Stable Diffusion Turbo example for Intel Arc A770 running with IPEX (intel extension for pytorch)

app.py

from diffusers import AutoPipelineForText2Image, DPMSolverMultistepScheduler, AutoencoderKL
import torch
import intel_extension_for_pytorch as ipex


# Initialize LoRA model and weights
#lora_model_id = "Linaqruf/style-enhancer-xl-lora"
#lora_filename = "style-enhancer-xl.safetensors"

#FOR REAL PHOTO AND ART
lora_model_id = "PvDeep/Add-Detail-XL"
lora_filename = "add-detail-xl.safetensors"

#FOR ART ONLY
#lora_model_id = "Outrun32/lora_xl_more_art"
#lora_filename = "xl_more_art-full_v1.safetensors"

# Load VAE component
vae = AutoencoderKL.from_pretrained(
    "madebyollin/sdxl-vae-fp16-fix", 
    torch_dtype=torch.float16
)

pipe = AutoPipelineForText2Image.from_pretrained('lykon/dreamshaper-xl-turbo', vae=vae,torch_dtype=torch.float16, variant="fp16")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(
    pipe.scheduler.config,
    algorithm_type="sde-dpmsolver++",
    use_karras_sigmas=True
    )

pipe = pipe.to("xpu")

# Load and fuse LoRA weights
pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
pipe.fuse_lora(lora_scale=0.6)

#prompt = "anime woman, night, blue light behind her, ((Galaxy, Lens flare)), short hair, flower field, night sky, cinematic shot. Wallpaper. (Blue color schema), detailed background, a city in the distance"
#prompt = "cinematic film still, close up, photo of a cute Pokémon, in the style of hyper-realistic fantasy,, sony fe 12-24mm f/2.8 gm, close up, 32k uhd, light navy and light amber, kushan empirem alluring, perfect skin, seductive, amazing quality, wallpaper, analog film grain"
prompt = "Kawaii Chameleon Painter changing its body colors to match its vibrant painting. Render this in an anime style,focusing on the chameleon's cute,wide eyes and intricate patterns on its body"

negative_prompt = "(low quality, worst quality:1.4), cgi, text, signature, watermark, extra limbs, ((nipples))"

generator = torch.manual_seed(0)
image = pipe(prompt=prompt,negative_prompt=negative_prompt, num_inference_steps=8, clip_skip=2, guidance_scale=2, width=1024, height=960, generator=generator).images[0]  

# Unfuse LoRA before saving the image
pipe.unfuse_lora()

image.save("./image.png")