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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, AutoencoderTiny, DiffusionPipeline
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
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"
id = "black-forest-labs/FLUX.1-schnell"
revision = "741f7c3ce8b383c54771c7003378a50191e9efe9"
vae_id = "madebyollin/taef1"
vae_revision = "2d552378e58c9c94201075708d7de4e1163b2689"
def load_pipeline() -> Pipeline:
path = os.path.join(HF_HUB_CACHE, "models--freaky231--flux.1-schnell-int8/snapshots/c33fa7f79751fe42b0a7de7f72edb5d1b86f32a7/transformer")
transformer = FluxTransformer2DModel.from_pretrained(
path,
use_safetensors=False,
local_files_only=True,
torch_dtype=torch.bfloat16)
vae = AutoencoderTiny.from_pretrained(
vae_id,
revision=vae_revision,
local_files_only=True,
torch_dtype=torch.bfloat16
)
pipeline = DiffusionPipeline.from_pretrained(
id,
revision=revision,
transformer=transformer,
vae=vae,
local_files_only=True,
torch_dtype=torch.bfloat16,
)
pipeline.to(memory_format=torch.channels_last)
pipeline.to("cuda")
for _ in range(2):
pipeline("satiety, unwitherable, Pygmy, ramlike, Curtis, fingerstone, rewhisper", num_inference_steps=4)
return pipeline
@torch.inference_mode()
def infer(request: TextToImageRequest, pipeline: Pipeline, generator: torch.Generator) -> Image:
generator = Generator(pipeline.device).manual_seed(request.seed)
try:
prompt = request.prompt
except Exception as e:
prompt = "satiety, unwitherable, Pygmy, ramlike, Curtis, fingerstone, rewhisper"
return pipeline(
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__":
pipe_ = load_pipeline()
for _ in range(2):
request = TextToImageRequest(prompt='dog',
height=None,
width=None,
seed=666)
infer(request, pipe_) |