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0f476ea18b
This generalizes the Adapter abstraction to higher-level constructs such as high-level LoRA (targeting e.g. the SD UNet), ControlNet and Reference-Only Control. Some adapters now work by adapting child models with "sub-adapters" that they inject / eject when needed.
25 lines
778 B
Python
25 lines
778 B
Python
import torch
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from refiners.adapters.adapter import Adapter
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from refiners.adapters.range_adapter import RangeEncoder
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from refiners.fluxion.layers import Chain, Linear
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class DummyLinearAdapter(Chain, Adapter[Linear]):
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def __init__(self, target: Linear):
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with self.setup_adapter(target):
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super().__init__(target)
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def test_range_encoder_dtype_after_adaptation(test_device: torch.device): # FG-433
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dtype = torch.float64
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chain = Chain(RangeEncoder(320, 1280, device=test_device, dtype=dtype))
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adaptee = chain.RangeEncoder.Linear_1
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adapter = DummyLinearAdapter(adaptee).inject(chain.RangeEncoder)
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assert adapter.parent == chain.RangeEncoder
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x = torch.tensor([42], device=test_device)
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y = chain(x)
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assert y.dtype == dtype
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