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import gradio as gr
import os
import numpy as np
import random
from huggingface_hub import login
import torch
from diffusers import StableDiffusionXLPipeline, AutoencoderKL
from blora_utils import BLOCKS, filter_lora, scale_lora
hf_token = os.environ.get("HF_TOKEN")
login(token=hf_token)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipeline = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
vae=vae,
torch_dtype=torch.float16,
).to("cuda")
def load_b_lora_to_unet(pipe, content_lora_model_id: str = '', style_lora_model_id: str = '', content_alpha: float = 1.,
style_alpha: float = 1.) -> None:
try:
# Get Content B-LoRA SD
if content_lora_model_id:
content_B_LoRA_sd, _ = pipe.lora_state_dict(content_lora_model_id, use_auth_token=True)
content_B_LoRA = filter_lora(content_B_LoRA_sd, BLOCKS['content'])
content_B_LoRA = scale_lora(content_B_LoRA, content_alpha)
else:
content_B_LoRA = {}
# Get Style B-LoRA SD
if style_lora_model_id:
style_B_LoRA_sd, _ = pipe.lora_state_dict(style_lora_model_id, use_auth_token=True)
style_B_LoRA = filter_lora(style_B_LoRA_sd, BLOCKS['style'])
style_B_LoRA = scale_lora(style_B_LoRA, style_alpha)
else:
style_B_LoRA = {}
# Merge B-LoRAs SD
res_lora = {**content_B_LoRA, **style_B_LoRA}
# Load
pipe.load_lora_into_unet(res_lora, None, pipe.unet)
except Exception as e:
raise type(e)(f'failed to load_b_lora_to_unet, due to: {e}')
def main(content_b_lora, style_b_lora, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
if content_b_lora is None:
content_B_LoRA_path = ''
else:
content_B_LoRA_path = content_b_lora
if style_b_lora is None:
style_B_LoRA_path = ''
else:
style_B_LoRA_path = style_b_lora
content_alpha,style_alpha = 1,1.1
load_b_lora_to_unet(pipeline, content_B_LoRA_path, style_B_LoRA_path, content_alpha, style_alpha)
prompt = prompt
image = pipeline(
prompt,
generator=generator,
num_images_per_prompt=1,
width = width,
height = height,
).images[0]
pipeline.unload_lora_weights()
return image
css="""
#col-container {
margin: 0 auto;
max-width: 520px;
}
"""
if torch.cuda.is_available():
power_device = "GPU"
else:
power_device = "CPU"
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""
# Text-to-Image Gradio Template
Currently running on {power_device}.
""")
with gr.Row():
content_b_lora = gr.Textbox(label="B-LoRa for content")
style_b_lora = gr.Textbox(label="B-LoRa for style")
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.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
visible=False,
)
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():
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=512,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=512,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=0.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=50,
)
run_button.click(
fn = main,
inputs = [content_b_lora, style_b_lora, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs = [result]
)
demo.queue().launch() |