app.py

#!/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<p>Running on CPU 🥶 This demo may not work on CPU.</p>"

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)