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Update blocks/inpainting.py
Browse files- blocks/inpainting.py +71 -60
blocks/inpainting.py
CHANGED
@@ -24,7 +24,7 @@ class StableDiffusionInpaintGenerator:
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device = get_device()
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self.pipe = get_scheduler_list(pipe=self.pipe, scheduler=scheduler)
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self.pipe.to(device)
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-
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return self.pipe
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def generate_image(
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@@ -33,7 +33,6 @@ class StableDiffusionInpaintGenerator:
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model_path: str,
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prompt: str,
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negative_prompt: str,
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num_images_per_prompt: int,
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scheduler: str,
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guidance_scale: int,
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num_inference_step: int,
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@@ -58,7 +57,7 @@ class StableDiffusionInpaintGenerator:
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image=image,
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mask_image=mask_image,
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negative_prompt=negative_prompt,
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num_images_per_prompt=
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num_inference_steps=num_inference_step,
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guidance_scale=guidance_scale,
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generator=generator,
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@@ -83,7 +82,7 @@ class StableDiffusionInpaintGenerator:
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stable_diffusion_inpaint_prompt = gr.Textbox(
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lines=1,
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placeholder="Prompt",
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show_label=False,
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elem_id="prompt-text-input-inpainting",
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value=''
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@@ -91,7 +90,7 @@ class StableDiffusionInpaintGenerator:
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stable_diffusion_inpaint_negative_prompt = gr.Textbox(
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lines=1,
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placeholder="Negative Prompt",
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show_label=False,
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elem_id = "negative-prompt-text-input-inpainting",
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value=''
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@@ -109,6 +108,8 @@ class StableDiffusionInpaintGenerator:
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lines=1,
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placeholder="Generated Prompt",
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show_label=False,
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)
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stable_diffusion_inpaint_model_id = gr.Dropdown(
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@@ -116,63 +117,62 @@ class StableDiffusionInpaintGenerator:
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value=list(INPAINT_MODEL_LIST.keys())[0],
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label="Inpaint Model Selection",
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elem_id="model-dropdown-inpainting",
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)
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with gr.Row():
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with gr.Column():
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stable_diffusion_inpaint_guidance_scale = gr.Slider(
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minimum=0.1,
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maximum=15,
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step=0.1,
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value=7.5,
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label="Guidance Scale",
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elem_id = "guidance-scale-slider-inpainting"
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)
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stable_diffusion_inpaint_num_inference_step = gr.Slider(
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minimum=1,
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maximum=100,
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step=1,
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value=50,
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label="Num Inference Step",
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elem_id = "num-inference-step-slider-inpainting"
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)
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stable_diffusion_inpiant_num_images_per_prompt = gr.Slider(
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minimum=1,
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maximum=10,
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step=1,
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value=1,
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label="Number Of Images",
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)
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with gr.Row():
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with gr.Column():
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stable_diffusion_inpaint_scheduler = gr.Dropdown(
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choices=SCHEDULER_LIST,
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value=SCHEDULER_LIST[0],
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label="Scheduler",
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elem_id="scheduler-dropdown-inpainting",
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)
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stable_diffusion_inpaint_size = gr.Slider(
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minimum=128,
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maximum=1280,
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step=32,
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value=512,
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label="Image Size",
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elem_id="image-size-slider-inpainting",
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)
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stable_diffusion_inpaint_seed_generator = gr.Slider(
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label="Seed(0 for random)",
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minimum=0,
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maximum=1000000,
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value=0,
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elem_id="seed-slider-inpainting",
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)
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stable_diffusion_inpaint_predict = gr.Button(
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value="
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)
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with gr.Column():
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@@ -189,6 +189,18 @@ class StableDiffusionInpaintGenerator:
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loading_icon = gr.HTML(loading_icon_html)
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share_button = gr.Button("Save artwork", elem_id="share-btn-inpainting")
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stable_diffusion_inpaint_predict.click(
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fn=StableDiffusionInpaintGenerator().generate_image,
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inputs=[
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@@ -196,7 +208,6 @@ class StableDiffusionInpaintGenerator:
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stable_diffusion_inpaint_model_id,
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stable_diffusion_inpaint_prompt,
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stable_diffusion_inpaint_negative_prompt,
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stable_diffusion_inpiant_num_images_per_prompt,
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stable_diffusion_inpaint_scheduler,
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stable_diffusion_inpaint_guidance_scale,
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stable_diffusion_inpaint_num_inference_step,
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device = get_device()
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self.pipe = get_scheduler_list(pipe=self.pipe, scheduler=scheduler)
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self.pipe.to(device)
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self.pipe.enable_attention_slicing()
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return self.pipe
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def generate_image(
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model_path: str,
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prompt: str,
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negative_prompt: str,
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scheduler: str,
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guidance_scale: int,
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num_inference_step: int,
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image=image,
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mask_image=mask_image,
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negative_prompt=negative_prompt,
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num_images_per_prompt=1,
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num_inference_steps=num_inference_step,
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guidance_scale=guidance_scale,
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generator=generator,
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stable_diffusion_inpaint_prompt = gr.Textbox(
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lines=1,
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placeholder="Prompt, keywords that explains how you want to modify the image.",
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show_label=False,
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elem_id="prompt-text-input-inpainting",
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value=''
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stable_diffusion_inpaint_negative_prompt = gr.Textbox(
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lines=1,
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placeholder="Negative Prompt, keywords that describe what you don't want in your image",
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show_label=False,
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elem_id = "negative-prompt-text-input-inpainting",
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value=''
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lines=1,
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placeholder="Generated Prompt",
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show_label=False,
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info="Auto generated prompts for inspiration.",
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)
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stable_diffusion_inpaint_model_id = gr.Dropdown(
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value=list(INPAINT_MODEL_LIST.keys())[0],
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label="Inpaint Model Selection",
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elem_id="model-dropdown-inpainting",
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info="Select the model you want to use for inpainting."
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)
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stable_diffusion_inpaint_scheduler = gr.Dropdown(
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choices=SCHEDULER_LIST,
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value=SCHEDULER_LIST[0],
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label="Scheduler",
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elem_id="scheduler-dropdown-inpainting",
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info="Scheduler list for models. Different schdulers result in different outputs."
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)
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stable_diffusion_inpaint_guidance_scale = gr.Slider(
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minimum=0.1,
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maximum=15,
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step=0.1,
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value=7.5,
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label="Guidance Scale",
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elem_id = "guidance-scale-slider-inpainting",
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info = "Guidance scale determines how much the prompt will affect the image. Higher the value, more the effect."
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)
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stable_diffusion_inpaint_num_inference_step = gr.Slider(
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minimum=1,
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maximum=100,
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step=1,
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value=50,
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label="Num Inference Step",
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elem_id = "num-inference-step-slider-inpainting",
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info = "Number of inference step determines the quality of the image. Higher the number, better the quality."
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)
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stable_diffusion_inpaint_size = gr.Slider(
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minimum=128,
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maximum=1280,
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step=32,
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value=512,
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label="Image Size",
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elem_id="image-size-slider-inpainting",
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info = "Image size determines the height and width of the generated image. Higher the value, better the quality however slower the computation."
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)
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stable_diffusion_inpaint_seed_generator = gr.Slider(
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label="Seed(0 for random)",
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minimum=0,
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maximum=1000000,
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value=0,
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elem_id="seed-slider-inpainting",
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info="Set the seed to a specific value to reproduce the results."
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)
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stable_diffusion_inpaint_predict = gr.Button(
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value="Generate image"
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)
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with gr.Column():
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loading_icon = gr.HTML(loading_icon_html)
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share_button = gr.Button("Save artwork", elem_id="share-btn-inpainting")
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gr.HTML(
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"""
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<div id="model-description-img2img">
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<h3>Inpainting Models</h3>
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<p>Inpainting models will take a masked image and modify the masked image with the given prompt.</p>
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<p>Prompt should describe how you want to modify the image. For example, if you want to modify the image to have a blue sky, you can use the prompt "sky is blue".</p>
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<p>Negative prompt should describe what you don't want in your image. For example, if you don't want the image to have a red sky, you can use the negative prompt "sky is red".</p>
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<hr>
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<p>Stable Diffusion 1 & 2: Default model for many tasks. </p>
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</div>
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"""
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)
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stable_diffusion_inpaint_predict.click(
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fn=StableDiffusionInpaintGenerator().generate_image,
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inputs=[
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stable_diffusion_inpaint_model_id,
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stable_diffusion_inpaint_prompt,
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stable_diffusion_inpaint_negative_prompt,
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stable_diffusion_inpaint_scheduler,
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stable_diffusion_inpaint_guidance_scale,
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stable_diffusion_inpaint_num_inference_step,
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