app/pipelineSDXLTurbo.py

from diffusers import (
    StableDiffusionXLControlNetImg2ImgPipeline,
    ControlNetModel,
    AutoencoderKL,
    AutoencoderTiny,
)
from compel import Compel, ReturnedEmbeddingsType
from pydantic import BaseModel, Field
from utils.canny_gpu import SobelOperator
import torch

try:
    import intel_extension_for_pytorch as ipex  # type: ignore
except:
    pass

import psutil
from PIL import Image
import math
import time


controlnet_model = "diffusers/controlnet-canny-sdxl-1.0"
model_id = "stabilityai/sdxl-turbo"
taesd_model = "madebyollin/taesdxl"

default_prompt = "Portrait of The Joker halloween costume, face painting, with , glare pose, detailed, intricate, full of colour, cinematic lighting, trending on artstation, 8k, hyperrealistic, focused, extreme details, unreal engine 5 cinematic, masterpiece"
default_negative_prompt = "blurry, low quality, render, 3D, oversaturated"

class Pipeline:
    class Info(BaseModel):
        name: str = "controlnet+SDXL+Turbo"
        title: str = "SDXL Turbo + Controlnet"
        description: str = "Generates an image from a text prompt"
        input_mode: str = "image"

    class InputParams(BaseModel):
        prompt: str = Field(
            default_prompt,
            title="Prompt",
            field="textarea",
            id="prompt",
        )
        negative_prompt: str = Field(
            default_negative_prompt,
            title="Negative Prompt",
            field="textarea",
            id="negative_prompt",
            hide=True,
        )
        seed: int = Field(
            2159232, min=0, title="Seed", field="seed", hide=True, id="seed"
        )
        steps: int = Field(
            2, min=1, max=15, title="Steps", field="range", hide=True, id="steps"
        )
        width: int = Field(
            512, min=2, max=15, title="Width", disabled=True, hide=True, id="width"
        )
        height: int = Field(
            512, min=2, max=15, title="Height", disabled=True, hide=True, id="height"
        )
        guidance_scale: float = Field(
            1.0,
            min=0,
            max=10,
            step=0.001,
            title="Guidance Scale",
            field="range",
            hide=True,
            id="guidance_scale",
        )
        strength: float = Field(
            0.5,
            min=0.25,
            max=1.0,
            step=0.001,
            title="Strength",
            field="range",
            hide=True,
            id="strength",
        )
        controlnet_scale: float = Field(
            0.5,
            min=0,
            max=1.0,
            step=0.001,
            title="Controlnet Scale",
            field="range",
            hide=True,
            id="controlnet_scale",
        )
        controlnet_start: float = Field(
            0.0,
            min=0,
            max=1.0,
            step=0.001,
            title="Controlnet Start",
            field="range",
            hide=True,
            id="controlnet_start",
        )
        controlnet_end: float = Field(
            1.0,
            min=0,
            max=1.0,
            step=0.001,
            title="Controlnet End",
            field="range",
            hide=True,
            id="controlnet_end",
        )
        canny_low_threshold: float = Field(
            0.31,
            min=0,
            max=1.0,
            step=0.001,
            title="Canny Low Threshold",
            field="range",
            hide=True,
            id="canny_low_threshold",
        )
        canny_high_threshold: float = Field(
            0.125,
            min=0,
            max=1.0,
            step=0.001,
            title="Canny High Threshold",
            field="range",
            hide=True,
            id="canny_high_threshold",
        )
        debug_canny: bool = Field(
            False,
            title="Debug Canny",
            field="checkbox",
            hide=True,
            id="debug_canny",
        )

    def __init__(self, device: torch.device, torch_dtype: torch.dtype):
        controlnet_canny = ControlNetModel.from_pretrained(
            controlnet_model, torch_dtype=torch_dtype
        ).to(device)

        vae = AutoencoderKL.from_pretrained(
            "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch_dtype
        )

        self.pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(
            model_id,
            controlnet=controlnet_canny,
            vae=vae,
        )
     
        self.canny_torch = SobelOperator(device=device)

        self.pipe.set_progress_bar_config(disable=True)
        self.pipe.to(device=device, dtype=torch_dtype).to(device)
        if device.type != "mps":
            self.pipe.unet.to(memory_format=torch.channels_last)

        if psutil.virtual_memory().total < 64 * 1024**3:
            self.pipe.enable_attention_slicing()

        self.pipe.compel_proc = Compel(
            tokenizer=[self.pipe.tokenizer, self.pipe.tokenizer_2],
            text_encoder=[self.pipe.text_encoder, self.pipe.text_encoder_2],
            returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
            requires_pooled=[False, True],
        )
        #if args.use_taesd:
        self.pipe.vae = AutoencoderTiny.from_pretrained(
            taesd_model, torch_dtype=torch_dtype, use_safetensors=True
        ).to(device)

        #if args.torch_compile:
        self.pipe.unet = torch.compile(
            self.pipe.unet, mode="reduce-overhead", fullgraph=True
        )
        self.pipe.vae = torch.compile(
            self.pipe.vae, mode="reduce-overhead", fullgraph=True
        )
        self.pipe(
            prompt="warmup",
            image=[Image.new("RGB", (512, 512))],
            control_image=[Image.new("RGB", (512, 512))],
        )