Spaces:
Paused
Paused
File size: 2,250 Bytes
83f383e |
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 48 49 50 51 52 53 54 55 56 57 58 59 60 |
import gradio as gr
import torch
from diffusers import LTXPipeline
from diffusers.utils import export_to_video
import tempfile
import random
# Load the LTX Video model
pipe = LTXPipeline.from_pretrained("Lightricks/LTX-Video", torch_dtype=torch.bfloat16)
pipe.to("cuda")
def generate_video(prompt, negative_prompt, height, width, num_frames, num_inference_steps, seed):
if seed == -1:
seed = random.randint(0, 2**32 - 1)
generator = torch.Generator(device="cuda").manual_seed(seed)
video = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
height=height,
width=width,
num_frames=num_frames,
num_inference_steps=num_inference_steps,
generator=generator
).frames[0]
# Export video to temporary file
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmpfile:
export_to_video(video, tmpfile.name, fps=24)
return tmpfile.name
# Gradio Interface
title = "LTX-Video Generator"
description = "Generate high-quality videos from text using the Lightricks LTX-Video model."
with gr.Blocks(title=title) as demo:
gr.Markdown(f"## {title}\n{description}")
with gr.Row():
prompt = gr.Textbox(label="Prompt", value="A woman with long brown hair and light skin smiles at another woman...", lines=5)
negative_prompt = gr.Textbox(label="Negative Prompt", value="worst quality, inconsistent motion, blurry, jittery, distorted", lines=5)
with gr.Row():
height = gr.Slider(minimum=64, maximum=720, step=32, value=480, label="Height")
width = gr.Slider(minimum=64, maximum=1280, step=32, value=704, label="Width")
num_frames = gr.Slider(minimum=9, maximum=257, step=8, value=161, label="Number of Frames")
num_inference_steps = gr.Slider(minimum=10, maximum=100, step=1, value=50, label="Inference Steps")
seed = gr.Number(value=-1, label="Seed (set -1 for random)")
generate_btn = gr.Button("Generate Video")
output_video = gr.Video(label="Generated Video")
generate_btn.click(
fn=generate_video,
inputs=[prompt, negative_prompt, height, width, num_frames, num_inference_steps, seed],
outputs=output_video
)
demo.launch() |