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import os |
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import gc |
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import torch |
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import torch._dynamo |
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from typing import TypeAlias |
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from torch import Generator |
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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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AutoencoderTiny, |
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DiffusionPipeline, |
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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 torchao.quantization import quantize_, int8_weight_only |
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from transformers import T5EncoderModel |
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torch._dynamo.config.suppress_errors = True |
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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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os.environ["TOKENIZERS_PARALLELISM"] = "True" |
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CHECKPOINT = "winner632/flux1-schnell-int8wo" |
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REVISION = "d9ff2fc9ad81476d3ef3a5f40d273f0fa5a36f2b" |
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def clear_gpu_cache(): |
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"""Frees GPU memory to prevent memory leaks.""" |
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gc.collect() |
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torch.cuda.empty_cache() |
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torch.cuda.reset_max_memory_allocated() |
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torch.cuda.reset_peak_memory_stats() |
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def load_pipeline() -> Pipeline: |
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"""Loads the diffusion pipeline with quantization and optimizations.""" |
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clear_gpu_cache() |
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transformer_model = FluxTransformer2DModel.from_pretrained( |
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os.path.join( |
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HF_HUB_CACHE, |
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"models--winner632--flux1-schnell-int8wo/snapshots/d9ff2fc9ad81476d3ef3a5f40d273f0fa5a36f2b/transformer", |
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), |
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use_safetensors=True, |
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local_files_only=True, |
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torch_dtype=torch.bfloat16, |
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) |
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pipe = FluxPipeline.from_pretrained( |
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CHECKPOINT, |
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revision=REVISION, |
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transformer=transformer_model, |
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local_files_only=True, |
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torch_dtype=torch.bfloat16, |
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).to("cuda") |
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pipe.to(memory_format=torch.channels_last) |
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pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead") |
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quantize_(pipe.transformer, int8_weight_only()) |
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quantize_(pipe.vae, int8_weight_only()) |
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pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead") |
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with torch.no_grad(): |
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for _ in range(5): |
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pipe( |
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prompt="onomancy, aftergo, spirantic, Platyhelmia, modificator, drupaceous, jobbernowl, hereness", |
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width=1024, |
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height=1024, |
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guidance_scale=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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clear_gpu_cache() |
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return pipe |
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@torch.no_grad() |
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def infer( |
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request: TextToImageRequest, pipeline: Pipeline, generator: Generator |
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) -> Image: |
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"""Generates an image from text input using the loaded pipeline.""" |
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return pipeline( |
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request.prompt, |
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generator=generator, |
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guidance_scale=0e0, |
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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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output_type="pil", |
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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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