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on
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Running
on
Zero
| import gradio as gr | |
| import torch | |
| import os | |
| import spaces | |
| import uuid | |
| from diffusers import AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler | |
| from diffusers.utils import export_to_video | |
| from huggingface_hub import hf_hub_download | |
| from safetensors.torch import load_file | |
| from PIL import Image | |
| # Constants | |
| bases = { | |
| "ToonYou": "frankjoshua/toonyou_beta6", | |
| "epiCRealism": "emilianJR/epiCRealism" | |
| } | |
| step_loaded = None | |
| base_loaded = "ToonYou" | |
| # Ensure model and scheduler are initialized in GPU-enabled function | |
| if not torch.cuda.is_available(): | |
| raise NotImplementedError("No GPU detected!") | |
| device = "cuda" | |
| dtype = torch.float16 | |
| pipe = AnimateDiffPipeline.from_pretrained(bases[base_loaded], torch_dtype=dtype).to(device) | |
| pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", beta_schedule="linear") | |
| # Function | |
| def generate_image(prompt, base, step): | |
| global step_loaded | |
| global base_loaded | |
| print(prompt, base, step) | |
| if step_loaded != step: | |
| repo = "ByteDance/AnimateDiff-Lightning" | |
| ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors" | |
| pipe.unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device), strict=False) | |
| step_loaded = step | |
| if base_loaded != base: | |
| pipe.unet.load_state_dict(torch.load(hf_hub_download(bases[base], "unet/diffusion_pytorch_model.bin"), map_location=device), strict=False) | |
| base_loaded = base | |
| output = pipe(prompt=prompt, guidance_scale=1.0, num_inference_steps=step) | |
| name = str(uuid.uuid4()).replace("-", "") | |
| path = f"/tmp/{name}.mp4" | |
| export_to_video(output.frames[0], path, fps=10) | |
| return path | |
| # Gradio Interface | |
| with gr.Blocks(css="style.css") as demo: | |
| gr.HTML("<h1><center>AnimateDiff-Lightning ⚡</center></h1>") | |
| gr.HTML("<p><center>Lightning-fast text-to-video generation</center></p><p><center><a href='https://huggingface.co/ByteDance/AnimateDiff-Lightning'>https://huggingface.co/ByteDance/AnimateDiff-Lightning</a></center></p>") | |
| with gr.Group(): | |
| with gr.Row(): | |
| prompt = gr.Textbox( | |
| label='Prompt (English)', | |
| scale=8 | |
| ) | |
| select_base = gr.Dropdown( | |
| label='Base model', | |
| choices=[ | |
| "ToonYou", | |
| "epiCRealism", | |
| ], | |
| value=base_loaded, | |
| interactive=True | |
| ) | |
| select_step = gr.Dropdown( | |
| label='Inference steps', | |
| choices=[ | |
| ('1-Step', 1), | |
| ('2-Step', 2), | |
| ('4-Step', 4), | |
| ('8-Step', 8)], | |
| value=4, | |
| interactive=True | |
| ) | |
| submit = gr.Button( | |
| scale=1, | |
| variant='primary' | |
| ) | |
| video = gr.Video( | |
| label='AnimateDiff-Lightning', | |
| autoplay=True, | |
| height=512, | |
| width=512, | |
| elem_id="video_output" | |
| ) | |
| prompt.submit( | |
| fn=generate_image, | |
| inputs=[prompt, select_base, select_step], | |
| outputs=video, | |
| ) | |
| submit.click( | |
| fn=generate_image, | |
| inputs=[prompt, select_base, select_step], | |
| outputs=video, | |
| ) | |
| demo.queue().launch() |