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 import requests import pandas as pd from transformers import pipeline from gradio_imageslider import ImageSlider import numpy as np import warnings # 상단에 허깅페이스 USERNAME (해당 계정) 반드시 개별 지정할것 USERNAME = "openfree" huggingface_token = os.getenv("HF_TOKEN") translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en", device="cpu") #Load prompts for randomization df = pd.read_csv('prompts.csv', header=None) prompt_values = df.values.flatten() # 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" # 공통 FLUX 모델 로드 base_model = "black-forest-labs/FLUX.1-dev" pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype).to(device) # LoRA를 위한 설정 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) # Image-to-Image 파이프라인 설정 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 ).to(device) MAX_SEED = 2**32 - 1 MAX_PIXEL_BUDGET = 1024 * 1024 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 download_file(url, directory=None): if directory is None: directory = os.getcwd() # Use current working directory if not specified # Get the filename from the URL filename = url.split('/')[-1] # Full path for the downloaded file filepath = os.path.join(directory, filename) # Download the file response = requests.get(url) response.raise_for_status() # Raise an exception for bad status codes # Write the content to the file with open(filepath, 'wb') as file: file.write(response.content) return filepath def update_selection(evt: gr.SelectData, selected_indices, loras_state, width, height): selected_index = evt.index selected_indices = selected_indices or [] if selected_index in selected_indices: selected_indices.remove(selected_index) else: if len(selected_indices) < 3: selected_indices.append(selected_index) else: gr.Warning("You can select up to 3 LoRAs, remove one to select a new one.") return gr.update(), gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), width, height, gr.update(), gr.update(), gr.update() selected_info_1 = "Select LoRA 1" selected_info_2 = "Select LoRA 2" selected_info_3 = "Select LoRA 3" lora_scale_1 = 1.15 lora_scale_2 = 1.15 lora_scale_3 = 1.15 lora_image_1 = None lora_image_2 = None lora_image_3 = None if len(selected_indices) >= 1: lora1 = loras_state[selected_indices[0]] selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨" lora_image_1 = lora1['image'] if len(selected_indices) >= 2: lora2 = loras_state[selected_indices[1]] selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨" lora_image_2 = lora2['image'] if len(selected_indices) >= 3: lora3 = loras_state[selected_indices[2]] selected_info_3 = f"### LoRA 3 Selected: [{lora3['title']}](https://huggingface.co/{lora3['repo']}) ✨" lora_image_3 = lora3['image'] if selected_indices: last_selected_lora = loras_state[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_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, width, height, lora_image_1, lora_image_2, lora_image_3 def remove_lora(selected_indices, loras_state, index_to_remove): if len(selected_indices) > index_to_remove: selected_indices.pop(index_to_remove) selected_info_1 = "Select LoRA 1" selected_info_2 = "Select LoRA 2" selected_info_3 = "Select LoRA 3" lora_scale_1 = 1.15 lora_scale_2 = 1.15 lora_scale_3 = 1.15 lora_image_1 = None lora_image_2 = None lora_image_3 = None for i, idx in enumerate(selected_indices): lora = loras_state[idx] if i == 0: selected_info_1 = f"### LoRA 1 Selected: [{lora['title']}]({lora['repo']}) ✨" lora_image_1 = lora['image'] elif i == 1: selected_info_2 = f"### LoRA 2 Selected: [{lora['title']}]({lora['repo']}) ✨" lora_image_2 = lora['image'] elif i == 2: selected_info_3 = f"### LoRA 3 Selected: [{lora['title']}]({lora['repo']}) ✨" lora_image_3 = lora['image'] return selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3 def remove_lora_1(selected_indices, loras_state): return remove_lora(selected_indices, loras_state, 0) def remove_lora_2(selected_indices, loras_state): return remove_lora(selected_indices, loras_state, 1) def remove_lora_3(selected_indices, loras_state): return remove_lora(selected_indices, loras_state, 2) def randomize_loras(selected_indices, loras_state): try: if len(loras_state) < 3: raise gr.Error("Not enough LoRAs to randomize.") selected_indices = random.sample(range(len(loras_state)), 3) lora1 = loras_state[selected_indices[0]] lora2 = loras_state[selected_indices[1]] lora3 = loras_state[selected_indices[2]] selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨" selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨" selected_info_3 = f"### LoRA 3 Selected: [{lora3['title']}](https://huggingface.co/{lora3['repo']}) ✨" lora_scale_1 = 1.15 lora_scale_2 = 1.15 lora_scale_3 = 1.15 lora_image_1 = lora1.get('image', 'path/to/default/image.png') lora_image_2 = lora2.get('image', 'path/to/default/image.png') lora_image_3 = lora3.get('image', 'path/to/default/image.png') random_prompt = random.choice(prompt_values) return selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3, random_prompt except Exception as e: print(f"Error in randomize_loras: {str(e)}") return "Error", "Error", "Error", [], 1.15, 1.15, 1.15, 'path/to/default/image.png', 'path/to/default/image.png', 'path/to/default/image.png', "" def add_custom_lora(custom_lora, selected_indices, current_loras): if custom_lora: try: title, repo, path, trigger_word, image = check_custom_model(custom_lora) print(f"Loaded custom LoRA: {repo}") existing_item_index = next((index for (index, item) in enumerate(current_loras) if item['repo'] == repo), None) if existing_item_index is None: if repo.endswith(".safetensors") and repo.startswith("http"): repo = download_file(repo) new_item = { "image": image if image else "/home/user/app/custom.png", "title": title, "repo": repo, "weights": path, "trigger_word": trigger_word } print(f"New LoRA: {new_item}") existing_item_index = len(current_loras) current_loras.append(new_item) # Update gallery gallery_items = [(item["image"], item["title"]) for item in current_loras] # Update selected_indices if there's room if len(selected_indices) < 3: selected_indices.append(existing_item_index) else: gr.Warning("You can select up to 3 LoRAs, remove one to select a new one.") # Update selected_info and images selected_info_1 = "Select a LoRA 1" selected_info_2 = "Select a LoRA 2" selected_info_3 = "Select a LoRA 3" lora_scale_1 = 1.15 lora_scale_2 = 1.15 lora_scale_3 = 1.15 lora_image_1 = None lora_image_2 = None lora_image_3 = None if len(selected_indices) >= 1: lora1 = current_loras[selected_indices[0]] selected_info_1 = f"### LoRA 1 Selected: {lora1['title']} ✨" lora_image_1 = lora1['image'] if lora1['image'] else None if len(selected_indices) >= 2: lora2 = current_loras[selected_indices[1]] selected_info_2 = f"### LoRA 2 Selected: {lora2['title']} ✨" lora_image_2 = lora2['image'] if lora2['image'] else None if len(selected_indices) >= 3: lora3 = current_loras[selected_indices[2]] selected_info_3 = f"### LoRA 3 Selected: {lora3['title']} ✨" lora_image_3 = lora3['image'] if lora3['image'] else None print("Finished adding custom LoRA") return ( current_loras, gr.update(value=gallery_items), selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3 ) except Exception as e: print(e) gr.Warning(str(e)) return current_loras, gr.update(), gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update() else: return current_loras, gr.update(), gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update() def remove_custom_lora(selected_indices, current_loras): if current_loras: custom_lora_repo = current_loras[-1]['repo'] # Remove from loras list current_loras = current_loras[:-1] # Remove from selected_indices if selected custom_lora_index = len(current_loras) if custom_lora_index in selected_indices: selected_indices.remove(custom_lora_index) # Update gallery gallery_items = [(item["image"], item["title"]) for item in current_loras] # Update selected_info and images selected_info_1 = "Select a LoRA 1" selected_info_2 = "Select a LoRA 2" selected_info_3 = "Select a LoRA 3" lora_scale_1 = 1.15 lora_scale_2 = 1.15 lora_scale_3 = 1.15 lora_image_1 = None lora_image_2 = None lora_image_3 = None if len(selected_indices) >= 1: lora1 = current_loras[selected_indices[0]] selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨" lora_image_1 = lora1['image'] if len(selected_indices) >= 2: lora2 = current_loras[selected_indices[1]] selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨" lora_image_2 = lora2['image'] if len(selected_indices) >= 3: lora3 = current_loras[selected_indices[2]] selected_info_3 = f"### LoRA 3 Selected: [{lora3['title']}]({lora3['repo']}) ✨" lora_image_3 = lora3['image'] return ( current_loras, gr.update(value=gallery_items), selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3 ) def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress): print("Generating image...") 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 def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, seed): pipe_i2i.to("cuda") generator = torch.Generator(device="cuda").manual_seed(seed) 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, lora_scale_3, randomize_seed, seed, width, height, loras_state, progress=gr.Progress(track_tqdm=True)): try: # 한글 감지 및 번역 if any('\u3131' <= char <= '\u318E' or '\uAC00' <= char <= '\uD7A3' for char in prompt): translated = translator(prompt, max_length=512)[0]['translation_text'] print(f"Original prompt: {prompt}") print(f"Translated prompt: {translated}") prompt = translated if not selected_indices: raise gr.Error("You must select at least one LoRA before proceeding.") selected_loras = [loras_state[idx] for idx in selected_indices] # Build the prompt with trigger words prepends = [] appends = [] for lora in selected_loras: trigger_word = lora.get('trigger_word', '') if trigger_word: if lora.get("trigger_position") == "prepend": prepends.append(trigger_word) else: appends.append(trigger_word) prompt_mash = " ".join(prepends + [prompt] + appends) print("Prompt Mash: ", prompt_mash) # Unload previous LoRA weights with calculateDuration("Unloading LoRA"): pipe.unload_lora_weights() pipe_i2i.unload_lora_weights() print(f"Active adapters before loading: {pipe.get_active_adapters()}") # Load LoRA weights with respective scales lora_names = [] lora_weights = [] with calculateDuration("Loading LoRA weights"): for idx, lora in enumerate(selected_loras): try: lora_name = f"lora_{idx}" lora_path = lora['repo'] # Private 모델인 경우 특별 처리 if lora.get('private', False): lora_path = load_private_model(lora_path, huggingface_token) print(f"Using private model path: {lora_path}") if image_input is not None: pipe_i2i.load_lora_weights( lora_path, adapter_name=lora_name, token=huggingface_token ) else: pipe.load_lora_weights( lora_path, adapter_name=lora_name, token=huggingface_token ) lora_names.append(lora_name) lora_weights.append(lora_scale_1 if idx == 0 else lora_scale_2 if idx == 1 else lora_scale_3) print(f"Successfully loaded LoRA {lora_name} from {lora_path}") except Exception as e: print(f"Failed to load LoRA {lora_name}: {str(e)}") continue print("Loaded LoRAs:", lora_names) print("Adapter weights:", lora_weights) if lora_names: if image_input is not None: pipe_i2i.set_adapters(lora_names, adapter_weights=lora_weights) else: pipe.set_adapters(lora_names, adapter_weights=lora_weights) else: print("No LoRAs were successfully loaded.") return None, seed, gr.update(visible=False) print(f"Active adapters after loading: {pipe.get_active_adapters()}") # Randomize seed if needed 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) else: image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress) 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) if final_image is None: raise Exception("Failed to generate image") return final_image, seed, gr.update(visible=False) except Exception as e: print(f"Error in run_lora: {str(e)}") return None, seed, gr.update(visible=False) run_lora.zerogpu = True 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(f"Base model: {base_model}") if base_model not in ["black-forest-labs/FLUX.1-dev", "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() safetensors_name = None 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) raise gr.Error("Invalid Hugging Face repository with a *.safetensors LoRA") if not safetensors_name: raise gr.Error("No *.safetensors file found in the repository") return split_link[1], link, safetensors_name, trigger_word, image_url else: raise gr.Error("Invalid Hugging Face repository link") def check_custom_model(link): if link.endswith(".safetensors"): # Treat as direct link to the LoRA weights title = os.path.basename(link) repo = link path = None # No specific weight name trigger_word = "" image_url = None return title, repo, path, trigger_word, image_url elif link.startswith("https://"): if "huggingface.co" in link: link_split = link.split("huggingface.co/") return get_huggingface_safetensors(link_split[1]) else: raise Exception("Unsupported URL") else: # Assume it's a Hugging Face model path return get_huggingface_safetensors(link) def update_history(new_image, history): """Updates the history gallery with the new image.""" if history is None: history = [] if new_image is not None: history.insert(0, new_image) return history def refresh_models(huggingface_token): try: headers = { "Authorization": f"Bearer {huggingface_token}", "Accept": "application/json" } username = USERNAME api_url = f"https://huggingface.co/api/models?author={username}" response = requests.get(api_url, headers=headers) if response.status_code != 200: raise Exception(f"Failed to fetch models from HuggingFace. Status code: {response.status_code}") all_models = response.json() print(f"Found {len(all_models)} models for user {username}") user_models = [ model for model in all_models if model.get('tags') and ('flux' in [tag.lower() for tag in model.get('tags', [])] or 'flux-lora' in [tag.lower() for tag in model.get('tags', [])]) ] print(f"Found {len(user_models)} FLUX models") new_models = [] for model in user_models: try: model_id = model['id'] model_card_url = f"https://huggingface.co/api/models/{model_id}" model_info_response = requests.get(model_card_url, headers=headers) model_info = model_info_response.json() # 이미지 URL에 토큰을 포함시키는 방식으로 변경 is_private = model.get('private', False) base_image_name = "1732195028106__000001000_0.jpg" # 기본 이미지 이름 try: # 실제 이미지 파일 확인 fs = HfFileSystem(token=huggingface_token) samples_path = f"{model_id}/samples" files = fs.ls(samples_path, detail=True) jpg_files = [ f['name'] for f in files if isinstance(f, dict) and 'name' in f and f['name'].lower().endswith('.jpg') and any(char.isdigit() for char in os.path.basename(f['name'])) ] if jpg_files: base_image_name = os.path.basename(jpg_files[0]) except Exception as e: print(f"Error accessing samples folder for {model_id}: {str(e)}") # 이미지 URL 구성 (토큰 포함) if is_private: # Private 모델의 경우 로컬 캐시 경로 사용 cache_dir = f"models/{model_id.replace('/', '_')}/samples" os.makedirs(cache_dir, exist_ok=True) # 이미지 다운로드 image_url = f"https://huggingface.co/{model_id}/resolve/main/samples/{base_image_name}" local_image_path = os.path.join(cache_dir, base_image_name) if not os.path.exists(local_image_path): response = requests.get(image_url, headers=headers) if response.status_code == 200: with open(local_image_path, 'wb') as f: f.write(response.content) image_url = local_image_path else: image_url = f"https://huggingface.co/{model_id}/resolve/main/samples/{base_image_name}" model_info = { "image": image_url, "title": f"[Private] {model_id.split('/')[-1]}" if is_private else model_id.split('/')[-1], "repo": model_id, "weights": "pytorch_lora_weights.safetensors", "trigger_word": model_info.get('instance_prompt', ''), "private": is_private } new_models.append(model_info) print(f"Added model: {model_id} with image: {image_url}") except Exception as e: print(f"Error processing model {model['id']}: {str(e)}") continue updated_loras = new_models + [lora for lora in loras if lora['repo'] not in [m['repo'] for m in new_models]] print(f"Total models after refresh: {len(updated_loras)}") return updated_loras except Exception as e: print(f"Error refreshing models: {str(e)}") return loras def load_private_model(model_id, huggingface_token): """Private 모델을 로드하는 함수""" try: headers = {"Authorization": f"Bearer {huggingface_token}"} # 모델 다운로드 local_dir = snapshot_download( repo_id=model_id, token=huggingface_token, local_dir=f"models/{model_id.replace('/', '_')}", local_dir_use_symlinks=False ) # safetensors 파일 찾기 safetensors_file = None for root, dirs, files in os.walk(local_dir): for file in files: if file.endswith('.safetensors'): safetensors_file = os.path.join(root, file) break if safetensors_file: break if not safetensors_file: raise Exception(f"No .safetensors file found in {local_dir}") print(f"Found safetensors file: {safetensors_file}") return safetensors_file # 전체 경로를 반환 except Exception as e: print(f"Error loading private model {model_id}: {str(e)}") raise e custom_theme = gr.themes.Base( primary_hue="blue", secondary_hue="purple", neutral_hue="slate", ).set( button_primary_background_fill="*primary_500", button_primary_background_fill_dark="*primary_600", button_primary_background_fill_hover="*primary_400", button_primary_border_color="*primary_500", button_primary_border_color_dark="*primary_600", button_primary_text_color="white", button_primary_text_color_dark="white", button_secondary_background_fill="*neutral_100", button_secondary_background_fill_dark="*neutral_700", button_secondary_background_fill_hover="*neutral_50", button_secondary_text_color="*neutral_800", button_secondary_text_color_dark="white", background_fill_primary="*neutral_50", background_fill_primary_dark="*neutral_900", block_background_fill="white", block_background_fill_dark="*neutral_800", block_label_background_fill="*primary_500", block_label_background_fill_dark="*primary_600", block_label_text_color="white", block_label_text_color_dark="white", block_title_text_color="*neutral_800", block_title_text_color_dark="white", input_background_fill="white", input_background_fill_dark="*neutral_800", input_border_color="*neutral_200", input_border_color_dark="*neutral_700", input_placeholder_color="*neutral_400", input_placeholder_color_dark="*neutral_400", shadow_spread="8px", shadow_inset="0px 2px 4px 0px rgba(0,0,0,0.05)" ) css = ''' /* 기본 버튼 및 컴포넌트 스타일 */ #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: 0.25em } #lora_list { background: var(--block-background-fill); padding: 0 1em .3em; font-size: 90% } /* 커스텀 LoRA 카드 스타일 */ .custom_lora_card { margin-bottom: 1em } .card_internal { display: flex; height: 100px; margin-top: .5em } .card_internal img { margin-right: 1em } /* 유틸리티 클래스 */ .styler { --form-gap-width: 0px !important } /* 프로그레스 바 스타일 */ #progress { height: 30px; width: 90% !important; margin: 0 auto !important; } #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 } /* 컴포넌트 특정 스타일 */ #component-8, .button_total { height: 100%; align-self: stretch; } #loaded_loras [data-testid="block-info"] { font-size: 80% } #custom_lora_structure { background: var(--block-background-fill) } #custom_lora_btn { margin-top: auto; margin-bottom: 11px } #random_btn { font-size: 300% } #component-11 { align-self: stretch; } /* 갤러리 메인 스타일 */ #lora_gallery { margin: 20px 0; padding: 10px; border: 1px solid #ddd; border-radius: 12px; background: linear-gradient(to bottom right, #ffffff, #f8f9fa); width: 100% !important; height: 800px !important; box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1); display: block !important; } /* 갤러리 그리드 스타일 */ #gallery { display: grid !important; grid-template-columns: repeat(10, 1fr) !important; gap: 10px !important; padding: 10px !important; width: 100% !important; height: 100% !important; overflow-y: auto !important; max-width: 100% !important; } /* 갤러리 아이템 스타일 */ .gallery-item { position: relative !important; width: 100% !important; aspect-ratio: 1 !important; margin: 0 !important; box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1); transition: transform 0.3s ease, box-shadow 0.3s ease; border-radius: 12px; overflow: hidden; } .gallery-item img { width: 100% !important; height: 100% !important; object-fit: cover !important; border-radius: 12px !important; } /* 갤러리 그리드 래퍼 */ .wrap, .svelte-w6dy5e { display: grid !important; grid-template-columns: repeat(10, 1fr) !important; gap: 10px !important; width: 100% !important; max-width: 100% !important; } /* 컨테이너 공통 스타일 */ .container, .content, .block, .contain { width: 100% !important; max-width: 100% !important; margin: 0 !important; padding: 0 !important; } .row { width: 100% !important; margin: 0 !important; padding: 0 !important; } /* 버튼 스타일 */ .button_total { box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06); transition: all 0.3s ease; } .button_total:hover { transform: translateY(-2px); box-shadow: 0 10px 15px -3px rgba(0, 0, 0, 0.1), 0 4px 6px -2px rgba(0, 0, 0, 0.05); } /* 입력 필드 스타일 */ input, textarea { box-shadow: inset 0 2px 4px 0 rgba(0, 0, 0, 0.06); transition: all 0.3s ease; } input:focus, textarea:focus { box-shadow: 0 0 0 3px rgba(66, 153, 225, 0.5); } /* 컴포넌트 border-radius */ .gradio-container .input, .gradio-container .button, .gradio-container .block { border-radius: 12px; } /* 스크롤바 스타일 */ #gallery::-webkit-scrollbar { width: 8px; } #gallery::-webkit-scrollbar-track { background: #f1f1f1; border-radius: 4px; } #gallery::-webkit-scrollbar-thumb { background: #888; border-radius: 4px; } #gallery::-webkit-scrollbar-thumb:hover { background: #555; } /* Flex 컨테이너 */ .flex { width: 100% !important; max-width: 100% !important; display: flex !important; } /* Svelte 특정 클래스 */ .svelte-1p9xokt { width: 100% !important; max-width: 100% !important; } /* Footer 숨김 */ #footer { visibility: hidden; } /* 결과 이미지 및 컨테이너 스타일 */ #result_column, #result_column > div { display: flex !important; flex-direction: column !important; align-items: flex-start !important; /* center에서 flex-start로 변경 */ width: 100% !important; margin: 0 !important; /* auto에서 0으로 변경 */ } .generated-image, .generated-image > div { display: flex !important; justify-content: flex-start !important; /* center에서 flex-start로 변경 */ align-items: flex-start !important; /* center에서 flex-start로 변경 */ width: 90% !important; max-width: 768px !important; margin: 0 !important; /* auto에서 0으로 변경 */ margin-left: 20px !important; /* 왼쪽 여백 추가 */ } .generated-image img { margin: 0 !important; /* auto에서 0으로 변경 */ display: block !important; max-width: 100% !important; } /* 히스토리 갤러리도 좌측 정렬로 변경 */ .history-gallery { display: flex !important; justify-content: flex-start !important; /* center에서 flex-start로 변경 */ width: 90% !important; max-width: 90% !important; margin: 0 !important; /* auto에서 0으로 변경 */ margin-left: 20px !important; /* 왼쪽 여백 추가 */ /* 새로고침 버튼 스타일 */ #refresh-button { margin: 10px; padding: 8px 16px; background-color: #4a5568; color: white; border-radius: 8px; transition: all 0.3s ease; } #refresh-button:hover { background-color: #2d3748; transform: scale(1.05); } #refresh-button:active { transform: scale(0.95); } } ''' with gr.Blocks(theme=custom_theme, css=css, delete_cache=(60, 3600)) as app: loras_state = gr.State(loras) selected_indices = gr.State([]) gr.Markdown( """ # MixGen3: 멀티 Lora(이미지 학습) 통합 생성 모델 ### 사용 안내: 갤러리에서 원하는 모델을 선택(최대 3개까지) < 프롬프트에 한글 또는 영문으로 원하는 내용을 입력 < Generate 버튼 실행 """ ) # 새로고침 버튼 추가 with gr.Row(): refresh_button = gr.Button("🔄 모델 새로고침(나만의 맞춤 학습된 Private 모델 불러오기)", variant="secondary") with gr.Row(elem_id="lora_gallery", equal_height=True): gallery = gr.Gallery( value=[(item["image"], item["title"]) for item in loras], label="LoRA Explorer Gallery", columns=11, elem_id="gallery", height=800, object_fit="cover", show_label=True, allow_preview=False, show_share_button=False, container=True, preview=False ) with gr.Tab(label="Generate"): # Prompt and Generate Button 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): generate_button = gr.Button("Generate", variant="primary", elem_classes=["button_total"]) # LoRA Selection Area with gr.Row(elem_id="loaded_loras"): # Randomize Button with gr.Column(scale=1, min_width=25): randomize_button = gr.Button("🎲", variant="secondary", scale=1, elem_id="random_btn") # LoRA 1 with gr.Column(scale=8): with gr.Row(): with gr.Column(scale=0, min_width=50): lora_image_1 = gr.Image(label="LoRA 1 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50) with gr.Column(scale=3, min_width=100): selected_info_1 = gr.Markdown("Select a LoRA 1") with gr.Column(scale=5, min_width=50): lora_scale_1 = gr.Slider(label="LoRA 1 Scale", minimum=0, maximum=3, step=0.01, value=1.15) with gr.Row(): remove_button_1 = gr.Button("Remove", size="sm") # LoRA 2 with gr.Column(scale=8): with gr.Row(): with gr.Column(scale=0, min_width=50): lora_image_2 = gr.Image(label="LoRA 2 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50) with gr.Column(scale=3, min_width=100): selected_info_2 = gr.Markdown("Select a LoRA 2") with gr.Column(scale=5, min_width=50): lora_scale_2 = gr.Slider(label="LoRA 2 Scale", minimum=0, maximum=3, step=0.01, value=1.15) with gr.Row(): remove_button_2 = gr.Button("Remove", size="sm") # LoRA 3 with gr.Column(scale=8): with gr.Row(): with gr.Column(scale=0, min_width=50): lora_image_3 = gr.Image(label="LoRA 3 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50) with gr.Column(scale=3, min_width=100): selected_info_3 = gr.Markdown("Select a LoRA 3") with gr.Column(scale=5, min_width=50): lora_scale_3 = gr.Slider(label="LoRA 3 Scale", minimum=0, maximum=3, step=0.01, value=1.15) with gr.Row(): remove_button_3 = gr.Button("Remove", size="sm") # Result and Progress Area with gr.Column(elem_id="result_column"): progress_bar = gr.Markdown(elem_id="progress", visible=False) with gr.Column(elem_id="result_box"): # Box를 Column으로 변경 result = gr.Image( label="Generated Image", interactive=False, elem_classes=["generated-image"], container=True, elem_id="result_image", width="100%" ) with gr.Accordion("History", open=False): history_gallery = gr.Gallery( label="History", columns=6, object_fit="contain", interactive=False, elem_classes=["history-gallery"] ) # Advanced Settings 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) # Custom LoRA Section with gr.Column(): with gr.Group(): with gr.Row(elem_id="custom_lora_structure"): custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path or *.safetensors public URL", placeholder="ginipick/flux-lora-eric-cat", scale=3, min_width=150) add_custom_lora_button = gr.Button("Add Custom LoRA", elem_id="custom_lora_btn", scale=2, min_width=150) remove_custom_lora_button = gr.Button("Remove Custom LoRA", visible=False) 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") # Event Handlers gallery.select( update_selection, inputs=[selected_indices, loras_state, width, height], outputs=[prompt, selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, width, height, lora_image_1, lora_image_2, lora_image_3] ) remove_button_1.click( remove_lora_1, inputs=[selected_indices, loras_state], outputs=[selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3] ) remove_button_2.click( remove_lora_2, inputs=[selected_indices, loras_state], outputs=[selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3] ) remove_button_3.click( remove_lora_3, inputs=[selected_indices, loras_state], outputs=[selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3] ) randomize_button.click( randomize_loras, inputs=[selected_indices, loras_state], outputs=[selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3, prompt] ) add_custom_lora_button.click( add_custom_lora, inputs=[custom_lora, selected_indices, loras_state], outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3] ) remove_custom_lora_button.click( remove_custom_lora, inputs=[selected_indices, loras_state], outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3] ) 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, lora_scale_3, randomize_seed, seed, width, height, loras_state], outputs=[result, seed, progress_bar] ).then( fn=lambda x, history: update_history(x, history) if x is not None else history, inputs=[result, history_gallery], outputs=history_gallery ) # 새로고침 버튼 이벤트 핸들러 def refresh_gallery(): updated_loras = refresh_models(huggingface_token) return ( gr.update(value=[(item["image"], item["title"]) for item in updated_loras]), updated_loras ) refresh_button.click( refresh_gallery, outputs=[gallery, loras_state] ) if __name__ == "__main__": app.queue(max_size=20) app.launch(debug=True)