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 = [ # ... (style list remains the same) ] 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 ) MAX_SEED = np.iinfo(np.int32).max processor = HEDdetector.from_pretrained('lllyasviel/Annotators') def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: if randomize_seed: seed = random.randint(0, MAX_SEED) return seed @spaces.GPU(duration=120) 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 pipe.to(device) 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] pipe.to("cpu") torch.cuda.empty_cache() 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") style = gr.Dropdown(label="Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME) 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, # Add this new input 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)