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import gc |
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import os |
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from typing import TypeAlias |
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import torch |
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from PIL.Image import Image |
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from diffusers import FluxPipeline, FluxTransformer2DModel, AutoencoderKL, AutoencoderTiny |
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from huggingface_hub.constants import HF_HUB_CACHE |
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from pipelines.models import TextToImageRequest |
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from torch import Generator |
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from torchao.quantization import quantize_, int8_weight_only |
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from transformers import T5EncoderModel, CLIPTextModel, logging |
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Pipeline: TypeAlias = FluxPipeline |
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torch.backends.cudnn.benchmark = True |
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torch._inductor.config.conv_1x1_as_mm = True |
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torch._inductor.config.coordinate_descent_tuning = True |
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torch._inductor.config.epilogue_fusion = False |
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torch._inductor.config.coordinate_descent_check_all_directions = True |
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os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True" |
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CHECKPOINT = "jokerbit/flux.1-schnell-Robert-int8wo" |
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REVISION = "5ef0012f11a863e5111ec56540302a023bc8587b" |
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TinyVAE = "madebyollin/taef1" |
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TinyVAE_REV = "2d552378e58c9c94201075708d7de4e1163b2689" |
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def load_pipeline() -> Pipeline: |
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path = os.path.join(HF_HUB_CACHE, "models--jokerbit--flux.1-schnell-Robert-int8wo/snapshots/5ef0012f11a863e5111ec56540302a023bc8587b/transformer") |
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transformer = FluxTransformer2DModel.from_pretrained( |
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path, |
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use_safetensors=False, |
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local_files_only=True, |
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torch_dtype=torch.bfloat16) |
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vae = AutoencoderTiny.from_pretrained( |
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TinyVAE, |
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revision=TinyVAE_REV, |
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local_files_only=True, |
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torch_dtype=torch.bfloat16) |
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pipeline = FluxPipeline.from_pretrained( |
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CHECKPOINT, |
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revision=REVISION, |
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transformer=transformer, |
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vae=vae, |
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local_files_only=True, |
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torch_dtype=torch.bfloat16, |
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) |
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pipeline.to(memory_format=torch.channels_last) |
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pipeline.vae = torch.compile(pipeline.vae, mode="max-autotune", fullgraph=True) |
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pipeline.to("cuda") |
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for _ in range(2): |
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pipeline("cat", num_inference_steps=4) |
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return pipeline |
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@torch.inference_mode() |
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def infer(request: TextToImageRequest, pipeline: Pipeline, generator: torch.Generator) -> Image: |
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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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if __name__ == "__main__": |
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from time import perf_counter |
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PROMPT = 'martyr, semiconformity, peregrination, quip, twineless, emotionless, tawa, depickle' |
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request = TextToImageRequest(prompt=PROMPT, |
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height=None, |
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width=None, |
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seed=666) |
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start_time = perf_counter() |
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pipe_ = load_pipeline() |
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stop_time = perf_counter() |
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print(f"Pipeline is loaded in {stop_time - start_time}s") |
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for _ in range(4): |
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start_time = perf_counter() |
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infer(request, pipe_) |
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stop_time = perf_counter() |
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print(f"Request in {stop_time - start_time}s") |
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