Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -28,7 +28,7 @@ def error_str(error, title="Error"):
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{error}""" if error else ""
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@spaces.GPU
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def inference(prompt, guidance, steps, width=
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if torch.cuda.is_available():
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generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
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@@ -108,7 +108,7 @@ with gr.Blocks(css=css) as demo:
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prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="[your prompt]")
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generate = gr.Button(value="Generate")
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image_out = gr.Image(height=
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error_output = gr.Markdown()
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with gr.Column(scale=45):
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@@ -118,17 +118,17 @@ with gr.Blocks(css=css) as demo:
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with gr.Row():
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guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
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steps = gr.Slider(label="Steps", value=
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with gr.Row():
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width = gr.Slider(label="Width", value=
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height = gr.Slider(label="Height", value=
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seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
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with gr.Tab("Image to image"):
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with gr.Group():
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image = gr.Image(label="Image", height=
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strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
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inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt]
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{error}""" if error else ""
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@spaces.GPU
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def inference(prompt, guidance, steps, width=768, height=768, seed=0, img=None, strength=0.5, neg_prompt=""):
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if torch.cuda.is_available():
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generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
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prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="[your prompt]")
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generate = gr.Button(value="Generate")
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image_out = gr.Image(height=768)
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error_output = gr.Markdown()
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with gr.Column(scale=45):
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with gr.Row():
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guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
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steps = gr.Slider(label="Steps", value=24, minimum=4, maximum=50, step=1)
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with gr.Row():
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width = gr.Slider(label="Width", value=768, minimum=64, maximum=1024, step=64)
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height = gr.Slider(label="Height", value=768, minimum=64, maximum=1024, step=64)
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seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
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with gr.Tab("Image to image"):
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with gr.Group():
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image = gr.Image(label="Image", height=768, type="pil")
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strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
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inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt]
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