Revert "Wan refactor"

diffsynth/models/utils.py

@@ -62,16 +62,16 @@ def load_state_dict_from_folder(file_path, torch_dtype=None):
     return state_dict
 
 
-def load_state_dict(file_path, torch_dtype=None, device="cpu"):
+def load_state_dict(file_path, torch_dtype=None):
     if file_path.endswith(".safetensors"):
-        return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype, device=device)
+        return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype)
     else:
-        return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype, device=device)
+        return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype)
 
 
-def load_state_dict_from_safetensors(file_path, torch_dtype=None, device="cpu"):
+def load_state_dict_from_safetensors(file_path, torch_dtype=None):
     state_dict = {}
-    with safe_open(file_path, framework="pt", device=device) as f:
+    with safe_open(file_path, framework="pt", device="cpu") as f:
         for k in f.keys():
             state_dict[k] = f.get_tensor(k)
             if torch_dtype is not None:
@@ -79,8 +79,8 @@ def load_state_dict_from_safetensors(file_path, torch_dtype=None, device="cpu"):
     return state_dict
 
 
-def load_state_dict_from_bin(file_path, torch_dtype=None, device="cpu"):
-    state_dict = torch.load(file_path, map_location=device, weights_only=True)
+def load_state_dict_from_bin(file_path, torch_dtype=None):
+    state_dict = torch.load(file_path, map_location="cpu", weights_only=True)
     if torch_dtype is not None:
         for i in state_dict:
             if isinstance(state_dict[i], torch.Tensor):

diffsynth/models/wan_video_dit.py

@@ -5,10 +5,6 @@ import math
 from typing import Tuple, Optional
 from einops import rearrange
 from .utils import hash_state_dict_keys
-
-from dchen.camera_adapter import SimpleAdapter
-
-
 try:
     import flash_attn_interface
     FLASH_ATTN_3_AVAILABLE = True
@@ -276,9 +272,6 @@ class WanModel(torch.nn.Module):
         num_layers: int,
         has_image_input: bool,
         has_image_pos_emb: bool = False,
-        has_ref_conv: bool = False,
-        add_control_adapter: bool = False,
-        in_dim_control_adapter: int = 24,
     ):
         super().__init__()
         self.dim = dim
@@ -310,22 +303,10 @@ class WanModel(torch.nn.Module):
 
         if has_image_input:
             self.img_emb = MLP(1280, dim, has_pos_emb=has_image_pos_emb)  # clip_feature_dim = 1280
-        if has_ref_conv:
-            self.ref_conv = nn.Conv2d(16, dim, kernel_size=(2, 2), stride=(2, 2))
         self.has_image_pos_emb = has_image_pos_emb
-        self.has_ref_conv = has_ref_conv
 
-        if add_control_adapter:
-            self.control_adapter = SimpleAdapter(in_dim_control_adapter, dim, kernel_size=patch_size[1:], stride=patch_size[1:])
-        else:
-            self.control_adapter = None
-
-    def patchify(self, x: torch.Tensor, control_camera_latents_input: torch.Tensor = None):
+    def patchify(self, x: torch.Tensor):
         x = self.patch_embedding(x)
-        if self.control_adapter is not None and control_camera_latents_input is not None:
-            y_camera = self.control_adapter(control_camera_latents_input)
-            x = [u + v for u, v in zip(x, y_camera)]
-            x = x[0].unsqueeze(0)
         grid_size = x.shape[2:]
         x = rearrange(x, 'b c f h w -> b (f h w) c').contiguous()
         return x, grid_size  # x, grid_size: (f, h, w)
@@ -551,7 +532,6 @@ class WanModelStateDictConverter:
                 "eps": 1e-6
             }
         elif hash_state_dict_keys(state_dict) == "349723183fc063b2bfc10bb2835cf677":
-            # 1.3B PAI control
             config = {
                 "has_image_input": True,
                 "patch_size": [1, 2, 2],
@@ -566,7 +546,6 @@ class WanModelStateDictConverter:
                 "eps": 1e-6
             }
         elif hash_state_dict_keys(state_dict) == "efa44cddf936c70abd0ea28b6cbe946c":
-            # 14B PAI control
             config = {
                 "has_image_input": True,
                 "patch_size": [1, 2, 2],
@@ -595,74 +574,6 @@ class WanModelStateDictConverter:
                 "eps": 1e-6,
                 "has_image_pos_emb": True
             }
-        elif hash_state_dict_keys(state_dict) == "70ddad9d3a133785da5ea371aae09504":
-            # 1.3B PAI control v1.1
-            config = {
-                "has_image_input": True,
-                "patch_size": [1, 2, 2],
-                "in_dim": 48,
-                "dim": 1536,
-                "ffn_dim": 8960,
-                "freq_dim": 256,
-                "text_dim": 4096,
-                "out_dim": 16,
-                "num_heads": 12,
-                "num_layers": 30,
-                "eps": 1e-6,
-                "has_ref_conv": True
-            }
-        elif hash_state_dict_keys(state_dict) == "26bde73488a92e64cc20b0a7485b9e5b":
-            # 14B PAI control v1.1
-            config = {
-                "has_image_input": True,
-                "patch_size": [1, 2, 2],
-                "in_dim": 48,
-                "dim": 5120,
-                "ffn_dim": 13824,
-                "freq_dim": 256,
-                "text_dim": 4096,
-                "out_dim": 16,
-                "num_heads": 40,
-                "num_layers": 40,
-                "eps": 1e-6,
-                "has_ref_conv": True
-            }
-        elif hash_state_dict_keys(state_dict) == "ac6a5aa74f4a0aab6f64eb9a72f19901":
-            # 1.3B PAI control-camera v1.1
-            config = {
-                "has_image_input": True,
-                "patch_size": [1, 2, 2],
-                "in_dim": 32,
-                "dim": 1536,
-                "ffn_dim": 8960,
-                "freq_dim": 256,
-                "text_dim": 4096,
-                "out_dim": 16,
-                "num_heads": 12,
-                "num_layers": 30,
-                "eps": 1e-6,
-                "has_ref_conv": False,
-                "add_control_adapter": True,
-                "in_dim_control_adapter": 24,
-            }
-        elif hash_state_dict_keys(state_dict) == "b61c605c2adbd23124d152ed28e049ae":
-            # 14B PAI control-camera v1.1
-            config = {
-                "has_image_input": True,
-                "patch_size": [1, 2, 2],
-                "in_dim": 32,
-                "dim": 5120,
-                "ffn_dim": 13824,
-                "freq_dim": 256,
-                "text_dim": 4096,
-                "out_dim": 16,
-                "num_heads": 40,
-                "num_layers": 40,
-                "eps": 1e-6,
-                "has_ref_conv": False,
-                "add_control_adapter": True,
-                "in_dim_control_adapter": 24,
-            }
         else:
             config = {}
         return state_dict, config

diffsynth/models/wan_video_vae.py

@@ -774,11 +774,18 @@ class WanVideoVAE(nn.Module):
 
 
     def decode(self, hidden_states, device, tiled=False, tile_size=(34, 34), tile_stride=(18, 16)):
-        if tiled:
-            video = self.tiled_decode(hidden_states, device, tile_size, tile_stride)
-        else:
-            video = self.single_decode(hidden_states, device)
-        return video
+        hidden_states = [hidden_state.to("cpu") for hidden_state in hidden_states]
+        videos = []
+        for hidden_state in hidden_states:
+            hidden_state = hidden_state.unsqueeze(0)
+            if tiled:
+                video = self.tiled_decode(hidden_state, device, tile_size, tile_stride)
+            else:
+                video = self.single_decode(hidden_state, device)
+            video = video.squeeze(0)
+            videos.append(video)
+        videos = torch.stack(videos)
+        return videos
 
 
     @staticmethod