from .svd_image_encoder import SVDImageEncoder from .sdxl_ipadapter import IpAdapterImageProjModel, IpAdapterModule, SDXLIpAdapterStateDictConverter from transformers import CLIPImageProcessor import torch class IpAdapterCLIPImageEmbedder(SVDImageEncoder): def __init__(self): super().__init__() self.image_processor = CLIPImageProcessor() def forward(self, image): pixel_values = self.image_processor(images=image, return_tensors="pt").pixel_values pixel_values = pixel_values.to(device=self.embeddings.class_embedding.device, dtype=self.embeddings.class_embedding.dtype) return super().forward(pixel_values) class SDIpAdapter(torch.nn.Module): def __init__(self): super().__init__() shape_list = [(768, 320)] * 2 + [(768, 640)] * 2 + [(768, 1280)] * 5 + [(768, 640)] * 3 + [(768, 320)] * 3 + [(768, 1280)] * 1 self.ipadapter_modules = torch.nn.ModuleList([IpAdapterModule(*shape) for shape in shape_list]) self.image_proj = IpAdapterImageProjModel(cross_attention_dim=768, clip_embeddings_dim=1024, clip_extra_context_tokens=4) self.set_full_adapter() def set_full_adapter(self): block_ids = [1, 4, 9, 12, 17, 20, 40, 43, 46, 50, 53, 56, 60, 63, 66, 29] self.call_block_id = {(i, 0): j for j, i in enumerate(block_ids)} def set_less_adapter(self): # IP-Adapter for SD v1.5 doesn't support this feature. self.set_full_adapter() def forward(self, hidden_states, scale=1.0): hidden_states = self.image_proj(hidden_states) hidden_states = hidden_states.view(1, -1, hidden_states.shape[-1]) ip_kv_dict = {} for (block_id, transformer_id) in self.call_block_id: ipadapter_id = self.call_block_id[(block_id, transformer_id)] ip_k, ip_v = self.ipadapter_modules[ipadapter_id](hidden_states) if block_id not in ip_kv_dict: ip_kv_dict[block_id] = {} ip_kv_dict[block_id][transformer_id] = { "ip_k": ip_k, "ip_v": ip_v, "scale": scale } return ip_kv_dict @staticmethod def state_dict_converter(): return SDIpAdapterStateDictConverter() class SDIpAdapterStateDictConverter(SDXLIpAdapterStateDictConverter): def __init__(self): pass