app.py

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 = """
# OpenVision

Midjourney Aesthetic for All Your Images:

OpenVision is a style enhancement of ProteusV0.4 that seamlessly incorporates the captivating Midjourney aesthetic into every image you generate.

Model by [Corcel.io](https://huggingface.co/Corcelio/mobius)
"""
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")


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)