VictorMylle/Kohya-ss-sd-scripts
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Merge branch 'dev' into sd3

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Kohya S
2025-03-21 22:07:50 +09:00
parent d151833526 5253a38783
commit 6364379f17
5 changed files with 11 additions and 4 deletions
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7
README.md
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@@ -752,6 +752,8 @@ Not available yet.
[__Change History__](#change-history) is moved to the bottom of the page. [__Change History__](#change-history) is moved to the bottom of the page.
更新履歴は[ページ末尾](#change-history)に移しました。 更新履歴は[ページ末尾](#change-history)に移しました。
Latest update: 2025-03-21 (Version 0.9.1)
[日本語版READMEはこちら](./README-ja.md) [日本語版READMEはこちら](./README-ja.md)
The development version is in the `dev` branch. Please check the dev branch for the latest changes. The development version is in the `dev` branch. Please check the dev branch for the latest changes.
@@ -895,6 +897,11 @@ The majority of scripts is licensed under ASL 2.0 (including codes from Diffuser
## Change History ## Change History
### Mar 21, 2025 / 2025-03-21 Version 0.9.1
- Fixed a bug where some of LoRA modules for CLIP Text Encoder were not trained. Thank you Nekotekina for PR [#1964](https://github.com/kohya-ss/sd-scripts/pull/1964)
- The LoRA modules for CLIP Text Encoder are now 264 modules, which is the same as before. Only 88 modules were trained in the previous version.
### Jan 17, 2025 / 2025-01-17 Version 0.9.0 ### Jan 17, 2025 / 2025-01-17 Version 0.9.0
- __important__ The dependent libraries are updated. Please see [Upgrade](#upgrade) and update the libraries. - __important__ The dependent libraries are updated. Please see [Upgrade](#upgrade) and update the libraries.

2
networks/dylora.py
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@@ -268,7 +268,7 @@ def create_network_from_weights(multiplier, file, vae, text_encoder, unet, weigh
class DyLoRANetwork(torch.nn.Module): class DyLoRANetwork(torch.nn.Module):
UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel"] UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel"]
UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"] UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"] TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPSdpaAttention", "CLIPMLP"]
LORA_PREFIX_UNET = "lora_unet" LORA_PREFIX_UNET = "lora_unet"
LORA_PREFIX_TEXT_ENCODER = "lora_te" LORA_PREFIX_TEXT_ENCODER = "lora_te"

2
networks/lora.py
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@@ -866,7 +866,7 @@ class LoRANetwork(torch.nn.Module):
UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel"] UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel"]
UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"] UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"] TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPSdpaAttention", "CLIPMLP"]
LORA_PREFIX_UNET = "lora_unet" LORA_PREFIX_UNET = "lora_unet"
LORA_PREFIX_TEXT_ENCODER = "lora_te" LORA_PREFIX_TEXT_ENCODER = "lora_te"

2
networks/lora_diffusers.py
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@@ -278,7 +278,7 @@ def merge_lora_weights(pipe, weights_sd: Dict, multiplier: float = 1.0):
class LoRANetwork(torch.nn.Module): class LoRANetwork(torch.nn.Module):
UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel"] UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel"]
UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"] UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"] TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPSdpaAttention", "CLIPMLP"]
LORA_PREFIX_UNET = "lora_unet" LORA_PREFIX_UNET = "lora_unet"
LORA_PREFIX_TEXT_ENCODER = "lora_te" LORA_PREFIX_TEXT_ENCODER = "lora_te"

2
networks/lora_fa.py
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@@ -755,7 +755,7 @@ class LoRANetwork(torch.nn.Module):
UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel"] UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel"]
UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"] UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"] TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPSdpaAttention", "CLIPMLP"]
LORA_PREFIX_UNET = "lora_unet" LORA_PREFIX_UNET = "lora_unet"
LORA_PREFIX_TEXT_ENCODER = "lora_te" LORA_PREFIX_TEXT_ENCODER = "lora_te"