import os
import random
import uuid
import json
import gradio as gr
import numpy as np
from PIL import Image
import spaces
import torch
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
css = '''
.gradio-container {
max-width: 150%;
margin: 0 auto;
}
h1 { text-align: center; }
footer { visibility: hidden; }
'''
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)",
"A glass cup of cold coffee placed on a rustic wooden table, surrounded by soft morning light. The coffee is rich, dark, and topped with a light layer of creamy froth, droplets of condensation sliding down the glass.",
"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_ID = os.getenv("MODEL_VAL_PATH") # SG161222/RealVisXL_V5.0_Lightning or SG161222/RealVisXL_V4.0_Lightning
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")
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()
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
def generate(
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 = 10,
progress=gr.Progress(track_tqdm=True),
):
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
with gr.Blocks(theme="YTheme/GMaterial",css=css) as demo:
gr.Markdown("## SDXL:Text to Image [10-Images]")
with gr.Row():
with gr.Column(scale=1):
with gr.Row():
prompt = gr.Text(
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0, variant="primary")
result = gr.Gallery(show_label=False, format="png", columns=2, object_fit="contain")
with gr.Accordion("Advanced Settings", open=False):
num_images = gr.Slider(
label="Number of Images",
minimum=1,
maximum=15,
step=1,
value=10,
)
with gr.Row():
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,
)
with gr.Column(scale=1):
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=[
prompt,
negative_prompt,
use_negative_prompt,
seed,
width,
height,
guidance_scale,
num_inference_steps,
randomize_seed,
num_images
],
outputs=[result, seed],
api_name="run",
)
if __name__ == "__main__":
demo.queue(max_size=40).launch(ssr_mode=False)