Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,098 Bytes
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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
css="""
#col-container {
margin: 0 auto;
max-width: 900px;
}
"""
js_func = """
function refresh() {
const url = new URL(window.location);
if (url.searchParams.get('__theme') !== 'dark') {
url.searchParams.set('__theme', 'dark');
window.location.href = url.href;
}
}
"""
with gr.Blocks(css=css, theme=gr.themes.Ocean(), js=js_func) as demo:
gr.Markdown("# LTX Video 0.9.7 Distilled")
with gr.Row():
with gr.Column():
with gr.Group():
image = gr.Image(label="")
prompt = gr.Textbox(label="prompt")
run_button = gr.Button()
with gr.Column():
output = gr.Video(interactive=False)
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()
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