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support wan-flf2v

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2025-04-17 14:47:55 +08:00
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examples/wanvideo/README.md
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@@ -18,6 +18,7 @@ pip install -e .
|Wan Team|14B text-to-video|[Link](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B)|[wan_14b_text_to_video.py](./wan_14b_text_to_video.py)|
|Wan Team|14B image-to-video 480P|[Link](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P)|[wan_14b_image_to_video.py](./wan_14b_image_to_video.py)|
|Wan Team|14B image-to-video 720P|[Link](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P)|[wan_14b_image_to_video.py](./wan_14b_image_to_video.py)|
|Wan Team|14B first-last-frame-to-video 720P|[Link](https://modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P)|[wan_14B_flf2v.py](./wan_14B_flf2v.py)|
|DiffSynth-Studio Team|1.3B aesthetics LoRA|[Link](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-lora-aesthetics-v1)|Please see the [model card](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-lora-aesthetics-v1).|
|DiffSynth-Studio Team|1.3B Highres-fix LoRA|[Link](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-lora-highresfix-v1)|Please see the [model card](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-lora-highresfix-v1).|
|DiffSynth-Studio Team|1.3B ExVideo LoRA|[Link](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-lora-exvideo-v1)|Please see the [model card](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-lora-exvideo-v1).|
@@ -110,6 +111,12 @@ https://github.com/user-attachments/assets/3908bc64-d451-485a-8b61-28f6d32dd92f
https://github.com/user-attachments/assets/c0bdd5ca-292f-45ed-b9bc-afe193156e75
14B first-last-frame-to-video
|First frame|Last frame|Video|
|-|-|-|
|![Image](https://github.com/user-attachments/assets/b0d8225b-aee0-4129-b8e5-58c8523221a6)|![Image](https://github.com/user-attachments/assets/2f0c9bc5-07e2-45fa-8320-53d63a4fd203)|https://github.com/user-attachments/assets/2a6a2681-622c-4512-b852-5f22e73830b1|
## Train
We support Wan-Video LoRA training and full training. Here is a tutorial. This is an experimental feature. Below is a video sample generated from the character Keqing LoRA:

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examples/wanvideo/wan_14B_flf2v.py Normal file
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@@ -0,0 +1,52 @@
import torch
from diffsynth import ModelManager, WanVideoPipeline, save_video, VideoData
from modelscope import snapshot_download, dataset_snapshot_download
from PIL import Image
# Download models
snapshot_download("Wan-AI/Wan2.1-FLF2V-14B-720P", local_dir="models/Wan-AI/Wan2.1-FLF2V-14B-720P")
# Load models
model_manager = ModelManager(device="cpu")
model_manager.load_models(
["models/Wan-AI/Wan2.1-FLF2V-14B-720P/models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"],
torch_dtype=torch.float32, # Image Encoder is loaded with float32
)
model_manager.load_models(
[
[
"models/Wan-AI/Wan2.1-FLF2V-14B-720P/diffusion_pytorch_model-00001-of-00007.safetensors",
"models/Wan-AI/Wan2.1-FLF2V-14B-720P/diffusion_pytorch_model-00002-of-00007.safetensors",
"models/Wan-AI/Wan2.1-FLF2V-14B-720P/diffusion_pytorch_model-00003-of-00007.safetensors",
"models/Wan-AI/Wan2.1-FLF2V-14B-720P/diffusion_pytorch_model-00004-of-00007.safetensors",
"models/Wan-AI/Wan2.1-FLF2V-14B-720P/diffusion_pytorch_model-00005-of-00007.safetensors",
"models/Wan-AI/Wan2.1-FLF2V-14B-720P/diffusion_pytorch_model-00006-of-00007.safetensors",
"models/Wan-AI/Wan2.1-FLF2V-14B-720P/diffusion_pytorch_model-00007-of-00007.safetensors",
],
"models/Wan-AI/Wan2.1-FLF2V-14B-720P/models_t5_umt5-xxl-enc-bf16.pth",
"models/Wan-AI/Wan2.1-FLF2V-14B-720P/Wan2.1_VAE.pth",
],
torch_dtype=torch.bfloat16, # You can set `torch_dtype=torch.float8_e4m3fn` to enable FP8 quantization.
)
pipe = WanVideoPipeline.from_model_manager(model_manager, torch_dtype=torch.bfloat16, device="cuda")
pipe.enable_vram_management(num_persistent_param_in_dit=None)
# Download example image
dataset_snapshot_download(
dataset_id="DiffSynth-Studio/examples_in_diffsynth",
local_dir="./",
allow_file_pattern=["data/examples/wan/first_frame.jpeg", "data/examples/wan/last_frame.jpeg"]
)
# First and last frame to video
video = pipe(
prompt="写实风格，一个女生手持枯萎的花站在花园中，镜头逐渐拉远，记录下花园的全貌。",
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",
num_inference_steps=30,
input_image=Image.open("data/examples/wan/first_frame.jpeg").resize((960, 960)),
end_image=Image.open("data/examples/wan/last_frame.jpeg").resize((960, 960)),
height=960, width=960,
seed=1, tiled=True
)
save_video(video, "video.mp4", fps=15, quality=5)