Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -31,6 +31,7 @@ SAMPLER_MAP = {
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}
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def inference(
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prompt: str,
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negative_prompt: str,
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guidance_scale: float = 8.0,
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@@ -42,7 +43,7 @@ def inference(
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if prompt is None or prompt == "":
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raise gr.Error("Prompt is required")
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-
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pipe.scheduler = SAMPLER_MAP[sampler](pipe.scheduler.config)
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generator = torch.manual_seed(seed) if seed != -1 else torch.Generator()
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@@ -51,7 +52,7 @@ def inference(
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prompt=prompt,
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negative_prompt=negative_prompt,
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image=None,
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-
control_image=
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guidance_scale=float(guidance_scale),
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controlnet_conditioning_scale=float(controlnet_conditioning_scale),
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generator=generator,
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@@ -71,7 +72,7 @@ with gr.Blocks() as app:
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with gr.Row():
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with gr.Column():
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prompt = gr.Textbox(label="Prompt")
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negative_prompt = gr.Textbox(label="Negative Prompt", value="ugly, disfigured, low quality, blurry, nsfw")
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with gr.Accordion(label="Advanced Options", open=False):
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@@ -86,7 +87,7 @@ with gr.Blocks() as app:
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run_btn.click(
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inference,
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inputs=[prompt, negative_prompt, guidance_scale, controlnet_conditioning_scale, strength, seed, sampler],
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outputs=[result_image]
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)
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}
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def inference(
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control_image: Image.Image,
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prompt: str,
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negative_prompt: str,
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guidance_scale: float = 8.0,
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if prompt is None or prompt == "":
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raise gr.Error("Prompt is required")
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control_image = control_image.resize((512, 512))
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pipe.scheduler = SAMPLER_MAP[sampler](pipe.scheduler.config)
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generator = torch.manual_seed(seed) if seed != -1 else torch.Generator()
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prompt=prompt,
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negative_prompt=negative_prompt,
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image=None,
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control_image=control_image,
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guidance_scale=float(guidance_scale),
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controlnet_conditioning_scale=float(controlnet_conditioning_scale),
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generator=generator,
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with gr.Row():
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with gr.Column():
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control_image = gr.Image(label="Control Image", type="pil")
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prompt = gr.Textbox(label="Prompt")
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negative_prompt = gr.Textbox(label="Negative Prompt", value="ugly, disfigured, low quality, blurry, nsfw")
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with gr.Accordion(label="Advanced Options", open=False):
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run_btn.click(
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inference,
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inputs=[control_image, prompt, negative_prompt, guidance_scale, controlnet_conditioning_scale, strength, seed, sampler],
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outputs=[result_image]
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)
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