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Add gradio local inference demo #740

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b3a7ca3
Add gradio local inference demo
JiayiZhangA Aug 15, 2025
9ce4794
Update gradio local demo
JiayiZhangA Aug 16, 2025
199bb85
Update readme
JiayiZhangA Aug 16, 2025
426f1fd
Update error handling for VSA
JiayiZhangA Aug 17, 2025
7117a81
Update generate_video
JiayiZhangA Aug 17, 2025
f1ed7ef
pre-commit fix
JiayiZhangA Aug 17, 2025
7b72aa3
update gradio_local_demo and video_generator
JiayiZhangA Aug 21, 2025
3bb3c06
update gradio_local_demo and video_generator
JiayiZhangA Aug 21, 2025
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680 changes: 680 additions & 0 deletions examples/inference/gradio/local/full.svg
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169 changes: 169 additions & 0 deletions examples/inference/gradio/local/gradio_demo.py
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import argparse
import os
from copy import deepcopy

import gradio as gr
import torch

from fastvideo import VideoGenerator
from fastvideo.configs.sample.base import SamplingParam

if __name__ == "__main__":
os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
parser = argparse.ArgumentParser(description="FastVideo Gradio Demo")
parser.add_argument("--model_path",
type=str,
default="FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
help="Path to the model")
parser.add_argument("--num_gpus",
type=int,
default=1,
help="Number of GPUs to use")
parser.add_argument("--output_path",
type=str,
default="my_videos/",
help="Path to save generated videos")
parsed_args = parser.parse_args()


generator = VideoGenerator.from_pretrained(
model_path=parsed_args.model_path, num_gpus=parsed_args.num_gpus)

default_params = SamplingParam.from_pretrained(parsed_args.model_path)

def generate_video(
prompt,
negative_prompt,
use_negative_prompt,
seed,
guidance_scale,
num_frames,
height,
width,
num_inference_steps,
randomize_seed=False,
):
params = deepcopy(default_params)
params.prompt = prompt
params.negative_prompt = negative_prompt
params.seed = seed
params.guidance_scale = guidance_scale
params.num_frames = num_frames
params.height = height
params.width = width
params.num_inference_steps = num_inference_steps

if randomize_seed:
params.seed = torch.randint(0, 1000000, (1, )).item()

if use_negative_prompt and negative_prompt:
params.negative_prompt = negative_prompt
else:
params.negative_prompt = default_params.negative_prompt

generator.generate_video(prompt=prompt, sampling_param=params,output_path=parsed_args.output_path,save_video=True)
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medium

The prompt, output_path, and save_video arguments are passed to generator.generate_video even though they are also part of the sampling_param object. This is redundant and can be confusing. It would be cleaner to set all parameters in the params object and pass only that to the generator.

        params.output_path = parsed_args.output_path
        params.save_video = True
        generator.generate_video(sampling_param=params)


output_path = os.path.join(parsed_args.output_path,
f"{params.prompt[:100]}.mp4")
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high

The output video filename is constructed directly from the user's prompt. If the prompt contains special characters, such as /, it could lead to the video being saved in an unintended directory or cause a file creation error. The prompt should be sanitized before being used as a filename.

Suggested change
output_path = os.path.join(parsed_args.output_path,
f"{params.prompt[:100]}.mp4")
safe_prompt = "".join(c for c in params.prompt[:100] if c.isalnum() or c in (' ', '_')).rstrip().replace(' ', '_')
output_path = os.path.join(parsed_args.output_path,
f"{safe_prompt}.mp4")


return output_path, params.seed

examples = [
"A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes wide with interest.",
]

with gr.Blocks() as demo:
gr.Markdown("# FastVideo Inference Demo")

with gr.Group():
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0)
result = gr.Video(label="Result", show_label=False)

with gr.Accordion("Advanced options", open=False):
with gr.Group():
with gr.Row():
height = gr.Slider(
label="Height",
minimum=256,
maximum=1024,
step=32,
value=default_params.height,
)
width = gr.Slider(label="Width",
minimum=256,
maximum=1024,
step=32,
value=default_params.width)

with gr.Row():
num_frames = gr.Slider(
label="Number of Frames",
minimum=21,
maximum=163,
value=default_params.num_frames,
)
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=1,
maximum=12,
value=default_params.guidance_scale,
)
num_inference_steps = gr.Slider(
label="Inference Steps",
minimum=3,
maximum=100,
value=default_params.num_inference_steps,
)

with gr.Row():
use_negative_prompt = gr.Checkbox(
label="Use negative prompt", value=False)
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter your negative prompt",
visible=False,
)

seed = gr.Slider(label="Seed",
minimum=0,
maximum=1000000,
step=1,
value=default_params.seed)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
seed_output = gr.Number(label="Used Seed")

gr.Examples(examples=examples, inputs=prompt)

use_negative_prompt.change(
fn=lambda x: gr.update(visible=x),
inputs=use_negative_prompt,
outputs=negative_prompt,
)

run_button.click(
fn=generate_video,
inputs=[
prompt,
negative_prompt,
use_negative_prompt,
seed,
guidance_scale,
num_frames,
height,
width,
num_inference_steps,
randomize_seed,
],
outputs=[result, seed_output],
)

demo.queue(max_size=20).launch(server_name="0.0.0.0", server_port=8888)
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