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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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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 torchao.quantization import quantize_, int8_weight_only |
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from transformers import T5EncoderModel, CLIPTextModel, logging |
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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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Pipeline = FluxPipeline |
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torch.backends.cudnn.benchmark = True |
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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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torch._dynamo.config.suppress_errors = True |
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os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True" |
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repo = "smash3211/Flux.1.schnell" |
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revision = "26534bc47459428a6763951757fd63892119ee08" |
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vae_repo = "smash3211/tae1-update" |
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vae_revision = "4aa8fbe28d8631db070810bc2b9ff9f9320effda" |
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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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f"models--{repo.split('/')[0]}--{repo.split('/')[1]}/snapshots/{revision}/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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) |
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vae = AutoencoderTiny.from_pretrained( |
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vae_repo, |
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revision=vae_revision, |
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local_files_only=True, |
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torch_dtype=torch.bfloat16, |
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) |
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vae_path = os.path.join( |
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HF_HUB_CACHE, |
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f"models--{vae_repo.split('/')[0]}--{vae_repo.split('/')[1]}/snapshots/{vae_revision}", |
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) |
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vae.encoder.load_state_dict(torch.load(f"{vae_path}/encoder.pth"), strict=False) |
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vae.decoder.load_state_dict(torch.load(f"{vae_path}/decoder.pth"), strict=False) |
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pipeline = FluxPipeline.from_pretrained( |
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repo, |
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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('cuda') |
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pipeline.to(memory_format=torch.channels_last) |
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quantize_(pipeline.vae, int8_weight_only()) |
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pipeline.vae = torch.compile(pipeline.vae, mode="max-autotune", fullgraph=True) |
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for _ in range(4): |
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pipeline(prompt="satiety, unwitherable, Pygmy, ramlike, Curtis, fingerstone, rewhisper", num_inference_steps=4) |
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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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prompt = 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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print("load pipeline...") |
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diffusion_pipeline = load_pipeline() |
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sample_request = TextToImageRequest( |
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prompt="A futuristic cityscape with neon lights", |
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height=1024, |
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width=1024, |
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) |
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generator = torch.Generator(device="cuda").manual_seed(42) |
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print("Generating image...") |
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generated_img = infer(sample_request, diffusion_pipeline, generator) |
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generated_img.show() |
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