No stop

README.md

@@ -5,10 +5,9 @@ colorFrom: red
 colorTo: gray
 sdk: gradio
 python_version: 3.10.12
-sdk_version: 5.4.0
+sdk_version: 3.50.2
 app_file: app.py
 pinned: false
-short_description: watermark-free Modelscope-based video generation
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,31 +1,19 @@
 import gradio as gr
-import cv2
-import numpy as np
-import tempfile
-import imageio
+from share_btn import community_icon_html, loading_icon_html, share_js
 import torch
 from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+from diffusers.utils import export_to_video
 
 pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)
 pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
 pipe.enable_model_cpu_offload()
 
-def export_to_video(frames: np.ndarray, fps: int) -> str:
-    frames = np.clip((frames * 255), 0, 255).astype(np.uint8)
-    out_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
-    writer = imageio.get_writer(out_file.name, format="FFMPEG", fps=fps)
-    for frame in frames:
-        writer.append_data(frame)
-    writer.close()
-    return out_file.name
-
-def infer(prompt, progress=gr.Progress(track_tqdm=True)):
+def infer(prompt):
     negative_prompt = "text, watermark, copyright, blurry, nsfw"
-    video_frames = pipe(prompt, negative_prompt=negative_prompt, num_inference_steps=40, height=320, width=576, num_frames=24).frames[0]
-   
-    video_path = export_to_video(video_frames, 12)
+    video_frames = pipe(prompt, negative_prompt=negative_prompt, num_inference_steps=40, height=320, width=576, num_frames=24).frames
+    video_path = export_to_video(video_frames)
     print(video_path)
-    return video_path
+    return video_path, gr.Group.update(visible=True)
 
 css = """
 #col-container {max-width: 510px; margin-left: auto; margin-right: auto;}
@@ -138,6 +126,11 @@ with gr.Blocks(css=css) as demo:
         video_result = gr.Video(label="Video Output", elem_id="video-output")
 
         with gr.Row():
+            with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
+                community_icon = gr.HTML(community_icon_html)
+                loading_icon = gr.HTML(loading_icon_html)
+                share_button = gr.Button("Share with Community", elem_id="share-btn")
+
             gr.Markdown("""
                 [![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-lg.svg#center)](https://huggingface.co/spaces/fffiloni/zeroscope-cloning?duplicate=true)
             """)
@@ -163,8 +156,10 @@ with gr.Blocks(css=css) as demo:
         
     submit_btn.click(fn=infer,
                     inputs=[prompt_in],
-                    outputs=[video_result],
+                    outputs=[video_result, share_group],
                     api_name="zrscp")
+    
+    share_button.click(None, [], [], _js=share_js)
 
-demo.queue(max_size=12).launch(show_api=False)
+demo.queue(max_size=12).launch(show_api=True)
         

requirements.txt

@@ -3,6 +3,4 @@ transformers
 accelerate
 torch
 opencv-python
-imageio[ffmpeg]==2.34.1
-numpy
-huggingface_hub==0.25.2
+