| from diffusers import ( |
| DiffusionPipeline, |
| AutoencoderKL, |
| AutoencoderTiny, |
| FluxPipeline, |
| FluxTransformer2DModel |
| ) |
| 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 |
| from pipelines.models import TextToImageRequest |
| from torch import Generator |
| import time |
| import math |
| from typing import Type, Dict, Any, Tuple, Callable, Optional, Union |
| import numpy as np |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from torchao.quantization import quantize_, float8_weight_only |
| from utils import _load |
| import torchvision |
| import os |
| from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe |
|
|
| os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True" |
| os.environ["TOKENIZERS_PARALLELISM"] = "True" |
| torch._dynamo.config.suppress_errors = True |
| torch.backends.cuda.matmul.allow_tf32 = True |
| torch.backends.cudnn.enabled = True |
| torch.backends.cudnn.benchmark = True |
|
|
|
|
| Pipeline = None |
| ckpt_id = "black-forest-labs/FLUX.1-schnell" |
| ckpt_revision = "741f7c3ce8b383c54771c7003378a50191e9efe9" |
| TinyVAE = "madebyollin/taef1" |
| TinyVAE_REV = "2d552378e58c9c94201075708d7de4e1163b2689" |
|
|
| def empty_cache(): |
| gc.collect() |
| torch.cuda.empty_cache() |
| torch.cuda.reset_max_memory_allocated() |
| torch.cuda.reset_peak_memory_stats() |
|
|
| def filter_state_dict(model, state_dict_path): |
| global E |
| state_dict = torch.load(state_dict_path, map_location="cpu", weights_only=True) |
| prefix = 'encoder.' if type(model) == E else 'decoder.' |
| return {k.strip(prefix): v for k, v in state_dict.items() if k.strip(prefix) in model.state_dict() and v.size() == model.state_dict()[k.strip(prefix)].size()} |
| |
| 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) |
| vae = AutoencoderTiny.from_pretrained( |
| TinyVAE, |
| revision=TinyVAE_REV, |
| local_files_only=True, |
| torch_dtype=torch.bfloat16) |
| vae.encoder=_load(vae.encoder, "E", dtype=torch.bfloat16); vae.decoder=_load(vae.decoder, "D", dtype=torch.bfloat16) |
| |
| pipeline = FluxPipeline.from_pretrained(ckpt_id, revision=ckpt_revision, transformer=transformer, vae=vae, local_files_only=True, torch_dtype=torch.bfloat16,) |
| pipeline.to("cuda") |
|
|
| pipeline.to(memory_format=torch.channels_last) |
| |
| |
| pipeline = apply_cache_on_pipe(pipeline,residual_diff_threshold=0.7) |
| pipeline.vae = torch.compile(pipeline.vae) |
| quantize_(pipeline.vae, float8_weight_only()) |
| |
| for _ in range(2): |
| pipeline(prompt="insensible, timbale, pothery, electrovital, centrodesmus", width=1024, height=1024, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256) |
| |
| return pipeline |
|
|
|
|
| sample = 1 |
| @torch.no_grad() |
| def infer(request: TextToImageRequest, pipeline: Pipeline, generator: Generator) -> Image: |
| global sample |
| if not sample: |
| sample=1 |
| empty_cache() |
| image=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="pt").images[0] |
| return torchvision.transforms.functional.to_pil_image(image.to(torch.float32).mul_(2).sub_(1)) |
|
|
