cavargas10 commited on
Commit
74a6aa8
·
verified ·
1 Parent(s): 8261bcc

Update app.py

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Files changed (1) hide show
  1. app.py +4 -23
app.py CHANGED
@@ -1,6 +1,5 @@
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  import gradio as gr
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  import spaces
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- from gradio_litmodel3d import LitModel3D
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  import os
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  import shutil
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  os.environ['SPCONV_ALGO'] = 'native'
@@ -224,27 +223,9 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
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  with gr.Accordion(label="Generation Settings", open=False):
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  with gr.Tab(label="sketch-to-image generation"):
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  negative_prompt = gr.Textbox(label="Negative prompt")
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- num_steps = gr.Slider(
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- label="Number of steps",
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- minimum=1,
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- maximum=20,
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- step=1,
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- value=8,
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- )
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- guidance_scale = gr.Slider(
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- label="Guidance scale",
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- minimum=0.1,
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- maximum=10.0,
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- step=0.1,
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- value=5,
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- )
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- controlnet_conditioning_scale = gr.Slider(
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- label="controlnet conditioning scale",
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- minimum=0.5,
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- maximum=5.0,
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- step=0.01,
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- value=0.85,
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- )
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  with gr.Tab(label="3D generation"):
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  seed = gr.Slider(0, MAX_SEED, label="Seed", value=0, step=1)
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  randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
@@ -265,7 +246,7 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
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  with gr.Column():
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  video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300)
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  image_prompt_processed = gr.Image(label="processed sketch", interactive=False, type="pil", height=512)
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- model_output = LitModel3D(label="Extracted GLB/Gaussian", exposure=10.0, height=300)
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  with gr.Row():
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  download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
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  download_gs = gr.DownloadButton(label="Download Gaussian", interactive=False)
 
1
  import gradio as gr
2
  import spaces
 
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  import os
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  import shutil
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  os.environ['SPCONV_ALGO'] = 'native'
 
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  with gr.Accordion(label="Generation Settings", open=False):
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  with gr.Tab(label="sketch-to-image generation"):
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  negative_prompt = gr.Textbox(label="Negative prompt")
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+ num_steps = gr.Slider(1, 20, label="Number of steps", value=8, step=1)
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+ guidance_scale = gr.Slider(0.1, 10.0, label="Guidance scale", value=5, step=0.1)
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+ controlnet_conditioning_scale = gr.Slider(0.5, 5.0, label="controlnet conditioning scale", value=0.85, step=0.01)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  with gr.Tab(label="3D generation"):
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  seed = gr.Slider(0, MAX_SEED, label="Seed", value=0, step=1)
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  randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
 
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  with gr.Column():
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  video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300)
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  image_prompt_processed = gr.Image(label="processed sketch", interactive=False, type="pil", height=512)
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+ model_output = gr.Model3D(label="Extracted GLB/Gaussian", height=300)
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  with gr.Row():
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  download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
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  download_gs = gr.DownloadButton(label="Download Gaussian", interactive=False)