add wan2.2-fun-A14B inp, control and control-camera (#839)

* update wan2.2-fun

* update wan2.2-fun

* update wan2.2-fun

* add examples

* update wan2.2-fun

* update wan2.2-fun

* Rename Wan2.2-Fun-A14B-Inp.py to Wan2.2-Fun-A14B-InP.py

---------

Co-authored-by: lzw478614@alibaba-inc.com <lzw478614@alibaba-inc.com>

Author: lzws

diffsynth/configs/model_config.py

@@ -150,6 +150,8 @@
     (None, "b61c605c2adbd23124d152ed28e049ae", ["wan_video_dit"], [WanModel], "civitai"), 
     (None, "1f5ab7703c6fc803fdded85ff040c316", ["wan_video_dit"], [WanModel], "civitai"),
     (None, "5b013604280dd715f8457c6ed6d6a626", ["wan_video_dit"], [WanModel], "civitai"),
+    (None, "2267d489f0ceb9f21836532952852ee5", ["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"),
     (None, "cb104773c6c2cb6df4f9529ad5c60d0b", ["wan_video_dit"], [WanModel], "diffusers"),

diffsynth/models/wan_video_camera_controller.py

@@ -182,7 +182,7 @@ def process_pose_file(cam_params, width=672, height=384, original_pose_width=128
 
 
 def generate_camera_coordinates(
-    direction: Literal["Left", "Right", "Up", "Down", "LeftUp", "LeftDown", "RightUp", "RightDown"],
+    direction: Literal["Left", "Right", "Up", "Down", "LeftUp", "LeftDown", "RightUp", "RightDown", "In", "Out"],
     length: int,
     speed: float = 1/54,
     origin=(0, 0.532139961, 0.946026558, 0.5, 0.5, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0)
@@ -198,5 +198,9 @@ def generate_camera_coordinates(
             coor[13] += speed
         if "Down" in direction:
             coor[13] -= speed
+        if "In" in direction:
+            coor[18] -= speed
+        if "Out" in direction:
+            coor[18] += speed
         coordinates.append(coor)
     return coordinates

diffsynth/models/wan_video_dit.py

@@ -294,6 +294,7 @@ def __init__(
     ):
         super().__init__()
         self.dim = dim
+        self.in_dim = in_dim
         self.freq_dim = freq_dim
         self.has_image_input = has_image_input
         self.patch_size = patch_size
@@ -713,6 +714,42 @@ def from_civitai(self, state_dict):
                 "eps": 1e-6,
                 "require_clip_embedding": False,
             }
+        elif hash_state_dict_keys(state_dict) == "2267d489f0ceb9f21836532952852ee5":
+            # Wan2.2-Fun-A14B-Control
+            config = {
+                "has_image_input": False,
+                "patch_size": [1, 2, 2],
+                "in_dim": 52,
+                "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,
+                "require_clip_embedding": False,
+            }
+        elif hash_state_dict_keys(state_dict) == "47dbeab5e560db3180adf51dc0232fb1":
+            # Wan2.2-Fun-A14B-Control-Camera
+            config = {
+                "has_image_input": False,
+                "patch_size": [1, 2, 2],
+                "in_dim": 36,
+                "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,
+                "require_clip_embedding": False,
+            }
         else:
             config = {}
         return state_dict, config

diffsynth/pipelines/wan_video_new.py

@@ -663,22 +663,23 @@ def process(self, pipe: WanVideoPipeline, input_image, latents, height, width, t
 class WanVideoUnit_FunControl(PipelineUnit):
     def __init__(self):
         super().__init__(
-            input_params=("control_video", "num_frames", "height", "width", "tiled", "tile_size", "tile_stride", "clip_feature", "y"),
+            input_params=("control_video", "num_frames", "height", "width", "tiled", "tile_size", "tile_stride", "clip_feature", "y", "latents"),
             onload_model_names=("vae",)
         )
 
-    def process(self, pipe: WanVideoPipeline, control_video, num_frames, height, width, tiled, tile_size, tile_stride, clip_feature, y):
+    def process(self, pipe: WanVideoPipeline, control_video, num_frames, height, width, tiled, tile_size, tile_stride, clip_feature, y, latents):
         if control_video is None:
             return {}
         pipe.load_models_to_device(self.onload_model_names)
         control_video = pipe.preprocess_video(control_video)
         control_latents = pipe.vae.encode(control_video, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device)
         control_latents = control_latents.to(dtype=pipe.torch_dtype, device=pipe.device)
+        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, 16, (num_frames - 1) // 4 + 1, height//8, width//8), 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)
         else:
-            y = y[:, -16:]
+            y = y[:, -y_dim:]
         y = torch.concat([control_latents, y], dim=1)
         return {"clip_feature": clip_feature, "y": y}
 
@@ -698,6 +699,8 @@ def process(self, pipe: WanVideoPipeline, reference_image, height, width):
         reference_image = reference_image.resize((width, height))
         reference_latents = pipe.preprocess_video([reference_image])
         reference_latents = pipe.vae.encode(reference_latents, device=pipe.device)
+        if pipe.image_encoder is None:
+            return {"reference_latents": reference_latents}
         clip_feature = pipe.preprocess_image(reference_image)
         clip_feature = pipe.image_encoder.encode_image([clip_feature])
         return {"reference_latents": reference_latents, "clip_feature": clip_feature}
@@ -707,13 +710,14 @@ def process(self, pipe: WanVideoPipeline, reference_image, height, width):
 class WanVideoUnit_FunCameraControl(PipelineUnit):
     def __init__(self):
         super().__init__(
-            input_params=("height", "width", "num_frames", "camera_control_direction", "camera_control_speed", "camera_control_origin", "latents", "input_image"),
+            input_params=("height", "width", "num_frames", "camera_control_direction", "camera_control_speed", "camera_control_origin", "latents", "input_image", "tiled", "tile_size", "tile_stride"),
             onload_model_names=("vae",)
         )
 
-    def process(self, pipe: WanVideoPipeline, height, width, num_frames, camera_control_direction, camera_control_speed, camera_control_origin, latents, input_image):
+    def process(self, pipe: WanVideoPipeline, height, width, num_frames, camera_control_direction, camera_control_speed, camera_control_origin, latents, input_image, tiled, tile_size, tile_stride):
         if camera_control_direction is None:
             return {}
+        pipe.load_models_to_device(self.onload_model_names)
         camera_control_plucker_embedding = pipe.dit.control_adapter.process_camera_coordinates(
             camera_control_direction, num_frames, height, width, camera_control_speed, camera_control_origin)
 
@@ -728,14 +732,27 @@ def process(self, pipe: WanVideoPipeline, height, width, num_frames, camera_cont
         control_camera_latents = control_camera_latents.contiguous().view(b, f // 4, 4, c, h, w).transpose(2, 3)
         control_camera_latents = control_camera_latents.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2)
         control_camera_latents_input = control_camera_latents.to(device=pipe.device, dtype=pipe.torch_dtype)
-
+        
         input_image = input_image.resize((width, height))
         input_latents = pipe.preprocess_video([input_image])
-        pipe.load_models_to_device(self.onload_model_names)
         input_latents = pipe.vae.encode(input_latents, device=pipe.device)
         y = torch.zeros_like(latents).to(pipe.device)
         y[:, :, :1] = input_latents
         y = y.to(dtype=pipe.torch_dtype, device=pipe.device)
+
+        if y.shape[1] != pipe.dit.in_dim - latents.shape[1]:
+            image = pipe.preprocess_image(input_image.resize((width, height))).to(pipe.device)
+            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[:, 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.transpose(1, 2)[0]
+            y = torch.cat([msk,y])
+            y = y.unsqueeze(0)
+            y = y.to(dtype=pipe.torch_dtype, device=pipe.device)
         return {"control_camera_latents_input": control_camera_latents_input, "y": y}
 
 
@@ -1048,7 +1065,7 @@ def model_fn_wan_video(
     if clip_feature is not None and dit.require_clip_embedding:
         clip_embdding = dit.img_emb(clip_feature)
         context = torch.cat([clip_embdding, context], dim=1)
-    
+
     # Add camera control
     x, (f, h, w) = dit.patchify(x, control_camera_latents_input)
 

examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py

@@ -0,0 +1,43 @@
+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-A14B-Control-Camera", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="Wan2.1_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-A14B-Control.py

@@ -0,0 +1,35 @@
+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-A14B-Control", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="Wan2.1_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)

examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py

@@ -0,0 +1,35 @@
+import torch
+from diffsynth import save_video
+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-A14B-InP", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
+        ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="Wan2.1_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"
+)
+image = Image.open("data/examples/wan/input_image.jpg")
+
+# First and last frame to video
+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",
+    input_image=image,
+    seed=0, tiled=True,
+    # You can input `end_image=xxx` to control the last frame of the video.
+    # The model will automatically generate the dynamic content between `input_image` and `end_image`.
+)
+save_video(video, "video.mp4", fps=15, quality=5)