Delete app-backup.py

app-backup.py

@@ -1,831 +0,0 @@
-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, FluxControlNetModel
-from diffusers.pipelines import FluxControlNetPipeline
-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
-
-
-huggingface_token = os.getenv("HUGGINFACE_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)
-
-# Upscale을 위한 ControlNet 설정
-controlnet = FluxControlNetModel.from_pretrained(
- "jasperai/Flux.1-dev-Controlnet-Upscaler", torch_dtype=torch.bfloat16
-).to(device)
-
-# Upscale 파이프라인 설정 (기존 pipe 재사용)
-pipe_upscale = FluxControlNetPipeline(
- vae=pipe.vae,
- text_encoder=pipe.text_encoder,
- text_encoder_2=pipe.text_encoder_2,
- tokenizer=pipe.tokenizer,
- tokenizer_2=pipe.tokenizer_2,
- transformer=pipe.transformer,
- scheduler=pipe.scheduler,
- controlnet=controlnet
-).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) < 2:
- selected_indices.append(selected_index)
- else:
- gr.Warning("You can select up to 2 LoRAs, remove one to select a new one.")
- return gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), width, height, gr.update(), gr.update()
-
- selected_info_1 = "Select a LoRA 1"
- selected_info_2 = "Select a LoRA 2"
- lora_scale_1 = 1.15
- lora_scale_2 = 1.15
- lora_image_1 = None
- lora_image_2 = 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 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_indices, lora_scale_1, lora_scale_2, width, height, lora_image_1, lora_image_2
-
-def remove_lora_1(selected_indices, loras_state):
- if len(selected_indices) >= 1:
- selected_indices.pop(0)
- selected_info_1 = "Select a LoRA 1"
- selected_info_2 = "Select a LoRA 2"
- lora_scale_1 = 1.15
- lora_scale_2 = 1.15
- lora_image_1 = None
- lora_image_2 = None
- if len(selected_indices) >= 1:
- lora1 = loras_state[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 = loras_state[selected_indices[1]]
- selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
- lora_image_2 = lora2['image']
- return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2
-
-def remove_lora_2(selected_indices, loras_state):
- if len(selected_indices) >= 2:
- selected_indices.pop(1)
- selected_info_1 = "Select LoRA 1"
- selected_info_2 = "Select LoRA 2"
- lora_scale_1 = 1.15
- lora_scale_2 = 1.15
- lora_image_1 = None
- lora_image_2 = None
- if len(selected_indices) >= 1:
- lora1 = loras_state[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 = loras_state[selected_indices[1]]
- selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
- lora_image_2 = lora2['image']
- return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2
-
-def randomize_loras(selected_indices, loras_state):
- try:
- if len(loras_state) < 2:
- raise gr.Error("Not enough LoRAs to randomize.")
- selected_indices = random.sample(range(len(loras_state)), 2)
- lora1 = loras_state[selected_indices[0]]
- lora2 = loras_state[selected_indices[1]]
- 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']}) ✨"
- lora_scale_1 = 1.15
- lora_scale_2 = 1.15
- lora_image_1 = lora1['image']
- lora_image_2 = lora2['image']
- random_prompt = random.choice(prompt_values)
- return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2, random_prompt
- except Exception as e:
- print(f"Error in randomize_loras: {str(e)}")
- return "Error", "Error", [], 1.15, 1.15, None, None, ""
-
-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) < 2:
- selected_indices.append(existing_item_index)
- else:
- gr.Warning("You can select up to 2 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"
- lora_scale_1 = 1.15
- lora_scale_2 = 1.15
- lora_image_1 = None
- lora_image_2 = 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
- print("Finished adding custom LoRA")
- return (
- current_loras,
- gr.update(value=gallery_items),
- selected_info_1, 
- selected_info_2,
- selected_indices,
- lora_scale_1,
- lora_scale_2,
- lora_image_1,
- lora_image_2
- )
- except Exception as e:
- print(e)
- gr.Warning(str(e))
- return current_loras, gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), gr.update()
- else:
- return current_loras, gr.update(), gr.update(), gr.update(), selected_indices, 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"
- lora_scale_1 = 1.15
- lora_scale_2 = 1.15
- lora_image_1 = None
- lora_image_2 = 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']
- return (
- current_loras,
- gr.update(value=gallery_items),
- selected_info_1,
- selected_info_2,
- selected_indices,
- lora_scale_1,
- lora_scale_2,
- lora_image_1,
- lora_image_2
- )
-
-@spaces.GPU(duration=75)
-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
-
-@spaces.GPU(duration=75)
-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, 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(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):
- lora_name = f"lora_{idx}"
- lora_names.append(lora_name)
- lora_weights.append(lora_scale_1 if idx == 0 else lora_scale_2)
- lora_path = lora['repo']
- weight_name = lora.get("weights")
- print(f"Lora Path: {lora_path}")
- if image_input is not None:
- if weight_name:
- pipe_i2i.load_lora_weights(lora_path, weight_name=weight_name, low_cpu_mem_usage=True, adapter_name=lora_name)
- else:
- pipe_i2i.load_lora_weights(lora_path, low_cpu_mem_usage=True, adapter_name=lora_name)
- else:
- if weight_name:
- pipe.load_lora_weights(lora_path, weight_name=weight_name, low_cpu_mem_usage=True, adapter_name=lora_name)
- else:
- pipe.load_lora_weights(lora_path, low_cpu_mem_usage=True, adapter_name=lora_name)
- print("Loaded LoRAs:", lora_names)
- print("Adapter weights:", lora_weights)
- 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)
- print(pipe.get_active_adapters())
- # 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)
- 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'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
- 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
-
-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: 0.25em}
-#gallery .grid-wrap{height: 5vh}
-#lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%}
-.custom_lora_card{margin-bottom: 1em}
-.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}
-#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;}
-footer {visibility: hidden;}
-'''
-
-# 업스케일 관련 함수 추가
-def process_input(input_image, upscale_factor, **kwargs):
- w, h = input_image.size
- w_original, h_original = w, h
- aspect_ratio = w / h
-
- was_resized = False
-
- max_size = int(np.sqrt(MAX_PIXEL_BUDGET / (upscale_factor ** 2)))
- if w > max_size or h > max_size:
- if w > h:
- w_new = max_size
- h_new = int(w_new / aspect_ratio)
- else:
- h_new = max_size
- w_new = int(h_new * aspect_ratio)
- 
- input_image = input_image.resize((w_new, h_new), Image.LANCZOS)
- was_resized = True
- gr.Info(f"Input image resized to {w_new}x{h_new} to fit within pixel budget after upscaling.")
-
- # resize to multiple of 8
- w, h = input_image.size
- w = w - w % 8
- h = h - h % 8
-
- return input_image.resize((w, h)), w_original, h_original, was_resized
- 
-from PIL import Image
-import numpy as np
-
-@spaces.GPU
-def infer_upscale(
- seed,
- randomize_seed,
- input_image,
- num_inference_steps,
- upscale_factor,
- controlnet_conditioning_scale,
- progress=gr.Progress(track_tqdm=True),
-):
- if input_image is None:
- return None, seed, gr.update(), gr.update(), gr.update(), gr.update(), gr.update(visible=True, value="Please upload an image for upscaling.")
-
- try:
- if randomize_seed:
- seed = random.randint(0, MAX_SEED)
- 
- input_image, w_original, h_original, was_resized = process_input(input_image, upscale_factor)
-
- # rescale with upscale factor
- w, h = input_image.size
- control_image = input_image.resize((w * upscale_factor, h * upscale_factor), Image.LANCZOS)
-
- generator = torch.Generator(device=device).manual_seed(seed)
-
- gr.Info("Upscaling image...")
- # 모든 텐서를 동일한 디바이스로 이동
- pipe_upscale.to(device)
- 
- # Ensure the image is in RGB format
- if control_image.mode != 'RGB':
- control_image = control_image.convert('RGB')
- 
- # Convert to tensor and add batch dimension
- control_image = torch.from_numpy(np.array(control_image)).permute(2, 0, 1).float().unsqueeze(0).to(device) / 255.0
- 
- with torch.no_grad():
- image = pipe_upscale(
- prompt="",
- control_image=control_image,
- controlnet_conditioning_scale=controlnet_conditioning_scale,
- num_inference_steps=num_inference_steps,
- guidance_scale=3.5,
- generator=generator,
- ).images[0]
-
- # Convert the image back to PIL Image
- if isinstance(image, torch.Tensor):
- image = image.cpu().permute(1, 2, 0).numpy()
- 
- # Ensure the image data is in the correct range
- image = np.clip(image * 255, 0, 255).astype(np.uint8)
- image = Image.fromarray(image)
-
- if was_resized:
- gr.Info(
- f"Resizing output image to targeted {w_original * upscale_factor}x{h_original * upscale_factor} size."
- )
- image = image.resize((w_original * upscale_factor, h_original * upscale_factor), Image.LANCZOS)
-
- return image, seed, num_inference_steps, upscale_factor, controlnet_conditioning_scale, gr.update(), gr.update(visible=False)
- except Exception as e:
- print(f"Error in infer_upscale: {str(e)}")
- import traceback
- traceback.print_exc()
- return None, seed, gr.update(), gr.update(), gr.update(), gr.update(), gr.update(visible=True, value=f"Error: {str(e)}")
- 
-def check_upscale_input(input_image, *args):
- if input_image is None:
- return gr.update(interactive=False), *args, gr.update(visible=True, value="Please upload an image for upscaling.")
- return gr.update(interactive=True), *args, gr.update(visible=False)
- 
-with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css, delete_cache=(60, 3600)) as app:
- loras_state = gr.State(loras)
- 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):
- generate_button = gr.Button("Generate", variant="primary", elem_classes=["button_total"])
- 
- with gr.Row(elem_id="loaded_loras"):
- with gr.Column(scale=1, min_width=25):
- randomize_button = gr.Button("🎲", variant="secondary", scale=1, elem_id="random_btn")
- 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")
- 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")
- 
- with gr.Row():
- 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")
- gallery = gr.Gallery(
- [(item["image"], item["title"]) for item in loras],
- label="Or pick from the LoRA Explorer gallery",
- allow_preview=False,
- columns=4,
- elem_id="gallery"
- )
- with gr.Column():
- progress_bar = gr.Markdown(elem_id="progress", visible=False)
- result = gr.Image(label="Generated Image", interactive=False)
- with gr.Accordion("History", open=False):
- history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", interactive=False)
-
- 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)
-
-# 업스케일 관련 UI 추가
- with gr.Row():
- upscale_button = gr.Button("Upscale", interactive=False)
-
- with gr.Row():
- with gr.Column(scale=4):
- upscale_input = gr.Image(label="Input Image for Upscaling", type="pil")
- with gr.Column(scale=1):
- upscale_steps = gr.Slider(
- label="Number of Inference Steps for Upscaling",
- minimum=8,
- maximum=50,
- step=1,
- value=28,
- )
- upscale_factor = gr.Slider(
- label="Upscale Factor",
- minimum=1,
- maximum=4,
- step=1,
- value=4,
- )
- controlnet_conditioning_scale = gr.Slider(
- label="Controlnet Conditioning Scale",
- minimum=0.1,
- maximum=1.0,
- step=0.05,
- value=0.5, # 기본값을 0.5로 낮춤
- ) 
- upscale_seed = gr.Slider(
- label="Seed for Upscaling",
- minimum=0,
- maximum=MAX_SEED,
- step=1,
- value=42,
- )
- upscale_randomize_seed = gr.Checkbox(label="Randomize seed for Upscaling", value=True)
- upscale_error = gr.Markdown(visible=False, value="Please provide an input image for upscaling.")
- 
- with gr.Row():
- upscale_result = gr.Image(label="Upscaled Image", type="pil")
- upscale_seed_output = gr.Number(label="Seed Used", precision=0)
-
-
- gallery.select(
- update_selection,
- inputs=[selected_indices, loras_state, width, height],
- outputs=[prompt, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, width, height, lora_image_1, lora_image_2]
- )
- remove_button_1.click(
- remove_lora_1,
- inputs=[selected_indices, loras_state],
- outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
- )
- remove_button_2.click(
- remove_lora_2,
- inputs=[selected_indices, loras_state],
- outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
- )
- randomize_button.click(
- randomize_loras,
- inputs=[selected_indices, loras_state],
- outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2, 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_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
- )
- remove_custom_lora_button.click(
- remove_custom_lora,
- inputs=[selected_indices, loras_state],
- outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
- )
-
- 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, 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,
- )
-
- upscale_input.upload(
- lambda x: gr.update(interactive=x is not None),
- inputs=[upscale_input],
- outputs=[upscale_button]
- )
- 
- upscale_error = gr.Markdown(visible=False, value="")
-
- upscale_button.click(
- infer_upscale,
- inputs=[
- upscale_seed,
- upscale_randomize_seed,
- upscale_input,
- upscale_steps,
- upscale_factor,
- controlnet_conditioning_scale,
- ],
- outputs=[
- upscale_result,
- upscale_seed_output,
- upscale_steps,
- upscale_factor,
- controlnet_conditioning_scale,
- upscale_randomize_seed,
- upscale_error
- ],
-
- ).then(
- infer_upscale,
- inputs=[
- upscale_seed,
- upscale_randomize_seed,
- upscale_input,
- upscale_steps,
- upscale_factor,
- controlnet_conditioning_scale,
- ],
- outputs=[upscale_result, upscale_seed_output]
- )
-
-
-if __name__ == "__main__":
- app.queue(max_size=20)
- app.launch(debug=True)