Spaces:
Running
Running
add files
Browse files- .DS_Store +0 -0
- .gitattributes +2 -0
- README.md +7 -4
- app.py +215 -0
- examples/.DS_Store +0 -0
- examples/anime_1.png +3 -0
- examples/anime_2.jpg +3 -0
- examples/bus1.png +3 -0
- examples/dogs.jpg +3 -0
- examples/festival_3.png +3 -0
- examples/general_2.png +3 -0
- examples/mid65_general.png +3 -0
- examples/portrait1.png +3 -0
- examples/vintage_family_photo.jpg +3 -0
- requirements.txt +5 -0
.DS_Store
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Binary file (6.15 kB). View file
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.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.png filter=lfs diff=lfs merge=lfs -text
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*.jpg filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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-
title: Puncta Lite
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emoji:
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colorFrom:
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colorTo:
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sdk: gradio
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sdk_version: 5.29.1
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app_file: app.py
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pinned: false
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short_description: High quality image upscaler
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: Puncta Upscaler Lite
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emoji: π
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colorFrom: green
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colorTo: indigo
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sdk: gradio
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sdk_version: 5.29.1
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app_file: app.py
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pinned: false
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short_description: High quality image upscaler
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tags:
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- upscaler
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- super-resolution
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import os
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import io
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import requests
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from uuid import uuid4
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import boto3
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from botocore.client import Config
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from PIL import Image
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import gradio as gr
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from dotenv import load_dotenv
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load_dotenv()
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css = """
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#col-container {
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margin: 0 auto;
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max-width: 512px;
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}
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"""
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MAX_PIXEL_BUDGET = 1024 * 1024
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def upload_to_r2(file_path, object_key, content_type):
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s3 = boto3.client(
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's3',
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endpoint_url=os.getenv('R2_ENDPOINT'),
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aws_access_key_id=os.getenv('R2_ACCESS_KEY_ID'),
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aws_secret_access_key=os.getenv('R2_SECRET_ACCESS_KEY'),
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config=Config(signature_version='s3v4'),
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region_name='auto'
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)
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with open(file_path, 'rb') as f:
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s3.put_object(
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Bucket=os.getenv('R2_BUCKET'),
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Key=object_key,
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Body=f,
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ContentType=content_type
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)
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download_url = s3.generate_presigned_url(
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'get_object',
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Params={
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'Bucket': os.getenv('R2_BUCKET'),
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'Key': object_key
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},
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ExpiresIn=3600 # url expiration time in seconds
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)
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return download_url
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def process_input(input_image, upscale_factor):
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w, h = input_image.size
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w_original, h_original = w, h
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aspect_ratio = w / h
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was_resized = False
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# compute minimum dimension after upscaling
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min_dimension = min(w, h) * upscale_factor
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# if minimum dimension is above 1024, adjust scale factor
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if min_dimension > 1024:
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new_scale = 1024 / min(w, h)
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upscale_factor = min(2, new_scale) # cap at 2x if needed
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gr.Info(f'Adjusted scale factor to {upscale_factor}x to maintain minimum dimension of 1024 pixels')
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if w * h * upscale_factor**2 > MAX_PIXEL_BUDGET:
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gr.Info(
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f'Requested output image is too large. Resizing input to fit within pixel budget.'
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)
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input_image = input_image.resize(
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(
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int(aspect_ratio * MAX_PIXEL_BUDGET**0.5 // upscale_factor),
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int(MAX_PIXEL_BUDGET**0.5 // aspect_ratio // upscale_factor),
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)
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)
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was_resized = True
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return input_image, w_original, h_original, was_resized, upscale_factor
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def infer(
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input_image,
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upscale_factor,
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image_category,
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):
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true_input_image = input_image
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input_image, w_original, h_original, was_resized, adjusted_scale = process_input(
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input_image, upscale_factor
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)
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temp_input_path = 'temp_input.png'
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input_image.save(temp_input_path)
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image_url = upload_to_r2(temp_input_path, str(uuid4()), 'image/png')
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gr.Info('Upscaling image...')
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try:
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resp = requests.get(
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os.getenv('ENDPOINT') ,
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headers={
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'Modal-Key': os.getenv('AUTH_KEY'),
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'Modal-Secret': os.getenv('AUTH_SECRET'),
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},
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params={
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'image_url': image_url,
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'image_category': image_category,
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'scale_factor': adjusted_scale,
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'output_format': 'png',
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'upload_to_r2': False
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}
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)
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if resp.status_code != 200:
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raise gr.Error(f'API request failed with status {resp.status_code}: {resp.text}')
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# save the response image
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output_path = 'output.png'
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with open(output_path, 'wb') as f:
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f.write(resp.content)
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output_image = Image.open(output_path)
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if was_resized:
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gr.Info(
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f'Resizing output image to targeted {w_original * adjusted_scale}x{h_original * adjusted_scale} size.'
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)
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output_image = output_image.resize((int(w_original * adjusted_scale), int(h_original * adjusted_scale)))
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return output_image
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except Exception as e:
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raise gr.Error(f'Error during upscaling: {str(e)}')
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finally:
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if os.path.exists(temp_input_path):
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os.remove(temp_input_path)
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if os.path.exists(output_path):
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os.remove(output_path)
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with gr.Blocks(css=css) as demo:
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gr.Markdown(
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"""
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# π Puncta Lite - AI Image Upscaler
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This is a lite version of Puncta's AI image upscaler. For more advanced features and higher quality results, visit [puncta.ai](https://www.puncta.ai/).
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*Note*: This demo is limited to a maximum output resolution of 1024x1024 pixels. For higher resolution upscaling, please visit our full version at puncta.ai.
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"""
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)
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with gr.Row():
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with gr.Column(scale=1):
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input_im = gr.Image(label='Input Image', type='pil')
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with gr.Column(scale=1):
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output_im = gr.Image(label='Output Image', type='pil')
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with gr.Row():
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with gr.Column(scale=1):
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upscale_factor = gr.Slider(
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label='Upscale Factor',
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minimum=2,
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maximum=4,
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step=1,
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value=2,
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)
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with gr.Column(scale=1):
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image_category = gr.Dropdown(
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label='Image Category',
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choices=['general', 'portrait', 'outdoor', 'digital art'],
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value='general'
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)
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with gr.Row():
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run_button = gr.Button(value='Upscale Image', variant='primary"')
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examples = gr.Examples(
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examples=[
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['examples/dogs.jpg', 2, 'outdoor'],
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['examples/portrait1.png', 3, 'portrait'],
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['examples/anime_1.png', 2, 'digital art'],
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['examples/vintage_family_photo.jpg', 2, 'portrait'],
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['examples/bus1.png', 3, 'general'],
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['examples/festival_3.png', 2, 'general'],
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['examples/general_2.png', 2, 'general'],
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['examples/anime_2.jpg', 3, 'digital art'],
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],
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inputs=[
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input_im,
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upscale_factor,
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image_category,
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],
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fn=infer,
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outputs=output_im,
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cache_examples=True,
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)
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gr.Markdown("**Disclaimer:**")
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gr.Markdown(
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"This demo is for testing purposes only. For commercial use and higher quality results, please visit [puncta.ai](https://www.puncta.ai/)."
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)
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gr.on(
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[run_button.click],
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fn=infer,
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inputs=[
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input_im,
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upscale_factor,
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image_category,
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],
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outputs=output_im,
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show_api=False,
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)
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demo.queue().launch(share=False, show_api=False)
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examples/.DS_Store
ADDED
Binary file (6.15 kB). View file
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examples/anime_1.png
ADDED
![]() |
Git LFS Details
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examples/anime_2.jpg
ADDED
![]() |
Git LFS Details
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examples/bus1.png
ADDED
![]() |
Git LFS Details
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examples/dogs.jpg
ADDED
![]() |
Git LFS Details
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examples/festival_3.png
ADDED
![]() |
Git LFS Details
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examples/general_2.png
ADDED
![]() |
Git LFS Details
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examples/mid65_general.png
ADDED
![]() |
Git LFS Details
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examples/portrait1.png
ADDED
![]() |
Git LFS Details
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examples/vintage_family_photo.jpg
ADDED
![]() |
Git LFS Details
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requirements.txt
ADDED
@@ -0,0 +1,5 @@
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1 |
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gradio==4.29.0
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2 |
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gradio_imageslider
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3 |
+
requests
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4 |
+
Pillow
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5 |
+
boto3
|