File size: 2,111 Bytes
656dc9e feb74fc 656dc9e feb74fc 656dc9e 01f387a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | import gradio as gr
import os
from glob import glob
from diffusers.utils import load_image
import spaces
from panna.pipeline import PipelineSVDUpscale
model = PipelineSVDUpscale(upscaler="instruct_ir")
def infer(init_image, upscaler_prompt, num_frames, motion_bucket_id, noise_aug_strength, decode_chunk_size, fps, seed):
base_count = len(glob(os.path.join(tmp_output_dir, "*.mp4")))
video_path = os.path.join(tmp_output_dir, f"{base_count:06d}.mp4")
model(
init_image,
output_path=video_path,
prompt=upscaler_prompt,
num_frames=num_frames,
motion_bucket_id=motion_bucket_id,
noise_aug_strength=noise_aug_strength,
decode_chunk_size=decode_chunk_size,
fps=fps,
seed=seed
)
return video_path
with gr.Blocks() as demo:
gr.Markdown(title)
with gr.Row():
with gr.Column():
image = gr.Image(label="Upload your image", type="pil")
run_button = gr.Button("Generate")
video = gr.Video()
with gr.Accordion("Advanced options", open=False):
upscaler_prompt = gr.Text("Correct the motion blur in this image so it is more clear", label="Prompt for upscaler", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False)
seed = gr.Slider(label="Seed", minimum=0, maximum=1_000_000, step=1, value=0)
num_frames = gr.Slider(label="Number of frames", minimum=1, maximum=100, step=1, value=25)
motion_bucket_id = gr.Slider(label="Motion bucket id", minimum=1, maximum=255, step=1, value=127)
noise_aug_strength = gr.Slider(label="Noise strength", minimum=0, maximum=1, step=0.01, value=0.02)
fps = gr.Slider(label="Frames per second", minimum=5, maximum=30, step=1, value=7)
decode_chunk_size = gr.Slider(label="Decode chunk size", minimum=1, maximum=25, step=1, value=7)
run_button.click(
fn=infer,
inputs=[image, upscaler_prompt, num_frames, motion_bucket_id, noise_aug_strength, decode_chunk_size, fps, seed],
outputs=[video]
)
gr.Examples(inputs=image)
demo.launch() |