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Fabrice-TIERCELIN
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0539164
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Parent(s):
943f713
Run examples on click
Browse files- gradio_demo.py +47 -15
gradio_demo.py
CHANGED
@@ -67,6 +67,23 @@ if torch.cuda.device_count() > 0:
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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, 2147483647)
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@@ -198,14 +215,7 @@ def stage2_process(
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print("Final prompt: " + str(a_prompt))
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noisy_image = noisy_image if denoise_image is None else denoise_image
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if 1 < downscale:
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input_height, input_width, input_channel =
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print('downscale')
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print(downscale)
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print(input_width)
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print(input_height)
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print(input_width // downscale)
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print(input_height // downscale)
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print((input_width // downscale, input_height // downscale))
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noisy_image = np.array(Image.fromarray(noisy_image).resize((input_width // downscale, input_height // downscale), Image.LANCZOS))
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if allocation == 1:
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@@ -390,7 +400,7 @@ def restore(
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hours = math.floor(minutes / 60)
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minutes = minutes - (hours * 60)
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information = ("Start the process again if you want a different result. " if randomize_seed else "") + \
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"Wait " + str(allocation) + " min before a new run to avoid quota penalty. " + \
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"The image(s) has(ve) been generated in " + \
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((str(hours) + " h, ") if hours != 0 else "") + \
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((str(minutes) + " min, ") if hours != 0 or minutes != 0 else "") + \
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@@ -474,7 +484,7 @@ title_html = """
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This demo can handle huge images but the process will be aborted if it lasts more than 10 min.
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Please leave a message in discussion if you encounter issues.
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<p><center><a href="https://arxiv.org/abs/2401.13627">Paper</a>   <a href="http://supir.xpixel.group/">Project Page</a>   <a href="https://
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"""
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@@ -503,8 +513,13 @@ with gr.Blocks(title="SUPIR") as interface:
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gr.HTML(title_html)
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input_image = gr.Image(label="Input", show_label=True, type="numpy", height=600, elem_id="image-input")
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with gr.Group():
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prompt = gr.Textbox(label="Image description", info="Help the AI understand what the image represents; describe as much as possible; I advise you to write in English
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prompt_hint = gr.HTML("You can use a <a href='"'https://huggingface.co/spaces/MaziyarPanahi/llava-llama-3-8b'"'>LlaVa space</a> to auto-generate the description of your image.")
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upscale = gr.Radio([["x1", 1], ["x2", 2], ["x3", 3], ["x4", 4], ["x5", 5], ["x6", 6], ["x7", 7], ["x8", 8]], label="Upscale factor", info="Resolution x1 to x8", value=2, interactive=True)
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allocation = gr.Radio([["1 min", 1], ["2 min", 2], ["3 min", 3], ["4 min", 4], ["5 min", 5], ["6 min", 6], ["7 min", 7], ["8 min", 8], ["9 min", 9], ["10 min", 10]], label="GPU allocation time", info="lower=May abort run, higher=Quota penalty for next runs", value=6, interactive=True)
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@@ -591,6 +606,7 @@ with gr.Blocks(title="SUPIR") as interface:
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event_id = gr.Textbox(label="Event ID", value="", visible=False)
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gr.Examples(
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fn = stage2_process,
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inputs = [
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input_image,
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@@ -696,6 +712,25 @@ with gr.Blocks(title="SUPIR") as interface:
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with gr.Row():
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gr.Markdown(claim_md)
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denoise_button.click(fn = check, inputs = [
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input_image
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], outputs = [], queue = False, show_progress = False).success(fn = stage1_process, inputs = [
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@@ -724,10 +759,7 @@ with gr.Blocks(title="SUPIR") as interface:
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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).success(fn
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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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denoise_image,
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prompt,
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else:
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llava_agent = None
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def check_upload(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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return [gr.update(visible = False)] * 2
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+
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def rotate_anti_90(image_array):
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if image_array is None:
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raise gr.Error("Please provide an image to rotate.")
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return np.array(list(zip(*image_array))[::-1])
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def rotate_90(image_array):
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if image_array is None:
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raise gr.Error("Please provide an image to rotate.")
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return np.array(list(zip(*image_array[::-1])))
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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, 2147483647)
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print("Final prompt: " + str(a_prompt))
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noisy_image = noisy_image if denoise_image is None else denoise_image
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if 1 < downscale:
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input_height, input_width, input_channel = noisy_image.shape
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noisy_image = np.array(Image.fromarray(noisy_image).resize((input_width // downscale, input_height // downscale), Image.LANCZOS))
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if allocation == 1:
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hours = math.floor(minutes / 60)
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minutes = minutes - (hours * 60)
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information = ("Start the process again if you want a different result. " if randomize_seed else "") + \
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"Wait " + str(allocation) + " min before a new run to avoid quota penalty or use another computer. " + \
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"The image(s) has(ve) been generated in " + \
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((str(hours) + " h, ") if hours != 0 else "") + \
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((str(minutes) + " min, ") if hours != 0 or minutes != 0 else "") + \
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This demo can handle huge images but the process will be aborted if it lasts more than 10 min.
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Please leave a message in discussion if you encounter issues.
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+
<p><center><a href="https://arxiv.org/abs/2401.13627">Paper</a>   <a href="http://supir.xpixel.group/">Project Page</a>   <a href="https://huggingface.co/blog/MonsterMMORPG/supir-sota-image-upscale-better-than-magnific-ai">Local Install Guide</a></center></p>
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"""
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gr.HTML(title_html)
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input_image = gr.Image(label="Input", show_label=True, type="numpy", height=600, elem_id="image-input")
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with gr.Row():
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with gr.Column():
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rotate_anti_90_button = gr.Button(value="⤴ Rotate -90°", elem_id="rotate_anti_90_button", visible=False)
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with gr.Column():
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rotate_90_button = gr.Button(value="⤵ Rotate +90°", elem_id="rotate_90_button", visible=False)
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with gr.Group():
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prompt = gr.Textbox(label="Image description", info="Help the AI understand what the image represents; describe as much as possible, especially the details we can't see on the original image; I advise you to write in English because other languages may not be handled", value="", placeholder="A 27 years old woman, walking, in Santiago, morning, Summer, photorealistic", lines=3)
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prompt_hint = gr.HTML("You can use a <a href='"'https://huggingface.co/spaces/MaziyarPanahi/llava-llama-3-8b'"'>LlaVa space</a> to auto-generate the description of your image.")
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upscale = gr.Radio([["x1", 1], ["x2", 2], ["x3", 3], ["x4", 4], ["x5", 5], ["x6", 6], ["x7", 7], ["x8", 8]], label="Upscale factor", info="Resolution x1 to x8", value=2, interactive=True)
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allocation = gr.Radio([["1 min", 1], ["2 min", 2], ["3 min", 3], ["4 min", 4], ["5 min", 5], ["6 min", 6], ["7 min", 7], ["8 min", 8], ["9 min", 9], ["10 min", 10]], label="GPU allocation time", info="lower=May abort run, higher=Quota penalty for next runs", value=6, interactive=True)
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event_id = gr.Textbox(label="Event ID", value="", visible=False)
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gr.Examples(
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run_on_click = True,
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fn = stage2_process,
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inputs = [
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input_image,
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with gr.Row():
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gr.Markdown(claim_md)
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input_image.upload(fn = check_upload, inputs = [
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input_image
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], outputs = [
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rotate_anti_90_button,
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rotate_90_button
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], queue = False, show_progress = False)
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rotate_anti_90_button.click(fn = rotate_anti_90, inputs = [
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input_image
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], outputs = [
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input_image
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], queue = False, show_progress = False)
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rotate_90_button.click(fn = rotate_90, inputs = [
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input_image
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], outputs = [
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input_image
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], queue = False, show_progress = False)
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denoise_button.click(fn = check, inputs = [
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input_image
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], outputs = [], queue = False, show_progress = False).success(fn = stage1_process, inputs = [
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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).success(fn=stage2_process, inputs = [
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input_image,
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denoise_image,
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prompt,
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