70 lines
3.7 KiB
Python
70 lines
3.7 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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from PIL import Image
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from diffsynth import save_video, VideoData
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from chronoedit_diffsynth.wan_video_new_chronoedit import WanVideoPipeline, ModelConfig
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pipe = WanVideoPipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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use_usp=True, # torchrun --standalone --nproc_per_node=8
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model_configs=[
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ModelConfig(
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path=[
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"checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00001-of-00014.safetensors",
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"checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00002-of-00014.safetensors",
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"checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00003-of-00014.safetensors",
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"checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00004-of-00014.safetensors",
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"checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00005-of-00014.safetensors",
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"checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00006-of-00014.safetensors",
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"checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00007-of-00014.safetensors",
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"checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00008-of-00014.safetensors",
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"checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00009-of-00014.safetensors",
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"checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00010-of-00014.safetensors",
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"checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00011-of-00014.safetensors",
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"checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00012-of-00014.safetensors",
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"checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00013-of-00014.safetensors",
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"checkpoints/ChronoEdit-14B-Diffusers/transformer/diffusion_pytorch_model-00014-of-00014.safetensors",
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],
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# offload_device="cpu"
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),
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ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
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ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
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ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"),
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],
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)
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# pipe.load_lora(pipe.dit, "models/train/ChronoEdit-14B_lora/epoch-4.safetensors", alpha=1)
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pipe.enable_vram_management()
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image = Image.open("assets/images/input.jpg")
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prompt = "Add a person wearing red jacket."
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video = pipe(
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prompt=prompt,
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negative_prompt="",
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input_image=image,
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seed=0, tiled=False,
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num_frames=5, # 5 or 29
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height=720, width=1280,
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cfg_scale=5.0,
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num_inference_steps=50,
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enable_temporal_reasoning=False,
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num_temporal_reasoning_steps=0,
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)
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save_video(video, "output.mp4", fps=4, quality=5)
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