| from diffusers import FluxPipeline, AutoencoderKL, AutoencoderTiny |
| from diffusers.image_processor import VaeImageProcessor |
| from diffusers.schedulers import FlowMatchEulerDiscreteScheduler |
| from huggingface_hub.constants import HF_HUB_CACHE |
| from transformers import T5EncoderModel, T5TokenizerFast, CLIPTokenizer, CLIPTextModel |
| import torch |
| import torch._dynamo |
| import gc |
| from PIL import Image as img |
| from PIL.Image import Image |
| from pipelines.models import TextToImageRequest |
| from torch import Generator |
| import time |
| from diffusers import FluxTransformer2DModel, DiffusionPipeline |
| from torchao.quantization import quantize_, int8_weight_only, fpx_weight_only |
| import os |
|
|
| os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True" |
| torch._dynamo.config.suppress_errors = True |
|
|
| Pipeline = None |
|
|
| ckpt_id = "black-forest-labs/FLUX.1-schnell" |
| ckpt_revision = "741f7c3ce8b383c54771c7003378a50191e9efe9" |
|
|
|
|
| def empty_cache(): |
| gc.collect() |
| torch.cuda.empty_cache() |
| torch.cuda.reset_max_memory_allocated() |
| torch.cuda.reset_peak_memory_stats() |
|
|
|
|
| def load_text_encoder_2(): |
| id = "city96/t5-v1_1-xxl-encoder-bf16" |
| revision = "1b9c856aadb864af93c1dcdc226c2774fa67bc86" |
| |
| return T5EncoderModel.from_pretrained( |
| id, |
| revision=revision, |
| torch_dtype=torch.bfloat16, |
| ).to(memory_format=torch.channels_last) |
| |
| def load_transformer_model(): |
| id = "position025/FLUX.1-schnell-8-pda" |
| revision = "cdee1db6a8b1858435ec4397b19ed703265d2153" |
| |
| transformer_path = os.path.join( |
| HF_HUB_CACHE, |
| f"models--{id.split('/')[0]}--{id.split('/')[1]}/snapshots/{revision}", |
| ) |
| return FluxTransformer2DModel.from_pretrained( |
| transformer_path, torch_dtype=torch.bfloat16, use_safetensors=False |
| ).to(memory_format=torch.channels_last) |
| |
| |
|
|
| def load_pipeline() -> Pipeline: |
| empty_cache() |
|
|
| text_encoder_2 = load_text_encoder_2() |
| transformer_model = load_transformer_model() |
| |
| pipeline = FluxPipeline.from_pretrained( |
| ckpt_id, |
| revision=ckpt_revision, |
| transformer=transformer_model, |
| text_encoder_2=text_encoder_2, |
| torch_dtype=torch.bfloat16, |
| ).to("cuda") |
| |
| pipeline.transformer = torch.compile(pipeline.transformer, mode="reduce-overhead") |
| for _ in range(3): |
| 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) |
| |
| empty_cache() |
| return pipeline |
|
|
|
|
| @torch.no_grad() |
| 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] |
|
|
