app.py

# Imports
import gradio as gr
import random
import spaces
import torch
import numpy
import uuid
import json
import os
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
from PIL import Image

# 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 = "(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"
DEFAULT_HEIGHT = 1024
DEFAULT_WIDTH = 1024

REPO = "sd-community/sdxl-flash"
REPO_WEIGHT = "ehristoforu/dalle-3-xl-v2"
WEIGHT = "dalle-3-xl-lora-v2.safetensors"
ADAPTER = "dalle"

model = StableDiffusionXLPipeline.from_pretrained(REPO, torch_dtype=torch.float16, use_safetensors=True, add_watermarker=False)
model.scheduler = EulerAncestralDiscreteScheduler.from_config(model.scheduler.config)
model.load_lora_weights(REPO_WEIGHT, weight_name=WEIGHT, adapter_name=ADAPTER)
model.set_adapters(ADAPTER, adapter_weights=[0.7])
model.to(DEVICE)

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

# Functions
def save_image(img):
    unique_name = str(uuid.uuid4()) + ".png"
    img.save(unique_name)
    return unique_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, negative_input=DEFAULT_NEGATIVE_INPUT, height=DEFAULT_HEIGHT, width=DEFAULT_WIDTH, steps=1, guidance=0, number=1, seed=None):
    
    seed = get_seed(seed)

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

    model.to(DEVICE)
    parameters  = {
        "prompt": input,
        "negative_prompt": negative_input,
        "height": height,
        "width": width,
        "num_inference_steps": steps,
        "guidance_scale": guidance,
        "num_images_per_prompt": number,
        "cross_attention_kwargs": {"scale": 0.65},
        "generator": torch.Generator().manual_seed(seed),
        "use_resolution_binning": True,
        "output_type":"pil",
    }
    
    images = model(**parameters).images
    image_paths = [save_image(img) 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():
        input = gr.Textbox(lines=1, value=DEFAULT_INPUT, label="Input")
        negative_input = gr.Textbox(lines=1, value=DEFAULT_NEGATIVE_INPUT, label="Input Negative")
        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=0, maximum=100, step=1, value=8, label="Steps")
        guidance = gr.Slider(minimum=0, maximum=100, step=0.001, value=3, 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, negative_input, height, width, steps, guidance, number, seed], outputs=[images], queue=False)
    maintain.click(cloud, inputs=[], outputs=[], queue=False)

main.launch(show_api=True)