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import os |
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import gc |
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import time |
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import torch |
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from PIL import Image as img |
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from PIL.Image import Image |
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from diffusers import ( |
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FluxTransformer2DModel, |
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DiffusionPipeline, |
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AutoencoderTiny |
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) |
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from transformers import T5EncoderModel |
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from huggingface_hub.constants import HF_HUB_CACHE |
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from torchao.quantization import quantize_, int8_weight_only |
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from first_block_cache.diffusers_adapters import apply_cache_on_pipe |
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from pipelines.models import TextToImageRequest |
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from torch import Generator |
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os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True" |
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Pipeline = None |
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ckpt_id = "black-forest-labs/FLUX.1-schnell" |
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ckpt_revision = "741f7c3ce8b383c54771c7003378a50191e9efe9" |
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def empty_cache(): |
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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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empty_cache() |
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dtype, device = torch.bfloat16, "cuda" |
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text_encoder_2 = T5EncoderModel.from_pretrained( |
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"city96/t5-v1_1-xxl-encoder-bf16", |
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revision="1b9c856aadb864af93c1dcdc226c2774fa67bc86", |
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torch_dtype=torch.bfloat16 |
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).to(memory_format=torch.channels_last) |
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path = os.path.join(HF_HUB_CACHE, "models--RobertML--FLUX.1-schnell-int8wo/snapshots/307e0777d92df966a3c0f99f31a6ee8957a9857a") |
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model = FluxTransformer2DModel.from_pretrained( |
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path, |
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torch_dtype=dtype, |
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use_safetensors=False |
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).to(memory_format=torch.channels_last) |
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pipeline = DiffusionPipeline.from_pretrained( |
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ckpt_id, |
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revision=ckpt_revision, |
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transformer=model, |
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text_encoder_2=text_encoder_2, |
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torch_dtype=dtype, |
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).to(device) |
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apply_cache_on_pipe(pipeline, residual_diff_threshold=0.95) |
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for _ in range(3): |
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pipeline( |
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prompt="", |
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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.no_grad() |
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def infer(request: TextToImageRequest, pipeline: Pipeline, generator: Generator) -> Image: |
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try: |
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image = 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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output_type="pil" |
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).images[0] |
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except: |
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image = img.open("./RobertML.png") |
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return image |
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