support Wan2.2-Fun-5B-Control[-Camera] inference

diffsynth/configs/model_config.py

@@ -153,6 +153,8 @@
     (None, "1f5ab7703c6fc803fdded85ff040c316", ["wan_video_dit"], [WanModel], "civitai"),
     (None, "5b013604280dd715f8457c6ed6d6a626", ["wan_video_dit"], [WanModel], "civitai"),
     (None, "2267d489f0ceb9f21836532952852ee5", ["wan_video_dit"], [WanModel], "civitai"),
+    (None, "8cf5720f1d99f2d3d9f4d059c99f7e25", ["wan_video_dit"], [WanModel], "civitai"),
+    (None, "0e2ab7dec4711919374f3d7ffdea90be", ["wan_video_dit"], [WanModel], "civitai"),    
     (None, "47dbeab5e560db3180adf51dc0232fb1", ["wan_video_dit"], [WanModel], "civitai"),
     (None, "a61453409b67cd3246cf0c3bebad47ba", ["wan_video_dit", "wan_video_vace"], [WanModel, VaceWanModel], "civitai"),
     (None, "7a513e1f257a861512b1afd387a8ecd9", ["wan_video_dit", "wan_video_vace"], [WanModel, VaceWanModel], "civitai"),

diffsynth/models/wan_video_camera_controller.py

@@ -6,15 +6,15 @@
 from typing_extensions import Literal
 
 class SimpleAdapter(nn.Module):
-    def __init__(self, in_dim, out_dim, kernel_size, stride, num_residual_blocks=1):
+    def __init__(self, in_dim, out_dim, kernel_size, stride, downscale_factor=8, num_residual_blocks=1):
         super(SimpleAdapter, self).__init__()
 
         # Pixel Unshuffle: reduce spatial dimensions by a factor of 8
-        self.pixel_unshuffle = nn.PixelUnshuffle(downscale_factor=8)
+        self.pixel_unshuffle = nn.PixelUnshuffle(downscale_factor=downscale_factor)
 
         # Convolution: reduce spatial dimensions by a factor
         #  of 2 (without overlap)
-        self.conv = nn.Conv2d(in_dim * 64, out_dim, kernel_size=kernel_size, stride=stride, padding=0)
+        self.conv = nn.Conv2d(in_dim * downscale_factor * downscale_factor, out_dim, kernel_size=kernel_size, stride=stride, padding=0)
 
         # Residual blocks for feature extraction
         self.residual_blocks = nn.Sequential(

diffsynth/models/wan_video_dit.py

@@ -287,6 +287,7 @@ def __init__(
         has_ref_conv: bool = False,
         add_control_adapter: bool = False,
         in_dim_control_adapter: int = 24,
+        downscale_factor_control_adapter: int = 8,
         seperated_timestep: bool = False,
         require_vae_embedding: bool = True,
         require_clip_embedding: bool = True,
@@ -328,11 +329,13 @@ def __init__(
         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.ref_conv = nn.Conv2d(out_dim, 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:])
+            self.control_adapter = SimpleAdapter(in_dim_control_adapter, dim,
+                                                 kernel_size=patch_size[1:], stride=patch_size[1:],
+                                                 downscale_factor=downscale_factor_control_adapter)
         else:
             self.control_adapter = None
 
@@ -750,6 +753,47 @@ def from_civitai(self, state_dict):
                 "in_dim_control_adapter": 24,
                 "require_clip_embedding": False,
             }
+        elif hash_state_dict_keys(state_dict) == "8cf5720f1d99f2d3d9f4d059c99f7e25":
+            # Wan2.2-Fun-5B-Control
+            config = {
+                "has_image_input": False,
+                "patch_size": [1, 2, 2],
+                "in_dim": 148,
+                "dim": 3072,
+                "ffn_dim": 14336,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 48,
+                "num_heads": 24,
+                "num_layers": 30,
+                "eps": 1e-6,
+                "has_ref_conv": True,
+                "require_clip_embedding": False,
+                "seperated_timestep": True,
+                "require_vae_embedding": True,
+                "fuse_vae_embedding_in_latents": True,
+            }
+        elif hash_state_dict_keys(state_dict) == "0e2ab7dec4711919374f3d7ffdea90be":
+            # Wan2.2-Fun-5B-Control-Camera
+            config = {
+                "has_image_input": False,
+                "patch_size": [1, 2, 2],
+                "in_dim": 100,
+                "dim": 3072,
+                "ffn_dim": 14336,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 48,
+                "num_heads": 24,
+                "num_layers": 30,
+                "eps": 1e-6,
+                "has_ref_conv": False,
+                "require_clip_embedding": False,
+                "seperated_timestep": True,
+                "add_control_adapter": True,
+                "downscale_factor_control_adapter": 16,
+                "in_dim_control_adapter": 24,
+            }
         else:
             config = {}
         return state_dict, config

diffsynth/pipelines/wan_video_new.py

@@ -600,7 +600,7 @@ def process(self, pipe: WanVideoPipeline, input_image, end_image, num_frames, he
         pipe.load_models_to_device(self.onload_model_names)
         image = pipe.preprocess_image(input_image.resize((width, height))).to(pipe.device)
         clip_context = pipe.image_encoder.encode_image([image])
-        msk = torch.ones(1, num_frames, height//8, width//8, device=pipe.device)
+        msk = torch.ones(1, num_frames, height//(pipe.height_division_factor//2), width//(pipe.width_division_factor//2), device=pipe.device)
         msk[:, 1:] = 0
         if end_image is not None:
             end_image = pipe.preprocess_image(end_image.resize((width, height))).to(pipe.device)
@@ -612,7 +612,7 @@ def process(self, pipe: WanVideoPipeline, input_image, end_image, num_frames, he
             vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1)
 
         msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1)
-        msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8)
+        msk = msk.view(1, msk.shape[1] // 4, 4, height//(pipe.height_division_factor//2), width//(pipe.width_division_factor//2))
         msk = msk.transpose(1, 2)[0]
 
         y = pipe.vae.encode([vae_input.to(dtype=pipe.torch_dtype, device=pipe.device)], device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
@@ -659,7 +659,7 @@ def process(self, pipe: WanVideoPipeline, input_image, end_image, num_frames, he
             return {}
         pipe.load_models_to_device(self.onload_model_names)
         image = pipe.preprocess_image(input_image.resize((width, height))).to(pipe.device)
-        msk = torch.ones(1, num_frames, height//8, width//8, device=pipe.device)
+        msk = torch.ones(1, num_frames, height//(pipe.height_division_factor//2), width//(pipe.width_division_factor//2), device=pipe.device)
         msk[:, 1:] = 0
         if end_image is not None:
             end_image = pipe.preprocess_image(end_image.resize((width, height))).to(pipe.device)
@@ -669,7 +669,7 @@ def process(self, pipe: WanVideoPipeline, input_image, end_image, num_frames, he
             vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1)
 
         msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1)
-        msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8)
+        msk = msk.view(1, msk.shape[1] // 4, 4, height//(pipe.height_division_factor//2), width//(pipe.width_division_factor//2))
         msk = msk.transpose(1, 2)[0]
 
         y = pipe.vae.encode([vae_input.to(dtype=pipe.torch_dtype, device=pipe.device)], device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
@@ -719,9 +719,11 @@ def process(self, pipe: WanVideoPipeline, control_video, num_frames, height, wid
         y_dim = pipe.dit.in_dim-control_latents.shape[1]-latents.shape[1]
         if clip_feature is None or y is None:
             clip_feature = torch.zeros((1, 257, 1280), dtype=pipe.torch_dtype, device=pipe.device)
-            y = torch.zeros((1, y_dim, (num_frames - 1) // 4 + 1, height//8, width//8), dtype=pipe.torch_dtype, device=pipe.device)
+            height_division_factor, width_division_factor = pipe.height_division_factor // 2, pipe.width_division_factor // 2
+            y = torch.zeros((1, y_dim, (num_frames - 1) // 4 + 1, height//height_division_factor, width//width_division_factor), dtype=pipe.torch_dtype, device=pipe.device)
         else:
             y = y[:, -y_dim:]
+
         y = torch.concat([control_latents, y], dim=1)
         return {"clip_feature": clip_feature, "y": y}
 
@@ -787,10 +789,10 @@ def process(self, pipe: WanVideoPipeline, height, width, num_frames, camera_cont
             vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1)
             y = pipe.vae.encode([vae_input.to(dtype=pipe.torch_dtype, device=pipe.device)], device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
             y = y.to(dtype=pipe.torch_dtype, device=pipe.device)
-            msk = torch.ones(1, num_frames, height//8, width//8, device=pipe.device)
+            msk = torch.ones(1, num_frames, height//(pipe.height_division_factor//2), width//(pipe.width_division_factor//2), device=pipe.device)
             msk[:, 1:] = 0
             msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1)
-            msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8)
+            msk = msk.view(1, msk.shape[1] // 4, 4, height//(pipe.height_division_factor//2), width//(pipe.width_division_factor//2))
             msk = msk.transpose(1, 2)[0]
             y = torch.cat([msk,y])
             y = y.unsqueeze(0)

examples/wanvideo/model_inference/Wan2.2-Fun-5B-Control-Camera.py

@@ -0,0 +1,41 @@
+import torch
+from diffsynth import save_video,VideoData
+from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
+from PIL import Image
+from modelscope import dataset_snapshot_download
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="PAI/Wan2.2-Fun-5B-Control-Camera", origin_file_pattern="diffusion_pytorch_model.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-5B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-5B-Control-Camera", origin_file_pattern="Wan2.2_VAE.pth", offload_device="cpu"),
+    ],
+)
+pipe.enable_vram_management()
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/examples_in_diffsynth",
+    local_dir="./",
+    allow_file_pattern=f"data/examples/wan/input_image.jpg"
+)
+input_image = Image.open("data/examples/wan/input_image.jpg")
+
+video = pipe(
+    prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。",
+    negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+    seed=0, tiled=True,
+    input_image=input_image,
+    camera_control_direction="Left", camera_control_speed=0.01,
+)
+save_video(video, "video_left.mp4", fps=15, quality=5)
+
+video = pipe(
+    prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。",
+    negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+    seed=0, tiled=True,
+    input_image=input_image,
+    camera_control_direction="Up", camera_control_speed=0.01,
+)
+save_video(video, "video_up.mp4", fps=15, quality=5)

examples/wanvideo/model_inference/Wan2.2-Fun-5B-Control.py

@@ -0,0 +1,34 @@
+import torch
+from diffsynth import save_video,VideoData
+from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
+from PIL import Image
+from modelscope import dataset_snapshot_download
+
+pipe = WanVideoPipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="PAI/Wan2.2-Fun-5B-Control", origin_file_pattern="diffusion_pytorch_model.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-5B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-5B-Control", origin_file_pattern="Wan2.2_VAE.pth", offload_device="cpu"),
+    ],
+)
+pipe.enable_vram_management()
+
+dataset_snapshot_download(
+    dataset_id="DiffSynth-Studio/examples_in_diffsynth",
+    local_dir="./",
+    allow_file_pattern=["data/examples/wan/control_video.mp4", "data/examples/wan/reference_image_girl.png"]
+)
+
+# Control video
+control_video = VideoData("data/examples/wan/control_video.mp4", height=832, width=576)
+reference_image = Image.open("data/examples/wan/reference_image_girl.png").resize((576, 832))
+video = pipe(
+    prompt="扁平风格动漫，一位长发少女优雅起舞。她五官精致，大眼睛明亮有神，黑色长发柔顺光泽。身穿淡蓝色T恤和深蓝色牛仔短裤。背景是粉色。",
+    negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
+    control_video=control_video, reference_image=reference_image,
+    height=832, width=576, num_frames=49,
+    seed=1, tiled=True
+)
+save_video(video, "video.mp4", fps=15, quality=5)