Added IP adapter training. Not functioning correctly yet

.gitmodules

@@ -7,3 +7,6 @@
 [submodule "repositories/batch_annotator"]
 	path = repositories/batch_annotator
 	url = https://github.com/ostris/batch-annotator
+[submodule "repositories/ipadapter"]
+	path = repositories/ipadapter
+	url = https://github.com/tencent-ailab/IP-Adapter.git

extensions_built_in/sd_trainer/SDTrainer.py

@@ -2,9 +2,11 @@ import os.path
 from collections import OrderedDict
 
 from PIL import Image
+from diffusers import T2IAdapter
 from torch.utils.data import DataLoader
 
 from toolkit.data_transfer_object.data_loader import DataLoaderBatchDTO
+from toolkit.ip_adapter import IPAdapter
 from toolkit.prompt_utils import concat_prompt_embeds, split_prompt_embeds
 from toolkit.stable_diffusion_model import StableDiffusion, BlankNetwork
 from toolkit.train_tools import get_torch_dtype, apply_snr_weight
@@ -115,13 +117,19 @@ class SDTrainer(BaseSDTrainProcess):
                 conditional_embeds = conditional_embeds.detach()
             # flush()
             pred_kwargs = {}
-            if self.adapter:
+            if self.adapter and isinstance(self.adapter, T2IAdapter):
                 down_block_additional_residuals = self.adapter(adapter_images)
                 down_block_additional_residuals = [
                     sample.to(dtype=dtype) for sample in down_block_additional_residuals
                 ]
                 pred_kwargs['down_block_additional_residuals'] = down_block_additional_residuals
 
+            if self.adapter and isinstance(self.adapter, IPAdapter):
+                with torch.no_grad():
+                    conditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(adapter_images)
+                conditional_embeds = self.adapter(conditional_embeds, conditional_clip_embeds)
+
+
             noise_pred = self.sd.predict_noise(
                 latents=noisy_latents.to(self.device_torch, dtype=dtype),
                 conditional_embeddings=conditional_embeds.to(self.device_torch, dtype=dtype),
@@ -164,6 +172,9 @@ class SDTrainer(BaseSDTrainProcess):
                 loss = apply_snr_weight(loss, timesteps, self.sd.noise_scheduler, self.train_config.min_snr_gamma)
 
             loss = loss.mean()
+            # check if nan
+            if torch.isnan(loss):
+                raise ValueError("loss is nan")
 
             # IMPORTANT if gradient checkpointing do not leave with network when doing backward
             # it will destroy the gradients. This is because the network is a context manager

jobs/process/BaseSDTrainProcess.py

@@ -15,6 +15,7 @@ from toolkit.basic import value_map
 from toolkit.data_loader import get_dataloader_from_datasets
 from toolkit.data_transfer_object.data_loader import FileItemDTO, DataLoaderBatchDTO
 from toolkit.embedding import Embedding
+from toolkit.ip_adapter import IPAdapter
 from toolkit.lora_special import LoRASpecialNetwork
 from toolkit.lycoris_special import LycorisSpecialNetwork
 from toolkit.network_mixins import Network
@@ -22,7 +23,8 @@ from toolkit.optimizer import get_optimizer
 from toolkit.paths import CONFIG_ROOT
 from toolkit.progress_bar import ToolkitProgressBar
 from toolkit.sampler import get_sampler
-from toolkit.saving import save_t2i_from_diffusers, load_t2i_model
+from toolkit.saving import save_t2i_from_diffusers, load_t2i_model, save_ip_adapter_from_diffusers, \
+    load_ip_adapter_model
 
 from toolkit.scheduler import get_lr_scheduler
 from toolkit.sd_device_states_presets import get_train_sd_device_state_preset
@@ -118,9 +120,11 @@ class BaseSDTrainProcess(BaseTrainProcess):
 
         # to hold network if there is one
         self.network: Union[Network, None] = None
-        self.adapter: Union[T2IAdapter, None] = None
+        self.adapter: Union[T2IAdapter, IPAdapter, None] = None
         self.embedding: Union[Embedding, None] = None
 
+        is_training_adapter = self.adapter_config is not None and self.adapter_config.train
+
         # get the device state preset based on what we are training
         self.train_device_state_preset = get_train_sd_device_state_preset(
             device=self.device_torch,
@@ -128,17 +132,17 @@ class BaseSDTrainProcess(BaseTrainProcess):
             train_text_encoder=self.train_config.train_text_encoder,
             cached_latents=self.is_latents_cached,
             train_lora=self.network_config is not None,
-            train_adapter=self.adapter_config is not None,
+            train_adapter=is_training_adapter,
             train_embedding=self.embed_config is not None,
         )
 
         # fine_tuning here is for training actual SD network, not LoRA, embeddings, etc. it is (Dreambooth, etc)
         self.is_fine_tuning = True
-        if self.network_config is not None or self.adapter_config is not None or self.embed_config is not None:
+        if self.network_config is not None or is_training_adapter or self.embed_config is not None:
             self.is_fine_tuning = False
 
         self.named_lora = False
-        if self.embed_config is not None or self.adapter_config is not None:
+        if self.embed_config is not None or is_training_adapter:
             self.named_lora = True
     def post_process_generate_image_config_list(self, generate_image_config_list: List[GenerateImageConfig]):
         # override in subclass
@@ -179,7 +183,7 @@ class BaseSDTrainProcess(BaseTrainProcess):
                 )
 
             extra_args = {}
-            if self.adapter_config is not None:
+            if self.adapter_config is not None and self.adapter_config.test_img_path is not None:
                 extra_args['adapter_image_path'] = self.adapter_config.test_img_path
 
             gen_img_config_list.append(GenerateImageConfig(
@@ -318,22 +322,33 @@ class BaseSDTrainProcess(BaseTrainProcess):
                     emb_file_path = os.path.splitext(emb_file_path)[0] + ".pt"
                 self.embedding.save(emb_file_path)
 
-            if self.adapter is not None:
+            if self.adapter is not None and self.adapter_config.train:
                 adapter_name = self.job.name
                 if self.network_config is not None or self.embedding is not None:
                     # add _lora to name
-                    adapter_name += '_t2i'
+                    if self.adapter_config.type == 't2i':
+                        adapter_name += '_t2i'
+                    else:
+                        adapter_name += '_ip'
 
                 filename = f'{adapter_name}{step_num}.safetensors'
                 file_path = os.path.join(self.save_root, filename)
                 # save adapter
                 state_dict = self.adapter.state_dict()
-                save_t2i_from_diffusers(
-                    state_dict,
-                    output_file=file_path,
-                    meta=save_meta,
-                    dtype=get_torch_dtype(self.save_config.dtype)
-                )
+                if self.adapter_config.type == 't2i':
+                    save_t2i_from_diffusers(
+                        state_dict,
+                        output_file=file_path,
+                        meta=save_meta,
+                        dtype=get_torch_dtype(self.save_config.dtype)
+                    )
+                else:
+                    save_ip_adapter_from_diffusers(
+                        state_dict,
+                        output_file=file_path,
+                        meta=save_meta,
+                        dtype=get_torch_dtype(self.save_config.dtype)
+                    )
         else:
             self.sd.save(
                 file_path,
@@ -527,6 +542,50 @@ class BaseSDTrainProcess(BaseTrainProcess):
 
         return noisy_latents, noise, timesteps, conditioned_prompts, imgs
 
+    def setup_adapter(self):
+        dtype = get_torch_dtype(self.train_config.dtype)
+        is_t2i = self.adapter_config.type == 't2i'
+        if is_t2i:
+            self.adapter = T2IAdapter(
+                in_channels=self.adapter_config.in_channels,
+                channels=self.adapter_config.channels,
+                num_res_blocks=self.adapter_config.num_res_blocks,
+                downscale_factor=self.adapter_config.downscale_factor,
+                adapter_type=self.adapter_config.adapter_type,
+            )
+        else:
+            self.adapter = IPAdapter(
+                sd=self.sd,
+                adapter_config=self.adapter_config,
+            )
+        self.adapter.to(self.device_torch, dtype=dtype)
+        # t2i adapter
+        suffix = 't2i' if is_t2i else 'ip'
+        adapter_name = self.name
+        if self.network_config is not None:
+            adapter_name = f"{adapter_name}_{suffix}"
+        latest_save_path = self.get_latest_save_path(adapter_name)
+        if latest_save_path is not None:
+            # load adapter from path
+            print(f"Loading adapter from {latest_save_path}")
+            if is_t2i:
+                loaded_state_dict = load_t2i_model(
+                    latest_save_path,
+                    self.device,
+                    dtype=dtype
+                )
+            else:
+                loaded_state_dict = load_ip_adapter_model(
+                    latest_save_path,
+                    self.device,
+                    dtype=dtype
+                )
+            self.adapter.load_state_dict(loaded_state_dict)
+            if self.adapter_config.train:
+                self.load_training_state_from_metadata(latest_save_path)
+        # set trainable params
+        self.sd.adapter = self.adapter
+
     def run(self):
         # torch.autograd.set_detect_anomaly(True)
         # run base process run
@@ -741,35 +800,12 @@ class BaseSDTrainProcess(BaseTrainProcess):
                 flush()
 
             if self.adapter_config is not None:
-                self.adapter = T2IAdapter(
-                    in_channels=self.adapter_config.in_channels,
-                    channels=self.adapter_config.channels,
-                    num_res_blocks=self.adapter_config.num_res_blocks,
-                    downscale_factor=self.adapter_config.downscale_factor,
-                    adapter_type=self.adapter_config.adapter_type,
-                )
-                self.adapter.to(self.device_torch, dtype=dtype)
-                # t2i adapter
-                adapter_name = self.name
-                if self.network_config is not None:
-                    adapter_name = f"{adapter_name}_t2i"
-                latest_save_path = self.get_latest_save_path(adapter_name)
-                if latest_save_path is not None:
-                    # load adapter from path
-                    print(f"Loading adapter from {latest_save_path}")
-                    loaded_state_dict = load_t2i_model(
-                        latest_save_path,
-                        self.device,
-                        dtype=dtype
-                    )
-                    self.adapter.load_state_dict(loaded_state_dict)
-                    self.load_training_state_from_metadata(latest_save_path)
+                self.setup_adapter()
                 # set trainable params
                 params.append({
                     'params': self.adapter.parameters(),
                     'lr': self.train_config.adapter_lr
                 })
-                self.sd.adapter = self.adapter
                 flush()
 
             params = self.load_additional_training_modules(params)
@@ -785,6 +821,9 @@ class BaseSDTrainProcess(BaseTrainProcess):
                 unet_lr=self.train_config.lr,
                 default_lr=self.train_config.lr
             )
+            # we may be using it for prompt injections
+            if self.adapter_config is not None:
+                self.setup_adapter()
         flush()
         ### HOOK ###
         params = self.hook_add_extra_train_params(params)

toolkit/config_modules.py

@@ -61,8 +61,11 @@ class NetworkConfig:
         self.dropout: Union[float, None] = kwargs.get('dropout', None)
 
 
+AdapterTypes = Literal['t2i', 'ip', 'ip+']
+
 class AdapterConfig:
     def __init__(self, **kwargs):
+        self.type: AdapterTypes = kwargs.get('type', 't2i')  # t2i, ip
         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)
@@ -70,6 +73,9 @@ class AdapterConfig:
         self.adapter_type: str = kwargs.get('adapter_type', 'full_adapter')
         self.image_dir: str = kwargs.get('image_dir', None)
         self.test_img_path: str = kwargs.get('test_img_path', None)
+        self.train: str = kwargs.get('train', False)
+        self.image_encoder_path: str = kwargs.get('image_encoder_path', None)
+        self.name_or_path = kwargs.get('name_or_path', None)
 
 
 class EmbeddingConfig:

toolkit/ip_adapter.py

@@ -0,0 +1,157 @@
+import torch
+import sys
+
+from PIL import Image
+from torch.nn import Parameter
+from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
+
+from toolkit.paths import REPOS_ROOT
+from toolkit.train_tools import get_torch_dtype
+
+sys.path.append(REPOS_ROOT)
+from typing import TYPE_CHECKING, Union, Iterator, Mapping, Any, Tuple, List
+from collections import OrderedDict
+from ipadapter.ip_adapter.attention_processor import AttnProcessor, IPAttnProcessor
+from ipadapter.ip_adapter.ip_adapter import ImageProjModel
+from ipadapter.ip_adapter.resampler import Resampler
+from toolkit.config_modules import AdapterConfig
+from toolkit.prompt_utils import PromptEmbeds
+import weakref
+
+if TYPE_CHECKING:
+    from toolkit.stable_diffusion_model import StableDiffusion
+
+
+# loosely based on # ref https://github.com/tencent-ailab/IP-Adapter/blob/main/tutorial_train.py
+class IPAdapter(torch.nn.Module):
+    """IP-Adapter"""
+
+    def __init__(self, sd: 'StableDiffusion', adapter_config: 'AdapterConfig'):
+        super().__init__()
+        self.config = adapter_config
+        self.sd_ref: weakref.ref = weakref.ref(sd)
+        self.clip_image_processor = CLIPImageProcessor()
+        self.device = self.sd_ref().unet.device
+        self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(adapter_config.image_encoder_path)
+        if adapter_config.type == 'ip':
+            # ip-adapter
+            image_proj_model = ImageProjModel(
+                cross_attention_dim=sd.unet.config['cross_attention_dim'],
+                clip_embeddings_dim=self.image_encoder.config.projection_dim,
+                clip_extra_context_tokens=4,
+            )
+        elif adapter_config.type == 'ip+':
+            # ip-adapter-plus
+            num_tokens = 16
+            image_proj_model = Resampler(
+                dim=sd.unet.config['cross_attention_dim'],
+                depth=4,
+                dim_head=64,
+                heads=12,
+                num_queries=num_tokens,
+                embedding_dim=self.image_encoder.config.hidden_size,
+                output_dim=sd.unet.config['cross_attention_dim'],
+                ff_mult=4
+            )
+        else:
+            raise ValueError(f"unknown adapter type: {adapter_config.type}")
+
+        # init adapter modules
+        attn_procs = {}
+        unet_sd = sd.unet.state_dict()
+        for name in sd.unet.attn_processors.keys():
+            cross_attention_dim = None if name.endswith("attn1.processor") else sd.unet.config['cross_attention_dim']
+            if name.startswith("mid_block"):
+                hidden_size = sd.unet.config['block_out_channels'][-1]
+            elif name.startswith("up_blocks"):
+                block_id = int(name[len("up_blocks.")])
+                hidden_size = list(reversed(sd.unet.config['block_out_channels']))[block_id]
+            elif name.startswith("down_blocks"):
+                block_id = int(name[len("down_blocks.")])
+                hidden_size = sd.unet.config['block_out_channels'][block_id]
+            else:
+                # they didnt have this, but would lead to undefined below
+                raise ValueError(f"unknown attn processor name: {name}")
+            if cross_attention_dim is None:
+                attn_procs[name] = AttnProcessor()
+            else:
+                layer_name = name.split(".processor")[0]
+                weights = {
+                    "to_k_ip.weight": unet_sd[layer_name + ".to_k.weight"],
+                    "to_v_ip.weight": unet_sd[layer_name + ".to_v.weight"],
+                }
+                attn_procs[name] = IPAttnProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim)
+                attn_procs[name].load_state_dict(weights)
+        sd.unet.set_attn_processor(attn_procs)
+        adapter_modules = torch.nn.ModuleList(sd.unet.attn_processors.values())
+
+        sd.adapter = self
+        self.unet_ref: weakref.ref = weakref.ref(sd.unet)
+        self.image_proj_model = image_proj_model
+        self.adapter_modules = adapter_modules
+
+    def to(self, *args, **kwargs):
+        super().to(*args, **kwargs)
+        self.image_encoder.to(*args, **kwargs)
+        self.image_proj_model.to(*args, **kwargs)
+        self.adapter_modules.to(*args, **kwargs)
+        return self
+
+    def load_ip_adapter(self, state_dict: Union[OrderedDict, dict]):
+        self.image_proj_model.load_state_dict(state_dict["image_proj"])
+        ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
+        ip_layers.load_state_dict(state_dict["ip_adapter"])
+
+    def state_dict(self) -> OrderedDict:
+        state_dict = OrderedDict()
+        state_dict["image_proj"] = self.image_proj_model.state_dict()
+        state_dict["ip_adapter"] = self.adapter_modules.state_dict()
+        return state_dict
+
+    def set_scale(self, scale):
+        for attn_processor in self.pipe.unet.attn_processors.values():
+            if isinstance(attn_processor, IPAttnProcessor):
+                attn_processor.scale = scale
+
+    @torch.no_grad()
+    def get_clip_image_embeds_from_pil(self, pil_image: Union[Image.Image, List[Image.Image]], drop=False) -> torch.Tensor:
+        # todo: add support for sdxl
+        if isinstance(pil_image, Image.Image):
+            pil_image = [pil_image]
+        clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
+        clip_image = clip_image.to(self.device, dtype=torch.float16)
+        if drop:
+            clip_image = clip_image * 0
+        clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
+        return clip_image_embeds
+
+    @torch.no_grad()
+    def get_clip_image_embeds_from_tensors(self, tensors_0_1: torch.Tensor, drop=False) -> torch.Tensor:
+        # tensors should be 0-1
+        # todo: add support for sdxl
+        if tensors_0_1.ndim == 3:
+            tensors_0_1 = tensors_0_1.unsqueeze(0)
+        tensors_0_1 = tensors_0_1.to(self.device, dtype=torch.float16)
+        clip_image = self.clip_image_processor(images=tensors_0_1, return_tensors="pt", do_resize=False).pixel_values
+        clip_image = clip_image.to(self.device, dtype=torch.float16)
+        if drop:
+            clip_image = clip_image * 0
+        clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
+        return clip_image_embeds
+
+    # use drop for prompt dropout, or negatives
+    def forward(self, embeddings: PromptEmbeds, clip_image_embeds: torch.Tensor) -> PromptEmbeds:
+        clip_image_embeds = clip_image_embeds.detach()
+        clip_image_embeds = clip_image_embeds.to(self.device, dtype=get_torch_dtype(self.sd_ref().dtype))
+        image_prompt_embeds = self.image_proj_model(clip_image_embeds.detach())
+        embeddings.text_embeds = torch.cat([embeddings.text_embeds, image_prompt_embeds], dim=1)
+        return embeddings
+
+    def parameters(self, recurse: bool = True) -> Iterator[Parameter]:
+        for attn_processor in self.adapter_modules:
+            yield from attn_processor.parameters(recurse)
+        yield from self.image_proj_model.parameters(recurse)
+
+    def load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True):
+        self.image_proj_model.load_state_dict(state_dict["image_proj"], strict=strict)
+        self.adapter_modules.load_state_dict(state_dict["ip_adapter"], strict=strict)

toolkit/network_mixins.py

@@ -306,6 +306,7 @@ class ToolkitNetworkMixin:
             extra_dict = None
         return extra_dict
 
+    @torch.no_grad()
     def _update_torch_multiplier(self: Network):
         # builds a tensor for fast usage in the forward pass of the network modules
         # without having to set it in every single module every time it changes

toolkit/saving.py

@@ -193,3 +193,42 @@ def load_t2i_model(
         # todo see if we need to convert dict
         converted_state_dict[key] = value.detach().to(device, dtype=dtype)
     return converted_state_dict
+
+
+IP_ADAPTER_MODULES = ['image_proj', 'ip_adapter']
+
+def save_ip_adapter_from_diffusers(
+        combined_state_dict: 'OrderedDict',
+        output_file: str,
+        meta: 'OrderedDict',
+        dtype=get_torch_dtype('fp16'),
+):
+    # todo: test compatibility with non diffusers
+    converted_state_dict = OrderedDict()
+    for module_name, state_dict in combined_state_dict.items():
+        for key, value in state_dict.items():
+            converted_state_dict[f"{module_name}.{key}"] = value.detach().to('cpu', dtype=dtype)
+
+    # make sure parent folder exists
+    os.makedirs(os.path.dirname(output_file), exist_ok=True)
+    save_file(converted_state_dict, output_file, metadata=meta)
+
+
+def load_ip_adapter_model(
+        path_to_file,
+        device: Union[str] = 'cpu',
+        dtype: torch.dtype = torch.float32
+):
+    # check if it is safetensors or checkpoint
+    if path_to_file.endswith('.safetensors'):
+        raw_state_dict = load_file(path_to_file, device)
+        combined_state_dict = OrderedDict()
+        for combo_key, value in raw_state_dict.items():
+            key_split = combo_key.split('.')
+            module_name = key_split.pop(0)
+            if module_name not in combined_state_dict:
+                combined_state_dict[module_name] = OrderedDict()
+            combined_state_dict[module_name]['.'.join(key_split)] = value.detach().to(device, dtype=dtype)
+        return combined_state_dict
+    else:
+        return torch.load(path_to_file, map_location=device)

toolkit/stable_diffusion_model.py

@@ -13,8 +13,9 @@ from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import
 from safetensors.torch import save_file, load_file
 from torch.nn import Parameter
 from tqdm import tqdm
-from torchvision.transforms import Resize
+from torchvision.transforms import Resize, transforms
 
+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
 from toolkit import train_tools
@@ -123,7 +124,7 @@ class StableDiffusion:
 
         # to hold network if there is one
         self.network = None
-        self.adapter: Union['T2IAdapter', None] = None
+        self.adapter: Union['T2IAdapter', 'IPAdapter', None] = None
         self.is_xl = model_config.is_xl
         self.is_v2 = model_config.is_v2
 
@@ -302,12 +303,13 @@ class StableDiffusion:
             Pipe = StableDiffusionPipeline
 
         extra_args = {}
-        if self.adapter:
-            if self.is_xl:
-                Pipe = StableDiffusionXLAdapterPipeline
-            else:
-                Pipe = StableDiffusionAdapterPipeline
-            extra_args['adapter'] = self.adapter
+        if self.adapter is not None:
+            if isinstance(self.adapter, T2IAdapter):
+                if self.is_xl:
+                    Pipe = StableDiffusionXLAdapterPipeline
+                else:
+                    Pipe = StableDiffusionAdapterPipeline
+                extra_args['adapter'] = self.adapter
 
         # TODO add clip skip
         if self.is_xl:
@@ -358,11 +360,19 @@ class StableDiffusion:
                     gen_config = image_configs[i]
 
                     extra = {}
-                    if gen_config.adapter_image_path is not None:
+                    if self.adapter is not None and gen_config.adapter_image_path is not None:
                         validation_image = Image.open(gen_config.adapter_image_path).convert("RGB")
-                        validation_image = validation_image.resize((gen_config.width * 2, gen_config.height * 2))
-                        extra['image'] = validation_image
-                        extra['adapter_conditioning_scale'] = 1.0
+                        if isinstance(self.adapter, T2IAdapter):
+                            # not sure why this is double??
+                            validation_image = validation_image.resize((gen_config.width * 2, gen_config.height * 2))
+                            extra['image'] = validation_image
+                            extra['adapter_conditioning_scale'] = 1.0
+                        if isinstance(self.adapter, IPAdapter):
+                            transform = transforms.Compose([
+                                transforms.Resize(gen_config.width, interpolation=transforms.InterpolationMode.BILINEAR),
+                                transforms.PILToTensor(),
+                            ])
+                            validation_image = transform(validation_image)
 
                     if self.network is not None:
                         self.network.multiplier = gen_config.network_multiplier
@@ -382,6 +392,14 @@ class StableDiffusion:
                         unconditional_embeds,
                     )
 
+                    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)
+                        unconditional_clip_embeds = self.adapter.get_clip_image_embeds_from_tensors(validation_image, True)
+                        conditional_embeds = self.adapter(conditional_embeds, conditional_clip_embeds)
+                        unconditional_embeds = self.adapter(unconditional_embeds, unconditional_clip_embeds)
+
+
                     # todo do we disable text encoder here as well if disabled for model, or only do that for training?
                     if self.is_xl:
                         # fix guidance rescale for sdxl
@@ -931,10 +949,18 @@ class StableDiffusion:
                 'requires_grad': self.text_encoder.text_model.final_layer_norm.weight.requires_grad
             }
         if self.adapter is not None:
+            if isinstance(self.adapter, IPAdapter):
+                requires_grad = self.adapter.adapter_modules.training
+                adapter_device = self.unet.device
+            elif isinstance(self.adapter, T2IAdapter):
+                requires_grad = self.adapter.adapter.conv_in.weight.requires_grad
+                adapter_device = self.adapter.device
+            else:
+                raise ValueError(f"Unknown adapter type: {type(self.adapter)}")
             self.device_state['adapter'] = {
                 'training': self.adapter.training,
-                'device': self.adapter.device,
-                'requires_grad': self.adapter.adapter.conv_in.weight.requires_grad,
+                'device': adapter_device,
+                'requires_grad': requires_grad,
             }
 
     def restore_device_state(self):