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https://github.com/finegrain-ai/refiners.git
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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.
86 lines
2.4 KiB
Python
86 lines
2.4 KiB
Python
from typing import Iterator
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import torch
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import pytest
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import refiners.fluxion.layers as fl
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from refiners.foundationals.latent_diffusion import SD1UNet, SD1ControlnetAdapter
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.controlnet import Controlnet
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@pytest.fixture(scope="module", params=[True, False])
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def unet(request: pytest.FixtureRequest) -> Iterator[SD1UNet]:
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with_parent: bool = request.param
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unet = SD1UNet(in_channels=9, clip_embedding_dim=768)
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if with_parent:
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fl.Chain(unet)
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yield unet
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@torch.no_grad()
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def test_single_controlnet(unet: SD1UNet) -> None:
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original_parent = unet.parent
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cn = SD1ControlnetAdapter(unet, name="cn")
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assert unet.parent == original_parent
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assert len(list(unet.walk(Controlnet))) == 0
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with pytest.raises(ValueError) as exc:
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cn.eject()
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assert "not in" in str(exc.value)
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cn.inject()
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assert unet.parent == cn
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assert len(list(unet.walk(Controlnet))) == 1
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with pytest.raises(AssertionError) as exc:
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cn.inject()
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assert "already injected" in str(exc.value)
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cn.eject()
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assert unet.parent == original_parent
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assert len(list(unet.walk(Controlnet))) == 0
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@torch.no_grad()
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def test_two_controlnets_eject_bottom_up(unet: SD1UNet) -> None:
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original_parent = unet.parent
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cn1 = SD1ControlnetAdapter(unet, name="cn1").inject()
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cn2 = SD1ControlnetAdapter(unet, name="cn2").inject()
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assert unet.parent == cn2
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assert unet in cn2
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assert unet not in cn1
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assert cn2.parent == cn1
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assert cn2 in cn1
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assert cn1.parent == original_parent
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assert len(list(unet.walk(Controlnet))) == 2
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assert cn1.target == unet
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assert cn1.lookup_actual_target() == cn2
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cn2.eject()
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assert unet.parent == cn1
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assert unet in cn2
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assert cn2 not in cn1
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assert unet in cn1
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assert len(list(unet.walk(Controlnet))) == 1
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cn1.eject()
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assert unet.parent == original_parent
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assert len(list(unet.walk(Controlnet))) == 0
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@torch.no_grad()
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def test_two_controlnets_eject_top_down(unet: SD1UNet) -> None:
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original_parent = unet.parent
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cn1 = SD1ControlnetAdapter(unet, name="cn1").inject()
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cn2 = SD1ControlnetAdapter(unet, name="cn2").inject()
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cn1.eject()
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assert cn2.parent == original_parent
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assert unet.parent == cn2
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cn2.eject()
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assert unet.parent == original_parent
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assert len(list(unet.walk(Controlnet))) == 0
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