Spaces:
Running
on
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Running
on
Zero
Commit
•
7813cdf
1
Parent(s):
c3029a6
Seed
Browse files- gradio_demo.py +30 -15
gradio_demo.py
CHANGED
@@ -65,10 +65,18 @@ if torch.cuda.device_count() > 0:
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else:
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llava_agent = None
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def check(input_image):
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if input_image is None:
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raise gr.Error("Please provide an image to restore.")
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@spaces.GPU(duration=180)
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def stage1_process(input_image, gamma_correction):
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print('Start stage1_process')
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@@ -114,7 +122,7 @@ def stage2_process(input_image, prompt, a_prompt, n_prompt, num_samples, upscale
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print('Start stage2_process')
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if torch.cuda.device_count() == 0:
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gr.Warning('Set this space to GPU config to make it work.')
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return None, None
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torch.cuda.set_device(SUPIR_device)
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event_id = str(time.time_ns())
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event_dict = {'event_id': event_id, 'localtime': time.ctime(), 'prompt': prompt, 'a_prompt': a_prompt,
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@@ -169,7 +177,7 @@ def stage2_process(input_image, prompt, a_prompt, n_prompt, num_samples, upscale
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for i, result in enumerate(results):
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Image.fromarray(result).save(f'./history/{event_id[:5]}/{event_id[5:]}/HQ_{i}.png')
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print('End stage2_process')
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-
return [input_image] + results, event_id
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def load_and_reset(param_setting):
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print('Start load_and_reset')
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@@ -231,10 +239,10 @@ else:
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title_md = """
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<h1><center>SUPIR Image Upscaler</center></h1>
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SUPIR is a practicing model scaling for photo-realistic image restoration. It is still a research project under tested and is not yet a stable commercial product.
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<a href="https://arxiv.org/abs/2401.13627">Paper</a>   <a href="http://supir.xpixel.group/">Project Page</a>   <a href="https://github.com/Fanghua-Yu/SUPIR/blob/master/assets/DemoGuide.png">How to play</a>   <a href="https://huggingface.co/blog/MonsterMMORPG/supir-sota-image-upscale-better-than-magnific-ai">Local Install Guide</a>
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<p style="background-color: blue;">
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"""
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@@ -249,7 +257,7 @@ The service is a research preview intended for non-commercial use only, subject
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"""
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# Gradio interface
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with gr.Blocks(title=
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with gr.Row():
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gr.HTML(title_md)
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@@ -275,13 +283,14 @@ with gr.Blocks(title='SUPIR') as interface:
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with gr.Accordion("Restoring options", open=False):
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num_samples = gr.Slider(label="Num Samples", info="Number of generated results; I discourage to increase because the process is limited to 3 min", minimum=1, maximum=4 if not args.use_image_slider else 1
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, value=1, step=1)
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upscale = gr.Slider(label="Upscale", info="
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edm_steps = gr.Slider(label="Steps", info="lower=faster, higher=more details", minimum=1, maximum=200, value=default_setting.edm_steps if torch.cuda.device_count() > 0 else 1, step=1)
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s_cfg = gr.Slider(label="Text Guidance Scale", info="lower=follow the image, higher=follow the prompt", minimum=1.0, maximum=15.0,
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value=default_setting.s_cfg_Quality if torch.cuda.device_count() > 0 else 1.0, step=0.1)
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s_stage2 = gr.Slider(label="Restoring Guidance Strength", minimum=0., maximum=1., value=1., step=0.05)
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s_stage1 = gr.Slider(label="Pre-denoising Guidance Strength", minimum=-1.0, maximum=6.0, value=-1.0, step=1.0)
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-
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s_churn = gr.Slider(label="S-Churn", minimum=0, maximum=40, value=5, step=1)
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s_noise = gr.Slider(label="S-Noise", minimum=1.0, maximum=1.1, value=1.003, step=0.001)
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a_prompt = gr.Textbox(label="Default Positive Prompt",
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@@ -329,9 +338,9 @@ with gr.Blocks(title='SUPIR') as interface:
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result_gallery = ImageSlider(label='Output', show_label=False, elem_id="gallery1")
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with gr.Row():
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with gr.Column():
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denoise_button = gr.Button(value="Pre-denoise
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with gr.Column():
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llave_button = gr.Button(value="Generate description by LlaVa (
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with gr.Column():
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diffusion_button = gr.Button(value="🚀 Restore", variant = "primary")
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with gr.Row():
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@@ -369,9 +378,17 @@ with gr.Blocks(title='SUPIR') as interface:
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prompt
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])
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diffusion_button.click(fn =
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input_image
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], outputs = [], queue = False, show_progress = False).
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input_image,
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prompt,
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a_prompt,
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@@ -396,9 +413,7 @@ with gr.Blocks(title='SUPIR') as interface:
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model_select
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], outputs = [
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result_gallery,
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event_id
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fb_score,
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fb_text
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])
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restart_button.click(fn = load_and_reset, inputs = [
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else:
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llava_agent = None
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def update_seed(is_randomize_seed, seed):
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if is_randomize_seed:
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return random.randint(0, max_64_bit_int)
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return seed
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def check(input_image):
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if input_image is None:
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raise gr.Error("Please provide an image to restore.")
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def reset_feedback():
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return 3, ''
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@spaces.GPU(duration=180)
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def stage1_process(input_image, gamma_correction):
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print('Start stage1_process')
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print('Start stage2_process')
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if torch.cuda.device_count() == 0:
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gr.Warning('Set this space to GPU config to make it work.')
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return None, None
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torch.cuda.set_device(SUPIR_device)
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event_id = str(time.time_ns())
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event_dict = {'event_id': event_id, 'localtime': time.ctime(), 'prompt': prompt, 'a_prompt': a_prompt,
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for i, result in enumerate(results):
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Image.fromarray(result).save(f'./history/{event_id[:5]}/{event_id[5:]}/HQ_{i}.png')
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print('End stage2_process')
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return [input_image] + results, event_id
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def load_and_reset(param_setting):
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print('Start load_and_reset')
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title_md = """
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<h1><center>SUPIR Image Upscaler</center></h1>
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<p>SUPIR is a practicing model scaling for photo-realistic image restoration. It is still a research project under tested and is not yet a stable commercial product.
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<a href="https://arxiv.org/abs/2401.13627">Paper</a>   <a href="http://supir.xpixel.group/">Project Page</a>   <a href="https://github.com/Fanghua-Yu/SUPIR/blob/master/assets/DemoGuide.png">How to play</a>   <a href="https://huggingface.co/blog/MonsterMMORPG/supir-sota-image-upscale-better-than-magnific-ai">Local Install Guide</a></p>
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<p style="background-color: blue;">LLaVa is disabled.</p>
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"""
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"""
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# Gradio interface
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with gr.Blocks(title="SUPIR") as interface:
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with gr.Row():
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gr.HTML(title_md)
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with gr.Accordion("Restoring options", open=False):
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num_samples = gr.Slider(label="Num Samples", info="Number of generated results; I discourage to increase because the process is limited to 3 min", minimum=1, maximum=4 if not args.use_image_slider else 1
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, value=1, step=1)
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upscale = gr.Slider(label="Upscale factor", info="Resolution x1, x2, x3, x4, x5, x6, x7 or x8", minimum=1, maximum=8, value=1, step=1)
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edm_steps = gr.Slider(label="Steps", info="lower=faster, higher=more details", minimum=1, maximum=200, value=default_setting.edm_steps if torch.cuda.device_count() > 0 else 1, step=1)
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s_cfg = gr.Slider(label="Text Guidance Scale", info="lower=follow the image, higher=follow the prompt", minimum=1.0, maximum=15.0,
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value=default_setting.s_cfg_Quality if torch.cuda.device_count() > 0 else 1.0, step=0.1)
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s_stage2 = gr.Slider(label="Restoring Guidance Strength", minimum=0., maximum=1., value=1., step=0.05)
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s_stage1 = gr.Slider(label="Pre-denoising Guidance Strength", minimum=-1.0, maximum=6.0, value=-1.0, step=1.0)
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randomize_seed = gr.Checkbox(label = "\U0001F3B2 Randomize seed", value = True, info = "If checked, result is always different")
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seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True)
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s_churn = gr.Slider(label="S-Churn", minimum=0, maximum=40, value=5, step=1)
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s_noise = gr.Slider(label="S-Noise", minimum=1.0, maximum=1.1, value=1.003, step=0.001)
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a_prompt = gr.Textbox(label="Default Positive Prompt",
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result_gallery = ImageSlider(label='Output', show_label=False, elem_id="gallery1")
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with gr.Row():
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with gr.Column():
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denoise_button = gr.Button(value="Pre-denoise")
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with gr.Column():
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llave_button = gr.Button(value="Generate description by LlaVa (disabled)")
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with gr.Column():
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diffusion_button = gr.Button(value="🚀 Restore", variant = "primary")
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with gr.Row():
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prompt
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])
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diffusion_button.click(fn = update_seed, inputs = [
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randomize_seed,
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seed
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], outputs = [
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seed
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], queue = False, show_progress = False).then(fn = check, inputs = [
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input_image
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], outputs = [], queue = False, show_progress = False).then(fn = reset_feedback, inputs = [], outputs = [
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fb_score,
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fb_text
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], queue = False, show_progress = False).success(fn=stage2_process, inputs = [
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input_image,
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prompt,
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a_prompt,
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model_select
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], outputs = [
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result_gallery,
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event_id
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])
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restart_button.click(fn = load_and_reset, inputs = [
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