| 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_) |
