#!/usr/bin/env python 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, StableDiffusionXLInpaintPipeline, DPMSolverMultistepScheduler ) DESCRIPTION = """ # [Visionix Playground](https://huggingface.co/spaces/ehristoforu/Visionix-Playground) """ 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") pipe = StableDiffusionXLPipeline.from_pretrained( "ehristoforu/Visionix-alpha", torch_dtype=torch.float16, use_safetensors=True, ) pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) pipe.to('cuda') pipe_inpaint = StableDiffusionXLInpaintPipeline.from_single_file( "https://huggingface.co/ehristoforu/Visionix-alpha-inpainting/blob/main/Visionix-alpha-inpainting.safetensors", torch_dtype=torch.float16, use_safetensors=True, ) pipe_inpaint.scheduler = DPMSolverMultistepScheduler.from_config(pipe_inpaint.scheduler.config) pipe_inpaint.to('cuda') def get_model(model): if model == "Alpha inpaint": return gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) else: return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False) 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(durarion=50, enable_queue=True) def generate( model, inpaint_image, mask_image, blur_factor, strength, prompt: str, negative_prompt: str = "", use_negative_prompt: bool = False, seed: int = 0, width: int = 1024, height: int = 1024, guidance_scale: float = 5.5, 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 = None if model == "Alpha": images = pipe( prompt=prompt, negative_prompt=f"{negative_prompt}", width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=25, num_images_per_prompt=1, output_type="pil", ).images elif model == "Alpha inpaint": blurred_mask = pipe_inpaint.mask_processor.blur(mask_image, blur_factor=blur_factor) images = pipe_inpaint( prompt=prompt, image=inpaint_image, mask_image=blurred_mask, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=25, strength=strength, 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 = [ "neon holography crystal cat", "a cat eating a piece of cheese", "an astronaut riding a horse in space", "a cartoon of a boy playing with a tiger", "a cute robot artist painting on an easel, concept art", "a close up of a woman wearing a transparent, prismatic, elaborate nemeses headdress, over the should pose, brown skin-tone" ] css = ''' .gradio-container{max-width: 560px !important} h1{text-align:center} footer { visibility: hidden } ''' with gr.Blocks(title="Visionix Playground", css=css) as demo: gr.Markdown(DESCRIPTION) gr.DuplicateButton( value="Duplicate Space for private use", elem_id="duplicate-button", visible=False, ) with gr.Row(): model = gr.Radio( label="Model", choices=["Alpha", "Alpha inpaint"], value="Alpha", interactive=True, ) md_mask = gr.Markdown(""" ⚠️ To generate an inpaint mask, go [here](https://huggingface.co/spaces/ehristoforu/inpaint-mask-maker). """, visible=False) inpaint_image = gr.Image(label="Inpaint Image", interactive=True, scale=5, visible=False, type="pil") mask_image = gr.Image(label="Mask Image", interactive=True, scale=5, visible=False, type="pil") blur_factor = gr.Slider(label="Mask Blur Factor", minimum=0, maximum=100, value=4, step=1, interactive=True, visible=False) strength = gr.Slider(label="Denoising Strength", minimum=0.00, maximum=1.00, value=0.70, step=0.01, interactive=True, 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=8, lines=6, value="cartoon, 3D, disfigured, bad, art, deformed, extra limbs, weird, blurry, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn, hands, poorly drawn face, mutation, ugly, bad, anatomy, bad proportions, extra limbs, clone, clone-faced, cross proportions, missing arms, malformed limbs, missing legs, mutated, hands, fused fingers, too many fingers, photo shop, video game, ugly, tiling, cross-eye, mutation of eyes, long neck, bonnet, hat, beanie, cap, B&W", 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=5.5, ) 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, ) model.change( fn=get_model, inputs=model, outputs=[md_mask, inpaint_image, mask_image, blur_factor, strength], api_name=False, ) gr.on( triggers=[ prompt.submit, negative_prompt.submit, run_button.click, ], fn=generate, inputs=[ model, inpaint_image, mask_image, blur_factor, strength, 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)