            qq, kk = apply_rotary_emb(q, k, freqs_cis_img)
-            assert qq.shape == q.shape and kk.shape == k.shape, f'qq: {qq.shape}, q: {q.shape}, kk: {kk.shape}, k: {k.shape}'
-            q, k = qq, kk
-
-        qkv = torch.stack([q, k, v], dim=2)     # [b, s, 3, h, d]
-        context = self.inner_attn(qkv)
-        out = self.out_proj(context.view(b, s, d))
-        out = self.proj_drop(out)
-
-        out_tuple = (out,)
-
-        return out_tuple
-
-
-class FlashCrossMHAModified(nn.Module):
-    """
-    Use QK Normalization.
-    """
-    def __init__(self,
-                 qdim,
-                 kdim,
-                 num_heads,
-                 qkv_bias=True,
-                 qk_norm=False,
-                 attn_drop=0.0,
-                 proj_drop=0.0,
-                 device=None,
-                 dtype=None,
-                 norm_layer=nn.LayerNorm,
-                 ):
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        super().__init__()
-        self.qdim = qdim
-        self.kdim = kdim
-        self.num_heads = num_heads
-        assert self.qdim % num_heads == 0, "self.qdim must be divisible by num_heads"
-        self.head_dim = self.qdim // num_heads
-        assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8"
-
-        self.scale = self.head_dim ** -0.5
-
-        self.q_proj = nn.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs)
-        self.kv_proj = nn.Linear(kdim, 2 * qdim, bias=qkv_bias, **factory_kwargs)
-
-        # TODO: eps should be 1 / 65530 if using fp16
-        self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
-        self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
-
-        self.inner_attn = FlashCrossAttention(attention_dropout=attn_drop)
-        self.out_proj = nn.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x, y, freqs_cis_img=None):
-        """
-        Parameters
-        ----------
-        x: torch.Tensor
-            (batch, seqlen1, hidden_dim) (where hidden_dim = num_heads * head_dim)
-        y: torch.Tensor
-            (batch, seqlen2, hidden_dim2)
-        freqs_cis_img: torch.Tensor
-            (batch, hidden_dim // num_heads), RoPE for image
-        """
-        b, s1, _ = x.shape     # [b, s1, D]
-        _, s2, _ = y.shape     # [b, s2, 1024]
-
-        q = self.q_proj(x).view(b, s1, self.num_heads, self.head_dim)       # [b, s1, h, d]
-        kv = self.kv_proj(y).view(b, s2, 2, self.num_heads, self.head_dim)  # [b, s2, 2, h, d]
-        k, v = kv.unbind(dim=2)                 # [b, s2, h, d]
-        q = self.q_norm(q).half()               # [b, s1, h, d]
-        k = self.k_norm(k).half()               # [b, s2, h, d]
-
-        # Apply RoPE if needed
-        if freqs_cis_img is not None:
-            qq, _ = apply_rotary_emb(q, None, freqs_cis_img)
-            assert qq.shape == q.shape, f'qq: {qq.shape}, q: {q.shape}'
-            q = qq                              # [b, s1, h, d]
-        kv = torch.stack([k, v], dim=2)         # [b, s1, 2, h, d]
-        context = self.inner_attn(q, kv)        # [b, s1, h, d]
-        context = context.view(b, s1, -1)       # [b, s1, D]
-
-        out = self.out_proj(context)
-        out = self.proj_drop(out)
-
-        out_tuple = (out,)
-
-        return out_tuple
-
-
-class CrossAttention(nn.Module):
-    """
-    Use QK Normalization.
-    """
-    def __init__(self,
-                 qdim,
-                 kdim,
-                 num_heads,
-                 qkv_bias=True,
-                 qk_norm=False,
-                 attn_drop=0.0,
-                 proj_drop=0.0,
-                 device=None,
-                 dtype=None,
-                 norm_layer=nn.LayerNorm,
-                 ):
-        factory_kwargs = {'device': device, 'dtype': dtype}
-        super().__init__()
-        self.qdim = qdim
-        self.kdim = kdim
-        self.num_heads = num_heads
-        assert self.qdim % num_heads == 0, "self.qdim must be divisible by num_heads"
-        self.head_dim = self.qdim // num_heads
-        assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8"
-        self.scale = self.head_dim ** -0.5
-
-        self.q_proj = nn.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs)
-        self.kv_proj = nn.Linear(kdim, 2 * qdim, bias=qkv_bias, **factory_kwargs)
-
-        # TODO: eps should be 1 / 65530 if using fp16
-        self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
-        self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.out_proj = nn.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x, y, freqs_cis_img=None):
-        """
-        Parameters
-        ----------
-        x: torch.Tensor
-            (batch, seqlen1, hidden_dim) (where hidden_dim = num heads * head dim)
-        y: torch.Tensor
-            (batch, seqlen2, hidden_dim2)
-        freqs_cis_img: torch.Tensor
-            (batch, hidden_dim // 2), RoPE for image
-        """
-        b, s1, c = x.shape     # [b, s1, D]
-        _, s2, c = y.shape     # [b, s2, 1024]
-
-        q = self.q_proj(x).view(b, s1, self.num_heads, self.head_dim)   # [b, s1, h, d]
-        kv = self.kv_proj(y).view(b, s2, 2, self.num_heads, self.head_dim)    # [b, s2, 2, h, d]
-        k, v = kv.unbind(dim=2) # [b, s, h, d]
-        q = self.q_norm(q)
-        k = self.k_norm(k)
-
-        # Apply RoPE if needed
-        if freqs_cis_img is not None:
-            qq, _ = apply_rotary_emb(q, None, freqs_cis_img)
-            assert qq.shape == q.shape, f'qq: {qq.shape}, q: {q.shape}'
-            q = qq
-
-        q = q * self.scale
-        q = q.transpose(-2, -3).contiguous()        # q ->  B, L1, H, C - B, H, L1, C
-        k = k.permute(0, 2, 3, 1).contiguous()      # k ->  B, L2, H, C - B, H, C, L2
-        attn = q @ k                                # attn -> B, H, L1, L2
-        attn = attn.softmax(dim=-1)                 # attn -> B, H, L1, L2
-        attn = self.attn_drop(attn)
-        x = attn @ v.transpose(-2, -3)              # v -> B, L2, H, C - B, H, L2, C    x-> B, H, L1, C
-        context = x.transpose(1, 2)                 # context -> B, H, L1, C - B, L1, H, C
-
-        context = context.contiguous().view(b, s1, -1)
-
-        out = self.out_proj(context)  # context.reshape - B, L1, -1
-        out = self.proj_drop(out)
-
-        out_tuple = (out,)
-
-        return out_tuple
-
-
-class Attention(nn.Module):
-    """
-    We rename some layer names to align with flash attention
-    """
-    def __init__(self, dim, num_heads, qkv_bias=True, qk_norm=False, attn_drop=0., proj_drop=0.,
-                 norm_layer=nn.LayerNorm,
-                 ):
-        super().__init__()
-        self.dim = dim
-        self.num_heads = num_heads
-        assert self.dim % num_heads == 0, 'dim should be divisible by num_heads'
-        self.head_dim = self.dim // num_heads
-        # This assertion is aligned with flash attention
-        assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8"
-        self.scale = self.head_dim ** -0.5
-
-        # qkv --> Wqkv
-        self.Wqkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        # TODO: eps should be 1 / 65530 if using fp16
-        self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
-        self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.out_proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-    def forward(self, x, freqs_cis_img=None):
-        B, N, C = x.shape
-        qkv = self.Wqkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)   # [3, b, h, s, d]
-        q, k, v = qkv.unbind(0)     # [b, h, s, d]
-        q = self.q_norm(q)          # [b, h, s, d]
-        k = self.k_norm(k)          # [b, h, s, d]
-
-        # Apply RoPE if needed
-        if freqs_cis_img is not None:
-            qq, kk = apply_rotary_emb(q, k, freqs_cis_img, head_first=True)
-            assert qq.shape == q.shape and kk.shape == k.shape, \
-                f'qq: {qq.shape}, q: {q.shape}, kk: {kk.shape}, k: {k.shape}'
-            q, k = qq, kk
-
-        # just use SDP here for now
-        x = torch.nn.functional.scaled_dot_product_attention(
-                q, k, v,
-        ).permute(0, 2, 1, 3).contiguous().reshape(B, N, C)
-        x = self.out_proj(x)
-        x = self.proj_drop(x)
-
-        out_tuple = (x,)
-
-        return out_tuple
diff --git a/HunYuanDiT/models/embedders.py b/HunYuanDiT/models/embedders.py
deleted file mode 100644
index 9fe08cb..0000000
--- a/HunYuanDiT/models/embedders.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import math
-import torch
-import torch.nn as nn
-from einops import repeat
-
-from timm.models.layers import to_2tuple
-
-
-class PatchEmbed(nn.Module):
-    """ 2D Image to Patch Embedding
-
-    Image to Patch Embedding using Conv2d
-
-    A convolution based approach to patchifying a 2D image w/ embedding projection.
-
-    Based on the impl in https://github.com/google-research/vision_transformer
-
-    Hacked together by / Copyright 2020 Ross Wightman
-
-    Remove the _assert function in forward function to be compatible with multi-resolution images.
-    """
-    def __init__(
-            self,
-            img_size=224,
-            patch_size=16,
-            in_chans=3,
-            embed_dim=768,
-            norm_layer=None,
-            flatten=True,
-            bias=True,
-    ):
-        super().__init__()
-        if isinstance(img_size, int):
-            img_size = to_2tuple(img_size)
-        elif isinstance(img_size, (tuple, list)) and len(img_size) == 2:
-            img_size = tuple(img_size)
-        else:
-            raise ValueError(f"img_size must be int or tuple/list of length 2. Got {img_size}")
-        patch_size = to_2tuple(patch_size)
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
-        self.num_patches = self.grid_size[0] * self.grid_size[1]
-        self.flatten = flatten
-
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
-        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
-
-    def update_image_size(self, img_size):
-        self.img_size = img_size
-        self.grid_size = (img_size[0] // self.patch_size[0], img_size[1] // self.patch_size[1])
-        self.num_patches = self.grid_size[0] * self.grid_size[1]
-
-    def forward(self, x):
-        # B, C, H, W = x.shape
-        # _assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).")
-        # _assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).")
-        x = self.proj(x)
-        if self.flatten:
-            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
-        x = self.norm(x)
-        return x
-
-
-def timestep_embedding(t, dim, max_period=10000, repeat_only=False):
-    """
-    Create sinusoidal timestep embeddings.
-    :param t: a 1-D Tensor of N indices, one per batch element.
-                      These may be fractional.
-    :param dim: the dimension of the output.
-    :param max_period: controls the minimum frequency of the embeddings.
-    :return: an (N, D) Tensor of positional embeddings.
-    """
-    # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
-    if not repeat_only:
-        half = dim // 2
-        freqs = torch.exp(
-            -math.log(max_period)
-            * torch.arange(start=0, end=half, dtype=torch.float32)
-            / half
-        ).to(device=t.device)   # size: [dim/2], 一个指数衰减的曲线
-        args = t[:, None].float() * freqs[None]
-        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-        if dim % 2:
-            embedding = torch.cat(
-                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
-            )
-    else:
-        embedding = repeat(t, "b -> b d", d=dim)
-    return embedding
-
-
-class TimestepEmbedder(nn.Module):
-    """
-    Embeds scalar timesteps into vector representations.
-    """
-    def __init__(self, hidden_size, frequency_embedding_size=256, out_size=None):
-        super().__init__()
-        if out_size is None:
-            out_size = hidden_size
-        self.mlp = nn.Sequential(
-            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
-            nn.SiLU(),
-            nn.Linear(hidden_size, out_size, bias=True),
-        )
-        self.frequency_embedding_size = frequency_embedding_size
-
-    def forward(self, t):
-        t_freq = timestep_embedding(t, self.frequency_embedding_size).type(self.mlp[0].weight.dtype)
-        t_emb = self.mlp(t_freq)
-        return t_emb
diff --git a/HunYuanDiT/models/models.py b/HunYuanDiT/models/models.py
deleted file mode 100644
index 7dae413..0000000
--- a/HunYuanDiT/models/models.py
+++ /dev/null
@@ -1,439 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from timm.models.vision_transformer import Mlp
-
-from .attn_layers import Attention, FlashCrossMHAModified, FlashSelfMHAModified, CrossAttention
-from .embedders import TimestepEmbedder, PatchEmbed, timestep_embedding
-from .norm_layers import RMSNorm
-from .poolers import AttentionPool
-from .posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop
-
-def modulate(x, shift, scale):
-    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
-
-
-class FP32_Layernorm(nn.LayerNorm):
-    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
-        origin_dtype = inputs.dtype
-        return F.layer_norm(inputs.float(), self.normalized_shape, self.weight.float(), self.bias.float(),
-                            self.eps).to(origin_dtype)
-
-
-class FP32_SiLU(nn.SiLU):
-    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
-        return torch.nn.functional.silu(inputs.float(), inplace=False).to(inputs.dtype)
-
-
-class HunYuanDiTBlock(nn.Module):
-    """
-    A HunYuanDiT block with `add` conditioning.
-    """
-    def __init__(self,
-                 hidden_size,
-                 c_emb_size,
-                 num_heads,
-                 mlp_ratio=4.0,
-                 text_states_dim=1024,
-                 use_flash_attn=False,
-                 qk_norm=False,
-                 norm_type="layer",
-                 skip=False,
-                 ):
-        super().__init__()
-        self.use_flash_attn = use_flash_attn
-        use_ele_affine = True
-
-        if norm_type == "layer":
-            norm_layer = FP32_Layernorm
-        elif norm_type == "rms":
-            norm_layer = RMSNorm
-        else:
-            raise ValueError(f"Unknown norm_type: {norm_type}")
-
-        # ========================= Self-Attention =========================
-        self.norm1 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6)
-        if use_flash_attn:
-            self.attn1 = FlashSelfMHAModified(hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=qk_norm)
-        else:
-            self.attn1 = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=qk_norm)
-
-        # ========================= FFN =========================
-        self.norm2 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6)
-        mlp_hidden_dim = int(hidden_size * mlp_ratio)
-        approx_gelu = lambda: nn.GELU(approximate="tanh")
-        self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0)
-
-        # ========================= Add =========================
-        # Simply use add like SDXL.
-        self.default_modulation = nn.Sequential(
-            FP32_SiLU(),
-            nn.Linear(c_emb_size, hidden_size, bias=True)
-        )
-
-        # ========================= Cross-Attention =========================
-        if use_flash_attn:
-            self.attn2 = FlashCrossMHAModified(hidden_size, text_states_dim, num_heads=num_heads, qkv_bias=True,
-                                               qk_norm=qk_norm)
-        else:
-            self.attn2 = CrossAttention(hidden_size, text_states_dim, num_heads=num_heads, qkv_bias=True,
-                                        qk_norm=qk_norm)
-        self.norm3 = norm_layer(hidden_size, elementwise_affine=True, eps=1e-6)
-
-        # ========================= Skip Connection =========================
-        if skip:
-            self.skip_norm = norm_layer(2 * hidden_size, elementwise_affine=True, eps=1e-6)
-            self.skip_linear = nn.Linear(2 * hidden_size, hidden_size)
-        else:
-            self.skip_linear = None
-
-    def forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None):
-        # Long Skip Connection
-        if self.skip_linear is not None:
-            cat = torch.cat([x, skip], dim=-1)
-            cat = self.skip_norm(cat)
-            x = self.skip_linear(cat)
-
-        # Self-Attention
-        shift_msa = self.default_modulation(c).unsqueeze(dim=1)
-        attn_inputs = (
-            self.norm1(x) + shift_msa, freq_cis_img,
-        )
-        x = x + self.attn1(*attn_inputs)[0]
-
-        # Cross-Attention
-        cross_inputs = (
-            self.norm3(x), text_states, freq_cis_img
-        )
-        x = x + self.attn2(*cross_inputs)[0]
-
-        # FFN Layer
-        mlp_inputs = self.norm2(x)
-        x = x + self.mlp(mlp_inputs)
-
-        return x
-
-
-class FinalLayer(nn.Module):
-    """
-    The final layer of HunYuanDiT.
-    """
-    def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels):
-        super().__init__()
-        self.norm_final = nn.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6)
-        self.linear = nn.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True)
-        self.adaLN_modulation = nn.Sequential(
-            FP32_SiLU(),
-            nn.Linear(c_emb_size, 2 * final_hidden_size, bias=True)
-        )
-
-    def forward(self, x, c):
-        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
-        x = modulate(self.norm_final(x), shift, scale)
-        x = self.linear(x)
-        return x
-
-
-class HunYuanDiT(nn.Module):
-    """
-    HunYuanDiT: Diffusion model with a Transformer backbone.
-
-    Parameters
-    ----------
-    args: argparse.Namespace
-        The arguments parsed by argparse.
-    input_size: tuple
-        The size of the input image.
-    patch_size: int
-        The size of the patch.
-    in_channels: int
-        The number of input channels.
-    hidden_size: int
-        The hidden size of the transformer backbone.
-    depth: int
-        The number of transformer blocks.
-    num_heads: int
-        The number of attention heads.
-    mlp_ratio: float
-        The ratio of the hidden size of the MLP in the transformer block.
-    log_fn: callable
-        The logging function.
-    """
-    def __init__(
-            self, args,
-            input_size=(32, 32),
-            patch_size=2,
-            in_channels=4,
-            hidden_size=1152,
-            depth=28,
-            num_heads=16,
-            mlp_ratio=4.0,
-            log_fn=print,
-            cond_style=True,
-            cond_res=True,
-            **kwargs,
-    ):
-        super().__init__()
-        self.args = args
-        self.log_fn = log_fn
-        self.depth = depth
-        self.learn_sigma = args.learn_sigma
-        self.in_channels = in_channels
-        self.out_channels = in_channels * 2 if args.learn_sigma else in_channels
-        self.patch_size = patch_size
-        self.num_heads = num_heads
-        self.hidden_size = hidden_size
-        self.head_size = hidden_size // num_heads
-        self.text_states_dim = args.text_states_dim
-        self.text_states_dim_t5 = args.text_states_dim_t5
-        self.text_len = args.text_len
-        self.text_len_t5 = args.text_len_t5
-        self.norm = args.norm
-        self.cond_res = cond_res
-        self.cond_style = cond_style
-
-        use_flash_attn = args.infer_mode == 'fa'
-        if use_flash_attn:
-            log_fn(f"    Enable Flash Attention.")
-        qk_norm = True  # See http://arxiv.org/abs/2302.05442 for details.
-
-        self.mlp_t5 = nn.Sequential(
-            nn.Linear(self.text_states_dim_t5, self.text_states_dim_t5 * 4, bias=True),
-            FP32_SiLU(),
-            nn.Linear(self.text_states_dim_t5 * 4, self.text_states_dim, bias=True),
-        )
-        # learnable replace
-        self.text_embedding_padding = nn.Parameter(
-            torch.randn(self.text_len + self.text_len_t5, self.text_states_dim, dtype=torch.float32))
-
-        # Attention pooling
-        self.pooler = AttentionPool(self.text_len_t5, self.text_states_dim_t5, num_heads=8, output_dim=1024)
-
-
-        self.extra_in_dim = 0        
-        if self.cond_res:
-                # Image size and crop size conditions
-                self.extra_in_dim += 256 * 6
-        if self.cond_style:
-                # Here we use a default learned embedder layer for future extension.
-                self.style_embedder = nn.Embedding(1, hidden_size)
-                self.extra_in_dim += hidden_size
-
-        # Text embedding for `add`
-        self.last_size = input_size
-        self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size)
-        self.t_embedder = TimestepEmbedder(hidden_size)
-        self.extra_in_dim += 1024
-        self.extra_embedder = nn.Sequential(
-            nn.Linear(self.extra_in_dim, hidden_size * 4),
-            FP32_SiLU(),
-            nn.Linear(hidden_size * 4, hidden_size, bias=True),
-        )
-
-        # Image embedding
-        num_patches = self.x_embedder.num_patches
-        log_fn(f"    Number of tokens: {num_patches}")
-
-        # HUnYuanDiT Blocks
-        self.blocks = nn.ModuleList([
-            HunYuanDiTBlock(hidden_size=hidden_size,
-                            c_emb_size=hidden_size,
-                            num_heads=num_heads,
-                            mlp_ratio=mlp_ratio,
-                            text_states_dim=self.text_states_dim,
-                            use_flash_attn=use_flash_attn,
-                            qk_norm=qk_norm,
-                            norm_type=self.norm,
-                            skip=layer > depth // 2,
-                            )
-            for layer in range(depth)
-        ])
-
-        self.final_layer = FinalLayer(hidden_size, hidden_size, patch_size, self.out_channels)
-        self.unpatchify_channels = self.out_channels
-
-    def forward_raw(self,
-                x,
-                t,
-                encoder_hidden_states=None,
-                text_embedding_mask=None,
-                encoder_hidden_states_t5=None,
-                text_embedding_mask_t5=None,
-                image_meta_size=None,
-                style=None,
-                cos_cis_img=None,
-                sin_cis_img=None,
-                return_dict=False,
-                ):
-        """
-        Forward pass of the encoder.
-
-        Parameters
-        ----------
-        x: torch.Tensor
-            (B, D, H, W)
-        t: torch.Tensor
-            (B)
-        encoder_hidden_states: torch.Tensor
-            CLIP text embedding, (B, L_clip, D)
-        text_embedding_mask: torch.Tensor
-            CLIP text embedding mask, (B, L_clip)
-        encoder_hidden_states_t5: torch.Tensor
-            T5 text embedding, (B, L_t5, D)
-        text_embedding_mask_t5: torch.Tensor
-            T5 text embedding mask, (B, L_t5)
-        image_meta_size: torch.Tensor
-            (B, 6)
-        style: torch.Tensor
-            (B)
-        cos_cis_img: torch.Tensor
-        sin_cis_img: torch.Tensor
-        return_dict: bool
-            Whether to return a dictionary.
-        """
-
-        text_states = encoder_hidden_states                     # 2,77,1024
-        text_states_t5 = encoder_hidden_states_t5               # 2,256,2048
-        text_states_mask = text_embedding_mask.bool()           # 2,77
-        text_states_t5_mask = text_embedding_mask_t5.bool()     # 2,256
-        b_t5, l_t5, c_t5 = text_states_t5.shape
-        text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5))
-        text_states = torch.cat([text_states, text_states_t5.view(b_t5, l_t5, -1)], dim=1)  # 2,205，1024
-        clip_t5_mask = torch.cat([text_states_mask, text_states_t5_mask], dim=-1)
-
-        clip_t5_mask = clip_t5_mask
-        text_states = torch.where(clip_t5_mask.unsqueeze(2), text_states, self.text_embedding_padding.to(text_states))
-
-        _, _, oh, ow = x.shape
-        th, tw = oh // self.patch_size, ow // self.patch_size
-
-        # ========================= Build time and image embedding =========================
-        t = self.t_embedder(t)
-        x = self.x_embedder(x)
-
-        # Get image RoPE embedding according to `reso`lution.
-        freqs_cis_img = (cos_cis_img, sin_cis_img)
-
-        # ========================= Concatenate all extra vectors =========================
-        # Build text tokens with pooling
-        extra_vec = self.pooler(encoder_hidden_states_t5)
-
-        if self.cond_res:
-                # Build image meta size tokens
-                image_meta_size = timestep_embedding(image_meta_size.view(-1), 256)   # [B * 6, 256]
-                # if self.args.use_fp16:
-                    # image_meta_size = image_meta_size.half()
-        
-                image_meta_size = image_meta_size.view(-1, 6 * 256)
-                extra_vec = torch.cat([extra_vec, image_meta_size], dim=1)  # [B, D + 6 * 256]
-
-        if self.cond_style:
-                # Build style tokens
-                style_embedding = self.style_embedder(style)
-                extra_vec = torch.cat([extra_vec, style_embedding], dim=1)
-
-        # Concatenate all extra vectors
-        c = t + self.extra_embedder(extra_vec.to(self.dtype))  # [B, D]
-
-        # ========================= Forward pass through HunYuanDiT blocks =========================
-        skips = []
-        for layer, block in enumerate(self.blocks):
-            if layer > self.depth // 2:
-                skip = skips.pop()
-                x = block(x, c, text_states, freqs_cis_img, skip)   # (N, L, D)
-            else:
-                x = block(x, c, text_states, freqs_cis_img)         # (N, L, D)
-
-            if layer < (self.depth // 2 - 1):
-                skips.append(x)
-
-        # ========================= Final layer =========================
-        x = self.final_layer(x, c)                              # (N, L, patch_size ** 2 * out_channels)
-        x = self.unpatchify(x, th, tw)                          # (N, out_channels, H, W)
-
-        if return_dict:
-            return {'x': x}
-        return x
-   
-    def calc_rope(self, height, width):
-        """
-        Probably not the best in terms of perf to have this here
-        """
-        th = height // 8 // self.patch_size
-        tw = width // 8 // self.patch_size
-        base_size = 512 // 8 // self.patch_size
-        start, stop = get_fill_resize_and_crop((th, tw), base_size)
-        sub_args = [start, stop, (th, tw)]
-        rope = get_2d_rotary_pos_embed(self.head_size, *sub_args)
-        return rope
-
-    def forward(self, x, timesteps, context, context_mask=None, context_t5=None, context_t5_mask=None, src_size_cond=(1024,1024), **kwargs):
-        """
-        Forward pass that adapts comfy input to original forward function
-        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
-        timesteps: (N,) tensor of diffusion timesteps
-        context: (N, 1, 77, C) CLIP conditioning
-        context_t5: (N, 1, 256, C) MT5 conditioning
-        """
-        # context_mask = torch.zeros(x.shape[0], 77, device=x.device)
-        # context_t5_mask = torch.zeros(x.shape[0], 256, device=x.device)
-
-        # style
-        style = torch.as_tensor([0] * (x.shape[0]), device=x.device)
-
-        # image size - todo separate for cond/uncond when batched
-        if torch.is_tensor(src_size_cond):
-                src_size_cond = (int(src_size_cond[0][0]), int(src_size_cond[0][1]))
-        
-        image_size = (x.shape[2]//2*16, x.shape[3]//2*16)
-        size_cond = list(src_size_cond) + [image_size[1], image_size[0], 0, 0]
-        image_meta_size = torch.as_tensor([size_cond] * x.shape[0], device=x.device)
-
-        # RoPE
-        rope = self.calc_rope(*image_size)
-
-        # Update x_embedder if image size changed
-        if self.last_size != image_size:
-                from tqdm import tqdm
-                tqdm.write(f"HyDiT: New image size {image_size}")
-                self.x_embedder.update_image_size(
-                        (image_size[0]//8, image_size[1]//8), 
-                )
-                self.last_size = image_size
-
-        # Run original forward pass
-        out = self.forward_raw(
-                x = x.to(self.dtype),
-                t = timesteps.to(self.dtype),
-                encoder_hidden_states = context.to(self.dtype),
-                text_embedding_mask   = context_mask.to(self.dtype),
-                encoder_hidden_states_t5 = context_t5.to(self.dtype),
-                text_embedding_mask_t5   = context_t5_mask.to(self.dtype),
-                image_meta_size = image_meta_size.to(self.dtype),
-                style = style,
-                cos_cis_img = rope[0],
-                sin_cis_img = rope[1],
-        )
-        
-        # return
-        out = out.to(torch.float)
-        if self.learn_sigma:
-                eps, rest = out[:, :self.in_channels], out[:, self.in_channels:]
-                return eps
-        else:
-                return out
-
-    def unpatchify(self, x, h, w):
-        """
-        x: (N, T, patch_size**2 * C)
-        imgs: (N, H, W, C)
-        """
-        c = self.unpatchify_channels
-        p = self.x_embedder.patch_size[0]
-        # h = w = int(x.shape[1] ** 0.5)
-        assert h * w == x.shape[1]
-
-        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
-        x = torch.einsum('nhwpqc->nchpwq', x)
-        imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
-        return imgs
diff --git a/HunYuanDiT/models/norm_layers.py b/HunYuanDiT/models/norm_layers.py
deleted file mode 100644
index 5204ad9..0000000
--- a/HunYuanDiT/models/norm_layers.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import torch
-import torch.nn as nn
-
-
-class RMSNorm(nn.Module):
-    def __init__(self, dim: int, elementwise_affine=True, eps: float = 1e-6):
-        """
-        Initialize the RMSNorm normalization layer.
-
-        Args:
-            dim (int): The dimension of the input tensor.
-            eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
-
-        Attributes:
-            eps (float): A small value added to the denominator for numerical stability.
-            weight (nn.Parameter): Learnable scaling parameter.
-
-        """
-        super().__init__()
-        self.eps = eps
-        if elementwise_affine:
-            self.weight = nn.Parameter(torch.ones(dim))
-
-    def _norm(self, x):
-        """
-        Apply the RMSNorm normalization to the input tensor.
-
-        Args:
-            x (torch.Tensor): The input tensor.
-
-        Returns:
-            torch.Tensor: The normalized tensor.
-
-        """
-        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
-
-    def forward(self, x):
-        """
-        Forward pass through the RMSNorm layer.
-
-        Args:
-            x (torch.Tensor): The input tensor.
-
-        Returns:
-            torch.Tensor: The output tensor after applying RMSNorm.
-
-        """
-        output = self._norm(x.float()).type_as(x)
-        if hasattr(self, "weight"):
-            output = output * self.weight
-        return output
-
-
-class GroupNorm32(nn.GroupNorm):
-    def __init__(self, num_groups, num_channels, eps=1e-5, dtype=None):
-        super().__init__(num_groups=num_groups, num_channels=num_channels, eps=eps, dtype=dtype)
-
-    def forward(self, x):
-        y = super().forward(x).to(x.dtype)
-        return y
-
-def normalization(channels, dtype=None):
-    """
-    Make a standard normalization layer.
-    :param channels: number of input channels.
-    :return: an nn.Module for normalization.
-    """
-    return GroupNorm32(num_channels=channels, num_groups=32, dtype=dtype)
diff --git a/HunYuanDiT/models/poolers.py b/HunYuanDiT/models/poolers.py
deleted file mode 100644
index a4adcac..0000000
--- a/HunYuanDiT/models/poolers.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class AttentionPool(nn.Module):
-    def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(torch.randn(spacial_dim + 1, embed_dim) / embed_dim ** 0.5)
-        self.k_proj = nn.Linear(embed_dim, embed_dim)
-        self.q_proj = nn.Linear(embed_dim, embed_dim)
-        self.v_proj = nn.Linear(embed_dim, embed_dim)
-        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
-        self.num_heads = num_heads
-
-    def forward(self, x):
-        x = x.permute(1, 0, 2)  # NLC -> LNC
-        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (L+1)NC
-        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (L+1)NC
-        x, _ = F.multi_head_attention_forward(
-            query=x[:1], key=x, value=x,
-            embed_dim_to_check=x.shape[-1],
-            num_heads=self.num_heads,
-            q_proj_weight=self.q_proj.weight,
-            k_proj_weight=self.k_proj.weight,
-            v_proj_weight=self.v_proj.weight,
-            in_proj_weight=None,
-            in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
-            bias_k=None,
-            bias_v=None,
-            add_zero_attn=False,
-            dropout_p=0,
-            out_proj_weight=self.c_proj.weight,
-            out_proj_bias=self.c_proj.bias,
-            use_separate_proj_weight=True,
-            training=self.training,
-            need_weights=False
-        )
-        return x.squeeze(0)
diff --git a/HunYuanDiT/models/posemb_layers.py b/HunYuanDiT/models/posemb_layers.py
deleted file mode 100644
index 62c83df..0000000
--- a/HunYuanDiT/models/posemb_layers.py
+++ /dev/null
@@ -1,225 +0,0 @@
-import torch
-import numpy as np
-from typing import Union
-
-
-def _to_tuple(x):
-    if isinstance(x, int):
-        return x, x
-    else:
-        return x
-
-
-def get_fill_resize_and_crop(src, tgt):    # src 来源的分辨率   tgt base 分辨率
-    th, tw = _to_tuple(tgt)
-    h, w = _to_tuple(src)
-
-    tr = th / tw        # base 分辨率
-    r = h / w           # 目标分辨率
-
-    # resize
-    if r > tr:
-        resize_height = th
-        resize_width = int(round(th / h * w))
-    else:
-        resize_width = tw
-        resize_height = int(round(tw / w * h))    # 根据base分辨率，将目标分辨率resize下来
-
-    crop_top = int(round((th - resize_height) / 2.0))
-    crop_left = int(round((tw - resize_width) / 2.0))
-
-    return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
-
-
-def get_meshgrid(start, *args):
-    if len(args) == 0:
-        # start is grid_size
-        num = _to_tuple(start)
-        start = (0, 0)
-        stop = num
-    elif len(args) == 1:
-        # start is start, args[0] is stop, step is 1
-        start = _to_tuple(start)
-        stop = _to_tuple(args[0])
-        num = (stop[0] - start[0], stop[1] - start[1])
-    elif len(args) == 2:
-        # start is start, args[0] is stop, args[1] is num
-        start = _to_tuple(start)       # 左上角   eg: 12,0
-        stop = _to_tuple(args[0])      # 右下角   eg: 20,32
-        num = _to_tuple(args[1])       # 目标大小  eg: 32,124
-    else:
-        raise ValueError(f"len(args) should be 0, 1 or 2, but got {len(args)}")
-
-    grid_h = np.linspace(start[0], stop[0], num[0], endpoint=False, dtype=np.float32) # 12-20 中间差值32份   0-32 中间差值124份
-    grid_w = np.linspace(start[1], stop[1], num[1], endpoint=False, dtype=np.float32)
-    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
-    grid = np.stack(grid, axis=0)   # [2, W, H]
-    return grid
-
-#################################################################################
-#                   Sine/Cosine Positional Embedding Functions                  #
-#################################################################################
-# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
-
-def get_2d_sincos_pos_embed(embed_dim, start, *args, cls_token=False, extra_tokens=0):
-    """
-    grid_size: int of the grid height and width
-    return:
-    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
-    """
-    grid = get_meshgrid(start, *args)   # [2, H, w]
-    # grid_h = np.arange(grid_size, dtype=np.float32)
-    # grid_w = np.arange(grid_size, dtype=np.float32)
-    # grid = np.meshgrid(grid_w, grid_h)  # here w goes first
-    # grid = np.stack(grid, axis=0)   # [2, W, H]
-
-    grid = grid.reshape([2, 1, *grid.shape[1:]])
-    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
-    if cls_token and extra_tokens > 0:
-        pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
-    return pos_embed
-
-
-def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
-    assert embed_dim % 2 == 0
-
-    # use half of dimensions to encode grid_h
-    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
-    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
-
-    emb = np.concatenate([emb_h, emb_w], axis=1)    # (H*W, D)
-    return emb
-
-
-def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
-    """
-    embed_dim: output dimension for each position
-    pos: a list of positions to be encoded: size (W,H)
-    out: (M, D)
-    """
-    assert embed_dim % 2 == 0
-    omega = np.arange(embed_dim // 2, dtype=np.float64)
-    omega /= embed_dim / 2.
-    omega = 1. / 10000**omega  # (D/2,)
-
-    pos = pos.reshape(-1)  # (M,)
-    out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product
-
-    emb_sin = np.sin(out)   # (M, D/2)
-    emb_cos = np.cos(out)   # (M, D/2)
-
-    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
-    return emb
-
-
-#################################################################################
-#                   Rotary Positional Embedding Functions                       #
-#################################################################################
-# https://github.com/facebookresearch/llama/blob/main/llama/model.py#L443
-
-def get_2d_rotary_pos_embed(embed_dim, start, *args, use_real=True):
-    """
-    This is a 2d version of precompute_freqs_cis, which is a RoPE for image tokens with 2d structure.
-
-    Parameters
-    ----------
-    embed_dim: int
-        embedding dimension size
-    start: int or tuple of int
-        If len(args) == 0, start is num; If len(args) == 1, start is start, args[0] is stop, step is 1;
-        If len(args) == 2, start is start, args[0] is stop, args[1] is num.
-    use_real: bool
-        If True, return real part and imaginary part separately. Otherwise, return complex numbers.
-
-    Returns
-    -------
-    pos_embed: torch.Tensor
-        [HW, D/2]
-    """
-    grid = get_meshgrid(start, *args)   # [2, H, w]
-    grid = grid.reshape([2, 1, *grid.shape[1:]])   # 返回一个采样矩阵  分辨率与目标分辨率一致
-    pos_embed = get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real)
-    return pos_embed
-
-
-def get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False):
-    assert embed_dim % 4 == 0
-
-    # use half of dimensions to encode grid_h
-    emb_h = get_1d_rotary_pos_embed(embed_dim // 2, grid[0].reshape(-1), use_real=use_real)  # (H*W, D/4)
-    emb_w = get_1d_rotary_pos_embed(embed_dim // 2, grid[1].reshape(-1), use_real=use_real)  # (H*W, D/4)
-
-    if use_real:
-        cos = torch.cat([emb_h[0], emb_w[0]], dim=1)    # (H*W, D/2)
-        sin = torch.cat([emb_h[1], emb_w[1]], dim=1)    # (H*W, D/2)
-        return cos, sin
-    else:
-        emb = torch.cat([emb_h, emb_w], dim=1)    # (H*W, D/2)
-        return emb
-
-
-def get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta: float = 10000.0, use_real=False):
-    """
-    Precompute the frequency tensor for complex exponentials (cis) with given dimensions.
-
-    This function calculates a frequency tensor with complex exponentials using the given dimension 'dim'
-    and the end index 'end'. The 'theta' parameter scales the frequencies.
-    The returned tensor contains complex values in complex64 data type.
-
-    Args:
-        dim (int): Dimension of the frequency tensor.
-        pos (np.ndarray, int): Position indices for the frequency tensor. [S] or scalar
-        theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0.
-        use_real (bool, optional): If True, return real part and imaginary part separately.
-                                   Otherwise, return complex numbers.
-
-    Returns:
-        torch.Tensor: Precomputed frequency tensor with complex exponentials. [S, D/2]
-
-    """
-    if isinstance(pos, int):
-        pos = np.arange(pos)
-    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))  # [D/2]
-    t = torch.from_numpy(pos).to(freqs.device)  # type: ignore  # [S]
-    freqs = torch.outer(t, freqs).float()  # type: ignore   # [S, D/2]
-    if use_real:
-        freqs_cos = freqs.cos().repeat_interleave(2, dim=1)  # [S, D]
-        freqs_sin = freqs.sin().repeat_interleave(2, dim=1)  # [S, D]
-        return freqs_cos, freqs_sin
-    else:
-        freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64     # [S, D/2]
-        return freqs_cis
-
-
-
-def calc_sizes(rope_img, patch_size, th, tw):
-    """ 计算 RoPE 的尺寸. """
-    if rope_img == 'extend':
-        # 拓展模式
-        sub_args = [(th, tw)]
-    elif rope_img.startswith('base'):
-        # 基于一个尺寸, 其他尺寸插值获得.
-        base_size = int(rope_img[4:]) // 8 // patch_size            # 基于512作为base，其他根据512差值得到
-        start, stop = get_fill_resize_and_crop((th, tw), base_size)   # 需要在32x32里面 crop的左上角和右下角
-        sub_args = [start, stop, (th, tw)]
-    else:
-        raise ValueError(f"Unknown rope_img: {rope_img}")
-    return sub_args
-
-
-def init_image_posemb(rope_img,
-                      resolutions,
-                      patch_size,
-                      hidden_size,
-                      num_heads,
-                      log_fn,
-                      rope_real=True,
-                      ):
-    freqs_cis_img = {}
-    for reso in resolutions:
-        th, tw = reso.height // 8 // patch_size, reso.width // 8 // patch_size
-        sub_args = calc_sizes(rope_img, patch_size, th, tw)      #  [左上角, 右下角, 目标高宽]   需要在32x32里面 crop的左上角和右下角
-        freqs_cis_img[str(reso)] = get_2d_rotary_pos_embed(hidden_size // num_heads, *sub_args, use_real=rope_real)
-        log_fn(f"    Using image RoPE ({rope_img}) ({'real' if rope_real else 'complex'}): {sub_args} | ({reso}) "
-               f"{freqs_cis_img[str(reso)][0].shape if rope_real else freqs_cis_img[str(reso)].shape}")
-    return freqs_cis_img
diff --git a/HunYuanDiT/mt5_tokenizer/config.json b/HunYuanDiT/mt5_tokenizer/config.json
deleted file mode 100644
index d5c7028..0000000
--- a/HunYuanDiT/mt5_tokenizer/config.json
+++ /dev/null
@@ -1,33 +0,0 @@
-{
-  "_name_or_path": "mt5",
-  "architectures": [
-    "MT5ForConditionalGeneration"
-  ],
-  "classifier_dropout": 0.0,
-  "d_ff": 5120,
-  "d_kv": 64,
-  "d_model": 2048,
-  "decoder_start_token_id": 0,
-  "dense_act_fn": "gelu_new",
-  "dropout_rate": 0.1,
-  "eos_token_id": 1,
-  "feed_forward_proj": "gated-gelu",
-  "initializer_factor": 1.0,
-  "is_encoder_decoder": true,
-  "is_gated_act": true,
-  "layer_norm_epsilon": 1e-06,
-  "model_type": "mt5",
-  "num_decoder_layers": 24,
-  "num_heads": 32,
-  "num_layers": 24,
-  "output_past": true,
-  "pad_token_id": 0,
-  "relative_attention_max_distance": 128,
-  "relative_attention_num_buckets": 32,
-  "tie_word_embeddings": false,
-  "tokenizer_class": "T5Tokenizer",
-  "torch_dtype": "float16",
-  "transformers_version": "4.40.2",
-  "use_cache": true,
-  "vocab_size": 250112
-}
diff --git a/HunYuanDiT/mt5_tokenizer/special_tokens_map.json b/HunYuanDiT/mt5_tokenizer/special_tokens_map.json
deleted file mode 100644
index 6dc4d43..0000000
--- a/HunYuanDiT/mt5_tokenizer/special_tokens_map.json
+++ /dev/null
@@ -1 +0,0 @@
-{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
\ No newline at end of file
diff --git a/HunYuanDiT/mt5_tokenizer/spiece.model b/HunYuanDiT/mt5_tokenizer/spiece.model
deleted file mode 100644
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diff --git a/HunYuanDiT/mt5_tokenizer/tokenizer_config.json b/HunYuanDiT/mt5_tokenizer/tokenizer_config.json
deleted file mode 100644
index 712e82c..0000000
--- a/HunYuanDiT/mt5_tokenizer/tokenizer_config.json
+++ /dev/null
@@ -1 +0,0 @@
-{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "extra_ids": 0, "additional_special_tokens": null, "special_tokens_map_file": "/home/patrick/.cache/torch/transformers/685ac0ca8568ec593a48b61b0a3c272beee9bc194a3c7241d15dcadb5f875e53.f76030f3ec1b96a8199b2593390c610e76ca8028ef3d24680000619ffb646276", "tokenizer_file": null, "name_or_path": "google/mt5-small"}
\ No newline at end of file
diff --git a/HunYuanDiT/nodes.py b/HunYuanDiT/nodes.py
deleted file mode 100644
index dbce038..0000000
--- a/HunYuanDiT/nodes.py
+++ /dev/null
@@ -1,198 +0,0 @@
-import os
-import folder_paths
-from copy import deepcopy
-
-from .conf import hydit_conf
-from .loader import load_hydit
-
-class HYDiTCheckpointLoader:
-	@classmethod
-	def INPUT_TYPES(s):
-		return {
-			"required": {
-				"ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
-				"model": (list(hydit_conf.keys()),{"default":"G/2"}),
-			}
-		}
-	RETURN_TYPES = ("MODEL",)
-	RETURN_NAMES = ("model",)
-	FUNCTION = "load_checkpoint"
-	CATEGORY = "ExtraModels/HunyuanDiT"
-	TITLE = "Hunyuan DiT Checkpoint Loader"
-
-	def load_checkpoint(self, ckpt_name, model):
-		ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
-		model_conf = hydit_conf[model]
-		model = load_hydit(
-			model_path = ckpt_path,
-			model_conf = model_conf,
-		)
-		return (model,)
-
-#### temp stuff for the text encoder ####
-import torch
-from .tenc import load_clip, load_t5
-from ..utils.dtype import string_to_dtype
-dtypes = [
-	"default",
-	"auto (comfy)",
-	"FP32",
-	"FP16",
-	"BF16"
-]
-
-class HYDiTTextEncoderLoader:
-	@classmethod
-	def INPUT_TYPES(s):
-		devices = ["auto", "cpu", "gpu"]
-		# hack for using second GPU as offload
-		for k in range(1, torch.cuda.device_count()):
-			devices.append(f"cuda:{k}")
-		return {
-			"required": {
-				"clip_name": (folder_paths.get_filename_list("clip"),),
-				"mt5_name": (folder_paths.get_filename_list("t5"),),
-				"device": (devices, {"default":"cpu"}),
-				"dtype": (dtypes,),
-			}
-		}
-	
-	RETURN_TYPES = ("CLIP", "T5")
-	FUNCTION = "load_model"
-	CATEGORY = "ExtraModels/HunyuanDiT"
-	TITLE = "Hunyuan DiT Text Encoder Loader"
-	
-	def load_model(self, clip_name, mt5_name, device, dtype):
-		dtype = string_to_dtype(dtype, "text_encoder")
-		if device == "cpu":
-			assert dtype in [None, torch.float32, torch.bfloat16], f"Can't use dtype '{dtype}' with CPU! Set dtype to 'default' or 'bf16'."
-
-		clip = load_clip(
-			model_path = folder_paths.get_full_path("clip", clip_name),
-			device = device,
-			dtype = dtype,
-		)
-		t5 = load_t5(
-			model_path = folder_paths.get_full_path("t5", mt5_name),
-			device = device,
-			dtype = dtype,
-		)
-		return(clip, t5)
-
-class HYDiTTextEncode:
-	@classmethod
-	def INPUT_TYPES(s):
-		return {
-			"required": {
-				"text": ("STRING", {"multiline": True}),
-				"text_t5": ("STRING", {"multiline": True}),
-				"CLIP": ("CLIP",),
-				"T5": ("T5",),
-			}
-		}
-
-	RETURN_TYPES = ("CONDITIONING",)
-	FUNCTION = "encode"
-	CATEGORY = "ExtraModels/HunyuanDiT"
-	TITLE = "Hunyuan DiT Text Encode"
-
-	def encode(self, text, text_t5, CLIP, T5):
-		# T5
-		T5.load_model()
-		t5_pre = T5.tokenizer(
-			text_t5,
-			max_length            = T5.cond_stage_model.max_length,
-			padding               = 'max_length',
-			truncation            = True,
-			return_attention_mask = True,
-			add_special_tokens    = True,
-			return_tensors        = 'pt'
-		)
-		t5_mask = t5_pre["attention_mask"]
-		with torch.no_grad():
-			t5_outs = T5.cond_stage_model.transformer(
-				input_ids = t5_pre["input_ids"].to(T5.load_device),
-				attention_mask = t5_mask.to(T5.load_device),
-				output_hidden_states = True,
-			)
-			# to-do: replace -1 for clip skip
-			t5_embs = t5_outs["hidden_states"][-1].float().cpu()
-
-		# "clip"
-		CLIP.load_model()
-		clip_pre = CLIP.tokenizer(
-			text,
-			max_length            = CLIP.cond_stage_model.max_length,
-			padding               = 'max_length',
-			truncation            = True,
-			return_attention_mask = True,
-			add_special_tokens    = True,
-			return_tensors        = 'pt'
-		)
-		clip_mask = clip_pre["attention_mask"]
-		with torch.no_grad():
-			clip_outs = CLIP.cond_stage_model.transformer(
-				input_ids = clip_pre["input_ids"].to(CLIP.load_device),
-				attention_mask = clip_mask.to(CLIP.load_device),
-			)
-			# to-do: add hidden states
-			clip_embs = clip_outs[0].float().cpu()
-		
-		# combined cond
-		return ([[
-			clip_embs, {
-				"context_t5": t5_embs,
-				"context_mask": clip_mask.float(),
-				"context_t5_mask": t5_mask.float()
-			}
-		]],)
-
-class HYDiTTextEncodeSimple(HYDiTTextEncode):
-	@classmethod
-	def INPUT_TYPES(s):
-		return {
-			"required": {
-				"text": ("STRING", {"multiline": True}),
-				"CLIP": ("CLIP",),
-				"T5": ("T5",),
-			}
-		}
-
-	FUNCTION = "encode_simple"
-	TITLE = "Hunyuan DiT Text Encode (simple)"
-
-	def encode_simple(self, text, **args):
-		return self.encode(text=text, text_t5=text, **args)
-
-class HYDiTSrcSizeCond:
-	@classmethod
-	def INPUT_TYPES(s):
-		return {
-			"required": {
-				"cond": ("CONDITIONING", ),
-				"width": ("INT", {"default": 1024.0, "min": 0, "max": 8192, "step": 16}),
-				"height": ("INT", {"default": 1024.0, "min": 0, "max": 8192, "step": 16}),
-			}
-		}
-
-	RETURN_TYPES = ("CONDITIONING",)
-	RETURN_NAMES = ("cond",)
-	FUNCTION = "add_cond"
-	CATEGORY = "ExtraModels/HunyuanDiT"
-	TITLE = "Hunyuan DiT Size Conditioning (advanced)"
-
-	def add_cond(self, cond, width, height):
-		cond = deepcopy(cond)
-		for c in range(len(cond)):
-			cond[c][1].update({
-				"src_size_cond": [[height, width]],
-			})
-		return (cond,)
-
-NODE_CLASS_MAPPINGS = {
-	"HYDiTCheckpointLoader": HYDiTCheckpointLoader,
-	"HYDiTTextEncoderLoader": HYDiTTextEncoderLoader,
-	"HYDiTTextEncode": HYDiTTextEncode,
-	"HYDiTTextEncodeSimple": HYDiTTextEncodeSimple,
-	"HYDiTSrcSizeCond": HYDiTSrcSizeCond,
-}
diff --git a/HunYuanDiT/tenc.py b/HunYuanDiT/tenc.py
deleted file mode 100644
index a2d7107..0000000
--- a/HunYuanDiT/tenc.py
+++ /dev/null
@@ -1,180 +0,0 @@
-# This is for loading the CLIP (bert?) + mT5 encoder for HunYuanDiT
-import os
-import torch
-from transformers import AutoTokenizer, modeling_utils
-from transformers import T5Config, T5EncoderModel, BertConfig, BertModel
-
-from comfy import model_management
-import comfy.model_patcher
-import comfy.utils
-
-class mT5Model(torch.nn.Module):
-	def __init__(self, textmodel_json_config=None, device="cpu", max_length=256, freeze=True, dtype=None):
-		super().__init__()
-		self.device = device
-		self.dtype = dtype
-		self.max_length = max_length
-		if textmodel_json_config is None:
-			textmodel_json_config = os.path.join(
-				os.path.dirname(os.path.realpath(__file__)),
-				f"config_mt5.json"
-			)
-		config = T5Config.from_json_file(textmodel_json_config)
-		with modeling_utils.no_init_weights():
-			self.transformer = T5EncoderModel(config)
-		self.to(dtype)
-		if freeze:
-			self.freeze()
-
-	def freeze(self):
-		self.transformer = self.transformer.eval()
-		for param in self.parameters():
-			param.requires_grad = False
-
-	def load_sd(self, sd):
-		return self.transformer.load_state_dict(sd, strict=False)
-
-	def to(self, *args, **kwargs):
-		return self.transformer.to(*args, **kwargs)
-
-class hyCLIPModel(torch.nn.Module):
-	def __init__(self, textmodel_json_config=None, device="cpu", max_length=77, freeze=True, dtype=None):
-		super().__init__()
-		self.device = device
-		self.dtype = dtype
-		self.max_length = max_length
-		if textmodel_json_config is None:
-			textmodel_json_config = os.path.join(
-				os.path.dirname(os.path.realpath(__file__)),
-				f"config_clip.json"
-			)
-		config = BertConfig.from_json_file(textmodel_json_config)
-		with modeling_utils.no_init_weights():
-			self.transformer = BertModel(config)
-		self.to(dtype)
-		if freeze:
-			self.freeze()
-
-	def freeze(self):
-		self.transformer = self.transformer.eval()
-		for param in self.parameters():
-			param.requires_grad = False
-
-	def load_sd(self, sd):
-		return self.transformer.load_state_dict(sd, strict=False)
-
-	def to(self, *args, **kwargs):
-		return self.transformer.to(*args, **kwargs)
-
-class EXM_HyDiT_Tenc_Temp:
-	def __init__(self, no_init=False, device="cpu", dtype=None, model_class="mT5", *kwargs):
-		if no_init:
-			return
-
-		size = 8 if model_class == "mT5" else 2
-		if dtype == torch.float32:
-			size *= 2
-		size *= (1024**3)
-
-		if device == "auto":
-			self.load_device = model_management.text_encoder_device()
-			self.offload_device = model_management.text_encoder_offload_device()
-			self.init_device = "cpu"
-		elif device == "cpu":
-			size = 0 # doesn't matter
-			self.load_device = "cpu"
-			self.offload_device = "cpu"
-			self.init_device="cpu"
-		elif device.startswith("cuda"):
-			print("Direct CUDA device override!\nVRAM will not be freed by default.")
-			size = 0 # not used
-			self.load_device = device
-			self.offload_device = device
-			self.init_device = device
-		else:
-			self.load_device = model_management.get_torch_device()
-			self.offload_device = "cpu"
-			self.init_device="cpu"
-
-		self.dtype = dtype
-		self.device = self.load_device
-		if model_class == "mT5":
-			self.cond_stage_model = mT5Model(
-				device         = self.load_device,
-				dtype          = self.dtype,
-			)
-			tokenizer_args = {"subfolder": "t2i/mt5"} # web
-			tokenizer_path = os.path.join( # local
-				os.path.dirname(os.path.realpath(__file__)),
-				"mt5_tokenizer",
-			)
-		else:
-			self.cond_stage_model = hyCLIPModel(
-				device         = self.load_device,
-				dtype          = self.dtype,
-			)
-			tokenizer_args = {"subfolder": "t2i/tokenizer",} # web
-			tokenizer_path = os.path.join( # local
-				os.path.dirname(os.path.realpath(__file__)),
-				"tokenizer",
-			)
-		# self.tokenizer = AutoTokenizer.from_pretrained(
-			# "Tencent-Hunyuan/HunyuanDiT",
-			# **tokenizer_args
-		# )
-		self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
-		self.patcher = comfy.model_patcher.ModelPatcher(
-			self.cond_stage_model,
-			load_device    = self.load_device,
-			offload_device = self.offload_device,
-			size           = size,
-		)
-
-	def clone(self):
-		n = EXM_HyDiT_Tenc_Temp(no_init=True)
-		n.patcher = self.patcher.clone()
-		n.cond_stage_model = self.cond_stage_model
-		n.tokenizer = self.tokenizer
-		return n
-
-	def load_sd(self, sd):
-		return self.cond_stage_model.load_sd(sd)
-
-	def get_sd(self):
-		return self.cond_stage_model.state_dict()
-
-	def load_model(self):
-		if self.load_device != "cpu":
-			model_management.load_model_gpu(self.patcher)
-		return self.patcher
-
-	def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
-		return self.patcher.add_patches(patches, strength_patch, strength_model)
-
-	def get_key_patches(self):
-		return self.patcher.get_key_patches()
-
-def load_clip(model_path, **kwargs):
-	model = EXM_HyDiT_Tenc_Temp(model_class="clip", **kwargs)
-	sd = comfy.utils.load_torch_file(model_path)
-
-	prefix = "bert."
-	state_dict = {}
-	for key in sd:
-		nkey = key
-		if key.startswith(prefix):
-				nkey = key[len(prefix):]
-		state_dict[nkey] = sd[key]
-
-	m, e = model.load_sd(state_dict)
-	if len(m) > 0 or len(e) > 0:
-		print(f"HYDiT: clip missing {len(m)} keys ({len(e)} extra)")
-	return model
-
-def load_t5(model_path, **kwargs):
-	model = EXM_HyDiT_Tenc_Temp(model_class="mT5", **kwargs)
-	sd = comfy.utils.load_torch_file(model_path)
-	m, e = model.load_sd(sd)
-	if len(m) > 0 or len(e) > 0:
-		print(f"HYDiT: mT5 missing {len(m)} keys ({len(e)} extra)")
-	return model
diff --git a/HunYuanDiT/tokenizer/config.json b/HunYuanDiT/tokenizer/config.json
deleted file mode 100644
index f629874..0000000
--- a/HunYuanDiT/tokenizer/config.json
+++ /dev/null
@@ -1,34 +0,0 @@
-{
-  "_name_or_path": "hfl/chinese-roberta-wwm-ext-large",
-  "architectures": [
-    "BertModel"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "bos_token_id": 0,
-  "classifier_dropout": null,
-  "directionality": "bidi",
-  "eos_token_id": 2,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 1024,
-  "initializer_range": 0.02,
-  "intermediate_size": 4096,
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 16,
-  "num_hidden_layers": 24,
-  "output_past": true,
-  "pad_token_id": 0,
-  "pooler_fc_size": 768,
-  "pooler_num_attention_heads": 12,
-  "pooler_num_fc_layers": 3,
-  "pooler_size_per_head": 128,
-  "pooler_type": "first_token_transform",
-  "position_embedding_type": "absolute",
-  "torch_dtype": "float32",
-  "transformers_version": "4.22.1",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 47020
-}
diff --git a/HunYuanDiT/tokenizer/special_tokens_map.json b/HunYuanDiT/tokenizer/special_tokens_map.json
deleted file mode 100644
index a8b3208..0000000
--- a/HunYuanDiT/tokenizer/special_tokens_map.json
+++ /dev/null
@@ -1,7 +0,0 @@
-{
-  "cls_token": "[CLS]",
-  "mask_token": "[MASK]",
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "unk_token": "[UNK]"
-}
diff --git a/HunYuanDiT/tokenizer/tokenizer_config.json b/HunYuanDiT/tokenizer/tokenizer_config.json
deleted file mode 100644
index a143560..0000000
--- a/HunYuanDiT/tokenizer/tokenizer_config.json
+++ /dev/null
@@ -1,16 +0,0 @@
-{
-  "cls_token": "[CLS]",
-  "do_basic_tokenize": true,
-  "do_lower_case": true,
-  "mask_token": "[MASK]",
-  "name_or_path": "hfl/chinese-roberta-wwm-ext",
-  "never_split": null,
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "special_tokens_map_file": "/home/chenweifeng/.cache/huggingface/hub/models--hfl--chinese-roberta-wwm-ext/snapshots/5c58d0b8ec1d9014354d691c538661bf00bfdb44/special_tokens_map.json",
-  "strip_accents": null,
-  "tokenize_chinese_chars": true,
-  "tokenizer_class": "BertTokenizer",
-  "unk_token": "[UNK]",
-  "model_max_length": 77
-}
diff --git a/HunYuanDiT/tokenizer/vocab.txt b/HunYuanDiT/tokenizer/vocab.txt
deleted file mode 100644
index 6246906..0000000
--- a/HunYuanDiT/tokenizer/vocab.txt
+++ /dev/null
@@ -1,47020 +0,0 @@
-[PAD]
-[unused1]
-[unused2]
-[unused3]
-[unused4]
-[unused5]
-[unused6]
-[unused7]
-[unused8]
-[unused9]
-[unused10]
-[unused11]
-[unused12]
-[unused13]
-[unused14]
-[unused15]
-[unused16]
-[unused17]
-[unused18]
-[unused19]
-[unused20]
-[unused21]
-[unused22]
-[unused23]
-[unused24]
-[unused25]
-[unused26]
-[unused27]
-[unused28]
-[unused29]
-[unused30]
-[unused31]
-[unused32]
-[unused33]
-[unused34]
-[unused35]
-[unused36]
-[unused37]
-[unused38]
-[unused39]
-[unused40]
-[unused41]
-[unused42]
-[unused43]
-[unused44]
-[unused45]
-[unused46]
-[unused47]
-[unused48]
-[unused49]
-[unused50]
-[unused51]
-[unused52]
-[unused53]
-[unused54]
-[unused55]
-[unused56]
-[unused57]
-[unused58]
-[unused59]
-[unused60]
-[unused61]
-[unused62]
-[unused63]
-[unused64]
-[unused65]
-[unused66]
-[unused67]
-[unused68]
-[unused69]
-[unused70]
-[unused71]
-[unused72]
-[unused73]
-[unused74]
-[unused75]
-[unused76]
-[unused77]
-[unused78]
-[unused79]
-[unused80]
-[unused81]
-[unused82]
-[unused83]
-[unused84]
-[unused85]
-[unused86]
-[unused87]
-[unused88]
-[unused89]
-[unused90]
-[unused91]
-[unused92]
-[unused93]
-[unused94]
-[unused95]
-[unused96]
-[unused97]
-[unused98]
-[unused99]
-[UNK]
-[CLS]
-[SEP]
-[MASK]
-<S>
-<T>
-!
-"
-#
-$
-%
-&
-'
-(
-)
-*
-+
-,
--
-.
-/
-0
-1
-2
-3
-4
-5
-6
-7
-8
-9
-:
-;
-<
-=
->
-?
-@
-[
-\
-]
-^
-_
-a
-b
-c
-d
-e
-f
-g
-h
-i
-j
-k
-l
-m
-n
-o
-p
-q
-r
-s
-t
-u
-v
-w
-x
-y
-z
-{
-|
-}
-~
-£
-¤
-¥
-§
-©
-«
-®
-°
-±
-²
-³
-µ
-·
-¹
-º
-»
-¼
-×
-ß
-æ
-÷
-ø
-đ
-ŋ
-ɔ
-ə
-ɡ
-ʰ
-ˇ
-ˈ
-ˊ
-ˋ
-ˍ
-ː
-˙
-˚
-ˢ
-α
-β
-γ
-δ
-ε
-η
-θ
-ι
-κ
-λ
-μ
-ν
-ο
-π
-ρ
-ς
-σ
-τ
-υ
-φ
-χ
-ψ
-ω
-а
-б
-в
-г
-д
-е
-ж
-з
-и
-к
-л
-м
-н
-о
-п
-р
-с
-т
-у
-ф
-х
-ц
-ч
-ш
-ы
-ь
-я
-і
-ا
-ب
-ة
-ت
-د
-ر
-س
-ع
-ل
-م
-ن
-ه
-و
-ي
-۩
-ก
-ง
-น
-ม
-ย
-ร
-อ
-า
-เ
-๑
-་
-ღ
-ᄀ
-ᄁ
-ᄂ
-ᄃ
-ᄅ
-ᄆ
-ᄇ
-ᄈ
-ᄉ
-ᄋ
-ᄌ
-ᄎ
-ᄏ
-ᄐ
-ᄑ
-ᄒ
-ᅡ
-ᅢ
-ᅣ
-ᅥ
-ᅦ
-ᅧ
-ᅨ
-ᅩ
-ᅪ
-ᅬ
-ᅭ
-ᅮ
-ᅯ
-ᅲ
-ᅳ
-ᅴ
-ᅵ
-ᆨ
-ᆫ
-ᆯ
-ᆷ
-ᆸ
-ᆺ
-ᆻ
-ᆼ
-ᗜ
-ᵃ
-ᵉ
-ᵍ
-ᵏ
-ᵐ
-ᵒ
-ᵘ
-‖
-„
-†
-•
-‥
-‧
- 
-‰
-′
-″
-‹
-›
-※
-‿
-⁄
-ⁱ
-⁺
-ⁿ
-₁
-₂
-₃
-₄
-€
-℃
-№
-™
-ⅰ
-ⅱ
-ⅲ
-ⅳ
-ⅴ
-←
-↑
-→
-↓
-↔
-↗
-↘
-⇒
-∀
-−
-∕
-∙
-√
-∞
-∟
-∠
-∣
-∥
-∩
-∮
-∶
-∼
-∽
-≈
-≒
-≡
-≤
-≥
-≦
-≧
-≪
-≫
-⊙
-⋅
-⋈
-⋯
-⌒
-①
-②
-③
-④
-⑤
-⑥
-⑦
-⑧
-⑨
-⑩
-⑴
-⑵
-⑶
-⑷
-⑸
-⒈
-⒉
-⒊
-⒋
-ⓒ
-ⓔ
-ⓘ
-─
-━
-│
-┃
-┅
-┆
-┊
-┌
-└
-├
-┣
-═
-║
-╚
-╞
-╠
-╭
-╮
-╯
-╰
-╱
-╳
-▂
-▃
-▅
-▇
-█
-▉
-▋
-▌
-▍
-▎
-■
-□
-▪
-▫
-▬
-▲
-△
-▶
-►
-▼
-▽
-◆
-◇
-○
-◎
-●
-◕
-◠
-◢
-◤
-☀
-★
-☆
-☕
-☞
-☺
-☼
-♀
-♂
-♠
-♡
-♣
-♥
-♦
-♪
-♫
-♬
-✈
-✔
-✕
-✖
-✦
-✨
-✪
-✰
-✿
-❀
-❤
-➜
-➤
-⦿
-、
-。
-〃
-々
-〇
-〈
-〉
-《
-》
-「
-」
-『
-』
-【
-】
-〓
-〔
-〕
-〖
-〗
-〜
-〝
-〞
-ぁ
-あ
-ぃ
-い
-う
-ぇ
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-##dion
-##etched
-papyrus
-##lster
-##nsor
-##toy
-bobbed
-dismounted
-endelle
-inhuman
-motorola
-wince
-wreath
-##ticus
-hideout
-inspections
-sanjay
-disgrace
-infused
-pudding
-stalks
-##urbed
-arsenic
-leases
-##hyl
-##rrard
-collarbone
-##waite
-##wil
-dowry
-##bant
-##edance
-genealogical
-nitrate
-salamanca
-scandals
-thyroid
-necessitated
-##`
-##¡
-##¢
-##¦
-##¨
-##ª
-##¬
-##´
-##¶
-##¾
-##¿
-##ð
-##þ
-##ħ
-##œ
-##ƒ
-##ɐ
-##ɑ
-##ɒ
-##ɕ
-##ɣ
-##ɨ
-##ɪ
-##ɫ
-##ɬ
-##ɯ
-##ɲ
-##ɴ
-##ɹ
-##ɾ
-##ʀ
-##ʁ
-##ʂ
-##ʃ
-##ʉ
-##ʊ
-##ʋ
-##ʌ
-##ʎ
-##ʐ
-##ʑ
-##ʒ
-##ʔ
-##ʲ
-##ʳ
-##ʷ
-##ʸ
-##ʻ
-##ʼ
-##ʾ
-##ʿ
-##ˡ
-##ˣ
-##ˤ
-##ζ
-##ξ
-##щ
-##ъ
-##э
-##ю
-##ђ
-##є
-##ј
-##љ
-##њ
-##ћ
-##ӏ
-##ա
-##բ
-##գ
-##դ
-##ե
-##թ
-##ի
-##լ
-##կ
-##հ
-##մ
-##յ
-##ն
-##ո
-##պ
-##ս
-##վ
-##տ
-##ր
-##ւ
-##ք
-##־
-##א
-##ב
-##ג
-##ד
-##ו
-##ז
-##ח
-##ט
-##י
-##ך
-##כ
-##ל
-##ם
-##מ
-##ן
-##נ
-##ס
-##ע
-##ף
-##פ
-##ץ
-##צ
-##ק
-##ר
-##ש
-##ת
-##،
-##ء
-##ث
-##ج
-##ح
-##خ
-##ذ
-##ز
-##ش
-##ص
-##ض
-##ط
-##ظ
-##غ
-##ـ
-##ف
-##ق
-##ك
-##ى
-##ٹ
-##پ
-##چ
-##ک
-##گ
-##ں
-##ھ
-##ہ
-##ے
-##अ
-##आ
-##उ
-##ए
-##क
-##ख
-##ग
-##च
-##ज
-##ट
-##ड
-##ण
-##त
-##थ
-##द
-##ध
-##न
-##प
-##ब
-##भ
-##म
-##य
-##र
-##ल
-##व
-##श
-##ष
-##स
-##ह
-##ा
-##ि
-##ी
-##ो
-##।
-##॥
-##ং
-##অ
-##আ
-##ই
-##উ
-##এ
-##ও
-##ক
-##খ
-##গ
-##চ
-##ছ
-##জ
-##ট
-##ড
-##ণ
-##ত
-##থ
-##দ
-##ধ
-##ন
-##প
-##ব
-##ভ
-##ম
-##য
-##র
-##ল
-##শ
-##ষ
-##স
-##হ
-##া
-##ি
-##ী
-##ে
-##க
-##ச
-##ட
-##த
-##ந
-##ன
-##ப
-##ம
-##ய
-##ர
-##ல
-##ள
-##வ
-##ா
-##ி
-##ு
-##ே
-##ை
-##ನ
-##ರ
-##ಾ
-##ක
-##ය
-##ර
-##ල
-##ව
-##ා
-##ต
-##ท
-##พ
-##ล
-##ว
-##ส
-##།
-##ག
-##ང
-##ད
-##ན
-##པ
-##བ
-##མ
-##འ
-##ར
-##ལ
-##ས
-##မ
-##ა
-##ბ
-##გ
-##დ
-##ე
-##ვ
-##თ
-##ი
-##კ
-##ლ
-##მ
-##ნ
-##ო
-##რ
-##ს
-##ტ
-##უ
-##ᄊ
-##ᴬ
-##ᴮ
-##ᴰ
-##ᴵ
-##ᴺ
-##ᵀ
-##ᵇ
-##ᵈ
-##ᵖ
-##ᵗ
-##ᵣ
-##ᵤ
-##ᵥ
-##ᶜ
-##ᶠ
-##‐
-##‑
-##‒
-##–
-##—
-##―
-##‘
-##’
-##‚
-##“
-##”
-##‡
-##…
-##⁰
-##⁴
-##⁵
-##⁶
-##⁷
-##⁸
-##⁹
-##⁻
-##₅
-##₆
-##₇
-##₈
-##₉
-##₊
-##₍
-##₎
-##ₐ
-##ₑ
-##ₒ
-##ₓ
-##ₕ
-##ₖ
-##ₗ
-##ₘ
-##ₚ
-##ₛ
-##ₜ
-##₤
-##₩
-##₱
-##₹
-##ℓ
-##ℝ
-##⅓
-##⅔
-##↦
-##⇄
-##⇌
-##∂
-##∅
-##∆
-##∇
-##∈
-##∗
-##∘
-##∧
-##∨
-##∪
-##⊂
-##⊆
-##⊕
-##⊗
-##☉
-##♯
-##⟨
-##⟩
-##ⱼ
-##⺩
-##⺼
-##⽥
-##亻
-##宀
-##彳
-##忄
-##扌
-##氵
-##疒
-##糹
-##訁
-##辶
-##阝
-##龸
-##ﬁ
-##ﬂ
diff --git a/__init__.py b/__init__.py
index 5dc119f..eeb3dc1 100644
--- a/__init__.py
+++ b/__init__.py
@@ -22,10 +22,6 @@
     from .PixArt.nodes import NODE_CLASS_MAPPINGS as PixArt_Nodes
     NODE_CLASS_MAPPINGS.update(PixArt_Nodes)
 
-    # HYDiT
-    from .HunYuanDiT.nodes import NODE_CLASS_MAPPINGS as HunYuanDiT_Nodes
-    NODE_CLASS_MAPPINGS.update(HunYuanDiT_Nodes)
-
     # VAE
     from .VAE.nodes import NODE_CLASS_MAPPINGS as VAE_Nodes
     NODE_CLASS_MAPPINGS.update(VAE_Nodes)

From d5a47fa3e5f8ff3b552d15e7545b8170f06ab984 Mon Sep 17 00:00:00 2001
From: City <125218114+city96@users.noreply.github.com>
Date: Wed, 11 Dec 2024 00:52:48 +0100
Subject: [PATCH 03/15] Use native ops for PixArt

---
 PixArt/config.py         |   3 +-
 PixArt/loader.py         |  12 +---
 PixArt/model/blocks.py   | 150 ++++++++++++++++++++-------------------
 PixArt/model/pixartms.py | 149 +++++++++++++++++++-------------------
 4 files changed, 157 insertions(+), 157 deletions(-)

diff --git a/PixArt/config.py b/PixArt/config.py
index 14975f7..e11e26a 100644
--- a/PixArt/config.py
+++ b/PixArt/config.py
@@ -93,8 +93,7 @@ def model_config_from_unet(sd):
                 config["pe_interpolation"] = 1
     model_config = PixArtConfig(config)
     model_config.unet_class = model_class
-    logging.info(f"Detected PixArt model as [{model_class}]")
-    logging.info(f"PixArt config:\n{config}")
+    logging.debug(f"PixArt config: {model_class}\n{config}")
     return model_config
 
 resolutions = {
diff --git a/PixArt/loader.py b/PixArt/loader.py
index b3627d6..d63b1d8 100644
--- a/PixArt/loader.py
+++ b/PixArt/loader.py
@@ -19,14 +19,8 @@ def __init__(self, *args, **kwargs):
     def extra_conds(self, **kwargs):
         out = super().extra_conds(**kwargs)
 
-        img_hw = kwargs.get("img_hw", None)
-        if img_hw is not None:
-            out["img_hw"] = comfy.conds.CONDRegular(torch.tensor(img_hw))
-
-        aspect_ratio = kwargs.get("aspect_ratio", None)
-        if aspect_ratio is not None:
-            out["aspect_ratio"] = comfy.conds.CONDRegular(torch.tensor(aspect_ratio))
-
+        for name in ["width", "height", "aspect_ratio", "img_hw"]: # TODO: remove last one
+            out[name] = comfy.conds.CONDRegular(torch.tensor(name))
         return out
 
 def load_pixart_state_dict(sd, model_options={}):
@@ -49,7 +43,7 @@ def load_pixart_state_dict(sd, model_options={}):
     load_device = model_management.get_torch_device()
     offload_device = comfy.model_management.unet_offload_device()
 
-    dtype = model_options.get("dtype", torch.float16) # TODO: fix this
+    dtype = model_options.get("dtype", None)
     weight_dtype = comfy.utils.weight_dtype(sd)
     unet_weight_dtype = list(model_config.supported_inference_dtypes)
 
diff --git a/PixArt/model/blocks.py b/PixArt/model/blocks.py
index df2eae0..3f3c2d9 100644
--- a/PixArt/model/blocks.py
+++ b/PixArt/model/blocks.py
@@ -12,9 +12,10 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from timm.models.vision_transformer import Mlp, Attention as Attention_
 from einops import rearrange
 
+from .utils import to_2tuple
+
 sdpa_32b = None
 Q_4GB_LIMIT = 32000000
 """If q is greater than this, the operation will likely require >4GB VRAM, which will fail on Intel Arc Alchemist GPUs without a workaround."""
@@ -44,7 +45,7 @@ def t2i_modulate(x, shift, scale):
     return x * (1 + scale) + shift
 
 class MultiHeadCrossAttention(nn.Module):
-    def __init__(self, d_model, num_heads, attn_drop=0., proj_drop=0., **block_kwargs):
+    def __init__(self, d_model, num_heads, attn_drop=0., proj_drop=0., dtype=None, device=None, operations=None, **block_kwargs):
         super(MultiHeadCrossAttention, self).__init__()
         assert d_model % num_heads == 0, "d_model must be divisible by num_heads"
 
@@ -52,10 +53,10 @@ def __init__(self, d_model, num_heads, attn_drop=0., proj_drop=0., **block_kwarg
         self.num_heads = num_heads
         self.head_dim = d_model // num_heads
 
-        self.q_linear = nn.Linear(d_model, d_model)
-        self.kv_linear = nn.Linear(d_model, d_model*2)
+        self.q_linear = operations.Linear(d_model, d_model, dtype=dtype, device=device)
+        self.kv_linear = operations.Linear(d_model, d_model*2, dtype=dtype, device=device)
         self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(d_model, d_model)
+        self.proj = operations.Linear(d_model, d_model, dtype=dtype, device=device)
         self.proj_drop = nn.Dropout(proj_drop)
 
     def forward(self, x, cond, mask=None):
@@ -111,7 +112,7 @@ def forward(self, x, cond, mask=None):
         return x
 
 
-class AttentionKVCompress(Attention_):
+class AttentionKVCompress(nn.Module):
     """Multi-head Attention block with KV token compression and qk norm."""
 
     def __init__(
@@ -122,6 +123,9 @@ def __init__(
         sampling='conv',
         sr_ratio=1,
         qk_norm=False,
+        dtype=None,
+        device=None,
+        operations=None,
         **block_kwargs,
     ):
         """
@@ -130,19 +134,26 @@ def __init__(
             num_heads (int): Number of attention heads.
             qkv_bias (bool:  If True, add a learnable bias to query, key, value.
         """
-        super().__init__(dim, num_heads=num_heads, qkv_bias=qkv_bias, **block_kwargs)
+        super().__init__()
+        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.scale = self.head_dim ** -0.5
+
+        self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
+        self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)
 
         self.sampling=sampling    # ['conv', 'ave', 'uniform', 'uniform_every']
         self.sr_ratio = sr_ratio
         if sr_ratio > 1 and sampling == 'conv':
             # Avg Conv Init.
-            self.sr = nn.Conv2d(dim, dim, groups=dim, kernel_size=sr_ratio, stride=sr_ratio)
-            self.sr.weight.data.fill_(1/sr_ratio**2)
-            self.sr.bias.data.zero_()
-            self.norm = nn.LayerNorm(dim)
+            self.sr = operations.Conv2d(dim, dim, groups=dim, kernel_size=sr_ratio, stride=sr_ratio, dtype=dtype, device=device)
+            # self.sr.weight.data.fill_(1/sr_ratio**2)
+            # self.sr.bias.data.zero_()
+            self.norm = operations.LayerNorm(dim, dtype=dtype, device=device)
         if qk_norm:
-            self.q_norm = nn.LayerNorm(dim)
-            self.k_norm = nn.LayerNorm(dim)
+            self.q_norm = operations.LayerNorm(dim, dtype=dtype, device=device)
+            self.k_norm = operations.LayerNorm(dim, dtype=dtype, device=device)
         else:
             self.q_norm = nn.Identity()
             self.k_norm = nn.Identity()
@@ -204,14 +215,12 @@ def forward(self, x, mask=None, HW=None, block_id=None):
         if model_management.xformers_enabled():
             x = xformers.ops.memory_efficient_attention(
                 q, k, v,
-                p=self.attn_drop.p,
+                p=0,
                 attn_bias=attn_bias
             )
         else:
             q, k, v = map(lambda t: t.transpose(1, 2),(q, k, v),)
-
-            p = getattr(self.attn_drop, "p", 0) # IPEX.optimize() will turn attn_drop into an Identity()
-            
+            p = 0
             if sdpa_32b is not None and (q.element_size() * q.nelement()) > Q_4GB_LIMIT:
                 sdpa = sdpa_32b
             else:
@@ -224,30 +233,6 @@ def forward(self, x, mask=None, HW=None, block_id=None):
             ).transpose(1, 2).contiguous()
         x = x.view(B, N, C)
         x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-#################################################################################
-#   AMP attention with fp32 softmax to fix loss NaN problem during training     #
-#################################################################################
-class Attention(Attention_):
-    def forward(self, x):
-        B, N, C = x.shape
-        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)
-        use_fp32_attention = getattr(self, 'fp32_attention', False)
-        if use_fp32_attention:
-            q, k = q.float(), k.float()
-        with torch.cuda.amp.autocast(enabled=not use_fp32_attention):
-            attn = (q @ k.transpose(-2, -1)) * self.scale
-            attn = attn.softmax(dim=-1)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
         return x
 
 
@@ -256,13 +241,13 @@ class FinalLayer(nn.Module):
     The final layer of PixArt.
     """
 
-    def __init__(self, hidden_size, patch_size, out_channels):
+    def __init__(self, hidden_size, patch_size, out_channels, dtype=None, device=None, operations=None):
         super().__init__()
-        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
+        self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
         self.adaLN_modulation = nn.Sequential(
             nn.SiLU(),
-            nn.Linear(hidden_size, 2 * hidden_size, bias=True)
+            operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device)
         )
 
     def forward(self, x, c):
@@ -271,23 +256,23 @@ def forward(self, x, c):
         x = self.linear(x)
         return x
 
-
 class T2IFinalLayer(nn.Module):
     """
     The final layer of PixArt.
     """
 
-    def __init__(self, hidden_size, patch_size, out_channels):
+    def __init__(self, hidden_size, patch_size, out_channels, dtype=None, device=None, operations=None):
         super().__init__()
-        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
+        self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
         self.scale_shift_table = nn.Parameter(torch.randn(2, hidden_size) / hidden_size ** 0.5)
         self.out_channels = out_channels
 
     def forward(self, x, t):
+        dtype = x.dtype
         shift, scale = (self.scale_shift_table[None] + t[:, None]).chunk(2, dim=1)
         x = t2i_modulate(self.norm_final(x), shift, scale)
-        x = self.linear(x)
+        x = self.linear(x.to(dtype))
         return x
 
 
@@ -296,13 +281,13 @@ class MaskFinalLayer(nn.Module):
     The final layer of PixArt.
     """
 
-    def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels):
+    def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels, dtype=None, device=None, operations=None):
         super().__init__()
-        self.norm_final = nn.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6)
-        self.linear = nn.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True)
+        self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = operations.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
         self.adaLN_modulation = nn.Sequential(
             nn.SiLU(),
-            nn.Linear(c_emb_size, 2 * final_hidden_size, bias=True)
+            operations.Linear(c_emb_size, 2 * final_hidden_size, bias=True, dtype=dtype, device=device)
         )
     def forward(self, x, t):
         shift, scale = self.adaLN_modulation(t).chunk(2, dim=1)
@@ -316,13 +301,13 @@ class DecoderLayer(nn.Module):
     The final layer of PixArt.
     """
 
-    def __init__(self, hidden_size, decoder_hidden_size):
+    def __init__(self, hidden_size, decoder_hidden_size, dtype=None, device=None, operations=None):
         super().__init__()
-        self.norm_decoder = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.linear = nn.Linear(hidden_size, decoder_hidden_size, bias=True)
+        self.norm_decoder = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = operations.Linear(hidden_size, decoder_hidden_size, bias=True, dtype=dtype, device=device)
         self.adaLN_modulation = nn.Sequential(
             nn.SiLU(),
-            nn.Linear(hidden_size, 2 * hidden_size, bias=True)
+            operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device)
         )
     def forward(self, x, t):
         shift, scale = self.adaLN_modulation(t).chunk(2, dim=1)
@@ -339,12 +324,12 @@ class TimestepEmbedder(nn.Module):
     Embeds scalar timesteps into vector representations.
     """
 
-    def __init__(self, hidden_size, frequency_embedding_size=256):
+    def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):
         super().__init__()
         self.mlp = nn.Sequential(
-            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
+            operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device),
             nn.SiLU(),
-            nn.Linear(hidden_size, hidden_size, bias=True),
+            operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
         )
         self.frequency_embedding_size = frequency_embedding_size
 
@@ -368,9 +353,9 @@ def timestep_embedding(t, dim, max_period=10000):
             embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
         return embedding
 
-    def forward(self, t):
+    def forward(self, t, dtype):
         t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
-        t_emb = self.mlp(t_freq.to(t.dtype))
+        t_emb = self.mlp(t_freq.to(dtype))
         return t_emb
 
 
@@ -379,12 +364,12 @@ class SizeEmbedder(TimestepEmbedder):
     Embeds scalar timesteps into vector representations.
     """
 
-    def __init__(self, hidden_size, frequency_embedding_size=256):
-        super().__init__(hidden_size=hidden_size, frequency_embedding_size=frequency_embedding_size)
+    def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):
+        super().__init__(hidden_size=hidden_size, frequency_embedding_size=frequency_embedding_size, operations=operations)
         self.mlp = nn.Sequential(
-            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
+            operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device),
             nn.SiLU(),
-            nn.Linear(hidden_size, hidden_size, bias=True),
+            operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
         )
         self.frequency_embedding_size = frequency_embedding_size
         self.outdim = hidden_size
@@ -409,10 +394,10 @@ class LabelEmbedder(nn.Module):
     Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
     """
 
-    def __init__(self, num_classes, hidden_size, dropout_prob):
+    def __init__(self, num_classes, hidden_size, dropout_prob, dtype=None, device=None, operations=None):
         super().__init__()
         use_cfg_embedding = dropout_prob > 0
-        self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
+        self.embedding_table = operations.Embedding(num_classes + use_cfg_embedding, hidden_size, dtype=dtype, device=device),
         self.num_classes = num_classes
         self.dropout_prob = dropout_prob
 
@@ -434,15 +419,31 @@ def forward(self, labels, train, force_drop_ids=None):
         embeddings = self.embedding_table(labels)
         return embeddings
 
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, dtype=None, device=None, operations=None) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+
+        self.fc1 = operations.Linear(in_features, hidden_features, bias=True, dtype=dtype, device=device)
+        self.act = act_layer()
+        self.fc2 = operations.Linear(hidden_features, out_features, bias=True, dtype=dtype, device=device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.act(self.fc1(x))
+        return self.fc2(x)
 
 class CaptionEmbedder(nn.Module):
     """
     Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
     """
 
-    def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120):
+    def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120, dtype=None, device=None, operations=None):
         super().__init__()
-        self.y_proj = Mlp(in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, drop=0)
+        self.y_proj = Mlp(
+            in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer,
+            dtype=dtype, device=device, operations=operations,
+        )
         self.register_buffer("y_embedding", nn.Parameter(torch.randn(token_num, in_channels) / in_channels ** 0.5))
         self.uncond_prob = uncond_prob
 
@@ -472,9 +473,12 @@ class CaptionEmbedderDoubleBr(nn.Module):
     Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
     """
 
-    def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120):
+    def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120, dtype=None, device=None, operations=None):
         super().__init__()
-        self.proj = Mlp(in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, drop=0)
+        self.proj = Mlp(
+            in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer,
+            dtype=dtype, device=device, operations=operations,
+        )
         self.embedding = nn.Parameter(torch.randn(1, in_channels) / 10 ** 0.5)
         self.y_embedding = nn.Parameter(torch.randn(token_num, in_channels) / 10 ** 0.5)
         self.uncond_prob = uncond_prob
diff --git a/PixArt/model/pixartms.py b/PixArt/model/pixartms.py
index 0bb5b36..a9e7035 100644
--- a/PixArt/model/pixartms.py
+++ b/PixArt/model/pixartms.py
@@ -11,11 +11,9 @@
 import torch
 import torch.nn as nn
 from tqdm import tqdm
-from timm.models.layers import DropPath
-from timm.models.vision_transformer import Mlp
 
 from .utils import auto_grad_checkpoint, to_2tuple
-from .blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, SizeEmbedder
+from .blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, SizeEmbedder, Mlp
 from .pixart import PixArt, get_2d_sincos_pos_embed
 
 
@@ -31,12 +29,15 @@ def __init__(
             norm_layer=None,
             flatten=True,
             bias=True,
+            dtype=None,
+            device=None,
+            operations=None
     ):
         super().__init__()
         patch_size = to_2tuple(patch_size)
         self.patch_size = patch_size
         self.flatten = flatten
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
+        self.proj = operations.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device)
         self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
 
     def forward(self, x):
@@ -52,29 +53,34 @@ class PixArtMSBlock(nn.Module):
     A PixArt block with adaptive layer norm zero (adaLN-Zero) conditioning.
     """
     def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0., input_size=None,
-                 sampling=None, sr_ratio=1, qk_norm=False, **block_kwargs):
+                 sampling=None, sr_ratio=1, qk_norm=False, dtype=None, device=None, operations=None, **block_kwargs):
         super().__init__()
         self.hidden_size = hidden_size
-        self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
         self.attn = AttentionKVCompress(
             hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio,
-            qk_norm=qk_norm, **block_kwargs
+            qk_norm=qk_norm, dtype=dtype, device=device, operations=operations, **block_kwargs
         )
-        self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, **block_kwargs)
-        self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.cross_attn = MultiHeadCrossAttention(
+            hidden_size, num_heads, dtype=dtype, device=device, operations=operations, **block_kwargs
+        )
+        self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
         # to be compatible with lower version pytorch
         approx_gelu = lambda: nn.GELU(approximate="tanh")
-        self.mlp = Mlp(in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0)
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.mlp = Mlp(
+            in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu,
+            dtype=dtype, device=device, operations=operations
+        )
         self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5)
 
     def forward(self, x, y, t, mask=None, HW=None, **kwargs):
         B, N, C = x.shape
+        dtype = x.dtype
 
-        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None] + t.reshape(B, 6, -1)).chunk(6, dim=1)
-        x = x + self.drop_path(gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW))
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None].to(x.dtype) + t.reshape(B, 6, -1)).chunk(6, dim=1)
+        x = x + (gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW))
         x = x + self.cross_attn(x, y, mask)
-        x = x + self.drop_path(gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp)))
+        x = x + (gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp)))
 
         return x
 
@@ -105,40 +111,52 @@ def __init__(
             micro_condition=True,
             qk_norm=False,
             kv_compress_config=None,
+            dtype=None,
+            device=None,
+            operations=None,
             **kwargs,
     ):
-        super().__init__(
-            input_size=input_size,
-            patch_size=patch_size,
-            in_channels=in_channels,
-            hidden_size=hidden_size,
-            depth=depth,
-            num_heads=num_heads,
-            mlp_ratio=mlp_ratio,
-            class_dropout_prob=class_dropout_prob,
-            learn_sigma=learn_sigma,
-            pred_sigma=pred_sigma,
-            drop_path=drop_path,
-            pe_interpolation=pe_interpolation,
-            config=config,
-            model_max_length=model_max_length,
-            qk_norm=qk_norm,
-            kv_compress_config=kv_compress_config,
-            **kwargs,
-        )
-        self.dtype = torch.get_default_dtype()
+        nn.Module.__init__(self)
+        self.dtype = dtype
+        self.pred_sigma = pred_sigma
+        self.in_channels = in_channels
+        self.out_channels = in_channels * 2 if pred_sigma else in_channels
+        self.patch_size = patch_size
+        self.num_heads = num_heads
+        self.pe_interpolation = pe_interpolation
+        self.pe_precision = pe_precision
+        self.depth = depth
+
         self.h = self.w = 0
         approx_gelu = lambda: nn.GELU(approximate="tanh")
         self.t_block = nn.Sequential(
             nn.SiLU(),
-            nn.Linear(hidden_size, 6 * hidden_size, bias=True)
+            operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device)
+        )
+        self.x_embedder = PatchEmbed(
+            patch_size, in_channels, hidden_size, bias=True,
+            dtype=dtype, device=device, operations=operations
+        )
+        self.t_embedder = TimestepEmbedder(
+            hidden_size, dtype=dtype, device=device, operations=operations,
         )
-        self.x_embedder = PatchEmbed(patch_size, in_channels, hidden_size, bias=True)
-        self.y_embedder = CaptionEmbedder(in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob, act_layer=approx_gelu, token_num=model_max_length)
+        self.y_embedder = CaptionEmbedder(
+            in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob,
+            act_layer=approx_gelu, token_num=model_max_length,
+            dtype=dtype, device=device, operations=operations,
+        )
+
         self.micro_conditioning = micro_condition
         if self.micro_conditioning:
-            self.csize_embedder = SizeEmbedder(hidden_size//3)  # c_size embed
-            self.ar_embedder = SizeEmbedder(hidden_size//3)     # aspect ratio embed
+
+            self.csize_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations)
+            self.ar_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations)
+
+        # Will use fixed sin-cos embedding:
+        num_patches = (input_size // patch_size) * (input_size // patch_size)
+        self.base_size = input_size // self.patch_size
+        self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size))
+
         drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)]  # stochastic depth decay rule
         if kv_compress_config is None:
             kv_compress_config = {
@@ -153,12 +171,17 @@ def __init__(
                 sampling=kv_compress_config['sampling'],
                 sr_ratio=int(kv_compress_config['scale_factor']) if i in kv_compress_config['kv_compress_layer'] else 1,
                 qk_norm=qk_norm,
+                dtype=dtype,
+                device=device,
+                operations=operations,
             )
             for i in range(depth)
         ])
-        self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels)
+        self.final_layer = T2IFinalLayer(
+            hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations
+        )
 
-    def forward_raw(self, x, t, y, mask=None, data_info=None, **kwargs):
+    def forward_orig(self, x, timestep, y, mask=None, data_info=None, **kwargs):
         """
         Original forward pass of PixArt.
         x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
@@ -166,9 +189,6 @@ def forward_raw(self, x, t, y, mask=None, data_info=None, **kwargs):
         y: (N, 1, 120, C) tensor of class labels
         """
         bs = x.shape[0]
-        x = x.to(self.dtype)
-        timestep = t.to(self.dtype)
-        y = y.to(self.dtype)
         
         pe_interpolation = self.pe_interpolation
         if pe_interpolation is None or self.pe_precision is not None:
@@ -181,10 +201,10 @@ def forward_raw(self, x, t, y, mask=None, data_info=None, **kwargs):
                 self.pos_embed.shape[-1], (self.h, self.w), pe_interpolation=pe_interpolation,
                 base_size=self.base_size
             )
-        ).unsqueeze(0).to(device=x.device, dtype=self.dtype)
+        ).to(device=x.device, dtype=x.dtype).unsqueeze(0)
 
         x = self.x_embedder(x) + pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
-        t = self.t_embedder(timestep)  # (N, D)
+        t = self.t_embedder(timestep, x.dtype)  # (N, D)
 
         if self.micro_conditioning:
             c_size, ar = data_info['img_hw'].to(self.dtype), data_info['aspect_ratio'].to(self.dtype)
@@ -212,46 +232,29 @@ def forward_raw(self, x, t, y, mask=None, data_info=None, **kwargs):
         
         return x
 
-    def forward(self, x, timesteps, context, img_hw=None, aspect_ratio=None, **kwargs):
-        """
-        Forward pass that adapts comfy input to original forward function
-        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
-        timesteps: (N,) tensor of diffusion timesteps
-        context: (N, 1, 120, C) conditioning
-        img_hw: height|width conditioning
-        aspect_ratio: aspect ratio conditioning
-        """
+    def forward(self, x, timesteps, context, width=None, height=None, img_hw=None, aspect_ratio=None, **kwargs):
+        bs, c, h, w = x.shape
+        dtype = self.dtype
+        device = x.device
+
         ## size/ar from cond with fallback based on the latent image shape.
         bs = x.shape[0]
         data_info = {}
         if img_hw is None:
-            data_info["img_hw"] = torch.tensor(
-                [[x.shape[2]*8, x.shape[3]*8]],
-                dtype=self.dtype,
-                device=x.device
-            ).repeat(bs, 1)
+            data_info["img_hw"] = torch.tensor([h*8, w*8], dtype=dtype, device=device).repeat(bs, 1)
         else:
             data_info["img_hw"] = img_hw.to(dtype=x.dtype, device=x.device)
-        if aspect_ratio is None or True:
-            data_info["aspect_ratio"] = torch.tensor(
-                [[x.shape[2]/x.shape[3]]],
-                dtype=self.dtype,
-                device=x.device
-            ).repeat(bs, 1)
+        if aspect_ratio is None:
+            data_info["aspect_ratio"] = torch.tensor([h/w], dtype=dtype, device=device).repeat(bs, 1)
         else:
-            data_info["aspect_ratio"] = aspect_ratio.to(dtype=x.dtype, device=x.device)
+            data_info["aspect_ratio"] = aspect_ratio.to(dtype=dtype, device=device)
 
         ## Still accepts the input w/o that dim but returns garbage
         if len(context.shape) == 3:
             context = context.unsqueeze(1)
 
         ## run original forward pass
-        out = self.forward_raw(
-            x = x.to(self.dtype),
-            t = timesteps.to(self.dtype),
-            y = context.to(self.dtype),
-            data_info=data_info,
-        )
+        out = self.forward_orig(x, timesteps, context, data_info=data_info)
 
         ## only return EPS
         out = out.to(torch.float)

From 6a881400027c19be7c6b621f1f0e54a57c61863c Mon Sep 17 00:00:00 2001
From: City <125218114+city96@users.noreply.github.com>
Date: Wed, 11 Dec 2024 17:24:18 +0100
Subject: [PATCH 04/15] Unused

---
 PixArt/config.py |  4 +---
 PixArt/loader.py | 24 +++++-------------------
 nodes/pixart.py  |  0
 3 files changed, 6 insertions(+), 22 deletions(-)
 delete mode 100644 nodes/pixart.py

diff --git a/PixArt/config.py b/PixArt/config.py
index e11e26a..43082a1 100644
--- a/PixArt/config.py
+++ b/PixArt/config.py
@@ -1,12 +1,10 @@
-"""
-Model config and setting logic
-"""
 import math
 import logging
 
 import comfy.supported_models_base
 import comfy.supported_models
 import comfy.latent_formats
+import comfy.model_base
 
 from .model.pixart import PixArt
 from .model.pixartms import PixArtMS
diff --git a/PixArt/loader.py b/PixArt/loader.py
index d63b1d8..9692a71 100644
--- a/PixArt/loader.py
+++ b/PixArt/loader.py
@@ -1,28 +1,14 @@
-import comfy.supported_models_base
-import comfy.latent_formats
-import comfy.model_detection
-import comfy.model_patcher
-import comfy.model_base
-import comfy.utils
-import comfy.conds
 import logging
-import torch
 
+import comfy.utils
+import comfy.model_base
+import comfy.model_patcher
+import comfy.model_detection
 from comfy import model_management
+
 from .diffusers_convert import convert_state_dict
 from .config import model_config_from_unet
 
-class PixArtModel(comfy.model_base.BaseModel):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-    
-    def extra_conds(self, **kwargs):
-        out = super().extra_conds(**kwargs)
-
-        for name in ["width", "height", "aspect_ratio", "img_hw"]: # TODO: remove last one
-            out[name] = comfy.conds.CONDRegular(torch.tensor(name))
-        return out
-
 def load_pixart_state_dict(sd, model_options={}):
     # prefix / format
     sd = sd.get("model", sd) # ref ckpt
diff --git a/nodes/pixart.py b/nodes/pixart.py
deleted file mode 100644
index e69de29..0000000

From 06a936813f37001c519ecbcfa5f089a44a4bf8ca Mon Sep 17 00:00:00 2001
From: City <125218114+city96@users.noreply.github.com>
Date: Wed, 11 Dec 2024 17:32:57 +0100
Subject: [PATCH 05/15] Consolidate loader logic

---
 PixArt/config.py | 134 ----------------------------------------
 PixArt/loader.py | 157 ++++++++++++++++++++++++++++++++++++++---------
 utils/loader.py  |  35 +++++++++++
 3 files changed, 163 insertions(+), 163 deletions(-)
 delete mode 100644 PixArt/config.py
 create mode 100644 utils/loader.py

diff --git a/PixArt/config.py b/PixArt/config.py
deleted file mode 100644
index 43082a1..0000000
--- a/PixArt/config.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import math
-import logging
-
-import comfy.supported_models_base
-import comfy.supported_models
-import comfy.latent_formats
-import comfy.model_base
-
-from .model.pixart import PixArt
-from .model.pixartms import PixArtMS
-from ..text_encoders.pixart.tenc import PixArtTokenizer, PixArtT5XXL
-
-class PixArtConfig(comfy.supported_models_base.BASE):
-    unet_class = PixArtMS
-    unet_config = {}
-    unet_extra_config = {}
-
-    latent_format = comfy.latent_formats.SD15
-    sampling_settings = {
-        "beta_schedule" : "sqrt_linear",
-        "linear_start"  : 0.0001,
-        "linear_end"    : 0.02,
-        "timesteps"     : 1000,
-    }
-
-    def model_type(self, state_dict, prefix=""):
-        return comfy.model_base.ModelType.EPS
-
-    def get_model(self, state_dict, prefix="", device=None):
-        return PixArtModel(model_config=self, unet_model=self.unet_class, device=device)
-
-    def clip_target(self, state_dict={}):
-        return comfy.supported_models_base.ClipTarget(PixArtTokenizer, PixArtT5XXL)
-
-class PixArtModel(comfy.model_base.BaseModel):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    def extra_conds(self, **kwargs):
-        out = super().extra_conds(**kwargs)
-        return out
-
-def model_config_from_unet(sd):
-    """
-    Guess config based on (converted) state dict.
-    """
-    # Shared settings based on DiT_XL_2 - could be enumerated
-    config = {
-        "num_heads"   :   16, # get from attention
-        "patch_size"  :    2, # final layer I guess?
-        "hidden_size" : 1152, # pos_embed.shape[2]
-    }
-    config["depth"] = sum([key.endswith(".attn.proj.weight") for key in sd.keys()]) or 28
-
-    try:
-        # this is not present in the diffusers version for sigma?
-        config["model_max_length"] = sd["y_embedder.y_embedding"].shape[0]
-    except KeyError:
-        # need better logic to guess this
-        config["model_max_length"] = 300
-
-    if "pos_embed" in sd:
-        config["input_size"] = int(math.sqrt(sd["pos_embed"].shape[1])) * config["patch_size"]
-        config["pe_interpolation"] = config["input_size"] // (512//8) # dumb guess
-
-    model_config = PixArtModel
-    if config["model_max_length"] == 300:
-        # Sigma
-        model_class = PixArtMS
-        model_config.latent_format = comfy.latent_formats.SDXL
-        config["micro_condition"] = False
-        if "input_size" not in config:
-            # The diffusers weights for 1K/2K are exactly the same...?
-            # replace patch embed logic with HyDiT?
-            logging.warn(f"PixArt: diffusers weights - 2K model will be broken, use manual loading!")
-            config["input_size"] = 1024//8
-    else:
-        # Alpha
-        if "csize_embedder.mlp.0.weight" in sd:
-            # MS (microconds)
-            model_class = PixArtMS
-            config["micro_condition"] = True
-            if "input_size" not in config:
-                config["input_size"] = 1024//8
-                config["pe_interpolation"] = 2
-        else:
-            # PixArt
-            model_class = PixArt
-            if "input_size" not in config:
-                config["input_size"] = 512//8
-                config["pe_interpolation"] = 1
-    model_config = PixArtConfig(config)
-    model_config.unet_class = model_class
-    logging.debug(f"PixArt config: {model_class}\n{config}")
-    return model_config
-
-resolutions = {
-    "PixArt 512": {
-        0.25: [256,1024], 0.26: [256, 992], 0.27: [256, 960], 0.28: [256, 928],
-        0.32: [288, 896], 0.33: [288, 864], 0.35: [288, 832], 0.40: [320, 800],
-        0.42: [320, 768], 0.48: [352, 736], 0.50: [352, 704], 0.52: [352, 672],
-        0.57: [384, 672], 0.60: [384, 640], 0.68: [416, 608], 0.72: [416, 576],
-        0.78: [448, 576], 0.82: [448, 544], 0.88: [480, 544], 0.94: [480, 512],
-        1.00: [512, 512], 1.07: [512, 480], 1.13: [544, 480], 1.21: [544, 448],
-        1.29: [576, 448], 1.38: [576, 416], 1.46: [608, 416], 1.67: [640, 384],
-        1.75: [672, 384], 2.00: [704, 352], 2.09: [736, 352], 2.40: [768, 320],
-        2.50: [800, 320], 2.89: [832, 288], 3.00: [864, 288], 3.11: [896, 288],
-        3.62: [928, 256], 3.75: [960, 256], 3.88: [992, 256], 4.00: [1024,256]
-    },
-    "PixArt 1024": {
-        0.25: [512, 2048], 0.26: [512, 1984], 0.27: [512, 1920], 0.28: [512, 1856],
-        0.32: [576, 1792], 0.33: [576, 1728], 0.35: [576, 1664], 0.40: [640, 1600],
-        0.42: [640, 1536], 0.48: [704, 1472], 0.50: [704, 1408], 0.52: [704, 1344],
-        0.57: [768, 1344], 0.60: [768, 1280], 0.68: [832, 1216], 0.72: [832, 1152],
-        0.78: [896, 1152], 0.82: [896, 1088], 0.88: [960, 1088], 0.94: [960, 1024],
-        1.00: [1024,1024], 1.07: [1024, 960], 1.13: [1088, 960], 1.21: [1088, 896],
-        1.29: [1152, 896], 1.38: [1152, 832], 1.46: [1216, 832], 1.67: [1280, 768],
-        1.75: [1344, 768], 2.00: [1408, 704], 2.09: [1472, 704], 2.40: [1536, 640],
-        2.50: [1600, 640], 2.89: [1664, 576], 3.00: [1728, 576], 3.11: [1792, 576],
-        3.62: [1856, 512], 3.75: [1920, 512], 3.88: [1984, 512], 4.00: [2048, 512],
-    },
-    "PixArt 2K": {
-        0.25: [1024, 4096], 0.26: [1024, 3968], 0.27: [1024, 3840], 0.28: [1024, 3712],
-        0.32: [1152, 3584], 0.33: [1152, 3456], 0.35: [1152, 3328], 0.40: [1280, 3200],
-        0.42: [1280, 3072], 0.48: [1408, 2944], 0.50: [1408, 2816], 0.52: [1408, 2688],
-        0.57: [1536, 2688], 0.60: [1536, 2560], 0.68: [1664, 2432], 0.72: [1664, 2304],
-        0.78: [1792, 2304], 0.82: [1792, 2176], 0.88: [1920, 2176], 0.94: [1920, 2048],
-        1.00: [2048, 2048], 1.07: [2048, 1920], 1.13: [2176, 1920], 1.21: [2176, 1792],
-        1.29: [2304, 1792], 1.38: [2304, 1664], 1.46: [2432, 1664], 1.67: [2560, 1536],
-        1.75: [2688, 1536], 2.00: [2816, 1408], 2.09: [2944, 1408], 2.40: [3072, 1280],
-        2.50: [3200, 1280], 2.89: [3328, 1152], 3.00: [3456, 1152], 3.11: [3584, 1152],
-        3.62: [3712, 1024], 3.75: [3840, 1024], 3.88: [3968, 1024], 4.00: [4096, 1024]
-    }
-}
diff --git a/PixArt/loader.py b/PixArt/loader.py
index 9692a71..bc0ca00 100644
--- a/PixArt/loader.py
+++ b/PixArt/loader.py
@@ -1,13 +1,49 @@
+import math
 import logging
 
 import comfy.utils
 import comfy.model_base
-import comfy.model_patcher
 import comfy.model_detection
-from comfy import model_management
 
+import comfy.supported_models_base
+import comfy.supported_models
+import comfy.latent_formats
+
+from .model.pixart import PixArt
+from .model.pixartms import PixArtMS
 from .diffusers_convert import convert_state_dict
-from .config import model_config_from_unet
+from ..utils.loader import load_state_dict_from_config
+from ..text_encoders.pixart.tenc import PixArtTokenizer, PixArtT5XXL
+
+class PixArtConfig(comfy.supported_models_base.BASE):
+    unet_class = PixArtMS
+    unet_config = {}
+    unet_extra_config = {}
+
+    latent_format = comfy.latent_formats.SD15
+    sampling_settings = {
+        "beta_schedule" : "sqrt_linear",
+        "linear_start"  : 0.0001,
+        "linear_end"    : 0.02,
+        "timesteps"     : 1000,
+    }
+
+    def model_type(self, state_dict, prefix=""):
+        return comfy.model_base.ModelType.EPS
+
+    def get_model(self, state_dict, prefix="", device=None):
+        return PixArtModel(model_config=self, unet_model=self.unet_class, device=device)
+
+    def clip_target(self, state_dict={}):
+        return comfy.supported_models_base.ClipTarget(PixArtTokenizer, PixArtT5XXL)
+
+class PixArtModel(comfy.model_base.BaseModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        return out
 
 def load_pixart_state_dict(sd, model_options={}):
     # prefix / format
@@ -23,34 +59,97 @@ def load_pixart_state_dict(sd, model_options={}):
 
     # model config
     model_config = model_config_from_unet(sd)
+    return load_state_dict_from_config(model_config, sd, model_options)
 
-    # TODO: move lines below to utils
-    parameters = comfy.utils.calculate_parameters(sd)
-    load_device = model_management.get_torch_device()
-    offload_device = comfy.model_management.unet_offload_device()
+def model_config_from_unet(sd):
+    """
+    Guess config based on (converted) state dict.
+    """
+    # Shared settings based on DiT_XL_2 - could be enumerated
+    config = {
+        "num_heads"   :   16, # get from attention
+        "patch_size"  :    2, # final layer I guess?
+        "hidden_size" : 1152, # pos_embed.shape[2]
+    }
+    config["depth"] = sum([key.endswith(".attn.proj.weight") for key in sd.keys()]) or 28
 
-    dtype = model_options.get("dtype", None)
-    weight_dtype = comfy.utils.weight_dtype(sd)
-    unet_weight_dtype = list(model_config.supported_inference_dtypes)
+    try:
+        # this is not present in the diffusers version for sigma?
+        config["model_max_length"] = sd["y_embedder.y_embedding"].shape[0]
+    except KeyError:
+        # need better logic to guess this
+        config["model_max_length"] = 300
 
-    if weight_dtype is not None and model_config.scaled_fp8 is None:
-        unet_weight_dtype.append(weight_dtype)
+    if "pos_embed" in sd:
+        config["input_size"] = int(math.sqrt(sd["pos_embed"].shape[1])) * config["patch_size"]
+        config["pe_interpolation"] = config["input_size"] // (512//8) # dumb guess
 
-    if dtype is None:
-        unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype)
+    model_config = PixArtModel
+    if config["model_max_length"] == 300:
+        # Sigma
+        model_class = PixArtMS
+        model_config.latent_format = comfy.latent_formats.SDXL
+        config["micro_condition"] = False
+        if "input_size" not in config:
+            # The diffusers weights for 1K/2K are exactly the same...?
+            # replace patch embed logic with HyDiT?
+            logging.warn(f"PixArt: diffusers weights - 2K model will be broken, use manual loading!")
+            config["input_size"] = 1024//8
     else:
-        unet_dtype = dtype
-
-    manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
-    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
-    model_config.custom_operations = model_options.get("custom_operations", model_config.custom_operations)
-    if model_options.get("fp8_optimizations", False):
-        model_config.optimizations["fp8"] = True
-
-    model = model_config.get_model(sd, "")
-    model = model.to(offload_device).eval()
-    model.load_model_weights(sd, "")
-    left_over = sd.keys()
-    if len(left_over) > 0:
-        logging.info("left over keys in unet: {}".format(left_over))
-    return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)
+        # Alpha
+        if "csize_embedder.mlp.0.weight" in sd:
+            # MS (microconds)
+            model_class = PixArtMS
+            config["micro_condition"] = True
+            if "input_size" not in config:
+                config["input_size"] = 1024//8
+                config["pe_interpolation"] = 2
+        else:
+            # PixArt
+            model_class = PixArt
+            if "input_size" not in config:
+                config["input_size"] = 512//8
+                config["pe_interpolation"] = 1
+    model_config = PixArtConfig(config)
+    model_config.unet_class = model_class
+    logging.debug(f"PixArt config: {model_class}\n{config}")
+    return model_config
+
+resolutions = {
+    "PixArt 512": {
+        0.25: [256,1024], 0.26: [256, 992], 0.27: [256, 960], 0.28: [256, 928],
+        0.32: [288, 896], 0.33: [288, 864], 0.35: [288, 832], 0.40: [320, 800],
+        0.42: [320, 768], 0.48: [352, 736], 0.50: [352, 704], 0.52: [352, 672],
+        0.57: [384, 672], 0.60: [384, 640], 0.68: [416, 608], 0.72: [416, 576],
+        0.78: [448, 576], 0.82: [448, 544], 0.88: [480, 544], 0.94: [480, 512],
+        1.00: [512, 512], 1.07: [512, 480], 1.13: [544, 480], 1.21: [544, 448],
+        1.29: [576, 448], 1.38: [576, 416], 1.46: [608, 416], 1.67: [640, 384],
+        1.75: [672, 384], 2.00: [704, 352], 2.09: [736, 352], 2.40: [768, 320],
+        2.50: [800, 320], 2.89: [832, 288], 3.00: [864, 288], 3.11: [896, 288],
+        3.62: [928, 256], 3.75: [960, 256], 3.88: [992, 256], 4.00: [1024,256]
+    },
+    "PixArt 1024": {
+        0.25: [512, 2048], 0.26: [512, 1984], 0.27: [512, 1920], 0.28: [512, 1856],
+        0.32: [576, 1792], 0.33: [576, 1728], 0.35: [576, 1664], 0.40: [640, 1600],
+        0.42: [640, 1536], 0.48: [704, 1472], 0.50: [704, 1408], 0.52: [704, 1344],
+        0.57: [768, 1344], 0.60: [768, 1280], 0.68: [832, 1216], 0.72: [832, 1152],
+        0.78: [896, 1152], 0.82: [896, 1088], 0.88: [960, 1088], 0.94: [960, 1024],
+        1.00: [1024,1024], 1.07: [1024, 960], 1.13: [1088, 960], 1.21: [1088, 896],
+        1.29: [1152, 896], 1.38: [1152, 832], 1.46: [1216, 832], 1.67: [1280, 768],
+        1.75: [1344, 768], 2.00: [1408, 704], 2.09: [1472, 704], 2.40: [1536, 640],
+        2.50: [1600, 640], 2.89: [1664, 576], 3.00: [1728, 576], 3.11: [1792, 576],
+        3.62: [1856, 512], 3.75: [1920, 512], 3.88: [1984, 512], 4.00: [2048, 512],
+    },
+    "PixArt 2K": {
+        0.25: [1024, 4096], 0.26: [1024, 3968], 0.27: [1024, 3840], 0.28: [1024, 3712],
+        0.32: [1152, 3584], 0.33: [1152, 3456], 0.35: [1152, 3328], 0.40: [1280, 3200],
+        0.42: [1280, 3072], 0.48: [1408, 2944], 0.50: [1408, 2816], 0.52: [1408, 2688],
+        0.57: [1536, 2688], 0.60: [1536, 2560], 0.68: [1664, 2432], 0.72: [1664, 2304],
+        0.78: [1792, 2304], 0.82: [1792, 2176], 0.88: [1920, 2176], 0.94: [1920, 2048],
+        1.00: [2048, 2048], 1.07: [2048, 1920], 1.13: [2176, 1920], 1.21: [2176, 1792],
+        1.29: [2304, 1792], 1.38: [2304, 1664], 1.46: [2432, 1664], 1.67: [2560, 1536],
+        1.75: [2688, 1536], 2.00: [2816, 1408], 2.09: [2944, 1408], 2.40: [3072, 1280],
+        2.50: [3200, 1280], 2.89: [3328, 1152], 3.00: [3456, 1152], 3.11: [3584, 1152],
+        3.62: [3712, 1024], 3.75: [3840, 1024], 3.88: [3968, 1024], 4.00: [4096, 1024]
+    }
+}
diff --git a/utils/loader.py b/utils/loader.py
new file mode 100644
index 0000000..f3f1b77
--- /dev/null
+++ b/utils/loader.py
@@ -0,0 +1,35 @@
+import logging
+import comfy.utils
+import comfy.model_patcher
+from comfy import model_management
+
+def load_state_dict_from_config(model_config, sd, model_options={}):
+    parameters = comfy.utils.calculate_parameters(sd)
+    load_device = model_management.get_torch_device()
+    offload_device = comfy.model_management.unet_offload_device()
+
+    dtype = model_options.get("dtype", None)
+    weight_dtype = comfy.utils.weight_dtype(sd)
+    unet_weight_dtype = list(model_config.supported_inference_dtypes)
+
+    if weight_dtype is not None and model_config.scaled_fp8 is None:
+        unet_weight_dtype.append(weight_dtype)
+
+    if dtype is None:
+        unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype)
+    else:
+        unet_dtype = dtype
+
+    manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
+    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
+    model_config.custom_operations = model_options.get("custom_operations", model_config.custom_operations)
+    if model_options.get("fp8_optimizations", False):
+        model_config.optimizations["fp8"] = True
+
+    model = model_config.get_model(sd, "")
+    model = model.to(offload_device).eval()
+    model.load_model_weights(sd, "")
+    left_over = sd.keys()
+    if len(left_over) > 0:
+        logging.info("left over keys in unet: {}".format(left_over))
+    return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)

From 5505ab4f40a70ff29ef63e34b26d9fa523b81ec2 Mon Sep 17 00:00:00 2001
From: City <125218114+city96@users.noreply.github.com>
Date: Wed, 11 Dec 2024 18:55:17 +0100
Subject: [PATCH 06/15] Sana loader logic

---
 Sana/conf.py   |  98 -------------------------
 Sana/loader.py | 192 +++++++++++++++++++++++++------------------------
 Sana/nodes.py  | 191 ++++++++++++++----------------------------------
 nodes.py       |   2 +
 4 files changed, 153 insertions(+), 330 deletions(-)
 delete mode 100644 Sana/conf.py

diff --git a/Sana/conf.py b/Sana/conf.py
deleted file mode 100644
index 3719c0f..0000000
--- a/Sana/conf.py
+++ /dev/null
@@ -1,98 +0,0 @@
-"""
-List of all Sana model types / settings
-"""
-
-sampling_settings = {
-	"shift": 3.0,
-}
-
-sana_conf = {
-	"SanaMS_600M_P1_D28": {
-		"target": "SanaMS",
-		"unet_config": {
-			"in_channels": 32,
-			"depth": 28,
-			"hidden_size": 1152,
-			"patch_size": 1,
-			"num_heads": 16,
-			"linear_head_dim": 32,
-			"model_max_length": 300,
-			"y_norm": True,
-			"attn_type": "linear",
-			"ffn_type": "glumbconv",
-			"mlp_ratio": 2.5,
-			"mlp_acts": ["silu", "silu", None],
-			"use_pe": False,
-			"pred_sigma": False,
-			"learn_sigma": False,
-			"fp32_attention": True,
-		},
-		"sampling_settings" : sampling_settings,
-	},
-	"SanaMS_1600M_P1_D20": {
-		"target": "SanaMS",
-		"unet_config": {
-			"in_channels": 32,
-			"depth": 20,
-			"hidden_size": 2240,
-			"patch_size": 1,
-			"num_heads": 20,
-			"linear_head_dim": 32,
-			"model_max_length": 300,
-			"y_norm": True,
-			"attn_type": "linear",
-			"ffn_type": "glumbconv",
-			"mlp_ratio": 2.5,
-			"mlp_acts": ["silu", "silu", None],
-			"use_pe": False,
-			"pred_sigma": False,
-			"learn_sigma": False,
-			"fp32_attention": True,
-		},
-		"sampling_settings" : sampling_settings,
-	},
-}
-
-sana_res = {
-	"1024px": { # models/SanaMS 1024x1024
-		'0.25': [512, 2048], '0.26': [512, 1984], '0.27': [512, 1920], '0.28': [512, 1856],
-		'0.32': [576, 1792], '0.33': [576, 1728], '0.35': [576, 1664], '0.40': [640, 1600],
-		'0.42': [640, 1536], '0.48': [704, 1472], '0.50': [704, 1408], '0.52': [704, 1344],
-		'0.57': [768, 1344], '0.60': [768, 1280], '0.68': [832, 1216], '0.72': [832, 1152],
-		'0.78': [896, 1152], '0.82': [896, 1088], '0.88': [960, 1088], '0.94': [960, 1024],
-		'1.00': [1024,1024], '1.07': [1024, 960], '1.13': [1088, 960], '1.21': [1088, 896],
-		'1.29': [1152, 896], '1.38': [1152, 832], '1.46': [1216, 832], '1.67': [1280, 768],
-		'1.75': [1344, 768], '2.00': [1408, 704], '2.09': [1472, 704], '2.40': [1536, 640],
-		'2.50': [1600, 640], '2.89': [1664, 576], '3.00': [1728, 576], '3.11': [1792, 576],
-		'3.62': [1856, 512], '3.75': [1920, 512], '3.88': [1984, 512], '4.00': [2048, 512],
-	},
-	"512px": { # models/SanaMS 512x512
-		'0.25': [256,1024], '0.26': [256, 992], '0.27': [256, 960], '0.28': [256, 928],
-		'0.32': [288, 896], '0.33': [288, 864], '0.35': [288, 832], '0.40': [320, 800],
-		'0.42': [320, 768], '0.48': [352, 736], '0.50': [352, 704], '0.52': [352, 672],
-		'0.57': [384, 672], '0.60': [384, 640], '0.68': [416, 608], '0.72': [416, 576],
-		'0.78': [448, 576], '0.82': [448, 544], '0.88': [480, 544], '0.94': [480, 512],
-		'1.00': [512, 512], '1.07': [512, 480], '1.13': [544, 480], '1.21': [544, 448],
-		'1.29': [576, 448], '1.38': [576, 416], '1.46': [608, 416], '1.67': [640, 384],
-		'1.75': [672, 384], '2.00': [704, 352], '2.09': [736, 352], '2.40': [768, 320],
-		'2.50': [800, 320], '2.89': [832, 288], '3.00': [864, 288], '3.11': [896, 288],
-		'3.62': [928, 256], '3.75': [960, 256], '3.88': [992, 256], '4.00': [1024,256]
-	},
-	"2K": {
-		'0.25': [1024, 4096], '0.26': [1024, 3968], '0.27': [1024, 3840], '0.28': [1024, 3712],
-		'0.32': [1152, 3584], '0.33': [1152, 3456], '0.35': [1152, 3328], '0.40': [1280, 3200],
-		'0.42': [1280, 3072], '0.48': [1408, 2944], '0.50': [1408, 2816], '0.52': [1408, 2688],
-		'0.57': [1536, 2688], '0.60': [1536, 2560], '0.68': [1664, 2432], '0.72': [1664, 2304],
-		'0.78': [1792, 2304], '0.82': [1792, 2176], '0.88': [1920, 2176], '0.94': [1920, 2048],
-		'1.00': [2048, 2048], '1.07': [2048, 1920], '1.13': [2176, 1920], '1.21': [2176, 1792],
-		'1.29': [2304, 1792], '1.38': [2304, 1664], '1.46': [2432, 1664], '1.67': [2560, 1536],
-		'1.75': [2688, 1536], '2.00': [2816, 1408], '2.09': [2944, 1408], '2.40': [3072, 1280],
-		'2.50': [3200, 1280], '2.89': [3328, 1152], '3.00': [3456, 1152], '3.11': [3584, 1152],
-		'3.62': [3712, 1024], '3.75': [3840, 1024], '3.88': [3968, 1024], '4.00': [4096, 1024]
-	}
-}
-# These should be the same
-sana_res.update({
-	"SanaMS_600M_P1_D28": sana_res["1024px"],
-	"SanaMS_1600M_P1_D20": sana_res["1024px"],
-})
diff --git a/Sana/loader.py b/Sana/loader.py
index 806ca64..60eea7a 100644
--- a/Sana/loader.py
+++ b/Sana/loader.py
@@ -1,100 +1,104 @@
+import logging
+
+import comfy.utils
+import comfy.model_base
+import comfy.model_detection
+
 import comfy.supported_models_base
+import comfy.supported_models
 import comfy.latent_formats
-import comfy.model_patcher
-import comfy.model_base
-import comfy.utils
-import comfy.conds
-import torch
-import math 
-from comfy import model_management
-from comfy.latent_formats import LatentFormat
-from .diffusers_convert import convert_state_dict
 
+from .models.sana import Sana
+from .models.sana_multi_scale import SanaMS
+from .diffusers_convert import convert_state_dict
+from ..utils.loader import load_state_dict_from_config
 
-class SanaLatent(LatentFormat):
+class SanaLatent(comfy.latent_formats.LatentFormat):
+    scale_factor = 0.41407
     latent_channels = 32
-    def __init__(self):
-        self.scale_factor = 0.41407
-
-
-class EXM_Sana(comfy.supported_models_base.BASE):
-	unet_config = {}
-	unet_extra_config = {}
-	latent_format = SanaLatent
-
-	def __init__(self, model_conf):
-		self.model_target = model_conf.get("target")
-		self.unet_config = model_conf.get("unet_config", {})
-		self.sampling_settings = model_conf.get("sampling_settings", {})
-		self.latent_format = self.latent_format()
-		# UNET is handled by extension
-		self.unet_config["disable_unet_model_creation"] = True
-
-	def model_type(self, state_dict, prefix=""):
-		return comfy.model_base.ModelType.FLOW
-
-
-class EXM_Sana_Model(comfy.model_base.BaseModel):
-	def __init__(self, *args, **kwargs):
-		super().__init__(*args, **kwargs)
-	
-	def extra_conds(self, **kwargs):
-		out = super().extra_conds(**kwargs)
-
-		cn_hint = kwargs.get("cn_hint", None)
-		if cn_hint is not None:
-			out["cn_hint"] = comfy.conds.CONDRegular(cn_hint)
-
-		return out
-
-
-def load_sana(model_path, model_conf):
-	state_dict = comfy.utils.load_torch_file(model_path)
-	state_dict = state_dict.get("model", state_dict)
-
-	# prefix
-	for prefix in ["model.diffusion_model.",]:
-		if any(True for x in state_dict if x.startswith(prefix)):
-			state_dict = {k[len(prefix):]:v for k,v in state_dict.items()}
-
-	# diffusers
-	if "adaln_single.linear.weight" in state_dict:
-		state_dict = convert_state_dict(state_dict) # Diffusers
-
-	parameters = comfy.utils.calculate_parameters(state_dict)
-	unet_dtype = comfy.model_management.unet_dtype()
-	load_device = comfy.model_management.get_torch_device()
-	offload_device = comfy.model_management.unet_offload_device()
-
-	# ignore fp8/etc and use directly for now
-	manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device)
-	if manual_cast_dtype:
-		print(f"Sana: falling back to {manual_cast_dtype}")
-		unet_dtype = manual_cast_dtype
-
-	model_conf = EXM_Sana(model_conf) # convert to object
-	model = EXM_Sana_Model( # same as comfy.model_base.BaseModel
-		model_conf,
-		model_type=comfy.model_base.ModelType.FLOW,
-		device=model_management.get_torch_device()
-	)
-
-	if model_conf.model_target == "SanaMS":
-		from .models.sana_multi_scale import SanaMS
-		model.diffusion_model = SanaMS(**model_conf.unet_config)
-	else:
-		raise NotImplementedError(f"Unknown model target '{model_conf.model_target}'")
-
-	m, u = model.diffusion_model.load_state_dict(state_dict, strict=False)
-	if len(m) > 0: print("Missing UNET keys", m)
-	if len(u) > 0: print("Leftover UNET keys", u)
-	model.diffusion_model.dtype = unet_dtype
-	model.diffusion_model.eval()
-	model.diffusion_model.to(unet_dtype)
 
-	model_patcher = comfy.model_patcher.ModelPatcher(
-		model,
-		load_device = load_device,
-		offload_device = offload_device,
-	)
-	return model_patcher
+class SanaConfig(comfy.supported_models_base.BASE):
+    unet_class = SanaMS
+    unet_config = {}
+    unet_extra_config = {}
+
+    latent_format = SanaLatent
+    sampling_settings = {
+        "shift": 3.0,
+    }
+
+    def model_type(self, state_dict, prefix=""):
+        return comfy.model_base.ModelType.FLOW
+
+    def get_model(self, state_dict, prefix="", device=None):
+        return SanaModel(
+            model_config=self,
+            model_type=comfy.model_base.ModelType.FLOW,
+            unet_model=self.unet_class,
+            device=device
+        )
+
+class SanaModel(comfy.model_base.BaseModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+def load_sana_state_dict(sd, model_options={}):
+    # prefix / format
+    sd = sd.get("model", sd) # ref ckpt
+    diffusion_model_prefix = comfy.model_detection.unet_prefix_from_state_dict(sd)
+    temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True)
+    if len(temp_sd) > 0:
+        sd = temp_sd
+
+    # diffusers convert
+    if "adaln_single.linear.weight" in sd:
+        sd = convert_state_dict(sd)
+
+    # model config
+    model_config = model_config_from_unet(sd)
+    return load_state_dict_from_config(model_config, sd, model_options)
+
+def model_config_from_unet(sd):
+    """
+    Guess config based on (converted) state dict.
+    """
+    # shared settings that match between all models
+    # TODO: some can (should) be enumerated
+    config = {
+        "in_channels": 32,
+        "linear_head_dim": 32,
+        "model_max_length": 300,
+        "y_norm": True,
+        "attn_type": "linear",
+        "ffn_type": "glumbconv",
+        "mlp_ratio": 2.5,
+        "mlp_acts": ["silu", "silu", None],
+        "use_pe": False,
+        "pred_sigma": False,
+        "learn_sigma": False,
+        "fp32_attention": True,
+        "patch_size": 1,
+    }
+    config["depth"] = sum([key.endswith(".point_conv.conv.weight") for key in sd.keys()]) or 28
+
+    if "x_embedder.proj.bias" in sd:
+        config["hidden_size"] = sd["x_embedder.proj.bias"].shape[0]
+    
+    if config["hidden_size"] == 1152:
+        config["num_heads"] = 16
+    elif config["hidden_size"] == 2240:
+        config["num_heads"] = 20
+    else:
+        raise RuntimeError(f"Unknown model config.")
+    
+    model_config = SanaConfig(config)
+    logging.debug(f"Sana config:\n{config}")
+    return model_config
+
+# 512/1024/2K match, TODO: 4K is new, add on release
+from ..PixArt.loader import resolutions as pixart_res
+resolutions = {
+    "Sana 512": pixart_res["PixArt 512"],
+    "Sana 1024": pixart_res["PixArt 1024"],
+    "Sana 2K": pixart_res["PixArt 2K"],
+}
diff --git a/Sana/nodes.py b/Sana/nodes.py
index abce9ba..ca1628b 100644
--- a/Sana/nodes.py
+++ b/Sana/nodes.py
@@ -2,151 +2,66 @@
 import folder_paths
 from nodes import EmptyLatentImage
 
-from .conf import sana_conf, sana_res
-from .loader import load_sana
-
-dtypes = [
-	"auto",
-	"FP32",
-	"FP16",
-	"BF16"
-]
-
-class SanaCheckpointLoader:
-	@classmethod
-	def INPUT_TYPES(s):
-		return {
-			"required": {
-				"ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
-				"model": (list(sana_conf.keys()),),
-			}
-		}
-	RETURN_TYPES = ("MODEL",)
-	RETURN_NAMES = ("model",)
-	FUNCTION = "load_checkpoint"
-	CATEGORY = "ExtraModels/Sana"
-	TITLE = "Sana Checkpoint Loader"
-
-	def load_checkpoint(self, ckpt_name, model):
-		ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
-		model_conf = sana_conf[model]
-		model = load_sana(
-			model_path = ckpt_path,
-			model_conf = model_conf,
-		)
-		return (model,)
-
-
 class EmptySanaLatentImage(EmptyLatentImage):
-	CATEGORY = "ExtraModels/Sana"
-	TITLE = "Empty Sana Latent Image"
-
-	def generate(self, width, height, batch_size=1):
-		latent = torch.zeros([batch_size, 32, height // 32, width // 32], device=self.device)
-		return ({"samples":latent}, )
-
-
-class SanaResolutionSelect():
-	@classmethod
-	def INPUT_TYPES(s):
-		return {
-			"required": {
-				"model": (list(sana_res.keys()),),
-				"ratio": (list(sana_res["1024px"].keys()),{"default":"1.00"}),
-			}
-		}
-	RETURN_TYPES = ("INT","INT")
-	RETURN_NAMES = ("width","height")
-	FUNCTION = "get_res"
-	CATEGORY = "ExtraModels/Sana"
-	TITLE = "Sana Resolution Select"
-
-	def get_res(self, model, ratio):
-		width, height = sana_res[model][ratio]
-		return (width,height)
-
-
-class SanaResolutionCond:
-	@classmethod
-	def INPUT_TYPES(s):
-		return {
-			"required": {
-				"cond": ("CONDITIONING", ),
-				"width": ("INT", {"default": 1024.0, "min": 0, "max": 8192}),
-				"height": ("INT", {"default": 1024.0, "min": 0, "max": 8192}),
-			}
-		}
-
-	RETURN_TYPES = ("CONDITIONING",)
-	RETURN_NAMES = ("cond",)
-	FUNCTION = "add_cond"
-	CATEGORY = "ExtraModels/Sana"
-	TITLE = "Sana Resolution Conditioning"
-	
-	def add_cond(self, cond, width, height):
-		for c in range(len(cond)):
-			cond[c][1].update({
-				"img_hw": [[height, width]],
-				"aspect_ratio": [[height/width]],
-			})
-		return (cond,)
+    CATEGORY = "ExtraModels/Sana"
+    TITLE = "Empty Sana Latent Image"
 
+    def generate(self, width, height, batch_size=1):
+        latent = torch.zeros([batch_size, 32, height // 32, width // 32], device=self.device)
+        return ({"samples":latent}, )
 
 class SanaTextEncode:
-	@classmethod
-	def INPUT_TYPES(s):
-		return {
-			"required": {
-				"text": ("STRING", {"multiline": True}),
-				"GEMMA": ("GEMMA",),
-			}
-		}
-
-	RETURN_TYPES = ("CONDITIONING",)
-	FUNCTION = "encode"
-	CATEGORY = "ExtraModels/Sana"
-	TITLE = "Sana Text Encode"
-
-	def encode(self, text, GEMMA=None):
-		tokenizer = GEMMA["tokenizer"]
-		text_encoder = GEMMA["text_encoder"]
-		
-		with torch.no_grad():
-			chi_prompt = "\n".join(preset_te_prompt)
-			full_prompt = chi_prompt + text
-			num_chi_tokens = len(tokenizer.encode(chi_prompt))
-			max_length = num_chi_tokens + 300 - 2
-			
-			tokens = tokenizer(
-				[full_prompt],
-				max_length=max_length,
-				padding="max_length",
-				truncation=True,
-				return_tensors="pt"
-			).to(text_encoder.device)
-			
-			select_idx = [0] + list(range(-300 + 1, 0))
-			embs = text_encoder(tokens.input_ids, tokens.attention_mask)[0][:, None][:, :, select_idx]
-			emb_masks = tokens.attention_mask[:, select_idx]
-		embs = embs * emb_masks.unsqueeze(-1)
-			
-		return ([[embs, {}]], )
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "text": ("STRING", {"multiline": True}),
+                "GEMMA": ("GEMMA",),
+            }
+        }
+
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "encode"
+    CATEGORY = "ExtraModels/Sana"
+    TITLE = "Sana Text Encode"
+
+    def encode(self, text, GEMMA=None):
+        tokenizer = GEMMA["tokenizer"]
+        text_encoder = GEMMA["text_encoder"]
+        
+        with torch.no_grad():
+            chi_prompt = "\n".join(preset_te_prompt)
+            full_prompt = chi_prompt + text
+            num_chi_tokens = len(tokenizer.encode(chi_prompt))
+            max_length = num_chi_tokens + 300 - 2
+            
+            tokens = tokenizer(
+                [full_prompt],
+                max_length=max_length,
+                padding="max_length",
+                truncation=True,
+                return_tensors="pt"
+            ).to(text_encoder.device)
+            
+            select_idx = [0] + list(range(-300 + 1, 0))
+            embs = text_encoder(tokens.input_ids, tokens.attention_mask)[0][:, None][:, :, select_idx]
+            emb_masks = tokens.attention_mask[:, select_idx]
+        embs = embs * emb_masks.unsqueeze(-1)
+            
+        return ([[embs, {}]], )
 
 preset_te_prompt = [
-	'Given a user prompt, generate an "Enhanced prompt" that provides detailed visual descriptions suitable for image generation. Evaluate the level of detail in the user prompt:',
-	'- If the prompt is simple, focus on adding specifics about colors, shapes, sizes, textures, and spatial relationships to create vivid and concrete scenes.',
-	'- If the prompt is already detailed, refine and enhance the existing details slightly without overcomplicating.',
-	'Here are examples of how to transform or refine prompts:',
-	'- User Prompt: A cat sleeping -> Enhanced: A small, fluffy white cat curled up in a round shape, sleeping peacefully on a warm sunny windowsill, surrounded by pots of blooming red flowers.',
-	'- User Prompt: A busy city street -> Enhanced: A bustling city street scene at dusk, featuring glowing street lamps, a diverse crowd of people in colorful clothing, and a double-decker bus passing by towering glass skyscrapers.',
-	'Please generate only the enhanced description for the prompt below and avoid including any additional commentary or evaluations:',
-	'User Prompt: '
+    'Given a user prompt, generate an "Enhanced prompt" that provides detailed visual descriptions suitable for image generation. Evaluate the level of detail in the user prompt:',
+    '- If the prompt is simple, focus on adding specifics about colors, shapes, sizes, textures, and spatial relationships to create vivid and concrete scenes.',
+    '- If the prompt is already detailed, refine and enhance the existing details slightly without overcomplicating.',
+    'Here are examples of how to transform or refine prompts:',
+    '- User Prompt: A cat sleeping -> Enhanced: A small, fluffy white cat curled up in a round shape, sleeping peacefully on a warm sunny windowsill, surrounded by pots of blooming red flowers.',
+    '- User Prompt: A busy city street -> Enhanced: A bustling city street scene at dusk, featuring glowing street lamps, a diverse crowd of people in colorful clothing, and a double-decker bus passing by towering glass skyscrapers.',
+    'Please generate only the enhanced description for the prompt below and avoid including any additional commentary or evaluations:',
+    'User Prompt: '
 ]
 
 NODE_CLASS_MAPPINGS = {
-	"SanaCheckpointLoader" : SanaCheckpointLoader,
-	"SanaResolutionSelect" : SanaResolutionSelect,
-	"SanaTextEncode" : SanaTextEncode,
-	"SanaResolutionCond" : SanaResolutionCond,
-	"EmptySanaLatentImage": EmptySanaLatentImage,
+    "SanaTextEncode" : SanaTextEncode,
+    "EmptySanaLatentImage": EmptySanaLatentImage,
 }
diff --git a/nodes.py b/nodes.py
index eab0d3d..31d7f53 100644
--- a/nodes.py
+++ b/nodes.py
@@ -2,9 +2,11 @@
 import comfy.utils
 
 from .PixArt.loader import load_pixart_state_dict
+from .Sana.loader import load_sana_state_dict
 
 loaders = {
     "PixArt": load_pixart_state_dict,
+    "Sana": load_sana_state_dict,
 }
 
 class EXMUnetLoader:

From 7e19ac5e439f35f009e668dd33e8cac5bba9f078 Mon Sep 17 00:00:00 2001
From: City <125218114+city96@users.noreply.github.com>
Date: Wed, 11 Dec 2024 22:01:11 +0100
Subject: [PATCH 07/15] Use native ops for sana

---
 Sana/loader.py                  |  11 +--
 Sana/models/act.py              |   4 +-
 Sana/models/basic_modules.py    | 102 +++++++++++++----------
 Sana/models/norms.py            |   6 +-
 Sana/models/sana.py             | 100 +++++++++++------------
 Sana/models/sana_blocks.py      | 140 ++++++++++++++++++++++----------
 Sana/models/sana_multi_scale.py | 110 +++++++++++++------------
 7 files changed, 274 insertions(+), 199 deletions(-)

diff --git a/Sana/loader.py b/Sana/loader.py
index 60eea7a..b2aea1b 100644
--- a/Sana/loader.py
+++ b/Sana/loader.py
@@ -31,16 +31,11 @@ def model_type(self, state_dict, prefix=""):
         return comfy.model_base.ModelType.FLOW
 
     def get_model(self, state_dict, prefix="", device=None):
-        return SanaModel(
-            model_config=self,
-            model_type=comfy.model_base.ModelType.FLOW,
-            unet_model=self.unet_class,
-            device=device
-        )
+        return SanaModel(model_config=self, unet_model=self.unet_class, device=device)
 
 class SanaModel(comfy.model_base.BaseModel):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
+    def __init__(self, *args, model_type=comfy.model_base.ModelType.FLOW, unet_model=SanaMS, **kwargs):
+        super().__init__(*args, model_type=model_type, unet_model=unet_model, **kwargs)
 
 def load_sana_state_dict(sd, model_options={}):
     # prefix / format
diff --git a/Sana/models/act.py b/Sana/models/act.py
index 9df6a7a..7993065 100644
--- a/Sana/models/act.py
+++ b/Sana/models/act.py
@@ -37,7 +37,7 @@
 }
 
 
-def build_act(name: str or None, **kwargs) -> nn.Module or None:
+def build_act(name, **kwargs):
     if name in REGISTERED_ACT_DICT:
         act_cls, default_args = copy.deepcopy(REGISTERED_ACT_DICT[name])
         for key in default_args:
@@ -50,7 +50,7 @@ def build_act(name: str or None, **kwargs) -> nn.Module or None:
         raise ValueError(f"do not support: {name}")
 
 
-def get_act_name(act: nn.Module or None) -> str or None:
+def get_act_name(act):
     if act is None:
         return None
     module2name = {}
diff --git a/Sana/models/basic_modules.py b/Sana/models/basic_modules.py
index ece579a..6b279a4 100644
--- a/Sana/models/basic_modules.py
+++ b/Sana/models/basic_modules.py
@@ -17,13 +17,31 @@
 # This file is modified from https://github.com/PixArt-alpha/PixArt-sigma
 import torch
 import torch.nn as nn
-from timm.models.vision_transformer import Mlp
+#from timm.models.vision_transformer import Mlp
 
 from .act import build_act, get_act_name
 from .norms import build_norm, get_norm_name
 from .utils import get_same_padding, val2tuple
 
 
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, drop=None, dtype=None, device=None, operations=None) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+
+        self.fc1 = operations.Linear(in_features, hidden_features, bias=bias, dtype=dtype, device=device)
+        self.act = act_layer()
+        self.fc2 = operations.Linear(hidden_features, out_features, bias=bias, dtype=dtype, device=device)
+
+        self.drop1 = nn.Identity()
+        self.drop2 = nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.act(self.fc1(x))
+        return self.fc2(x)
+
+
 class ConvLayer(nn.Module):
     def __init__(
         self,
@@ -33,11 +51,14 @@ def __init__(
         stride=1,
         dilation=1,
         groups=1,
-        padding: int or None = None,
+        padding=None,
         use_bias=False,
         dropout=0.0,
         norm="bn2d",
         act="relu",
+        dtype=None,
+        device=None,
+        operations=None,
     ):
         super().__init__()
         if padding is None:
@@ -54,7 +75,7 @@ def __init__(
         self.use_bias = use_bias
 
         self.dropout = nn.Dropout2d(dropout, inplace=False) if dropout > 0 else None
-        self.conv = nn.Conv2d(
+        self.conv = operations.Conv2d(
             in_dim,
             out_dim,
             kernel_size=(kernel_size, kernel_size),
@@ -63,6 +84,8 @@ def __init__(
             dilation=(dilation, dilation),
             groups=groups,
             bias=use_bias,
+            dtype=dtype,
+            device=device,
         )
         self.norm = build_norm(norm, num_features=out_dim)
         self.act = build_act(act)
@@ -86,11 +109,14 @@ def __init__(
         out_feature=None,
         kernel_size=3,
         stride=1,
-        padding: int or None = None,
+        padding=None,
         use_bias=False,
         norm=(None, None, None),
         act=("silu", "silu", None),
         dilation=1,
+        dtype=None,
+        device=None,
+        operations=None,
     ):
         out_feature = out_feature or in_features
         super().__init__()
@@ -106,6 +132,9 @@ def __init__(
             use_bias=use_bias[0],
             norm=norm[0],
             act=act[0],
+            dtype=dtype,
+            device=device,
+            operations=operations,
         )
         self.depth_conv = ConvLayer(
             hidden_features * 2,
@@ -118,6 +147,9 @@ def __init__(
             norm=norm[1],
             act=None,
             dilation=dilation,
+            dtype=dtype,
+            device=device,
+            operations=operations,
         )
         self.point_conv = ConvLayer(
             hidden_features,
@@ -126,6 +158,9 @@ def __init__(
             use_bias=use_bias[2],
             norm=norm[2],
             act=act[2],
+            dtype=dtype,
+            device=device,
+            operations=operations,
         )
         # from IPython import embed; embed(header='debug dilate conv')
 
@@ -189,10 +224,13 @@ def __init__(
         stride=1,
         mid_dim=None,
         expand=6,
-        padding: int or None = None,
+        padding=None,
         use_bias=False,
         norm=(None, None, "ln2d"),
         act=("silu", "silu", None),
+        dtype=None,
+        device=None,
+        operations=None,
     ):
         super().__init__()
         use_bias = val2tuple(use_bias, 3)
@@ -208,6 +246,9 @@ def __init__(
             use_bias=use_bias[0],
             norm=norm[0],
             act=None,
+            dtype=dtype,
+            device=device,
+            operations=operations,
         )
         self.glu_act = build_act(act[0], inplace=False)
         self.depth_conv = ConvLayer(
@@ -220,6 +261,9 @@ def __init__(
             use_bias=use_bias[1],
             norm=norm[1],
             act=act[1],
+            dtype=dtype,
+            device=device,
+            operations=operations,
         )
         self.point_conv = ConvLayer(
             mid_dim,
@@ -228,6 +272,9 @@ def __init__(
             use_bias=use_bias[2],
             norm=norm[2],
             act=act[2],
+            dtype=dtype,
+            device=device,
+            operations=operations,
         )
 
     def forward(self, x: torch.Tensor, HW=None) -> torch.Tensor:
@@ -283,6 +330,9 @@ def __init__(
         stride=1,
         dilation=1,
         padding=None,
+        dtype=None,
+        device=None,
+        operations=None,
     ):
         super().__init__(
             in_features=in_features,
@@ -291,6 +341,9 @@ def __init__(
             act_layer=act_layer,
             bias=bias,
             drop=drop,
+            dtype=dtype,
+            device=device,
+            operations=operations,
         )
         hidden_features = hidden_features or in_features
         self.hidden_features = hidden_features
@@ -298,7 +351,7 @@ def __init__(
             padding = get_same_padding(kernel_size)
             padding *= dilation
 
-        self.conv = nn.Conv2d(
+        self.conv = operations.Conv2d(
             hidden_features,
             hidden_features,
             kernel_size=(kernel_size, kernel_size),
@@ -307,6 +360,8 @@ def __init__(
             dilation=(dilation, dilation),
             groups=hidden_features,
             bias=bias,
+            dtype=dtype,
+            device=device,
         )
 
     def forward(self, x, HW=None):
@@ -324,38 +379,3 @@ def forward(self, x, HW=None):
         x = self.fc2(x)
         x = self.drop2(x)
         return x
-
-
-class Mlp(Mlp):
-    """MLP as used in Vision Transformer, MLP-Mixer and related networks"""
-
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, drop=0.0):
-        super().__init__(
-            in_features=in_features,
-            hidden_features=hidden_features,
-            out_features=out_features,
-            act_layer=act_layer,
-            bias=bias,
-            drop=drop,
-        )
-
-    def forward(self, x, HW=None):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop1(x)
-        x = self.fc2(x)
-        x = self.drop2(x)
-        return x
-
-
-if __name__ == "__main__":
-    model = GLUMBConv(
-        1152,
-        1152 * 4,
-        1152,
-        use_bias=(True, True, False),
-        norm=(None, None, None),
-        act=("silu", "silu", None),
-    ).cuda()
-    input = torch.randn(4, 256, 1152).cuda()
-    output = model(input)
diff --git a/Sana/models/norms.py b/Sana/models/norms.py
index 4731d69..5174fdc 100644
--- a/Sana/models/norms.py
+++ b/Sana/models/norms.py
@@ -48,7 +48,7 @@ def extra_repr(self) -> str:
 }
 
 
-def build_norm(name="bn2d", num_features=None, affine=True, **kwargs) -> nn.Module or None:
+def build_norm(name="bn2d", num_features=None, affine=True, **kwargs):
     if name in ["ln", "ln2d"]:
         kwargs["normalized_shape"] = num_features
         kwargs["elementwise_affine"] = affine
@@ -67,7 +67,7 @@ def build_norm(name="bn2d", num_features=None, affine=True, **kwargs) -> nn.Modu
         raise ValueError("do not support: %s" % name)
 
 
-def get_norm_name(norm: nn.Module or None) -> str or None:
+def get_norm_name(norm):
     if norm is None:
         return None
     module2name = {}
@@ -171,7 +171,7 @@ def remove_bn(model: nn.Module) -> None:
             m.forward = lambda x: x
 
 
-def set_norm_eps(model: nn.Module, eps: float or None = None, momentum: float or None = None) -> None:
+def set_norm_eps(model, eps=None, momentum=None):
     for m in model.modules():
         if isinstance(m, (nn.GroupNorm, nn.LayerNorm, _BatchNorm)):
             if eps is not None:
diff --git a/Sana/models/sana.py b/Sana/models/sana.py
index 81c2211..085eb65 100644
--- a/Sana/models/sana.py
+++ b/Sana/models/sana.py
@@ -20,7 +20,6 @@
 import numpy as np
 import torch
 import torch.nn as nn
-from timm.models.layers import DropPath
 
 from .basic_modules import DWMlp, GLUMBConv, MBConvPreGLU, Mlp
 from .sana_blocks import (
@@ -55,10 +54,13 @@ def __init__(
         ffn_type="mlp",
         mlp_acts=("silu", "silu", None),
         linear_head_dim=32,
+        dtype=None,
+        device=None,
+        operations=None,
         **block_kwargs,
     ):
         super().__init__()
-        self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
         if attn_type == "flash":
             # flash self attention
             self.attn = FlashAttention(
@@ -66,20 +68,28 @@ def __init__(
                 num_heads=num_heads,
                 qkv_bias=True,
                 qk_norm=qk_norm,
+                dtype=dtype,
+                device=device,
+                operations=operations,
                 **block_kwargs,
             )
         elif attn_type == "linear":
             # linear self attention
             # TODO: Here the num_heads set to 36 for tmp used
             self_num_heads = hidden_size // linear_head_dim
-            self.attn = LiteLA(hidden_size, hidden_size, heads=self_num_heads, eps=1e-8, qk_norm=qk_norm)
+            self.attn = LiteLA(
+                hidden_size, hidden_size, heads=self_num_heads, eps=1e-8, qk_norm=qk_norm,
+                dtype=dtype, device=device, operations=operations,
+            )
         elif attn_type == "vanilla":
             # vanilla self attention
-            self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True)
+            self.attn = Attention(
+                hidden_size, num_heads=num_heads, qkv_bias=True, dtype=dtype, device=device, operations=operations,
+            )
         else:
             raise ValueError(f"{attn_type} type is not defined.")
 
-        self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, **block_kwargs)
+        self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, dtype=dtype, device=device, operations=operations, **block_kwargs)
         self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
         # to be compatible with lower version pytorch
         if ffn_type == "dwmlp":
@@ -94,6 +104,9 @@ def __init__(
                 use_bias=(True, True, False),
                 norm=(None, None, None),
                 act=mlp_acts,
+                dtype=dtype,
+                device=device,
+                operations=operations,
             )
         elif ffn_type == "glumbconv_dilate":
             self.mlp = GLUMBConv(
@@ -103,6 +116,9 @@ def __init__(
                 norm=(None, None, None),
                 act=mlp_acts,
                 dilation=2,
+                dtype=dtype,
+                device=device,
+                operations=operations,
             )
         elif ffn_type == "mbconvpreglu":
             self.mlp = MBConvPreGLU(
@@ -112,15 +128,19 @@ def __init__(
                 use_bias=(True, True, False),
                 norm=None,
                 act=("silu", "silu", None),
+                dtype=dtype,
+                device=device,
+                operations=operations,
             )
         elif ffn_type == "mlp":
             approx_gelu = lambda: nn.GELU(approximate="tanh")
             self.mlp = Mlp(
-                in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0
+                in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0,
+                # dtype=dtype, device=device, operations=operations,
             )
         else:
             raise ValueError(f"{ffn_type} type is not defined.")
-        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+        self.drop_path = nn.Identity() #DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
         self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size**0.5)
 
     def forward(self, x, y, t, mask=None, **kwargs):
@@ -170,9 +190,13 @@ def __init__(
         patch_embed_kernel=None,
         mlp_acts=("silu", "silu", None),
         linear_head_dim=32,
+        dtype=None,
+        device=None,
+        operations=None,
         **kwargs,
     ):
         super().__init__()
+        self.dtype = torch.float32
         self.pred_sigma = pred_sigma
         self.in_channels = in_channels
         self.out_channels = in_channels * 2 if pred_sigma else in_channels
@@ -187,22 +211,28 @@ def __init__(
 
         kernel_size = patch_embed_kernel or patch_size
         self.x_embedder = PatchEmbed(
-            input_size, patch_size, in_channels, hidden_size, kernel_size=kernel_size, bias=True
+            input_size, patch_size, in_channels, hidden_size, kernel_size=kernel_size, bias=True,
+            dtype=dtype, device=device, operations=operations
         )
-        self.t_embedder = TimestepEmbedder(hidden_size)
+        self.t_embedder = TimestepEmbedder(hidden_size, dtype=dtype, device=device, operations=operations)
         num_patches = self.x_embedder.num_patches
         self.base_size = input_size // self.patch_size
         # Will use fixed sin-cos embedding:
         self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size))
 
         approx_gelu = lambda: nn.GELU(approximate="tanh")
-        self.t_block = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
+        self.t_block = nn.Sequential(
+            nn.SiLU(), operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device)
+        )
         self.y_embedder = CaptionEmbedder(
             in_channels=caption_channels,
             hidden_size=hidden_size,
             uncond_prob=class_dropout_prob,
             act_layer=approx_gelu,
             token_num=model_max_length,
+            dtype=dtype,
+            device=device,
+            operations=operations
         )
         if self.y_norm:
             self.attention_y_norm = RMSNorm(hidden_size, scale_factor=y_norm_scale_factor, eps=norm_eps)
@@ -220,29 +250,32 @@ def __init__(
                     ffn_type=ffn_type,
                     mlp_acts=mlp_acts,
                     linear_head_dim=linear_head_dim,
+                    dtype=dtype,
+                    device=device,
+                    operations=operations
                 )
                 for i in range(depth)
             ]
         )
-        self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels)
+        self.final_layer = T2IFinalLayer(
+            hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations
+        )
 
-    def forward(self, x, timestep, y, mask=None, data_info=None, **kwargs):
+    def forward(self, x, timestep, context, mask=None, data_info=None, **kwargs):
         """
         Forward pass of Sana.
         x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
         t: (N,) tensor of diffusion timesteps
         y: (N, 1, 120, C) tensor of class labels
         """
-        x = x.to(self.dtype)
-        timestep = timestep.to(self.dtype)
-        y = y.to(self.dtype)
-        pos_embed = self.pos_embed.to(self.dtype)
+        y = context # remap comfy cond name
+        pos_embed = self.pos_embed.to(x.dtype)
         self.h, self.w = x.shape[-2] // self.patch_size, x.shape[-1] // self.patch_size
         if self.use_pe:
             x = self.x_embedder(x) + pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
         else:
             x = self.x_embedder(x)
-        t = self.t_embedder(timestep.to(x.dtype))  # (N, D)
+        t = self.t_embedder(timestep, x.dtype)  # (N, D)
         t0 = self.t_block(t)
         y = self.y_embedder(y, self.training)  # (N, 1, L, D)
         if self.y_norm:
@@ -292,39 +325,6 @@ def unpatchify(self, x):
         imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
         return imgs
 
-    def initialize_weights(self):
-        # Initialize transformer layers:
-        def _basic_init(module):
-            if isinstance(module, nn.Linear):
-                torch.nn.init.xavier_uniform_(module.weight)
-                if module.bias is not None:
-                    nn.init.constant_(module.bias, 0)
-
-        self.apply(_basic_init)
-
-        if self.use_pe:
-            # Initialize (and freeze) pos_embed by sin-cos embedding:
-            pos_embed = get_2d_sincos_pos_embed(
-                self.pos_embed.shape[-1],
-                int(self.x_embedder.num_patches**0.5),
-                pe_interpolation=self.pe_interpolation,
-                base_size=self.base_size,
-            )
-            self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
-
-        # Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
-        w = self.x_embedder.proj.weight.data
-        nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
-
-        # Initialize timestep embedding MLP:
-        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
-        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
-        nn.init.normal_(self.t_block[1].weight, std=0.02)
-
-        # Initialize caption embedding MLP:
-        nn.init.normal_(self.y_embedder.y_proj.fc1.weight, std=0.02)
-        nn.init.normal_(self.y_embedder.y_proj.fc2.weight, std=0.02)
-
 
 def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0, pe_interpolation=1.0, base_size=16):
     """
diff --git a/Sana/models/sana_blocks.py b/Sana/models/sana_blocks.py
index cdd37e4..d6350ad 100644
--- a/Sana/models/sana_blocks.py
+++ b/Sana/models/sana_blocks.py
@@ -22,12 +22,11 @@
 import torch.nn as nn
 import torch.nn.functional as F
 from einops import rearrange
-from timm.models.vision_transformer import Attention as Attention_
-from timm.models.vision_transformer import Mlp
 from transformers import AutoModelForCausalLM
 
 from .norms import RMSNorm
 from .utils import get_same_padding, to_2tuple
+from .basic_modules import Mlp
 
 sdpa_32b = None
 Q_4GB_LIMIT = 32000000
@@ -42,7 +41,7 @@
     import xformers.ops
 else:
     if model_management.xpu_available:
-        import intel_extension_for_pytorch as ipex
+        import intel_extension_for_pytorch as ipex # type: ignore
         import os
         if not torch.xpu.has_fp64_dtype() and not os.environ.get('IPEX_FORCE_ATTENTION_SLICE', None):
             from ...utils.IPEX.attention import scaled_dot_product_attention_32_bit
@@ -61,7 +60,7 @@ def t2i_modulate(x, shift, scale):
 
 
 class MultiHeadCrossAttention(nn.Module):
-    def __init__(self, d_model, num_heads, attn_drop=0.0, proj_drop=0.0, qk_norm=False, **block_kwargs):
+    def __init__(self, d_model, num_heads, attn_drop=0.0, proj_drop=0.0, qk_norm=False, dtype=None, device=None, operations=None, **block_kwargs):
         super().__init__()
         assert d_model % num_heads == 0, "d_model must be divisible by num_heads"
 
@@ -69,10 +68,10 @@ def __init__(self, d_model, num_heads, attn_drop=0.0, proj_drop=0.0, qk_norm=Fal
         self.num_heads = num_heads
         self.head_dim = d_model // num_heads
 
-        self.q_linear = nn.Linear(d_model, d_model)
-        self.kv_linear = nn.Linear(d_model, d_model * 2)
+        self.q_linear = operations.Linear(d_model, d_model, dtype=dtype, device=device)
+        self.kv_linear = operations.Linear(d_model, d_model * 2, dtype=dtype, device=device)
         self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(d_model, d_model)
+        self.proj = operations.Linear(d_model, d_model, dtype=dtype, device=device)
         self.proj_drop = nn.Dropout(proj_drop)
         if qk_norm:
             # not used for now
@@ -135,7 +134,7 @@ def forward(self, x, cond, mask=None):
         return x
 
 
-class LiteLA(Attention_):
+class LiteLA(torch.nn.Module): # from attention
     r"""Lightweight linear attention"""
 
     PAD_VAL = 1
@@ -151,9 +150,20 @@ def __init__(
         use_bias=False,
         qk_norm=False,
         norm_eps=1e-5,
+        dtype=None,
+        device=None,
+        operations=None,
     ):
+        super().__init__()
         heads = heads or int(out_dim // dim * heads_ratio)
-        super().__init__(in_dim, num_heads=heads, qkv_bias=use_bias)
+
+        # assert dim % heads == 0, 'dim should be divisible by num_heads'
+        self.num_heads = heads
+        self.head_dim = in_dim // heads
+        self.scale = self.head_dim ** -0.5
+
+        self.qkv = operations.Linear(in_dim, in_dim * 3, bias=use_bias, dtype=dtype, device=device)
+        self.proj = operations.Linear(in_dim, in_dim, dtype=dtype, device=device)
 
         self.in_dim = in_dim
         self.out_dim = out_dim
@@ -346,7 +356,7 @@ def __call__(self, x: torch.Tensor, mask=None, HW=None, block_id=None) -> torch.
         return out
 
 
-class FlashAttention(Attention_):
+class FlashAttention(torch.nn.Module): # from attention
     """Multi-head Flash Attention block with qk norm."""
 
     def __init__(
@@ -395,7 +405,7 @@ def forward(self, x, mask=None, HW=None, block_id=None):
             attn_bias = torch.zeros([B * self.num_heads, q.shape[1], k.shape[1]], dtype=q.dtype, device=q.device)
             attn_bias.masked_fill_(mask.squeeze(1).repeat(self.num_heads, 1, 1) == 0, float("-inf"))
 
-        if _xformers_available:
+        if model_management.xformers_enabled():
             x = xformers.ops.memory_efficient_attention(q, k, v, p=self.attn_drop.p, attn_bias=attn_bias)
         else:
             q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
@@ -418,7 +428,31 @@ def forward(self, x, mask=None, HW=None, block_id=None):
 #################################################################################
 #   AMP attention with fp32 softmax to fix loss NaN problem during training     #
 #################################################################################
-class Attention(Attention_):
+class Attention(torch.nn.Module):
+    def __init__(
+        self,
+        dim,
+        num_heads=8,
+        qkv_bias=True,
+        sampling='conv',
+        sr_ratio=1,
+        qk_norm=False,
+        dtype=None,
+        device=None,
+        operations=None,
+        **block_kwargs,
+    ):
+        super().__init__()
+        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.scale = self.head_dim ** -0.5
+
+        self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
+        self.q_norm = nn.Identity()
+        self.k_norm = nn.Identity()
+        self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)
+
     def forward(self, x, HW=None):
         B, N, C = x.shape
         qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
@@ -432,11 +466,11 @@ def forward(self, x, HW=None):
             attn = (q @ k.transpose(-2, -1)) * self.scale
             attn = attn.softmax(dim=-1)
 
-        attn = self.attn_drop(attn)
+        #attn = self.attn_drop(attn)
 
         x = (attn @ v).transpose(1, 2).reshape(B, N, C)
         x = self.proj(x)
-        x = self.proj_drop(x)
+        #x = self.proj_drop(x)
         return x
 
 
@@ -445,11 +479,14 @@ class FinalLayer(nn.Module):
     The final layer of Sana.
     """
 
-    def __init__(self, hidden_size, patch_size, out_channels):
+    def __init__(self, hidden_size, patch_size, out_channels, dtype=None, device=None, operations=None):
         super().__init__()
-        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
-        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
+        self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device)
+        )
 
     def forward(self, x, c):
         shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
@@ -463,17 +500,18 @@ class T2IFinalLayer(nn.Module):
     The final layer of Sana.
     """
 
-    def __init__(self, hidden_size, patch_size, out_channels):
+    def __init__(self, hidden_size, patch_size, out_channels, dtype=None, device=None, operations=None):
         super().__init__()
-        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
-        self.scale_shift_table = nn.Parameter(torch.randn(2, hidden_size) / hidden_size**0.5)
+        self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
+        self.scale_shift_table = nn.Parameter(torch.randn(2, hidden_size) / hidden_size ** 0.5)
         self.out_channels = out_channels
 
     def forward(self, x, t):
+        dtype = x.dtype
         shift, scale = (self.scale_shift_table[None] + t[:, None]).chunk(2, dim=1)
         x = t2i_modulate(self.norm_final(x), shift, scale)
-        x = self.linear(x)
+        x = self.linear(x.to(dtype))
         return x
 
 
@@ -482,12 +520,14 @@ class MaskFinalLayer(nn.Module):
     The final layer of Sana.
     """
 
-    def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels):
+    def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels, dtype=None, device=None, operations=None):
         super().__init__()
-        self.norm_final = nn.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6)
-        self.linear = nn.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True)
-        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(c_emb_size, 2 * final_hidden_size, bias=True))
-
+        self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = operations.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(c_emb_size, 2 * final_hidden_size, bias=True, dtype=dtype, device=device)
+        )
     def forward(self, x, t):
         shift, scale = self.adaLN_modulation(t).chunk(2, dim=1)
         x = modulate(self.norm_final(x), shift, scale)
@@ -500,12 +540,14 @@ class DecoderLayer(nn.Module):
     The final layer of Sana.
     """
 
-    def __init__(self, hidden_size, decoder_hidden_size):
+    def __init__(self, hidden_size, decoder_hidden_size, dtype=None, device=None, operations=None):
         super().__init__()
-        self.norm_decoder = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.linear = nn.Linear(hidden_size, decoder_hidden_size, bias=True)
-        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
-
+        self.norm_decoder = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = operations.Linear(hidden_size, decoder_hidden_size, bias=True, dtype=dtype, device=device)
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device)
+        )
     def forward(self, x, t):
         shift, scale = self.adaLN_modulation(t).chunk(2, dim=1)
         x = modulate(self.norm_decoder(x), shift, scale)
@@ -521,12 +563,12 @@ class TimestepEmbedder(nn.Module):
     Embeds scalar timesteps into vector representations.
     """
 
-    def __init__(self, hidden_size, frequency_embedding_size=256):
+    def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):
         super().__init__()
         self.mlp = nn.Sequential(
-            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
+            operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device),
             nn.SiLU(),
-            nn.Linear(hidden_size, hidden_size, bias=True),
+            operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
         )
         self.frequency_embedding_size = frequency_embedding_size
 
@@ -551,9 +593,9 @@ def timestep_embedding(t, dim, max_period=10000):
             embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
         return embedding
 
-    def forward(self, t):
-        t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(self.dtype)
-        t_emb = self.mlp(t_freq)
+    def forward(self, t, dtype):
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
+        t_emb = self.mlp(t_freq.to(dtype))
         return t_emb
 
     @property
@@ -644,10 +686,14 @@ def __init__(
         uncond_prob,
         act_layer=nn.GELU(approximate="tanh"),
         token_num=120,
+        dtype=None,
+        device=None,
+        operations=None,
     ):
         super().__init__()
         self.y_proj = Mlp(
-            in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, drop=0
+            in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, drop=0,
+            dtype=dtype, device=device, operations=operations
         )
         self.register_buffer("y_embedding", nn.Parameter(torch.randn(token_num, in_channels) / in_channels**0.5))
         self.uncond_prob = uncond_prob
@@ -734,6 +780,9 @@ def __init__(
         norm_layer=None,
         flatten=True,
         bias=True,
+        dtype=None,
+        device=None,
+        operations=None,
     ):
         super().__init__()
         kernel_size = kernel_size or patch_size
@@ -746,8 +795,8 @@ def __init__(
         self.flatten = flatten
         if not padding and kernel_size % 2 > 0:
             padding = get_same_padding(kernel_size)
-        self.proj = nn.Conv2d(
-            in_chans, embed_dim, kernel_size=kernel_size, stride=patch_size, padding=padding, bias=bias
+        self.proj = operations.Conv2d(
+            in_chans, embed_dim, kernel_size=kernel_size, stride=patch_size, padding=padding, bias=bias, dtype=dtype, device=device
         )
         self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
 
@@ -775,6 +824,9 @@ def __init__(
         norm_layer=None,
         flatten=True,
         bias=True,
+        dtype=None,
+        device=None,
+        operations=None,
     ):
         super().__init__()
         kernel_size = kernel_size or patch_size
@@ -783,8 +835,8 @@ def __init__(
         self.flatten = flatten
         if not padding and kernel_size % 2 > 0:
             padding = get_same_padding(kernel_size)
-        self.proj = nn.Conv2d(
-            in_chans, embed_dim, kernel_size=kernel_size, stride=patch_size, padding=padding, bias=bias
+        self.proj = operations.Conv2d(
+            in_chans, embed_dim, kernel_size=kernel_size, stride=patch_size, padding=padding, bias=bias, dtype=dtype, device=device
         )
         self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
 
diff --git a/Sana/models/sana_multi_scale.py b/Sana/models/sana_multi_scale.py
index 2d5452c..e69e183 100644
--- a/Sana/models/sana_multi_scale.py
+++ b/Sana/models/sana_multi_scale.py
@@ -17,7 +17,6 @@
 # This file is modified from https://github.com/PixArt-alpha/PixArt-sigma
 import torch
 import torch.nn as nn
-from timm.models.layers import DropPath
 
 from .basic_modules import DWMlp, GLUMBConv, MBConvPreGLU, Mlp
 from .sana import Sana, get_2d_sincos_pos_embed
@@ -52,11 +51,14 @@ def __init__(
         mlp_acts=("silu", "silu", None),
         linear_head_dim=32,
         cross_norm=False,
+        dtype=None,
+        device=None,
+        operations=None,
         **block_kwargs,
     ):
         super().__init__()
         self.hidden_size = hidden_size
-        self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
         if attn_type == "flash":
             # flash self attention
             self.attn = FlashAttention(
@@ -64,25 +66,34 @@ def __init__(
                 num_heads=num_heads,
                 qkv_bias=True,
                 qk_norm=qk_norm,
+                dtype=dtype,
+                device=device,
+                operations=operations,
                 **block_kwargs,
             )
         elif attn_type == "linear":
             # linear self attention
             # TODO: Here the num_heads set to 36 for tmp used
             self_num_heads = hidden_size // linear_head_dim
-            self.attn = LiteLA(hidden_size, hidden_size, heads=self_num_heads, eps=1e-8, qk_norm=qk_norm)
+            self.attn = LiteLA(
+                hidden_size, hidden_size, heads=self_num_heads, eps=1e-8, qk_norm=qk_norm,
+                dtype=dtype, device=device, operations=operations,
+            )
         elif attn_type == "vanilla":
             # vanilla self attention
-            self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True)
+            self.attn = Attention(
+                hidden_size, num_heads=num_heads, qkv_bias=True, dtype=dtype, device=device, operations=operations,
+            )
         else:
             raise ValueError(f"{attn_type} type is not defined.")
 
-        self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, qk_norm=cross_norm, **block_kwargs)
+        self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, qk_norm=cross_norm, dtype=dtype, device=device, operations=operations, **block_kwargs)
         self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
         if ffn_type == "dwmlp":
             approx_gelu = lambda: nn.GELU(approximate="tanh")
             self.mlp = DWMlp(
-                in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0
+                in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0,
+                dtype=dtype, device=device, operations=operations,
             )
         elif ffn_type == "glumbconv":
             self.mlp = GLUMBConv(
@@ -91,6 +102,9 @@ def __init__(
                 use_bias=(True, True, False),
                 norm=(None, None, None),
                 act=mlp_acts,
+                dtype=dtype,
+                device=device,
+                operations=operations,
             )
         elif ffn_type == "glumbconv_dilate":
             self.mlp = GLUMBConv(
@@ -100,11 +114,15 @@ def __init__(
                 norm=(None, None, None),
                 act=mlp_acts,
                 dilation=2,
+                dtype=dtype,
+                device=device,
+                operations=operations,
             )
         elif ffn_type == "mlp":
             approx_gelu = lambda: nn.GELU(approximate="tanh")
             self.mlp = Mlp(
-                in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0
+                in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0,
+                dtype=dtype, device=device, operations=operations,
             )
         elif ffn_type == "mbconvpreglu":
             self.mlp = MBConvPreGLU(
@@ -114,17 +132,20 @@ def __init__(
                 use_bias=(True, True, False),
                 norm=None,
                 act=mlp_acts,
+                dtype=dtype,
+                device=device,
+                operations=operations,
             )
         else:
             raise ValueError(f"{ffn_type} type is not defined.")
-        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+        self.drop_path = nn.Identity() # DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
         self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size**0.5)
 
     def forward(self, x, y, t, mask=None, HW=None, **kwargs):
         B, N, C = x.shape
 
         shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
-            self.scale_shift_table[None] + t.reshape(B, 6, -1)
+            self.scale_shift_table[None].to(x.dtype) + t.reshape(B, 6, -1)
         ).chunk(6, dim=1)
         x = x + self.drop_path(gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW))
         x = x + self.cross_attn(x, y, mask)
@@ -169,6 +190,9 @@ def __init__(
         mlp_acts=("silu", "silu", None),
         linear_head_dim=32,
         cross_norm=False,
+        dtype=None,
+        device=None,
+        operations=None,
         **kwargs,
     ):
         super().__init__(
@@ -197,22 +221,33 @@ def __init__(
             patch_embed_kernel=patch_embed_kernel,
             mlp_acts=mlp_acts,
             linear_head_dim=linear_head_dim,
+            dtype=dtype,
+            device=device,
+            operations=operations,
             **kwargs,
         )
-        self.dtype = torch.get_default_dtype()
+        self.dtype = dtype
         self.h = self.w = 0
         approx_gelu = lambda: nn.GELU(approximate="tanh")
-        self.t_block = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True))
+        self.t_block = nn.Sequential(
+            nn.SiLU(), operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device)
+        )
         self.pos_embed_ms = None
 
         kernel_size = patch_embed_kernel or patch_size
-        self.x_embedder = PatchEmbedMS(patch_size, in_channels, hidden_size, kernel_size=kernel_size, bias=True)
+        self.x_embedder = PatchEmbedMS(
+            patch_size, in_channels, hidden_size, kernel_size=kernel_size, bias=True,
+            dtype=dtype, device=device, operations=operations,
+        )
         self.y_embedder = CaptionEmbedder(
             in_channels=caption_channels,
             hidden_size=hidden_size,
             uncond_prob=class_dropout_prob,
             act_layer=approx_gelu,
             token_num=model_max_length,
+            dtype=dtype,
+            device=device,
+            operations=operations,
         )
         drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)]  # stochastic depth decay rule
         self.blocks = nn.ModuleList(
@@ -229,13 +264,16 @@ def __init__(
                     mlp_acts=mlp_acts,
                     linear_head_dim=linear_head_dim,
                     cross_norm=cross_norm,
+                    dtype=dtype,
+                    device=device,
+                    operations=operations,
                 )
                 for i in range(depth)
             ]
         )
-        self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels)
-
-        self.initialize()
+        self.final_layer = T2IFinalLayer(
+            hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations
+        )
 
     def forward(self, x, timesteps, context, **kwargs):
         """
@@ -251,7 +289,7 @@ def forward(self, x, timesteps, context, **kwargs):
             context = context.unsqueeze(1)
 
         ## run original forward pass
-        out = self.forward_raw(
+        out = self.forward_orig(
             x = x.to(self.dtype),
             timestep = timesteps.to(self.dtype),
             y = context.to(self.dtype),
@@ -262,7 +300,7 @@ def forward(self, x, timesteps, context, **kwargs):
         
         return out
 
-    def forward_raw(self, x, timestep, y, mask=None, data_info=None, **kwargs):
+    def forward(self, x, timestep, context, mask=None, data_info=None, **kwargs):
         """
         Forward pass of Sana.
         x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
@@ -270,9 +308,7 @@ def forward_raw(self, x, timestep, y, mask=None, data_info=None, **kwargs):
         y: (N, 1, 120, C) tensor of class labels
         """
         bs = x.shape[0]
-        x = x.to(self.dtype)
-        timestep = timestep.to(self.dtype)
-        y = y.to(self.dtype)
+        y = context
         self.h, self.w = x.shape[-2] // self.patch_size, x.shape[-1] // self.patch_size
         if self.use_pe:
             x = self.x_embedder(x)
@@ -285,16 +321,13 @@ def forward_raw(self, x, timestep, y, mask=None, data_info=None, **kwargs):
                             pe_interpolation=self.pe_interpolation,
                             base_size=self.base_size,
                         )
-                    )
-                    .unsqueeze(0)
-                    .to(x.device)
-                    .to(self.dtype)
+                    ).unsqueeze(0).to(x.device).to(x.dtype)
                 )
             x += self.pos_embed_ms  # (N, T, D), where T = H * W / patch_size ** 2
         else:
             x = self.x_embedder(x)
 
-        t = self.t_embedder(timestep)  # (N, D)
+        t = self.t_embedder(timestep, x.dtype)  # (N, D)
 
         y_lens = ((y != 0).sum(dim=3) > 0).sum(dim=2).squeeze().tolist()
         y_lens = [y_lens[1]] * bs
@@ -311,9 +344,7 @@ def forward_raw(self, x, timestep, y, mask=None, data_info=None, **kwargs):
         y = y.squeeze(1).masked_select(mask.unsqueeze(-1).bool()).view(1, -1, y.shape[-1])
 
         for block in self.blocks:
-            x = auto_grad_checkpoint(
-                block, x, y, t0, y_lens, (self.h, self.w), **kwargs
-            )  # (N, T, D) #support grad checkpoint
+            x = block(x, y, t0, y_lens, (self.h, self.w), **kwargs) # (N, T, D) #
 
         x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
         x = self.unpatchify(x)  # (N, out_channels, H, W)
@@ -348,26 +379,3 @@ def unpatchify(self, x):
         x = torch.einsum("nhwpqc->nchpwq", x)
         imgs = x.reshape(shape=(x.shape[0], c, self.h * p, self.w * p))
         return imgs
-
-    def initialize(self):
-        # Initialize transformer layers:
-        def _basic_init(module):
-            if isinstance(module, nn.Linear):
-                torch.nn.init.xavier_uniform_(module.weight)
-                if module.bias is not None:
-                    nn.init.constant_(module.bias, 0)
-
-        self.apply(_basic_init)
-
-        # Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
-        w = self.x_embedder.proj.weight.data
-        nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
-
-        # Initialize timestep embedding MLP:
-        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
-        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
-        nn.init.normal_(self.t_block[1].weight, std=0.02)
-
-        # Initialize caption embedding MLP:
-        nn.init.normal_(self.y_embedder.y_proj.fc1.weight, std=0.02)
-        nn.init.normal_(self.y_embedder.y_proj.fc2.weight, std=0.02)

From 2beff5a4aa48b31d51d31a26975f36981b2dfa1a Mon Sep 17 00:00:00 2001
From: City <125218114+city96@users.noreply.github.com>
Date: Wed, 11 Dec 2024 22:33:57 +0100
Subject: [PATCH 08/15] Faster model initialization

don't initialize base Sana model for SanaMS
---
 Sana/loader.py                  |  7 ++++
 Sana/models/sana.py             | 24 +++++++++-----
 Sana/models/sana_multi_scale.py | 58 ++++++++++++++-------------------
 3 files changed, 48 insertions(+), 41 deletions(-)

diff --git a/Sana/loader.py b/Sana/loader.py
index b2aea1b..129e898 100644
--- a/Sana/loader.py
+++ b/Sana/loader.py
@@ -1,3 +1,4 @@
+import math
 import logging
 
 import comfy.utils
@@ -76,6 +77,12 @@ def model_config_from_unet(sd):
     }
     config["depth"] = sum([key.endswith(".point_conv.conv.weight") for key in sd.keys()]) or 28
 
+    if "pos_embed" in sd:
+        config["input_size"] = int(math.sqrt(sd["pos_embed"].shape[1])) * config["patch_size"]
+    else:
+        # TODO: this isn't optimal though most models don't use it
+        config["use_pe"] = False
+
     if "x_embedder.proj.bias" in sd:
         config["hidden_size"] = sd["x_embedder.proj.bias"].shape[0]
     
diff --git a/Sana/models/sana.py b/Sana/models/sana.py
index 085eb65..113adfb 100644
--- a/Sana/models/sana.py
+++ b/Sana/models/sana.py
@@ -34,7 +34,7 @@
     t2i_modulate,
 )
 from .norms import RMSNorm
-from .utils import auto_grad_checkpoint, to_2tuple
+from .utils import to_2tuple
 
 
 class SanaBlock(nn.Module):
@@ -166,7 +166,7 @@ class Sana(nn.Module):
 
     def __init__(
         self,
-        input_size=32,
+        input_size=None,
         patch_size=1,
         in_channels=32,
         hidden_size=1152,
@@ -196,7 +196,7 @@ def __init__(
         **kwargs,
     ):
         super().__init__()
-        self.dtype = torch.float32
+        self.dtype = dtype
         self.pred_sigma = pred_sigma
         self.in_channels = in_channels
         self.out_channels = in_channels * 2 if pred_sigma else in_channels
@@ -215,10 +215,18 @@ def __init__(
             dtype=dtype, device=device, operations=operations
         )
         self.t_embedder = TimestepEmbedder(hidden_size, dtype=dtype, device=device, operations=operations)
+
+        if input_size is not None:
+            self.base_size = input_size // self.patch_size
+        else:
+            self.base_size = None
+
         num_patches = self.x_embedder.num_patches
-        self.base_size = input_size // self.patch_size
-        # Will use fixed sin-cos embedding:
-        self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size))
+        if self.use_pe and num_patches is not None:
+            #Will use fixed sin-cos embedding:
+            self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size))
+        else:
+            self.pos_embed = None
 
         approx_gelu = lambda: nn.GELU(approximate="tanh")
         self.t_block = nn.Sequential(
@@ -269,9 +277,9 @@ def forward(self, x, timestep, context, mask=None, data_info=None, **kwargs):
         y: (N, 1, 120, C) tensor of class labels
         """
         y = context # remap comfy cond name
-        pos_embed = self.pos_embed.to(x.dtype)
         self.h, self.w = x.shape[-2] // self.patch_size, x.shape[-1] // self.patch_size
         if self.use_pe:
+            pos_embed = self.pos_embed.to(x.dtype)
             x = self.x_embedder(x) + pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
         else:
             x = self.x_embedder(x)
@@ -290,7 +298,7 @@ def forward(self, x, timestep, context, mask=None, data_info=None, **kwargs):
             y_lens = [y.shape[2]] * y.shape[0]
             y = y.squeeze(1).view(1, -1, x.shape[-1])
         for block in self.blocks:
-            x = auto_grad_checkpoint(block, x, y, t0, y_lens)  # (N, T, D) #support grad checkpoint
+            x = block(x, y, t0, y_lens) # (N, T, D)
         x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
         x = self.unpatchify(x)  # (N, out_channels, H, W)
         return x
diff --git a/Sana/models/sana_multi_scale.py b/Sana/models/sana_multi_scale.py
index e69e183..a00e6f5 100644
--- a/Sana/models/sana_multi_scale.py
+++ b/Sana/models/sana_multi_scale.py
@@ -23,6 +23,7 @@
 from .sana_blocks import (
     Attention,
     CaptionEmbedder,
+    TimestepEmbedder,
     FlashAttention,
     LiteLA,
     MultiHeadCrossAttention,
@@ -30,8 +31,7 @@
     T2IFinalLayer,
     t2i_modulate,
 )
-from .utils import auto_grad_checkpoint
-
+from .norms import RMSNorm
 
 class SanaMSBlock(nn.Module):
     """
@@ -195,44 +195,33 @@ def __init__(
         operations=None,
         **kwargs,
     ):
-        super().__init__(
-            input_size=input_size,
-            patch_size=patch_size,
-            in_channels=in_channels,
-            hidden_size=hidden_size,
-            depth=depth,
-            num_heads=num_heads,
-            mlp_ratio=mlp_ratio,
-            class_dropout_prob=class_dropout_prob,
-            learn_sigma=learn_sigma,
-            pred_sigma=pred_sigma,
-            drop_path=drop_path,
-            caption_channels=caption_channels,
-            pe_interpolation=pe_interpolation,
-            config=config,
-            model_max_length=model_max_length,
-            qk_norm=qk_norm,
-            y_norm=y_norm,
-            norm_eps=norm_eps,
-            attn_type=attn_type,
-            ffn_type=ffn_type,
-            use_pe=use_pe,
-            y_norm_scale_factor=y_norm_scale_factor,
-            patch_embed_kernel=patch_embed_kernel,
-            mlp_acts=mlp_acts,
-            linear_head_dim=linear_head_dim,
-            dtype=dtype,
-            device=device,
-            operations=operations,
-            **kwargs,
-        )
+        nn.Module.__init__(self)
         self.dtype = dtype
+        self.pred_sigma = pred_sigma
+        self.in_channels = in_channels
+        self.out_channels = in_channels * 2 if pred_sigma else in_channels
+        self.patch_size = patch_size
+        self.num_heads = num_heads
+        self.pe_interpolation = pe_interpolation
+        self.depth = depth
+        self.use_pe = use_pe
+        self.y_norm = y_norm
+        self.model_max_length = model_max_length
+        self.fp32_attention = kwargs.get("use_fp32_attention", False)
         self.h = self.w = 0
+
         approx_gelu = lambda: nn.GELU(approximate="tanh")
         self.t_block = nn.Sequential(
             nn.SiLU(), operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device)
         )
+
+        self.t_embedder = TimestepEmbedder(hidden_size, dtype=dtype, device=device, operations=operations)
+
         self.pos_embed_ms = None
+        if input_size is not None:
+            self.base_size = input_size // self.patch_size
+        else:
+            self.base_size = None
 
         kernel_size = patch_embed_kernel or patch_size
         self.x_embedder = PatchEmbedMS(
@@ -249,6 +238,9 @@ def __init__(
             device=device,
             operations=operations,
         )
+        if self.y_norm:
+            self.attention_y_norm = RMSNorm(hidden_size, scale_factor=y_norm_scale_factor, eps=norm_eps)
+        
         drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)]  # stochastic depth decay rule
         self.blocks = nn.ModuleList(
             [

From d1f751677afa50614a9fdb1d6d76595466f3238d Mon Sep 17 00:00:00 2001
From: City <125218114+city96@users.noreply.github.com>
Date: Wed, 11 Dec 2024 22:34:31 +0100
Subject: [PATCH 09/15] Patch size logic from common dit

---
 Sana/models/sana_blocks.py | 33 +++++++++++++++++++++++++++------
 1 file changed, 27 insertions(+), 6 deletions(-)

diff --git a/Sana/models/sana_blocks.py b/Sana/models/sana_blocks.py
index d6350ad..09ca72a 100644
--- a/Sana/models/sana_blocks.py
+++ b/Sana/models/sana_blocks.py
@@ -28,6 +28,8 @@
 from .utils import get_same_padding, to_2tuple
 from .basic_modules import Mlp
 
+import comfy.ldm.common_dit
+
 sdpa_32b = None
 Q_4GB_LIMIT = 32000000
 """If q is greater than this, the operation will likely require >4GB VRAM, which will fail on Intel Arc Alchemist GPUs without a workaround."""
@@ -780,6 +782,8 @@ def __init__(
         norm_layer=None,
         flatten=True,
         bias=True,
+        dynamic_img_pad=True,
+        padding_mode='circular',
         dtype=None,
         device=None,
         operations=None,
@@ -788,11 +792,20 @@ def __init__(
         kernel_size = kernel_size or patch_size
         img_size = to_2tuple(img_size)
         patch_size = to_2tuple(patch_size)
-        self.img_size = img_size
         self.patch_size = patch_size
-        self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
-        self.num_patches = self.grid_size[0] * self.grid_size[1]
+
+        if img_size is not None and False:
+            self.img_size = img_size
+            self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
+            self.num_patches = self.grid_size[0] * self.grid_size[1]
+        else:
+            self.img_size = None
+            self.grid_size = None
+            self.num_patches = None
+        
         self.flatten = flatten
+        self.dynamic_img_pad = dynamic_img_pad
+        self.padding_mode = padding_mode
         if not padding and kernel_size % 2 > 0:
             padding = get_same_padding(kernel_size)
         self.proj = operations.Conv2d(
@@ -802,11 +815,13 @@ def __init__(
 
     def forward(self, x):
         B, C, H, W = x.shape
-        assert (H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).")
-        assert (W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).")
+        # assert (H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).")
+        # assert (W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).")
+        if self.dynamic_img_pad:
+            x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode=self.padding_mode)
         x = self.proj(x)
         if self.flatten:
-            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
+            x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
         x = self.norm(x)
         return x
 
@@ -824,6 +839,8 @@ def __init__(
         norm_layer=None,
         flatten=True,
         bias=True,
+        dynamic_img_pad=True,
+        padding_mode='circular',
         dtype=None,
         device=None,
         operations=None,
@@ -833,6 +850,8 @@ def __init__(
         patch_size = to_2tuple(patch_size)
         self.patch_size = patch_size
         self.flatten = flatten
+        self.dynamic_img_pad = dynamic_img_pad
+        self.padding_mode = padding_mode
         if not padding and kernel_size % 2 > 0:
             padding = get_same_padding(kernel_size)
         self.proj = operations.Conv2d(
@@ -841,6 +860,8 @@ def __init__(
         self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
 
     def forward(self, x):
+        if self.dynamic_img_pad:
+            x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode=self.padding_mode)
         x = self.proj(x)
         if self.flatten:
             x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC

From ca2bc8bc65a9a5bab8c40d1d846b34c92a6dfc26 Mon Sep 17 00:00:00 2001
From: City <125218114+city96@users.noreply.github.com>
Date: Wed, 11 Dec 2024 22:37:13 +0100
Subject: [PATCH 10/15] Make mlp args match the one from timm

---
 PixArt/model/blocks.py | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/PixArt/model/blocks.py b/PixArt/model/blocks.py
index 3f3c2d9..19b76d2 100644
--- a/PixArt/model/blocks.py
+++ b/PixArt/model/blocks.py
@@ -419,20 +419,25 @@ def forward(self, labels, train, force_drop_ids=None):
         embeddings = self.embedding_table(labels)
         return embeddings
 
+
 class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, dtype=None, device=None, operations=None) -> None:
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, drop=None, dtype=None, device=None, operations=None) -> None:
         super().__init__()
         out_features = out_features or in_features
         hidden_features = hidden_features or in_features
 
-        self.fc1 = operations.Linear(in_features, hidden_features, bias=True, dtype=dtype, device=device)
+        self.fc1 = operations.Linear(in_features, hidden_features, bias=bias, dtype=dtype, device=device)
         self.act = act_layer()
-        self.fc2 = operations.Linear(hidden_features, out_features, bias=True, dtype=dtype, device=device)
+        self.fc2 = operations.Linear(hidden_features, out_features, bias=bias, dtype=dtype, device=device)
+
+        self.drop1 = nn.Identity()
+        self.drop2 = nn.Identity()
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         x = self.act(self.fc1(x))
         return self.fc2(x)
 
+
 class CaptionEmbedder(nn.Module):
     """
     Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.

From ee982c5d735a25b7fdb64e5cd987e052597de1e8 Mon Sep 17 00:00:00 2001
From: City <125218114+city96@users.noreply.github.com>
Date: Wed, 11 Dec 2024 22:44:09 +0100
Subject: [PATCH 11/15] Remove timm as a dependency

---
 PixArt/model/pixart.py | 20 +++++++++++++-------
 requirements.txt       |  1 -
 2 files changed, 13 insertions(+), 8 deletions(-)

diff --git a/PixArt/model/pixart.py b/PixArt/model/pixart.py
index f200177..a5ca872 100644
--- a/PixArt/model/pixart.py
+++ b/PixArt/model/pixart.py
@@ -8,17 +8,23 @@
 # GLIDE: https://github.com/openai/glide-text2im
 # MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
 # --------------------------------------------------------
-import math
 import torch
 import torch.nn as nn
-import os
 import numpy as np
-from timm.models.layers import DropPath
-from timm.models.vision_transformer import PatchEmbed, Mlp
-
 
 from .utils import auto_grad_checkpoint, to_2tuple
-from .blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, LabelEmbedder, FinalLayer
+from .blocks import (
+    t2i_modulate,
+    CaptionEmbedder,
+    AttentionKVCompress,
+    MultiHeadCrossAttention,
+    T2IFinalLayer,
+    TimestepEmbedder,
+    LabelEmbedder,
+    FinalLayer,
+    Mlp
+)
+from comfy.ldm.modules.diffusionmodules.mmdit import PatchEmbed
 
 
 class PixArtBlock(nn.Module):
@@ -37,7 +43,7 @@ def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0, input_siz
         # to be compatible with lower version pytorch
         approx_gelu = lambda: nn.GELU(approximate="tanh")
         self.mlp = Mlp(in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0)
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.drop_path = nn.Identity() #DropPath(drop_path) if drop_path > 0. else nn.Identity()
         self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5)
         self.sampling = sampling
         self.sr_ratio = sr_ratio
diff --git a/requirements.txt b/requirements.txt
index 1e54893..017b5b8 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,3 @@
-timm>=0.6.13
 sentencepiece>=0.1.97
 transformers>=4.34.1
 accelerate>=0.23.0

From 4a11543beb8612ea254f1da2553046ba25412943 Mon Sep 17 00:00:00 2001
From: City <125218114+city96@users.noreply.github.com>
Date: Wed, 11 Dec 2024 22:48:51 +0100
Subject: [PATCH 12/15] Add switch for new xformers

#86 - patch provided by @SaiZyca
---
 PixArt/model/blocks.py     | 10 +++++++---
 Sana/models/sana_blocks.py |  7 +++++--
 2 files changed, 12 insertions(+), 5 deletions(-)

diff --git a/PixArt/model/blocks.py b/PixArt/model/blocks.py
index 19b76d2..d540d96 100644
--- a/PixArt/model/blocks.py
+++ b/PixArt/model/blocks.py
@@ -14,6 +14,7 @@
 import torch.nn.functional as F
 from einops import rearrange
 
+import comfy.ldm.common_dit
 from .utils import to_2tuple
 
 sdpa_32b = None
@@ -25,11 +26,14 @@
 
 from comfy import model_management
 if model_management.xformers_enabled():
-    import xformers
     import xformers.ops
+    if int((xformers.__version__).split(".")[2]) >= 28:
+        block_diagonal_mask_from_seqlens = xformers.ops.fmha.attn_bias.BlockDiagonalMask.from_seqlens
+    else:
+        block_diagonal_mask_from_seqlens = xformers.ops.fmha.BlockDiagonalMask.from_seqlens
 else:
     if model_management.xpu_available:
-        import intel_extension_for_pytorch as ipex
+        import intel_extension_for_pytorch as ipex # type: ignore
         import os
         if not torch.xpu.has_fp64_dtype() and not os.environ.get('IPEX_FORCE_ATTENTION_SLICE', None):
             from ...utils.IPEX.attention import scaled_dot_product_attention_32_bit
@@ -70,7 +74,7 @@ def forward(self, x, cond, mask=None):
         if model_management.xformers_enabled():
             attn_bias = None
             if mask is not None:
-                attn_bias = xformers.ops.fmha.BlockDiagonalMask.from_seqlens([N] * B, mask)
+                attn_bias = block_diagonal_mask_from_seqlens([N] * B, mask)
             x = xformers.ops.memory_efficient_attention(
                 q, k, v,
                 p=self.attn_drop.p,
diff --git a/Sana/models/sana_blocks.py b/Sana/models/sana_blocks.py
index 09ca72a..894b23c 100644
--- a/Sana/models/sana_blocks.py
+++ b/Sana/models/sana_blocks.py
@@ -39,8 +39,11 @@
 
 from comfy import model_management
 if model_management.xformers_enabled():
-    import xformers
     import xformers.ops
+    if int((xformers.__version__).split(".")[2]) >= 28:
+        block_diagonal_mask_from_seqlens = xformers.ops.fmha.attn_bias.BlockDiagonalMask.from_seqlens
+    else:
+        block_diagonal_mask_from_seqlens = xformers.ops.fmha.BlockDiagonalMask.from_seqlens
 else:
     if model_management.xpu_available:
         import intel_extension_for_pytorch as ipex # type: ignore
@@ -94,7 +97,7 @@ def forward(self, x, cond, mask=None):
         if model_management.xformers_enabled():
             attn_bias = None
             if mask is not None:
-                attn_bias = xformers.ops.fmha.BlockDiagonalMask.from_seqlens([N] * B, mask)
+                attn_bias = block_diagonal_mask_from_seqlens([N] * B, mask)
             x = xformers.ops.memory_efficient_attention(
                 q, k, v,
                 p=self.attn_drop.p,

From 7b3f2ced4e13f7cc57bad3f5e62d791db5f7b0fb Mon Sep 17 00:00:00 2001
From: City <125218114+city96@users.noreply.github.com>
Date: Thu, 12 Dec 2024 06:59:37 +0100
Subject: [PATCH 13/15] Add fast rgb preview to Sana

---
 Sana/loader.py | 42 +++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 41 insertions(+), 1 deletion(-)

diff --git a/Sana/loader.py b/Sana/loader.py
index 129e898..413ffb0 100644
--- a/Sana/loader.py
+++ b/Sana/loader.py
@@ -15,8 +15,11 @@
 from ..utils.loader import load_state_dict_from_config
 
 class SanaLatent(comfy.latent_formats.LatentFormat):
-    scale_factor = 0.41407
     latent_channels = 32
+    def __init__(self):
+        self.scale_factor = 0.41407
+        self.latent_rgb_factors = latent_rgb_factors.copy()
+        self.latent_rgb_factors_bias = latent_rgb_factors_bias.copy()
 
 class SanaConfig(comfy.supported_models_base.BASE):
     unet_class = SanaMS
@@ -97,6 +100,43 @@ def model_config_from_unet(sd):
     logging.debug(f"Sana config:\n{config}")
     return model_config
 
+# for fast latent preview
+latent_rgb_factors = [
+    [-2.0022e-03, -6.0736e-03, -1.7096e-03],
+    [ 1.4221e-03,  3.6703e-03,  4.1083e-03],
+    [ 1.0081e-02,  2.6456e-04, -1.4333e-02],
+    [-2.4253e-03,  3.0967e-03, -1.0301e-03],
+    [ 2.2158e-03,  7.7274e-03, -1.3151e-02],
+    [ 1.1235e-02,  5.7630e-03,  3.6146e-03],
+    [-7.2899e-02,  1.1062e-02,  3.6103e-02],
+    [ 3.2346e-02,  2.8678e-02,  2.5014e-02],
+    [ 1.6469e-03, -1.1364e-03,  2.8366e-03],
+    [-3.5597e-02, -2.3447e-02, -3.1172e-03],
+    [-1.9985e-04, -2.0647e-03, -1.2702e-02],
+    [ 2.1318e-04,  1.2196e-03, -8.3461e-04],
+    [ 1.3766e-02,  2.7559e-03,  7.3567e-03],
+    [ 1.3027e-02,  2.6365e-03,  3.0405e-03],
+    [ 1.5335e-02,  9.4682e-03,  6.7312e-03],
+    [ 5.1827e-03, -9.4865e-03,  8.5080e-03],
+    [ 1.4365e-02, -3.2867e-03,  9.5108e-03],
+    [-4.1216e-03, -1.9177e-03, -3.3726e-03],
+    [-2.4757e-03,  5.1739e-04,  2.0280e-03],
+    [-3.5950e-03,  1.0720e-03,  5.3043e-03],
+    [-5.1758e-03,  8.1040e-03, -3.7564e-02],
+    [-3.8555e-03, -1.5529e-03, -3.5799e-03],
+    [-6.6175e-03, -6.8484e-03, -9.9609e-03],
+    [-2.1656e-03,  5.5770e-05,  1.4936e-03],
+    [-9.2857e-02, -1.1379e-01, -1.0919e-01],
+    [ 7.7044e-04,  5.5594e-03,  3.4755e-02],
+    [ 1.2714e-02,  2.9729e-02,  3.1989e-03],
+    [-1.1805e-03,  9.0548e-03, -4.1063e-04],
+    [ 8.3309e-04,  4.9694e-03,  2.3087e-03],
+    [ 7.8456e-03,  3.9750e-03,  3.5655e-03],
+    [-1.7552e-03,  4.9306e-03,  1.4210e-02],
+    [-1.4790e-03,  2.8837e-03, -4.5687e-03]
+]
+latent_rgb_factors_bias = [0.4358, 0.3814, 0.3388]
+
 # 512/1024/2K match, TODO: 4K is new, add on release
 from ..PixArt.loader import resolutions as pixart_res
 resolutions = {

From c52d82758a8472eae05e1d6831ab66060dd1ffab Mon Sep 17 00:00:00 2001
From: City <125218114+city96@users.noreply.github.com>
Date: Thu, 12 Dec 2024 19:51:17 +0100
Subject: [PATCH 14/15] Rename/move

---
 PixArt/loader.py                     |  4 +--
 PixArt/{model => models}/LICENSE     |  0
 PixArt/{model => models}/__init__.py |  0
 PixArt/{model => models}/blocks.py   |  0
 PixArt/{model => models}/pixart.py   |  0
 PixArt/{model => models}/pixartms.py |  0
 PixArt/{model => models}/utils.py    |  0
 Sana/models/__init__.py              |  0
 __init__.py                          |  6 +----
 nodes.py                             | 34 +++++++++++++++++++++++++
 text_encoders/nodes.py               | 37 ----------------------------
 11 files changed, 37 insertions(+), 44 deletions(-)
 rename PixArt/{model => models}/LICENSE (100%)
 rename PixArt/{model => models}/__init__.py (100%)
 rename PixArt/{model => models}/blocks.py (100%)
 rename PixArt/{model => models}/pixart.py (100%)
 rename PixArt/{model => models}/pixartms.py (100%)
 rename PixArt/{model => models}/utils.py (100%)
 create mode 100644 Sana/models/__init__.py
 delete mode 100644 text_encoders/nodes.py

diff --git a/PixArt/loader.py b/PixArt/loader.py
index bc0ca00..4fcee7c 100644
--- a/PixArt/loader.py
+++ b/PixArt/loader.py
@@ -9,8 +9,8 @@
 import comfy.supported_models
 import comfy.latent_formats
 
-from .model.pixart import PixArt
-from .model.pixartms import PixArtMS
+from .models.pixart import PixArt
+from .models.pixartms import PixArtMS
 from .diffusers_convert import convert_state_dict
 from ..utils.loader import load_state_dict_from_config
 from ..text_encoders.pixart.tenc import PixArtTokenizer, PixArtT5XXL
diff --git a/PixArt/model/LICENSE b/PixArt/models/LICENSE
similarity index 100%
rename from PixArt/model/LICENSE
rename to PixArt/models/LICENSE
diff --git a/PixArt/model/__init__.py b/PixArt/models/__init__.py
similarity index 100%
rename from PixArt/model/__init__.py
rename to PixArt/models/__init__.py
diff --git a/PixArt/model/blocks.py b/PixArt/models/blocks.py
similarity index 100%
rename from PixArt/model/blocks.py
rename to PixArt/models/blocks.py
diff --git a/PixArt/model/pixart.py b/PixArt/models/pixart.py
similarity index 100%
rename from PixArt/model/pixart.py
rename to PixArt/models/pixart.py
diff --git a/PixArt/model/pixartms.py b/PixArt/models/pixartms.py
similarity index 100%
rename from PixArt/model/pixartms.py
rename to PixArt/models/pixartms.py
diff --git a/PixArt/model/utils.py b/PixArt/models/utils.py
similarity index 100%
rename from PixArt/model/utils.py
rename to PixArt/models/utils.py
diff --git a/Sana/models/__init__.py b/Sana/models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/__init__.py b/__init__.py
index eeb3dc1..2f9bee0 100644
--- a/__init__.py
+++ b/__init__.py
@@ -6,11 +6,7 @@
 else:
     NODE_CLASS_MAPPINGS = {}
 
-    # All text encoders
-    from .text_encoders.nodes import NODE_CLASS_MAPPINGS as Tenc_Nodes
-    NODE_CLASS_MAPPINGS.update(Tenc_Nodes)
-
-    # Generic nodes
+    # Generic/universal nodes
     from .nodes import NODE_CLASS_MAPPINGS as Base_Nodes
     NODE_CLASS_MAPPINGS.update(Base_Nodes)
 
diff --git a/nodes.py b/nodes.py
index 31d7f53..c7ba2f5 100644
--- a/nodes.py
+++ b/nodes.py
@@ -3,6 +3,7 @@
 
 from .PixArt.loader import load_pixart_state_dict
 from .Sana.loader import load_sana_state_dict
+from .text_encoders.tenc import load_text_encoder, tenc_names
 
 loaders = {
     "PixArt": load_pixart_state_dict,
@@ -31,6 +32,39 @@ def load_unet(self, unet_name, model_type):
         sd = comfy.utils.load_torch_file(unet_path)
         return (loader_fn(sd),)
 
+class EXMCLIPLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        files = []
+        files += folder_paths.get_filename_list("clip")
+        # if "clip_gguf" in folder_paths.folder_names_and_paths:
+        #     files += folder_paths.get_filename_list("clip_gguf")
+        return {
+            "required": {
+                "clip_name": (files, ),
+                "type": (["PixArt", "MiaoBi", "Sana"],),
+            }
+        }
+    
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "load_clip"
+    CATEGORY = "ExtraModels"
+    TITLE = "CLIPLoader (ExtraModels)"
+
+    def load_clip(self, clip_name, type):
+        clip_path = folder_paths.get_full_path("clip", clip_name)
+        clip_type = tenc_names.get(type, None)
+
+        clip = load_text_encoder(
+            ckpt_paths =[clip_path],
+            embedding_directory = folder_paths.get_folder_paths("embeddings"),
+            clip_type = clip_type
+        )
+        return (clip,)
+
+#class EXMResolutionSelect:
+
 NODE_CLASS_MAPPINGS = {
     "EXMUnetLoader": EXMUnetLoader,
+    "EXMCLIPLoader": EXMCLIPLoader,
 }
diff --git a/text_encoders/nodes.py b/text_encoders/nodes.py
deleted file mode 100644
index 5bda333..0000000
--- a/text_encoders/nodes.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import folder_paths
-
-from .tenc import load_text_encoder, tenc_names
-
-class EXMCLIPLoader:
-    @classmethod
-    def INPUT_TYPES(s):
-        files = []
-        files += folder_paths.get_filename_list("clip")
-        # if "clip_gguf" in folder_paths.folder_names_and_paths:
-        #     files += folder_paths.get_filename_list("clip_gguf")
-        return {
-            "required": {
-                "clip_name": (files, ),
-                "type": (["PixArt", "MiaoBi", "Sana"],),
-            }
-        }
-    
-    RETURN_TYPES = ("CLIP",)
-    FUNCTION = "load_clip"
-    CATEGORY = "ExtraModels"
-    TITLE = "CLIPLoader (ExtraModels)"
-
-    def load_clip(self, clip_name, type):
-        clip_path = folder_paths.get_full_path("clip", clip_name)
-        clip_type = tenc_names.get(type, None)
-
-        clip = load_text_encoder(
-            ckpt_paths =[clip_path],
-            embedding_directory = folder_paths.get_folder_paths("embeddings"),
-            clip_type = clip_type
-        )
-        return (clip,)
-
-NODE_CLASS_MAPPINGS = {
-    "EXMCLIPLoader": EXMCLIPLoader,
-}

From 0f567fc4a751635f5bce2628709be093dc1f9896 Mon Sep 17 00:00:00 2001
From: City <125218114+city96@users.noreply.github.com>
Date: Fri, 13 Dec 2024 01:04:31 +0100
Subject: [PATCH 15/15] Gemma first implementation

---
 Sana/models/sana_multi_scale.py               |    2 +
 text_encoders/pixart/tenc.py                  |    6 +-
 text_encoders/sana/config.json                |   35 +
 text_encoders/sana/gemma.py                   |  379 +++++
 text_encoders/sana/tenc.py                    |   46 +
 text_encoders/tenc.py                         |    9 +
 .../gemma_tokenizer/special_tokens_map.json   |   34 +
 .../gemma_tokenizer/tokenizer.model           |  Bin 0 -> 4241003 bytes
 .../gemma_tokenizer/tokenizer_config.json     | 1516 +++++++++++++++++
 9 files changed, 2025 insertions(+), 2 deletions(-)
 create mode 100644 text_encoders/sana/config.json
 create mode 100644 text_encoders/sana/gemma.py
 create mode 100644 text_encoders/sana/tenc.py
 create mode 100644 text_encoders/tokenizers/gemma_tokenizer/special_tokens_map.json
 create mode 100644 text_encoders/tokenizers/gemma_tokenizer/tokenizer.model
 create mode 100644 text_encoders/tokenizers/gemma_tokenizer/tokenizer_config.json

diff --git a/Sana/models/sana_multi_scale.py b/Sana/models/sana_multi_scale.py
index a00e6f5..bd5bab8 100644
--- a/Sana/models/sana_multi_scale.py
+++ b/Sana/models/sana_multi_scale.py
@@ -301,6 +301,8 @@ def forward(self, x, timestep, context, mask=None, data_info=None, **kwargs):
         """
         bs = x.shape[0]
         y = context
+        if len(y.shape) == 3:
+            y = y.unsqueeze(1)
         self.h, self.w = x.shape[-2] // self.patch_size, x.shape[-1] // self.patch_size
         if self.use_pe:
             x = self.x_embedder(x)
diff --git a/text_encoders/pixart/tenc.py b/text_encoders/pixart/tenc.py
index 456cd92..04ab372 100644
--- a/text_encoders/pixart/tenc.py
+++ b/text_encoders/pixart/tenc.py
@@ -1,10 +1,12 @@
+import os
+import torch
+
 from comfy import sd1_clip
 import comfy.text_encoders.t5
 import comfy.text_encoders.sd3_clip
 import comfy.model_management
+
 from transformers import T5TokenizerFast
-import torch
-import os
 
 class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel):
     def __init__(self, **kwargs):
diff --git a/text_encoders/sana/config.json b/text_encoders/sana/config.json
new file mode 100644
index 0000000..05131f6
--- /dev/null
+++ b/text_encoders/sana/config.json
@@ -0,0 +1,35 @@
+{
+  "architectures": [
+    "Gemma2ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": 50.0,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "eos_token_id": [
+    1,
+    107
+  ],
+  "final_logit_softcapping": 30.0,
+  "head_dim": 256,
+  "hidden_act": "gelu_pytorch_tanh",
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 2304,
+  "initializer_range": 0.02,
+  "intermediate_size": 9216,
+  "max_position_embeddings": 8192,
+  "model_type": "gemma2",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 26,
+  "num_key_value_heads": 4,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.42.4",
+  "use_cache": true,
+  "vocab_size": 256000
+}
diff --git a/text_encoders/sana/gemma.py b/text_encoders/sana/gemma.py
new file mode 100644
index 0000000..3b6679c
--- /dev/null
+++ b/text_encoders/sana/gemma.py
@@ -0,0 +1,379 @@
+# Copyright 2024 Google Inc. HuggingFace Inc. team. All rights reserved.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import torch
+import torch.nn as nn
+import importlib
+
+def rotate_half(x):
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+def repeat_kv(hidden_states, n_rep):
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+def sdpa_attention_forward(config, query, key, value, mask=None, **kwargs):
+    key = repeat_kv(key, config["num_key_value_groups"])
+    value = repeat_kv(value, config["num_key_value_groups"])
+
+    causal_mask = mask
+    if mask is not None:
+        causal_mask = causal_mask[:, :, :, : key.shape[-2]]
+
+    # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+    # Reference: https://github.com/pytorch/pytorch/issues/112577.
+    if query.device.type == "cuda" and causal_mask is not None:
+        query = query.contiguous()
+        key = key.contiguous()
+        value = value.contiguous()
+
+    # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+    # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+    is_causal = True if causal_mask is None and query.shape[1] > 1 else False
+
+    attn_output = torch.nn.functional.scaled_dot_product_attention(
+        query,
+        key,
+        value,
+        attn_mask=causal_mask,
+        dropout_p=0.0,
+        is_causal=is_causal,
+        scale=config["scaling"],
+    )
+    attn_output = attn_output.transpose(1, 2).contiguous()
+    return attn_output, None
+
+def eager_attention_forward(config, query, key, value, mask, **kwargs):
+    key_states = repeat_kv(key, config["num_key_value_groups"])
+    value_states = repeat_kv(value, config["num_key_value_groups"])
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * config["scaling"]
+
+    if config["attn_logit_softcapping"] is not None:
+        attn_weights = attn_weights / config["attn_logit_softcapping"]
+        attn_weights = torch.tanh(attn_weights)
+        attn_weights = attn_weights * config["attn_logit_softcapping"]
+    if mask is not None:  # no matter the length, we just slice it
+        causal_mask = mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+
+    # upcast attention to fp32
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    #attn_weights = nn.functional.dropout(attn_weights, p=0, training=config.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+    return attn_output, attn_weights
+
+# torch 2.0 can't pass scale arg to sdpa
+if int((torch.__version__).split(".")[1]) >= 1:
+    attention_forward = sdpa_attention_forward
+else:
+    attention_forward = eager_attention_forward
+
+class Gemma2RMSNorm(nn.Module):
+    def __init__(self, dim, eps=1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.zeros(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        output = self._norm(x.float())
+        # Llama does x.to(float16) * w whilst Gemma2 is (x * w).to(float16)
+        # See https://github.com/huggingface/transformers/pull/29402
+        output = output * (1.0 + self.weight.float())
+        return output.type_as(x)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.eps}"
+
+class Gemma2MLP(nn.Module):
+    def __init__(self, config, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config["hidden_size"]
+        self.intermediate_size = config["intermediate_size"]
+        self.gate_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=False, dtype=dtype, device=device)
+        self.up_proj = operations.Linear(self.hidden_size, self.intermediate_size, bias=False, dtype=dtype, device=device)
+        self.down_proj = operations.Linear(self.intermediate_size, self.hidden_size, bias=False, dtype=dtype, device=device)
+        if config["hidden_activation"] != "gelu_pytorch_tanh":
+            raise NotImplementedError("Unknown act mode")
+        self.act_fn = torch.nn.GELU()
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+class Gemma2RotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float() / self.dim))
+        self.register_buffer("inv_freq", tensor=inv_freq, persistent=False)
+
+    @torch.no_grad()
+    def forward(self, x, position_ids, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        self.inv_freq.to(x.device)
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        # Force float32 since bfloat16 loses precision on long contexts
+        # See https://github.com/huggingface/transformers/pull/29285
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+class Gemma2Attention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config, layer_idx=None, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+
+        self.attention_dropout = config["attention_dropout"]
+        self.hidden_size = config["hidden_size"]
+        self.num_heads = config["num_attention_heads"]
+        self.head_dim = config["head_dim"]
+        self.num_key_value_heads = config["num_key_value_heads"]
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config["max_position_embeddings"]
+        self.rope_theta = config["rope_theta"]
+        self.is_causal = True
+        self.scaling = config["query_pre_attn_scalar"]**-0.5
+        self.sliding_window = config["sliding_window"] if not bool(layer_idx % 2) else None
+        self.attn_logit_softcapping = config["attn_logit_softcapping"]
+        if self.hidden_size % self.num_heads != 0:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        self.q_proj = operations.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config["attention_bias"], dtype=dtype, device=device)
+        self.k_proj = operations.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config["attention_bias"], dtype=dtype, device=device)
+        self.v_proj = operations.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config["attention_bias"], dtype=dtype, device=device)
+        self.o_proj = operations.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config["attention_bias"], dtype=dtype, device=device)
+        self.rotary_emb = Gemma2RotaryEmbedding(
+            self.head_dim,
+            max_position_embeddings=self.max_position_embeddings,
+            base=self.rope_theta,
+        )
+
+    def forward(self, hidden_states, attention_mask=None, position_ids=None, past_key_value=None, output_attentions=False, use_cache=False, cache_position= None):
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {
+                "sin": sin,
+                "cos": cos,
+                "sliding_window": self.sliding_window,
+                "cache_position": cache_position,
+            }
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        config = {
+            "scaling": self.scaling,
+            "num_key_value_groups": self.num_key_value_groups,
+            "max_position_embeddings": self.max_position_embeddings,
+            "attn_logit_softcapping": self.attn_logit_softcapping,
+        }
+        attn_output, attn_weights = attention_forward(config, query_states, key_states, value_states, attention_mask, output_attentions=output_attentions)
+
+        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+class Gemma2DecoderLayer(nn.Module):
+    def __init__(self, config, layer_idx, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.hidden_size = config["hidden_size"]
+        self.config = config
+        self.is_sliding = not bool(layer_idx % 2)
+
+        self.self_attn = Gemma2Attention(config=config, layer_idx=layer_idx, dtype=dtype, device=device, operations=operations)
+        self.mlp = Gemma2MLP(config, dtype=dtype, device=device, operations=operations)
+
+        self.input_layernorm = Gemma2RMSNorm(config["hidden_size"], eps=config["rms_norm_eps"])
+        self.post_attention_layernorm = Gemma2RMSNorm(config["hidden_size"], eps=config["rms_norm_eps"])
+
+        self.pre_feedforward_layernorm = Gemma2RMSNorm(config["hidden_size"], eps=config["rms_norm_eps"])
+        self.post_feedforward_layernorm = Gemma2RMSNorm(config["hidden_size"], eps=config["rms_norm_eps"])
+        self.sliding_window = config["sliding_window"]
+
+    def forward(self, hidden_states, attention_mask=None, position_ids=None, past_key_value=None, output_attentions=False, use_cache=False, cache_position=None):
+        if self.is_sliding and attention_mask is not None:  # efficient SDPA and no padding
+            # # Flash-attn is a 2D tensor
+            # if self.config["_attn_implementation == "flash_attention_2":
+            #     if past_key_value is not None:  # when decoding
+            #         attention_mask = attention_mask[:, -self.sliding_window :]
+            # else:
+                min_dtype = torch.finfo(hidden_states.dtype).min
+                sliding_window_mask = torch.tril(
+                    torch.ones_like(attention_mask, dtype=torch.bool), diagonal=-self.sliding_window
+                )
+                attention_mask = torch.where(sliding_window_mask, min_dtype, attention_mask)
+                if attention_mask.shape[-1] <= 1:  # when decoding
+                    attention_mask = attention_mask[:, :, :, -self.sliding_window :]
+
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+        )
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.pre_feedforward_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = self.post_feedforward_layernorm(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+def prepare_causal_mask(input_tensor, attention_mask):
+    dtype, device = input_tensor.dtype, input_tensor.device
+    batch_size=input_tensor.shape[0]
+    sequence_length = input_tensor.shape[1]
+    target_length = attention_mask.shape[-1] if attention_mask is not None else input_tensor.shape[1]
+
+    min_dtype = torch.finfo(dtype).min
+    causal_mask = torch.full(
+        (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
+    )
+    if sequence_length != 1:
+        causal_mask = torch.triu(causal_mask, diagonal=1)
+    #causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
+    causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+    if attention_mask is not None:
+        causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
+        mask_length = attention_mask.shape[-1]
+        padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+        padding_mask = padding_mask == 0
+        causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+            padding_mask, min_dtype
+        )
+
+    return causal_mask
+
+class Gemma2Model(torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        self.padding_idx = 0
+        self.hidden_size = config_dict["hidden_size"]
+        self.embed_tokens = operations.Embedding(config_dict["vocab_size"], self.hidden_size, self.padding_idx, device=device, dtype=dtype)
+        self.num_layers = config_dict["num_hidden_layers"]
+        self.layers = nn.ModuleList(
+            [Gemma2DecoderLayer(config_dict, layer_idx, dtype=dtype, device=device, operations=operations) for layer_idx in range(config_dict["num_hidden_layers"])]
+        )
+        self.norm = Gemma2RMSNorm(self.hidden_size, eps=config_dict["rms_norm_eps"])
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+    
+    def forward(self, input_ids=None, attention_mask=None, position_ids=None, intermediate_output=None, final_layer_norm_intermediate=False, *args, **kwargs):
+        inputs_embeds = self.embed_tokens(input_ids, out_dtype=kwargs.get("dtype", torch.float32))
+        hidden_states = inputs_embeds
+        intermediate = None
+
+        if attention_mask is not None and position_ids is None:
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+
+        # normalized
+        # Gemma2 downcasts the below to float16, causing sqrt(3072)=55.4256 to become 55.5
+        # See https://github.com/huggingface/transformers/pull/29402
+        normalizer = torch.tensor(self.hidden_size**0.5, dtype=hidden_states.dtype)
+        hidden_states = hidden_states * normalizer
+
+        causal_mask = prepare_causal_mask(inputs_embeds, attention_mask)
+
+        if intermediate_output is not None:
+            if intermediate_output < 0:
+                intermediate_output = len(self.layers) + intermediate_output
+
+        for i, decoder_layer in enumerate(self.layers):
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask,
+                position_ids=position_ids,
+                past_key_value=None,
+                output_attentions=False,
+                use_cache=False,
+            )
+            if i == intermediate_output:
+                intermediate = hidden_states.clone()
+            hidden_states = layer_outputs[0]
+
+        hidden_states = self.norm(hidden_states)
+        if intermediate is not None and final_layer_norm_intermediate:
+            intermediate = self.norm(intermediate)
+
+        return hidden_states, intermediate
diff --git a/text_encoders/sana/tenc.py b/text_encoders/sana/tenc.py
new file mode 100644
index 0000000..45da847
--- /dev/null
+++ b/text_encoders/sana/tenc.py
@@ -0,0 +1,46 @@
+import os
+import torch
+
+from comfy import sd1_clip
+import comfy.model_management
+from .gemma import Gemma2Model
+
+from transformers import GemmaTokenizer as TFGemmaTokenizer
+
+class GemmaClipModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "config.json")
+        special_tokens = {"start": 2, "end": 1, "pad": 0}
+        super().__init__(device=device, layer="last", layer_idx=None, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens=special_tokens, model_class=Gemma2Model, enable_attention_masks=True, return_attention_masks=False, model_options=model_options)
+
+class SanaClipModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, name="gemma", clip_model=GemmaClipModel, model_options=model_options)
+
+class GemmaTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer_path = os.path.join(
+            os.path.dirname(os.path.dirname(os.path.realpath(__file__))),
+            "tokenizers", "gemma_tokenizer",
+        )
+        # TODO: reenable proper logic here - needs comfy version 44db978 or higher
+        super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=2304, embedding_key='gemma', tokenizer_class=TFGemmaTokenizer, has_start_token=False, pad_to_max_length=True, max_length=300, min_length=1)
+        self.start_token = 2
+        self.end_token = 1
+        self.pad_token = 0
+
+    def tokenize_with_weights(self, text, return_word_ids=False):
+        # TODO: see above, this is still just a wrapper for now
+        tokens = self.tokenizer(
+            text,
+            max_length=300,
+            padding="max_length", 
+            truncation=True,
+            return_tensors="pt"
+        )
+        batched_tokens = [(x.item(), 1.0) for x in tokens.input_ids[0]]
+        return [batched_tokens]
+
+class SanaTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="gemma", tokenizer=GemmaTokenizer)
diff --git a/text_encoders/tenc.py b/text_encoders/tenc.py
index 239d97d..8ec8acc 100644
--- a/text_encoders/tenc.py
+++ b/text_encoders/tenc.py
@@ -4,8 +4,10 @@
 import comfy.sd
 import comfy.utils
 import comfy.text_encoders
+import comfy.model_management
 
 from .pixart.tenc import pixart_te, PixArtTokenizer
+from .sana.tenc import SanaClipModel, SanaTokenizer
 
 class TencType(Enum):
     # offset in case we ever integrate w/ original
@@ -44,6 +46,8 @@ class EmptyClass:
     for i in range(len(clip_data)):
         if "transformer.resblocks.0.ln_1.weight" in clip_data[i]:
             clip_data[i] = comfy.utils.clip_text_transformers_convert(clip_data[i], "", "")
+        elif "model.layers.25.post_feedforward_layernorm.weight" in clip_data[i]:
+            clip_data[i] = {k[len("model."):]:v for k,v in clip_data[i].items()}
         else:
             if "text_projection" in clip_data[i]:
                 clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node
@@ -54,6 +58,11 @@ class EmptyClass:
     if clip_type == TencType.PixArt:
         clip_target.clip = pixart_te(**comfy.sd.t5xxl_detect(clip_data))
         clip_target.tokenizer = PixArtTokenizer
+    elif clip_type == TencType.Sana:
+        clip_target.clip = SanaClipModel
+        clip_target.tokenizer = SanaTokenizer
+    else:
+        raise NotImplementedError(f"Unknown tenc: {clip_type}")
 
     parameters = 0
     tokenizer_data = {}
diff --git a/text_encoders/tokenizers/gemma_tokenizer/special_tokens_map.json b/text_encoders/tokenizers/gemma_tokenizer/special_tokens_map.json
new file mode 100644
index 0000000..8d6368f
--- /dev/null
+++ b/text_encoders/tokenizers/gemma_tokenizer/special_tokens_map.json
@@ -0,0 +1,34 @@
+{
+  "additional_special_tokens": [
+    "<start_of_turn>",
+    "<end_of_turn>"
+  ],
+  "bos_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<eos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/text_encoders/tokenizers/gemma_tokenizer/tokenizer.model b/text_encoders/tokenizers/gemma_tokenizer/tokenizer.model
new file mode 100644
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HcmV?d00001

diff --git a/text_encoders/tokenizers/gemma_tokenizer/tokenizer_config.json b/text_encoders/tokenizers/gemma_tokenizer/tokenizer_config.json
new file mode 100644
index 0000000..f23acea
--- /dev/null
+++ b/text_encoders/tokenizers/gemma_tokenizer/tokenizer_config.json
@@ -0,0 +1,1516 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<eos>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<bos>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<mask>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "5": {
+      "content": "<2mass>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "6": {
+      "content": "[@BOS@]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "7": {
+      "content": "<unused0>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "8": {
+      "content": "<unused1>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "9": {
+      "content": "<unused2>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "10": {
+      "content": "<unused3>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "11": {
+      "content": "<unused4>",
+      "lstrip": false,
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