| |
|
|
| import os |
| import torch |
| import ldm_patched.controlnet.cldm |
| import ldm_patched.k_diffusion.sampling |
| import ldm_patched.ldm.modules.attention |
| import ldm_patched.ldm.modules.diffusionmodules.model |
| import ldm_patched.ldm.modules.diffusionmodules.openaimodel |
| import ldm_patched.ldm.modules.diffusionmodules.openaimodel |
| import ldm_patched.modules.args_parser |
| import ldm_patched.modules.model_base |
| import ldm_patched.modules.model_management |
| import ldm_patched.modules.model_patcher |
| import ldm_patched.modules.samplers |
| import ldm_patched.modules.sd |
| import ldm_patched.modules.sd1_clip |
| import ldm_patched.modules.clip_vision |
| import ldm_patched.modules.ops as ops |
|
|
| from modules.ops import use_patched_ops |
| from transformers import CLIPTextModel, CLIPTextConfig, modeling_utils, CLIPVisionConfig, CLIPVisionModelWithProjection |
|
|
|
|
| def patched_encode_token_weights(self, token_weight_pairs): |
| to_encode = list() |
| max_token_len = 0 |
| has_weights = False |
| for x in token_weight_pairs: |
| tokens = list(map(lambda a: a[0], x)) |
| max_token_len = max(len(tokens), max_token_len) |
| has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x)) |
| to_encode.append(tokens) |
|
|
| sections = len(to_encode) |
| if has_weights or sections == 0: |
| to_encode.append(ldm_patched.modules.sd1_clip.gen_empty_tokens(self.special_tokens, max_token_len)) |
|
|
| out, pooled = self.encode(to_encode) |
| if pooled is not None: |
| first_pooled = pooled[0:1].to(ldm_patched.modules.model_management.intermediate_device()) |
| else: |
| first_pooled = pooled |
|
|
| output = [] |
| for k in range(0, sections): |
| z = out[k:k + 1] |
| if has_weights: |
| original_mean = z.mean() |
| z_empty = out[-1] |
| for i in range(len(z)): |
| for j in range(len(z[i])): |
| weight = token_weight_pairs[k][j][1] |
| if weight != 1.0: |
| z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j] |
| new_mean = z.mean() |
| z = z * (original_mean / new_mean) |
| output.append(z) |
|
|
| if len(output) == 0: |
| return out[-1:].to(ldm_patched.modules.model_management.intermediate_device()), first_pooled |
| return torch.cat(output, dim=-2).to(ldm_patched.modules.model_management.intermediate_device()), first_pooled |
|
|
|
|
| def patched_SDClipModel__init__(self, max_length=77, freeze=True, layer="last", layer_idx=None, |
| textmodel_json_config=None, dtype=None, special_tokens=None, |
| layer_norm_hidden_state=True, **kwargs): |
| torch.nn.Module.__init__(self) |
| assert layer in self.LAYERS |
|
|
| if special_tokens is None: |
| special_tokens = {"start": 49406, "end": 49407, "pad": 49407} |
|
|
| if textmodel_json_config is None: |
| textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(ldm_patched.modules.sd1_clip.__file__)), |
| "sd1_clip_config.json") |
|
|
| config = CLIPTextConfig.from_json_file(textmodel_json_config) |
| self.num_layers = config.num_hidden_layers |
|
|
| with use_patched_ops(ops.manual_cast): |
| with modeling_utils.no_init_weights(): |
| self.transformer = CLIPTextModel(config) |
|
|
| if dtype is not None: |
| self.transformer.to(dtype) |
|
|
| self.transformer.text_model.embeddings.to(torch.float32) |
|
|
| if freeze: |
| self.freeze() |
|
|
| self.max_length = max_length |
| self.layer = layer |
| self.layer_idx = None |
| self.special_tokens = special_tokens |
| self.text_projection = torch.nn.Parameter(torch.eye(self.transformer.get_input_embeddings().weight.shape[1])) |
| self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055)) |
| self.enable_attention_masks = False |
|
|
| self.layer_norm_hidden_state = layer_norm_hidden_state |
| if layer == "hidden": |
| assert layer_idx is not None |
| assert abs(layer_idx) < self.num_layers |
| self.clip_layer(layer_idx) |
| self.layer_default = (self.layer, self.layer_idx) |
|
|
|
|
| def patched_SDClipModel_forward(self, tokens): |
| backup_embeds = self.transformer.get_input_embeddings() |
| device = backup_embeds.weight.device |
| tokens = self.set_up_textual_embeddings(tokens, backup_embeds) |
| tokens = torch.LongTensor(tokens).to(device) |
|
|
| attention_mask = None |
| if self.enable_attention_masks: |
| attention_mask = torch.zeros_like(tokens) |
| max_token = self.transformer.get_input_embeddings().weight.shape[0] - 1 |
| for x in range(attention_mask.shape[0]): |
| for y in range(attention_mask.shape[1]): |
| attention_mask[x, y] = 1 |
| if tokens[x, y] == max_token: |
| break |
|
|
| outputs = self.transformer(input_ids=tokens, attention_mask=attention_mask, |
| output_hidden_states=self.layer == "hidden") |
| self.transformer.set_input_embeddings(backup_embeds) |
|
|
| if self.layer == "last": |
| z = outputs.last_hidden_state |
| elif self.layer == "pooled": |
| z = outputs.pooler_output[:, None, :] |
| else: |
| z = outputs.hidden_states[self.layer_idx] |
| if self.layer_norm_hidden_state: |
| z = self.transformer.text_model.final_layer_norm(z) |
|
|
| if hasattr(outputs, "pooler_output"): |
| pooled_output = outputs.pooler_output.float() |
| else: |
| pooled_output = None |
|
|
| if self.text_projection is not None and pooled_output is not None: |
| pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float() |
|
|
| return z.float(), pooled_output |
|
|
|
|
| def patched_ClipVisionModel__init__(self, json_config): |
| config = CLIPVisionConfig.from_json_file(json_config) |
|
|
| self.load_device = ldm_patched.modules.model_management.text_encoder_device() |
| self.offload_device = ldm_patched.modules.model_management.text_encoder_offload_device() |
|
|
| if ldm_patched.modules.model_management.should_use_fp16(self.load_device, prioritize_performance=False): |
| self.dtype = torch.float16 |
| else: |
| self.dtype = torch.float32 |
|
|
| with use_patched_ops(ops.manual_cast): |
| with modeling_utils.no_init_weights(): |
| self.model = CLIPVisionModelWithProjection(config) |
|
|
| self.model.to(self.dtype) |
| self.patcher = ldm_patched.modules.model_patcher.ModelPatcher( |
| self.model, |
| load_device=self.load_device, |
| offload_device=self.offload_device |
| ) |
|
|
|
|
| def patched_ClipVisionModel_encode_image(self, image): |
| ldm_patched.modules.model_management.load_model_gpu(self.patcher) |
| pixel_values = ldm_patched.modules.clip_vision.clip_preprocess(image.to(self.load_device)) |
| outputs = self.model(pixel_values=pixel_values, output_hidden_states=True) |
|
|
| for k in outputs: |
| t = outputs[k] |
| if t is not None: |
| if k == 'hidden_states': |
| outputs["penultimate_hidden_states"] = t[-2].to(ldm_patched.modules.model_management.intermediate_device()) |
| outputs["hidden_states"] = None |
| else: |
| outputs[k] = t.to(ldm_patched.modules.model_management.intermediate_device()) |
|
|
| return outputs |
|
|
|
|
| def patch_all_clip(): |
| ldm_patched.modules.sd1_clip.ClipTokenWeightEncoder.encode_token_weights = patched_encode_token_weights |
| ldm_patched.modules.sd1_clip.SDClipModel.__init__ = patched_SDClipModel__init__ |
| ldm_patched.modules.sd1_clip.SDClipModel.forward = patched_SDClipModel_forward |
| ldm_patched.modules.clip_vision.ClipVisionModel.__init__ = patched_ClipVisionModel__init__ |
| ldm_patched.modules.clip_vision.ClipVisionModel.encode_image = patched_ClipVisionModel_encode_image |
| return |
|
|
