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
DESCRIPTIONx = """## STABLE HAMSTER"""
# Use environment variables for flexibility
MODEL_ID = os.getenv("MODEL_REPO")
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: 560px !important}
h1{text-align:center}
footer {
visibility: hidden
}
'''
# Define the content of index.html with a loading animation and delay
index_html_content = """
Loading...
"""
# Save index.html to the current directory
with open("assets/index.html", "w") as f:
f.write(index_html_content)
# Embed index.html into Gradio UI
with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
gr.HTML(index_html_content) # Embed the HTML content here
gr.Markdown(DESCRIPTIONx)
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()