product_diffusion_api/routers/sdxl_text_to_image.py

import sys
sys.path.append("../scripts")  # Path of the scripts directory
import config
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel
import base64
from io import BytesIO
from typing import List
import uuid
from diffusers import DiffusionPipeline
import torch
from functools import lru_cache
from s3_manager import S3ManagerService
from PIL import Image
import io
from scripts.utils import accelerator

device = accelerator()
torch._inductor.config.conv_1x1_as_mm = True
torch._inductor.config.coordinate_descent_tuning = True
torch._inductor.config.epilogue_fusion = False
torch._inductor.config.coordinate_descent_check_all_directions = True
torch._inductor.config.force_fuse_int_mm_with_mul = True
torch._inductor.config.use_mixed_mm = True

router = APIRouter()





def pil_to_b64_json(image):
    """
    Converts a PIL image to a base64-encoded JSON object.

    Args:
        image (PIL.Image.Image): The PIL image object to be converted.

    Returns:
        dict: A dictionary containing the image ID and the base64-encoded image.

    """
    image_id = str(uuid.uuid4())
    buffered = BytesIO()
    image.save(buffered, format="PNG")
    b64_image = base64.b64encode(buffered.getvalue()).decode("utf-8")
    return {"image_id": image_id, "b64_image": b64_image}


def pil_to_s3_json(image: Image.Image, file_name) -> str:
    """
    Uploads a PIL image to Amazon S3 and returns a JSON object containing the image ID and the signed URL.

    Args:
        image (PIL.Image.Image): The PIL image to be uploaded.
        file_name (str): The name of the file.

    Returns:
        dict: A JSON object containing the image ID and the signed URL.

    """
    image_id = str(uuid.uuid4())
    s3_uploader = S3ManagerService()
    image_bytes = io.BytesIO()
    image.save(image_bytes, format="PNG")
    image_bytes.seek(0)

    unique_file_name = s3_uploader.generate_unique_file_name(file_name)
    s3_uploader.upload_file(image_bytes, unique_file_name)
    signed_url = s3_uploader.generate_signed_url(
        unique_file_name, exp=43200
    )  # 12 hours
    return {"image_id": image_id, "url": signed_url}


@lru_cache(maxsize=1)
def load_pipeline(model_name, adapter_name):
    """
    Load the diffusion pipeline with the specified model and adapter names.

    Args:
        model_name (str): The name of the pretrained model.
        adapter_name (str): The name of the adapter.

    Returns:
        DiffusionPipeline: The loaded diffusion pipeline.
    """
    pipe = DiffusionPipeline.from_pretrained(model_name, torch_dtype=torch.bfloat16).to(device)
    pipe.load_lora_weights(adapter_name)
    pipe.fuse_lora()
    pipe.unload_lora_weights()
    pipe.unet.to(memory_format=torch.channels_last)
    pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead")
    pipe.vae.decode = torch.compile(pipe.vae.decode, mode="reduce-overhead")
    pipe.fuse_qkv_projections()
    return pipe


loaded_pipeline = load_pipeline(config.MODEL_NAME, config.ADAPTER_NAME)


# SDXLLoraInference class for running inference
class SDXLLoraInference:
    """
    Class for performing SDXL Lora inference.

    Args:
        prompt (str): The prompt for generating the image.
        negative_prompt (str): The negative prompt for generating the image.
        num_images (int): The number of images to generate.
        num_inference_steps (int): The number of inference steps to perform.
        guidance_scale (float): The scale for guiding the generation process.

    Attributes:
        pipe (DiffusionPipeline): The pre-trained diffusion pipeline.
        prompt (str): The prompt for generating the image.
        negative_prompt (str): The negative prompt for generating the image.
        num_images (int): The number of images to generate.
        num_inference_steps (int): The number of inference steps to perform.
        guidance_scale (float): The scale for guiding the generation process.

    Methods:
        run_inference: Runs the inference process and returns the generated image.
    """

    def __init__(
        self,
        prompt: str,
        negative_prompt: str,
        num_images: int,
        num_inference_steps: int,
        guidance_scale: float,
        mode :str
    ) -> None:
        self.pipe = loaded_pipeline
        self.prompt = prompt
        self.negative_prompt = negative_prompt
        self.num_images = num_images
        self.num_inference_steps = num_inference_steps
        self.guidance_scale = guidance_scale
        self.mode = mode 
        

    def run_inference(self) -> str:
        """
        Runs the inference process and returns the generated image.

        Parameters:
            mode (str): The mode for returning the generated image.
                        Possible values: "b64_json", "s3_json".
                        Defaults to "b64_json".

        Returns:
            str: The generated image in the specified format.
        """
        image = self.pipe(
            prompt=self.prompt,
            num_inference_steps=self.num_inference_steps,
            guidance_scale=self.guidance_scale,
            negative_prompt=self.negative_prompt,
            num_images_per_prompt=self.num_images,
        ).images[0]
        
        if self.mode == "s3_json":
            s3_url = pil_to_s3_json(image,'sdxl_image')
            return s3_url
        elif self.mode == "b64_json":
            return pil_to_b64_json(image)
        else:
            raise ValueError("Invalid mode. Supported modes are 'b64_json' and 's3_json'.")


# Input format for single request
class InputFormat(BaseModel):
    prompt: str
    num_inference_steps: int
    guidance_scale: float
    negative_prompt: str
    num_images: int
    mode: str


# Input format for batch requests
class BatchInputFormat(BaseModel):
    batch_input: List[InputFormat]


# Endpoint for single request
@router.post("/sdxl_v0_lora_inference")
async def sdxl_v0_lora_inference(data: InputFormat):
    inference = SDXLLoraInference(
        data.prompt,
        data.negative_prompt,
        data.num_images,
        data.num_inference_steps,
        data.guidance_scale,
        data.mode
    )
    output_json = inference.run_inference()
    return output_json



@router.post("/sdxl_v0_lora_inference/batch")
async def sdxl_v0_lora_inference_batch(data: BatchInputFormat):
    """
    Perform batch inference for SDXL V0 LoRa model.

    Args:
        data (BatchInputFormat): The input data containing a batch of requests.

    Returns:
        dict: A dictionary containing the message and processed requests data.

    Raises:
        HTTPException: If the number of requests exceeds the maximum queue size.
    """
    MAX_QUEUE_SIZE = 64

    if len(data.batch_input) > MAX_QUEUE_SIZE:
        raise HTTPException(
            status_code=400,
            detail=f"Number of requests exceeds maximum queue size ({MAX_QUEUE_SIZE})",
        )

    processed_requests = []
    for item in data.batch_input:
        inference = SDXLLoraInference(
            item.prompt,
            item.negative_prompt,
            item.num_images,
            item.num_inference_steps,
            item.guidance_scale,
            item.mode,
        )
        output_json = inference.run_inference()
        processed_requests.append(output_json)

    return {"message": "Requests processed successfully", "data": processed_requests}