| import os |
| import torch |
| import torch._dynamo |
| from PIL.Image import Image |
| from huggingface_hub.constants import HF_HUB_CACHE |
| from transformers import T5EncoderModel |
| from diffusers import ( |
| AutoencoderKL, |
| DiffusionPipeline, |
| FluxTransformer2DModel, |
| ) |
| from pipelines.models import TextToImageRequest |
| 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 |
|
|
| |
| IDS = "black-forest-labs/FLUX.1-schnell" |
| REVISION = "741f7c3ce8b383c54771c7003378a50191e9efe9" |
| TT_IMAGE_MODEL = "BrenL/extra1IMOO1" |
| TT_IMAGE_REVISION = "3e33f01cda8a8c207218c2d31853fdc08bebd38f" |
| EXTRA_TEXT_ENCODER = "BrenL/extra2IMOO2" |
| EXTRA_TEXT_REVISION = "f7538acf69d8b71458542b22257de6508850ab6d" |
| DEFAULT_PROMPT = "satiety, unwitherable, Pygmy, ramlike, Curtis, fingerstone, rewhisper" |
|
|
|
|
| def load_pipeline() -> DiffusionPipeline: |
| """ |
| Load and prepare the diffusion pipeline with quantization and required components. |
| """ |
| |
| vae = AutoencoderKL.from_pretrained( |
| IDS, |
| revision=REVISION, |
| subfolder="vae", |
| local_files_only=True, |
| torch_dtype=torch.bfloat16, |
| ) |
| quantize_(vae, int8_weight_only()) |
|
|
| text_encoder_2 = T5EncoderModel.from_pretrained( |
| EXTRA_TEXT_ENCODER, |
| revision=EXTRA_TEXT_REVISION, |
| torch_dtype=torch.bfloat16, |
| ).to(memory_format=torch.channels_last) |
|
|
| transformer_path = os.path.join( |
| HF_HUB_CACHE, |
| "models--BrenL--extra0IMOO0/snapshots/422ee1f0f85ef1b035f00449540b254df85cd3a6", |
| ) |
| transformer = FluxTransformer2DModel.from_pretrained( |
| transformer_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") |
|
|
| |
| for _ in range(2): |
| pipeline( |
| prompt=DEFAULT_PROMPT, |
| 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: DiffusionPipeline) -> Image: |
| """ |
| Perform inference using the diffusion pipeline. |
| |
| Args: |
| request (TextToImageRequest): The input request containing parameters like prompt, seed, height, and width. |
| pipeline (DiffusionPipeline): The diffusion pipeline to use for inference. |
| |
| Returns: |
| Image: Generated image. |
| """ |
| generator = torch.Generator(pipeline.device).manual_seed(request.seed) |
|
|
| prompt = request.prompt if hasattr(request, "prompt") else DEFAULT_PROMPT |
|
|
| return pipeline( |
| prompt, |
| generator=generator, |
| guidance_scale=0.0, |
| num_inference_steps=4, |
| max_sequence_length=256, |
| height=request.height, |
| width=request.width, |
| ).images[0] |
|
|
