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from PIL.Image import Image
from diffusers import FluxPipeline, FluxTransformer2DModel, AutoencoderKL, AutoencoderTiny
from huggingface_hub.constants import HF_HUB_CACHE
from pipelines.models import TextToImageRequest
from torch import Generator
from torchao.quantization import quantize_, int8_weight_only
from transformers import T5EncoderModel, CLIPTextModel, logging
import gc
import os
from typing import TypeAlias
import torch
Pipeline = FluxPipeline
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.benchmark = True
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._dynamo.config.suppress_errors = True
os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True"
CHECKPOINT = "manbeast3b/Flux.1.schnell-quant2"
REVISION = "44eb293715147878512da10bf3bc47cd14ec8c55"
TinyVAE = "madebyollin/taef1"
TinyVAE_REV = "2d552378e58c9c94201075708d7de4e1163b2689"
def load_pipeline() -> Pipeline:
path = os.path.join(HF_HUB_CACHE, "models--manbeast3b--Flux.1.schnell-quant2/snapshots/44eb293715147878512da10bf3bc47cd14ec8c55/transformer")
transformer = FluxTransformer2DModel.from_pretrained(
path,
use_safetensors=False,
local_files_only=True,
torch_dtype=torch.bfloat16)
vae = AutoencoderTiny.from_pretrained(
TinyVAE,
revision=TinyVAE_REV,
local_files_only=True,
torch_dtype=torch.bfloat16)
vae.encoder.load_state_dict(torch.load("encoder.pth"), strict=False)
vae.decoder.load_state_dict(torch.load("decoder.pth"), strict=False)
pipeline = FluxPipeline.from_pretrained(CHECKPOINT,revision=REVISION,transformer=transformer,vae=vae,local_files_only=True,torch_dtype=torch.bfloat16).to("cuda")
pipeline.to(memory_format=torch.channels_last)
quantize_(pipeline.vae, int8_weight_only())
pipeline.vae = torch.compile(pipeline.vae, mode="max-autotune", fullgraph=True)
with torch.inference_mode():
for _ in range(2):
pipeline("meow", num_inference_steps=4)
return pipeline
@torch.inference_mode()
def infer(request: TextToImageRequest, pipeline: Pipeline, generator: torch.Generator) -> Image:
return pipeline(
request.prompt,
generator=generator,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
height=request.height,
width=request.width,
).images[0]
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