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| 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 | |
| from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download | |
| import os | |
| import copy | |
| import random | |
| import time | |
| import requests | |
| import pandas as pd | |
| from env import models, num_loras, num_cns | |
| 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 flux import (search_civitai_lora, select_civitai_lora, search_civitai_lora_json, | |
| download_my_lora, get_all_lora_tupled_list, apply_lora_prompt, | |
| update_loras, get_t2i_model_info) | |
| 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) | |
| good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype) | |
| pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1) | |
| 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) | |
| 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, progress=gr.Progress(track_tqdm=True)): | |
| global pipe, pipe_i2i, taef1, good_vae, controlnet_union, controlnet, last_model, last_cn_on, dtype | |
| 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) | |
| 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) | |
| controlnet = FluxMultiControlNetModel([controlnet_union]) | |
| controlnet.config = controlnet_union.config | |
| pipe = FluxControlNetPipeline.from_pretrained(repo_id, controlnet=controlnet, torch_dtype=dtype) | |
| 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) | |
| 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}") | |
| pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=dtype) | |
| 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) | |
| 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: {e}") | |
| raise gr.Error(f"Model load Error: {e}") from e | |
| return gr.update(visible=True) | |
| change_base_model.zerogpu = True | |
| 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): | |
| 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 | |
| ) | |
| 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 | |
| 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 | |
| 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) | |
| 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'<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) | |
| 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) | |
| 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 = [] | |
| 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 {text-align:center; !important} | |
| ''' | |
| with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', fill_width=True, css=css, delete_cache=(60, 3600)) as app: | |
| with gr.Tab("FLUX LoRA the Explorer"): | |
| title = gr.HTML( | |
| """<h1><img src="https://huggingface.co/spaces/John6666/flux-lora-the-explorer/resolve/main/flux_lora.png" alt="LoRA">FLUX LoRA the Explorer Mod</h1>""", | |
| 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" | |
| ) | |
| 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", 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) | |
| with gr.Group(): | |
| model_name = gr.Dropdown(label="Base Model", info="You can enter a huggingface model repo_id to want to use.", choices=models, value=models[0], allow_custom_value=True) | |
| 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) | |
| 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=["Highest Rated", "Most Downloaded", "Newest"], value="Most Downloaded") | |
| lora_search_civitai_period = gr.Radio(label="Period", choices=["AllTime", "Year", "Month", "Week", "Day"], value="Month") | |
| with gr.Row(): | |
| lora_search_civitai_query = gr.Textbox(label="Query", placeholder="flux", lines=1) | |
| lora_search_civitai_tag = gr.Textbox(label="Tag", 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) | |
| 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=True): | |
| 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], | |
| 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=change_base_model, | |
| inputs=[model_name, cn_on, disable_model_cache], | |
| 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, | |
| ) | |
| 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, lora_search_civitai_tag.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], | |
| outputs=[lora_search_civitai_result, lora_search_civitai_desc, lora_search_civitai_submit, lora_search_civitai_query], | |
| 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) | |
| 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, | |
| 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, | |
| 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, [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() |