import json import random import requests import gradio as gr import numpy as np import spaces import torch from diffusers import DiffusionPipeline, LCMScheduler from PIL import Image import os # Load the JSON data with open("sdxl_lora.json", "r") as file: data = json.load(file) sdxl_loras_raw = sorted(data, key=lambda x: x["likes"], reverse=True) DEVICE = "cuda" if torch.cuda.is_available() else "cpu" model_id = "stabilityai/stable-diffusion-xl-base-1.0" pipe = DiffusionPipeline.from_pretrained(model_id, variant="fp16") pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) pipe.to(device=DEVICE, dtype=torch.float16) MAX_SEED = np.iinfo(np.int32).max MAX_IMAGE_SIZE = 1024 def update_selection(selected_state: gr.SelectData, gr_sdxl_loras): lora_id = gr_sdxl_loras[selected_state.index]["repo"] trigger_word = gr_sdxl_loras[selected_state.index]["trigger_word"] return lora_id, trigger_word def load_lora_for_style(style_repo): pipe.unload_lora_weights() pipe.load_lora_weights("jasperai/flash-sdxl", adapter_name="lora") pipe.set_adapters(["lora", new_adapter_id], adapter_weights=[1.0, user_lora_weight]) if new_adapter_id not in loaded_adapters["unet"]: gr.Info("Swapping LoRA") pipe.unload_lora_weights() pipe.load_lora_weights(flash_sdxl_id, adapter_name="lora") pipe.load_lora_weights(user_lora_selector, adapter_name=new_adapter_id) def get_image(image_data): if isinstance(image_data, str): return image_data if isinstance(image_data, dict): local_path = image_data.get('local_path') hf_url = image_data.get('hf_url') else: print(f"Unexpected image_data format: {type(image_data)}") return None # Try loading from local path first if local_path and os.path.exists(local_path): try: Image.open(local_path).verify() # Verify that it's a valid image return local_path except Exception as e: print(f"Error loading local image {local_path}: {e}") # If local path fails or doesn't exist, try URL if hf_url: try: response = requests.get(hf_url) if response.status_code == 200: img = Image.open(requests.get(hf_url, stream=True).raw) img.verify() # Verify that it's a valid image img.save(local_path) # Save for future use return local_path else: print(f"Failed to fetch image from URL {hf_url}. Status code: {response.status_code}") except Exception as e: print(f"Error loading image from URL {hf_url}: {e}") print(f"Failed to load image for {image_data}") return None @spaces.GPU def infer( pre_prompt, prompt, seed, randomize_seed, num_inference_steps, negative_prompt, guidance_scale, user_lora_selector, user_lora_weight, progress=gr.Progress(track_tqdm=True), ): load_lora_for_style(user_lora_selector) if randomize_seed: seed = random.randint(0, MAX_SEED) generator = torch.Generator().manual_seed(seed) if pre_prompt != "": prompt = f"{pre_prompt} {prompt}" image = pipe( prompt=prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, generator=generator, ).images[0] return image css = """ body { background-color: #1a1a1a; color: #ffffff; } .container { max-width: 900px; margin: auto; padding: 20px; } h1, h2 { color: #4CAF50; text-align: center; } .gallery { display: flex; flex-wrap: wrap; justify-content: center; } .gallery img { margin: 10px; border-radius: 10px; transition: transform 0.3s ease; } .gallery img:hover { transform: scale(1.05); } .gradio-slider input[type="range"] { background-color: #4CAF50; } .gradio-button { background-color: #4CAF50 !important; } """ with gr.Blocks(css=css) as demo: gr.Markdown( """ # ⚡ FlashDiffusion: Araminta K's FlashLoRA Showcase ⚡ This interactive demo showcases [Araminta K's models](https://huggingface.co/alvdansen) using [Flash Diffusion](https://gojasper.github.io/flash-diffusion-project/) technology. ## Acknowledgments - Original Flash Diffusion technology by the Jasper AI team - Based on the paper: [Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation](http://arxiv.org/abs/2406.02347) by Clément Chadebec, Onur Tasar, Eyal Benaroche and Benjamin Aubin - Models showcased here are created by Araminta K at Alvdansen Labs Explore the power of FlashLoRA with Araminta K's unique artistic styles! """ ) gr_sdxl_loras = gr.State(value=sdxl_loras_raw) gr_lora_id = gr.State(value="") with gr.Row(): with gr.Column(scale=2): gallery = gr.Gallery( value=[(get_image(item["image"]), item["title"]) for item in sdxl_loras_raw if get_image(item["image"]) is not None], label="SDXL LoRA Gallery", show_label=False, elem_id="gallery", columns=3, height=600, ) user_lora_selector = gr.Textbox( label="Current Selected LoRA", interactive=False, ) with gr.Column(scale=3): prompt = gr.Textbox( label="Prompt", placeholder="Enter your prompt", lines=3, ) with gr.Row(): run_button = gr.Button("Run", variant="primary") clear_button = gr.Button("Clear") result = gr.Image(label="Result", height=512) with gr.Accordion("Advanced Settings", open=False): pre_prompt = gr.Textbox( label="Pre-Prompt", placeholder="Pre Prompt from the LoRA config", lines=2, ) with gr.Row(): seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) num_inference_steps = gr.Slider( label="Number of inference steps", minimum=4, maximum=8, step=1, value=4, ) guidance_scale = gr.Slider( label="Guidance Scale", minimum=1, maximum=6, step=0.5, value=1, ) negative_prompt = gr.Textbox( label="Negative Prompt", placeholder="Enter a negative Prompt", lines=2, ) gr.on( [run_button.click, prompt.submit], fn=infer, inputs=[ pre_prompt, prompt, seed, randomize_seed, num_inference_steps, negative_prompt, guidance_scale, user_lora_selector, gr.Slider(label="Selected LoRA Weight", minimum=0.5, maximum=3, step=0.1, value=1), ], outputs=[result], ) clear_button.click(lambda: "", outputs=[prompt, result]) gallery.select( fn=update_selection, inputs=[gr_sdxl_loras], outputs=[user_lora_selector, pre_prompt], ) gr.Markdown( """ ## Unleash Your Creativity! This showcase brings together the speed of Flash Diffusion and the artistic flair of Araminta K's models. Craft your prompts, adjust the settings, and watch as AI brings your ideas to life in stunning detail. Remember to use this tool ethically and respect copyright and individual privacy. Enjoy exploring these unique artistic styles! """ ) demo.queue().launch()