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from huggingface_hub.constants import HF_HUB_CACHE |
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from transformers import T5EncoderModel, T5TokenizerFast, CLIPTokenizer, CLIPTextModel |
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import torch |
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import torch._dynamo |
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import gc |
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import os |
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from diffusers import FluxPipeline, AutoencoderKL, AutoencoderTiny |
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from PIL.Image import Image |
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from pipelines.models import TextToImageRequest |
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from torch import Generator |
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from diffusers import FluxTransformer2DModel, DiffusionPipeline |
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from torchao.quantization import quantize_, int8_weight_only, fpx_weight_only |
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os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True" |
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os.environ["TOKENIZERS_PARALLELISM"] = "True" |
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torch._dynamo.config.suppress_errors = True |
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Pipeline = None |
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ckpt_id = "manbeast3b/flux.1-schnell-full1" |
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ckpt_revision = "cb1b599b0d712b9aab2c4df3ad27b050a27ec146" |
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def load_pipeline() -> Pipeline: |
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path = os.path.join(HF_HUB_CACHE, "models--manbeast3b--flux.1-schnell-full1/snapshots/cb1b599b0d712b9aab2c4df3ad27b050a27ec146/transformer") |
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transformer = FluxTransformer2DModel.from_pretrained(path, torch_dtype=torch.bfloat16, use_safetensors=False) |
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pipeline = FluxPipeline.from_pretrained(ckpt_id, revision=ckpt_revision, transformer=transformer, local_files_only=True, torch_dtype=torch.bfloat16,) |
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pipeline.transformer = torch.compile(pipeline.transformer, mode="max-autotune", fullgraph=True) |
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basepath = os.path.join(HF_HUB_CACHE, "models--manbeast3b--Flux.1.schnell_eagle5_1_0.1_unst_8_try/snapshots/0d3ce1d07195ccfe8eafe821ee80b34d74a3c2d7") |
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pipeline.vae.encoder.load_state_dict(torch.load(os.path.join(basepath, "encoder.pth")), strict=False) |
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pipeline.vae.decoder.load_state_dict(torch.load(os.path.join(basepath, "decoder.pth")), strict=False) |
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quantize_(pipeline.vae, int8_weight_only()) |
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pipeline.to("cuda") |
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for _ in range(3): |
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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) |
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return pipeline |
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@torch.no_grad() |
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def infer(request: TextToImageRequest, pipeline: Pipeline) -> Image: |
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generator = Generator(pipeline.device).manual_seed(request.seed) |
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return pipeline( |
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request.prompt, |
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generator=generator, |
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guidance_scale=0.0, |
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num_inference_steps=4, |
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max_sequence_length=256, |
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height=request.height, |
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width=request.width, |
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).images[0] |
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