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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import os | |
| import random | |
| import gc | |
| import toml | |
| import gradio as gr | |
| import numpy as np | |
| import utils | |
| import torch | |
| import json | |
| import PIL.Image | |
| import base64 | |
| import safetensors | |
| from io import BytesIO | |
| from typing import Tuple | |
| from datetime import datetime | |
| from PIL import PngImagePlugin | |
| import gradio_user_history as gr_user_history | |
| from huggingface_hub import hf_hub_download | |
| from safetensors.torch import load_file | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer | |
| from lora_diffusers import LoRANetwork, create_network_from_weights | |
| from diffusers.models import AutoencoderKL | |
| from diffusers import ( | |
| StableDiffusionXLPipeline, | |
| StableDiffusionXLImg2ImgPipeline, | |
| DPMSolverMultistepScheduler, | |
| DPMSolverSinglestepScheduler, | |
| KDPM2DiscreteScheduler, | |
| EulerDiscreteScheduler, | |
| EulerAncestralDiscreteScheduler, | |
| HeunDiscreteScheduler, | |
| LMSDiscreteScheduler, | |
| DDIMScheduler, | |
| DEISMultistepScheduler, | |
| UniPCMultistepScheduler, | |
| ) | |
| DESCRIPTION = "Animagine XL 3.0" | |
| if not torch.cuda.is_available(): | |
| DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU. </p>" | |
| IS_COLAB = utils.is_google_colab() or os.getenv("IS_COLAB") == "1" | |
| MAX_SEED = np.iinfo(np.int32).max | |
| HF_TOKEN = os.getenv("HF_TOKEN") | |
| CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1" | |
| MIN_IMAGE_SIZE = int(os.getenv("MIN_IMAGE_SIZE", "512")) | |
| MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048")) | |
| USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1" | |
| ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1" | |
| MODEL = os.getenv("MODEL", "https://huggingface.co/Linaqruf/animagine-xl-3.0/blob/main/animagine-xl-3.0.safetensors") | |
| torch.backends.cudnn.deterministic = True | |
| torch.backends.cudnn.benchmark = False | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| if torch.cuda.is_available(): | |
| vae = AutoencoderKL.from_pretrained( | |
| "madebyollin/sdxl-vae-fp16-fix", | |
| torch_dtype=torch.float16, | |
| ) | |
| pipeline = StableDiffusionXLPipeline.from_single_file if MODEL.endswith(".safetensors") else StableDiffusionXLPipeline.from_pretrained | |
| pipe = pipeline( | |
| MODEL, | |
| vae=vae, | |
| torch_dtype=torch.float16, | |
| custom_pipeline="lpw_stable_diffusion_xl", | |
| use_safetensors=True, | |
| use_auth_token=HF_TOKEN, | |
| variant="fp16", | |
| ) | |
| if ENABLE_CPU_OFFLOAD: | |
| pipe.enable_model_cpu_offload() | |
| else: | |
| pipe.to(device) | |
| if USE_TORCH_COMPILE: | |
| pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) | |
| else: | |
| pipe = None | |
| def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| return seed | |
| def seed_everything(seed): | |
| torch.manual_seed(seed) | |
| torch.cuda.manual_seed_all(seed) | |
| np.random.seed(seed) | |
| generator = torch.Generator() | |
| generator.manual_seed(seed) | |
| return generator | |
| def get_image_path(base_path: str): | |
| extensions = [".jpg", ".jpeg", ".png", ".bmp", ".gif"] | |
| for ext in extensions: | |
| image_path = base_path + ext | |
| if os.path.exists(image_path): | |
| return image_path | |
| return None | |
| def update_selection(selected_state: gr.SelectData): | |
| lora_repo = sdxl_loras[selected_state.index]["repo"] | |
| lora_weight = sdxl_loras[selected_state.index]["multiplier"] | |
| updated_selected_info = f"{lora_repo}" | |
| return ( | |
| updated_selected_info, | |
| selected_state, | |
| lora_weight, | |
| ) | |
| def parse_aspect_ratio(aspect_ratio): | |
| if aspect_ratio == "Custom": | |
| return None, None | |
| width, height = aspect_ratio.split(" x ") | |
| return int(width), int(height) | |
| def aspect_ratio_handler(aspect_ratio, custom_width, custom_height): | |
| if aspect_ratio == "Custom": | |
| return custom_width, custom_height | |
| else: | |
| width, height = parse_aspect_ratio(aspect_ratio) | |
| return width, height | |
| def create_network(text_encoders, unet, state_dict, multiplier, device): | |
| network = create_network_from_weights( | |
| text_encoders, | |
| unet, | |
| state_dict, | |
| multiplier, | |
| ) | |
| network.load_state_dict(state_dict) | |
| network.to(device, dtype=unet.dtype) | |
| network.apply_to(multiplier=multiplier) | |
| return network | |
| def get_scheduler(scheduler_config, name): | |
| scheduler_map = { | |
| "DPM++ 2M Karras": lambda: DPMSolverMultistepScheduler.from_config( | |
| scheduler_config, use_karras_sigmas=True | |
| ), | |
| "DPM++ SDE Karras": lambda: DPMSolverSinglestepScheduler.from_config( | |
| scheduler_config, use_karras_sigmas=True | |
| ), | |
| "DPM++ 2M SDE Karras": lambda: DPMSolverMultistepScheduler.from_config( | |
| scheduler_config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++" | |
| ), | |
| "Euler": lambda: EulerDiscreteScheduler.from_config(scheduler_config), | |
| "Euler a": lambda: EulerAncestralDiscreteScheduler.from_config( | |
| scheduler_config | |
| ), | |
| "DDIM": lambda: DDIMScheduler.from_config(scheduler_config), | |
| } | |
| return scheduler_map.get(name, lambda: None)() | |
| def free_memory(): | |
| torch.cuda.empty_cache() | |
| gc.collect() | |
| def preprocess_prompt( | |
| style_dict, | |
| style_name: str, | |
| positive: str, | |
| negative: str = "", | |
| add_style: bool = True, | |
| ) -> Tuple[str, str]: | |
| p, n = style_dict.get(style_name, style_dict["(None)"]) | |
| if add_style and positive.strip(): | |
| formatted_positive = p.format(prompt=positive) | |
| else: | |
| formatted_positive = positive | |
| combined_negative = n + negative | |
| return formatted_positive, combined_negative | |
| def common_upscale(samples, width, height, upscale_method): | |
| return torch.nn.functional.interpolate( | |
| samples, size=(height, width), mode=upscale_method | |
| ) | |
| def upscale(samples, upscale_method, scale_by): | |
| width = round(samples.shape[3] * scale_by) | |
| height = round(samples.shape[2] * scale_by) | |
| s = common_upscale(samples, width, height, upscale_method) | |
| return s | |
| def load_and_convert_thumbnail(model_path: str): | |
| with safetensors.safe_open(model_path, framework="pt") as f: | |
| metadata = f.metadata() | |
| if "modelspec.thumbnail" in metadata: | |
| base64_data = metadata["modelspec.thumbnail"] | |
| prefix, encoded = base64_data.split(",", 1) | |
| image_data = base64.b64decode(encoded) | |
| image = PIL.Image.open(BytesIO(image_data)) | |
| return image | |
| return None | |
| def load_wildcard_files(wildcard_dir): | |
| wildcard_files = {} | |
| for file in os.listdir(wildcard_dir): | |
| if file.endswith(".txt"): | |
| key = f"__{file.split('.')[0]}__" # Create a key like __character__ | |
| wildcard_files[key] = os.path.join(wildcard_dir, file) | |
| return wildcard_files | |
| def get_random_line_from_file(file_path): | |
| with open(file_path, 'r') as file: | |
| lines = file.readlines() | |
| if not lines: | |
| return "" | |
| return random.choice(lines).strip() | |
| def add_wildcard(prompt, wildcard_files): | |
| for key, file_path in wildcard_files.items(): | |
| if key in prompt: | |
| wildcard_line = get_random_line_from_file(file_path) | |
| prompt = prompt.replace(key, wildcard_line) | |
| return prompt | |
| def generate( | |
| prompt: str, | |
| negative_prompt: str = "", | |
| seed: int = 0, | |
| custom_width: int = 1024, | |
| custom_height: int = 1024, | |
| guidance_scale: float = 7.0, | |
| num_inference_steps: int = 28, | |
| use_lora: bool = False, | |
| lora_weight: float = 1.0, | |
| selected_state: str = "", | |
| sampler: str = "Euler a", | |
| aspect_ratio_selector: str = "896 x 1152", | |
| style_selector: str = "(None)", | |
| quality_selector: str = "Standard", | |
| use_upscaler: bool = False, | |
| upscaler_strength: float = 0.5, | |
| upscale_by: float = 1.5, | |
| add_quality_tags: bool = True, | |
| profile: gr.OAuthProfile | None = None, | |
| progress=gr.Progress(track_tqdm=True), | |
| ) -> PIL.Image.Image: | |
| generator = seed_everything(seed) | |
| network = None | |
| network_state = {"current_lora": None, "multiplier": None} | |
| width, height = aspect_ratio_handler( | |
| aspect_ratio_selector, | |
| custom_width, | |
| custom_height, | |
| ) | |
| prompt = add_wildcard(prompt, wildcard_files) | |
| prompt, negative_prompt = preprocess_prompt( | |
| quality_prompt, quality_selector, prompt, negative_prompt, add_quality_tags | |
| ) | |
| prompt, negative_prompt = preprocess_prompt( | |
| styles, style_selector, prompt, negative_prompt | |
| ) | |
| if width % 8 != 0: | |
| width = width - (width % 8) | |
| if height % 8 != 0: | |
| height = height - (height % 8) | |
| if use_lora: | |
| if not selected_state: | |
| raise Exception("You must Select a LoRA") | |
| repo_name = sdxl_loras[selected_state.index]["repo"] | |
| full_path_lora = saved_names[selected_state.index] | |
| weight_name = sdxl_loras[selected_state.index]["weights"] | |
| lora_sd = load_file(full_path_lora) | |
| text_encoders = [pipe.text_encoder, pipe.text_encoder_2] | |
| if network_state["current_lora"] != repo_name: | |
| network = create_network( | |
| text_encoders, | |
| pipe.unet, | |
| lora_sd, | |
| lora_weight, | |
| device, | |
| ) | |
| network_state["current_lora"] = repo_name | |
| network_state["multiplier"] = lora_weight | |
| elif network_state["multiplier"] != lora_weight: | |
| network = create_network( | |
| text_encoders, | |
| pipe.unet, | |
| lora_sd, | |
| lora_weight, | |
| device, | |
| ) | |
| network_state["multiplier"] = lora_weight | |
| else: | |
| if network: | |
| network.unapply_to() | |
| network = None | |
| network_state = { | |
| "current_lora": None, | |
| "multiplier": None, | |
| } | |
| backup_scheduler = pipe.scheduler | |
| pipe.scheduler = get_scheduler(pipe.scheduler.config, sampler) | |
| if use_upscaler: | |
| upscaler_pipe = StableDiffusionXLImg2ImgPipeline(**pipe.components) | |
| metadata = { | |
| "prompt": prompt, | |
| "negative_prompt": negative_prompt, | |
| "resolution": f"{width} x {height}", | |
| "guidance_scale": guidance_scale, | |
| "num_inference_steps": num_inference_steps, | |
| "seed": seed, | |
| "sampler": sampler, | |
| "sdxl_style": style_selector, | |
| "add_quality_tags": add_quality_tags, | |
| "quality_tags": quality_selector, | |
| } | |
| if use_lora: | |
| metadata["use_lora"] = {"selected_lora": repo_name, "multiplier": lora_weight} | |
| else: | |
| metadata["use_lora"] = None | |
| if use_upscaler: | |
| new_width = int(width * upscale_by) | |
| new_height = int(height * upscale_by) | |
| metadata["use_upscaler"] = { | |
| "upscale_method": "nearest-exact", | |
| "upscaler_strength": upscaler_strength, | |
| "upscale_by": upscale_by, | |
| "new_resolution": f"{new_width} x {new_height}", | |
| } | |
| else: | |
| metadata["use_upscaler"] = None | |
| print(json.dumps(metadata, indent=4)) | |
| try: | |
| if use_upscaler: | |
| latents = pipe( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| width=width, | |
| height=height, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=num_inference_steps, | |
| generator=generator, | |
| output_type="latent", | |
| ).images | |
| upscaled_latents = upscale(latents, "nearest-exact", upscale_by) | |
| image = upscaler_pipe( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| image=upscaled_latents, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=num_inference_steps, | |
| strength=upscaler_strength, | |
| generator=generator, | |
| output_type="pil", | |
| ).images[0] | |
| else: | |
| image = pipe( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| width=width, | |
| height=height, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=num_inference_steps, | |
| generator=generator, | |
| output_type="pil", | |
| ).images[0] | |
| if network: | |
| network.unapply_to() | |
| network = None | |
| if profile is not None: | |
| gr_user_history.save_image( | |
| label=prompt, | |
| image=image, | |
| profile=profile, | |
| metadata=metadata, | |
| ) | |
| if image and IS_COLAB: | |
| current_time = datetime.now().strftime("%Y%m%d_%H%M%S") | |
| output_directory = "./outputs" | |
| os.makedirs(output_directory, exist_ok=True) | |
| filename = f"image_{current_time}.png" | |
| filepath = os.path.join(output_directory, filename) | |
| # Convert metadata to a string and save as a text chunk in the PNG | |
| metadata_str = json.dumps(metadata) | |
| info = PngImagePlugin.PngInfo() | |
| info.add_text("metadata", metadata_str) | |
| image.save(filepath, "PNG", pnginfo=info) | |
| print(f"Image saved as {filepath} with metadata") | |
| return image, metadata | |
| except Exception as e: | |
| print(f"An error occurred: {e}") | |
| raise | |
| finally: | |
| if network: | |
| network.unapply_to() | |
| network = None | |
| if use_lora: | |
| del lora_sd, text_encoders | |
| if use_upscaler: | |
| del upscaler_pipe | |
| pipe.scheduler = backup_scheduler | |
| free_memory() | |
| examples = [ | |
| "1girl, arima kana, oshi no ko, solo, idol, idol clothes, one eye closed, red shirt, black skirt, black headwear, gloves, stage light, singing, open mouth, crowd, smile, pointing at viewer", | |
| "1girl, c.c., code geass, white shirt, long sleeves, turtleneck, sitting, looking at viewer, eating, pizza, plate, fork, knife, table, chair, table, restaurant, cinematic angle, cinematic lighting", | |
| "1girl, sakurauchi riko, \(love live\), queen hat, noble coat, red coat, noble shirt, sitting, crossed legs, gentle smile, parted lips, throne, cinematic angle", | |
| "1girl, amiya \(arknights\), arknights, dirty face, outstretched hand, close-up, cinematic angle, foreshortening, dark, dark background", | |
| "A boy and a girl, Emiya Shirou and Artoria Pendragon from fate series, having their breakfast in the dining room. Emiya Shirou wears white t-shirt and jacket. Artoria Pendragon wears white dress with blue neck ribbon. Rice, soup, and minced meats are served on the table. They look at each other while smiling happily", | |
| ] | |
| quality_prompt_list = [ | |
| { | |
| "name": "(None)", | |
| "prompt": "{prompt}", | |
| "negative_prompt": "nsfw, lowres, ", | |
| }, | |
| { | |
| "name": "Standard", | |
| "prompt": "{prompt}, masterpiece, best quality", | |
| "negative_prompt": "nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, ", | |
| }, | |
| { | |
| "name": "Light", | |
| "prompt": "{prompt}, (masterpiece), best quality, perfect face", | |
| "negative_prompt": "nsfw, (low quality, worst quality:1.2), 3d, watermark, signature, ugly, poorly drawn, ", | |
| }, | |
| { | |
| "name": "Heavy", | |
| "prompt": "{prompt}, (masterpiece), (best quality), (ultra-detailed), illustration, disheveled hair, perfect composition, moist skin, intricate details, earrings", | |
| "negative_prompt": "nsfw, longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair, extra digit, fewer digits, cropped, worst quality, low quality, ", | |
| }, | |
| ] | |
| sampler_list = [ | |
| "DPM++ 2M Karras", | |
| "DPM++ SDE Karras", | |
| "DPM++ 2M SDE Karras", | |
| "Euler", | |
| "Euler a", | |
| "DDIM", | |
| ] | |
| aspect_ratios = [ | |
| "1024 x 1024", | |
| "1152 x 896", | |
| "896 x 1152", | |
| "1216 x 832", | |
| "832 x 1216", | |
| "1344 x 768", | |
| "768 x 1344", | |
| "1536 x 640", | |
| "640 x 1536", | |
| "Custom", | |
| ] | |
| style_list = [ | |
| { | |
| "name": "(None)", | |
| "prompt": "{prompt}", | |
| "negative_prompt": "", | |
| }, | |
| { | |
| "name": "Cinematic", | |
| "prompt": "{prompt}, cinematic still, emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy", | |
| "negative_prompt": "nsfw, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", | |
| }, | |
| { | |
| "name": "Photographic", | |
| "prompt": "{prompt}, cinematic photo, 35mm photograph, film, bokeh, professional, 4k, highly detailed", | |
| "negative_prompt": "nsfw, drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly", | |
| }, | |
| { | |
| "name": "Anime", | |
| "prompt": "{prompt}, anime artwork, anime style, key visual, vibrant, studio anime, highly detailed", | |
| "negative_prompt": "nsfw, photo, deformed, black and white, realism, disfigured, low contrast", | |
| }, | |
| { | |
| "name": "Manga", | |
| "prompt": "{prompt}, manga style, vibrant, high-energy, detailed, iconic, Japanese comic style", | |
| "negative_prompt": "nsfw, ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style", | |
| }, | |
| { | |
| "name": "Digital Art", | |
| "prompt": "{prompt}, concept art, digital artwork, illustrative, painterly, matte painting, highly detailed", | |
| "negative_prompt": "nsfw, photo, photorealistic, realism, ugly", | |
| }, | |
| { | |
| "name": "Pixel art", | |
| "prompt": "{prompt}, pixel-art, low-res, blocky, pixel art style, 8-bit graphics", | |
| "negative_prompt": "nsfw, sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic", | |
| }, | |
| { | |
| "name": "Fantasy art", | |
| "prompt": "{prompt}, ethereal fantasy concept art, magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy", | |
| "negative_prompt": "nsfw, photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white", | |
| }, | |
| { | |
| "name": "Neonpunk", | |
| "prompt": "{prompt}, neonpunk style, cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional", | |
| "negative_prompt": "nsfw, painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured", | |
| }, | |
| { | |
| "name": "3D Model", | |
| "prompt": "{prompt}, professional 3d model, octane render, highly detailed, volumetric, dramatic lighting", | |
| "negative_prompt": "nsfw, ugly, deformed, noisy, low poly, blurry, painting", | |
| }, | |
| ] | |
| thumbnail_cache = {} | |
| with open("lora.toml", "r") as file: | |
| data = toml.load(file) | |
| sdxl_loras = [] | |
| saved_names = [] | |
| for item in data["data"]: | |
| model_path = hf_hub_download(item["repo"], item["weights"], token=HF_TOKEN) | |
| saved_names.append(model_path) # Store the path in saved_names | |
| if model_path not in thumbnail_cache: | |
| thumbnail_image = load_and_convert_thumbnail(model_path) | |
| thumbnail_cache[model_path] = thumbnail_image | |
| else: | |
| thumbnail_image = thumbnail_cache[model_path] | |
| sdxl_loras.append( | |
| { | |
| "image": thumbnail_image, # Storing the PIL image object | |
| "title": item["title"], | |
| "repo": item["repo"], | |
| "weights": item["weights"], | |
| "multiplier": item.get("multiplier", "1.0"), | |
| } | |
| ) | |
| styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list} | |
| quality_prompt = { | |
| k["name"]: (k["prompt"], k["negative_prompt"]) for k in quality_prompt_list | |
| } | |
| # saved_names = [ | |
| # hf_hub_download(item["repo"], item["weights"], token=HF_TOKEN) | |
| # for item in sdxl_loras | |
| # ] | |
| wildcard_files = load_wildcard_files("wildcard") | |
| with gr.Blocks(css="style.css", theme="NoCrypt/miku@1.2.1") as demo: | |
| title = gr.HTML( | |
| f"""<h1><span>{DESCRIPTION}</span></h1>""", | |
| elem_id="title", | |
| ) | |
| gr.Markdown( | |
| f"""Gradio demo for [cagliostrolab/animagine-xl-3.0](https://huggingface.co/cagliostrolab/animagine-xl-3.0)""", | |
| elem_id="subtitle", | |
| ) | |
| gr.Markdown( | |
| f"""Prompting is a bit different in this iteration, we train the model like this: | |
| ``` | |
| 1girl/1boy, character name, from what series, everything else in any order. | |
| ``` | |
| Prompting Tips | |
| ``` | |
| 1. Quality Tags: `masterpiece, best quality, high quality, normal quality, worst quality, low quality` | |
| 2. Year Tags: `oldest, early, mid, late, newest` | |
| 3. Rating tags: `rating: general, rating: sensitive, rating: questionable, rating: explicit, nsfw` | |
| 4. Escape character: `character name \(series\)` | |
| 5. Recommended settings: `Euler a, cfg 5-7, 25-28 steps` | |
| 6. It's recommended to use the exact danbooru tags for more accurate result | |
| 7. To use character wildcard, add this syntax to the prompt `__character__`. | |
| ``` | |
| """, | |
| elem_id="subtitle", | |
| ) | |
| gr.DuplicateButton( | |
| value="Duplicate Space for private use", | |
| elem_id="duplicate-button", | |
| visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", | |
| ) | |
| selected_state = gr.State() | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| with gr.Tab("Txt2img"): | |
| with gr.Group(): | |
| prompt = gr.Text( | |
| label="Prompt", | |
| max_lines=5, | |
| placeholder="Enter your prompt", | |
| ) | |
| negative_prompt = gr.Text( | |
| label="Negative Prompt", | |
| max_lines=5, | |
| placeholder="Enter a negative prompt", | |
| ) | |
| with gr.Accordion(label="Quality Tags", open=True): | |
| add_quality_tags = gr.Checkbox(label="Add Quality Tags", value=True) | |
| quality_selector = gr.Dropdown( | |
| label="Quality Tags Presets", | |
| interactive=True, | |
| choices=list(quality_prompt.keys()), | |
| value="Standard", | |
| ) | |
| with gr.Row(): | |
| use_lora = gr.Checkbox(label="Use LoRA", value=False) | |
| with gr.Group(visible=False) as lora_group: | |
| selector_info = gr.Text( | |
| label="Selected LoRA", | |
| max_lines=1, | |
| value="No LoRA selected.", | |
| ) | |
| lora_selection = gr.Gallery( | |
| value=[(item["image"], item["title"]) for item in sdxl_loras], | |
| label="Animagine XL 2.0 LoRA", | |
| show_label=False, | |
| columns=2, | |
| show_share_button=False, | |
| ) | |
| lora_weight = gr.Slider( | |
| label="Multiplier", | |
| minimum=-2, | |
| maximum=2, | |
| step=0.05, | |
| value=1, | |
| ) | |
| with gr.Tab("Advanced Settings"): | |
| with gr.Group(): | |
| style_selector = gr.Radio( | |
| label="Style Preset", | |
| container=True, | |
| interactive=True, | |
| choices=list(styles.keys()), | |
| value="(None)", | |
| ) | |
| with gr.Group(): | |
| aspect_ratio_selector = gr.Radio( | |
| label="Aspect Ratio", | |
| choices=aspect_ratios, | |
| value="896 x 1152", | |
| container=True, | |
| ) | |
| with gr.Group(): | |
| use_upscaler = gr.Checkbox(label="Use Upscaler", value=False) | |
| with gr.Row() as upscaler_row: | |
| upscaler_strength = gr.Slider( | |
| label="Strength", | |
| minimum=0, | |
| maximum=1, | |
| step=0.05, | |
| value=0.55, | |
| visible=False, | |
| ) | |
| upscale_by = gr.Slider( | |
| label="Upscale by", | |
| minimum=1, | |
| maximum=1.5, | |
| step=0.1, | |
| value=1.5, | |
| visible=False, | |
| ) | |
| with gr.Group(visible=False) as custom_resolution: | |
| with gr.Row(): | |
| custom_width = gr.Slider( | |
| label="Width", | |
| minimum=MIN_IMAGE_SIZE, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=8, | |
| value=1024, | |
| ) | |
| custom_height = gr.Slider( | |
| label="Height", | |
| minimum=MIN_IMAGE_SIZE, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=8, | |
| value=1024, | |
| ) | |
| with gr.Group(): | |
| sampler = gr.Dropdown( | |
| label="Sampler", | |
| choices=sampler_list, | |
| interactive=True, | |
| value="Euler a", | |
| ) | |
| with gr.Group(): | |
| seed = gr.Slider( | |
| label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0 | |
| ) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| with gr.Group(): | |
| with gr.Row(): | |
| guidance_scale = gr.Slider( | |
| label="Guidance scale", | |
| minimum=1, | |
| maximum=12, | |
| step=0.1, | |
| value=7.0, | |
| ) | |
| num_inference_steps = gr.Slider( | |
| label="Number of inference steps", | |
| minimum=1, | |
| maximum=50, | |
| step=1, | |
| value=28, | |
| ) | |
| with gr.Tab("Past Generation"): | |
| gr_user_history.render() | |
| with gr.Column(scale=3): | |
| with gr.Blocks(): | |
| run_button = gr.Button("Generate", variant="primary") | |
| result = gr.Image(label="Result", show_label=False) | |
| with gr.Accordion(label="Generation Parameters", open=False): | |
| gr_metadata = gr.JSON(label="Metadata", show_label=False) | |
| gr.Examples( | |
| examples=examples, | |
| inputs=prompt, | |
| outputs=[result, gr_metadata], | |
| fn=generate, | |
| cache_examples=CACHE_EXAMPLES, | |
| ) | |
| lora_selection.select( | |
| update_selection, | |
| outputs=[ | |
| selector_info, | |
| selected_state, | |
| lora_weight, | |
| ], | |
| queue=False, | |
| show_progress=False, | |
| ) | |
| use_lora.change( | |
| fn=lambda x: gr.update(visible=x), | |
| inputs=use_lora, | |
| outputs=lora_group, | |
| queue=False, | |
| api_name=False, | |
| ) | |
| use_upscaler.change( | |
| fn=lambda x: [gr.update(visible=x), gr.update(visible=x)], | |
| inputs=use_upscaler, | |
| outputs=[upscaler_strength, upscale_by], | |
| queue=False, | |
| api_name=False, | |
| ) | |
| aspect_ratio_selector.change( | |
| fn=lambda x: gr.update(visible=x == "Custom"), | |
| inputs=aspect_ratio_selector, | |
| outputs=custom_resolution, | |
| queue=False, | |
| api_name=False, | |
| ) | |
| inputs = [ | |
| prompt, | |
| negative_prompt, | |
| seed, | |
| custom_width, | |
| custom_height, | |
| guidance_scale, | |
| num_inference_steps, | |
| use_lora, | |
| lora_weight, | |
| selected_state, | |
| sampler, | |
| aspect_ratio_selector, | |
| style_selector, | |
| quality_selector, | |
| use_upscaler, | |
| upscaler_strength, | |
| upscale_by, | |
| add_quality_tags | |
| ] | |
| prompt.submit( | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| api_name=False, | |
| ).then( | |
| fn=generate, | |
| inputs=inputs, | |
| outputs=result, | |
| api_name="run", | |
| ) | |
| negative_prompt.submit( | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| api_name=False, | |
| ).then( | |
| fn=generate, | |
| inputs=inputs, | |
| outputs=result, | |
| api_name=False, | |
| ) | |
| run_button.click( | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| api_name=False, | |
| ).then( | |
| fn=generate, | |
| inputs=inputs, | |
| outputs=[result, gr_metadata], | |
| api_name=False, | |
| ) | |
| demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB) | |