import os import gradio as gr import json import logging import torch from PIL import Image import spaces from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL, AutoPipelineForImage2Image from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images from diffusers.utils import load_image from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download 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 dtype = torch.bfloat16 device = "cuda" if torch.cuda.is_available() else "cpu" base_model = "black-forest-labs/FLUX.1-dev" taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device) good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device) pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device) pipe_i2i = AutoPipelineForImage2Image.from_pretrained(base_model, vae=good_vae, transformer=pipe.transformer, text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer, text_encoder_2=pipe.text_encoder_2, tokenizer_2=pipe.tokenizer_2, torch_dtype=dtype ) MAX_SEED = 2**32-1 pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe) 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, selected_indices, width, height): selected_index = evt.index selected_indices = selected_indices or [] if selected_index in selected_indices: # LoRA is already selected, remove it selected_indices.remove(selected_index) else: if len(selected_indices) < 2: selected_indices.append(selected_index) else: raise gr.Error("You can select up to 2 LoRAs only.") # Initialize outputs selected_info_1 = "" selected_info_2 = "" if len(selected_indices) >= 1: lora1 = loras[selected_indices[0]] selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨" if len(selected_indices) >= 2: lora2 = loras[selected_indices[1]] selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨" # Update prompt placeholder based on last selected LoRA if selected_indices: last_selected_lora = loras[selected_indices[-1]] new_placeholder = f"Type a prompt for {last_selected_lora['title']}" else: new_placeholder = "Type a prompt after selecting a LoRA" return ( gr.update(placeholder=new_placeholder), selected_info_1, selected_info_2, selected_indices, width, height, ) def remove_lora_1(selected_indices): selected_indices = selected_indices or [] if len(selected_indices) >= 1: selected_indices.pop(0) # Update selected_info_1 and selected_info_2 selected_info_1 = "" selected_info_2 = "" if len(selected_indices) >= 1: lora1 = loras[selected_indices[0]] selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨" if len(selected_indices) >= 2: lora2 = loras[selected_indices[1]] selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨" return selected_info_1, selected_info_2, selected_indices def remove_lora_2(selected_indices): selected_indices = selected_indices or [] if len(selected_indices) >= 2: selected_indices.pop(1) # Update selected_info_1 and selected_info_2 selected_info_1 = "" selected_info_2 = "" if len(selected_indices) >= 1: lora1 = loras[selected_indices[0]] selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨" if len(selected_indices) >= 2: lora2 = loras[selected_indices[1]] selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨" return selected_info_1, selected_info_2, selected_indices @spaces.GPU(duration=70) def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress): pipe.to("cuda") generator = torch.Generator(device="cuda").manual_seed(seed) with calculateDuration("Generating image"): # Generate image for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images( prompt=prompt_mash, num_inference_steps=steps, guidance_scale=cfg_scale, width=width, height=height, generator=generator, joint_attention_kwargs={"scale": 1.0}, output_type="pil", good_vae=good_vae, ): yield img @spaces.GPU(duration=70) def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, seed): generator = torch.Generator(device="cuda").manual_seed(seed) pipe_i2i.to("cuda") image_input = load_image(image_input_path) final_image = pipe_i2i( prompt=prompt_mash, image=image_input, strength=image_strength, num_inference_steps=steps, guidance_scale=cfg_scale, width=width, height=height, generator=generator, joint_attention_kwargs={"scale": 1.0}, output_type="pil", ).images[0] return final_image def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, progress=gr.Progress(track_tqdm=True)): if not selected_indices: raise gr.Error("You must select at least one LoRA before proceeding.") selected_loras = [loras[idx] for idx in selected_indices] # Build the prompt with trigger words prompt_mash = prompt for lora in selected_loras: trigger_word = lora.get('trigger_word', '') if trigger_word: if lora.get("trigger_position") == "prepend": prompt_mash = f"{trigger_word} {prompt_mash}" else: prompt_mash = f"{prompt_mash} {trigger_word}" # Unload previous LoRA weights with calculateDuration("Unloading LoRA"): pipe.unload_lora_weights() pipe_i2i.unload_lora_weights() # Load LoRA weights with respective scales with calculateDuration("Loading LoRA weights"): for idx, lora in enumerate(selected_loras): lora_path = lora['repo'] scale = lora_scale_1 if idx == 0 else lora_scale_2 if image_input is not None: if "weights" in lora: pipe_i2i.load_lora_weights(lora_path, weight_name=lora["weights"], multiplier=scale) else: pipe_i2i.load_lora_weights(lora_path, multiplier=scale) else: if "weights" in lora: pipe.load_lora_weights(lora_path, weight_name=lora["weights"], multiplier=scale) else: pipe.load_lora_weights(lora_path, multiplier=scale) # Set random seed for reproducibility with calculateDuration("Randomizing seed"): if randomize_seed: seed = random.randint(0, MAX_SEED) # Generate image if image_input is not None: final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, seed) yield final_image, seed, gr.update(visible=False) else: image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress) # Consume the generator to get the final image final_image = None step_counter = 0 for image in image_generator: step_counter+=1 final_image = image progress_bar = f'
' yield image, seed, gr.update(value=progress_bar, visible=True) yield final_image, seed, gr.update(value=progress_bar, visible=False) def get_huggingface_safetensors(link): split_link = link.split("/") if(len(split_link) == 2): model_card = ModelCard.load(link) base_model = model_card.data.get("base_model") print(base_model) if((base_model != "black-forest-labs/FLUX.1-dev") and (base_model != "black-forest-labs/FLUX.1-schnell")): raise Exception("Not a FLUX LoRA!") image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None) trigger_word = model_card.data.get("instance_prompt", "") image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None fs = HfFileSystem() try: list_of_files = fs.ls(link, detail=False) for file in list_of_files: if(file.endswith(".safetensors")): safetensors_name = file.split("/")[-1] if (not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp"))): image_elements = file.split("/") image_url = f"https://huggingface.co/{link}/resolve/main/{image_elements[-1]}" except Exception as e: print(e) gr.Warning(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA") raise Exception(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA") return split_link[1], link, safetensors_name, trigger_word, image_url def check_custom_model(link): if(link.startswith("https://")): if(link.startswith("https://huggingface.co") or link.startswith("https://www.huggingface.co")): link_split = link.split("huggingface.co/") return get_huggingface_safetensors(link_split[1]) else: return get_huggingface_safetensors(link) def add_custom_lora(custom_lora): global loras if(custom_lora): try: title, repo, path, trigger_word, image = check_custom_model(custom_lora) print(f"Loaded custom LoRA: {repo}") card = f'''
Loaded custom LoRA:

{title}

{"Using: "+trigger_word+" as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}
''' existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None) if(not existing_item_index): new_item = { "image": image, "title": title, "repo": repo, "weights": path, "trigger_word": trigger_word } print(new_item) existing_item_index = len(loras) loras.append(new_item) return gr.update(visible=True, value=card), gr.update(visible=True), gr.Gallery(selected_index=None), f"Custom: {path}", existing_item_index, trigger_word except Exception as e: gr.Warning(f"Invalid LoRA: either you entered an invalid link, or a non-FLUX LoRA") return gr.update(visible=True, value=f"Invalid LoRA: either you entered an invalid link, a non-FLUX LoRA"), gr.update(visible=True), gr.update(), "", None, "" else: return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, "" def remove_custom_lora(): return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, "" run_lora.zerogpu = True 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} #lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%} .card_internal{display: flex;height: 100px;margin-top: .5em} .card_internal img{margin-right: 1em} .styler{--form-gap-width: 0px !important} #progress{height:30px} #progress .generating{display:none} .progress-container {width: 100%;height: 30px;background-color: #f0f0f0;border-radius: 15px;overflow: hidden;margin-bottom: 20px} .progress-bar {height: 100%;background-color: #4f46e5;width: calc(var(--current) / var(--total) * 100%);transition: width 0.5s ease-in-out} ''' with gr.Blocks(theme=gr.themes.Soft(), css=css, delete_cache=(60, 3600)) as app: title = gr.HTML( """

LoRA LoRA Lab

""", elem_id="title", ) selected_indices = gr.State([]) 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(): gallery = gr.Gallery( [(item["image"], item["title"]) for item in loras], label="LoRA Gallery", allow_preview=False, columns=3, elem_id="gallery" ) with gr.Group(): custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path", placeholder="multimodalart/vintage-ads-flux") gr.Markdown("[Check the list of FLUX LoRas](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list") custom_lora_info = gr.HTML(visible=False) custom_lora_button = gr.Button("Remove custom LoRA", visible=False) with gr.Column(): progress_bar = gr.Markdown(elem_id="progress",visible=False) result = gr.Image(label="Generated Image") with gr.Row(): with gr.Column(): selected_info_1 = gr.Markdown("") lora_scale_1 = gr.Slider(label="LoRA 1 Scale", minimum=0, maximum=3, step=0.01, value=0.95) remove_button_1 = gr.Button("Remove LoRA 1") with gr.Column(): selected_info_2 = gr.Markdown("") lora_scale_2 = gr.Slider(label="LoRA 2 Scale", minimum=0, maximum=3, step=0.01, value=0.95) remove_button_2 = gr.Button("Remove LoRA 2") with gr.Row(): with gr.Accordion("Advanced Settings", open=False): with gr.Row(): input_image = gr.Image(label="Input image", type="filepath") image_strength = gr.Slider(label="Denoise Strength", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.75) 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) gallery.select( update_selection, inputs=[selected_indices, width, height], outputs=[prompt, selected_info_1, selected_info_2, selected_indices, width, height] ) remove_button_1.click( remove_lora_1, inputs=[selected_indices], outputs=[selected_info_1, selected_info_2, selected_indices] ) remove_button_2.click( remove_lora_2, inputs=[selected_indices], outputs=[selected_info_1, selected_info_2, selected_indices] ) custom_lora.input( add_custom_lora, inputs=[custom_lora], outputs=[custom_lora_info, custom_lora_button, gallery, selected_info_1, selected_indices, prompt] ) custom_lora_button.click( remove_custom_lora, outputs=[custom_lora_info, custom_lora_button, gallery, selected_info_1, selected_indices, custom_lora] ) gr.on( triggers=[generate_button.click, prompt.submit], fn=run_lora, inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height], outputs=[result, seed, progress_bar] ) app.queue() app.launch()