Fabrice-TIERCELIN
commited on
Commit
•
d4f0399
1
Parent(s):
1448721
10 min allocation
Browse files
app.py
CHANGED
@@ -137,9 +137,9 @@ def stage2_process(
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output_format,
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allocation
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):
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-
print("noisy_image
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print("rotation: " + str(rotation))
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-
print("denoise_image: " + str(denoise_image))
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print("prompt: " + str(prompt))
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print("a_prompt: " + str(a_prompt))
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print("n_prompt: " + str(n_prompt))
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@@ -315,7 +315,7 @@ def restore(
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allocation
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):
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start = time.time()
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-
print('
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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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@@ -360,7 +360,7 @@ def restore(
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# All the results have the same size
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result_height, result_width, result_channel = np.array(results[0]).shape
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print('<<==
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end = time.time()
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secondes = int(end - start)
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minutes = math.floor(secondes / 60)
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@@ -474,8 +474,8 @@ with gr.Blocks() as interface:
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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; you can write in any language", value="", placeholder="A 33 years old man, walking, in the street, 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]], label="GPU allocation time", info="lower=May abort run, higher=Quota penalty for next runs", value=6, interactive=True)
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-
output_format = gr.Radio([["As input", "input"], ["*.png", "png"], ["*.webp", "webp"], ["*.jpeg", "jpeg"], ["*.gif", "gif"], ["*.bmp", "bmp"]], label="Image format for result", info="File extention", value="
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with gr.Accordion("Pre-denoising (optional)", open=False):
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gamma_correction = gr.Slider(label="Gamma Correction", info = "lower=lighter, higher=darker", minimum=0.1, maximum=2.0, value=1.0, step=0.1)
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@@ -612,7 +612,7 @@ with gr.Blocks() as interface:
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False,
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0.,
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"v0-Q",
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-
"
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5
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],
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[
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@@ -643,7 +643,7 @@ with gr.Blocks() as interface:
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False,
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0.,
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"v0-Q",
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-
"
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4
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],
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[
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@@ -674,7 +674,7 @@ with gr.Blocks() as interface:
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False,
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0.,
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"v0-Q",
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-
"
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4
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],
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[
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@@ -705,7 +705,7 @@ with gr.Blocks() as interface:
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False,
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0.,
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"v0-Q",
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-
"
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4
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],
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],
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output_format,
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allocation
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):
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+
print("noisy_image:\n" + str(noisy_image))
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+
print("denoise_image:\n" + str(denoise_image))
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print("rotation: " + str(rotation))
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print("prompt: " + str(prompt))
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print("a_prompt: " + str(a_prompt))
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print("n_prompt: " + str(n_prompt))
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allocation
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):
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start = time.time()
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+
print('restore ==>>')
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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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# All the results have the same size
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result_height, result_width, result_channel = np.array(results[0]).shape
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+
print('<<== restore')
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end = time.time()
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secondes = int(end - start)
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minutes = math.floor(secondes / 60)
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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; you can write in any language", value="", placeholder="A 33 years old man, walking, in the street, 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 (discouraged)", 9], ["10 min (discouraged)", 10]], label="GPU allocation time", info="lower=May abort run, higher=Quota penalty for next runs", value=6, interactive=True)
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+
output_format = gr.Radio([["As input", "input"], ["*.png", "png"], ["*.webp", "webp"], ["*.jpeg", "jpeg"], ["*.gif", "gif"], ["*.bmp", "bmp"]], label="Image format for result", info="File extention", value="input", interactive=True)
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with gr.Accordion("Pre-denoising (optional)", open=False):
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gamma_correction = gr.Slider(label="Gamma Correction", info = "lower=lighter, higher=darker", minimum=0.1, maximum=2.0, value=1.0, step=0.1)
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False,
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0.,
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"v0-Q",
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+
"input",
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5
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],
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[
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False,
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0.,
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"v0-Q",
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+
"input",
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4
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],
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[
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False,
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0.,
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"v0-Q",
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+
"input",
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4
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],
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[
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False,
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0.,
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"v0-Q",
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+
"input",
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4
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],
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],
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