Update app.py

app.py

@@ -17,7 +17,7 @@ from huggingface_hub import hf_hub_download
 #gradio.helpers.CACHED_FOLDER = '/data/cache'
 
 pipe = StableVideoDiffusionPipeline.from_pretrained(
-    "multimodalart/stable-video-diffusion", torch_dtype=torch.float16, variant="fp16"
+    "monkseal555/ian1", torch_dtype=torch.float16, variant="fp16"
 )
 pipe.to("cuda")
 pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
@@ -86,8 +86,7 @@ def resize_image(image, output_size=(1024, 576)):
     return cropped_image
 
 with gr.Blocks() as demo:
-  gr.Markdown('''# Community demo for Stable Video Diffusion - Img2Vid - XT ([model](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt), [paper](https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets), [stability's ui waitlist](https://stability.ai/contact))
-#### Research release ([_non-commercial_](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/blob/main/LICENSE)): generate `4s` vid from a single image at (`25 frames` at `6 fps`). this demo uses [🧨 diffusers for low VRAM and fast generation](https://huggingface.co/docs/diffusers/main/en/using-diffusers/svd).
+  gr.Markdown('''generative radar predictor).
   ''')
   with gr.Row():
     with gr.Column():
@@ -104,15 +103,7 @@ with gr.Blocks() as demo:
   generate_btn.click(fn=sample, inputs=[image, seed, randomize_seed, motion_bucket_id, fps_id], outputs=[video, seed], api_name="video")
   gr.Examples(
     examples=[
-        "images/blink_meme.png",
-        "images/confused2_meme.png",
-        "images/disaster_meme.png",
-        "images/distracted_meme.png",
-        "images/hide_meme.png",
-        "images/nazare_meme.png",
-        "images/success_meme.png",
-        "images/willy_meme.png",
-        "images/wink_meme.png"
+        
     ],
     inputs=image,
     outputs=[video, seed],