linoyts HF staff commited on
Commit
5ecde43
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1 Parent(s): 4e6213f

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +40 -38
app.py CHANGED
@@ -391,6 +391,45 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
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  with gr.Row():
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  prompt = gr.Textbox(label="Prompt")
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  style = gr.Dropdown(label="Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
394
 
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  with gr.Tab(label="Multiple Images", id=1, visible=False) as multiimage_input_tab:
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  multiimage_prompt = gr.Gallery(label="Image Prompt", format="png", type="pil", height=300, columns=3)
@@ -400,44 +439,7 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
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  *NOTE: this is an experimental algorithm without training a specialized model. It may not produce the best results for all images, especially those having different poses or inconsistent details.*
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  """)
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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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-
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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=0.8,
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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)
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- gr.Markdown("Stage 1: Sparse Structure Generation")
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- with gr.Row():
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- ss_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
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- ss_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
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- gr.Markdown("Stage 2: Structured Latent Generation")
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- with gr.Row():
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- slat_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=3.0, step=0.1)
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- slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
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- multiimage_algo = gr.Radio(["stochastic", "multidiffusion"], label="Multi-image Algorithm", value="stochastic")
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-
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  #generate_btn = gr.Button("Generate")
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  with gr.Accordion(label="GLB Extraction Settings", open=False):
 
391
  with gr.Row():
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  prompt = gr.Textbox(label="Prompt")
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  style = gr.Dropdown(label="Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME)
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+
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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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+
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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)
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+ gr.Markdown("Stage 1: Sparse Structure Generation")
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+ with gr.Row():
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+ ss_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
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+ ss_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
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+ gr.Markdown("Stage 2: Structured Latent Generation")
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+ with gr.Row():
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+ slat_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=3.0, step=0.1)
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+ slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
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+ multiimage_algo = gr.Radio(["stochastic", "multidiffusion"], label="Multi-image Algorithm", value="stochastic")
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+
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434
  with gr.Tab(label="Multiple Images", id=1, visible=False) as multiimage_input_tab:
435
  multiimage_prompt = gr.Gallery(label="Image Prompt", format="png", type="pil", height=300, columns=3)
 
439
  *NOTE: this is an experimental algorithm without training a specialized model. It may not produce the best results for all images, especially those having different poses or inconsistent details.*
440
  """)
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+
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  #generate_btn = gr.Button("Generate")
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445
  with gr.Accordion(label="GLB Extraction Settings", open=False):