app.py

import gradio as gr
import spaces 
import torch
from diffusers import LTXConditionPipeline, LTXLatentUpsamplePipeline
from diffusers.pipelines.ltx.pipeline_ltx_condition import LTXVideoCondition
from diffusers.utils import export_to_video, load_video

pipe = LTXConditionPipeline.from_pretrained("a-r-r-o-w/LTX-Video-0.9.7-diffusers", torch_dtype=torch.bfloat16)
pipe_upsample = LTXLatentUpsamplePipeline.from_pretrained("a-r-r-o-w/LTX-Video-0.9.7-Latent-Spatial-Upsampler-diffusers", vae=pipe.vae, torch_dtype=torch.bfloat16)
pipe.to("cuda")
pipe_upsample.to("cuda")
pipe.vae.enable_tiling()


def round_to_nearest_resolution_acceptable_by_vae(height, width):
    height = height - (height % pipe.vae_temporal_compression_ratio)
    width = width - (width % pipe.vae_temporal_compression_ratio)
    return height, width
    
@spaces.GPU
def generate(prompt,
             negative_prompt,
             steps,
             seed):
    return


with gr.Blocks() as demo:

  gr.Markdown("# LTX Video 0.9.7 Distilled")

  prompt = gr.Textbox(label="prompt")
  output = gr.Video(interactive=False)
  run_button = gr.Button()

  with gr.Accordion("Advanced settings", open=False):
     n_prompt = gr.Textbox(label="negative prompt", value="", visible=False)  
     with gr.Row():
      seed = gr.Number(label="seed", value=0, precision=0)
      randomize_seed = gr.Checkbox(label="randomize seed")
     with gr.Row():
      steps = gr.Slider(label="Steps", minimum=1, maximum=30, value=8, step=1)
      num_frames = gr.Slider(label="# frames", minimum=1, maximum=30, value=8, step=1)
    
    
  

demo.launch()