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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 huggingface_hub.constants import HF_HUB_CACHE |
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from transformers import T5EncoderModel, T5TokenizerFast, CLIPTokenizer, CLIPTextModel |
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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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ids = "black-forest-labs/FLUX.1-schnell" |
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Revision = "741f7c3ce8b383c54771c7003378a50191e9efe9" |
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def load_pipeline() -> Pipeline: |
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ttimagemodel = "edgetensor/max-schnell" |
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ttimagerevision = "13313e1f7d5b9d655554366d7394c42fb4e765d4" |
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vae = AutoencoderKL.from_pretrained(ids,revision=Revision, subfolder="vae", local_files_only=True, torch_dtype=torch.bfloat16,) |
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quantize_(vae, int8_weight_only()) |
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text_encoder_2 = T5EncoderModel.from_pretrained("edgetensor/edgetensor-t5-v1_1-xxl-encoder-bf16", revision = "bfed5213335c2ead9c9b5aff657680db420a7c7d", torch_dtype=torch.bfloat16).to(memory_format=torch.channels_last) |
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path = os.path.join(HF_HUB_CACHE, "models--edgetensor--edgetensor-FLUX.1-schnell-int8wo/snapshots/6b4c594d4c510da13a40f8c3b483789eb82d36df") |
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transformer = FluxTransformer2DModel.from_pretrained(path, torch_dtype=torch.bfloat16, use_safetensors=False).to(memory_format=torch.channels_last) |
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pipeline = DiffusionPipeline.from_pretrained(ids, revision=Revision, transformer=transformer, text_encoder_2=text_encoder_2, torch_dtype=torch.bfloat16,) |
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pipeline.to("cuda") |
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for _ in range(2): |
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pipeline(prompt="satiety, unwitherable, Pygmy, ramlike, Curtis, fingerstone, rewhisper", 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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try: |
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prompt = request.prompt |
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except Exception as e: |
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prompt = "satiety, unwitherable, Pygmy, ramlike, Curtis, fingerstone, rewhisper" |
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return pipeline( |
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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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