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# onediff.compile
import os
from typing import TypeAlias
import torch
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
from functools import partial
Pipeline: TypeAlias = 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
os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True"
CHECKPOINT = "jokerbit/flux.1-schnell-Robert-int8wo"
REVISION = "5ef0012f11a863e5111ec56540302a023bc8587b"
TinyVAE = "madebyollin/taef1"
TinyVAE_REV = "2d552378e58c9c94201075708d7de4e1163b2689"
my_quantize = partial(quantize_, apply_tensor_subclass=int8_weight_only())
path = os.path.join(HF_HUB_CACHE, "models--jokerbit--flux.1-schnell-Robert-int8wo/snapshots/5ef0012f11a863e5111ec56540302a023bc8587b/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
)
pipeline = FluxPipeline.from_pretrained(
CHECKPOINT,
revision=REVISION,
transformer=transformer,
vae=vae,
local_files_only=True,
torch_dtype=torch.bfloat16,
)
pipeline.transformer.to(memory_format=torch.channels_last)
my_quantize(pipeline.vae)
pipeline.vae = torch.compile(pipeline.vae, mode="reduce-overhead")
pipeline.to("cuda")
def load_pipeline():
for _ in range(4):
pipeline("cat", 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]
if __name__ == "__main__":
from time import perf_counter
PROMPT = 'martyr, semiconformity, peregrination, quip, twineless, emotionless, tawa, depickle'
request = TextToImageRequest(prompt=PROMPT,
height=None,
width=None,
seed=666)
start_time = perf_counter()
pipe_ = load_pipeline()
stop_time = perf_counter()
print(f"Pipeline is loaded in {stop_time - start_time}s")
for _ in range(4):
start_time = perf_counter()
infer(request, pipe_)
stop_time = perf_counter()
print(f"Request in {stop_time - start_time}s")
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