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feat: add support for Segmind's Vega model (#1195)

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5 changed files with 28 additions and 6 deletions
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16
docs/distilled_sd.md
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@ -1,8 +1,8 @@
# Running distilled models: SSD1B and SDx.x with tiny U-Nets
# Running distilled models: SSD1B, Vega and SDx.x with tiny U-Nets
## Preface
These models feature a reduced U-Net architecture. Unlike standard SDXL models, the SSD-1B U-Net contains only one middle block and fewer attention layers in its up- and down-blocks, resulting in significantly smaller file sizes. Using these models can reduce inference time by more than 33%. For more details, refer to Segmind's paper: https://arxiv.org/abs/2401.02677v1.
These models feature a reduced U-Net architecture. Unlike standard SDXL models, the SSD-1B and Vega U-Net contains only one middle block and fewer attention layers in its up- and down-blocks, resulting in significantly smaller file sizes. Using these models can reduce inference time by more than 33%. For more details, refer to Segmind's paper: https://arxiv.org/abs/2401.02677v1.
Similarly, SD1.x- and SD2.x-style models with a tiny U-Net consist of only 6 U-Net blocks, leading to very small files and time savings of up to 50%. For more information, see the paper: https://arxiv.org/pdf/2305.15798.pdf.
## SSD1B
@ -17,7 +17,17 @@ Useful LoRAs are also available:
* https://huggingface.co/seungminh/lora-swarovski-SSD-1B/resolve/main/pytorch_lora_weights.safetensors
* https://huggingface.co/kylielee505/mylcmlorassd/resolve/main/pytorch_lora_weights.safetensors
These files can be used out-of-the-box, unlike the models described in the next section.
## Vega
Segmind's Vega model is available online here:
* https://huggingface.co/segmind/Segmind-Vega/resolve/main/segmind-vega.safetensors
VegaRT is an example for an LCM-LoRA:
* https://huggingface.co/segmind/Segmind-VegaRT/resolve/main/pytorch_lora_weights.safetensors
Both files can be used out-of-the-box, unlike the models described in next sections.
## SD1.x, SD2.x with tiny U-Nets

7
model.cpp
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@ -1040,6 +1040,7 @@ SDVersion ModelLoader::get_sd_version() {
int64_t patch_embedding_channels = 0;
bool has_img_emb = false;
bool has_middle_block_1 = false;
bool has_output_block_311 = false;
bool has_output_block_71 = false;
for (auto& [name, tensor_storage] : tensor_storage_map) {
@ -1100,6 +1101,9 @@ SDVersion ModelLoader::get_sd_version() {
tensor_storage.name.find("unet.mid_block.resnets.1.") != std::string::npos) {
has_middle_block_1 = true;
}
if (tensor_storage.name.find("model.diffusion_model.output_blocks.3.1.transformer_blocks.1") != std::string::npos) {
has_output_block_311 = true;
}
if (tensor_storage.name.find("model.diffusion_model.output_blocks.7.1") != std::string::npos) {
has_output_block_71 = true;
}
@ -1138,6 +1142,9 @@ SDVersion ModelLoader::get_sd_version() {
return VERSION_SDXL_PIX2PIX;
}
if (!has_middle_block_1) {
if (!has_output_block_311) {
return VERSION_SDXL_VEGA;
}
return VERSION_SDXL_SSD1B;
}
return VERSION_SDXL;

3
model.h
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@ -32,6 +32,7 @@ enum SDVersion {
VERSION_SDXL,
VERSION_SDXL_INPAINT,
VERSION_SDXL_PIX2PIX,
VERSION_SDXL_VEGA,
VERSION_SDXL_SSD1B,
VERSION_SVD,
VERSION_SD3,
@ -66,7 +67,7 @@ static inline bool sd_version_is_sd2(SDVersion version) {
}
static inline bool sd_version_is_sdxl(SDVersion version) {
if (version == VERSION_SDXL || version == VERSION_SDXL_INPAINT || version == VERSION_SDXL_PIX2PIX || version == VERSION_SDXL_SSD1B) {
if (version == VERSION_SDXL || version == VERSION_SDXL_INPAINT || version == VERSION_SDXL_PIX2PIX || version == VERSION_SDXL_SSD1B || version == VERSION_SDXL_VEGA) {
return true;
}
return false;

1
stable-diffusion.cpp
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@ -35,6 +35,7 @@ const char* model_version_to_str[] = {
"SDXL",
"SDXL Inpaint",
"SDXL Instruct-Pix2Pix",
"SDXL (Vega)",
"SDXL (SSD1B)",
"SVD",
"SD3.x",

7
unet.hpp
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@ -201,6 +201,9 @@ public:
num_head_channels = 64;
num_heads = -1;
use_linear_projection = true;
if (version == VERSION_SDXL_VEGA) {
transformer_depth = {1, 1, 2};
}
} else if (version == VERSION_SVD) {
in_channels = 8;
out_channels = 4;
@ -319,7 +322,7 @@ public:
}
if (!tiny_unet) {
blocks["middle_block.0"] = std::shared_ptr<GGMLBlock>(get_resblock(ch, time_embed_dim, ch));
if (version != VERSION_SDXL_SSD1B) {
if (version != VERSION_SDXL_SSD1B && version != VERSION_SDXL_VEGA) {
blocks["middle_block.1"] = std::shared_ptr<GGMLBlock>(get_attention_layer(ch,
n_head,
d_head,
@ -520,7 +523,7 @@ public:
// middle_block
if (!tiny_unet) {
h = resblock_forward("middle_block.0", ctx, h, emb, num_video_frames); // [N, 4*model_channels, h/8, w/8]
if (version != VERSION_SDXL_SSD1B) {
if (version != VERSION_SDXL_SSD1B && version != VERSION_SDXL_VEGA) {
h = attention_layer_forward("middle_block.1", ctx, h, context, num_video_frames); // [N, 4*model_channels, h/8, w/8]
h = resblock_forward("middle_block.2", ctx, h, emb, num_video_frames); // [N, 4*model_channels, h/8, w/8]
}