#!/usr/bin/env python
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, EulerAncestralDiscreteScheduler
DESCRIPTIONx = """
## TEXT 2 IMAGE PLAYGROUND ðŸ¥
"""
css = '''
.gradio-container{max-width: 900px !important}
h1{text-align:center}
footer {
visibility: hidden
}
'''
js_func = """
function refresh() {
const url = new URL(window.location);
if (url.searchParams.get('__theme') !== 'dark') {
url.searchParams.set('__theme', 'dark');
window.location.href = url.href;
}
}
"""
examples = [
"3d image, cute girl, in the style of Pixar --ar 1:2 --stylize 750, 4K resolution highlights, Sharp focus, octane render, ray tracing, Ultra-High-Definition, 8k, UHD, HDR, (Masterpiece:1.5), (best quality:1.5)",
"Cold coffee in a cup bokeh --ar 85:128 --v 6.0 --style raw5, 4K",
"Vector illustration of a horse, vector graphic design with flat colors on an brown background in the style of vector art, using simple shapes and graphics with simple details, professionally designed as a tshirt logo ready for print on a white background. --ar 89:82 --v 6.0 --style raw",
"Man in brown leather jacket posing for camera, in the style of sleek and stylized, clockpunk, subtle shades, exacting precision, ferrania p30 --ar 67:101 --v 5",
"Commercial photography, giant burger, white lighting, studio light, 8k octane rendering, high resolution photography, insanely detailed, fine details, on white isolated plain, 8k, commercial photography, stock photo, professional color grading, --v 4 --ar 9:16 "
]
MODEL_OPTIONS = {
"RealVisXL_V4.0_Lightning": "SG161222/RealVisXL_V4.0_Lightning",
"Animagine-XL-3.1": "cagliostrolab/animagine-xl-3.1"
}
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
BATCH_SIZE = int(os.getenv("BATCH_SIZE", "1"))
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def load_and_prepare_model(model_id):
pipe = StableDiffusionXLPipeline.from_pretrained(
model_id,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
use_safetensors=True,
add_watermarker=False,
).to(device)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
if USE_TORCH_COMPILE:
pipe.compile()
if ENABLE_CPU_OFFLOAD:
pipe.enable_model_cpu_offload()
return pipe
# Preload and compile both models
models = {key: load_and_prepare_model(value) for key, value in MODEL_OPTIONS.items()}
MAX_SEED = np.iinfo(np.int32).max
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(duration=60, enable_queue=True)
def generate(
model_choice: str,
prompt: str,
negative_prompt: str = "",
use_negative_prompt: bool = False,
seed: int = 1,
width: int = 1024,
height: int = 1024,
guidance_scale: float = 3,
num_inference_steps: int = 25,
randomize_seed: bool = False,
use_resolution_binning: bool = True,
num_images: int = 1,
progress=gr.Progress(track_tqdm=True),
):
global models
pipe = models[model_choice]
seed = int(randomize_seed_fn(seed, randomize_seed))
generator = torch.Generator(device=device).manual_seed(seed)
options = {
"prompt": [prompt] * num_images,
"negative_prompt": [negative_prompt] * num_images if use_negative_prompt else None,
"width": width,
"height": height,
"guidance_scale": guidance_scale,
"num_inference_steps": num_inference_steps,
"generator": generator,
"output_type": "pil",
}
if use_resolution_binning:
options["use_resolution_binning"] = True
images = []
for i in range(0, num_images, BATCH_SIZE):
batch_options = options.copy()
batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]
if "negative_prompt" in batch_options:
batch_options["negative_prompt"] = options["negative_prompt"][i:i+BATCH_SIZE]
images.extend(pipe(**batch_options).images)
image_paths = [save_image(img) for img in images]
return image_paths, seed
def load_predefined_images():
predefined_images = [
"assets/1.png",
"assets/2.png",
"assets/3.png",
"assets/4.png",
"assets/5.png",
"assets/6.png",
"assets/7.png",
"assets/8.png",
"assets/9.png",
"assets/10.png",
"assets/11.png",
"assets/12.png",
]
return predefined_images
with gr.Blocks(css=css, theme="bethecloud/storj_theme", js=js_func) as demo:
gr.Markdown(DESCRIPTIONx)
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, show_label=False)
with gr.Row():
model_choice = gr.Dropdown(
label="Model",
choices=list(MODEL_OPTIONS.keys()),
value="RealVisXL_V4.0_Lightning"
)
with gr.Accordion("Advanced options", open=True):
num_images = gr.Slider(
label="Number of Images",
minimum=1,
maximum=4,
step=1,
value=1,
)
with gr.Row():
with gr.Column(scale=1):
use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=5,
lines=4,
placeholder="Enter a negative prompt",
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",
visible=True,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row(visible=True):
width = gr.Slider(
label="Width",
minimum=512,
maximum=MAX_IMAGE_SIZE,
step=64,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=512,
maximum=MAX_IMAGE_SIZE,
step=64,
value=1024,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=0.1,
maximum=6,
step=0.1,
value=3.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=25,
step=1,
value=23,
)
gr.Examples(
examples=examples,
inputs=prompt,
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=[
model_choice,
prompt,
negative_prompt,
use_negative_prompt,
seed,
width,
height,
guidance_scale,
num_inference_steps,
randomize_seed,
num_images
],
outputs=[result, seed],
api_name="run",
)
gr.Markdown("### Image Gallery")
predefined_gallery = gr.Gallery(label="Image Gallery", columns=4, show_label=False, value=load_predefined_images())
if __name__ == "__main__":
demo.queue(max_size=40).launch(show_api=False)