Spaces:
Running on Zero
Running on Zero
Commit ·
f091221
1
Parent(s): d0a0381
app.py
Browse files
app.py
CHANGED
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@@ -54,21 +54,8 @@ class Img2Img:
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load_lora_model(self.lora_dir)
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def prompt_layout(self):
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with gr.Column():
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with gr.Row():
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self.prompt = gr.Textbox(label="prompt", lines=3)
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with gr.Row():
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self.negative_prompt = gr.Textbox(label="negative_prompt", lines=3, value="lowres, error, extra digit, fewer digits, cropped, worst quality,low quality, normal quality, jpeg artifacts, blurry")
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with gr.Row():
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self.prompt_analysis_button = gr.Button("prompt解析")
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self.process_prompt_analysis,
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inputs=[self.input_image_path],
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outputs=self.prompt
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)
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return [self.prompt, self.negative_prompt]
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def process_prompt_analysis(self, input_image_path):
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if self.tagger_model is None:
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@@ -92,12 +79,28 @@ class Img2Img:
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with gr.Row():
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with gr.Column():
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self.input_image_path = gr.Image(label="input_image", type='filepath')
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with gr.Column():
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self.output_image = gr.Image(type="pil", label="出力画像")
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generate_button.click(
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fn=self.predict,
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inputs=[self.input_image_path, self.prompt, self.negative_prompt, self.controlnet_scale],
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load_lora_model(self.lora_dir)
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def process_prompt_analysis(self, input_image_path):
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if self.tagger_model is None:
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with gr.Row():
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with gr.Column():
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self.input_image_path = gr.Image(label="input_image", type='filepath')
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with gr.Column():
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with gr.Row():
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self.prompt = gr.Textbox(label="prompt", lines=3)
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with gr.Row():
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self.negative_prompt = gr.Textbox(label="negative_prompt", lines=3, value="lowres, error, extra digit, fewer digits, cropped, worst quality,low quality, normal quality, jpeg artifacts, blurry")
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with gr.Row():
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prompt_analysis_button = gr.Button("prompt解析")
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with gr.Row():
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self.controlnet_scale = gr.Slider(minimum=0.5, maximum=1.25, value=1.0, step=0.01, label="線画忠実度")
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with gr.Row():
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generate_button = gr.Button("生成")
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with gr.Column():
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self.output_image = gr.Image(type="pil", label="出力画像")
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prompt_analysis_button.click(
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self.process_prompt_analysis,
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inputs=[self.input_image_path],
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outputs=self.prompt
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)
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generate_button.click(
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fn=self.predict,
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inputs=[self.input_image_path, self.prompt, self.negative_prompt, self.controlnet_scale],
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