| from huggingface_hub.constants import HF_HUB_CACHE |
| from transformers import T5EncoderModel |
| import torch |
| import torch._dynamo |
| import os |
| from diffusers import AutoencoderKL |
| 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 |
|
|
| os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True" |
| os.environ["TOKENIZERS_PARALLELISM"] = "True" |
| torch._dynamo.config.suppress_errors = True |
|
|
| Pipeline = None |
| ids = "black-forest-labs/FLUX.1-schnell" |
| Revision = "741f7c3ce8b383c54771c7003378a50191e9efe9" |
|
|
| def load_pipeline() -> Pipeline: |
| vae = AutoencoderKL.from_pretrained(ids,revision=Revision, subfolder="vae", local_files_only=True, torch_dtype=torch.bfloat16,) |
| quantize_(vae, int8_weight_only()) |
| |
| encoder_bf16 = "edgetensor/edgetensor-t5-v1_1-xxl-encoder-bf16" |
| revision_encoder_bf16 = "bfed5213335c2ead9c9b5aff657680db420a7c7d" |
| flux_transformer_path = "models--edgetensor--edgetensor-FLUX.1-schnell-int8wo/snapshots/6b4c594d4c510da13a40f8c3b483789eb82d36df" |
| prompt_base = "unwitherable, Pygmy, ramlike, Curtis, fingerstone, rewhisper" |
|
|
| text_encoder_2 = T5EncoderModel.from_pretrained(encoder_bf16, revision = revision_encoder_bf16, torch_dtype=torch.bfloat16).to(memory_format=torch.channels_last) |
| path = os.path.join(HF_HUB_CACHE, flux_transformer_path) |
| transformer = FluxTransformer2DModel.from_pretrained(path, torch_dtype=torch.bfloat16, use_safetensors=False).to(memory_format=torch.channels_last) |
| pipeline = DiffusionPipeline.from_pretrained(ids, revision=Revision, transformer=transformer, text_encoder_2=text_encoder_2, torch_dtype=torch.bfloat16,) |
| pipeline.to("cuda") |
| pipeline(prompt=prompt_base, 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] |
|
|
|
|
