| import gradio as gr | |
| import torch | |
| import torchvision | |
| from diffusers import I2VGenXLPipeline | |
| from diffusers.utils.loading_utils import load_image | |
| from PIL import Image | |
| def generate(image: Image.Image, prompt: str): | |
| negative_prompt = "Distorted, discontinuous, Ugly, blurry, low resolution, motionless, static, disfigured, disconnected limbs, Ugly faces, incomplete arms" | |
| generator = torch.manual_seed(8888) | |
| image = image.convert("RGB") | |
| pipeline = I2VGenXLPipeline.from_pretrained("ali-vilab/i2vgen-xl", torch_dtype=torch.float16, variant="fp16") | |
| pipeline.enable_model_cpu_offload() | |
| pipeline.unet.enable_forward_chunking() | |
| frames = pipeline( | |
| prompt=prompt, | |
| image=image, | |
| num_inference_steps=50, | |
| negative_prompt=negative_prompt, | |
| guidance_scale=9.0, | |
| generator=generator, | |
| decode_chunk_size=6, | |
| ).frames[0] | |
| torchvision.io.write_video("video.mp4", frames, fps=16) | |
| return "video.mp4" | |
| app = gr.Interface( | |
| fn=generate, | |
| inputs=[gr.Image(type="pil"), "text"], | |
| outputs=gr.Video() | |
| ) | |
| if __name__ == "__main__": | |
| app.launch() | |