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import gradio as gr
from gradio_client import Client, handle_file
def generate_video(input_image, prompt, negative_prompt, diffusion_step, height, width, scfg_scale, use_dctinit, dct_coefficients, noise_level, motion_bucket_id, seed):
client = Client("maxin-cn/Cinemo")
result = client.predict(
input_image=handle_file(input_image),
prompt=prompt,
negative_prompt=negative_prompt,
diffusion_step=diffusion_step,
height=height,
width=width,
scfg_scale=scfg_scale,
use_dctinit=use_dctinit,
dct_coefficients=dct_coefficients,
noise_level=noise_level,
motion_bucket_id=motion_bucket_id,
seed=seed,
api_name="/gen_video"
)
print("API response" , result)
video_path = result.get('video') # Extract the video file path from the API response
if video_path is None:
return "The API did not return a valid video. Please try again."
return video_path # Return the path to the video file
# Gradio Interface
with gr.Blocks() as demo:
with gr.Row():
input_image = gr.Image(label="Input Image", type="filepath")
with gr.Row():
prompt = gr.Textbox(label="Prompt")
negative_prompt = gr.Textbox(label="Negative Prompt")
with gr.Row():
diffusion_step = gr.Slider(label="Sampling steps", minimum=1, maximum=100, value=50)
height = gr.Slider(label="Height", minimum=64, maximum=1024, value=320)
width = gr.Slider(label="Width", minimum=64, maximum=1024, value=512)
scfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7.5)
with gr.Row():
use_dctinit = gr.Checkbox(label="Enable DCTInit", value=True)
dct_coefficients = gr.Slider(label="DCT Coefficients", minimum=0.0, maximum=1.0, value=0.23)
noise_level = gr.Slider(label="Noise Level", minimum=0, maximum=1000, value=985)
motion_bucket_id = gr.Slider(label="Motion Intensity", minimum=0, maximum=20, value=10)
seed = gr.Number(label="Seed", value=100)
video_output = gr.Video(label="Generated Video")
generate_button = gr.Button("Generate Video")
generate_button.click(generate_video, inputs=[input_image, prompt, negative_prompt, diffusion_step, height, width, scfg_scale, use_dctinit, dct_coefficients, noise_level, motion_bucket_id, seed], outputs=video_output)
demo.launch()
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