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
from typing import Tuple
import torch
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler

DESCRIPTION = """# InterDiffusion-4.0
### [https://huggingface.co/cutycat2000x/InterDiffusion-4.0](https://huggingface.co/cutycat2000x/InterDiffusion-4.0)"""

MAX_SEED = np.iinfo(np.int32).max
DEFAULT_STYLE_NAME = "(LoRA)"

def save_image(img):
    filename = str(uuid.uuid4()) + ".png"
    img.save(filename)
    return filename

def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    return random.randint(0, MAX_SEED) if randomize_seed else seed

style_list = [
    {
        "name": DEFAULT_STYLE_NAME,
        "prompt": "{prompt}",
        "negative_prompt": "",
    },
]
styles = {s["name"]: (s["prompt"], s["negative_prompt"]) for s in style_list}
STYLE_NAMES = list(styles.keys())

def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]:
    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
    return p.replace("{prompt}", positive), n + negative

if torch.cuda.is_available():
    pipe = StableDiffusionXLPipeline.from_pretrained(
        "cutycat2000x/InterDiffusion-4.0",
        torch_dtype=torch.float16,
        use_safetensors=True,
    )
    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
    pipe.load_lora_weights("cutycat2000x/LoRA2", weight_name="lora.safetensors", adapter_name="adapt")
    pipe.set_adapters("adapt")
    pipe.to("cuda")

@spaces.GPU(enable_queue=True)
def generate(prompt, negative_prompt, style, use_negative_prompt, num_inference_steps,
             num_images_per_prompt, seed, width, height, guidance_scale, randomize_seed, progress=gr.Progress(track_tqdm=True)):

    seed = randomize_seed_fn(seed, randomize_seed)
    if not use_negative_prompt:
        negative_prompt = ""
    prompt, negative_prompt = apply_style(style, prompt, negative_prompt)

    result = 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"
    )
    return result.images, seed


examples = [
    'a smiling girl with sparkles in her eyes, walking in a garden, in the morning --style anime',
    'firewatch landscape, Graphic Novel, Pastel Art...',
    'Cat on a tree sitting in between parrots.',
    'cat, 4k, hyperrealistic, Cinematic, unreal engine 5',
    'cinematic closeup of burning skull',
    'frozen elsa',
    'A rainbow tree, anime style, tree in focus',
    'A cat holding a sign that reads "Hello World"',
    'Odette the butterfly goddess wondering in the cosmos'
]

css = '''
.gradio-container{max-width: 560px !important}
h1{text-align:center}
footer { visibility: hidden }
'''

with gr.Blocks(css=css, theme="xiaobaiyuan/theme_brief") as demo:
    gr.Markdown(DESCRIPTION)

    with gr.Group():
        with gr.Row():
            prompt = gr.Textbox(
                label="Prompt", placeholder="Enter your prompt", lines=1
            )
            run_button = gr.Button("Run")

        result = gr.Gallery(label="Result", columns=1, preview=True)

    with gr.Accordion("Advanced options", open=False):
        use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
        negative_prompt = gr.Textbox(label="Negative prompt", lines=1, visible=True)
        num_inference_steps = gr.Slider(label="Steps", minimum=10, maximum=60, step=1, value=30)
        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)
        randomize_seed = gr.Checkbox(label="Randomize seed", value=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)
        guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=20.0, step=0.1, value=6.0)
        style_selection = gr.Radio(label="Image Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME)

    gr.Examples(
        examples=examples,
        inputs=prompt,
        outputs=[result, seed],
        fn=generate,
        cache_examples=False
    )

    use_negative_prompt.change(
        lambda x: gr.update(visible=x),
        inputs=use_negative_prompt,
        outputs=negative_prompt,
    )

    prompt.submit(
        fn=generate,
        inputs=[prompt, negative_prompt, style_selection, use_negative_prompt,
                num_inference_steps, num_images_per_prompt, seed,
                width, height, guidance_scale, randomize_seed],
        outputs=[result, seed],
    )

    run_button.click(
        fn=generate,
        inputs=[prompt, negative_prompt, style_selection, use_negative_prompt,
                num_inference_steps, num_images_per_prompt, seed,
                width, height, guidance_scale, randomize_seed],
        outputs=[result, seed],
    )

if __name__ == "__main__":
    demo.queue(max_size=20).launch(show_api=False, debug=False)