make refiner de-selectable
Browse files
app.py
CHANGED
@@ -11,6 +11,12 @@ def device_change(device, config):
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return config, str(config), assemble_code(config)
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def models_change(model, scheduler, config):
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config = set_config(config, 'model', model)
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@@ -210,7 +216,7 @@ with gr.Blocks(analytics_enabled=False) as demo:
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with gr.Row():
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with gr.Column(scale=1):
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in_use_safetensors = gr.Radio(label="Use safe tensors:", choices=["True", "False"], interactive=False)
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in_model_refiner = gr.
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with gr.Column(scale=1):
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in_safety_checker = gr.Radio(label="Enable safety checker:", value=config.value["safety_checker"], choices=["True", "False"])
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in_requires_safety_checker = gr.Radio(label="Requires safety checker:", value=config.value["requires_safety_checker"], choices=["True", "False"])
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@@ -229,7 +235,7 @@ with gr.Blocks(analytics_enabled=False) as demo:
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in_prompt = gr.TextArea(label="Prompt", value=config.value["prompt"])
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in_negative_prompt = gr.TextArea(label="Negative prompt", value=config.value["negative_prompt"])
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with gr.Row():
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in_inference_steps = gr.Number(label="Inference steps", value=config.value["inference_steps"])
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in_manual_seed = gr.Number(label="Manual seed", value=config.value["manual_seed"], info="Set this to -1 or leave it empty to randomly generate an image. A fixed value will result in a similar image for every run")
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in_guidance_scale = gr.Slider(minimum=0, maximum=1, step=0.01, label="Guidance Scale", value=config.value["guidance_scale"], info="A low guidance scale leads to a faster inference time, with the drawback that negative prompts don’t have any effect on the denoising process.")
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@@ -251,6 +257,7 @@ with gr.Blocks(analytics_enabled=False) as demo:
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in_allow_tensorfloat32.change(tensorfloat32_change, inputs=[in_allow_tensorfloat32, config], outputs=[config, out_config, out_code])
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in_variant.change(variant_change, inputs=[in_variant, config], outputs=[config, out_config, out_code])
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in_models.change(models_change, inputs=[in_models, in_schedulers, config], outputs=[out_model_description, in_model_refiner, in_use_safetensors, in_schedulers, config, out_config, out_code])
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in_safety_checker.change(safety_checker_change, inputs=[in_safety_checker, config], outputs=[config, out_config, out_code])
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in_requires_safety_checker.change(requires_safety_checker_change, inputs=[in_requires_safety_checker, config], outputs=[config, out_config, out_code])
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in_schedulers.change(schedulers_change, inputs=[in_schedulers, config], outputs=[out_scheduler_description, config, out_config, out_code])
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return config, str(config), assemble_code(config)
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def model_refiner_change(refiner, config):
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config = set_config(config, 'refiner', refiner)
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return config, str(config), assemble_code(config)
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def models_change(model, scheduler, config):
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config = set_config(config, 'model', model)
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with gr.Row():
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with gr.Column(scale=1):
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in_use_safetensors = gr.Radio(label="Use safe tensors:", choices=["True", "False"], interactive=False)
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in_model_refiner = gr.Dropdown(value="", choices=[""], label="Refiner", allow_custom_value=True, multiselect=False)
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with gr.Column(scale=1):
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in_safety_checker = gr.Radio(label="Enable safety checker:", value=config.value["safety_checker"], choices=["True", "False"])
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in_requires_safety_checker = gr.Radio(label="Requires safety checker:", value=config.value["requires_safety_checker"], choices=["True", "False"])
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in_prompt = gr.TextArea(label="Prompt", value=config.value["prompt"])
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in_negative_prompt = gr.TextArea(label="Negative prompt", value=config.value["negative_prompt"])
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with gr.Row():
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in_inference_steps = gr.Number(label="Inference steps", value=config.value["inference_steps"], info="Each step improves the final result but also results in higher computation")
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in_manual_seed = gr.Number(label="Manual seed", value=config.value["manual_seed"], info="Set this to -1 or leave it empty to randomly generate an image. A fixed value will result in a similar image for every run")
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in_guidance_scale = gr.Slider(minimum=0, maximum=1, step=0.01, label="Guidance Scale", value=config.value["guidance_scale"], info="A low guidance scale leads to a faster inference time, with the drawback that negative prompts don’t have any effect on the denoising process.")
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in_allow_tensorfloat32.change(tensorfloat32_change, inputs=[in_allow_tensorfloat32, config], outputs=[config, out_config, out_code])
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in_variant.change(variant_change, inputs=[in_variant, config], outputs=[config, out_config, out_code])
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in_models.change(models_change, inputs=[in_models, in_schedulers, config], outputs=[out_model_description, in_model_refiner, in_use_safetensors, in_schedulers, config, out_config, out_code])
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in_model_refiner.change(model_refiner_change, inputs=[in_model_refiner, config], outputs=[config, out_config, out_code])
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in_safety_checker.change(safety_checker_change, inputs=[in_safety_checker, config], outputs=[config, out_config, out_code])
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in_requires_safety_checker.change(requires_safety_checker_change, inputs=[in_requires_safety_checker, config], outputs=[config, out_config, out_code])
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in_schedulers.change(schedulers_change, inputs=[in_schedulers, config], outputs=[out_scheduler_description, config, out_config, out_code])
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