mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-21 21:58:47 +00:00
unnest Residual subchain by modifying its forward
And replaced the remaining Sum-Identity layers by Residual. The tolerance used to compare SAM's ViT models has been tweaked: for some reasons there is a small difference (in float32) in the neck layer (first conv2D) Co-authored-by: Cédric Deltheil <cedric@deltheil.me>
This commit is contained in:
Benjamin Trom
Cédric Deltheil
parent
46dd710076
commit
ea44262a39
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@ -65,11 +65,11 @@ def convert(args: Args) -> dict[str, torch.Tensor]:
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expected_target_order = [
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"DownBlocks.Chain_1.Passthrough.Conv2d",
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"DownBlocks.Chain_2.CLIPLCrossAttention.Chain.Chain_1.Conv2d",
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"DownBlocks.Chain_2.CLIPLCrossAttention.Chain.Chain_3.Conv2d",
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"DownBlocks.Chain_2.CLIPLCrossAttention.Chain_1.Conv2d",
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"DownBlocks.Chain_2.CLIPLCrossAttention.Chain_3.Conv2d",
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"DownBlocks.Chain_2.Passthrough.Conv2d",
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"DownBlocks.Chain_3.CLIPLCrossAttention.Chain.Chain_1.Conv2d",
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"DownBlocks.Chain_3.CLIPLCrossAttention.Chain.Chain_3.Conv2d",
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"DownBlocks.Chain_3.CLIPLCrossAttention.Chain_1.Conv2d",
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"DownBlocks.Chain_3.CLIPLCrossAttention.Chain_3.Conv2d",
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"DownBlocks.Chain_3.Passthrough.Conv2d",
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"DownBlocks.Chain_4.Passthrough.Conv2d",
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]
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@ -102,11 +102,11 @@ def convert(args: Args) -> dict[str, torch.Tensor]:
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]
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expected_target_order = [
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"DownBlocks.Chain_5.CLIPLCrossAttention.Chain.Chain_1.Conv2d",
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"DownBlocks.Chain_5.CLIPLCrossAttention.Chain.Chain_3.Conv2d",
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"DownBlocks.Chain_5.CLIPLCrossAttention.Chain_1.Conv2d",
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"DownBlocks.Chain_5.CLIPLCrossAttention.Chain_3.Conv2d",
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"DownBlocks.Chain_5.Passthrough.Conv2d",
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"DownBlocks.Chain_6.CLIPLCrossAttention.Chain.Chain_1.Conv2d",
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"DownBlocks.Chain_6.CLIPLCrossAttention.Chain.Chain_3.Conv2d",
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"DownBlocks.Chain_6.CLIPLCrossAttention.Chain_1.Conv2d",
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"DownBlocks.Chain_6.CLIPLCrossAttention.Chain_3.Conv2d",
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"DownBlocks.Chain_6.Passthrough.Conv2d",
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"DownBlocks.Chain_7.Passthrough.Conv2d",
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]
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@ -143,17 +143,17 @@ def convert(args: Args) -> dict[str, torch.Tensor]:
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]
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expected_target_order = [
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"DownBlocks.Chain_8.CLIPLCrossAttention.Chain.Chain_1.Conv2d",
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"DownBlocks.Chain_8.CLIPLCrossAttention.Chain.Chain_3.Conv2d",
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"DownBlocks.Chain_8.CLIPLCrossAttention.Chain_1.Conv2d",
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"DownBlocks.Chain_8.CLIPLCrossAttention.Chain_3.Conv2d",
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"DownBlocks.Chain_8.Passthrough.Conv2d",
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"DownBlocks.Chain_9.CLIPLCrossAttention.Chain.Chain_1.Conv2d",
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"DownBlocks.Chain_9.CLIPLCrossAttention.Chain.Chain_3.Conv2d",
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"DownBlocks.Chain_9.CLIPLCrossAttention.Chain_1.Conv2d",
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"DownBlocks.Chain_9.CLIPLCrossAttention.Chain_3.Conv2d",
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"DownBlocks.Chain_9.Passthrough.Conv2d",
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"DownBlocks.Chain_10.Passthrough.Conv2d",
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"DownBlocks.Chain_11.Passthrough.Conv2d",
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"DownBlocks.Chain_12.Passthrough.Conv2d",
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"MiddleBlock.CLIPLCrossAttention.Chain.Chain_1.Conv2d",
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"MiddleBlock.CLIPLCrossAttention.Chain.Chain_3.Conv2d",
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"MiddleBlock.CLIPLCrossAttention.Chain_1.Conv2d",
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"MiddleBlock.CLIPLCrossAttention.Chain_3.Conv2d",
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"MiddleBlock.Passthrough.Conv2d",
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]
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@ -105,17 +105,17 @@ def main() -> None:
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t_pfx, s_pfx = f"Transformer.TransformerLayer_{i+1}.Residual_", f"layers.{i}."
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image_proj_state_dict.update(
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{
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f"{t_pfx}1.Chain.PerceiverAttention.Distribute.LayerNorm_1.weight": w[f"{s_pfx}0.norm1.weight"],
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f"{t_pfx}1.Chain.PerceiverAttention.Distribute.LayerNorm_1.bias": w[f"{s_pfx}0.norm1.bias"],
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f"{t_pfx}1.Chain.PerceiverAttention.Distribute.LayerNorm_2.weight": w[f"{s_pfx}0.norm2.weight"],
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f"{t_pfx}1.Chain.PerceiverAttention.Distribute.LayerNorm_2.bias": w[f"{s_pfx}0.norm2.bias"],
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f"{t_pfx}1.Chain.PerceiverAttention.Parallel.Chain_2.Linear.weight": w[f"{s_pfx}0.to_q.weight"],
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f"{t_pfx}1.Chain.PerceiverAttention.Parallel.Chain_1.Linear.weight": w[f"{s_pfx}0.to_kv.weight"],
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f"{t_pfx}1.Chain.PerceiverAttention.Linear.weight": w[f"{s_pfx}0.to_out.weight"],
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f"{t_pfx}2.Chain.LayerNorm.weight": w[f"{s_pfx}1.0.weight"],
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f"{t_pfx}2.Chain.LayerNorm.bias": w[f"{s_pfx}1.0.bias"],
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f"{t_pfx}2.Chain.FeedForward.Linear_1.weight": w[f"{s_pfx}1.1.weight"],
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f"{t_pfx}2.Chain.FeedForward.Linear_2.weight": w[f"{s_pfx}1.3.weight"],
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f"{t_pfx}1.PerceiverAttention.Distribute.LayerNorm_1.weight": w[f"{s_pfx}0.norm1.weight"],
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f"{t_pfx}1.PerceiverAttention.Distribute.LayerNorm_1.bias": w[f"{s_pfx}0.norm1.bias"],
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f"{t_pfx}1.PerceiverAttention.Distribute.LayerNorm_2.weight": w[f"{s_pfx}0.norm2.weight"],
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f"{t_pfx}1.PerceiverAttention.Distribute.LayerNorm_2.bias": w[f"{s_pfx}0.norm2.bias"],
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f"{t_pfx}1.PerceiverAttention.Parallel.Chain_2.Linear.weight": w[f"{s_pfx}0.to_q.weight"],
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f"{t_pfx}1.PerceiverAttention.Parallel.Chain_1.Linear.weight": w[f"{s_pfx}0.to_kv.weight"],
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f"{t_pfx}1.PerceiverAttention.Linear.weight": w[f"{s_pfx}0.to_out.weight"],
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f"{t_pfx}2.LayerNorm.weight": w[f"{s_pfx}1.0.weight"],
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f"{t_pfx}2.LayerNorm.bias": w[f"{s_pfx}1.0.bias"],
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f"{t_pfx}2.FeedForward.Linear_1.weight": w[f"{s_pfx}1.1.weight"],
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f"{t_pfx}2.FeedForward.Linear_2.weight": w[f"{s_pfx}1.3.weight"],
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}
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)
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else:
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@ -55,7 +55,7 @@ def convert_point_encoder(prompt_encoder: nn.Module) -> dict[str, Tensor]:
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pe = prompt_encoder.pe_layer.positional_encoding_gaussian_matrix # type: ignore
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assert isinstance(pe, Tensor)
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state_dict: dict[str, Tensor] = {
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"Residual.Chain.PointTypeEmbedding.weight": nn.Parameter(data=torch.cat(tensors=point_embeddings, dim=0)),
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"Residual.PointTypeEmbedding.weight": nn.Parameter(data=torch.cat(tensors=point_embeddings, dim=0)),
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"CoordinateEncoder.Linear.weight": nn.Parameter(data=pe.T.contiguous()),
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}
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@ -80,10 +80,10 @@ def convert_vit(vit: nn.Module) -> dict[str, Tensor]:
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mapping = converter.map_state_dicts(source_args=(x,))
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assert mapping
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mapping["PositionalEncoder.Chain.Parameter.parameter"] = "pos_embed"
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mapping["PositionalEncoder.Parameter.parameter"] = "pos_embed"
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target_state_dict = refiners_sam_vit_h.state_dict()
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del target_state_dict["PositionalEncoder.Chain.Parameter.parameter"]
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del target_state_dict["PositionalEncoder.Parameter.parameter"]
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source_state_dict = vit.state_dict()
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pos_embed = source_state_dict["pos_embed"]
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@ -91,8 +91,8 @@ def convert_vit(vit: nn.Module) -> dict[str, Tensor]:
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target_rel_keys = [
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(
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f"Transformer.TransformerLayer_{i}.Residual_1.Chain.FusedSelfAttention.RelativePositionAttention.horizontal_embedding",
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f"Transformer.TransformerLayer_{i}.Residual_1.Chain.FusedSelfAttention.RelativePositionAttention.vertical_embedding",
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f"Transformer.TransformerLayer_{i}.Residual_1.FusedSelfAttention.RelativePositionAttention.horizontal_embedding",
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f"Transformer.TransformerLayer_{i}.Residual_1.FusedSelfAttention.RelativePositionAttention.vertical_embedding",
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)
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for i in range(1, 33)
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]
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@ -112,11 +112,11 @@ def convert_vit(vit: nn.Module) -> dict[str, Tensor]:
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source_state_dict=source_state_dict, target_state_dict=target_state_dict, state_dict_mapping=mapping
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)
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converted_source["PositionalEncoder.Chain.Parameter.parameter"] = pos_embed # type: ignore
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converted_source["PositionalEncoder.Parameter.parameter"] = pos_embed # type: ignore
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converted_source.update(rel_items)
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refiners_sam_vit_h.load_state_dict(state_dict=converted_source)
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assert converter.compare_models((x,), threshold=1e-3)
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assert converter.compare_models((x,), threshold=1e-2)
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return converted_source
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@ -176,6 +176,12 @@ download_t2i_adapter () {
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curl -LO https://huggingface.co/TencentARC/t2iadapter_depth_sd15v2/raw/main/config.json
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curl -LO https://huggingface.co/TencentARC/t2iadapter_depth_sd15v2/resolve/main/diffusion_pytorch_model.bin
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popd
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mkdir -p tests/weights/TencentARC/t2i-adapter-canny-sdxl-1.0
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pushd tests/weights/TencentARC/t2i-adapter-canny-sdxl-1.0
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curl -LO https://huggingface.co/TencentARC/t2i-adapter-canny-sdxl-1.0/raw/main/config.json
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curl -LO https://huggingface.co/TencentARC/t2i-adapter-canny-sdxl-1.0/resolve/main/diffusion_pytorch_model.safetensors
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popd
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}
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download_sam () {
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@ -191,18 +197,18 @@ convert_sd15 () {
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--from "tests/weights/runwayml/stable-diffusion-v1-5" \
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--to "tests/weights/CLIPTextEncoderL.safetensors" \
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--half
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check_hash "tests/weights/CLIPTextEncoderL.safetensors" bef71657
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check_hash "tests/weights/CLIPTextEncoderL.safetensors" 6c9cbc59
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python scripts/conversion/convert_diffusers_autoencoder_kl.py \
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--from "tests/weights/runwayml/stable-diffusion-v1-5" \
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--to "tests/weights/lda.safetensors"
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check_hash "tests/weights/lda.safetensors" 28f38b35
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check_hash "tests/weights/lda.safetensors" 329e369c
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python scripts/conversion/convert_diffusers_unet.py \
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--from "tests/weights/runwayml/stable-diffusion-v1-5" \
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--to "tests/weights/unet.safetensors" \
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--half
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check_hash "tests/weights/unet.safetensors" d283a9a5
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check_hash "tests/weights/unet.safetensors" f81ac65a
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mkdir tests/weights/inpainting
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@ -210,7 +216,7 @@ convert_sd15 () {
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--from "tests/weights/runwayml/stable-diffusion-inpainting" \
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--to "tests/weights/inpainting/unet.safetensors" \
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--half
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check_hash "tests/weights/inpainting/unet.safetensors" 78069e20
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check_hash "tests/weights/inpainting/unet.safetensors" c07a8c61
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}
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convert_sdxl () {
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@ -219,19 +225,19 @@ convert_sdxl () {
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--to "tests/weights/DoubleCLIPTextEncoder.safetensors" \
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--subfolder2 text_encoder_2 \
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--half
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check_hash "tests/weights/DoubleCLIPTextEncoder.safetensors" a68fd375
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check_hash "tests/weights/DoubleCLIPTextEncoder.safetensors" 7f99c30b
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python scripts/conversion/convert_diffusers_autoencoder_kl.py \
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--from "tests/weights/stabilityai/stable-diffusion-xl-base-1.0" \
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--to "tests/weights/sdxl-lda.safetensors" \
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--half
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check_hash "tests/weights/sdxl-lda.safetensors" b00aaf87
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check_hash "tests/weights/sdxl-lda.safetensors" 7464e9dc
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python scripts/conversion/convert_diffusers_unet.py \
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--from "tests/weights/stabilityai/stable-diffusion-xl-base-1.0" \
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--to "tests/weights/sdxl-unet.safetensors" \
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--half
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check_hash "tests/weights/sdxl-unet.safetensors" 861b57fd
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check_hash "tests/weights/sdxl-unet.safetensors" 2e5c4911
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}
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convert_vae_ft_mse () {
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@ -239,7 +245,7 @@ convert_vae_ft_mse () {
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--from "tests/weights/stabilityai/sd-vae-ft-mse" \
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--to "tests/weights/lda_ft_mse.safetensors" \
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--half
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check_hash "tests/weights/lda_ft_mse.safetensors" 6cfb7776
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check_hash "tests/weights/lda_ft_mse.safetensors" 4d0bae7e
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}
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convert_lora () {
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@ -259,7 +265,7 @@ convert_preprocessors () {
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--from "tests/weights/carolineec/informativedrawings/model2.pth" \
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--to "tests/weights/informative-drawings.safetensors"
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rm -f src/model.py
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check_hash "tests/weights/informative-drawings.safetensors" 0294ac8a
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check_hash "tests/weights/informative-drawings.safetensors" 93dca207
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}
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convert_controlnet () {
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@ -268,27 +274,27 @@ convert_controlnet () {
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python scripts/conversion/convert_diffusers_controlnet.py \
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--from "tests/weights/lllyasviel/control_v11p_sd15_canny" \
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--to "tests/weights/controlnet/lllyasviel_control_v11p_sd15_canny.safetensors"
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check_hash "tests/weights/controlnet/lllyasviel_control_v11p_sd15_canny.safetensors" be9ffe47
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check_hash "tests/weights/controlnet/lllyasviel_control_v11p_sd15_canny.safetensors" 9a1a48cf
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python scripts/conversion/convert_diffusers_controlnet.py \
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--from "tests/weights/lllyasviel/control_v11f1p_sd15_depth" \
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--to "tests/weights/controlnet/lllyasviel_control_v11f1p_sd15_depth.safetensors"
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check_hash "tests/weights/controlnet/lllyasviel_control_v11f1p_sd15_depth.safetensors" bbeaa1ba
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check_hash "tests/weights/controlnet/lllyasviel_control_v11f1p_sd15_depth.safetensors" bbe7e5a6
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python scripts/conversion/convert_diffusers_controlnet.py \
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--from "tests/weights/lllyasviel/control_v11p_sd15_normalbae" \
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--to "tests/weights/controlnet/lllyasviel_control_v11p_sd15_normalbae.safetensors"
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check_hash "tests/weights/controlnet/lllyasviel_control_v11p_sd15_normalbae.safetensors" 24520c5b
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check_hash "tests/weights/controlnet/lllyasviel_control_v11p_sd15_normalbae.safetensors" 9fa88ed5
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python scripts/conversion/convert_diffusers_controlnet.py \
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--from "tests/weights/lllyasviel/control_v11p_sd15_lineart" \
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--to "tests/weights/controlnet/lllyasviel_control_v11p_sd15_lineart.safetensors"
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check_hash "tests/weights/controlnet/lllyasviel_control_v11p_sd15_lineart.safetensors" 5bc4de82
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check_hash "tests/weights/controlnet/lllyasviel_control_v11p_sd15_lineart.safetensors" c29e8c03
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python scripts/conversion/convert_diffusers_controlnet.py \
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--from "tests/weights/mfidabel/controlnet-segment-anything" \
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--to "tests/weights/controlnet/mfidabel_controlnet-segment-anything.safetensors"
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check_hash "tests/weights/controlnet/mfidabel_controlnet-segment-anything.safetensors" ba7059fc
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check_hash "tests/weights/controlnet/mfidabel_controlnet-segment-anything.safetensors" d536eebb
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}
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convert_unclip () {
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@ -296,7 +302,7 @@ convert_unclip () {
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--from "tests/weights/stabilityai/stable-diffusion-2-1-unclip" \
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--to "tests/weights/CLIPImageEncoderH.safetensors" \
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--half
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check_hash "tests/weights/CLIPImageEncoderH.safetensors" 654842e4
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check_hash "tests/weights/CLIPImageEncoderH.safetensors" 82918ff4
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}
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convert_ip_adapter () {
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@ -315,13 +321,13 @@ convert_ip_adapter () {
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--from "tests/weights/h94/IP-Adapter/models/ip-adapter-plus_sd15.bin" \
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--to "tests/weights/ip-adapter-plus_sd15.safetensors" \
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--half
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check_hash "tests/weights/ip-adapter-plus_sd15.safetensors" 9cea790f
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check_hash "tests/weights/ip-adapter-plus_sd15.safetensors" 346a31d1
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python scripts/conversion/convert_diffusers_ip_adapter.py \
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--from "tests/weights/h94/IP-Adapter/sdxl_models/ip-adapter-plus_sdxl_vit-h.bin" \
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--to "tests/weights/ip-adapter-plus_sdxl_vit-h.safetensors" \
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--half
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check_hash "tests/weights/ip-adapter-plus_sdxl_vit-h.safetensors" a090ab44
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check_hash "tests/weights/ip-adapter-plus_sdxl_vit-h.safetensors" d195feb3
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}
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convert_t2i_adapter () {
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@ -330,14 +336,20 @@ convert_t2i_adapter () {
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--from "tests/weights/TencentARC/t2iadapter_depth_sd15v2" \
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--to "tests/weights/T2I-Adapter/t2iadapter_depth_sd15v2.safetensors" \
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--half
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check_hash "tests/weights/T2I-Adapter/t2iadapter_depth_sd15v2.safetensors" 809a355f
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check_hash "tests/weights/T2I-Adapter/t2iadapter_depth_sd15v2.safetensors" bb2b3115
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python scripts/conversion/convert_diffusers_t2i_adapter.py \
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--from "tests/weights/TencentARC/t2i-adapter-canny-sdxl-1.0" \
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--to "tests/weights/T2I-Adapter/t2i-adapter-canny-sdxl-1.0.safetensors" \
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--half
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check_hash "tests/weights/T2I-Adapter/t2i-adapter-canny-sdxl-1.0.safetensors" f07249a6
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}
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convert_sam () {
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python scripts/conversion/convert_segment_anything.py \
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--from "tests/weights/sam_vit_h_4b8939.pth" \
|
||||
--to "tests/weights/segment-anything-h.safetensors"
|
||||
check_hash "tests/weights/segment-anything-h.safetensors" e11e1ec5
|
||||
check_hash "tests/weights/segment-anything-h.safetensors" 321d6f23
|
||||
}
|
||||
|
||||
download_all () {
|
||||
|
|
|
|||
|
|
@ -6,7 +6,6 @@ import traceback
|
|||
from typing import Any, Callable, Iterable, Iterator, TypeVar, cast, overload
|
||||
import torch
|
||||
from torch import Tensor, cat, device as Device, dtype as DType
|
||||
from refiners.fluxion.layers.basics import Identity
|
||||
from refiners.fluxion.layers.module import Module, ContextModule, ModuleTree, WeightedModule
|
||||
from refiners.fluxion.context import Contexts, ContextProvider
|
||||
from refiners.fluxion.utils import summarize_tensor
|
||||
|
|
@ -530,9 +529,12 @@ class Sum(Chain):
|
|||
return self.__class__ == Sum
|
||||
|
||||
|
||||
class Residual(Sum):
|
||||
def __init__(self, *modules: Module) -> None:
|
||||
super().__init__(Identity(), Chain(*modules))
|
||||
class Residual(Chain):
|
||||
_tag = "RES"
|
||||
|
||||
def forward(self, *inputs: Any) -> Any:
|
||||
assert len(inputs) == 1, "Residual connection can only be used with a single input."
|
||||
return super().forward(*inputs) + inputs[0]
|
||||
|
||||
|
||||
class Breakpoint(ContextModule):
|
||||
|
|
|
|||
|
|
@ -10,6 +10,7 @@ from refiners.fluxion.layers import (
|
|||
Sum,
|
||||
SelfAttention2d,
|
||||
Slicing,
|
||||
Residual,
|
||||
)
|
||||
from refiners.fluxion.utils import image_to_tensor, tensor_to_image
|
||||
from torch import Tensor, device as Device, dtype as DType
|
||||
|
|
@ -82,12 +83,9 @@ class Encoder(Chain):
|
|||
channels: int = layer[-1].out_channels # type: ignore
|
||||
layer.append(Downsample(channels=channels, scale_factor=2, device=device, dtype=dtype))
|
||||
|
||||
attention_layer = Sum(
|
||||
Identity(),
|
||||
Chain(
|
||||
GroupNorm(channels=resnet_sizes[-1], num_groups=32, eps=1e-6, device=device, dtype=dtype),
|
||||
SelfAttention2d(channels=resnet_sizes[-1], device=device, dtype=dtype),
|
||||
),
|
||||
attention_layer = Residual(
|
||||
GroupNorm(channels=resnet_sizes[-1], num_groups=32, eps=1e-6, device=device, dtype=dtype),
|
||||
SelfAttention2d(channels=resnet_sizes[-1], device=device, dtype=dtype),
|
||||
)
|
||||
resnet_layers[-1].insert_after_type(Resnet, attention_layer)
|
||||
super().__init__(
|
||||
|
|
@ -152,12 +150,9 @@ class Decoder(Chain):
|
|||
)
|
||||
for i in range(len(resnet_sizes))
|
||||
]
|
||||
attention_layer = Sum(
|
||||
Identity(),
|
||||
Chain(
|
||||
GroupNorm(channels=resnet_sizes[0], num_groups=32, eps=1e-6, device=device, dtype=dtype),
|
||||
SelfAttention2d(channels=resnet_sizes[0], device=device, dtype=dtype),
|
||||
),
|
||||
attention_layer = Residual(
|
||||
GroupNorm(channels=resnet_sizes[0], num_groups=32, eps=1e-6, device=device, dtype=dtype),
|
||||
SelfAttention2d(channels=resnet_sizes[0], device=device, dtype=dtype),
|
||||
)
|
||||
resnet_layers[0].insert(1, attention_layer)
|
||||
for _, layer in zip(range(3), resnet_layers[1:]):
|
||||
|
|
|
|||
|
|
@ -10,7 +10,6 @@ from refiners.fluxion.layers import (
|
|||
Parallel,
|
||||
LayerNorm,
|
||||
Attention,
|
||||
Sum,
|
||||
UseContext,
|
||||
Linear,
|
||||
GLU,
|
||||
|
|
@ -19,6 +18,7 @@ from refiners.fluxion.layers import (
|
|||
Conv2d,
|
||||
SelfAttention,
|
||||
SetContext,
|
||||
Residual,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -41,43 +41,34 @@ class CrossAttentionBlock(Chain):
|
|||
self.use_bias = use_bias
|
||||
|
||||
super().__init__(
|
||||
Sum(
|
||||
Identity(),
|
||||
Chain(
|
||||
LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype),
|
||||
SelfAttention(
|
||||
embedding_dim=embedding_dim, num_heads=num_heads, use_bias=use_bias, device=device, dtype=dtype
|
||||
),
|
||||
Residual(
|
||||
LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype),
|
||||
SelfAttention(
|
||||
embedding_dim=embedding_dim, num_heads=num_heads, use_bias=use_bias, device=device, dtype=dtype
|
||||
),
|
||||
),
|
||||
Sum(
|
||||
Identity(),
|
||||
Chain(
|
||||
LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype),
|
||||
Parallel(
|
||||
Identity(),
|
||||
UseContext(context=self.context, key=context_key),
|
||||
UseContext(context=self.context, key=context_key),
|
||||
),
|
||||
Attention(
|
||||
embedding_dim=embedding_dim,
|
||||
num_heads=num_heads,
|
||||
key_embedding_dim=context_embedding_dim,
|
||||
value_embedding_dim=context_embedding_dim,
|
||||
use_bias=use_bias,
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
),
|
||||
Residual(
|
||||
LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype),
|
||||
Parallel(
|
||||
Identity(),
|
||||
UseContext(context=self.context, key=context_key),
|
||||
UseContext(context=self.context, key=context_key),
|
||||
),
|
||||
Attention(
|
||||
embedding_dim=embedding_dim,
|
||||
num_heads=num_heads,
|
||||
key_embedding_dim=context_embedding_dim,
|
||||
value_embedding_dim=context_embedding_dim,
|
||||
use_bias=use_bias,
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
),
|
||||
),
|
||||
Sum(
|
||||
Identity(),
|
||||
Chain(
|
||||
LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype),
|
||||
Linear(in_features=embedding_dim, out_features=2 * 4 * embedding_dim, device=device, dtype=dtype),
|
||||
GLU(GeLU()),
|
||||
Linear(in_features=4 * embedding_dim, out_features=embedding_dim, device=device, dtype=dtype),
|
||||
),
|
||||
Residual(
|
||||
LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype),
|
||||
Linear(in_features=embedding_dim, out_features=2 * 4 * embedding_dim, device=device, dtype=dtype),
|
||||
GLU(GeLU()),
|
||||
Linear(in_features=4 * embedding_dim, out_features=embedding_dim, device=device, dtype=dtype),
|
||||
),
|
||||
)
|
||||
|
||||
|
|
@ -98,7 +89,7 @@ class StatefulFlatten(Chain):
|
|||
)
|
||||
|
||||
|
||||
class CrossAttentionBlock2d(Sum):
|
||||
class CrossAttentionBlock2d(Residual):
|
||||
def __init__(
|
||||
self,
|
||||
channels: int,
|
||||
|
|
@ -164,23 +155,20 @@ class CrossAttentionBlock2d(Sum):
|
|||
)
|
||||
|
||||
super().__init__(
|
||||
Identity(),
|
||||
in_block,
|
||||
Chain(
|
||||
in_block,
|
||||
Chain(
|
||||
CrossAttentionBlock(
|
||||
embedding_dim=channels,
|
||||
context_embedding_dim=context_embedding_dim,
|
||||
context_key=context_key,
|
||||
num_heads=num_attention_heads,
|
||||
use_bias=use_bias,
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
)
|
||||
for _ in range(num_attention_layers)
|
||||
),
|
||||
out_block,
|
||||
CrossAttentionBlock(
|
||||
embedding_dim=channels,
|
||||
context_embedding_dim=context_embedding_dim,
|
||||
context_key=context_key,
|
||||
num_heads=num_attention_heads,
|
||||
use_bias=use_bias,
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
)
|
||||
for _ in range(num_attention_layers)
|
||||
),
|
||||
out_block,
|
||||
)
|
||||
|
||||
def init_context(self) -> Contexts:
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
from refiners.fluxion.context import Contexts
|
||||
from refiners.fluxion.layers import Chain, Conv2d, SiLU, Lambda, Passthrough, UseContext, Sum, Identity, Slicing
|
||||
from refiners.fluxion.layers import Chain, Conv2d, SiLU, Lambda, Passthrough, UseContext, Slicing, Residual
|
||||
from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import (
|
||||
SD1UNet,
|
||||
DownBlocks,
|
||||
|
|
@ -92,12 +92,9 @@ class Controlnet(Passthrough):
|
|||
# We run the condition encoder at each step. Caching the result
|
||||
# is not worth it as subsequent runs take virtually no time (FG-374).
|
||||
self.DownBlocks[0].append(
|
||||
Sum(
|
||||
Identity(),
|
||||
Chain(
|
||||
UseContext("controlnet", f"condition_{name}"),
|
||||
ConditionEncoder(device=device, dtype=dtype),
|
||||
),
|
||||
Residual(
|
||||
UseContext("controlnet", f"condition_{name}"),
|
||||
ConditionEncoder(device=device, dtype=dtype),
|
||||
),
|
||||
)
|
||||
for residual_block in self.layers(ResidualBlock):
|
||||
|
|
|
|||
|
|
@ -131,7 +131,7 @@ def test_image_encoder(sam_h: SegmentAnythingH, facebook_sam_h: FacebookSAM, tru
|
|||
y_1 = facebook_sam_h.image_encoder(image_tensor)
|
||||
y_2 = sam_h.image_encoder(image_tensor)
|
||||
|
||||
assert torch.equal(input=y_1, other=y_2)
|
||||
assert torch.allclose(input=y_1, other=y_2, atol=1e-4)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
|
|
|
|||
Loading…
Reference in a new issue