import base64 import datetime import gradio as gr import numpy as np import os import pytz import psutil import re import random import torch import time import shutil # Added for zip functionality import zipfile from PIL import Image from io import BytesIO from diffusers import DiffusionPipeline, LCMScheduler, AutoencoderTiny try: import intel_extension_for_pytorch as ipex except: pass SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None) TORCH_COMPILE = os.environ.get("TORCH_COMPILE", None) HF_TOKEN = os.environ.get("HF_TOKEN", None) # check if MPS is available OSX only M1/M2/M3 chips mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available() xpu_available = hasattr(torch, "xpu") and torch.xpu.is_available() device = torch.device( "cuda" if torch.cuda.is_available() else "xpu" if xpu_available else "cpu" ) torch_device = device torch_dtype = torch.float16 # Function to encode a file to base64 def encode_file_to_base64(file_path): with open(file_path, "rb") as file: encoded = base64.b64encode(file.read()).decode() return encoded def create_zip_of_files(files): """ Create a zip file from a list of files. """ zip_name = "all_files.zip" with zipfile.ZipFile(zip_name, 'w') as zipf: for file in files: zipf.write(file) return zip_name def get_zip_download_link(zip_file): """ Generate a link to download the zip file. """ with open(zip_file, 'rb') as f: data = f.read() b64 = base64.b64encode(data).decode() href = f'Download All' return href # Function to clear all image files def clear_all_images(): base_dir = os.getcwd() # Get the current base directory img_files = [file for file in os.listdir(base_dir) if file.lower().endswith((".png", ".jpg", ".jpeg"))] # List all files ending with ".jpg" or ".jpeg" # Remove all image files for file in img_files: os.remove(file) print('removed:' + file) # add file save and download and clear: # Function to create a zip file from a list of files def create_zip(files): timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S") zip_filename = f"images_{timestamp}.zip" print('Creating file ' + zip_filename) with zipfile.ZipFile(zip_filename, 'w') as zipf: for file in files: zipf.write(file, os.path.basename(file)) print('added:' + file) return zip_filename # Function to save all images as a zip file and provide a base64 download link def save_all_images(images): if len(images) == 0: return None, None zip_filename = create_zip(images) # Create a zip file from the list of image files print(' Zip file created:' + zip_filename) # gr.Button(link="/file=" + zip_filename) # remove? zip_base64 = encode_file_to_base64(zip_filename) # Encode the zip file to base64 download_link = f'Download All' # redirect_button = gr.Button("Clear", variant='secondary') # redirect_button.click(None, None,None, _js="window.location.assign('https://google.com');") return zip_filename, download_link # Function to handle "Save All" button click def save_all_button_click(): images = [file for file in os.listdir() if file.lower().endswith((".png", ".jpg", ".jpeg"))] zip_filename, download_link = save_all_images(images) if zip_filename: print(zip_filename) gr.Button(link=zip_filename) # gr.File(value=zip_filename) if download_link: print(download_link) #gr.HTML(download_link) gr.Button(link=download_link) # Function to handle "Clear All" button click def clear_all_button_click(): clear_all_images() print(f"SAFETY_CHECKER: {SAFETY_CHECKER}") print(f"TORCH_COMPILE: {TORCH_COMPILE}") print(f"device: {device}") if mps_available: device = torch.device("mps") torch_device = "cpu" torch_dtype = torch.float32 if SAFETY_CHECKER == "True": pipe = DiffusionPipeline.from_pretrained("Lykon/dreamshaper-7") else: pipe = DiffusionPipeline.from_pretrained("Lykon/dreamshaper-7", safety_checker=None) pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) pipe.to(device=torch_device, dtype=torch_dtype).to(device) pipe.unet.to(memory_format=torch.channels_last) pipe.set_progress_bar_config(disable=True) # check if computer has less than 64GB of RAM using sys or os if psutil.virtual_memory().total < 64 * 1024**3: pipe.enable_attention_slicing() if TORCH_COMPILE: pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True) pipe(prompt="warmup", num_inference_steps=1, guidance_scale=8.0) # Load LCM LoRA pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5") pipe.fuse_lora() def safe_filename(text): """Generate a safe filename from a string.""" safe_text = re.sub(r'\W+', '_', text) timestamp = datetime.datetime.now().strftime("%Y%m%d") return f"{safe_text}_{timestamp}.png" def encode_image(image): """Encode image to base64.""" buffered = BytesIO() #image.save(buffered, format="PNG") return base64.b64encode(buffered.getvalue()).decode() def fake_gan(): base_dir = os.getcwd() # Get the current base directory img_files = [file for file in os.listdir(base_dir) if file.lower().endswith((".png", ".jpg", ".jpeg"))] # List all files ending with ".jpg" or ".jpeg" images = [(random.choice(img_files), os.path.splitext(file)[0]) for file in img_files] return images def predict(prompt, guidance, steps, seed=1231231): generator = torch.manual_seed(seed) last_time = time.time() results = pipe( prompt=prompt, generator=generator, num_inference_steps=steps, guidance_scale=guidance, width=512, height=512, # original_inference_steps=params.lcm_steps, output_type="pil", ) print(f"Pipe took {time.time() - last_time} seconds") nsfw_content_detected = ( results.nsfw_content_detected[0] if "nsfw_content_detected" in results else False ) if nsfw_content_detected: nsfw=gr.Button("🕹️NSFW🎨", scale=1) try: central = pytz.timezone('US/Central') safe_date_time = datetime.datetime.now().strftime("%Y%m%d") replaced_prompt = prompt.replace(" ", "_").replace("\n", "_") safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:90] filename = f"{safe_date_time}_{safe_prompt}.png" # Save the image if len(results.images) > 0: image_path = os.path.join("", filename) # Specify your directory results.images[0].save(image_path) print(f"#Image saved as {image_path}") gr.File(image_path) gr.Button(link=image_path) # encoded_image = encode_image(image) # html_link = f'Download Image' # gr.HTML(html_link) except: return results.images[0] return results.images[0] if len(results.images) > 0 else None css = """ #container{ margin: 0 auto; max-width: 40rem; } #intro{ max-width: 100%; text-align: center; margin: 0 auto; } """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="container"): gr.Markdown( """4📝RT🖼️Images - 🕹️ Real Time 🎨 Image Generator Gallery 🌐""", elem_id="intro", ) with gr.Row(): with gr.Row(): prompt = gr.Textbox( placeholder="Insert your prompt here:", scale=5, container=False ) generate_bt = gr.Button("Generate", scale=1) # Image Result from last prompt image = gr.Image(type="filepath") # Gallery of Generated Images with Image Names in Random Set to Download with gr.Row(variant="compact"): text = gr.Textbox( label="Image Sets", show_label=False, max_lines=1, placeholder="Enter your prompt", ) btn = gr.Button("Generate Gallery of Saved Images") gallery = gr.Gallery( label="Generated Images", show_label=False, elem_id="gallery" ) with gr.Row(variant="compact"): # Add "Save All" button with emoji save_all_button = gr.Button("💾 Save All", scale=1) # Add "Clear All" button with emoji clear_all_button = gr.Button("🗑️ Clear All", scale=1) # Advanced Generate Options with gr.Accordion("Advanced options", open=False): guidance = gr.Slider( label="Guidance", minimum=0.0, maximum=5, value=0.3, step=0.001 ) steps = gr.Slider(label="Steps", value=4, minimum=2, maximum=10, step=1) seed = gr.Slider( randomize=True, minimum=0, maximum=12013012031030, label="Seed", step=1 ) # Diffusers with gr.Accordion("Run with diffusers"): gr.Markdown( """## Running LCM-LoRAs it with `diffusers` ```bash pip install diffusers==0.23.0 ``` ```py from diffusers import DiffusionPipeline, LCMScheduler pipe = DiffusionPipeline.from_pretrained("Lykon/dreamshaper-7").to("cuda") pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5") #yes, it's a normal LoRA results = pipe( prompt="ImageEditor", num_inference_steps=4, guidance_scale=0.0, ) results.images[0] ``` """ ) # Function IO Eventing and Controls inputs = [prompt, guidance, steps, seed] generate_bt.click(fn=predict, inputs=inputs, outputs=image, show_progress=False) btn.click(fake_gan, None, gallery) prompt.input(fn=predict, inputs=inputs, outputs=image, show_progress=False) guidance.change(fn=predict, inputs=inputs, outputs=image, show_progress=False) steps.change(fn=predict, inputs=inputs, outputs=image, show_progress=False) seed.change(fn=predict, inputs=inputs, outputs=image, show_progress=False) # Attach click event handlers to the buttons save_all_button.click(save_all_button_click) clear_all_button.click(clear_all_button_click) demo.queue() demo.launch()