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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,
DiffusionPipeline,
AutoencoderTiny,
)
from huggingface_hub.constants import HF_HUB_CACHE
from pipelines.models import TextToImageRequest
from torch import Generator
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_rev = "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
).to(memory_format=torch.channels_last)
vae = AutoencoderTiny.from_pretrained(
vae_id, revision=vae_rev, local_files_only=True, torch_dtype=torch.bfloat16
)
text_encoder_2 = T5EncoderModel.from_pretrained(
"freaky231/t5-encoder-bf16",
revision="994f6e4720f69e67bfc8822cbb4063c9149b801b",
torch_dtype=torch.bfloat16,
).to(memory_format=torch.channels_last)
pipeline = DiffusionPipeline.from_pretrained(
id,
revision=revision,
transformer=transformer,
text_encoder_2=text_encoder_2,
vae=vae,
torch_dtype=torch.bfloat16,
)
pipeline.to("cuda")
for _ in range(2):
pipeline(
prompt="satiety, unwitherable, Pygmy, ramlike, Curtis, fingerstone, rewhisper",
width=1024,
height=1024,
guidance_scale=0.0,
num_inference_steps=4,
max_sequence_length=256,
)
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__":
pipe_ = load_pipeline()
for _ in range(4):
request = TextToImageRequest(prompt="cat", height=None, width=None, seed=3254)
infer(request, pipe_) |