feat: use i2vgenxl

app.py

@@ -1,14 +1,35 @@
 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(prompt: str):
-    video = torch.rand(240, 512, 512, 3)
-    torchvision.io.write_video(video, "video.mp4", fps=24)
+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"
 
-gr.Interface(
+app = gr.Interface(
     fn=generate,
-    inputs="text",
+    inputs=[gr.Image(type="pil"), "text"],
     outputs=gr.Video()
-).launch()
+)
+
+if __name__ == "__main__":
+    app.launch()
+