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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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from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe |
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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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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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CHECKPOINT = "black-forest-labs/FLUX.1-schnell" |
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REVISION = "741f7c3ce8b383c54771c7003378a50191e9efe9" |
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def load_pipeline() -> Pipeline: |
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text_encoder = CLIPTextModel.from_pretrained(CHECKPOINT, revision=REVISION, subfolder="text_encoder", local_files_only=True, torch_dtype=torch.bfloat16,) |
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text_encoder_2 = T5EncoderModel.from_pretrained(CHECKPOINT, revision=REVISION, subfolder="text_encoder_2", local_files_only=True, torch_dtype=torch.bfloat16,) |
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vae = AutoencoderKL.from_pretrained(CHECKPOINT, revision=REVISION, subfolder="vae", local_files_only=True, torch_dtype=torch.bfloat16,) |
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path = os.path.join(HF_HUB_CACHE, "models--RobertML--FLUX.1-schnell-int8wo/snapshots/307e0777d92df966a3c0f99f31a6ee8957a9857a") |
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transformer = FluxTransformer2DModel.from_pretrained(path, torch_dtype=torch.bfloat16, use_safetensors=False,) |
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pipeline = FluxPipeline.from_pretrained(CHECKPOINT, revision=REVISION, local_files_only=True, text_encoder=text_encoder, text_encoder_2=text_encoder_2, transformer=transformer, vae=vae, torch_dtype=torch.bfloat16,).to("cuda") |
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quantize_(pipeline.vae, int8_weight_only()) |
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pipeline = apply_cache_on_pipe(pipeline, residual_diff_threshold=0.345) |
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pipeline("") |
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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] |
