Source fastvideo/v1/examples/inference/gradio.
FastVideo VideoGenerator Gradio Demo#
This is a Gradio-based web interface for generating videos using the FastVideo framework. The demo allows users to create videos from text prompts with various customization options.
Overview#
The demo uses the FastVideo framework to generate videos based on text prompts. It provides a simple web interface built with Gradio that allows users to:
Enter text prompts to generate videos
Customize video parameters (dimensions, number of frames, etc.)
Use negative prompts to guide the generation process
Set or randomize seeds for reproducibility
Requirements#
Linux-based OS
Python 3.10
Cuda 12.4
FastVideo
Installation#
pip install fastvideo
Usage#
Run the demo with:
python fastvideo/v1/examples/inference/gradio/gradio_demo.py
This will start a web server at http://0.0.0.0:7860 where you can access the interface.
Model Initialization#
args = FastVideoArgs(model_path="FastVideo/FastHunyuan-Diffusers", num_gpus=2)
generator = VideoGenerator.from_pretrained(
model_path=args.model_path,
num_gpus=args.num_gpus
)
This demo initializes a VideoGenerator with the minimum required arguments for inference. Users can seamlessly adjust inference options between generations, including prompts, resolution, video length, or even the number of inference steps, without ever needing to reload the model.
Video Generation#
The core functionality is in the generate_video function, which:
Processes user inputs
Uses the FastVideo VideoGenerator from earlier to run inference (
generator.generate_video())Returns an output path that Gradio uses to display the generated video
Gradio Interface#
The interface is built with several components:
A text input for the prompt
A video display for the result
Inference options in a collapsible accordion:
Height and width sliders
Number of frames slider
Guidance scale slider
Inference steps slider
Negative prompt options
Seed controls
Inference Options#
Height/Width: Control the resolution of the generated video
Number of Frames: Set how many frames to generate
Guidance Scale: Control how closely the generation follows the prompt
Inference Steps: More steps can improve quality but take longer
Negative Prompt: Specify what you don’t want to see in the video
Seed: Control randomness for reproducible results
Example materials#
gradio_demo.py
import os
import gradio as gr
import torch
import argparse
from copy import deepcopy
from fastvideo.v1.fastvideo_args import FastVideoArgs
from fastvideo import VideoGenerator
from fastvideo.v1.configs.sample.base import SamplingParam
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="FastVideo Gradio Demo")
parser.add_argument("--model_path", type=str, default="FastVideo/FastHunyuan-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="outputs", help="Path to save generated videos")
parsed_args = parser.parse_args()
# args = FastVideoArgs(model_path="FastVideo/FastHunyuan-Diffusers", num_gpus=2)
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 not use_negative_prompt:
params.negative_prompt = None
generator.generate_video(
prompt=prompt,
sampling_param=params
)
output_path = os.path.join(parsed_args.output_path, f"{params.prompt[:100]}.mp4")
return output_path, params.seed
examples = [
"A hand enters the frame, pulling a sheet of plastic wrap over three balls of dough placed on a wooden surface. The plastic wrap is stretched to cover the dough more securely. The hand adjusts the wrap, ensuring that it is tight and smooth over the dough. The scene focuses on the hand’s movements as it secures the edges of the plastic wrap. No new objects appear, and the camera remains stationary, focusing on the action of covering the dough.",
"A vintage train snakes through the mountains, its plume of white steam rising dramatically against the jagged peaks. The cars glint in the late afternoon sun, their deep crimson and gold accents lending a touch of elegance. The tracks carve a precarious path along the cliffside, revealing glimpses of a roaring river far below. Inside, passengers peer out the large windows, their faces lit with awe as the landscape unfolds.",
"A crowded rooftop bar buzzes with energy, the city skyline twinkling like a field of stars in the background. Strings of fairy lights hang above, casting a warm, golden glow over the scene. Groups of people gather around high tables, their laughter blending with the soft rhythm of live jazz. The aroma of freshly mixed cocktails and charred appetizers wafts through the air, mingling with the cool night breeze.",
]
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=4,
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 a 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=default_params.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=7860)