SDXL-Flash / app.py
Niansuh's picture
Update app.py
05f674c verified
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
# Use environment variables for flexibility
MODEL_ID = os.getenv("MODEL_ID", "sd-community/sdxl-flash")
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")) # Allow generating multiple images at once
# Determine device and load model outside of function for efficiency
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)
# Torch compile for potential speedup (experimental)
if USE_TORCH_COMPILE:
pipe.compile()
# CPU offloading for larger RAM capacity (experimental)
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(duration=35, enable_queue=True)
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 = 30,
randomize_seed: bool = False,
use_resolution_binning: bool = True,
num_images: int = 1, # Number of images to generate
progress=gr.Progress(track_tqdm=True),
):
seed = int(randomize_seed_fn(seed, randomize_seed))
generator = torch.Generator(device=device).manual_seed(seed)
# Improved options handling
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",
}
# Use resolution binning for faster generation with less VRAM usage
if use_resolution_binning:
options["use_resolution_binning"] = True
# Generate images potentially in batches
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
examples = [
"a cat eating a piece of cheese",
"a ROBOT riding a BLUE horse on Mars, photorealistic, 4k",
"Ironman VS Hulk, ultrarealistic",
"Astronaut in a jungle, cold color palette, oil pastel, detailed, 8k",
"An alien holding a sign board containing the word 'Flash', futuristic, neonpunk",
"Kids going to school, Anime style"
]
css = '''
.gradio-container{max-width: 700px !important}
h1{text-align:center}
footer {
visibility: hidden
}
'''
with gr.Blocks(css=css) as demo:
gr.Markdown("""# SDXL Flash""")
with gr.Group():
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.Accordion("Advanced options", open=False):
num_images = gr.Slider(
label="Number of Images",
minimum=1,
maximum=4,
step=1,
value=1,
)
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, NSFW",
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=15,
step=1,
value=8,
)
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=50).launch()