app.py

# Imports
import gradio as gr
import random
import spaces
import torch
import uuid
import os

from diffusers import StableDiffusionXLPipeline, ControlNetModel
from diffusers.models import AutoencoderKL

# Pre-Initialize
DEVICE = "auto"
if DEVICE == "auto":
    DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
print(f"[SYSTEM] | Using {DEVICE} type compute device.")

# Variables
MAX_SEED = 9007199254740991
DEFAULT_INPUT = ""
DEFAULT_NEGATIVE_INPUT = "EasyNegative, (bad), [abstract], deformed, distorted, disfigured, disconnected, disgusting, displeasing, mutation, mutated, blur, blurry, fewer, extra, missing, unfinished, scribble, lowres, low quality, jpeg artifacts, chromatic aberration, extra digits, artistic error, text, error, username, scan, signature, watermark, ugly, amputation, limb, limbs, leg, legs, foot, feet, toe, toes, arm, arms, hand, hands, finger, fingers, head, heads, exposed, explicit, porn, nude, nudity, naked, nsfw"
DEFAULT_MODEL = "Default"
DEFAULT_HEIGHT = 1024
DEFAULT_WIDTH = 1024

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

vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
controlnet = ControlNetModel.from_pretrained("MakiPan/controlnet-encoded-hands-130k", torch_dtype=torch.float16)

repo_default = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash", vae=vae, controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True, add_watermarker=False)
repo_default.load_lora_weights("ehristoforu/dalle-3-xl-v2", adapter_name="base")
repo_default.load_lora_weights("Corcelio/mobius", adapter_name="base2")
repo_default.set_adapters(["base", "base2"], adapter_weights=[0.7, 0.7])

repo_pixel = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash", vae=vae, controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True, add_watermarker=False)
repo_pixel.load_lora_weights("artificialguybr/PixelArtRedmond", adapter_name="base")
repo_pixel.load_lora_weights("nerijs/pixel-art-xl", adapter_name="base2")
repo_pixel.set_adapters(["base", "base2"], adapter_weights=[1, 1])

repo_customs = {
    "Default": repo_default,
    "Realistic": StableDiffusionXLPipeline.from_pretrained("stablediffusionapi/NightVision_XL", vae=vae, controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=False, add_watermarker=False),
    "Anime": StableDiffusionXLPipeline.from_pretrained("cagliostrolab/animagine-xl-3.1", vae=vae, controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True, add_watermarker=False),
    "Pixel": repo_pixel,
}

# Functions
def save_image(img, seed):
    name = f"{seed}-{uuid.uuid4()}.png"
    img.save(name)
    return name
    
def get_seed(seed):
    seed = seed.strip()
    if seed.isdigit():
        return int(seed)
    else:
        return random.randint(0, MAX_SEED)

@spaces.GPU(duration=30)
def generate(input=DEFAULT_INPUT, filter_input="", negative_input=DEFAULT_NEGATIVE_INPUT, model=DEFAULT_MODEL, height=DEFAULT_HEIGHT, width=DEFAULT_WIDTH, steps=1, guidance=0, number=1, seed=None):

    repo = repo_customs[model or "Default"]
    filter_input = filter_input or ""
    negative_input = negative_input or DEFAULT_NEGATIVE_INPUT
    seed = get_seed(seed)

    print(input, filter_input, negative_input, model, height, width, steps, guidance, number, seed)

    if model == "Realistic":   
        steps = (not steps or steps < 0 and 30) or steps
        guidance = (not guidance or guidance < 0 and 7) or guidance
    elif model == "Anime":   
        steps = (not steps or steps < 0 and 16) or steps
        guidance = (not guidance or guidance < 0 and 7) or guidance
    elif model == "Pixel":   
        steps = (not steps or steps < 0 and 8) or steps
        guidance = (not guidance or guidance < 0 and 1.5) or guidance
    else:
        steps = (not steps or steps < 0 and 16) or steps
        guidance = (not guidance or guidance < 0 and 3) or guidance
        
    repo.to(DEVICE)
    
    parameters  = {
        "prompt": input,
        "negative_prompt": filter_input + negative_input,
        "height": height,
        "width": width,
        "num_inference_steps": steps,
        "guidance_scale": guidance,
        "num_images_per_prompt": number,
        "controlnet_conditioning_scale": 1,
        "cross_attention_kwargs": {"scale": 1},
        "generator": torch.Generator().manual_seed(seed),
        "use_resolution_binning": True,
        "output_type":"pil",
    }
    
    images = repo(**parameters).images
    image_paths = [save_image(img, seed) for img in images]
    print(image_paths)
    return image_paths

def cloud():
    print("[CLOUD] | Space maintained.")


# Initialize
with gr.Blocks(css=css) as main:
    with gr.Column():
        gr.Markdown("🪄 Generate high quality images on all styles between 10 to 20 seconds.")
        
    with gr.Column():
        input = gr.Textbox(lines=1, value=DEFAULT_INPUT, label="Input")
        filter_input = gr.Textbox(lines=1, value="", label="Input Filter")
        negative_input = gr.Textbox(lines=1, value=DEFAULT_NEGATIVE_INPUT, label="Input Negative")
        model = gr.Dropdown(label="Models", choices=["Default", "Realistic", "Anime", "Pixel"], value="Default")
        height = gr.Slider(minimum=1, maximum=2160, step=1, value=DEFAULT_HEIGHT, label="Height")
        width = gr.Slider(minimum=1, maximum=2160, step=1, value=DEFAULT_WIDTH, label="Width")
        steps = gr.Slider(minimum=-1, maximum=100, step=1, value=-1, label="Steps")
        guidance = gr.Slider(minimum=-1, maximum=100, step=0.001, value=-1, label = "Guidance")
        number = gr.Slider(minimum=1, maximum=4, step=1, value=1, label="Number")
        seed = gr.Textbox(lines=1, value="", label="Seed (Blank for random)")
        submit = gr.Button("▶")
        maintain = gr.Button("☁️")

    with gr.Column():
        images = gr.Gallery(columns=1, label="Image")
            
    submit.click(generate, inputs=[input, filter_input, negative_input, model, height, width, steps, guidance, number, seed], outputs=[images], queue=False)
    maintain.click(cloud, inputs=[], outputs=[], queue=False)

main.launch(show_api=True)