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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 ( |
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FluxPipeline, |
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FluxTransformer2DModel, |
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AutoencoderKL, |
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DiffusionPipeline, |
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AutoencoderTiny, |
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) |
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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 transformers import T5EncoderModel, CLIPTextModel |
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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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id = "black-forest-labs/FLUX.1-schnell" |
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revision = "741f7c3ce8b383c54771c7003378a50191e9efe9" |
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vae_id = "madebyollin/taef1" |
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vae_rev = "2d552378e58c9c94201075708d7de4e1163b2689" |
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def load_pipeline() -> Pipeline: |
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path = os.path.join( |
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HF_HUB_CACHE, |
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"models--freaky231--flux.1-schnell-int8/snapshots/c33fa7f79751fe42b0a7de7f72edb5d1b86f32a7/transformer", |
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) |
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transformer = FluxTransformer2DModel.from_pretrained( |
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path, use_safetensors=False, local_files_only=True, torch_dtype=torch.bfloat16 |
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).to(memory_format=torch.channels_last) |
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vae = AutoencoderTiny.from_pretrained( |
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vae_id, revision=vae_rev, local_files_only=True, torch_dtype=torch.bfloat16 |
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) |
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text_encoder_2 = T5EncoderModel.from_pretrained( |
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"freaky231/t5-encoder-bf16", |
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revision="994f6e4720f69e67bfc8822cbb4063c9149b801b", |
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torch_dtype=torch.bfloat16, |
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).to(memory_format=torch.channels_last) |
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pipeline = DiffusionPipeline.from_pretrained( |
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id, |
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revision=revision, |
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transformer=transformer, |
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text_encoder_2=text_encoder_2, |
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vae=vae, |
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torch_dtype=torch.bfloat16, |
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) |
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pipeline.to("cuda") |
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for _ in range(2): |
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pipeline( |
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prompt="satiety, unwitherable, Pygmy, ramlike, Curtis, fingerstone, rewhisper", |
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width=1024, |
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height=1024, |
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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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) |
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return pipeline |
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@torch.inference_mode() |
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def infer( |
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request: TextToImageRequest, pipeline: Pipeline, generator: torch.Generator |
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) -> 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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pipe_ = load_pipeline() |
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for _ in range(4): |
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request = TextToImageRequest(prompt="cat", height=None, width=None, seed=3254) |
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infer(request, pipe_) |
