Spaces:
Running
on
Zero
Running
on
Zero
Update gradio_seesr_turbo.py
Browse files- gradio_seesr_turbo.py +10 -5
gradio_seesr_turbo.py
CHANGED
@@ -47,6 +47,11 @@ snapshot_download(
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# Load scheduler, tokenizer and models.
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pretrained_model_path = 'preset/models/sd-turbo'
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seesr_model_path = 'preset/models/seesr'
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@@ -56,7 +61,7 @@ text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="t
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tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
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vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae")
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# feature_extractor = CLIPImageProcessor.from_pretrained(f"{pretrained_model_path}/feature_extractor")
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unet = UNet2DConditionModel.from_pretrained_orig(seesr_model_path, subfolder="unet")
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controlnet = ControlNetModel.from_pretrained(seesr_model_path, subfolder="controlnet")
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# Freeze vae and text_encoder
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@@ -181,8 +186,8 @@ with block:
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gr.Markdown(MARKDOWN)
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with gr.Row():
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with gr.Column():
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input_image = gr.Image(
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run_button = gr.Button(
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with gr.Accordion("Options", open=True):
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user_prompt = gr.Textbox(label="User Prompt", value="")
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positive_prompt = gr.Textbox(label="Positive Prompt", value="clean, high-resolution, 8k, best quality, masterpiece")
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@@ -198,7 +203,7 @@ with block:
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latent_tiled_overlap = gr.Slider(label="Diffusion Tile Overlap", minimum=4, maximum=16, value=4, step=1)
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scale_factor = gr.Number(label="SR Scale", value=4)
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with gr.Column():
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result_gallery = gr.Gallery(label="Output", show_label=False, elem_id="gallery")
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inputs = [
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input_image,
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@@ -215,5 +220,5 @@ with block:
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run_button.click(fn=process, inputs=inputs, outputs=[result_gallery])
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block.launch()
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snapshot_download(
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repo_id="xinyu1205/recognize_anything_model",
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local_dir="preset/models/"
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)
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# Load scheduler, tokenizer and models.
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pretrained_model_path = 'preset/models/sd-turbo'
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seesr_model_path = 'preset/models/seesr'
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tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
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vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae")
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# feature_extractor = CLIPImageProcessor.from_pretrained(f"{pretrained_model_path}/feature_extractor")
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unet = UNet2DConditionModel.from_pretrained_orig(pretrained_model_path, seesr_model_path, subfolder="unet")
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controlnet = ControlNetModel.from_pretrained(seesr_model_path, subfolder="controlnet")
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# Freeze vae and text_encoder
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gr.Markdown(MARKDOWN)
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with gr.Row():
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with gr.Column():
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input_image = gr.Image(type="pil")
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run_button = gr.Button("Run")
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with gr.Accordion("Options", open=True):
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user_prompt = gr.Textbox(label="User Prompt", value="")
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positive_prompt = gr.Textbox(label="Positive Prompt", value="clean, high-resolution, 8k, best quality, masterpiece")
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latent_tiled_overlap = gr.Slider(label="Diffusion Tile Overlap", minimum=4, maximum=16, value=4, step=1)
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scale_factor = gr.Number(label="SR Scale", value=4)
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with gr.Column():
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result_gallery = gr.Gallery(label="Output", show_label=False, elem_id="gallery")
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inputs = [
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input_image,
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]
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run_button.click(fn=process, inputs=inputs, outputs=[result_gallery])
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block.launch(share=True)
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