import spaces import gradio as gr import json import torch 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 diffusers import FluxControlNetPipeline, FluxControlNetModel, FluxMultiControlNetModel, FluxControlNetImg2ImgPipeline, FluxTransformer2DModel from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download, HfApi import os import copy import random import time import requests import pandas as pd from pathlib import Path from env import models, num_loras, num_cns, HF_TOKEN, single_file_base_models from mod import (clear_cache, get_repo_safetensors, is_repo_name, is_repo_exists, get_model_trigger, description_ui, compose_lora_json, is_valid_lora, fuse_loras, save_image, preprocess_i2i_image, get_trigger_word, enhance_prompt, set_control_union_image, get_control_union_mode, set_control_union_mode, get_control_params, translate_to_en) from modutils import (search_civitai_lora, select_civitai_lora, search_civitai_lora_json, download_my_lora_flux, get_all_lora_tupled_list, apply_lora_prompt_flux, update_loras_flux, update_civitai_selection, get_civitai_tag, CIVITAI_SORT, CIVITAI_PERIOD, get_t2i_model_info, download_hf_file, save_image_history) from tagger.tagger import predict_tags_wd, compose_prompt_to_copy from tagger.fl2flux import predict_tags_fl2_flux #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 base_model = models[0] controlnet_model_union_repo = 'InstantX/FLUX.1-dev-Controlnet-Union' #controlnet_model_union_repo = 'Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro' dtype = torch.bfloat16 #dtype = torch.float8_e4m3fn #device = "cuda" if torch.cuda.is_available() else "cpu" taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype, token=HF_TOKEN) good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype, token=HF_TOKEN) pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1, token=HF_TOKEN) 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, token=HF_TOKEN) controlnet_union = None controlnet = None last_model = models[0] last_cn_on = False #controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union_repo, torch_dtype=dtype) #controlnet = FluxMultiControlNetModel([controlnet_union]) #controlnet.config = controlnet_union.config MAX_SEED = 2**32-1 def unload_lora(): global pipe, pipe_i2i try: #pipe.unfuse_lora() pipe.unload_lora_weights() #pipe_i2i.unfuse_lora() pipe_i2i.unload_lora_weights() except Exception as e: print(e) # https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union # https://huggingface.co/spaces/jiuface/FLUX.1-dev-Controlnet-Union # https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux #@spaces.GPU() def change_base_model(repo_id: str, cn_on: bool, disable_model_cache: bool, model_type: str, progress=gr.Progress(track_tqdm=True)): global pipe, pipe_i2i, taef1, good_vae, controlnet_union, controlnet, last_model, last_cn_on, dtype safetensors_file = None single_file_base_model = single_file_base_models.get(model_type, models[0]) try: #if not disable_model_cache and (repo_id == last_model and cn_on is last_cn_on) or not is_repo_name(repo_id) or not is_repo_exists(repo_id): return gr.update(visible=True) if not disable_model_cache and (repo_id == last_model and cn_on is last_cn_on) or ((not is_repo_name(repo_id) or not is_repo_exists(repo_id)) and not ".safetensors" in repo_id): return gr.update(visible=True) unload_lora() pipe.to("cpu") pipe_i2i.to("cpu") good_vae.to("cpu") taef1.to("cpu") if controlnet is not None: controlnet.to("cpu") if controlnet_union is not None: controlnet_union.to("cpu") clear_cache() if cn_on: progress(0, desc=f"Loading model: {repo_id} / Loading ControlNet: {controlnet_model_union_repo}") print(f"Loading model: {repo_id} / Loading ControlNet: {controlnet_model_union_repo}") controlnet_union = FluxControlNetModel.from_pretrained(controlnet_model_union_repo, torch_dtype=dtype, token=HF_TOKEN) controlnet = FluxMultiControlNetModel([controlnet_union]) controlnet.config = controlnet_union.config if ".safetensors" in repo_id: safetensors_file = download_file_mod(repo_id) transformer = FluxTransformer2DModel.from_single_file(safetensors_file, subfolder="transformer", torch_dtype=dtype, config=single_file_base_model) pipe = FluxControlNetPipeline.from_pretrained(single_file_base_model, transformer=transformer, controlnet=controlnet, torch_dtype=dtype, token=HF_TOKEN) pipe_i2i = FluxControlNetImg2ImgPipeline.from_pretrained(single_file_base_model, controlnet=controlnet, vae=None, 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, token=HF_TOKEN) else: pipe = FluxControlNetPipeline.from_pretrained(repo_id, controlnet=controlnet, torch_dtype=dtype, token=HF_TOKEN) pipe_i2i = FluxControlNetImg2ImgPipeline.from_pretrained(repo_id, controlnet=controlnet, vae=None, 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, token=HF_TOKEN) last_model = repo_id last_cn_on = cn_on progress(1, desc=f"Model loaded: {repo_id} / ControlNet Loaded: {controlnet_model_union_repo}") print(f"Model loaded: {repo_id} / ControlNet Loaded: {controlnet_model_union_repo}") else: progress(0, desc=f"Loading model: {repo_id}") print(f"Loading model: {repo_id}") if ".safetensors" in repo_id: safetensors_file = download_file_mod(repo_id) transformer = FluxTransformer2DModel.from_single_file(safetensors_file, subfolder="transformer", torch_dtype=dtype, config=single_file_base_model) pipe = DiffusionPipeline.from_pretrained(single_file_base_model, transformer=transformer, torch_dtype=dtype, token=HF_TOKEN) pipe_i2i = AutoPipelineForImage2Image.from_pretrained(single_file_base_model, vae=None, 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, token=HF_TOKEN) else: pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=dtype, token=HF_TOKEN) pipe_i2i = AutoPipelineForImage2Image.from_pretrained(repo_id, vae=None, 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, token=HF_TOKEN) last_model = repo_id last_cn_on = cn_on progress(1, desc=f"Model loaded: {repo_id}") print(f"Model loaded: {repo_id}") except Exception as e: print(f"Model load Error: {repo_id} {e}") raise gr.Error(f"Model load Error: {repo_id} {e}") from e finally: if safetensors_file and Path(safetensors_file).exists(): Path(safetensors_file).unlink() return gr.update(visible=True) change_base_model.zerogpu = True def download_file_mod(url, directory=os.getcwd()): path = download_hf_file(directory, url) if not path: raise Exception(f"Download error: {url}") return path def is_repo_public(repo_id: str): api = HfApi() try: if api.repo_exists(repo_id=repo_id, token=False): return True else: return False except Exception as e: print(f"Error: Failed to connect {repo_id}. {e}") return False 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" 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 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 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): 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 def add_custom_lora(custom_lora, selected_indices, current_loras, gallery): if custom_lora: try: title, repo, path, trigger_word, image = check_custom_model(custom_lora) if "http" in image and not is_repo_public(repo): try: image = download_file_mod(image) except Exception as e: print(e) image = None 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) repo = download_file_mod(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, gallery): 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=70) @torch.inference_mode() def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, cn_on, progress=gr.Progress(track_tqdm=True)): global pipe, taef1, good_vae, controlnet, controlnet_union try: good_vae.to("cuda") taef1.to("cuda") generator = torch.Generator(device="cuda").manual_seed(int(float(seed))) with calculateDuration("Generating image"): # Generate image modes, images, scales = get_control_params() if not cn_on or len(modes) == 0: pipe.to("cuda") pipe.vae = taef1 pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe) progress(0, desc="Start Inference.") 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 else: pipe.to("cuda") pipe.vae = good_vae if controlnet_union is not None: controlnet_union.to("cuda") if controlnet is not None: controlnet.to("cuda") pipe.enable_model_cpu_offload() progress(0, desc="Start Inference with ControlNet.") for img in pipe( prompt=prompt_mash, control_image=images, control_mode=modes, num_inference_steps=steps, guidance_scale=cfg_scale, width=width, height=height, controlnet_conditioning_scale=scales, generator=generator, joint_attention_kwargs={"scale": 1.0}, ).images: yield img except Exception as e: print(e) raise gr.Error(f"Inference Error: {e}") from e @spaces.GPU(duration=70) @torch.inference_mode() def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, seed, cn_on, progress=gr.Progress(track_tqdm=True)): global pipe_i2i, good_vae, controlnet, controlnet_union try: good_vae.to("cuda") generator = torch.Generator(device="cuda").manual_seed(int(float(seed))) image_input = load_image(image_input_path) with calculateDuration("Generating image"): # Generate image modes, images, scales = get_control_params() if not cn_on or len(modes) == 0: pipe_i2i.to("cuda") pipe_i2i.vae = good_vae image_input = load_image(image_input_path) progress(0, desc="Start I2I Inference.") 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 else: pipe_i2i.to("cuda") pipe_i2i.vae = good_vae image_input = load_image(image_input_path) if controlnet_union is not None: controlnet_union.to("cuda") if controlnet is not None: controlnet.to("cuda") pipe_i2i.enable_model_cpu_offload() progress(0, desc="Start I2I Inference with ControlNet.") final_image = pipe_i2i( prompt=prompt_mash, control_image=images, control_mode=modes, image=image_input, strength=image_strength, num_inference_steps=steps, guidance_scale=cfg_scale, width=width, height=height, controlnet_conditioning_scale=scales, generator=generator, joint_attention_kwargs={"scale": 1.0}, output_type="pil", ).images[0] return final_image except Exception as e: print(e) raise gr.Error(f"I2I Inference Error: {e}") from e 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, lora_json, cn_on, translate_on, progress=gr.Progress(track_tqdm=True)): global pipe, pipe_i2i if not selected_indices and not is_valid_lora(lora_json): gr.Info("LoRA isn't selected.") # raise gr.Error("You must select a LoRA before proceeding.") progress(0, desc="Preparing Inference.") selected_loras = [loras_state[idx] for idx in selected_indices] if translate_on: prompt = translate_to_en(prompt) # 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"): unload_lora() print(pipe.get_active_adapters()) # print(pipe_i2i.get_active_adapters()) # clear_cache() # # Build the prompt for External LoRAs prompt_mash = prompt_mash + get_model_trigger(last_model) lora_names = [] lora_weights = [] if is_valid_lora(lora_json): # Load External LoRA weights with calculateDuration("Loading External LoRA weights"): if image_input is not None: pipe_i2i, lora_names, lora_weights = fuse_loras(pipe_i2i, lora_json) else: pipe, lora_names, lora_weights = fuse_loras(pipe, lora_json) trigger_word = get_trigger_word(lora_json) prompt_mash = f"{prompt_mash} {trigger_word}" print("Prompt Mash: ", prompt_mash) # # Load LoRA weights with respective scales if selected_indices: with calculateDuration("Loading LoRA weights"): for idx, lora in enumerate(selected_loras): lora_name = f"lora_{idx}" lora_names.append(lora_name) print(f"Lora Name: {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: pipe_i2i.load_lora_weights( lora_path, weight_name=weight_name if weight_name else None, low_cpu_mem_usage=False, adapter_name=lora_name, token=HF_TOKEN ) else: pipe.load_lora_weights( lora_path, weight_name=weight_name if weight_name else None, low_cpu_mem_usage=False, adapter_name=lora_name, token=HF_TOKEN ) print("Loaded LoRAs:", lora_names) if selected_indices or is_valid_lora(lora_json): 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()) # print(pipe_i2i.get_active_adapters()) # # Set random seed for reproducibility with calculateDuration("Randomizing seed"): if randomize_seed: seed = random.randint(0, MAX_SEED) # Generate image progress(0, desc="Running Inference.") if(image_input is not None): final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, seed, cn_on) yield save_image(final_image, None, last_model, prompt_mash, height, width, steps, cfg_scale, seed), seed, gr.update(visible=False) else: image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, cn_on) # 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 save_image(final_image, None, last_model, prompt_mash, height, width, steps, cfg_scale, seed), seed, gr.update(value=progress_bar, 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, token=HF_TOKEN) 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(token=HF_TOKEN) 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 = [] history.insert(0, new_image) return history css = ''' #gen_column{align-self: stretch} #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;} .info { align-items: center; text-align: center; } .desc [src$='#float'] { float: right; margin: 20px; } ''' with gr.Blocks(theme='Nymbo/Nymbo_Theme_5', fill_width=True, css=css, delete_cache=(60, 3600)) as app: with gr.Tab("FLUX LoRA the Explorer"): title = gr.HTML( """

LoRAFLUX LoRA the Explorer Mod

""", elem_id="title", ) loras_state = gr.State(loras) selected_indices = gr.State([]) with gr.Row(): with gr.Column(scale=3): with gr.Group(): with gr.Accordion("Generate Prompt from Image", open=False): tagger_image = gr.Image(label="Input image", type="pil", sources=["upload", "clipboard"], height=256) with gr.Accordion(label="Advanced options", open=False): tagger_general_threshold = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True) tagger_character_threshold = gr.Slider(label="Character threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.01, interactive=True) neg_prompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="", visible=False) v2_character = gr.Textbox(label="Character", placeholder="hatsune miku", scale=2, visible=False) v2_series = gr.Textbox(label="Series", placeholder="vocaloid", scale=2, visible=False) v2_copy = gr.Button(value="Copy to clipboard", size="sm", interactive=False, visible=False) tagger_algorithms = gr.CheckboxGroup(["Use WD Tagger", "Use Florence-2-Flux"], label="Algorithms", value=["Use WD Tagger"]) tagger_generate_from_image = gr.Button(value="Generate Prompt from Image") prompt = gr.Textbox(label="Prompt", lines=1, max_lines=8, placeholder="Type a prompt", show_copy_button=True) with gr.Row(): prompt_enhance = gr.Button(value="Enhance your prompt", variant="secondary") auto_trans = gr.Checkbox(label="Auto translate to English", value=False, elem_classes="info") with gr.Column(scale=1, elem_id="gen_column"): generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn", 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(): selected_info = gr.Markdown("") gallery = gr.Gallery([(item["image"], item["title"]) for item in loras], label="LoRA Gallery", allow_preview=False, columns=5, elem_id="gallery", show_share_button=False, interactive=False) 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="multimodalart/vintage-ads-flux", 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") with gr.Column(): progress_bar = gr.Markdown(elem_id="progress",visible=False) result = gr.Image(label="Generated Image", format="png", type="filepath", show_share_button=False, interactive=False) with gr.Accordion("History", open=False): history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", interactive=False, format="png", show_share_button=False, show_download_button=True) history_files = gr.Files(interactive=False, visible=False) history_clear_button = gr.Button(value="Clear History", variant="secondary") history_clear_button.click(lambda: ([], []), None, [history_gallery, history_files], queue=False, show_api=False) with gr.Group(): with gr.Row(): model_name = gr.Dropdown(label="Base Model", info="You can enter a huggingface model repo_id or path of single safetensors file to want to use.", choices=models, value=models[0], allow_custom_value=True, min_width=320, scale=5) model_type = gr.Radio(label="Model type", info="Model type of single safetensors file", choices=list(single_file_base_models.keys()), value=list(single_file_base_models.keys())[0], scale=1) model_info = gr.Markdown(elem_classes="info") with gr.Row(): with gr.Accordion("Advanced Settings", open=False): with gr.Row(): input_image = gr.Image(label="Input image", type="filepath", height=256, sources=["upload", "clipboard"], show_share_button=False) with gr.Column(): 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) input_image_preprocess = gr.Checkbox(True, label="Preprocess Input image") with gr.Column(): 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) 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(): randomize_seed = gr.Checkbox(True, label="Randomize seed") seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True) disable_model_cache = gr.Checkbox(False, label="Disable model caching") with gr.Accordion("External LoRA", open=True): with gr.Column(): deselect_lora_button = gr.Button("Remove External LoRAs", variant="secondary") lora_repo_json = gr.JSON(value=[{}] * num_loras, visible=False) lora_repo = [None] * num_loras lora_weights = [None] * num_loras lora_trigger = [None] * num_loras lora_wt = [None] * num_loras lora_info = [None] * num_loras lora_copy = [None] * num_loras lora_md = [None] * num_loras lora_num = [None] * num_loras with gr.Row(): for i in range(num_loras): with gr.Column(): lora_repo[i] = gr.Dropdown(label=f"LoRA {int(i+1)} Repo", choices=get_all_lora_tupled_list(), info="Input LoRA Repo ID", value="", allow_custom_value=True, min_width=320) with gr.Row(): lora_weights[i] = gr.Dropdown(label=f"LoRA {int(i+1)} Filename", choices=[], info="Optional", value="", allow_custom_value=True) lora_trigger[i] = gr.Textbox(label=f"LoRA {int(i+1)} Trigger Prompt", lines=1, max_lines=4, value="") lora_wt[i] = gr.Slider(label=f"LoRA {int(i+1)} Scale", minimum=-3, maximum=3, step=0.01, value=1.00) with gr.Row(): lora_info[i] = gr.Textbox(label="", info="Example of prompt:", value="", show_copy_button=True, interactive=False, visible=False) lora_copy[i] = gr.Button(value="Copy example to prompt", visible=False) lora_md[i] = gr.Markdown(value="", visible=False) lora_num[i] = gr.Number(i, visible=False) with gr.Accordion("From URL", open=True, visible=True): with gr.Row(): lora_search_civitai_basemodel = gr.CheckboxGroup(label="Search LoRA for", choices=["Flux.1 D", "Flux.1 S"], value=["Flux.1 D"]) lora_search_civitai_sort = gr.Radio(label="Sort", choices=CIVITAI_SORT, value="Most Downloaded") lora_search_civitai_period = gr.Radio(label="Period", choices=CIVITAI_PERIOD, value="Month") with gr.Row(): lora_search_civitai_query = gr.Textbox(label="Query", placeholder="flux", lines=1) lora_search_civitai_tag = gr.Dropdown(label="Tag", choices=get_civitai_tag(), value=get_civitai_tag()[0], allow_custom_value=True) lora_search_civitai_user = gr.Textbox(label="Username", lines=1) lora_search_civitai_submit = gr.Button("Search on Civitai") with gr.Row(): lora_search_civitai_json = gr.JSON(value={}, visible=False) lora_search_civitai_desc = gr.Markdown(value="", visible=False, elem_classes="desc") with gr.Accordion("Select from Gallery", open=False): lora_search_civitai_gallery = gr.Gallery([], label="Results", allow_preview=False, columns=5, show_share_button=False, interactive=False) lora_search_civitai_result = gr.Dropdown(label="Search Results", choices=[("", "")], value="", allow_custom_value=True, visible=False) lora_download_url = gr.Textbox(label="LoRA URL", placeholder="https://civitai.com/api/download/models/28907", lines=1) with gr.Row(): lora_download = [None] * num_loras for i in range(num_loras): lora_download[i] = gr.Button(f"Get and set LoRA to {int(i+1)}") with gr.Accordion("ControlNet (extremely slow)", open=True, visible=False): with gr.Column(): cn_on = gr.Checkbox(False, label="Use ControlNet") cn_mode = [None] * num_cns cn_scale = [None] * num_cns cn_image = [None] * num_cns cn_image_ref = [None] * num_cns cn_res = [None] * num_cns cn_num = [None] * num_cns with gr.Row(): for i in range(num_cns): with gr.Column(): cn_mode[i] = gr.Radio(label=f"ControlNet {int(i+1)} Mode", choices=get_control_union_mode(), value=get_control_union_mode()[0]) with gr.Row(): cn_scale[i] = gr.Slider(label=f"ControlNet {int(i+1)} Weight", minimum=0.0, maximum=1.0, step=0.01, value=0.75) cn_res[i] = gr.Slider(label=f"ControlNet {int(i+1)} Preprocess resolution", minimum=128, maximum=512, value=384, step=1) cn_num[i] = gr.Number(i, visible=False) with gr.Row(): cn_image_ref[i] = gr.Image(label="Image Reference", type="pil", format="png", height=256, sources=["upload", "clipboard"], show_share_button=False) cn_image[i] = gr.Image(label="Control Image", type="pil", format="png", height=256, show_share_button=False, interactive=False) 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, gallery], 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, gallery], 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=change_base_model, inputs=[model_name, cn_on, disable_model_cache, model_type], outputs=[result], queue=True, show_api=False, trigger_mode="once", ).success( 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, lora_repo_json, cn_on, auto_trans], outputs=[result, seed, progress_bar], queue=True, show_api=True, #).then( # Update the history gallery # fn=lambda x, history: update_history(x, history), # inputs=[result, history_gallery], # outputs=history_gallery, ).success(save_image_history, [result, history_gallery, history_files, model_name], [history_gallery, history_files], queue=False, show_api=False) input_image.upload(preprocess_i2i_image, [input_image, input_image_preprocess, height, width], [input_image], queue=False, show_api=False) gr.on( triggers=[model_name.change, cn_on.change], fn=get_t2i_model_info, inputs=[model_name], outputs=[model_info], queue=False, show_api=False, trigger_mode="once", ).then(change_base_model, [model_name, cn_on, disable_model_cache], [result], queue=True, show_api=False) prompt_enhance.click(enhance_prompt, [prompt], [prompt], queue=False, show_api=False) gr.on( triggers=[lora_search_civitai_submit.click, lora_search_civitai_query.submit], fn=search_civitai_lora, inputs=[lora_search_civitai_query, lora_search_civitai_basemodel, lora_search_civitai_sort, lora_search_civitai_period, lora_search_civitai_tag, lora_search_civitai_user, lora_search_civitai_gallery], outputs=[lora_search_civitai_result, lora_search_civitai_desc, lora_search_civitai_submit, lora_search_civitai_query, lora_search_civitai_gallery], scroll_to_output=True, queue=True, show_api=False, ) lora_search_civitai_json.change(search_civitai_lora_json, [lora_search_civitai_query, lora_search_civitai_basemodel], [lora_search_civitai_json], queue=True, show_api=True) # fn for api lora_search_civitai_result.change(select_civitai_lora, [lora_search_civitai_result], [lora_download_url, lora_search_civitai_desc], scroll_to_output=True, queue=False, show_api=False) lora_search_civitai_gallery.select(update_civitai_selection, None, [lora_search_civitai_result], queue=False, show_api=False) for i, l in enumerate(lora_repo): deselect_lora_button.click(lambda: ("", 1.0), None, [lora_repo[i], lora_wt[i]], queue=False, show_api=False) gr.on( triggers=[lora_download[i].click], fn=download_my_lora_flux, inputs=[lora_download_url, lora_repo[i]], outputs=[lora_repo[i]], scroll_to_output=True, queue=True, show_api=False, ) gr.on( triggers=[lora_repo[i].change, lora_wt[i].change], fn=update_loras_flux, inputs=[prompt, lora_repo[i], lora_wt[i]], outputs=[prompt, lora_repo[i], lora_wt[i], lora_info[i], lora_md[i]], queue=False, trigger_mode="once", show_api=False, ).success(get_repo_safetensors, [lora_repo[i]], [lora_weights[i]], queue=False, show_api=False ).success(apply_lora_prompt_flux, [lora_info[i]], [lora_trigger[i]], queue=False, show_api=False ).success(compose_lora_json, [lora_repo_json, lora_num[i], lora_repo[i], lora_wt[i], lora_weights[i], lora_trigger[i]], [lora_repo_json], queue=False, show_api=False) for i, m in enumerate(cn_mode): gr.on( triggers=[cn_mode[i].change, cn_scale[i].change], fn=set_control_union_mode, inputs=[cn_num[i], cn_mode[i], cn_scale[i]], outputs=[cn_on], queue=True, show_api=False, ).success(set_control_union_image, [cn_num[i], cn_mode[i], cn_image_ref[i], height, width, cn_res[i]], [cn_image[i]], queue=False, show_api=False) cn_image_ref[i].upload(set_control_union_image, [cn_num[i], cn_mode[i], cn_image_ref[i], height, width, cn_res[i]], [cn_image[i]], queue=False, show_api=False) tagger_generate_from_image.click(lambda: ("", "", ""), None, [v2_series, v2_character, prompt], queue=False, show_api=False, ).success( predict_tags_wd, [tagger_image, prompt, tagger_algorithms, tagger_general_threshold, tagger_character_threshold], [v2_series, v2_character, prompt, v2_copy], show_api=False, ).success(predict_tags_fl2_flux, [tagger_image, prompt, tagger_algorithms], [prompt], show_api=False, ).success(compose_prompt_to_copy, [v2_character, v2_series, prompt], [prompt], queue=False, show_api=False) with gr.Tab("FLUX Prompt Generator"): from prompt import (PromptGenerator, HuggingFaceInferenceNode, florence_caption, ARTFORM, PHOTO_TYPE, ROLES, HAIRSTYLES, LIGHTING, COMPOSITION, POSE, BACKGROUND, PHOTOGRAPHY_STYLES, DEVICE, PHOTOGRAPHER, ARTIST, DIGITAL_ARTFORM, PLACE, FEMALE_DEFAULT_TAGS, MALE_DEFAULT_TAGS, FEMALE_BODY_TYPES, MALE_BODY_TYPES, FEMALE_CLOTHING, MALE_CLOTHING, FEMALE_ADDITIONAL_DETAILS, MALE_ADDITIONAL_DETAILS, pg_title) prompt_generator = PromptGenerator() huggingface_node = HuggingFaceInferenceNode() gr.HTML(pg_title) with gr.Row(): with gr.Column(scale=2): with gr.Accordion("Basic Settings"): pg_custom = gr.Textbox(label="Custom Input Prompt (optional)") pg_subject = gr.Textbox(label="Subject (optional)") pg_gender = gr.Radio(["female", "male"], label="Gender", value="female") # Add the radio button for global option selection pg_global_option = gr.Radio( ["Disabled", "Random", "No Figure Rand"], label="Set all options to:", value="Disabled" ) with gr.Accordion("Artform and Photo Type", open=False): pg_artform = gr.Dropdown(["disabled", "random"] + ARTFORM, label="Artform", value="disabled") pg_photo_type = gr.Dropdown(["disabled", "random"] + PHOTO_TYPE, label="Photo Type", value="disabled") with gr.Accordion("Character Details", open=False): pg_body_types = gr.Dropdown(["disabled", "random"] + FEMALE_BODY_TYPES + MALE_BODY_TYPES, label="Body Types", value="disabled") pg_default_tags = gr.Dropdown(["disabled", "random"] + FEMALE_DEFAULT_TAGS + MALE_DEFAULT_TAGS, label="Default Tags", value="disabled") pg_roles = gr.Dropdown(["disabled", "random"] + ROLES, label="Roles", value="disabled") pg_hairstyles = gr.Dropdown(["disabled", "random"] + HAIRSTYLES, label="Hairstyles", value="disabled") pg_clothing = gr.Dropdown(["disabled", "random"] + FEMALE_CLOTHING + MALE_CLOTHING, label="Clothing", value="disabled") with gr.Accordion("Scene Details", open=False): pg_place = gr.Dropdown(["disabled", "random"] + PLACE, label="Place", value="disabled") pg_lighting = gr.Dropdown(["disabled", "random"] + LIGHTING, label="Lighting", value="disabled") pg_composition = gr.Dropdown(["disabled", "random"] + COMPOSITION, label="Composition", value="disabled") pg_pose = gr.Dropdown(["disabled", "random"] + POSE, label="Pose", value="disabled") pg_background = gr.Dropdown(["disabled", "random"] + BACKGROUND, label="Background", value="disabled") with gr.Accordion("Style and Artist", open=False): pg_additional_details = gr.Dropdown(["disabled", "random"] + FEMALE_ADDITIONAL_DETAILS + MALE_ADDITIONAL_DETAILS, label="Additional Details", value="disabled") pg_photography_styles = gr.Dropdown(["disabled", "random"] + PHOTOGRAPHY_STYLES, label="Photography Styles", value="disabled") pg_device = gr.Dropdown(["disabled", "random"] + DEVICE, label="Device", value="disabled") pg_photographer = gr.Dropdown(["disabled", "random"] + PHOTOGRAPHER, label="Photographer", value="disabled") pg_artist = gr.Dropdown(["disabled", "random"] + ARTIST, label="Artist", value="disabled") pg_digital_artform = gr.Dropdown(["disabled", "random"] + DIGITAL_ARTFORM, label="Digital Artform", value="disabled") pg_generate_button = gr.Button("Generate Prompt") with gr.Column(scale=2): with gr.Accordion("Image and Caption", open=False): pg_input_image = gr.Image(label="Input Image (optional)") pg_caption_output = gr.Textbox(label="Generated Caption", lines=3) pg_create_caption_button = gr.Button("Create Caption") pg_add_caption_button = gr.Button("Add Caption to Prompt") with gr.Accordion("Prompt Generation", open=True): pg_output = gr.Textbox(label="Generated Prompt / Input Text", lines=4) pg_t5xxl_output = gr.Textbox(label="T5XXL Output", visible=True) pg_clip_l_output = gr.Textbox(label="CLIP L Output", visible=True) pg_clip_g_output = gr.Textbox(label="CLIP G Output", visible=True) with gr.Column(scale=2): with gr.Accordion("Prompt Generation with LLM", open=False): pg_happy_talk = gr.Checkbox(label="Happy Talk", value=True) pg_compress = gr.Checkbox(label="Compress", value=True) pg_compression_level = gr.Radio(["soft", "medium", "hard"], label="Compression Level", value="hard") pg_poster = gr.Checkbox(label="Poster", value=False) pg_custom_base_prompt = gr.Textbox(label="Custom Base Prompt", lines=5) pg_generate_text_button = gr.Button("Generate Prompt with LLM (Llama 3.1 70B)") pg_text_output = gr.Textbox(label="Generated Text", lines=10) def create_caption(image): if image is not None: return florence_caption(image) return "" pg_create_caption_button.click( create_caption, inputs=[pg_input_image], outputs=[pg_caption_output] ) def generate_prompt_with_dynamic_seed(*args): # Generate a new random seed dynamic_seed = random.randint(0, 1000000) # Call the generate_prompt function with the dynamic seed result = prompt_generator.generate_prompt(dynamic_seed, *args) # Return the result along with the used seed return [dynamic_seed] + list(result) pg_generate_button.click( generate_prompt_with_dynamic_seed, inputs=[pg_custom, pg_subject, pg_gender, pg_artform, pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles, pg_additional_details, pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform, pg_place, pg_lighting, pg_clothing, pg_composition, pg_pose, pg_background, pg_input_image], outputs=[gr.Number(label="Used Seed", visible=False), pg_output, gr.Number(visible=False), pg_t5xxl_output, pg_clip_l_output, pg_clip_g_output] ) # pg_add_caption_button.click( prompt_generator.add_caption_to_prompt, inputs=[pg_output, pg_caption_output], outputs=[pg_output] ) pg_generate_text_button.click( huggingface_node.generate, inputs=[pg_output, pg_happy_talk, pg_compress, pg_compression_level, pg_poster, pg_custom_base_prompt], outputs=pg_text_output ) def update_all_options(choice): updates = {} if choice == "Disabled": for dropdown in [ pg_artform, pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles, pg_clothing, pg_place, pg_lighting, pg_composition, pg_pose, pg_background, pg_additional_details, pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform ]: updates[dropdown] = gr.update(value="disabled") elif choice == "Random": for dropdown in [ pg_artform, pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles, pg_clothing, pg_place, pg_lighting, pg_composition, pg_pose, pg_background, pg_additional_details, pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform ]: updates[dropdown] = gr.update(value="random") else: # No Figure Random for dropdown in [pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles, pg_clothing, pg_pose, pg_additional_details]: updates[dropdown] = gr.update(value="disabled") for dropdown in [pg_artform, pg_place, pg_lighting, pg_composition, pg_background, pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform]: updates[dropdown] = gr.update(value="random") return updates pg_global_option.change( update_all_options, inputs=[pg_global_option], outputs=[ pg_artform, pg_photo_type, pg_body_types, pg_default_tags, pg_roles, pg_hairstyles, pg_clothing, pg_place, pg_lighting, pg_composition, pg_pose, pg_background, pg_additional_details, pg_photography_styles, pg_device, pg_photographer, pg_artist, pg_digital_artform ] ) with gr.Tab("PNG Info"): def extract_exif_data(image): if image is None: return "" try: metadata_keys = ['parameters', 'metadata', 'prompt', 'Comment'] for key in metadata_keys: if key in image.info: return image.info[key] return str(image.info) except Exception as e: return f"Error extracting metadata: {str(e)}" with gr.Row(): with gr.Column(): image_metadata = gr.Image(label="Image with metadata", type="pil", sources=["upload"]) with gr.Column(): result_metadata = gr.Textbox(label="Metadata", show_label=True, show_copy_button=True, interactive=False, container=True, max_lines=99) image_metadata.change( fn=extract_exif_data, inputs=[image_metadata], outputs=[result_metadata], ) description_ui() gr.LoginButton() gr.DuplicateButton(value="Duplicate Space for private use (This demo does not work on CPU. Requires GPU Space)") app.queue() app.launch()