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import gradio as gr
import tempfile
import json
from inference_shutter_speed import load_models, run_inference, OmegaConf
import torch
# Initialize models once at startup
cfg = OmegaConf.load("configs/inference_genphoto/adv3_256_384_genphoto_relora_shutter_speed.yaml")
pipeline, device = load_models(cfg)
def generate_video(base_scene, shutter_speed_list):
try:
# Validate input
if len(json.loads(shutter_speed_list)) != 5:
raise ValueError("Exactly 5 shutter_speed values required")
# Run inference
video_path = run_inference(
pipeline=pipeline,
tokenizer=pipeline.tokenizer,
text_encoder=pipeline.text_encoder,
base_scene=base_scene,
shutter_speed_list=shutter_speed_list,
device=device
)
return video_path
except Exception as e:
raise gr.Error(f"Generation failed: {str(e)}")
# Example inputs
examples = [
[
"A brown and orange leather handbag with a paw print on it sits next to a book.",
"[0.11, 0.22, 0.33, 0.44, 0.55]"
],
[
"A variety of potted plants are displayed on a windowsill, with some of them placed in yellow and white bowls. ",
"[0.29, 0.49, 0.69, 0.79, 0.89]"
]
]
with gr.Blocks(title="Shutter Speed Effect Generator") as demo:
gr.Markdown("#Dynamic Shutter Speed Effect Generation")
with gr.Row():
with gr.Column():
scene_input = gr.Textbox(
label="Scene Description",
placeholder="Describe the scene you want to generate..."
)
shutter_speed_input = gr.Textbox(
label="Shutter Speed Values",
placeholder="Enter 5 comma-separated values from 0.1-1.0 (e.g., [0.15, 0.32, 0.53, 0.62, 0.82])"
)
submit_btn = gr.Button("Generate Video", variant="primary")
with gr.Column():
video_output = gr.Video(label="Generated Video")
error_output = gr.Textbox(label="Error Messages", visible=False)
gr.Examples(
examples=examples,
inputs=[scene_input, shutter_speed_input],
outputs=[video_output],
fn=generate_video,
cache_examples=True
)
submit_btn.click(
fn=generate_video,
inputs=[scene_input, shutter_speed_input],
outputs=[video_output],
)
if __name__ == "__main__":
demo.launch(share=True)
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