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import gradio as gr |
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import spaces |
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import torch |
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from diffusers import LTXConditionPipeline, LTXLatentUpsamplePipeline |
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from diffusers.pipelines.ltx.pipeline_ltx_condition import LTXVideoCondition |
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from diffusers.utils import export_to_video, load_video |
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pipe = LTXConditionPipeline.from_pretrained("a-r-r-o-w/LTX-Video-0.9.7-diffusers", torch_dtype=torch.bfloat16) |
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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) |
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pipe.to("cuda") |
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pipe_upsample.to("cuda") |
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pipe.vae.enable_tiling() |
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def round_to_nearest_resolution_acceptable_by_vae(height, width): |
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height = height - (height % pipe.vae_temporal_compression_ratio) |
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width = width - (width % pipe.vae_temporal_compression_ratio) |
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return height, width |
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@spaces.GPU |
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def generate(prompt, |
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negative_prompt, |
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steps, |
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seed): |
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return |
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css=""" |
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#col-container { |
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margin: 0 auto; |
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max-width: 900px; |
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} |
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""" |
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js_func = """ |
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function refresh() { |
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const url = new URL(window.location); |
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if (url.searchParams.get('__theme') !== 'dark') { |
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url.searchParams.set('__theme', 'dark'); |
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window.location.href = url.href; |
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} |
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} |
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""" |
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with gr.Blocks(css=css, theme=gr.themes.Ocean()) as demo: |
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gr.Markdown("# LTX Video 0.9.7 Distilled") |
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with gr.Row(): |
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with gr.Column(): |
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with gr.Group(): |
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image = gr.Image(label="") |
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prompt = gr.Textbox(label="prompt") |
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run_button = gr.Button() |
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with gr.Column(): |
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output = gr.Video(interactive=False) |
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with gr.Accordion("Advanced settings", open=False): |
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n_prompt = gr.Textbox(label="negative prompt", value="", visible=False) |
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with gr.Row(): |
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seed = gr.Number(label="seed", value=0, precision=0) |
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randomize_seed = gr.Checkbox(label="randomize seed") |
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with gr.Row(): |
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steps = gr.Slider(label="Steps", minimum=1, maximum=30, value=8, step=1) |
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num_frames = gr.Slider(label="# frames", minimum=1, maximum=30, value=8, step=1) |
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demo.launch() |
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