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import gradio as gr
import torch
from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
import spaces
import os
from PIL import Image
SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", "0") == "1"
# Constants
base = "stabilityai/stable-diffusion-xl-base-1.0"
repo = "ByteDance/SDXL-Lightning"
checkpoints = {
"1-Step" : ["sdxl_lightning_1step_unet_x0.safetensors", 1],
"2-Step" : ["sdxl_lightning_2step_unet.safetensors", 2],
"4-Step" : ["sdxl_lightning_4step_unet.safetensors", 4],
"8-Step" : ["sdxl_lightning_8step_unet.safetensors", 8],
}
# Ensure model and scheduler are initialized in GPU-enabled function
if torch.cuda.is_available():
pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to("cuda")
if SAFETY_CHECKER:
from safety_checker import StableDiffusionSafetyChecker
from transformers import CLIPFeatureExtractor
safety_checker = StableDiffusionSafetyChecker.from_pretrained(
"CompVis/stable-diffusion-safety-checker"
).to("cuda")
feature_extractor = CLIPFeatureExtractor.from_pretrained(
"openai/clip-vit-base-patch32"
)
def check_nsfw_images(
images: list[Image.Image],
) -> tuple[list[Image.Image], list[bool]]:
safety_checker_input = feature_extractor(images, return_tensors="pt").to("cuda")
has_nsfw_concepts = safety_checker(
images=[images],
clip_input=safety_checker_input.pixel_values.to("cuda")
)
return images, has_nsfw_concepts
# Function
@spaces.GPU(enable_queue=True)
def generate_image(prompt, ckpt):
if torch.cuda.is_available():
pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to("cuda") # Moved inside
checkpoint = checkpoints[ckpt][0]
num_inference_steps = checkpoints[ckpt][1]
if num_inference_steps==1:
# Ensure sampler uses "trailing" timesteps and "sample" prediction type for 1-step inference.
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample")
else:
# Ensure sampler uses "trailing" timesteps.
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
pipe.unet.load_state_dict(load_file(hf_hub_download(repo, checkpoint), device="cuda"))
results = pipe(prompt, num_inference_steps=num_inference_steps, guidance_scale=0)
if SAFETY_CHECKER:
images, has_nsfw_concepts = check_nsfw_images(results.images)
if any(has_nsfw_concepts):
gr.Warning("NSFW content detected.")
return Image.new("RGB", (512, 512))
return images[0]
return results.images[0]
# Gradio Interface
description = """
This demo utilizes the SDXL-Lightning model by ByteDance, which is a lightning-fast text-to-image generative model capable of producing high-quality images in 4 steps.
As a community effort, this demo was put together by AngryPenguin. Link to model: https://huggingface.co/ByteDance/SDXL-Lightning
"""
with gr.Blocks(css="style.css") as demo:
gr.HTML("<h1><center>Text-to-Image with SDXL-Lightning ⚡</center></h1>")
gr.Markdown(description)
with gr.Group():
with gr.Row():
prompt = gr.Textbox(label='Enter you image prompt:', scale=8)
ckpt = gr.Dropdown(label='Select inference steps',choices=['1-Step', '2-Step', '4-Step', '8-Step'], value='4-Step', interactive=True)
submit = gr.Button(scale=1, variant='primary')
img = gr.Image(label='SDXL-Lightning Generated Image')
prompt.submit(fn=generate_image,
inputs=[prompt, ckpt],
outputs=img,
)
submit.click(fn=generate_image,
inputs=[prompt, ckpt],
outputs=img,
)
demo.queue().launch() |