Duplicate from jbilcke-hf/video-upscaling-server-1

Co-authored-by: Julian Bilcke <jbilcke-hf@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+title: Zeroscope XL
+emoji: 🐡
+colorFrom: red
+colorTo: gray
+sdk: gradio
+sdk_version: 3.35.2
+app_file: app.py
+pinned: false
+duplicated_from: jbilcke-hf/video-upscaling-server-1
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import numpy as np
+import os
+from PIL import Image
+import cv2
+from moviepy.editor import VideoFileClip
+import torch
+from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+from diffusers.utils import export_to_video
+
+SECRET_TOKEN = os.getenv('SECRET_TOKEN', 'default_secret')
+DESCRIPTION = 'This space is an API service meant to be used by VideoChain and VideoQuest.\nWant to use this space for yourself? Please use the original code: [https://huggingface.co/spaces/fffiloni/zeroscope-XL](https://huggingface.co/spaces/fffiloni/zeroscope-XL)'
+
+pipe_xl = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_XL", torch_dtype=torch.float16, revision="refs/pr/17")
+pipe_xl.vae.enable_slicing()
+pipe_xl.scheduler = DPMSolverMultistepScheduler.from_config(pipe_xl.scheduler.config)
+pipe_xl.enable_model_cpu_offload()
+pipe_xl.to("cuda")
+
+def convert_mp4_to_frames(video_path):
+    # Read the video file
+    video = cv2.VideoCapture(video_path)
+
+    # Get the frames per second (fps) of the video
+    fps = video.get(cv2.CAP_PROP_FPS)
+
+    # Calculate the number of frames to extract
+    # Note: we cannot go beyond 3 seconds on the large A10G
+    # num_frames = int(fps * min(duration, 3))
+
+    # let's use a fixed max for now, no need to have a duration field
+    num_frames = int(fps * 3)
+
+    frames = []
+    frame_count = 0
+    
+    # Iterate through each frame
+    while True:
+        # Read a frame
+        ret, frame = video.read()
+        
+        # If the frame was not successfully read or we have reached the desired duration, break the loop
+        if not ret or frame_count == num_frames:
+            break
+        
+        # Convert BGR to RGB
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+        # Append the frame to the list of frames
+        frames.append(frame)
+
+        frame_count += 1
+
+    # Release the video object
+    video.release()
+
+    # Convert the list of frames to a numpy array
+    frames = np.array(frames)
+
+    return frames
+
+def infer(prompt, video_in, denoise_strength, secret_token):
+    if secret_token != SECRET_TOKEN:
+        raise gr.Error(
+            f'Invalid secret token. Please fork the original space if you want to use it for yourself.')
+        
+    negative_prompt = "text, watermark, copyright, blurry, cropped, noisy, pixelated, nsfw"
+
+
+    video = convert_mp4_to_frames(video_in)
+    video_resized = [Image.fromarray(frame).resize((1024, 576)) for frame in video]
+    video_frames = pipe_xl(prompt, negative_prompt=negative_prompt, video=video_resized, strength=denoise_strength).frames
+    video_path = export_to_video(video_frames, output_video_path="xl_result.mp4")
+    
+    return "xl_result.mp4", gr.Group.update(visible=True)
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(DESCRIPTION)
+    
+    with gr.Column():
+        secret_token = gr.Text(label='Secret Token', max_lines=1)
+        video_in = gr.Video(type="numpy", source="upload")
+        prompt_in = gr.Textbox(label="Prompt", elem_id="prompt-in")
+        denoise_strength = gr.Slider(label="Denoise strength", minimum=0.6, maximum=0.9, step=0.01, value=0.66)
+        #inference_steps = gr.Slider(label="Inference Steps", minimum=7, maximum=100, step=1, value=40, interactive=False)
+        submit_btn = gr.Button("Submit")
+        video_result = gr.Video(label="Video Output", elem_id="video-output")
+
+    submit_btn.click(fn=infer,
+                    inputs=[prompt_in, video_in, denoise_strength, secret_token],
+                    outputs=[video_result],
+                    api_name="zero_xl"
+                    )
+    
+demo.queue(max_size=6).launch()
+        

requirements.txt

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+git+https://github.com/huggingface/diffusers
+transformers
+accelerate
+torch
+opencv-python
+moviepy