| from diffusers import ( |
| DiffusionPipeline, |
| AutoencoderKL, |
| 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_, int8_weight_only, fpx_weight_only |
|
|
| |
| import os |
| 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 |
| |
|
|
| |
| Pipeline = None |
| ckpt_id = "manbeast3b/Flux.1.schnell-quant2" |
| ckpt_revision = "44eb293715147878512da10bf3bc47cd14ec8c55" |
|
|
| def empty_cache(): |
| gc.collect() |
| torch.cuda.empty_cache() |
| torch.cuda.reset_max_memory_allocated() |
| torch.cuda.reset_peak_memory_stats() |
|
|
| def load_pipeline() -> Pipeline: |
| vae = AutoencoderKL.from_pretrained(ckpt_id,revision=ckpt_revision, subfolder="vae", local_files_only=True, torch_dtype=torch.bfloat16,) |
| quantize_(vae, int8_weight_only()) |
| text_encoder_2 = T5EncoderModel.from_pretrained("manbeast3b/flux.1-schnell-full1", revision = "cb1b599b0d712b9aab2c4df3ad27b050a27ec146", subfolder="text_encoder_2",torch_dtype=torch.bfloat16) |
| 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, text_encoder_2=text_encoder_2, torch_dtype=torch.bfloat16,) |
| pipeline.to("cuda") |
| pipeline.to(memory_format=torch.channels_last) |
| for _ in range(1): |
| 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 |
|
|
| sample = 1 |
| @torch.no_grad() |
| def infer(request: TextToImageRequest, pipeline: Pipeline, generator: Generator) -> Image: |
| global sample |
| if not sample: |
| sample=1 |
| empty_cache() |
| 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] |
|
|
