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unnest Residual subchain by modifying its forward

Browse source 
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 2023-10-19 10:17:25 +02:00 committed by Cédric Deltheil
parent 46dd710076
commit ea44262a39
9 changed files with 119 additions and 125 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

28
scripts/conversion/convert_diffusers_controlnet.py
View file

@ -65,11 +65,11 @@ def convert(args: Args) -> dict[str, torch.Tensor]:
expected_target_order = [ expected_target_order = [
"DownBlocks.Chain_1.Passthrough.Conv2d", "DownBlocks.Chain_1.Passthrough.Conv2d",
"DownBlocks.Chain_2.CLIPLCrossAttention.Chain.Chain_1.Conv2d", "DownBlocks.Chain_2.CLIPLCrossAttention.Chain_1.Conv2d",
"DownBlocks.Chain_2.CLIPLCrossAttention.Chain.Chain_3.Conv2d", "DownBlocks.Chain_2.CLIPLCrossAttention.Chain_3.Conv2d",
"DownBlocks.Chain_2.Passthrough.Conv2d", "DownBlocks.Chain_2.Passthrough.Conv2d",
"DownBlocks.Chain_3.CLIPLCrossAttention.Chain.Chain_1.Conv2d", "DownBlocks.Chain_3.CLIPLCrossAttention.Chain_1.Conv2d",
"DownBlocks.Chain_3.CLIPLCrossAttention.Chain.Chain_3.Conv2d", "DownBlocks.Chain_3.CLIPLCrossAttention.Chain_3.Conv2d",
"DownBlocks.Chain_3.Passthrough.Conv2d", "DownBlocks.Chain_3.Passthrough.Conv2d",
"DownBlocks.Chain_4.Passthrough.Conv2d", "DownBlocks.Chain_4.Passthrough.Conv2d",
] ]
@ -102,11 +102,11 @@ def convert(args: Args) -> dict[str, torch.Tensor]:
] ]
expected_target_order = [ expected_target_order = [
"DownBlocks.Chain_5.CLIPLCrossAttention.Chain.Chain_1.Conv2d", "DownBlocks.Chain_5.CLIPLCrossAttention.Chain_1.Conv2d",
"DownBlocks.Chain_5.CLIPLCrossAttention.Chain.Chain_3.Conv2d", "DownBlocks.Chain_5.CLIPLCrossAttention.Chain_3.Conv2d",
"DownBlocks.Chain_5.Passthrough.Conv2d", "DownBlocks.Chain_5.Passthrough.Conv2d",
"DownBlocks.Chain_6.CLIPLCrossAttention.Chain.Chain_1.Conv2d", "DownBlocks.Chain_6.CLIPLCrossAttention.Chain_1.Conv2d",
"DownBlocks.Chain_6.CLIPLCrossAttention.Chain.Chain_3.Conv2d", "DownBlocks.Chain_6.CLIPLCrossAttention.Chain_3.Conv2d",
"DownBlocks.Chain_6.Passthrough.Conv2d", "DownBlocks.Chain_6.Passthrough.Conv2d",
"DownBlocks.Chain_7.Passthrough.Conv2d", "DownBlocks.Chain_7.Passthrough.Conv2d",
] ]
@ -143,17 +143,17 @@ def convert(args: Args) -> dict[str, torch.Tensor]:
] ]
expected_target_order = [ expected_target_order = [
"DownBlocks.Chain_8.CLIPLCrossAttention.Chain.Chain_1.Conv2d", "DownBlocks.Chain_8.CLIPLCrossAttention.Chain_1.Conv2d",
"DownBlocks.Chain_8.CLIPLCrossAttention.Chain.Chain_3.Conv2d", "DownBlocks.Chain_8.CLIPLCrossAttention.Chain_3.Conv2d",
"DownBlocks.Chain_8.Passthrough.Conv2d", "DownBlocks.Chain_8.Passthrough.Conv2d",
"DownBlocks.Chain_9.CLIPLCrossAttention.Chain.Chain_1.Conv2d", "DownBlocks.Chain_9.CLIPLCrossAttention.Chain_1.Conv2d",
"DownBlocks.Chain_9.CLIPLCrossAttention.Chain.Chain_3.Conv2d", "DownBlocks.Chain_9.CLIPLCrossAttention.Chain_3.Conv2d",
"DownBlocks.Chain_9.Passthrough.Conv2d", "DownBlocks.Chain_9.Passthrough.Conv2d",
"DownBlocks.Chain_10.Passthrough.Conv2d", "DownBlocks.Chain_10.Passthrough.Conv2d",
"DownBlocks.Chain_11.Passthrough.Conv2d", "DownBlocks.Chain_11.Passthrough.Conv2d",
"DownBlocks.Chain_12.Passthrough.Conv2d", "DownBlocks.Chain_12.Passthrough.Conv2d",
"MiddleBlock.CLIPLCrossAttention.Chain.Chain_1.Conv2d", "MiddleBlock.CLIPLCrossAttention.Chain_1.Conv2d",
"MiddleBlock.CLIPLCrossAttention.Chain.Chain_3.Conv2d", "MiddleBlock.CLIPLCrossAttention.Chain_3.Conv2d",
"MiddleBlock.Passthrough.Conv2d", "MiddleBlock.Passthrough.Conv2d",
] ]

22
scripts/conversion/convert_diffusers_ip_adapter.py
View file

@ -105,17 +105,17 @@ def main() -> None:
t_pfx, s_pfx = f"Transformer.TransformerLayer_{i+1}.Residual_", f"layers.{i}." t_pfx, s_pfx = f"Transformer.TransformerLayer_{i+1}.Residual_", f"layers.{i}."
image_proj_state_dict.update( image_proj_state_dict.update(
{ {
f"{t_pfx}1.Chain.PerceiverAttention.Distribute.LayerNorm_1.weight": w[f"{s_pfx}0.norm1.weight"], f"{t_pfx}1.PerceiverAttention.Distribute.LayerNorm_1.weight": w[f"{s_pfx}0.norm1.weight"],
f"{t_pfx}1.Chain.PerceiverAttention.Distribute.LayerNorm_1.bias": w[f"{s_pfx}0.norm1.bias"], f"{t_pfx}1.PerceiverAttention.Distribute.LayerNorm_1.bias": w[f"{s_pfx}0.norm1.bias"],
f"{t_pfx}1.Chain.PerceiverAttention.Distribute.LayerNorm_2.weight": w[f"{s_pfx}0.norm2.weight"], f"{t_pfx}1.PerceiverAttention.Distribute.LayerNorm_2.weight": w[f"{s_pfx}0.norm2.weight"],
f"{t_pfx}1.Chain.PerceiverAttention.Distribute.LayerNorm_2.bias": w[f"{s_pfx}0.norm2.bias"], f"{t_pfx}1.PerceiverAttention.Distribute.LayerNorm_2.bias": w[f"{s_pfx}0.norm2.bias"],
f"{t_pfx}1.Chain.PerceiverAttention.Parallel.Chain_2.Linear.weight": w[f"{s_pfx}0.to_q.weight"], f"{t_pfx}1.PerceiverAttention.Parallel.Chain_2.Linear.weight": w[f"{s_pfx}0.to_q.weight"],
f"{t_pfx}1.Chain.PerceiverAttention.Parallel.Chain_1.Linear.weight": w[f"{s_pfx}0.to_kv.weight"], f"{t_pfx}1.PerceiverAttention.Parallel.Chain_1.Linear.weight": w[f"{s_pfx}0.to_kv.weight"],
f"{t_pfx}1.Chain.PerceiverAttention.Linear.weight": w[f"{s_pfx}0.to_out.weight"], f"{t_pfx}1.PerceiverAttention.Linear.weight": w[f"{s_pfx}0.to_out.weight"],
f"{t_pfx}2.Chain.LayerNorm.weight": w[f"{s_pfx}1.0.weight"], f"{t_pfx}2.LayerNorm.weight": w[f"{s_pfx}1.0.weight"],
f"{t_pfx}2.Chain.LayerNorm.bias": w[f"{s_pfx}1.0.bias"], f"{t_pfx}2.LayerNorm.bias": w[f"{s_pfx}1.0.bias"],
f"{t_pfx}2.Chain.FeedForward.Linear_1.weight": w[f"{s_pfx}1.1.weight"], f"{t_pfx}2.FeedForward.Linear_1.weight": w[f"{s_pfx}1.1.weight"],
f"{t_pfx}2.Chain.FeedForward.Linear_2.weight": w[f"{s_pfx}1.3.weight"], f"{t_pfx}2.FeedForward.Linear_2.weight": w[f"{s_pfx}1.3.weight"],
} }
) )
else: else:

14
scripts/conversion/convert_segment_anything.py
View file

@ -55,7 +55,7 @@ def convert_point_encoder(prompt_encoder: nn.Module) -> dict[str, Tensor]:
pe = prompt_encoder.pe_layer.positional_encoding_gaussian_matrix # type: ignore pe = prompt_encoder.pe_layer.positional_encoding_gaussian_matrix # type: ignore
assert isinstance(pe, Tensor) assert isinstance(pe, Tensor)
state_dict: dict[str, Tensor] = { state_dict: dict[str, Tensor] = {
"Residual.Chain.PointTypeEmbedding.weight": nn.Parameter(data=torch.cat(tensors=point_embeddings, dim=0)), "Residual.PointTypeEmbedding.weight": nn.Parameter(data=torch.cat(tensors=point_embeddings, dim=0)),
"CoordinateEncoder.Linear.weight": nn.Parameter(data=pe.T.contiguous()), "CoordinateEncoder.Linear.weight": nn.Parameter(data=pe.T.contiguous()),
} }
@ -80,10 +80,10 @@ def convert_vit(vit: nn.Module) -> dict[str, Tensor]:
mapping = converter.map_state_dicts(source_args=(x,)) mapping = converter.map_state_dicts(source_args=(x,))
assert mapping assert mapping
mapping["PositionalEncoder.Chain.Parameter.parameter"] = "pos_embed" mapping["PositionalEncoder.Parameter.parameter"] = "pos_embed"
target_state_dict = refiners_sam_vit_h.state_dict() target_state_dict = refiners_sam_vit_h.state_dict()
del target_state_dict["PositionalEncoder.Chain.Parameter.parameter"] del target_state_dict["PositionalEncoder.Parameter.parameter"]
source_state_dict = vit.state_dict() source_state_dict = vit.state_dict()
pos_embed = source_state_dict["pos_embed"] pos_embed = source_state_dict["pos_embed"]
@ -91,8 +91,8 @@ def convert_vit(vit: nn.Module) -> dict[str, Tensor]:
target_rel_keys = [ target_rel_keys = [
( (
f"Transformer.TransformerLayer_{i}.Residual_1.Chain.FusedSelfAttention.RelativePositionAttention.horizontal_embedding", f"Transformer.TransformerLayer_{i}.Residual_1.FusedSelfAttention.RelativePositionAttention.horizontal_embedding",
f"Transformer.TransformerLayer_{i}.Residual_1.Chain.FusedSelfAttention.RelativePositionAttention.vertical_embedding", f"Transformer.TransformerLayer_{i}.Residual_1.FusedSelfAttention.RelativePositionAttention.vertical_embedding",
) )
for i in range(1, 33) for i in range(1, 33)
] ]
@ -112,11 +112,11 @@ def convert_vit(vit: nn.Module) -> dict[str, Tensor]:
source_state_dict=source_state_dict, target_state_dict=target_state_dict, state_dict_mapping=mapping source_state_dict=source_state_dict, target_state_dict=target_state_dict, state_dict_mapping=mapping
) )
converted_source["PositionalEncoder.Chain.Parameter.parameter"] = pos_embed # type: ignore converted_source["PositionalEncoder.Parameter.parameter"] = pos_embed # type: ignore
converted_source.update(rel_items) converted_source.update(rel_items)
refiners_sam_vit_h.load_state_dict(state_dict=converted_source) refiners_sam_vit_h.load_state_dict(state_dict=converted_source)
assert converter.compare_models((x,), threshold=1e-3) assert converter.compare_models((x,), threshold=1e-2)
return converted_source return converted_source

50
scripts/prepare-test-weights.sh
View file

@ -176,6 +176,12 @@ download_t2i_adapter () {
curl -LO https://huggingface.co/TencentARC/t2iadapter_depth_sd15v2/raw/main/config.json curl -LO https://huggingface.co/TencentARC/t2iadapter_depth_sd15v2/raw/main/config.json
curl -LO https://huggingface.co/TencentARC/t2iadapter_depth_sd15v2/resolve/main/diffusion_pytorch_model.bin curl -LO https://huggingface.co/TencentARC/t2iadapter_depth_sd15v2/resolve/main/diffusion_pytorch_model.bin
popd popd
mkdir -p tests/weights/TencentARC/t2i-adapter-canny-sdxl-1.0
pushd tests/weights/TencentARC/t2i-adapter-canny-sdxl-1.0
curl -LO https://huggingface.co/TencentARC/t2i-adapter-canny-sdxl-1.0/raw/main/config.json
curl -LO https://huggingface.co/TencentARC/t2i-adapter-canny-sdxl-1.0/resolve/main/diffusion_pytorch_model.safetensors
popd
} }
download_sam () { download_sam () {
@ -191,18 +197,18 @@ convert_sd15 () {
--from "tests/weights/runwayml/stable-diffusion-v1-5" \ --from "tests/weights/runwayml/stable-diffusion-v1-5" \
--to "tests/weights/CLIPTextEncoderL.safetensors" \ --to "tests/weights/CLIPTextEncoderL.safetensors" \
--half --half
check_hash "tests/weights/CLIPTextEncoderL.safetensors" bef71657 check_hash "tests/weights/CLIPTextEncoderL.safetensors" 6c9cbc59
python scripts/conversion/convert_diffusers_autoencoder_kl.py \ python scripts/conversion/convert_diffusers_autoencoder_kl.py \
--from "tests/weights/runwayml/stable-diffusion-v1-5" \ --from "tests/weights/runwayml/stable-diffusion-v1-5" \
--to "tests/weights/lda.safetensors" --to "tests/weights/lda.safetensors"
check_hash "tests/weights/lda.safetensors" 28f38b35 check_hash "tests/weights/lda.safetensors" 329e369c
python scripts/conversion/convert_diffusers_unet.py \ python scripts/conversion/convert_diffusers_unet.py \
--from "tests/weights/runwayml/stable-diffusion-v1-5" \ --from "tests/weights/runwayml/stable-diffusion-v1-5" \
--to "tests/weights/unet.safetensors" \ --to "tests/weights/unet.safetensors" \
--half --half
check_hash "tests/weights/unet.safetensors" d283a9a5 check_hash "tests/weights/unet.safetensors" f81ac65a
mkdir tests/weights/inpainting mkdir tests/weights/inpainting
@ -210,7 +216,7 @@ convert_sd15 () {
--from "tests/weights/runwayml/stable-diffusion-inpainting" \ --from "tests/weights/runwayml/stable-diffusion-inpainting" \
--to "tests/weights/inpainting/unet.safetensors" \ --to "tests/weights/inpainting/unet.safetensors" \
--half --half
check_hash "tests/weights/inpainting/unet.safetensors" 78069e20 check_hash "tests/weights/inpainting/unet.safetensors" c07a8c61
} }
convert_sdxl () { convert_sdxl () {
@ -219,19 +225,19 @@ convert_sdxl () {
--to "tests/weights/DoubleCLIPTextEncoder.safetensors" \ --to "tests/weights/DoubleCLIPTextEncoder.safetensors" \
--subfolder2 text_encoder_2 \ --subfolder2 text_encoder_2 \
--half --half
check_hash "tests/weights/DoubleCLIPTextEncoder.safetensors" a68fd375 check_hash "tests/weights/DoubleCLIPTextEncoder.safetensors" 7f99c30b
python scripts/conversion/convert_diffusers_autoencoder_kl.py \ python scripts/conversion/convert_diffusers_autoencoder_kl.py \
--from "tests/weights/stabilityai/stable-diffusion-xl-base-1.0" \ --from "tests/weights/stabilityai/stable-diffusion-xl-base-1.0" \
--to "tests/weights/sdxl-lda.safetensors" \ --to "tests/weights/sdxl-lda.safetensors" \
--half --half
check_hash "tests/weights/sdxl-lda.safetensors" b00aaf87 check_hash "tests/weights/sdxl-lda.safetensors" 7464e9dc
python scripts/conversion/convert_diffusers_unet.py \ python scripts/conversion/convert_diffusers_unet.py \
--from "tests/weights/stabilityai/stable-diffusion-xl-base-1.0" \ --from "tests/weights/stabilityai/stable-diffusion-xl-base-1.0" \
--to "tests/weights/sdxl-unet.safetensors" \ --to "tests/weights/sdxl-unet.safetensors" \
--half --half
check_hash "tests/weights/sdxl-unet.safetensors" 861b57fd check_hash "tests/weights/sdxl-unet.safetensors" 2e5c4911
} }
convert_vae_ft_mse () { convert_vae_ft_mse () {
@ -239,7 +245,7 @@ convert_vae_ft_mse () {
--from "tests/weights/stabilityai/sd-vae-ft-mse" \ --from "tests/weights/stabilityai/sd-vae-ft-mse" \
--to "tests/weights/lda_ft_mse.safetensors" \ --to "tests/weights/lda_ft_mse.safetensors" \
--half --half
check_hash "tests/weights/lda_ft_mse.safetensors" 6cfb7776 check_hash "tests/weights/lda_ft_mse.safetensors" 4d0bae7e
} }
convert_lora () { convert_lora () {
@ -259,7 +265,7 @@ convert_preprocessors () {
--from "tests/weights/carolineec/informativedrawings/model2.pth" \ --from "tests/weights/carolineec/informativedrawings/model2.pth" \
--to "tests/weights/informative-drawings.safetensors" --to "tests/weights/informative-drawings.safetensors"
rm -f src/model.py rm -f src/model.py
check_hash "tests/weights/informative-drawings.safetensors" 0294ac8a check_hash "tests/weights/informative-drawings.safetensors" 93dca207
} }
convert_controlnet () { convert_controlnet () {
@ -268,27 +274,27 @@ convert_controlnet () {
python scripts/conversion/convert_diffusers_controlnet.py \ python scripts/conversion/convert_diffusers_controlnet.py \
--from "tests/weights/lllyasviel/control_v11p_sd15_canny" \ --from "tests/weights/lllyasviel/control_v11p_sd15_canny" \
--to "tests/weights/controlnet/lllyasviel_control_v11p_sd15_canny.safetensors" --to "tests/weights/controlnet/lllyasviel_control_v11p_sd15_canny.safetensors"
check_hash "tests/weights/controlnet/lllyasviel_control_v11p_sd15_canny.safetensors" be9ffe47 check_hash "tests/weights/controlnet/lllyasviel_control_v11p_sd15_canny.safetensors" 9a1a48cf
python scripts/conversion/convert_diffusers_controlnet.py \ python scripts/conversion/convert_diffusers_controlnet.py \
--from "tests/weights/lllyasviel/control_v11f1p_sd15_depth" \ --from "tests/weights/lllyasviel/control_v11f1p_sd15_depth" \
--to "tests/weights/controlnet/lllyasviel_control_v11f1p_sd15_depth.safetensors" --to "tests/weights/controlnet/lllyasviel_control_v11f1p_sd15_depth.safetensors"
check_hash "tests/weights/controlnet/lllyasviel_control_v11f1p_sd15_depth.safetensors" bbeaa1ba check_hash "tests/weights/controlnet/lllyasviel_control_v11f1p_sd15_depth.safetensors" bbe7e5a6
python scripts/conversion/convert_diffusers_controlnet.py \ python scripts/conversion/convert_diffusers_controlnet.py \
--from "tests/weights/lllyasviel/control_v11p_sd15_normalbae" \ --from "tests/weights/lllyasviel/control_v11p_sd15_normalbae" \
--to "tests/weights/controlnet/lllyasviel_control_v11p_sd15_normalbae.safetensors" --to "tests/weights/controlnet/lllyasviel_control_v11p_sd15_normalbae.safetensors"
check_hash "tests/weights/controlnet/lllyasviel_control_v11p_sd15_normalbae.safetensors" 24520c5b check_hash "tests/weights/controlnet/lllyasviel_control_v11p_sd15_normalbae.safetensors" 9fa88ed5
python scripts/conversion/convert_diffusers_controlnet.py \ python scripts/conversion/convert_diffusers_controlnet.py \
--from "tests/weights/lllyasviel/control_v11p_sd15_lineart" \ --from "tests/weights/lllyasviel/control_v11p_sd15_lineart" \
--to "tests/weights/controlnet/lllyasviel_control_v11p_sd15_lineart.safetensors" --to "tests/weights/controlnet/lllyasviel_control_v11p_sd15_lineart.safetensors"
check_hash "tests/weights/controlnet/lllyasviel_control_v11p_sd15_lineart.safetensors" 5bc4de82 check_hash "tests/weights/controlnet/lllyasviel_control_v11p_sd15_lineart.safetensors" c29e8c03
python scripts/conversion/convert_diffusers_controlnet.py \ python scripts/conversion/convert_diffusers_controlnet.py \
--from "tests/weights/mfidabel/controlnet-segment-anything" \ --from "tests/weights/mfidabel/controlnet-segment-anything" \
--to "tests/weights/controlnet/mfidabel_controlnet-segment-anything.safetensors" --to "tests/weights/controlnet/mfidabel_controlnet-segment-anything.safetensors"
check_hash "tests/weights/controlnet/mfidabel_controlnet-segment-anything.safetensors" ba7059fc check_hash "tests/weights/controlnet/mfidabel_controlnet-segment-anything.safetensors" d536eebb
} }
convert_unclip () { convert_unclip () {
@ -296,7 +302,7 @@ convert_unclip () {
--from "tests/weights/stabilityai/stable-diffusion-2-1-unclip" \ --from "tests/weights/stabilityai/stable-diffusion-2-1-unclip" \
--to "tests/weights/CLIPImageEncoderH.safetensors" \ --to "tests/weights/CLIPImageEncoderH.safetensors" \
--half --half
check_hash "tests/weights/CLIPImageEncoderH.safetensors" 654842e4 check_hash "tests/weights/CLIPImageEncoderH.safetensors" 82918ff4
} }
convert_ip_adapter () { convert_ip_adapter () {
@ -315,13 +321,13 @@ convert_ip_adapter () {
--from "tests/weights/h94/IP-Adapter/models/ip-adapter-plus_sd15.bin" \ --from "tests/weights/h94/IP-Adapter/models/ip-adapter-plus_sd15.bin" \
--to "tests/weights/ip-adapter-plus_sd15.safetensors" \ --to "tests/weights/ip-adapter-plus_sd15.safetensors" \
--half --half
check_hash "tests/weights/ip-adapter-plus_sd15.safetensors" 9cea790f check_hash "tests/weights/ip-adapter-plus_sd15.safetensors" 346a31d1
python scripts/conversion/convert_diffusers_ip_adapter.py \ python scripts/conversion/convert_diffusers_ip_adapter.py \
--from "tests/weights/h94/IP-Adapter/sdxl_models/ip-adapter-plus_sdxl_vit-h.bin" \ --from "tests/weights/h94/IP-Adapter/sdxl_models/ip-adapter-plus_sdxl_vit-h.bin" \
--to "tests/weights/ip-adapter-plus_sdxl_vit-h.safetensors" \ --to "tests/weights/ip-adapter-plus_sdxl_vit-h.safetensors" \
--half --half
check_hash "tests/weights/ip-adapter-plus_sdxl_vit-h.safetensors" a090ab44 check_hash "tests/weights/ip-adapter-plus_sdxl_vit-h.safetensors" d195feb3
} }
convert_t2i_adapter () { convert_t2i_adapter () {
@ -330,14 +336,20 @@ convert_t2i_adapter () {
--from "tests/weights/TencentARC/t2iadapter_depth_sd15v2" \ --from "tests/weights/TencentARC/t2iadapter_depth_sd15v2" \
--to "tests/weights/T2I-Adapter/t2iadapter_depth_sd15v2.safetensors" \ --to "tests/weights/T2I-Adapter/t2iadapter_depth_sd15v2.safetensors" \
--half --half
check_hash "tests/weights/T2I-Adapter/t2iadapter_depth_sd15v2.safetensors" 809a355f check_hash "tests/weights/T2I-Adapter/t2iadapter_depth_sd15v2.safetensors" bb2b3115
python scripts/conversion/convert_diffusers_t2i_adapter.py \
--from "tests/weights/TencentARC/t2i-adapter-canny-sdxl-1.0" \
--to "tests/weights/T2I-Adapter/t2i-adapter-canny-sdxl-1.0.safetensors" \
--half
check_hash "tests/weights/T2I-Adapter/t2i-adapter-canny-sdxl-1.0.safetensors" f07249a6
} }
convert_sam () { convert_sam () {
python scripts/conversion/convert_segment_anything.py \ python scripts/conversion/convert_segment_anything.py \
--from "tests/weights/sam_vit_h_4b8939.pth" \ --from "tests/weights/sam_vit_h_4b8939.pth" \
--to "tests/weights/segment-anything-h.safetensors" --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 () { download_all () {

10
src/refiners/fluxion/layers/chain.py
View file

@ -6,7 +6,6 @@ import traceback
from typing import Any, Callable, Iterable, Iterator, TypeVar, cast, overload from typing import Any, Callable, Iterable, Iterator, TypeVar, cast, overload
import torch import torch
from torch import Tensor, cat, device as Device, dtype as DType 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.layers.module import Module, ContextModule, ModuleTree, WeightedModule
from refiners.fluxion.context import Contexts, ContextProvider from refiners.fluxion.context import Contexts, ContextProvider
from refiners.fluxion.utils import summarize_tensor from refiners.fluxion.utils import summarize_tensor
@ -530,9 +529,12 @@ class Sum(Chain):
return self.__class__ == Sum return self.__class__ == Sum
class Residual(Sum): class Residual(Chain):
def __init__(self, *modules: Module) -> None: _tag = "RES"
super().__init__(Identity(), Chain(*modules))
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): class Breakpoint(ContextModule):

19
src/refiners/foundationals/latent_diffusion/auto_encoder.py
View file

@ -10,6 +10,7 @@ from refiners.fluxion.layers import (
Sum, Sum,
SelfAttention2d, SelfAttention2d,
Slicing, Slicing,
Residual,
) )
from refiners.fluxion.utils import image_to_tensor, tensor_to_image from refiners.fluxion.utils import image_to_tensor, tensor_to_image
from torch import Tensor, device as Device, dtype as DType 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 channels: int = layer[-1].out_channels # type: ignore
layer.append(Downsample(channels=channels, scale_factor=2, device=device, dtype=dtype)) layer.append(Downsample(channels=channels, scale_factor=2, device=device, dtype=dtype))
attention_layer = Sum( attention_layer = Residual(
Identity(), GroupNorm(channels=resnet_sizes[-1], num_groups=32, eps=1e-6, device=device, dtype=dtype),
Chain( SelfAttention2d(channels=resnet_sizes[-1], device=device, dtype=dtype),
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) resnet_layers[-1].insert_after_type(Resnet, attention_layer)
super().__init__( super().__init__(
@ -152,12 +150,9 @@ class Decoder(Chain):
) )
for i in range(len(resnet_sizes)) for i in range(len(resnet_sizes))
] ]
attention_layer = Sum( attention_layer = Residual(
Identity(), GroupNorm(channels=resnet_sizes[0], num_groups=32, eps=1e-6, device=device, dtype=dtype),
Chain( SelfAttention2d(channels=resnet_sizes[0], device=device, dtype=dtype),
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) resnet_layers[0].insert(1, attention_layer)
for _, layer in zip(range(3), resnet_layers[1:]): for _, layer in zip(range(3), resnet_layers[1:]):

88
src/refiners/foundationals/latent_diffusion/cross_attention.py
View file

@ -10,7 +10,6 @@ from refiners.fluxion.layers import (
Parallel, Parallel,
LayerNorm, LayerNorm,
Attention, Attention,
Sum,
UseContext, UseContext,
Linear, Linear,
GLU, GLU,
@ -19,6 +18,7 @@ from refiners.fluxion.layers import (
Conv2d, Conv2d,
SelfAttention, SelfAttention,
SetContext, SetContext,
Residual,
) )
@ -41,43 +41,34 @@ class CrossAttentionBlock(Chain):
self.use_bias = use_bias self.use_bias = use_bias
super().__init__( super().__init__(
Sum( Residual(
Identity(), LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype),
Chain( SelfAttention(
LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype), embedding_dim=embedding_dim, num_heads=num_heads, use_bias=use_bias, device=device, dtype=dtype
SelfAttention(
embedding_dim=embedding_dim, num_heads=num_heads, use_bias=use_bias, device=device, dtype=dtype
),
), ),
), ),
Sum( Residual(
Identity(), LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype),
Chain( Parallel(
LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype), Identity(),
Parallel( UseContext(context=self.context, key=context_key),
Identity(), UseContext(context=self.context, key=context_key),
UseContext(context=self.context, key=context_key), ),
UseContext(context=self.context, key=context_key), Attention(
), embedding_dim=embedding_dim,
Attention( num_heads=num_heads,
embedding_dim=embedding_dim, key_embedding_dim=context_embedding_dim,
num_heads=num_heads, value_embedding_dim=context_embedding_dim,
key_embedding_dim=context_embedding_dim, use_bias=use_bias,
value_embedding_dim=context_embedding_dim, device=device,
use_bias=use_bias, dtype=dtype,
device=device,
dtype=dtype,
),
), ),
), ),
Sum( Residual(
Identity(), LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype),
Chain( Linear(in_features=embedding_dim, out_features=2 * 4 * embedding_dim, device=device, dtype=dtype),
LayerNorm(normalized_shape=embedding_dim, device=device, dtype=dtype), GLU(GeLU()),
Linear(in_features=embedding_dim, out_features=2 * 4 * embedding_dim, device=device, dtype=dtype), Linear(in_features=4 * embedding_dim, out_features=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__( def __init__(
self, self,
channels: int, channels: int,
@ -164,23 +155,20 @@ class CrossAttentionBlock2d(Sum):
) )
super().__init__( super().__init__(
Identity(), in_block,
Chain( Chain(
in_block, CrossAttentionBlock(
Chain( embedding_dim=channels,
CrossAttentionBlock( context_embedding_dim=context_embedding_dim,
embedding_dim=channels, context_key=context_key,
context_embedding_dim=context_embedding_dim, num_heads=num_attention_heads,
context_key=context_key, use_bias=use_bias,
num_heads=num_attention_heads, device=device,
use_bias=use_bias, dtype=dtype,
device=device, )
dtype=dtype, for _ in range(num_attention_layers)
)
for _ in range(num_attention_layers)
),
out_block,
), ),
out_block,
) )
def init_context(self) -> Contexts: def init_context(self) -> Contexts:

11
src/refiners/foundationals/latent_diffusion/stable_diffusion_1/controlnet.py
View file

@ -1,5 +1,5 @@
from refiners.fluxion.context import Contexts 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 ( from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import (
SD1UNet, SD1UNet,
DownBlocks, DownBlocks,
@ -92,12 +92,9 @@ class Controlnet(Passthrough):
# We run the condition encoder at each step. Caching the result # We run the condition encoder at each step. Caching the result
# is not worth it as subsequent runs take virtually no time (FG-374). # is not worth it as subsequent runs take virtually no time (FG-374).
self.DownBlocks[0].append( self.DownBlocks[0].append(
Sum( Residual(
Identity(), UseContext("controlnet", f"condition_{name}"),
Chain( ConditionEncoder(device=device, dtype=dtype),
UseContext("controlnet", f"condition_{name}"),
ConditionEncoder(device=device, dtype=dtype),
),
), ),
) )
for residual_block in self.layers(ResidualBlock): for residual_block in self.layers(ResidualBlock):

2
tests/foundationals/segment_anything/test_sam.py
View file

@ -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_1 = facebook_sam_h.image_encoder(image_tensor)
y_2 = 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() @torch.no_grad()