Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
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)
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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)
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@@ -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.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=0.8,
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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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#generate_btn = gr.Button("Generate")
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with gr.Accordion(label="GLB Extraction Settings", open=False):
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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)
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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)
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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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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)
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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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#generate_btn = gr.Button("Generate")
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with gr.Accordion(label="GLB Extraction Settings", open=False):
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