app.py

import gradio as gr
from diffusers import StableDiffusionXLPipeline, DDIMScheduler
import torch
import numpy as np
from PIL import Image
import io
import sys
import os
import sa_handler
import inversion

# Model Load
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")

# Function to process the image
def process_image(image, prompt, style, src_description, inference_steps, shared_score_shift, shared_score_scale, guidance_scale):
    src_prompt = f'{src_description}, {style}.'

    num_inference_steps = inference_steps
    x0 = np.array(Image.fromarray(image).resize((1024, 1024)))
    zts = inversion.ddim_inversion(pipeline, x0, src_prompt, num_inference_steps, 2)

    prompts = [
        src_prompt,
        f"{prompt}, {style}."
    ]

    handler = sa_handler.Handler(pipeline)
    sa_args = sa_handler.StyleAlignedArgs(
        share_group_norm=True, share_layer_norm=True, share_attention=True,
        adain_queries=True, adain_keys=True, adain_values=False,
        shared_score_shift=shared_score_shift, shared_score_scale=shared_score_scale,)
    handler.register(sa_args)

    zT, inversion_callback = inversion.make_inversion_callback(zts, offset=5)

    g_cpu = torch.Generator(device='cpu')
    g_cpu.manual_seed(10)

    latents = torch.randn(len(prompts), 4, 128, 128, device='cpu', generator=g_cpu,
                          dtype=pipeline.unet.dtype,).to('cuda:0')
    latents[0] = zT

    images_a = pipeline(prompts, latents=latents,
                        callback_on_step_end=inversion_callback,
                        num_inference_steps=num_inference_steps, guidance_scale=guidance_scale).images

    handler.remove()

    return Image.fromarray(images_a[1])

iface = gr.Interface(
    fn=process_image,
    inputs=[
        gr.Image(type="numpy"), 
        gr.Textbox(label="Enter your prompt"),
        gr.Textbox(label="Enter your style", default="medieval painting"),
        gr.Textbox(label="Enter source description", default="Man laying in a bed"),
        gr.Slider(minimum=5, maximum=50, step=1, default=50, label="Number of Inference Steps"),
        gr.Slider(minimum=1, maximum=2, step=0.01, default=1.5, label="Shared Score Shift"),
        gr.Slider(minimum=0, maximum=1, step=0.01, default=0.5, label="Shared Score Scale"),
        gr.Slider(minimum=5, maximum=120, step=1, default=10, label="Guidance Scale")
    ],
    outputs="image",
    title="Stable Diffusion XL with Style Alignment",
    description="Generate images in the style of your choice."
)

iface.launch()