Added some additional experimental things to the vision direct encoder

toolkit/config_modules.py

@@ -212,6 +212,11 @@ class AdapterConfig:
 
         self.flux_only_double: bool = kwargs.get('flux_only_double', False)
         
+        # train and use a conv layer to pool the embedding
+        self.conv_pooling: bool = kwargs.get('conv_pooling', False)
+        self.conv_pooling_stacks: int = kwargs.get('conv_pooling_stacks', 1)
+        self.sparse_autoencoder_dim: Optional[int] = kwargs.get('sparse_autoencoder_dim', None)
+
 
 class EmbeddingConfig:
     def __init__(self, **kwargs):

toolkit/custom_adapter.py

@@ -407,7 +407,7 @@ class CustomAdapter(torch.nn.Module):
         if 'vd_adapter' in state_dict:
             self.vd_adapter.load_state_dict(state_dict['vd_adapter'], strict=strict)
         if 'dvadapter' in state_dict:
-            self.vd_adapter.load_state_dict(state_dict['dvadapter'], strict=strict)
+            self.vd_adapter.load_state_dict(state_dict['dvadapter'], strict=False)
 
         if 'sv_adapter' in state_dict:
             self.single_value_adapter.load_state_dict(state_dict['sv_adapter'], strict=strict)
@@ -732,8 +732,7 @@ class CustomAdapter(torch.nn.Module):
     def train(self, mode: bool = True):
         if self.config.train_image_encoder:
             self.vision_encoder.train(mode)
-        else:
+        super().train(mode)
-            super().train(mode)
 
     def trigger_pre_te(
             self,
@@ -879,7 +878,10 @@ class CustomAdapter(torch.nn.Module):
                     elif self.config.clip_layer == 'last_hidden_state':
                         clip_image_embeds = clip_output.hidden_states[-1]
                     else:
-                        clip_image_embeds = clip_output.image_embeds
+                        if hasattr(clip_output, 'image_embeds'):
+                            clip_image_embeds = clip_output.image_embeds
+                        elif hasattr(clip_output, 'pooler_output'):
+                            clip_image_embeds = clip_output.pooler_output
                         # TODO should we always norm image embeds?
                         # get norm embeddings
                         l2_norm = torch.norm(clip_image_embeds, p=2)
@@ -931,8 +933,12 @@ class CustomAdapter(torch.nn.Module):
                     yield from attn_processor.parameters(recurse)
                 if self.config.train_image_encoder:
                     yield from self.vision_encoder.parameters(recurse)
-                if self.config.num_tokens:
+                if self.vd_adapter.resampler is not None:
                     yield from self.vd_adapter.resampler.parameters(recurse)
+                if self.vd_adapter.pool is not None:
+                    yield from self.vd_adapter.pool.parameters(recurse)
+                if self.vd_adapter.sparse_autoencoder is not None:
+                    yield from self.vd_adapter.sparse_autoencoder.parameters(recurse)
         elif self.config.type == 'te_augmenter':
             yield from self.te_augmenter.parameters(recurse)
             if self.config.train_image_encoder:

toolkit/dataloader_mixins.py

@@ -13,7 +13,7 @@ import numpy as np
 import torch
 from safetensors.torch import load_file, save_file
 from tqdm import tqdm
-from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
+from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection, SiglipImageProcessor
 
 from toolkit.basic import flush, value_map
 from toolkit.buckets import get_bucket_for_image_size, get_resolution
@@ -764,7 +764,8 @@ class ClipImageFileItemDTOMixin:
             return self.clip_image_path
 
     def load_clip_image(self: 'FileItemDTO'):
-        is_dynamic_size_and_aspect = isinstance(self.clip_image_processor, PixtralVisionImagePreprocessorCompatible)
+        is_dynamic_size_and_aspect = isinstance(self.clip_image_processor, PixtralVisionImagePreprocessorCompatible) or \
+                                    isinstance(self.clip_image_processor, SiglipImageProcessor)
         if self.is_vision_clip_cached:
             self.clip_image_embeds = load_file(self.get_clip_vision_embeddings_path())
 
@@ -794,21 +795,7 @@ class ClipImageFileItemDTOMixin:
             img = img.transpose(Image.FLIP_TOP_BOTTOM)
             
         if is_dynamic_size_and_aspect:
-            # just match the bucket size for now
+            pass  # let the image processor handle it
-            if self.dataset_config.buckets:
-                # scale and crop based on file item
-                img = img.resize((self.scale_to_width, self.scale_to_height), Image.BICUBIC)
-                # img = transforms.CenterCrop((self.crop_height, self.crop_width))(img)
-                # crop
-                img = img.crop((
-                    self.crop_x,
-                    self.crop_y,
-                    self.crop_x + self.crop_width,
-                    self.crop_y + self.crop_height
-                ))
-            else:
-                raise Exception("Control images not supported for non-bucket datasets")
-        
         elif img.width != img.height:
             min_size = min(img.width, img.height)
             if self.dataset_config.square_crop:

toolkit/models/vd_adapter.py

@@ -10,6 +10,7 @@ from collections import OrderedDict
 from diffusers import Transformer2DModel, FluxTransformer2DModel
 from transformers import T5EncoderModel, CLIPTextModel, CLIPTokenizer, T5Tokenizer, CLIPVisionModelWithProjection
 from toolkit.models.pixtral_vision import PixtralVisionEncoder, PixtralVisionImagePreprocessor, VisionLanguageAdapter
+from transformers import SiglipImageProcessor, SiglipVisionModel
 
 from toolkit.config_modules import AdapterConfig
 from toolkit.paths import REPOS_ROOT
@@ -21,6 +22,52 @@ if TYPE_CHECKING:
     from toolkit.custom_adapter import CustomAdapter
     
 
+# matches distribution of randn
+class Norm(nn.Module):
+    def __init__(self, target_mean=0.0, target_std=1.0, eps=1e-6):
+        super(Norm, self).__init__()
+        self.target_mean = target_mean
+        self.target_std = target_std
+        self.eps = eps
+
+    def forward(self, x):
+        dims = tuple(range(1, x.dim()))
+        mean = x.mean(dim=dims, keepdim=True)
+        std = x.std(dim=dims, keepdim=True)
+        
+        # Normalize
+        return self.target_std * (x - mean) / (std + self.eps) + self.target_mean
+    
+    
+class SparseAutoencoder(nn.Module):
+    def __init__(self, input_dim, hidden_dim, output_dim):
+        super(SparseAutoencoder, self).__init__()
+        self.encoder = nn.Sequential(
+            nn.Linear(input_dim, hidden_dim),
+            nn.GELU(),
+            nn.Linear(hidden_dim, output_dim),
+        )
+        self.norm = Norm()
+        self.decoder = nn.Sequential(
+            nn.Linear(output_dim, hidden_dim),
+            nn.GELU(),
+            nn.Linear(hidden_dim, input_dim),
+        )
+        self.last_run = None
+
+    def forward(self, x):
+        self.last_run = {
+            "input": x
+        }
+        x = self.encoder(x)
+        x = self.norm(x)
+        self.last_run["sparse"] = x
+        x = self.decoder(x)
+        x = self.norm(x)
+        self.last_run["output"] = x
+        return x
+
+
 class MLPR(nn.Module):  # MLP with reshaping
     def __init__(
             self,
@@ -466,12 +513,18 @@ class VisionDirectAdapter(torch.nn.Module):
         is_pixtral = self.config.image_encoder_arch == "pixtral"
 
         if adapter.config.clip_layer == "image_embeds":
-            self.token_size = vision_model.config.projection_dim
+            if isinstance(vision_model, SiglipVisionModel):
+                self.token_size = vision_model.config.hidden_size
+            else:
+                self.token_size = vision_model.config.projection_dim
         else:
             self.token_size = vision_model.config.hidden_size
             
         self.mid_size = self.token_size
         
+        if self.config.conv_pooling and self.config.conv_pooling_stacks > 1:
+            self.mid_size = self.mid_size * self.config.conv_pooling_stacks
+        
         # if pixtral, use cross attn dim for more sparse representation if only doing double transformers
         if is_pixtral and self.config.flux_only_double:
             if is_flux:
@@ -678,6 +731,27 @@ class VisionDirectAdapter(torch.nn.Module):
                 out_dim=self.mid_size,
             )
         
+        self.pool = None
+        self.sparse_autoencoder = None
+        if self.config.conv_pooling:
+            vision_config = self.adapter_ref().vision_encoder.config
+            # sequence_length = int((vision_config.image_size / vision_config.patch_size) ** 2 + 1)
+            # siglip doesnt add 1
+            sequence_length = int((vision_config.image_size / vision_config.patch_size) ** 2)
+            self.pool = nn.Sequential(
+                nn.Conv1d(sequence_length, self.config.conv_pooling_stacks, 1, bias=False),
+                Norm(),
+            )
+        if self.config.sparse_autoencoder_dim is not None:
+            hidden_dim  = self.token_size * 2
+            if hidden_dim > self.config.sparse_autoencoder_dim:
+                hidden_dim = self.config.sparse_autoencoder_dim
+            self.sparse_autoencoder = SparseAutoencoder(
+                input_dim=self.token_size,
+                hidden_dim=hidden_dim,
+                output_dim=self.config.sparse_autoencoder_dim
+            )
+
     def state_dict(self, destination=None, prefix='', keep_vars=False):
         if self.config.train_scaler:
             # only return the block scaler
@@ -699,6 +773,12 @@ class VisionDirectAdapter(torch.nn.Module):
             self.block_scaler.data = self.block_scaler.data.to(torch.float32)
         if self.resampler is not None:
             input = self.resampler(input)
+        if self.pool is not None:
+            input = self.pool(input)
+            if self.config.conv_pooling_stacks > 1:
+                input = torch.cat(torch.chunk(input, self.config.conv_pooling_stacks, dim=1), dim=2)
+        if self.sparse_autoencoder is not None:
+            input = self.sparse_autoencoder(input)
         return input
 
     def to(self, *args, **kwargs):