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| import os | |
| import random | |
| import spaces | |
| import gradio as gr | |
| import numpy as np | |
| import PIL.Image | |
| import torch | |
| import torchvision.transforms.functional as TF | |
| from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL | |
| from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler | |
| from controlnet_aux import PidiNetDetector, HEDdetector | |
| from diffusers.utils import load_image | |
| from huggingface_hub import HfApi, snapshot_download | |
| from pathlib import Path | |
| from PIL import Image, ImageOps | |
| import cv2 | |
| from gradio_imageslider import ImageSlider | |
| js_func = """ | |
| function refresh() { | |
| const url = new URL(window.location); | |
| } | |
| """ | |
| def nms(x, t, s): | |
| x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s) | |
| f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8) | |
| f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8) | |
| f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8) | |
| f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8) | |
| y = np.zeros_like(x) | |
| for f in [f1, f2, f3, f4]: | |
| np.putmask(y, cv2.dilate(x, kernel=f) == x, x) | |
| z = np.zeros_like(y, dtype=np.uint8) | |
| z[y > t] = 255 | |
| return z | |
| def HWC3(x): | |
| assert x.dtype == np.uint8 | |
| if x.ndim == 2: | |
| x = x[:, :, None] | |
| assert x.ndim == 3 | |
| H, W, C = x.shape | |
| assert C == 1 or C == 3 or C == 4 | |
| if C == 3: | |
| return x | |
| if C == 1: | |
| return np.concatenate([x, x, x], axis=2) | |
| if C == 4: | |
| color = x[:, :, 0:3].astype(np.float32) | |
| alpha = x[:, :, 3:4].astype(np.float32) / 255.0 | |
| y = color * alpha + 255.0 * (1.0 - alpha) | |
| y = y.clip(0, 255).astype(np.uint8) | |
| return y | |
| DESCRIPTION = '''''' | |
| if not torch.cuda.is_available(): | |
| DESCRIPTION += "" | |
| style_list = [ | |
| { | |
| "name": "(No style)", | |
| "prompt": "{prompt}", | |
| "negative_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality", | |
| }, | |
| { | |
| "name": "Cinematic", | |
| "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy", | |
| "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", | |
| }, | |
| { | |
| "name": "3D Model", | |
| "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting", | |
| "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting", | |
| }, | |
| { | |
| "name": "Anime", | |
| "prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed", | |
| "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast", | |
| }, | |
| { | |
| "name": "Digital Art", | |
| "prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed", | |
| "negative_prompt": "photo, photorealistic, realism, ugly", | |
| }, | |
| { | |
| "name": "Photographic", | |
| "prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed", | |
| "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly", | |
| }, | |
| { | |
| "name": "Pixel art", | |
| "prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics", | |
| "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic", | |
| }, | |
| { | |
| "name": "Fantasy art", | |
| "prompt": "ethereal fantasy concept art of {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy", | |
| "negative_prompt": "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": "neonpunk style {prompt} . 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": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured", | |
| }, | |
| { | |
| "name": "Manga", | |
| "prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style", | |
| "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style", | |
| }, | |
| ] | |
| styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list} | |
| STYLE_NAMES = list(styles.keys()) | |
| DEFAULT_STYLE_NAME = "(No style)" | |
| def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]: | |
| p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) | |
| return p.replace("{prompt}", positive), n + negative | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler") | |
| # Download the model files | |
| ckpt_dir_pony = snapshot_download(repo_id="John6666/pony-realism-v21main-sdxl") | |
| ckpt_dir_cyber = snapshot_download(repo_id="John6666/cyberrealistic-pony-v61-sdxl") | |
| ckpt_dir_stallion = snapshot_download(repo_id="John6666/stallion-dreams-pony-realistic-v1-sdxl") | |
| # Load the models | |
| vae_pony = AutoencoderKL.from_pretrained(os.path.join(ckpt_dir_pony, "vae"), torch_dtype=torch.float16) | |
| vae_cyber = AutoencoderKL.from_pretrained(os.path.join(ckpt_dir_cyber, "vae"), torch_dtype=torch.float16) | |
| vae_stallion = AutoencoderKL.from_pretrained(os.path.join(ckpt_dir_stallion, "vae"), torch_dtype=torch.float16) | |
| controlnet_pony = ControlNetModel.from_pretrained("xinsir/controlnet-union-sdxl-1.0", torch_dtype=torch.float16) | |
| controlnet_cyber = ControlNetModel.from_pretrained("xinsir/controlnet-union-sdxl-1.0", torch_dtype=torch.float16) | |
| controlnet_stallion = ControlNetModel.from_pretrained("xinsir/controlnet-union-sdxl-1.0", torch_dtype=torch.float16) | |
| pipe_pony = StableDiffusionXLControlNetPipeline.from_pretrained( | |
| ckpt_dir_pony, controlnet=controlnet_pony, vae=vae_pony, torch_dtype=torch.float16, scheduler=eulera_scheduler | |
| ) | |
| pipe_cyber = StableDiffusionXLControlNetPipeline.from_pretrained( | |
| ckpt_dir_cyber, controlnet=controlnet_cyber, vae=vae_cyber, torch_dtype=torch.float16, scheduler=eulera_scheduler | |
| ) | |
| pipe_stallion = StableDiffusionXLControlNetPipeline.from_pretrained( | |
| ckpt_dir_stallion, controlnet=controlnet_stallion, vae=vae_stallion, torch_dtype=torch.float16, scheduler=eulera_scheduler | |
| ) | |
| pipe_pony.to(device) | |
| pipe_cyber.to(device) | |
| pipe_stallion.to(device) | |
| MAX_SEED = np.iinfo(np.int32).max | |
| processor = HEDdetector.from_pretrained('lllyasviel/Annotators') | |
| def nms(x, t, s): | |
| x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s) | |
| f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8) | |
| f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8) | |
| f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8) | |
| f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8) | |
| y = np.zeros_like(x) | |
| for f in [f1, f2, f3, f4]: | |
| np.putmask(y, cv2.dilate(x, kernel=f) == x, x) | |
| z = np.zeros_like(y, dtype=np.uint8) | |
| z[y > t] = 255 | |
| return z | |
| def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| return seed | |
| def run( | |
| image: dict, | |
| prompt: str, | |
| negative_prompt: str, | |
| model_choice: str, # Add this new input | |
| style_name: str = DEFAULT_STYLE_NAME, | |
| num_steps: int = 25, | |
| guidance_scale: float = 5, | |
| controlnet_conditioning_scale: float = 1.0, | |
| seed: int = 0, | |
| use_hed: bool = False, | |
| use_canny: bool = False, | |
| progress=gr.Progress(track_tqdm=True), | |
| ) -> PIL.Image.Image: | |
| # Get the composite image from the EditorValue dict | |
| composite_image = image['composite'] | |
| width, height = composite_image.size | |
| # Calculate new dimensions to fit within 1024x1024 while maintaining aspect ratio | |
| max_size = 1024 | |
| ratio = min(max_size / width, max_size / height) | |
| new_width = int(width * ratio) | |
| new_height = int(height * ratio) | |
| # Resize the image | |
| resized_image = composite_image.resize((new_width, new_height), Image.LANCZOS) | |
| if use_canny: | |
| controlnet_img = np.array(resized_image) | |
| controlnet_img = cv2.Canny(controlnet_img, 100, 200) | |
| controlnet_img = HWC3(controlnet_img) | |
| image = Image.fromarray(controlnet_img) | |
| elif not use_hed: | |
| controlnet_img = resized_image | |
| image = resized_image | |
| else: | |
| controlnet_img = processor(resized_image, scribble=False) | |
| controlnet_img = np.array(controlnet_img) | |
| controlnet_img = nms(controlnet_img, 127, 3) | |
| controlnet_img = cv2.GaussianBlur(controlnet_img, (0, 0), 3) | |
| random_val = int(round(random.uniform(0.01, 0.10), 2) * 255) | |
| controlnet_img[controlnet_img > random_val] = 255 | |
| controlnet_img[controlnet_img < 255] = 0 | |
| image = Image.fromarray(controlnet_img) | |
| prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt) | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| # Select the appropriate pipe based on the model choice | |
| if model_choice == "Pony Realism v21": | |
| pipe = pipe_pony | |
| elif model_choice == "Cyber Realistic Pony v61": | |
| pipe = pipe_cyber | |
| else: # "Stallion Dreams Pony Realistic v1" | |
| pipe = pipe_stallion | |
| if use_canny: | |
| out = pipe( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| image=image, | |
| num_inference_steps=num_steps, | |
| generator=generator, | |
| controlnet_conditioning_scale=controlnet_conditioning_scale, | |
| guidance_scale=guidance_scale, | |
| width=new_width, | |
| height=new_height, | |
| ).images[0] | |
| else: | |
| out = pipe( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| image=image, | |
| num_inference_steps=num_steps, | |
| generator=generator, | |
| controlnet_conditioning_scale=controlnet_conditioning_scale, | |
| guidance_scale=guidance_scale, | |
| width=new_width, | |
| height=new_height, | |
| ).images[0] | |
| return (controlnet_img, out) | |
| with gr.Blocks(css="style.css", js=js_func) as demo: | |
| gr.Markdown(DESCRIPTION, elem_id="description") | |
| gr.DuplicateButton( | |
| value="Duplicate Space for private use", | |
| elem_id="duplicate-button", | |
| visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Group(): | |
| image = gr.ImageEditor(type="pil", label="Sketch your image or upload one", width=512, height=512) | |
| prompt = gr.Textbox(label="Prompt") | |
| model_choice = gr.Dropdown( | |
| ["Pony Realism v21", "Cyber Realistic Pony v61", "Stallion Dreams Pony Realistic v1"], | |
| label="Model Choice", | |
| value="Pony Realism v21" | |
| ) | |
| use_hed = gr.Checkbox(label="use HED detector", value=False, info="check this box if you upload an image and want to turn it to a sketch") | |
| use_canny = gr.Checkbox(label="use Canny", value=False, info="check this to use ControlNet canny instead of scribble") | |
| run_button = gr.Button("Run") | |
| with gr.Accordion("Advanced options", open=False): | |
| negative_prompt = gr.Textbox( | |
| label="Negative prompt", | |
| value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality", | |
| ) | |
| num_steps = gr.Slider( | |
| label="Number of steps", | |
| minimum=1, | |
| maximum=50, | |
| step=1, | |
| value=25, | |
| ) | |
| guidance_scale = gr.Slider( | |
| label="Guidance scale", | |
| minimum=0.1, | |
| maximum=10.0, | |
| step=0.1, | |
| value=5, | |
| ) | |
| controlnet_conditioning_scale = gr.Slider( | |
| label="controlnet conditioning scale", | |
| minimum=0.5, | |
| maximum=5.0, | |
| step=0.1, | |
| value=0.9, | |
| ) | |
| 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.Column(): | |
| with gr.Group(): | |
| image_slider = ImageSlider(position=0.5) | |
| inputs = [ | |
| image, | |
| prompt, | |
| negative_prompt, | |
| model_choice, | |
| style, # Replace 'style_name' with 'style' | |
| num_steps, | |
| guidance_scale, | |
| controlnet_conditioning_scale, | |
| seed, | |
| use_hed, | |
| use_canny | |
| ] | |
| outputs = [image_slider] | |
| run_button.click( | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| api_name=False, | |
| ).then(lambda x: None, inputs=None, outputs=image_slider).then( | |
| fn=run, inputs=inputs, outputs=outputs | |
| ) | |
| demo.queue().launch(show_error=True, ssl_verify=False) |