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import gradio as gr
import torch
from diffusers import StableDiffusionXLPipeline
from diffusers.schedulers import TCDScheduler
import spaces
from PIL import Image
SAFETY_CHECKER = True
# Constants
base = "stabilityai/stable-diffusion-xl-base-1.0"
repo = "ByteDance/SDXL-Lightning"
checkpoints = {
"2-Step": ["pcm_sdxl_smallcfg_2step_converted.safetensors", 2, 0.0],
"4-Step": ["pcm_sdxl_smallcfg_4step_converted.safetensors", 4, 0.0],
"8-Step": ["pcm_sdxl_smallcfg_8step_converted.safetensors", 8, 0.0],
"16-Step": ["pcm_sdxl_smallcfg_16step_converted.safetensors", 16, 0.0],
"Normal CFG 4-Step": ["pcm_sdxl_normalcfg_4step_converted.safetensors", 4, 7.5],
"Normal CFG 8-Step": ["pcm_sdxl_normalcfg_8step_converted.safetensors", 8, 7.5],
"Normal CFG 16-Step": ["pcm_sdxl_normalcfg_16step_converted.safetensors", 16, 7.5],
"LCM-Like LoRA": ["pcm_sdxl_lcmlike_lora_converted.safetensors", 16, 0.0],
}
loaded = None
# 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):
global loaded
print(prompt, ckpt)
checkpoint = checkpoints[ckpt][0]
num_inference_steps = checkpoints[ckpt][1]
guidance_scale = checkpoints[ckpt][2]
if loaded != num_inference_steps:
pipe.scheduler = TCDScheduler(
num_train_timesteps=1000,
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
timestep_spacing="trailing",
)
pipe.load_lora_weights(
"wangfuyun/PCM_Weights", weight_name=checkpoint, subfolder="sdxl"
)
loaded = num_inference_steps
results = pipe(
prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale
)
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
css = """
.gradio-container {
max-width: 60rem !important;
}
"""
with gr.Blocks(css=css) as demo:
gr.HTML("<h1><center>SDXL-Lightning ⚡</center></h1>")
gr.HTML(
"<p><center>Lightning-fast text-to-image generation</center></p><p><center><a href='https://huggingface.co/ByteDance/SDXL-Lightning'>https://huggingface.co/ByteDance/SDXL-Lightning</a></center></p>"
)
with gr.Group():
with gr.Row():
prompt = gr.Textbox(label="Enter your prompt (English)", scale=8)
ckpt = gr.Dropdown(
label="Select inference steps",
choices=list(checkpoints.keys()),
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()
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