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Update app.py
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app.py
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@@ -57,7 +57,7 @@ pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
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text_encoder_2=pipe.text_encoder_2,
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tokenizer_2=pipe.tokenizer_2,
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torch_dtype=dtype
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# 업스케일링을 위한 ControlNet 모델 로드
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controlnet = FluxControlNetModel.from_pretrained(
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@@ -74,10 +74,7 @@ pipe_controlnet = FluxControlNetPipeline(
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controlnet=controlnet,
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scheduler=pipe.scheduler
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# 'safety_checker'와 'feature_extractor'는 제거됨
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).to(device)
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MAX_SEED = 2**32 - 1
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MAX_PIXEL_BUDGET = 1024 * 1024
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@@ -667,11 +664,19 @@ with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css, delete_cache=(60, 3600)) as a
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fn=run_lora,
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inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, loras_state],
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outputs=[result, seed, progress_bar]
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)
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prompt.submit(
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fn=run_lora,
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inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, loras_state],
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outputs=[result, seed, progress_bar]
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)
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gallery.select(
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fn=update_selection,
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@@ -703,11 +708,6 @@ with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css, delete_cache=(60, 3600)) as a
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inputs=[selected_indices, loras_state],
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outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
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)
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history_gallery.load(
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fn=update_history,
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inputs=[result, history_gallery],
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outputs=history_gallery
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)
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with gr.Tab("Upscale"):
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with gr.Row():
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@@ -763,3 +763,4 @@ with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css, delete_cache=(60, 3600)) as a
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app.queue()
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app.launch()
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text_encoder_2=pipe.text_encoder_2,
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tokenizer_2=pipe.tokenizer_2,
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torch_dtype=dtype
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).to(device)
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# 업스케일링을 위한 ControlNet 모델 로드
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controlnet = FluxControlNetModel.from_pretrained(
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controlnet=controlnet,
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scheduler=pipe.scheduler
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# 'safety_checker'와 'feature_extractor'는 제거됨
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).to(device) # 'torch_dtype' 제거
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MAX_SEED = 2**32 - 1
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MAX_PIXEL_BUDGET = 1024 * 1024
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fn=run_lora,
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inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, loras_state],
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outputs=[result, seed, progress_bar]
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).then( # Update the history gallery
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fn=lambda x, history: update_history(x, history),
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inputs=[result, history_gallery],
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outputs=history_gallery,
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)
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prompt.submit(
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fn=run_lora,
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inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, loras_state],
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outputs=[result, seed, progress_bar]
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).then( # Update the history gallery
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fn=lambda x, history: update_history(x, history),
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inputs=[result, history_gallery],
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outputs=history_gallery,
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)
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gallery.select(
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fn=update_selection,
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inputs=[selected_indices, loras_state],
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outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
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)
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with gr.Tab("Upscale"):
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with gr.Row():
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app.queue()
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app.launch()
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