| import os |
| from typing import TypeAlias |
| import torch |
| from PIL.Image import Image |
| from diffusers import FluxPipeline, FluxTransformer2DModel |
| 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 |
|
|
| Pipeline = None |
|
|
| os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True" |
| os.environ["TOKENIZERS_PARALLELISM"] = "True" |
|
|
| def load_pipeline() -> Pipeline: |
| path = os.path.join(HF_HUB_CACHE, "models--farapart--flow.1-fast/snapshots/59ebc4a11e1a6d4fe2085988028c5252f3a07b74/transformer") |
| transformer = FluxTransformer2DModel.from_pretrained(path, use_safetensors=False, local_files_only=True, torch_dtype=torch.bfloat16) |
|
|
| pipeline = FluxPipeline.from_pretrained("farapart/flow.1-fast", revision="59ebc4a11e1a6d4fe2085988028c5252f3a07b74", transformer=transformer, local_files_only=True, torch_dtype=torch.bfloat16).to("cuda") |
| pipeline.to(memory_format=torch.channels_last) |
| |
| with torch.inference_mode(): |
| for _ in range(4): |
| pipeline(prompt="onomancy, aftergo, spirantic, Platyhelmia, modificator, drupaceous, jobbernowl, hereness", width=1024, height=1024, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256) |
| torch.cuda.empty_cache() |
| 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] |
