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| import gradio as gr | |
| from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler, LCMScheduler, AutoencoderKL,DiffusionPipeline | |
| import torch | |
| from typing import Tuple | |
| import numpy as np | |
| from huggingface_hub import hf_hub_download | |
| from safetensors.torch import load_file | |
| import spaces | |
| import os | |
| import random | |
| import uuid | |
| def save_image(img): | |
| unique_name = str(uuid.uuid4()) + ".png" | |
| img.save(unique_name) | |
| return unique_name | |
| def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| return seed | |
| MAX_SEED = np.iinfo(np.int32).max | |
| vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) | |
| # by PixArt-alpha/PixArt-Sigma | |
| style_list = [ | |
| { | |
| "name": "(No style)", | |
| "prompt": "{prompt}", | |
| "negative_prompt": "", | |
| }, | |
| { | |
| "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": "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": "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": "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", | |
| }, | |
| { | |
| "name": "Digital Art", | |
| "prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed", | |
| "negative_prompt": "photo, photorealistic, realism, 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": "3D Model", | |
| "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting", | |
| "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting", | |
| }, | |
| ] | |
| 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]) | |
| if not negative: | |
| negative = "" | |
| return p.replace("{prompt}", positive), n + negative | |
| JX_pipe = StableDiffusionXLPipeline.from_pretrained( | |
| "RunDiffusion/Juggernaut-X-Hyper", | |
| vae=vae, | |
| torch_dtype=torch.float16, | |
| ) | |
| JX_pipe.to("cuda") | |
| J10_pipe = StableDiffusionXLPipeline.from_pretrained( | |
| "RunDiffusion/Juggernaut-X-v10", | |
| vae=vae, | |
| torch_dtype=torch.float16, | |
| ) | |
| J10_pipe.to("cuda") | |
| J9_pipe = StableDiffusionXLPipeline.from_pretrained( | |
| "RunDiffusion/Juggernaut-XL-v9", | |
| vae=vae, | |
| torch_dtype=torch.float16, | |
| custom_pipeline="lpw_stable_diffusion_xl", | |
| use_safetensors=True, | |
| add_watermarker=False, | |
| variant="fp16", | |
| ) | |
| J9_pipe.to("cuda") | |
| def run_comparison(prompt: str, | |
| negative_prompt: str = "", | |
| style: str = DEFAULT_STYLE_NAME, | |
| use_negative_prompt: bool = False, | |
| num_inference_steps: int = 30, | |
| num_images_per_prompt: int = 2, | |
| seed: int = 0, | |
| width: int = 1024, | |
| height: int = 1024, | |
| guidance_scale: float = 3, | |
| randomize_seed: bool = False, | |
| progress=gr.Progress(track_tqdm=True), | |
| ): | |
| seed = int(randomize_seed_fn(seed, randomize_seed)) | |
| if not use_negative_prompt: | |
| negative_prompt = "" | |
| prompt, negative_prompt = apply_style(style, prompt, negative_prompt) | |
| image_r3 = JX_pipe(prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| width=width, | |
| height=height, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=num_inference_steps, | |
| num_images_per_prompt=num_images_per_prompt, | |
| cross_attention_kwargs={"scale": 0.65}, | |
| output_type="pil", | |
| ).images | |
| image_paths_r3 = [save_image(img) for img in image_r3] | |
| image_r4 = J10_pipe(prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| width=width, | |
| height=height, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=num_inference_steps, | |
| num_images_per_prompt=num_images_per_prompt, | |
| cross_attention_kwargs={"scale": 0.65}, | |
| output_type="pil", | |
| ).images | |
| image_paths_r4 = [save_image(img) for img in image_r4] | |
| image_r5 = J9_pipe(prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| width=width, | |
| height=height, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=num_inference_steps, | |
| num_images_per_prompt=num_images_per_prompt, | |
| cross_attention_kwargs={"scale": 0.65}, | |
| output_type="pil", | |
| ).images | |
| image_paths_r5 = [save_image(img) for img in image_r5] | |
| return image_paths_r3, image_paths_r4,image_paths_r5, seed | |
| examples = ["A dignified beaver wearing glasses, a vest, and colorful neck tie.", | |
| "The spirit of a tamagotchi wandering in the city of Barcelona", | |
| "an ornate, high-backed mahogany chair with a red cushion", | |
| "a sketch of a camel next to a stream", | |
| "a delicate porcelain teacup sits on a saucer, its surface adorned with intricate blue patterns", | |
| "a baby swan grafitti", | |
| "A bald eagle made of chocolate powder, mango, and whipped cream" | |
| ] | |
| with gr.Blocks(theme=gr.themes.Base()) as demo: | |
| gr.Markdown("## One step Juggernaut-XL comparison 🦶") | |
| gr.Markdown('Compare Juggernaut-XL variants and distillations able to generate images in a single diffusion step') | |
| prompt = gr.Textbox(label="Prompt") | |
| run = gr.Button("Run") | |
| with gr.Row(visible=True): | |
| style_selection = gr.Radio( | |
| show_label=True, | |
| container=True, | |
| interactive=True, | |
| choices=STYLE_NAMES, | |
| value=DEFAULT_STYLE_NAME, | |
| label="Image Style", | |
| ) | |
| with gr.Accordion("Advanced options", open=False): | |
| use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False, visible=True) | |
| negative_prompt = gr.Text( | |
| label="Negative prompt", | |
| max_lines=1, | |
| placeholder="Enter a negative prompt", | |
| visible=True, | |
| ) | |
| with gr.Row(): | |
| num_inference_steps = gr.Slider( | |
| label="Steps", | |
| minimum=10, | |
| maximum=60, | |
| step=1, | |
| value=30, | |
| ) | |
| with gr.Row(): | |
| num_images_per_prompt = gr.Slider( | |
| label="Images", | |
| minimum=1, | |
| maximum=5, | |
| step=1, | |
| value=2, | |
| ) | |
| seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=0, | |
| visible=True | |
| ) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| with gr.Row(visible=True): | |
| width = gr.Slider( | |
| label="Width", | |
| minimum=512, | |
| maximum=2048, | |
| step=8, | |
| value=1024, | |
| ) | |
| height = gr.Slider( | |
| label="Height", | |
| minimum=512, | |
| maximum=2048, | |
| step=8, | |
| value=1024, | |
| ) | |
| with gr.Row(): | |
| guidance_scale = gr.Slider( | |
| label="Guidance Scale", | |
| minimum=0.1, | |
| maximum=20.0, | |
| step=0.1, | |
| value=6, | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| image_r3 = gr.Gallery(label="Juggernaut-X",columns=1, preview=True,) | |
| gr.Markdown("## [Juggernaut-X](https://huggingface.co)") | |
| with gr.Column(): | |
| image_r4 = gr.Gallery(label="Juggernaut-X-10",columns=1, preview=True,) | |
| gr.Markdown("## [Juggernaut-XL-10](https://huggingface.co)") | |
| with gr.Column(): | |
| image_r5 = gr.Gallery(label="Juggernaut-XL-9",columns=1, preview=True,) | |
| gr.Markdown("## [Juggernaut-XL-9](https://huggingface.co)") | |
| image_outputs = [image_r3, image_r4, image_r5] | |
| gr.on( | |
| triggers=[prompt.submit, run.click], | |
| fn=run_comparison, | |
| inputs=[ | |
| prompt, | |
| negative_prompt, | |
| style_selection, | |
| use_negative_prompt, | |
| num_inference_steps, | |
| num_images_per_prompt, | |
| seed, | |
| width, | |
| height, | |
| guidance_scale, | |
| randomize_seed, | |
| ], | |
| outputs=image_outputs | |
| ) | |
| use_negative_prompt.change( | |
| fn=lambda x: gr.update(visible=x), | |
| inputs=use_negative_prompt, | |
| outputs=negative_prompt, | |
| api_name=False, | |
| ) | |
| gr.Examples( | |
| examples=examples, | |
| fn=run_comparison, | |
| inputs=prompt, | |
| outputs=image_outputs, | |
| cache_examples=False, | |
| run_on_click=True | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=20).launch(show_api=False, debug=False) |