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import gradio as gr
import torch
from PIL import Image
from diffusers.utils import numpy_to_pil
from diffusers import (
T2IAdapter,
StableDiffusionXLAdapterPipeline,
AutoencoderKL,
EulerAncestralDiscreteScheduler
)
from controlnet_aux import PidiNetDetector
# Global variable to store the pipeline
pipe = None
def load_pipe():
global pipe
if pipe is None:
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
adapter = T2IAdapter.from_pretrained(
"Adapter/t2iadapter",
subfolder="sketch_sdxl_1.0",
torch_dtype=torch.float16,
adapter_type="full_adapter_xl")
euler_a = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
model_id,
adapter=adapter,
vae=vae,
scheduler=euler_a,
torch_dtype=torch.float16,
variant="fp16",
).to("cuda")
pipe.enable_xformers_memory_efficient_attention()
def preprocess_image(uploaded_file):
if uploaded_file is None:
return None, "Please upload an image."
img_upload = Image.open(uploaded_file)
preprocessor = PidiNetDetector.from_pretrained("lllyasviel/Annotators").to("cuda")
img_preprocessed = preprocessor(
img_upload,
detect_resolution=1024,
image_resolution=1024,
apply_filter=True).convert("L")
return img_preprocessed, ""
def generate(prompt, uploaded_file, prompt_addition, negative_prompt, num_images, num_steps, guidance_scale, adapter_conditioning_scale, adapter_conditioning_factor):
global pipe
load_pipe() # Ensure the model is loaded
img_preprocessed, error_message = preprocess_image(uploaded_file)
if error_message:
return error_message
num_images = int(num_images)
params = {
"image": img_preprocessed,
"num_inference_steps": num_steps,
"prompt": f"{prompt},{prompt_addition}" if prompt_addition.strip() else prompt,
"negative_prompt": negative_prompt,
"guidance_scale": guidance_scale,
"adapter_conditioning_scale": adapter_conditioning_scale / 100,
"adapter_conditioning_factor": adapter_conditioning_factor / 100,
"num_images_per_prompt": num_images
}
generated_images = pipe(**params).images
return generated_images # Returning PIL images directly
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="Prompt", value="a robot elephant", placeholder="Enter a description for the image you want to generate")
prompt_addition = gr.Textbox(label="Prompt addition", value="in outer space, in the style of picasso, highly detailed")
negative_prompt = gr.Textbox(label="Negative prompt", value="disfigured, wrong number of digits, cropped, low quality")
num_images = gr.Slider(minimum=1, maximum=10, value=1, label="Number of images to generate")
num_steps = gr.Slider(minimum=1, maximum=100, value=20, label="Number of steps")
guidance_scale = gr.Slider(minimum=6, maximum=10, value=7, label="Guidance scale")
adapter_conditioning_scale = gr.Slider(minimum=0, maximum=100, value=90, label="Adapter conditioning scale")
adapter_conditioning_factor = gr.Slider(minimum=0, maximum=100, value=90, label="Adapter conditioning factor")
uploaded_file = gr.File(label="Upload image", type='filepath')
with gr.Column():
output_gallery = gr.Gallery(label="Generated images")
generate_button = gr.Button("Generate")
generate_button.click(
generate,
inputs=[prompt, uploaded_file, prompt_addition, negative_prompt, num_images, num_steps, guidance_scale, adapter_conditioning_scale, adapter_conditioning_factor],
outputs=[output_gallery]
)
demo.launch()
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