Added a clip vision adapter trainer. Only works for sd15 for now

extensions_built_in/sd_trainer/SDTrainer.py

@@ -5,6 +5,7 @@ from diffusers import T2IAdapter
 import torch.functional as F
 from toolkit import train_tools
 from toolkit.basic import value_map, adain, get_mean_std
+from toolkit.clip_vision_adapter import ClipVisionAdapter
 from toolkit.config_modules import GuidanceConfig
 from toolkit.data_transfer_object.data_loader import DataLoaderBatchDTO, FileItemDTO
 from toolkit.guidance import get_targeted_guidance_loss, get_guidance_loss
@@ -504,6 +505,7 @@ class SDTrainer(BaseSDTrainProcess):
             noise: torch.Tensor,
             **kwargs
     ):
+        # todo for embeddings, we need to run without trigger words
         was_unet_training = self.sd.unet.training
         was_network_active = False
         if self.network is not None:
@@ -519,13 +521,28 @@ class SDTrainer(BaseSDTrainProcess):
         # do a prediction here so we can match its output with network multiplier set to 0.0
         with torch.no_grad():
             dtype = get_torch_dtype(self.train_config.dtype)
+
+            embeds_to_use = conditional_embeds.clone().detach()
+            # handle clip vision adapter by removing triggers from prompt and replacing with the class name
+            if self.adapter is not None and isinstance(self.adapter, ClipVisionAdapter):
+                prompt_list = batch.get_caption_list()
+                for idx, prompt in enumerate(prompt_list):
+                    prompt = self.adapter.inject_trigger_class_name_into_prompt(prompt)
+                    prompt_list[idx] = prompt
+
+                embeds_to_use = self.sd.encode_prompt(
+                    prompt,
+                    long_prompts=self.do_long_prompts).to(
+                    self.device_torch,
+                    dtype=dtype).detach()
+
             # dont use network on this
             # self.network.multiplier = 0.0
             self.sd.unet.eval()
 
             prior_pred = self.sd.predict_noise(
                 latents=noisy_latents.to(self.device_torch, dtype=dtype).detach(),
-                conditional_embeddings=conditional_embeds.to(self.device_torch, dtype=dtype).detach(),
+                conditional_embeddings=embeds_to_use.to(self.device_torch, dtype=dtype).detach(),
                 timestep=timesteps,
                 guidance_scale=1.0,
                 **pred_kwargs  # adapter residuals in here
@@ -666,6 +683,9 @@ class SDTrainer(BaseSDTrainProcess):
             if self.embedding:
                 grad_on_text_encoder = True
 
+            if self.adapter and isinstance(self.adapter, ClipVisionAdapter):
+                grad_on_text_encoder = True
+
             # have a blank network so we can wrap it in a context and set multipliers without checking every time
             if self.network is not None:
                 network = self.network
@@ -745,6 +765,26 @@ class SDTrainer(BaseSDTrainProcess):
                     prompt_2 = prompt_2 + [self.train_config.negative_prompt for x in range(len(prompt_2))]
 
             with network:
+                # encode clip adapter here so embeds are active for tokenizer
+                if self.adapter and isinstance(self.adapter, ClipVisionAdapter):
+                    with self.timer('encode_clip_vision_embeds'):
+                        if has_clip_image:
+                            conditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(
+                                clip_images.detach().to(self.device_torch, dtype=dtype),
+                                is_training=True
+                            )
+                        else:
+                            # just do a blank one
+                            conditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(
+                                torch.zeros(
+                                    (noisy_latents.shape[0], 3, 512, 512),
+                                    device=self.device_torch, dtype=dtype
+                                ),
+                                is_training=True
+                            )
+                        # it will be injected into the tokenizer when called
+                        self.adapter(conditional_clip_embeds)
+
                 with self.timer('encode_prompt'):
                     if grad_on_text_encoder:
                         with torch.set_grad_enabled(True):
@@ -912,6 +952,10 @@ class SDTrainer(BaseSDTrainProcess):
             with self.timer('restore_embeddings'):
                 # Let's make sure we don't update any embedding weights besides the newly added token
                 self.embedding.restore_embeddings()
+        if self.adapter is not None and isinstance(self.adapter, ClipVisionAdapter):
+            with self.timer('restore_adapter'):
+                # Let's make sure we don't update any embedding weights besides the newly added token
+                self.adapter.restore_embeddings()
 
         loss_dict = OrderedDict(
             {'loss': loss.item()}

jobs/process/BaseSDTrainProcess.py

@@ -5,7 +5,7 @@ import json
 import shutil
 from collections import OrderedDict
 import os
-from typing import Union, List
+from typing import Union, List, Optional
 
 import numpy as np
 import yaml
@@ -17,6 +17,7 @@ import torch
 import torch.backends.cuda
 
 from toolkit.basic import value_map
+from toolkit.clip_vision_adapter import ClipVisionAdapter
 from toolkit.data_loader import get_dataloader_from_datasets, trigger_dataloader_setup_epoch
 from toolkit.data_transfer_object.data_loader import FileItemDTO, DataLoaderBatchDTO
 from toolkit.embedding import Embedding
@@ -138,7 +139,7 @@ class BaseSDTrainProcess(BaseTrainProcess):
 
         # to hold network if there is one
         self.network: Union[Network, None] = None
-        self.adapter: Union[T2IAdapter, IPAdapter, None] = None
+        self.adapter: Union[T2IAdapter, IPAdapter, ClipVisionAdapter, None] = None
         self.embedding: Union[Embedding, None] = None
 
         is_training_adapter = self.adapter_config is not None and self.adapter_config.train
@@ -202,7 +203,11 @@ class BaseSDTrainProcess(BaseTrainProcess):
             # ie test123 will become test123 test123_1 test123_2 etc. Do not add this yourself here
             if self.embedding is not None:
                 prompt = self.embedding.inject_embedding_to_prompt(
-                    prompt, add_if_not_present=False
+                    prompt, expand_token=True, add_if_not_present=False
+                )
+            if self.adapter is not None and isinstance(self.adapter, ClipVisionAdapter):
+                prompt = self.adapter.inject_trigger_into_prompt(
+                    prompt, expand_token=True, add_if_not_present=False
                 )
             if self.trigger_word is not None:
                 prompt = self.sd.inject_trigger_into_prompt(
@@ -400,6 +405,8 @@ class BaseSDTrainProcess(BaseTrainProcess):
                     # add _lora to name
                     if self.adapter_config.type == 't2i':
                         adapter_name += '_t2i'
+                    elif self.adapter_config.type == 'clip':
+                        adapter_name += '_clip'
                     else:
                         adapter_name += '_ip'
 
@@ -647,6 +654,13 @@ class BaseSDTrainProcess(BaseTrainProcess):
                             add_if_not_present=not is_reg,
                         )
 
+                    if self.adapter and isinstance(self.adapter, ClipVisionAdapter):
+                        prompt = self.adapter.inject_trigger_into_prompt(
+                            prompt,
+                            expand_token=True,
+                            add_if_not_present=not is_reg,
+                        )
+
                     # make sure trigger is in the prompts if not a regularization run
                     if self.trigger_word is not None:
                         prompt = self.sd.inject_trigger_into_prompt(
@@ -840,7 +854,12 @@ class BaseSDTrainProcess(BaseTrainProcess):
     def setup_adapter(self):
         # t2i adapter
         is_t2i = self.adapter_config.type == 't2i'
-        suffix = 't2i' if is_t2i else 'ip'
+        if self.adapter_config.type == 't2i':
+            suffix = 't2i'
+        elif self.adapter_config.type == 'clip':
+            suffix = 'clip'
+        else:
+            suffix = 'ip'
         adapter_name = self.name
         if self.network_config is not None:
             adapter_name = f"{adapter_name}_{suffix}"
@@ -865,6 +884,11 @@ class BaseSDTrainProcess(BaseTrainProcess):
                     downscale_factor=self.adapter_config.downscale_factor,
                     adapter_type=self.adapter_config.adapter_type,
                 )
+        elif self.adapter_config.type == 'clip':
+            self.adapter = ClipVisionAdapter(
+                sd=self.sd,
+                adapter_config=self.adapter_config,
+            )
         else:
             self.adapter = IPAdapter(
                 sd=self.sd,

toolkit/clip_vision_adapter.py

@@ -0,0 +1,331 @@
+from typing import TYPE_CHECKING, Mapping, Any
+
+import torch
+import weakref
+
+from toolkit.config_modules import AdapterConfig
+from toolkit.prompt_utils import PromptEmbeds
+from toolkit.train_tools import get_torch_dtype
+
+if TYPE_CHECKING:
+    from toolkit.stable_diffusion_model import StableDiffusion
+
+from transformers import (
+    CLIPImageProcessor,
+    CLIPVisionModelWithProjection,
+    CLIPVisionModel
+)
+
+from toolkit.resampler import Resampler
+
+import torch.nn as nn
+
+
+class Embedder(nn.Module):
+    def __init__(
+            self,
+            num_input_tokens: int = 50,
+            input_dim: int = 1024,
+            num_output_tokens: int = 8,
+            output_dim: int = 768,
+            mid_dim: int = 128,
+    ):
+        super(Embedder, self).__init__()
+        self.num_output_tokens = num_output_tokens
+        self.num_input_tokens = num_input_tokens
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+
+        # Convolutional layer to reduce channel dimension
+        self.conv = nn.Conv1d(in_channels=input_dim, out_channels=mid_dim, kernel_size=1)
+
+        # GELU Activation
+        self.gelu = nn.GELU()
+
+        # Layer Normalization
+        self.layer_norm = nn.LayerNorm(mid_dim)
+
+        # Adaptive pooling to change sequence length
+        self.adaptive_pool = nn.AdaptiveAvgPool1d(num_output_tokens)
+
+        # Linear layer for final transformation
+        self.final_linear = nn.Linear(mid_dim, output_dim)
+
+    def forward(self, x):
+        x = x.permute(0, 2, 1)  # Adjust for Conv1d
+        x = self.conv(x)
+        x = self.gelu(x)
+        x = self.layer_norm(x.permute(0, 2, 1)).permute(0, 2, 1)  # Apply LayerNorm
+        x = self.adaptive_pool(x)
+        x = x.permute(0, 2, 1)  # Adjust back
+        x = self.final_linear(x)
+        return x
+
+
+class ClipVisionAdapter(torch.nn.Module):
+    def __init__(self, sd: 'StableDiffusion', adapter_config: AdapterConfig):
+        super().__init__()
+        self.config = adapter_config
+        self.trigger = adapter_config.trigger
+        self.trigger_class_name = adapter_config.trigger_class_name
+        self.sd_ref: weakref.ref = weakref.ref(sd)
+        # embedding stuff
+        self.text_encoder_list = sd.text_encoder if isinstance(sd.text_encoder, list) else [sd.text_encoder]
+        self.tokenizer_list = sd.tokenizer if isinstance(sd.tokenizer, list) else [sd.tokenizer]
+        placeholder_tokens = [self.trigger]
+
+        # add dummy tokens for multi-vector
+        additional_tokens = []
+        for i in range(1, self.config.num_tokens):
+            additional_tokens.append(f"{self.trigger}_{i}")
+        placeholder_tokens += additional_tokens
+
+        # handle dual tokenizer
+        self.tokenizer_list = self.sd_ref().tokenizer if isinstance(self.sd_ref().tokenizer, list) else [
+            self.sd_ref().tokenizer]
+        self.text_encoder_list = self.sd_ref().text_encoder if isinstance(self.sd_ref().text_encoder, list) else [
+            self.sd_ref().text_encoder]
+
+        self.placeholder_token_ids = []
+        self.embedding_tokens = []
+
+        print(f"Adding {placeholder_tokens} tokens to tokenizer")
+        print(f"Adding {self.config.num_tokens} tokens to tokenizer")
+
+        for text_encoder, tokenizer in zip(self.text_encoder_list, self.tokenizer_list):
+            num_added_tokens = tokenizer.add_tokens(placeholder_tokens)
+            if num_added_tokens != self.config.num_tokens:
+                raise ValueError(
+                    f"The tokenizer already contains the token {self.trigger}. Please pass a different"
+                    f" `placeholder_token` that is not already in the tokenizer. Only added {num_added_tokens}"
+                )
+
+            # Convert the initializer_token, placeholder_token to ids
+            init_token_ids = tokenizer.encode(self.config.trigger_class_name, add_special_tokens=False)
+            # if length of token ids is more than number of orm embedding tokens fill with *
+            if len(init_token_ids) > self.config.num_tokens:
+                init_token_ids = init_token_ids[:self.config.num_tokens]
+            elif len(init_token_ids) < self.config.num_tokens:
+                pad_token_id = tokenizer.encode(["*"], add_special_tokens=False)
+                init_token_ids += pad_token_id * (self.config.num_tokens - len(init_token_ids))
+
+            placeholder_token_ids = tokenizer.encode(placeholder_tokens, add_special_tokens=False)
+            self.placeholder_token_ids.append(placeholder_token_ids)
+
+            # Resize the token embeddings as we are adding new special tokens to the tokenizer
+            text_encoder.resize_token_embeddings(len(tokenizer))
+
+            # Initialise the newly added placeholder token with the embeddings of the initializer token
+            token_embeds = text_encoder.get_input_embeddings().weight.data
+            with torch.no_grad():
+                for initializer_token_id, token_id in zip(init_token_ids, placeholder_token_ids):
+                    token_embeds[token_id] = token_embeds[initializer_token_id].clone()
+
+            # replace "[name] with this. on training. This is automatically generated in pipeline on inference
+            self.embedding_tokens.append(" ".join(tokenizer.convert_ids_to_tokens(placeholder_token_ids)))
+
+        # backup text encoder embeddings
+        self.orig_embeds_params = [x.get_input_embeddings().weight.data.clone() for x in self.text_encoder_list]
+
+        try:
+            self.clip_image_processor = CLIPImageProcessor.from_pretrained(self.config.image_encoder_path)
+        except EnvironmentError:
+            self.clip_image_processor = CLIPImageProcessor()
+        self.device = self.sd_ref().unet.device
+        self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+            self.config.image_encoder_path,
+            ignore_mismatched_sizes=True
+        ).to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype))
+        if self.config.train_image_encoder:
+            self.image_encoder.train()
+        else:
+            self.image_encoder.eval()
+        # self.embedder = Embedder(
+        #     num_output_tokens=self.config.num_tokens,
+        #     num_input_tokens=self.image_encoder.config.top_k,  # max_position_embeddings ?
+        #     input_dim=self.image_encoder.config.hidden_size,
+        #     output_dim=sd.unet.config['cross_attention_dim'],
+        # ).to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype))
+        heads = 12 if not sd.is_xl else 20
+        # dim = sd.unet.config['cross_attention_dim'] if not sd.is_xl else 1280
+        dim = sd.unet.config['cross_attention_dim']
+        self.embedder = Resampler(
+            dim=dim,
+            depth=4,
+            dim_head=64,
+            heads=heads,
+            num_queries=self.config.num_tokens,  # usually 16
+            embedding_dim=self.image_encoder.config.hidden_size,
+            output_dim=sd.unet.config['cross_attention_dim'],
+            ff_mult=4
+        ).to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype))
+
+        self.embedder.train()
+
+    def state_dict(self, *args, destination=None, prefix='', keep_vars=False):
+        state_dict = {
+            'embedder': self.embedder.state_dict(*args, destination=destination, prefix=prefix, keep_vars=keep_vars)
+        }
+        if self.config.train_image_encoder:
+            state_dict['image_encoder'] = self.image_encoder.state_dict(
+                *args, destination=destination, prefix=prefix,
+                keep_vars=keep_vars)
+
+        return state_dict
+
+    def load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True):
+        self.embedder.load_state_dict(state_dict["embedder"], strict=strict)
+        if self.config.train_image_encoder and 'image_encoder' in state_dict:
+            self.image_encoder.load_state_dict(state_dict["image_encoder"], strict=strict)
+
+    def parameters(self, *args, **kwargs):
+        yield from self.embedder.parameters(*args, **kwargs)
+
+    def named_parameters(self, *args, **kwargs):
+        yield from self.embedder.named_parameters(*args, **kwargs)
+
+    def get_clip_image_embeds_from_tensors(
+            self, tensors_0_1: torch.Tensor, drop=False,
+            is_training=False
+    ) -> torch.Tensor:
+        with torch.no_grad():
+            # tensors should be 0-1
+            # todo: add support for sdxl
+            if tensors_0_1.ndim == 3:
+                tensors_0_1 = tensors_0_1.unsqueeze(0)
+            # training tensors are 0 - 1
+            tensors_0_1 = tensors_0_1.to(self.device, dtype=torch.float16)
+            # if images are out of this range throw error
+            if tensors_0_1.min() < -0.3 or tensors_0_1.max() > 1.3:
+                raise ValueError("image tensor values must be between 0 and 1. Got min: {}, max: {}".format(
+                    tensors_0_1.min(), tensors_0_1.max()
+                ))
+
+            clip_image = self.clip_image_processor(
+                images=tensors_0_1,
+                return_tensors="pt",
+                do_resize=True,
+                do_rescale=False,
+            ).pixel_values
+            clip_image = clip_image.to(self.device, dtype=torch.float16).detach()
+            if drop:
+                clip_image = clip_image * 0
+        with torch.set_grad_enabled(is_training):
+            if is_training:
+                self.image_encoder.train()
+            else:
+                self.image_encoder.eval()
+            clip_output = self.image_encoder(clip_image, output_hidden_states=True)
+            clip_image_embeds = clip_output.hidden_states[-2]
+        return clip_image_embeds
+
+    import torch
+
+    def set_vec(self, new_vector, text_encoder_idx=0):
+        # Get the embedding layer
+        embedding_layer = self.text_encoder_list[text_encoder_idx].get_input_embeddings()
+
+        # Indices to replace in the embeddings
+        indices_to_replace = self.placeholder_token_ids[text_encoder_idx]
+
+        # Replace the specified embeddings with new_vector
+        for idx in indices_to_replace:
+            vector_idx = idx - indices_to_replace[0]
+            embedding_layer.weight[idx] = new_vector[vector_idx]
+
+    # adds it to the tokenizer
+    def forward(self, clip_image_embeds: torch.Tensor) -> PromptEmbeds:
+        clip_image_embeds = clip_image_embeds.to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype))
+        image_prompt_embeds = self.embedder(clip_image_embeds)
+        # todo add support for multiple batch sizes
+        if image_prompt_embeds.shape[0] != 1:
+            raise ValueError("Batch size must be 1 for embedder for now")
+
+        # output on sd1.5 is bs, num_tokens, 768
+        if len(self.text_encoder_list) == 1:
+            # add it to the text encoder
+            self.set_vec(image_prompt_embeds[0], text_encoder_idx=0)
+        else:
+            raise ValueError("Multiple text encoders not supported yet")
+        # just a place to put a breakpoint
+        pass
+
+    def restore_embeddings(self):
+        # Let's make sure we don't update any embedding weights besides the newly added token
+        for text_encoder, tokenizer, orig_embeds, placeholder_token_ids in zip(
+                self.text_encoder_list,
+                self.tokenizer_list,
+                self.orig_embeds_params,
+                self.placeholder_token_ids
+        ):
+            index_no_updates = torch.ones((len(tokenizer),), dtype=torch.bool)
+            index_no_updates[
+            min(placeholder_token_ids): max(placeholder_token_ids) + 1] = False
+            with torch.no_grad():
+                text_encoder.get_input_embeddings().weight[
+                    index_no_updates
+                ] = orig_embeds[index_no_updates]
+                # detach it all
+                text_encoder.get_input_embeddings().weight.detach_()
+
+    def enable_gradient_checkpointing(self):
+        self.image_encoder.gradient_checkpointing = True
+
+    def inject_trigger_into_prompt(self, prompt, expand_token=False, to_replace_list=None, add_if_not_present=True):
+        output_prompt = prompt
+        embedding_tokens = self.embedding_tokens[0]  # shoudl be the same
+        default_replacements = ["[name]", "[trigger]"]
+
+        replace_with = embedding_tokens if expand_token else self.trigger
+        if to_replace_list is None:
+            to_replace_list = default_replacements
+        else:
+            to_replace_list += default_replacements
+
+        # remove duplicates
+        to_replace_list = list(set(to_replace_list))
+
+        # replace them all
+        for to_replace in to_replace_list:
+            # replace it
+            output_prompt = output_prompt.replace(to_replace, replace_with)
+
+        # see how many times replace_with is in the prompt
+        num_instances = output_prompt.count(replace_with)
+
+        if num_instances == 0 and add_if_not_present:
+            # add it to the beginning of the prompt
+            output_prompt = replace_with + " " + output_prompt
+
+        if num_instances > 1:
+            print(
+                f"Warning: {replace_with} token appears {num_instances} times in prompt {output_prompt}. This may cause issues.")
+
+        return output_prompt
+
+    # reverses injection with class name. useful for normalizations
+    def inject_trigger_class_name_into_prompt(self, prompt):
+        output_prompt = prompt
+        embedding_tokens = self.embedding_tokens[0]  # shoudl be the same
+
+        default_replacements = ["[name]", "[trigger]", embedding_tokens, self.trigger]
+
+        replace_with = self.config.trigger_class_name
+        to_replace_list = default_replacements
+
+        # remove duplicates
+        to_replace_list = list(set(to_replace_list))
+
+        # replace them all
+        for to_replace in to_replace_list:
+            # replace it
+            output_prompt = output_prompt.replace(to_replace, replace_with)
+
+        # see how many times replace_with is in the prompt
+        num_instances = output_prompt.count(replace_with)
+
+        if num_instances > 1:
+            print(
+                f"Warning: {replace_with} token appears {num_instances} times in prompt {output_prompt}. This may cause issues.")
+
+        return output_prompt

toolkit/config_modules.py

@@ -14,6 +14,7 @@ SaveFormat = Literal['safetensors', 'diffusers']
 if TYPE_CHECKING:
     from toolkit.guidance import GuidanceType
 
+
 class SaveConfig:
     def __init__(self, **kwargs):
         self.save_every: int = kwargs.get('save_every', 1000)
@@ -47,7 +48,8 @@ class SampleConfig:
         self.guidance_rescale = kwargs.get('guidance_rescale', 0.0)
         self.ext: ImgExt = kwargs.get('format', 'jpg')
         self.adapter_conditioning_scale = kwargs.get('adapter_conditioning_scale', 1.0)
-        self.refiner_start_at = kwargs.get('refiner_start_at', 0.5)  # step to start using refiner on sample if it exists
+        self.refiner_start_at = kwargs.get('refiner_start_at',
+                                           0.5)  # step to start using refiner on sample if it exists
 
 
 class LormModuleSettingsConfig:
@@ -130,7 +132,7 @@ AdapterTypes = Literal['t2i', 'ip', 'ip+']
 
 class AdapterConfig:
     def __init__(self, **kwargs):
-        self.type: AdapterTypes = kwargs.get('type', 't2i')  # t2i, ip
+        self.type: AdapterTypes = kwargs.get('type', 't2i')  # t2i, ip, clip
         self.in_channels: int = kwargs.get('in_channels', 3)
         self.channels: List[int] = kwargs.get('channels', [320, 640, 1280, 1280])
         self.num_res_blocks: int = kwargs.get('num_res_blocks', 2)
@@ -153,15 +155,9 @@ class AdapterConfig:
         self.train_image_encoder: bool = kwargs.get('train_image_encoder', False)
         self.image_encoder_arch: str = kwargs.get('image_encoder_arch', 'clip')  # clip vit vit_hybrid
 
-
-
-class ClipTokenMakerConfig:
-    def __init__(self, **kwargs):
-        self.image_encoder_path: str = kwargs.get('image_encoder_path', None)
-        self.num_tokens: int = kwargs.get('num_tokens', 8)
-
-
-
+        # clip vision
+        self.trigger = kwargs.get('trigger', 'tri993r')
+        self.trigger_class_name = kwargs.get('trigger_class_name', 'person')
 
 
 class EmbeddingConfig:
@@ -401,7 +397,8 @@ class DatasetConfig:
         self.alpha_mask: bool = kwargs.get('alpha_mask', False)  # if true, will use alpha channel as mask
         self.mask_path: str = kwargs.get('mask_path',
                                          None)  # focus mask (black and white. White has higher loss than black)
-        self.unconditional_path: str = kwargs.get('unconditional_path', None)  # path where matching unconditional images are located
+        self.unconditional_path: str = kwargs.get('unconditional_path',
+                                                  None)  # path where matching unconditional images are located
         self.invert_mask: bool = kwargs.get('invert_mask', False)  # invert mask
         self.mask_min_value: float = kwargs.get('mask_min_value', 0.0)  # min value for . 0 - 1
         self.poi: Union[str, None] = kwargs.get('poi',

toolkit/resampler.py

@@ -0,0 +1,160 @@
+# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
+# and https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py
+# and https://github.com/tencent-ailab/IP-Adapter/blob/9fc189e3fb389cc2b60a7d0c0850e083a716ea6e/ip_adapter/resampler.py
+
+import math
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+from einops.layers.torch import Rearrange
+
+
+# FFN
+def FeedForward(dim, mult=4):
+    inner_dim = int(dim * mult)
+    return nn.Sequential(
+        nn.LayerNorm(dim),
+        nn.Linear(dim, inner_dim, bias=False),
+        nn.GELU(),
+        nn.Linear(inner_dim, dim, bias=False),
+    )
+
+
+def reshape_tensor(x, heads):
+    bs, length, width = x.shape
+    # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
+    x = x.view(bs, length, heads, -1)
+    # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
+    x = x.transpose(1, 2)
+    # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
+    x = x.reshape(bs, heads, length, -1)
+    return x
+
+
+class PerceiverAttention(nn.Module):
+    def __init__(self, *, dim, dim_head=64, heads=8):
+        super().__init__()
+        self.scale = dim_head ** -0.5
+        self.dim_head = dim_head
+        self.heads = heads
+        inner_dim = dim_head * heads
+
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+    def forward(self, x, latents):
+        """
+        Args:
+            x (torch.Tensor): image features
+                shape (b, n1, D)
+            latent (torch.Tensor): latent features
+                shape (b, n2, D)
+        """
+        x = self.norm1(x)
+        latents = self.norm2(latents)
+
+        b, l, _ = latents.shape
+
+        q = self.to_q(latents)
+        kv_input = torch.cat((x, latents), dim=-2)
+        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+
+        q = reshape_tensor(q, self.heads)
+        k = reshape_tensor(k, self.heads)
+        v = reshape_tensor(v, self.heads)
+
+        # attention
+        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
+        weight = (q * scale) @ (k * scale).transpose(-2, -1)  # More stable with f16 than dividing afterwards
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        out = weight @ v
+
+        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
+
+        return self.to_out(out)
+
+
+class Resampler(nn.Module):
+    def __init__(
+            self,
+            dim=1024,
+            depth=8,
+            dim_head=64,
+            heads=16,
+            num_queries=8,
+            embedding_dim=768,
+            output_dim=1024,
+            ff_mult=4,
+            max_seq_len: int = 257,  # CLIP tokens + CLS token
+            apply_pos_emb: bool = False,
+            num_latents_mean_pooled: int = 0,
+            # number of latents derived from mean pooled representation of the sequence
+    ):
+        super().__init__()
+        self.pos_emb = nn.Embedding(max_seq_len, embedding_dim) if apply_pos_emb else None
+
+        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim ** 0.5)
+
+        self.proj_in = nn.Linear(embedding_dim, dim)
+
+        self.proj_out = nn.Linear(dim, output_dim)
+        self.norm_out = nn.LayerNorm(output_dim)
+
+        self.to_latents_from_mean_pooled_seq = (
+            nn.Sequential(
+                nn.LayerNorm(dim),
+                nn.Linear(dim, dim * num_latents_mean_pooled),
+                Rearrange("b (n d) -> b n d", n=num_latents_mean_pooled),
+            )
+            if num_latents_mean_pooled > 0
+            else None
+        )
+
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                nn.ModuleList(
+                    [
+                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
+                        FeedForward(dim=dim, mult=ff_mult),
+                    ]
+                )
+            )
+
+    def forward(self, x):
+        if self.pos_emb is not None:
+            n, device = x.shape[1], x.device
+            pos_emb = self.pos_emb(torch.arange(n, device=device))
+            x = x + pos_emb
+
+        latents = self.latents.repeat(x.size(0), 1, 1)
+
+        x = self.proj_in(x)
+
+        if self.to_latents_from_mean_pooled_seq:
+            meanpooled_seq = masked_mean(x, dim=1, mask=torch.ones(x.shape[:2], device=x.device, dtype=torch.bool))
+            meanpooled_latents = self.to_latents_from_mean_pooled_seq(meanpooled_seq)
+            latents = torch.cat((meanpooled_latents, latents), dim=-2)
+
+        for attn, ff in self.layers:
+            latents = attn(x, latents) + latents
+            latents = ff(latents) + latents
+
+        latents = self.proj_out(latents)
+        return self.norm_out(latents)
+
+
+def masked_mean(t, *, dim, mask=None):
+    if mask is None:
+        return t.mean(dim=dim)
+
+    denom = mask.sum(dim=dim, keepdim=True)
+    mask = rearrange(mask, "b n -> b n 1")
+    masked_t = t.masked_fill(~mask, 0.0)
+
+    return masked_t.sum(dim=dim) / denom.clamp(min=1e-5)

toolkit/stable_diffusion_model.py

@@ -18,6 +18,7 @@ from torch.utils.checkpoint import checkpoint
 from tqdm import tqdm
 from torchvision.transforms import Resize, transforms
 
+from toolkit.clip_vision_adapter import ClipVisionAdapter
 from toolkit.ip_adapter import IPAdapter
 from library.model_util import convert_unet_state_dict_to_sd, convert_text_encoder_state_dict_to_sd_v2, \
     convert_vae_state_dict, load_vae
@@ -472,7 +473,7 @@ class StableDiffusion:
                             validation_image = validation_image.resize((gen_config.width * 2, gen_config.height * 2))
                             extra['image'] = validation_image
                             extra['adapter_conditioning_scale'] = gen_config.adapter_conditioning_scale
-                        if isinstance(self.adapter, IPAdapter):
+                        if isinstance(self.adapter, IPAdapter) or isinstance(self.adapter, ClipVisionAdapter):
                             transform = transforms.Compose([
                                 transforms.ToTensor(),
                             ])
@@ -483,6 +484,12 @@ class StableDiffusion:
                     torch.manual_seed(gen_config.seed)
                     torch.cuda.manual_seed(gen_config.seed)
 
+                    if self.adapter is not None and isinstance(self.adapter, ClipVisionAdapter) \
+                            and gen_config.adapter_image_path is not None:
+                        # run through the adapter to saturate the embeds
+                        conditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(validation_image)
+                        self.adapter(conditional_clip_embeds)
+
                     # encode the prompt ourselves so we can do fun stuff with embeddings
                     conditional_embeds = self.encode_prompt(gen_config.prompt, gen_config.prompt_2, force_all=True)
 
@@ -496,8 +503,8 @@ class StableDiffusion:
                         unconditional_embeds,
                     )
 
-                    if self.adapter is not None and isinstance(self.adapter,
-                                                               IPAdapter) and gen_config.adapter_image_path is not None:
+                    if self.adapter is not None and isinstance(self.adapter, IPAdapter) \
+                            and gen_config.adapter_image_path is not None:
 
                         # apply the image projection
                         conditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(validation_image)
@@ -1445,6 +1452,9 @@ class StableDiffusion:
             elif isinstance(self.adapter, T2IAdapter):
                 requires_grad = self.adapter.adapter.conv_in.weight.requires_grad
                 adapter_device = self.adapter.device
+            elif isinstance(self.adapter, ClipVisionAdapter):
+                requires_grad = self.adapter.embedder.training
+                adapter_device = self.adapter.device
             else:
                 raise ValueError(f"Unknown adapter type: {type(self.adapter)}")
             self.device_state['adapter'] = {