add conversion script

Author: a-r-r-o-w

scripts/convert_wan_to_diffusers.py

@@ -1,6 +1,6 @@
 import argparse
 import pathlib
-from typing import Any, Dict
+from typing import Any, Dict, Tuple
 
 import torch
 from accelerate import init_empty_weights
@@ -14,6 +14,8 @@
     WanImageToVideoPipeline,
     WanPipeline,
     WanTransformer3DModel,
+    WanVACEPipeline,
+    WanVACETransformer3DModel,
 )
 
 
@@ -59,7 +61,52 @@
     "attn2.norm_k_img": "attn2.norm_added_k",
 }
 
+VACE_TRANSFORMER_KEYS_RENAME_DICT = {
+    "time_embedding.0": "condition_embedder.time_embedder.linear_1",
+    "time_embedding.2": "condition_embedder.time_embedder.linear_2",
+    "text_embedding.0": "condition_embedder.text_embedder.linear_1",
+    "text_embedding.2": "condition_embedder.text_embedder.linear_2",
+    "time_projection.1": "condition_embedder.time_proj",
+    "head.modulation": "scale_shift_table",
+    "head.head": "proj_out",
+    "modulation": "scale_shift_table",
+    "ffn.0": "ffn.net.0.proj",
+    "ffn.2": "ffn.net.2",
+    # Hack to swap the layer names
+    # The original model calls the norms in following order: norm1, norm3, norm2
+    # We convert it to: norm1, norm2, norm3
+    "norm2": "norm__placeholder",
+    "norm3": "norm2",
+    "norm__placeholder": "norm3",
+    # # For the I2V model
+    # "img_emb.proj.0": "condition_embedder.image_embedder.norm1",
+    # "img_emb.proj.1": "condition_embedder.image_embedder.ff.net.0.proj",
+    # "img_emb.proj.3": "condition_embedder.image_embedder.ff.net.2",
+    # "img_emb.proj.4": "condition_embedder.image_embedder.norm2",
+    # # for the FLF2V model
+    # "img_emb.emb_pos": "condition_embedder.image_embedder.pos_embed",
+    # Add attention component mappings
+    "self_attn.q": "attn1.to_q",
+    "self_attn.k": "attn1.to_k",
+    "self_attn.v": "attn1.to_v",
+    "self_attn.o": "attn1.to_out.0",
+    "self_attn.norm_q": "attn1.norm_q",
+    "self_attn.norm_k": "attn1.norm_k",
+    "cross_attn.q": "attn2.to_q",
+    "cross_attn.k": "attn2.to_k",
+    "cross_attn.v": "attn2.to_v",
+    "cross_attn.o": "attn2.to_out.0",
+    "cross_attn.norm_q": "attn2.norm_q",
+    "cross_attn.norm_k": "attn2.norm_k",
+    "attn2.to_k_img": "attn2.add_k_proj",
+    "attn2.to_v_img": "attn2.add_v_proj",
+    "attn2.norm_k_img": "attn2.norm_added_k",
+    "before_proj": "proj_in",
+    "after_proj": "proj_out",
+}
+
 TRANSFORMER_SPECIAL_KEYS_REMAP = {}
+VACE_TRANSFORMER_SPECIAL_KEYS_REMAP = {}
 
 
 def update_state_dict_(state_dict: Dict[str, Any], old_key: str, new_key: str) -> Dict[str, Any]:
@@ -74,7 +121,7 @@ def load_sharded_safetensors(dir: pathlib.Path):
     return state_dict
 
 
-def get_transformer_config(model_type: str) -> Dict[str, Any]:
+def get_transformer_config(model_type: str) -> Tuple[Dict[str, Any], ...]:
     if model_type == "Wan-T2V-1.3B":
         config = {
             "model_id": "StevenZhang/Wan2.1-T2V-1.3B-Diff",
@@ -94,6 +141,8 @@ def get_transformer_config(model_type: str) -> Dict[str, Any]:
                 "text_dim": 4096,
             },
         }
+        RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT
+        SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP
     elif model_type == "Wan-T2V-14B":
         config = {
             "model_id": "StevenZhang/Wan2.1-T2V-14B-Diff",
@@ -113,6 +162,8 @@ def get_transformer_config(model_type: str) -> Dict[str, Any]:
                 "text_dim": 4096,
             },
         }
+        RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT
+        SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP
     elif model_type == "Wan-I2V-14B-480p":
         config = {
             "model_id": "StevenZhang/Wan2.1-I2V-14B-480P-Diff",
@@ -133,6 +184,8 @@ def get_transformer_config(model_type: str) -> Dict[str, Any]:
                 "text_dim": 4096,
             },
         }
+        RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT
+        SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP
     elif model_type == "Wan-I2V-14B-720p":
         config = {
             "model_id": "StevenZhang/Wan2.1-I2V-14B-720P-Diff",
@@ -153,6 +206,8 @@ def get_transformer_config(model_type: str) -> Dict[str, Any]:
                 "text_dim": 4096,
             },
         }
+        RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT
+        SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP
     elif model_type == "Wan-FLF2V-14B-720P":
         config = {
             "model_id": "ypyp/Wan2.1-FLF2V-14B-720P",  # This is just a placeholder
@@ -175,28 +230,80 @@ def get_transformer_config(model_type: str) -> Dict[str, Any]:
                 "pos_embed_seq_len": 257 * 2,
             },
         }
-    return config
+        RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT
+        SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP
+    elif model_type == "Wan-VACE-1.3B":
+        config = {
+            "model_id": "Wan-AI/Wan2.1-VACE-1.3B",
+            "diffusers_config": {
+                "added_kv_proj_dim": None,
+                "attention_head_dim": 128,
+                "cross_attn_norm": True,
+                "eps": 1e-06,
+                "ffn_dim": 8960,
+                "freq_dim": 256,
+                "in_channels": 16,
+                "num_attention_heads": 12,
+                "num_layers": 30,
+                "out_channels": 16,
+                "patch_size": [1, 2, 2],
+                "qk_norm": "rms_norm_across_heads",
+                "text_dim": 4096,
+                "vace_layers": [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28],
+                "vace_in_channels": 96,
+            },
+        }
+        RENAME_DICT = VACE_TRANSFORMER_KEYS_RENAME_DICT
+        SPECIAL_KEYS_REMAP = VACE_TRANSFORMER_SPECIAL_KEYS_REMAP
+    elif model_type == "Wan-VACE-14B":
+        config = {
+            "model_id": "Wan-AI/Wan2.1-VACE-14B",
+            "diffusers_config": {
+                "added_kv_proj_dim": None,
+                "attention_head_dim": 128,
+                "cross_attn_norm": True,
+                "eps": 1e-06,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "in_channels": 16,
+                "num_attention_heads": 40,
+                "num_layers": 40,
+                "out_channels": 16,
+                "patch_size": [1, 2, 2],
+                "qk_norm": "rms_norm_across_heads",
+                "text_dim": 4096,
+                "vace_layers": [0, 5, 10, 15, 20, 25, 30, 35],
+                "vace_in_channels": 96,
+            },
+        }
+        RENAME_DICT = VACE_TRANSFORMER_KEYS_RENAME_DICT
+        SPECIAL_KEYS_REMAP = VACE_TRANSFORMER_SPECIAL_KEYS_REMAP
+    return config, RENAME_DICT, SPECIAL_KEYS_REMAP
 
 
 def convert_transformer(model_type: str):
-    config = get_transformer_config(model_type)
+    config, RENAME_DICT, SPECIAL_KEYS_REMAP = get_transformer_config(model_type)
+
     diffusers_config = config["diffusers_config"]
     model_id = config["model_id"]
     model_dir = pathlib.Path(snapshot_download(model_id, repo_type="model"))
 
     original_state_dict = load_sharded_safetensors(model_dir)
 
     with init_empty_weights():
-        transformer = WanTransformer3DModel.from_config(diffusers_config)
+        if "VACE" not in model_type:
+            transformer = WanTransformer3DModel.from_config(diffusers_config)
+        else:
+            transformer = WanVACETransformer3DModel.from_config(diffusers_config)
 
     for key in list(original_state_dict.keys()):
         new_key = key[:]
-        for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items():
+        for replace_key, rename_key in RENAME_DICT.items():
             new_key = new_key.replace(replace_key, rename_key)
         update_state_dict_(original_state_dict, key, new_key)
 
     for key in list(original_state_dict.keys()):
-        for special_key, handler_fn_inplace in TRANSFORMER_SPECIAL_KEYS_REMAP.items():
+        for special_key, handler_fn_inplace in SPECIAL_KEYS_REMAP.items():
             if special_key not in key:
                 continue
             handler_fn_inplace(key, original_state_dict)
@@ -412,7 +519,7 @@ def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument("--model_type", type=str, default=None)
     parser.add_argument("--output_path", type=str, required=True)
-    parser.add_argument("--dtype", default="fp32")
+    parser.add_argument("--dtype", default="fp32", choices=["fp32", "fp16", "bf16", "none"])
     return parser.parse_args()
 
 
@@ -426,18 +533,20 @@ def get_args():
 if __name__ == "__main__":
     args = get_args()
 
-    transformer = None
-    dtype = DTYPE_MAPPING[args.dtype]
-
-    transformer = convert_transformer(args.model_type).to(dtype=dtype)
+    transformer = convert_transformer(args.model_type)
     vae = convert_vae()
-    text_encoder = UMT5EncoderModel.from_pretrained("google/umt5-xxl")
+    text_encoder = UMT5EncoderModel.from_pretrained("google/umt5-xxl", torch_dtype=torch.bfloat16)
     tokenizer = AutoTokenizer.from_pretrained("google/umt5-xxl")
     flow_shift = 16.0 if "FLF2V" in args.model_type else 3.0
     scheduler = UniPCMultistepScheduler(
         prediction_type="flow_prediction", use_flow_sigmas=True, num_train_timesteps=1000, flow_shift=flow_shift
     )
 
+    # If user has specified "none", we keep the original dtypes of the state dict without any conversion
+    if args.dtype != "none":
+        dtype = DTYPE_MAPPING[args.dtype]
+        transformer.to(dtype)
+
     if "I2V" in args.model_type or "FLF2V" in args.model_type:
         image_encoder = CLIPVisionModelWithProjection.from_pretrained(
             "laion/CLIP-ViT-H-14-laion2B-s32B-b79K", torch_dtype=torch.bfloat16
@@ -452,6 +561,14 @@ def get_args():
             image_encoder=image_encoder,
             image_processor=image_processor,
         )
+    elif "VACE" in args.model_type:
+        pipe = WanVACEPipeline(
+            transformer=transformer,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            vae=vae,
+            scheduler=scheduler,
+        )
     else:
         pipe = WanPipeline(
             transformer=transformer,