Create app.py

initial commit

app.py

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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
+
+
+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()