import os import random import uuid import gradio as gr import numpy as np from PIL import Image import spaces import torch from diffusers import ( StableDiffusionXLPipeline, KDPM2AncestralDiscreteScheduler, AutoencoderKL ) DESCRIPTION = """ # Mobius a diffusion model that pushes the boundaries of domain-agnostic debiasing and representation realignment. By employing a brand new constructive deconstruction framework, Mobius achieves unrivaled generalization across a vast array of styles and domains, eliminating the need for expensive pretraining from scratch. Model by [Corcel.io](https://huggingface.co/Corcelio/mobius) """ if not torch.cuda.is_available(): DESCRIPTION += "\n

Running on CPU 🥶 This demo may not work on CPU.

" MAX_SEED = np.iinfo(np.int32).max USE_TORCH_COMPILE = 0 ENABLE_CPU_OFFLOAD = 0 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") vae = AutoencoderKL.from_pretrained( "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16 ) # Configure the pipeline pipe = StableDiffusionXLPipeline.from_pretrained( "Corcelio/openvision", vae=vae, torch_dtype=torch.float16, ) pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipe.scheduler.config) pipe.to('cuda') 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 @spaces.GPU(enable_queue=True) def generate( prompt: str, negative_prompt: str = "", use_negative_prompt: bool = False, seed: int = 0, width: int = 1280, height: int = 1280, guidance_scale: float = 1.5, randomize_seed: bool = False, progress=gr.Progress(track_tqdm=True), ): pipe.to(device) seed = int(randomize_seed_fn(seed, randomize_seed)) if not use_negative_prompt: negative_prompt = "" # type: ignore images = pipe( prompt=f'''{prompt}''', negative_prompt=f"{negative_prompt}", width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=30, num_images_per_prompt=1, output_type="pil", ).images image_paths = [save_image(img) for img in images] print(image_paths) return image_paths, seed examples = [ "a cat wearing sunglasses in the summer", "an astronaut riding a horse on the moon", "anime boy, protagonist,", "A tiny robot taking a break under a tree in the garden", ] css = ''' .gradio-container{max-width: 560px !important} h1{text-align:center} footer { visibility: hidden } ''' with gr.Blocks(title="Mobius", css=css) as demo: gr.Markdown(DESCRIPTION) gr.DuplicateButton( value="Duplicate Space for private use", elem_id="duplicate-button", visible=False, ) with gr.Group(): with gr.Row(): prompt = gr.Text( label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False, ) run_button = gr.Button("Run", scale=0) result = gr.Gallery(label="Result", columns=1, preview=True, show_label=False) with gr.Accordion("Advanced options", open=False): with gr.Row(): use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True) negative_prompt = gr.Text( label="Negative prompt", max_lines=6, lines=4, value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, (NSFW:0.25)", placeholder="Enter a negative prompt", visible=True, ) 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, step=0.1, value=7.0, ) gr.Examples( examples=examples, inputs=prompt, outputs=[result, seed], fn=generate, cache_examples=False, ) use_negative_prompt.change( fn=lambda x: gr.update(visible=x), inputs=use_negative_prompt, outputs=negative_prompt, api_name=False, ) gr.on( triggers=[ prompt.submit, negative_prompt.submit, run_button.click, ], fn=generate, inputs=[ prompt, negative_prompt, use_negative_prompt, seed, width, height, guidance_scale, randomize_seed, ], outputs=[result, seed], api_name="run", ) if __name__ == "__main__": demo.queue(max_size=20).launch(show_api=False, debug=False)