| |
| from huggingface_hub.constants import HF_HUB_CACHE |
| from transformers import T5EncoderModel, T5TokenizerFast, CLIPTokenizer, CLIPTextModel |
| import torch |
| import torch._dynamo |
| import gc |
| import os |
| from diffusers import FluxPipeline, AutoencoderKL, AutoencoderTiny |
| from PIL.Image import Image |
| from pipelines.models import TextToImageRequest |
| from torch import Generator |
| from diffusers import FluxTransformer2DModel, DiffusionPipeline |
| from torchao.quantization import quantize_, int8_weight_only, fpx_weight_only |
|
|
| os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True" |
| os.environ["TOKENIZERS_PARALLELISM"] = "True" |
| torch._dynamo.config.suppress_errors = True |
|
|
| Pipeline = None |
| ckpt_id = "manbeast3b/flux.1-schnell-full1" |
| ckpt_revision = "cb1b599b0d712b9aab2c4df3ad27b050a27ec146" |
|
|
|
|
| def load_pipeline() -> Pipeline: |
| path = os.path.join(HF_HUB_CACHE, "models--manbeast3b--flux.1-schnell-full1/snapshots/cb1b599b0d712b9aab2c4df3ad27b050a27ec146/transformer") |
| transformer = FluxTransformer2DModel.from_pretrained(path, torch_dtype=torch.bfloat16, use_safetensors=False) |
| pipeline = FluxPipeline.from_pretrained(ckpt_id, revision=ckpt_revision, transformer=transformer, local_files_only=True, torch_dtype=torch.bfloat16,) |
| |
| pipeline.transformer = torch.compile(pipeline.transformer, mode="max-autotune", fullgraph=True) |
| |
| |
| |
| |
| basepath = os.path.join(HF_HUB_CACHE, "models--manbeast3b--Flux.1.schnell_eagle5_1_0.1_unst_8_try/snapshots/0d3ce1d07195ccfe8eafe821ee80b34d74a3c2d7") |
| pipeline.vae.encoder.load_state_dict(torch.load(os.path.join(basepath, "encoder.pth")), strict=False) |
| pipeline.vae.decoder.load_state_dict(torch.load(os.path.join(basepath, "decoder.pth")), strict=False) |
| quantize_(pipeline.vae, int8_weight_only()) |
| pipeline.to("cuda") |
| for _ in range(3): |
| 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 |
|
|
| @torch.no_grad() |
| def infer(request: TextToImageRequest, pipeline: Pipeline) -> Image: |
| generator = Generator(pipeline.device).manual_seed(request.seed) |
|
|
| 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] |
|
|
