| 134 | |
| 135 | @torch.inference_mode() |
| 136 | def get_image_embeds(self, clip_embed, clip_embed_zeroed, batch_size): |
| 137 | torch_device = model_management.get_torch_device() |
| 138 | intermediate_device = model_management.intermediate_device() |
| 139 | |
| 140 | if batch_size == 0: |
| 141 | batch_size = clip_embed.shape[0] |
| 142 | intermediate_device = torch_device |
| 143 | elif batch_size > clip_embed.shape[0]: |
| 144 | batch_size = clip_embed.shape[0] |
| 145 | |
| 146 | clip_embed = torch.split(clip_embed, batch_size, dim=0) |
| 147 | clip_embed_zeroed = torch.split(clip_embed_zeroed, batch_size, dim=0) |
| 148 | |
| 149 | image_prompt_embeds = [] |
| 150 | uncond_image_prompt_embeds = [] |
| 151 | |
| 152 | for ce, cez in zip(clip_embed, clip_embed_zeroed): |
| 153 | image_prompt_embeds.append(self.image_proj_model(ce.to(torch_device)).to(intermediate_device)) |
| 154 | uncond_image_prompt_embeds.append(self.image_proj_model(cez.to(torch_device)).to(intermediate_device)) |
| 155 | |
| 156 | del clip_embed, clip_embed_zeroed |
| 157 | |
| 158 | image_prompt_embeds = torch.cat(image_prompt_embeds, dim=0) |
| 159 | uncond_image_prompt_embeds = torch.cat(uncond_image_prompt_embeds, dim=0) |
| 160 | |
| 161 | torch.cuda.empty_cache() |
| 162 | |
| 163 | #image_prompt_embeds = self.image_proj_model(clip_embed) |
| 164 | #uncond_image_prompt_embeds = self.image_proj_model(clip_embed_zeroed) |
| 165 | return image_prompt_embeds, uncond_image_prompt_embeds |
| 166 | |
| 167 | @torch.inference_mode() |
| 168 | def get_image_embeds_faceid_plus(self, face_embed, clip_embed, s_scale, shortcut, batch_size): |