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import os |
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import random |
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import gradio as gr |
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import numpy as np |
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import PIL.Image |
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import torch |
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import torchvision.transforms.functional as TF |
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from diffusers import ( |
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AutoencoderKL, |
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EulerAncestralDiscreteScheduler, |
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StableDiffusionXLAdapterPipeline, |
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T2IAdapter, |
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) |
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DESCRIPTION = '''# T2I-Adapter-SDXL **Sketch** |
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To try out all the [6 T2I-Adapter](https://huggingface.co/collections/TencentARC/t2i-adapter-sdxl-64fac9cbf393f30370eeb02f) released for SDXL, [click here](https://huggingface.co/spaces/TencentARC/T2I-Adapter-SDXL) |
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''' |
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if not torch.cuda.is_available(): |
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DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" |
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style_list = [ |
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{ |
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"name": "(No style)", |
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"prompt": "{prompt}", |
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"negative_prompt": "", |
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}, |
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{ |
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"name": "Cinematic", |
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"prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy", |
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"negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", |
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}, |
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{ |
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"name": "3D Model", |
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"prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting", |
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"negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting", |
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}, |
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{ |
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"name": "Anime", |
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"prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed", |
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"negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast", |
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}, |
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{ |
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"name": "Digital Art", |
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"prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed", |
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"negative_prompt": "photo, photorealistic, realism, ugly", |
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}, |
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{ |
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"name": "Photographic", |
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"prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed", |
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"negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly", |
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}, |
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{ |
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"name": "Pixel art", |
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"prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics", |
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"negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic", |
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}, |
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{ |
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"name": "Fantasy art", |
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"prompt": "ethereal fantasy concept art of {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy", |
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"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", |
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}, |
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{ |
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"name": "Neonpunk", |
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"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", |
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"negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured", |
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}, |
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{ |
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"name": "Manga", |
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"prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style", |
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"negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style", |
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}, |
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] |
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styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list} |
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STYLE_NAMES = list(styles.keys()) |
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DEFAULT_STYLE_NAME = "(No style)" |
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def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]: |
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p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) |
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return p.replace("{prompt}", positive), n + negative |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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if torch.cuda.is_available(): |
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model_id = "stabilityai/stable-diffusion-xl-base-1.0" |
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adapter = T2IAdapter.from_pretrained( |
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"TencentARC/t2i-adapter-sketch-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" |
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) |
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scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") |
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pipe = StableDiffusionXLAdapterPipeline.from_pretrained( |
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model_id, |
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vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16), |
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adapter=adapter, |
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scheduler=scheduler, |
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torch_dtype=torch.float16, |
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variant="fp16", |
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) |
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pipe.to(device) |
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else: |
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pipe = None |
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MAX_SEED = np.iinfo(np.int32).max |
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def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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return seed |
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def run( |
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image: PIL.Image.Image, |
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prompt: str, |
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negative_prompt: str, |
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style_name: str = DEFAULT_STYLE_NAME, |
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num_steps: int = 25, |
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guidance_scale: float = 5, |
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adapter_conditioning_scale: float = 0.8, |
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adapter_conditioning_factor: float = 0.8, |
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seed: int = 0, |
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progress=gr.Progress(track_tqdm=True), |
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) -> PIL.Image.Image: |
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image = image.convert("RGB") |
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image = TF.to_tensor(image) > 0.5 |
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image = TF.to_pil_image(image.to(torch.float32)) |
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prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt) |
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generator = torch.Generator(device=device).manual_seed(seed) |
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out = pipe( |
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prompt=prompt, |
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negative_prompt=negative_prompt, |
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image=image, |
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num_inference_steps=num_steps, |
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generator=generator, |
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guidance_scale=guidance_scale, |
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adapter_conditioning_scale=adapter_conditioning_scale, |
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adapter_conditioning_factor=adapter_conditioning_factor, |
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).images[0] |
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return out |
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with gr.Blocks(css="style.css") as demo: |
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gr.Markdown(DESCRIPTION, elem_id="description") |
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gr.DuplicateButton( |
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value="Duplicate Space for private use", |
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elem_id="duplicate-button", |
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visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", |
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) |
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with gr.Row(): |
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with gr.Column(): |
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with gr.Group(): |
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image = gr.Image( |
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source="canvas", |
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tool="sketch", |
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type="pil", |
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image_mode="L", |
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invert_colors=True, |
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shape=(1024, 1024), |
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brush_radius=4, |
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height=440, |
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) |
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prompt = gr.Textbox(label="Prompt") |
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style = gr.Dropdown(label="Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME) |
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run_button = gr.Button("Run") |
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with gr.Accordion("Advanced options", open=False): |
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negative_prompt = gr.Textbox( |
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label="Negative prompt", |
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value=" extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured", |
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) |
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num_steps = gr.Slider( |
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label="Number of steps", |
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minimum=1, |
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maximum=50, |
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step=1, |
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value=25, |
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) |
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guidance_scale = gr.Slider( |
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label="Guidance scale", |
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minimum=0.1, |
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maximum=10.0, |
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step=0.1, |
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value=5, |
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) |
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adapter_conditioning_scale = gr.Slider( |
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label="Adapter conditioning scale", |
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minimum=0.5, |
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maximum=1, |
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step=0.1, |
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value=0.8, |
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) |
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adapter_conditioning_factor = gr.Slider( |
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label="Adapter conditioning factor", |
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info="Fraction of timesteps for which adapter should be applied", |
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minimum=0.5, |
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maximum=1, |
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step=0.1, |
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value=0.8, |
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) |
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seed = gr.Slider( |
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label="Seed", |
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minimum=0, |
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maximum=MAX_SEED, |
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step=1, |
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value=0, |
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) |
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
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with gr.Column(): |
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result = gr.Image(label="Result", height=400) |
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inputs = [ |
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image, |
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prompt, |
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negative_prompt, |
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style, |
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num_steps, |
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guidance_scale, |
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adapter_conditioning_scale, |
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adapter_conditioning_factor, |
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seed, |
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] |
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prompt.submit( |
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fn=randomize_seed_fn, |
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inputs=[seed, randomize_seed], |
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outputs=seed, |
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queue=False, |
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api_name=False, |
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).then( |
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fn=run, |
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inputs=inputs, |
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outputs=result, |
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api_name=False, |
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) |
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negative_prompt.submit( |
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fn=randomize_seed_fn, |
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inputs=[seed, randomize_seed], |
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outputs=seed, |
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queue=False, |
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api_name=False, |
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).then( |
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fn=run, |
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inputs=inputs, |
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outputs=result, |
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api_name=False, |
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) |
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run_button.click( |
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fn=randomize_seed_fn, |
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inputs=[seed, randomize_seed], |
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outputs=seed, |
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queue=False, |
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api_name=False, |
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).then( |
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fn=run, |
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inputs=inputs, |
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outputs=result, |
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api_name=False, |
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) |
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if __name__ == "__main__": |
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demo.queue(max_size=20).launch() |
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