import gradio as gr import json import logging import torch from PIL import Image import spaces from diffusers import DiffusionPipeline import copy import random import time # Load LoRAs from JSON file with open('loras.json', 'r') as f: loras = json.load(f) # Initialize the base model models = ["camenduru/FLUX.1-dev-diffusers", "black-forest-labs/FLUX.1-schnell", "sayakpaul/FLUX.1-merged", "John6666/blue-pencil-flux1-v001-fp8-flux"] base_model = models[0] pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16) MAX_SEED = 2**32-1 class calculateDuration: def __init__(self, activity_name=""): self.activity_name = activity_name def __enter__(self): self.start_time = time.time() return self def __exit__(self, exc_type, exc_value, traceback): self.end_time = time.time() self.elapsed_time = self.end_time - self.start_time if self.activity_name: print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds") else: print(f"Elapsed time: {self.elapsed_time:.6f} seconds") def update_selection(evt: gr.SelectData, width, height): selected_lora = loras[evt.index] new_placeholder = f"Type a prompt for {selected_lora['title']}" lora_repo = selected_lora["repo"] updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨" if "aspect" in selected_lora: if selected_lora["aspect"] == "portrait": width = 768 height = 1024 elif selected_lora["aspect"] == "landscape": width = 1024 height = 768 return ( gr.update(placeholder=new_placeholder), updated_text, evt.index, width, height, ) @spaces.GPU(duration=70) def generate_image(prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress): pipe.to("cuda") generator = torch.Generator(device="cuda").manual_seed(seed) with calculateDuration("Generating image"): # Generate image image = pipe( prompt=f"{prompt} {trigger_word}", num_inference_steps=steps, guidance_scale=cfg_scale, width=width, height=height, generator=generator, joint_attention_kwargs={"scale": lora_scale}, ).images[0] return image def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, lora_repo, lora_weights, lora_trigger, progress=gr.Progress(track_tqdm=True)): #if selected_index is None and not lora_repo: # raise gr.Error("You must select a LoRA before proceeding.") if selected_index is not None and not lora_repo: selected_lora = loras[selected_index] lora_path = selected_lora["repo"] trigger_word = selected_lora["trigger_word"] else: # override selected_lora = loras[0] lora_path = lora_repo trigger_word = lora_trigger # Load LoRA weights with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"): if selected_index is None and not lora_repo: # override pass elif lora_weights: # override pipe.load_lora_weights(lora_path, weight_name=lora_weights) elif "weights" in selected_lora: pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"]) else: pipe.load_lora_weights(lora_path) # Set random seed for reproducibility with calculateDuration("Randomizing seed"): if randomize_seed: seed = random.randint(0, MAX_SEED) image = generate_image(prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress) pipe.to("cpu") if selected_index is not None or lora_repo: pipe.unload_lora_weights() return image, seed run_lora.zerogpu = True def get_repo_safetensors(repo_id: str): from huggingface_hub import HfApi api = HfApi() try: if " " in repo_id or not api.repo_exists(repo_id): return gr.update(value="", choices=[]) files = api.list_repo_files(repo_id=repo_id) except Exception as e: print(f"Error: Failed to get {repo_id}'s info. ") print(e) return gr.update(choices=[]) files = [f for f in files if f.endswith(".safetensors")] if len(files) == 0: return gr.update(value="", choices=[]) else: return gr.update(value=files[0], choices=files) def change_base_model(repo_id: str): from huggingface_hub import HfApi global pipe api = HfApi() try: if " " in repo_id or not api.repo_exists(repo_id): return pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16) except Exception as e: print(e) css = ''' #gen_btn{height: 100%} #title{text-align: center} #title h1{font-size: 3em; display:inline-flex; align-items:center} #title img{width: 100px; margin-right: 0.5em} #gallery .grid-wrap{height: 10vh} ''' with gr.Blocks(theme=gr.themes.Soft(), css=css) as app: title = gr.HTML( """

LoRA FLUX LoRA the Explorer

""", elem_id="title", ) selected_index = gr.State(None) with gr.Row(): with gr.Column(scale=3): prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting a LoRA") with gr.Column(scale=1, elem_id="gen_column"): generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn") with gr.Row(): with gr.Column(scale=3): selected_info = gr.Markdown("") gallery = gr.Gallery( [(item["image"], item["title"]) for item in loras], label="LoRA Gallery", allow_preview=False, columns=3, elem_id="gallery" ) with gr.Column(scale=4): result = gr.Image(label="Generated Image") with gr.Row(): with gr.Accordion("Advanced Settings", open=False): with gr.Column(): with gr.Row(): cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5) steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28) with gr.Row(): width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024) height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024) with gr.Row(): randomize_seed = gr.Checkbox(True, label="Randomize seed") seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True) with gr.Row(): lora_repo = gr.Dropdown(label="LoRA Repo", choices=[], info="Input LoRA Repo ID", value="", allow_custom_value=True) lora_weights = gr.Dropdown(label="LoRA Filename", choices=[], info="Optional", value="", allow_custom_value=True) lora_trigger = gr.Textbox(label="LoRA Trigger Prompt", value="") lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=1, step=0.01, value=0.95) with gr.Row(): model_name = gr.Dropdown(label="Base Model", choices=models, value=models[0], allow_custom_value=True) gallery.select( update_selection, inputs=[width, height], outputs=[prompt, selected_info, selected_index, width, height] ) gr.on( triggers=[generate_button.click, prompt.submit], fn=run_lora, inputs=[prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, lora_repo, lora_weights, lora_trigger], outputs=[result, seed] ) lora_repo.change(get_repo_safetensors, [lora_repo], [lora_weights]) model_name.change(change_base_model, [model_name], None) app.queue() app.launch()