src/pipeline.py

from diffusers import AutoencoderKL, AutoencoderTiny
from diffusers.image_processor import VaeImageProcessor
import torch
import torch._dynamo
import gc
from PIL.Image import Image
from pipelines.models import TextToImageRequest
from torch import Generator
from diffusers import FluxPipeline
from torchao.quantization import quantize_, int8_weight_only, fpx_weight_only

Pipeline = None
MODEL_ID = "black-forest-labs/FLUX.1-schnell"
DTYPE = torch.bfloat16
def clear():
    gc.collect()
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()

def load_pipeline() -> Pipeline:    
    clear()
    # vae = AutoencoderKL.from_pretrained(
    #     MODEL_ID, subfolder="vae", torch_dtype=torch.bfloat16
    # )
    vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=DTYPE)
    # quantize_(vae, fpx_weight_only(3, 2))
    quantize_(vae, int8_weight_only())
    pipeline = FluxPipeline.from_pretrained(MODEL_ID,vae=vae,
                                        torch_dtype=DTYPE)
    torch.backends.cudnn.benchmark = True
    torch.backends.cuda.matmul.allow_tf32 = True
    torch.cuda.set_per_process_memory_fraction(0.99)
    pipeline.text_encoder.to(memory_format=torch.channels_last)
    pipeline.text_encoder_2.to(memory_format=torch.channels_last)
    pipeline.transformer.to(memory_format=torch.channels_last)
    pipeline.vae.to(memory_format=torch.channels_last)
    pipeline.vae = torch.compile(pipeline.vae)
    pipeline._exclude_from_cpu_offload = ["vae"]
    pipeline.enable_sequential_cpu_offload()
    clear()
    for _ in range(1):
        pipeline(prompt="unpervaded, unencumber, froggish, groundneedle, transnatural, fatherhood, outjump, cinerator", width=1024, height=1024, guidance_scale=0.1, num_inference_steps=4, max_sequence_length=256)
    return pipeline

# sample = True
# @torch.inference_mode()
# def infer(request: TextToImageRequest, pipeline: Pipeline) -> Image:
#     global sample
#     if sample:
#         clear()
#         sample = None
#     # torch.cuda.reset_peak_memory_stats()
#     generator = Generator("cuda").manual_seed(request.seed)
#     image=pipeline(request.prompt,generator=generator, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256, height=request.height, width=request.width, output_type="pil").images[0]
#     return(image)

sample = True
@torch.inference_mode()
def infer(request: TextToImageRequest, pipeline: Pipeline) -> Image:
    global sample
    if sample:
        clear()
        sample = None
    # torch.cuda.reset_peak_memory_stats()
    generator = Generator("cuda").manual_seed(request.seed)
    image = None
    with torch.backends.cuda.sdp_kernel(enable_flash=False, enable_math=False, enable_mem_efficient=True):
        image=pipeline(request.prompt,generator=generator, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256, height=request.height, width=request.width, output_type="pil").images[0]
    return(image)