diff --git a/diffsynth/configs/model_config.py b/diffsynth/configs/model_config.py index 9de5bfb..b4b847f 100644 --- a/diffsynth/configs/model_config.py +++ b/diffsynth/configs/model_config.py @@ -150,6 +150,8 @@ model_loader_configs = [ (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"), diff --git a/diffsynth/models/wan_video_camera_controller.py b/diffsynth/models/wan_video_camera_controller.py index 026b558..45a44ee 100644 --- a/diffsynth/models/wan_video_camera_controller.py +++ b/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 diff --git a/diffsynth/models/wan_video_dit.py b/diffsynth/models/wan_video_dit.py index 419f8cf..1a54728 100644 --- a/diffsynth/models/wan_video_dit.py +++ b/diffsynth/models/wan_video_dit.py @@ -294,6 +294,7 @@ class WanModel(torch.nn.Module): ): 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 @@ class WanModelStateDictConverter: "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 diff --git a/diffsynth/pipelines/wan_video_new.py b/diffsynth/pipelines/wan_video_new.py index 89adbdf..53df7d9 100644 --- a/diffsynth/pipelines/wan_video_new.py +++ b/diffsynth/pipelines/wan_video_new.py @@ -663,22 +663,23 @@ class WanVideoUnit_ImageEmbedderFused(PipelineUnit): 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 @@ class WanVideoUnit_FunReference(PipelineUnit): 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 @@ class WanVideoUnit_FunReference(PipelineUnit): 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 @@ class WanVideoUnit_FunCameraControl(PipelineUnit): 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) diff --git a/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py b/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py new file mode 100644 index 0000000..27cda27 --- /dev/null +++ b/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) \ No newline at end of file diff --git a/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py b/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py new file mode 100644 index 0000000..2941422 --- /dev/null +++ b/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) \ No newline at end of file diff --git a/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py b/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py new file mode 100644 index 0000000..c63e522 --- /dev/null +++ b/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)