StyleAligned_Transfer / demo_stylealigned_sdxl.py
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import gradio as gr
from diffusers import StableDiffusionXLPipeline, DDIMScheduler
import torch
import sa_handler
# init models
scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False,
set_alpha_to_one=False)
pipeline = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True,
scheduler=scheduler
).to("cuda")
# Configure the pipeline for CPU offloading and VAE slicing#pipeline.enable_sequential_cpu_offload()
pipeline.enable_model_cpu_offload()
pipeline.enable_vae_slicing()
# Initialize the style-aligned handler
handler = sa_handler.Handler(pipeline)
sa_args = sa_handler.StyleAlignedArgs(share_group_norm=False,
share_layer_norm=False,
share_attention=True,
adain_queries=True,
adain_keys=True,
adain_values=False,
)
handler.register(sa_args, )
# Define the function to generate style-aligned images
def style_aligned_sdxl(initial_prompt1, initial_prompt2, initial_prompt3, initial_prompt4,
initial_prompt5, style_prompt, seed):
try:
# Combine the style prompt with each initial prompt
gen = None if seed is None else torch.manual_seed(int(seed))
sets_of_prompts = [prompt + " in the style of " + style_prompt for prompt in [initial_prompt1, initial_prompt2, initial_prompt3, initial_prompt4, initial_prompt5] if prompt]
# Generate images using the pipeline
images = pipeline(sets_of_prompts, generator=gen).images
return images
except Exception as e:
raise gr.Error(f"Error in generating images: {e}")
with gr.Blocks() as demo:
gr.HTML('<h1 style="text-align: center;">StyleAligned SDXL</h1>')
with gr.Group():
with gr.Column():
with gr.Accordion(label='Enter upto 5 different initial prompts', open=True):
with gr.Row(variant='panel'):
# Textboxes for initial prompts
initial_prompt1 = gr.Textbox(label='Initial prompt 1', value='', show_label=False, container=False, placeholder='a toy train')
initial_prompt2 = gr.Textbox(label='Initial prompt 2', value='', show_label=False, container=False, placeholder='a toy airplane')
initial_prompt3 = gr.Textbox(label='Initial prompt 3', value='', show_label=False, container=False, placeholder='a toy bicycle')
initial_prompt4 = gr.Textbox(label='Initial prompt 4', value='', show_label=False, container=False, placeholder='a toy car')
initial_prompt5 = gr.Textbox(label='Initial prompt 5', value='', show_label=False, container=False, placeholder='a toy boat')
with gr.Row():
# Textbox for the style prompt
style_prompt = gr.Textbox(label="Enter a style prompt", placeholder='macro photo, 3d game asset', scale=3)
seed = gr.Number(value=1234, label="Seed", precision=0, step=1, scale=1,
info="Enter a seed of a previous run "
"or leave empty for a random generation.")
# Button to generate images
btn = gr.Button("Generate a set of Style-aligned SDXL images",)
# Display the generated images
output = gr.Gallery(label="Style aligned text-to-image on SDXL ", elem_id="gallery",columns=5, rows=1,
object_fit="contain", height="auto",)
# Button click event
btn.click(fn=style_aligned_sdxl,
inputs=[initial_prompt1, initial_prompt2, initial_prompt3, initial_prompt4, initial_prompt5,
style_prompt, seed],
outputs=output,
api_name="style_aligned_sdxl")
# Providing Example inputs for the demo
gr.Examples(examples=[
["a toy train", "a toy airplane", "a toy bicycle", "a toy car", "a toy boat", "macro photo. 3d game asset."],
["a toy train", "a toy airplane", "a toy bicycle", "a toy car", "a toy boat", "BW logo. high contrast."],
["a cat", "a dog", "a bear", "a man on a bicycle", "a girl working on laptop", "minimal origami."],
["a firewoman", "a Gardner", "a scientist", "a policewoman", "a saxophone player", "made of claymation, stop motion animation."],
["a firewoman", "a Gardner", "a scientist", "a policewoman", "a saxophone player", "sketch, character sheet."],
],
inputs=[initial_prompt1, initial_prompt2, initial_prompt3, initial_prompt4, initial_prompt5, style_prompt],
outputs=[output],
fn=style_aligned_sdxl)
# Launch the Gradio demo
demo.launch()