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@ -31,6 +31,7 @@ ______________________________________________________________________
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## Latest News 🔥
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- Added [ELLA](https://arxiv.org/abs/2403.05135) for better prompts handling (contributed by [@ily-R](https://github.com/ily-R))
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- Added the Box Segmenter all-in-one solution ([model](https://huggingface.co/finegrain/finegrain-box-segmenter), [HF Space](https://huggingface.co/spaces/finegrain/finegrain-object-cutter))
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- Added [MVANet](https://arxiv.org/abs/2404.07445) for high resolution segmentation
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- Added [IC-Light](https://github.com/lllyasviel/IC-Light) to manipulate the illumination of images
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@ -15,3 +15,5 @@
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::: refiners.foundationals.latent_diffusion.style_aligned
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::: refiners.foundationals.latent_diffusion.multi_diffusion
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::: refiners.foundationals.latent_diffusion.ella_adapter
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@ -210,6 +210,11 @@ class PerceiverResampler(fl.Chain):
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class ELLA(fl.Passthrough):
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"""ELLA latents encoder.
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See [[arXiv:2403.05135] ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment](https://arxiv.org/abs/2403.05135) for more details.
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"""
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def __init__(
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self,
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time_channel: int,
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@ -249,6 +254,8 @@ class ELLACrossAttentionAdapter(fl.Chain, Adapter[fl.UseContext]):
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class ELLAAdapter(Generic[T], fl.Chain, Adapter[T]):
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"""Adapter for [`ELLA`][refiners.foundationals.latent_diffusion.ella_adapter.ELLA]."""
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def __init__(self, target: T, latents_encoder: ELLA, weights: dict[str, Tensor] | None = None) -> None:
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if weights is not None:
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latents_encoder.load_state_dict(weights)
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@ -5,7 +5,15 @@ from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1U
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class SD1ELLAAdapter(ELLAAdapter[SD1UNet]):
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"""[`ELLA`][refiners.foundationals.latent_diffusion.ella_adapter.ELLA] adapter for Stable Diffusion 1.5."""
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def __init__(self, target: SD1UNet, weights: dict[str, Tensor] | None = None) -> None:
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"""Initialize the adapter.
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Args:
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target: The target model to adapt.
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weights: The weights of the ELLA adapter (see `scripts/conversion/convert_ella_adapter.py`).
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"""
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latents_encoder = ELLA(
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time_channel=320,
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timestep_embedding_dim=768,
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