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Update app.py
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app.py
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@@ -10,11 +10,10 @@ import random
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import torch
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import time
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import shutil # Added for zip functionality
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from PIL import Image
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from io import BytesIO
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from diffusers import DiffusionPipeline, LCMScheduler, AutoencoderTiny
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import zipfile
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import glob
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try:
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import intel_extension_for_pytorch as ipex
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@@ -33,54 +32,12 @@ device = torch.device(
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torch_device = device
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torch_dtype = torch.float16
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# Function to create a zip file from a list of files
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def create_zip(files):
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timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
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zip_filename = f"images_{timestamp}.zip"
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print('Creating file ' + zip_filename)
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with zipfile.ZipFile(zip_filename, 'w') as zipf:
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for file in files:
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zipf.write(file, os.path.basename(file))
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print('added:' + file)
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return zip_filename
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# Function to save all images as a zip file and provide a base64 download link
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def save_all_images(images):
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if len(images) == 0:
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return None, None
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zip_filename = create_zip(images) # Create a zip file from the list of image files
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print(' Zip file created:' + zip_filename)
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zip_base64 = encode_file_to_base64(zip_filename) # Encode the zip file to base64
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download_link = f'<a href="data:application/zip;base64,{zip_base64}" download="{zip_filename}">Download All</a>'
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return zip_filename, download_link
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# Function to handle "Save All" button click
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def save_all_button_click():
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images = [file for file in os.listdir() if file.lower().endswith((".png", ".jpg", ".jpeg"))]
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zip_filename, download_link = save_all_images(images)
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if zip_filename:
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print(zip_filename)
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gr.Button(link=zip_filename)
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if download_link:
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print(download_link)
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#gr.HTML(download_link)
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gr.Button(link=download_link)
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# Function to handle "Clear All" button click
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def clear_all_button_click():
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clear_all_images()
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# Function to encode a file to base64
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def encode_file_to_base64(file_path):
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with open(file_path, "rb") as file:
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encoded = base64.b64encode(file.read()).decode()
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return encoded
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def create_zip_of_files(files):
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"""
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Create a zip file from a list of files.
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@@ -102,8 +59,6 @@ def get_zip_download_link(zip_file):
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href = f'<a href="data:application/zip;base64,{b64}" download="{zip_file}">Download All</a>'
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return href
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# Function to clear all image files
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def clear_all_images():
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base_dir = os.getcwd() # Get the current base directory
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for file in img_files:
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os.remove(file)
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print('removed:' + file)
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# add file save and download and clear:
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# Function to create a zip file from a list of files
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def create_zip(files):
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@@ -324,11 +278,9 @@ with gr.Blocks(css=css) as demo:
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```py
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from diffusers import DiffusionPipeline, LCMScheduler
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pipe = DiffusionPipeline.from_pretrained("Lykon/dreamshaper-7").to("cuda")
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pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
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pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5") #yes, it's a normal LoRA
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results = pipe(
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prompt="ImageEditor",
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num_inference_steps=4,
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@@ -352,7 +304,5 @@ with gr.Blocks(css=css) as demo:
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save_all_button.click(save_all_button_click)
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clear_all_button.click(clear_all_button_click)
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demo.queue()
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demo.launch()
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import torch
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import time
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import shutil # Added for zip functionality
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import zipfile
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from PIL import Image
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from io import BytesIO
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from diffusers import DiffusionPipeline, LCMScheduler, AutoencoderTiny
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try:
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import intel_extension_for_pytorch as ipex
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torch_device = device
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torch_dtype = torch.float16
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# Function to encode a file to base64
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def encode_file_to_base64(file_path):
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with open(file_path, "rb") as file:
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encoded = base64.b64encode(file.read()).decode()
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return encoded
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def create_zip_of_files(files):
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"""
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Create a zip file from a list of files.
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href = f'<a href="data:application/zip;base64,{b64}" download="{zip_file}">Download All</a>'
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return href
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# Function to clear all image files
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def clear_all_images():
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base_dir = os.getcwd() # Get the current base directory
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for file in img_files:
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os.remove(file)
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print('removed:' + file)
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# add file save and download and clear:
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# Function to create a zip file from a list of files
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def create_zip(files):
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```py
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from diffusers import DiffusionPipeline, LCMScheduler
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pipe = DiffusionPipeline.from_pretrained("Lykon/dreamshaper-7").to("cuda")
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pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
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pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5") #yes, it's a normal LoRA
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results = pipe(
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prompt="ImageEditor",
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num_inference_steps=4,
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save_all_button.click(save_all_button_click)
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clear_all_button.click(clear_all_button_click)
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demo.queue()
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demo.launch()
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