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# 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 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, seed=None, progress=gr.Progress(track_tqdm=True)):
print(input, negative_input, height, width, steps, guidance, seed)
model.to(DEVICE)
parameters = {
"prompt": input,
"negative_prompt": negative_input,
"height": height,
"width": width,
"num_inference_steps": steps,
"guidance_scale": guidance,
"generator": torch.Generator().manual_seed(get_seed(seed)),
"use_resolution_binning": True,
"output_type":"pil",
}
image = model(**parameters).images[0]
return image
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")
seed = gr.Textbox(lines=1, value="", label="Seed (Blank for random)")
submit = gr.Button("▶")
maintain = gr.Button("☁️")
with gr.Column():
image = gr.Image(label="Image")
submit.click(generate, inputs=[input, negative_input, height, width, steps, guidance, seed], outputs=[image], queue=False)
maintain.click(cloud, inputs=[], outputs=[], queue=False)
main.launch(show_api=True) |