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import gc
import os
from typing import TypeAlias
import torch
from PIL.Image import Image
from diffusers import FluxPipeline, FluxTransformer2DModel, AutoencoderKL
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
Pipeline: TypeAlias = FluxPipeline
CHECKPOINT = "black-forest-labs/FLUX.1-schnell"
REVISION = "741f7c3ce8b383c54771c7003378a50191e9efe9"
def load_pipeline() -> Pipeline:
text_encoder = CLIPTextModel.from_pretrained(
CHECKPOINT,
revision=REVISION,
subfolder="text_encoder",
local_files_only=True,
torch_dtype=torch.bfloat16,
)
text_encoder_2 = T5EncoderModel.from_pretrained(
CHECKPOINT,
revision=REVISION,
subfolder="text_encoder_2",
local_files_only=True,
torch_dtype=torch.bfloat16,
)
vae = AutoencoderKL.from_pretrained(
CHECKPOINT,
revision=REVISION,
subfolder="vae",
local_files_only=True,
torch_dtype=torch.bfloat16,
)
path = os.path.join(HF_HUB_CACHE, "models--RobertML--FLUX.1-schnell-int8wo/snapshots/307e0777d92df966a3c0f99f31a6ee8957a9857a")
transformer = FluxTransformer2DModel.from_pretrained(
path,
torch_dtype=torch.bfloat16,
use_safetensors=False,
)
pipeline = FluxPipeline.from_pretrained(
CHECKPOINT,
revision=REVISION,
local_files_only=True,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
transformer=transformer,
vae=vae,
torch_dtype=torch.bfloat16,
).to("cuda")
pipeline.vae.to(memory_format=torch.channels_last)
pipeline.vae = torch.compile(pipeline.vae, mode="reduce-overhead")
pipeline("")
return pipeline
def infer(request: TextToImageRequest, pipeline: Pipeline) -> Image:
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_peak_memory_stats()
generator = Generator(pipeline.device).manual_seed(request.seed)
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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