| import os | |
| import gc | |
| import torch | |
| import torch._dynamo | |
| from PIL import Image | |
| from typing import Type | |
| from torch import Generator | |
| from transformers import T5EncoderModel | |
| from huggingface_hub.constants import HF_HUB_CACHE | |
| from pipelines.models import TextToImageRequest | |
| from torchao.quantization import quantize_, int8_weight_only | |
| from diffusers import AutoencoderKL, FluxPipeline, FluxTransformer2DModel | |
| os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True" | |
| os.environ["TOKENIZERS_PARALLELISM"] = "True" | |
| torch._dynamo.config.suppress_errors = True | |
| torch.backends.cuda.matmul.allow_tf32 = True | |
| torch.backends.cudnn.enabled = True | |
| torch.backends.cudnn.benchmark = True | |
| Pipeline = None | |
| def empty_cache(): | |
| gc.collect() | |
| torch.cuda.empty_cache() | |
| torch.cuda.reset_max_memory_allocated() | |
| torch.cuda.reset_peak_memory_stats() | |
| def load_pipeline() -> Pipeline: | |
| ckpt_id = "black-forest-labs/FLUX.1-schnell" | |
| ckpt_revision = "741f7c3ce8b383c54771c7003378a50191e9efe9" | |
| text_encoder_2 = T5EncoderModel.from_pretrained("db900/encode-stream", revision = "8a6b7bd09dc66733fa582900186f929353f63619", subfolder="text_encoder_2", torch_dtype=torch.bfloat16).to(memory_format=torch.channels_last) | |
| path = os.path.join(HF_HUB_CACHE, "models--db900--encode-stream/snapshots/8a6b7bd09dc66733fa582900186f929353f63619/transformer") | |
| transformer = FluxTransformer2DModel.from_pretrained(path, torch_dtype=torch.bfloat16, use_safetensors=False).to(memory_format=torch.channels_last) | |
| quantize_(AutoencoderKL.from_pretrained(ckpt_id, revision=ckpt_revision, subfolder="vae", local_files_only=True, torch_dtype=torch.bfloat16,), int8_weight_only()) | |
| pipeline = FluxPipeline.from_pretrained(ckpt_id, revision=ckpt_revision, transformer=transformer, text_encoder_2=text_encoder_2, torch_dtype=torch.bfloat16,) | |
| pipeline.to("cuda") | |
| with torch.inference_mode(): | |
| pipeline(prompt="insensible, timbale, pothery, electrovital, actinogram, taxis, intracerebellar, centrodesmus", width=1024, height=1024, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256) | |
| return pipeline | |
| def infer(request: TextToImageRequest, pipeline: Pipeline, generator: 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, output_type="pil").images[0] | |