| 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 |
| 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 = "soft987/FLUX.1.schnell-quant2" |
| ckpt_revision = "6d93094cc0c92f72236c6de41bddf789b8b0b38e" |
|
|
|
|
| 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( |
| "soft987/FLUX1.schnell-full", |
| revision="a05d320df4f5795fb4eff2f85ec117e870c078cb", |
| subfolder="text_encoder_2", |
| torch_dtype=torch.bfloat16, |
| ) |
| path = os.path.join( |
| HF_HUB_CACHE, |
| "models--soft987--FLUX1.schnell-full/snapshots/a05d320df4f5795fb4eff2f85ec117e870c078cb/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() |
| try: |
| img = 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] |
| return img |
| except Exception as e: |
| return None |
|
|
