268 lines
10 KiB
Python
268 lines
10 KiB
Python
# Consistent with Kohya/A1111 to reduce differences between model training and inference.
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import os
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import torch
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import ldm_patched.controlnet.cldm
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import ldm_patched.k_diffusion.sampling
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import ldm_patched.ldm.modules.attention
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import ldm_patched.ldm.modules.diffusionmodules.model
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import ldm_patched.ldm.modules.diffusionmodules.openaimodel
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import ldm_patched.ldm.modules.diffusionmodules.openaimodel
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import ldm_patched.modules.args_parser
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import ldm_patched.modules.model_base
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import ldm_patched.modules.model_management
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import ldm_patched.modules.model_patcher
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import ldm_patched.modules.ops
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import ldm_patched.modules.samplers
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import ldm_patched.modules.sd
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import ldm_patched.modules.sd1_clip
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import ldm_patched.modules.clip_vision
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import ldm_patched.modules.model_management as model_management
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import contextlib
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from transformers import CLIPTextModel, CLIPTextConfig, modeling_utils, CLIPVisionConfig, CLIPVisionModelWithProjection
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def encode_token_weights_fooocus(self, token_weight_pairs):
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to_encode = list()
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max_token_len = 0
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has_weights = False
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for x in token_weight_pairs:
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tokens = list(map(lambda a: a[0], x))
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max_token_len = max(len(tokens), max_token_len)
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has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x))
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to_encode.append(tokens)
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sections = len(to_encode)
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if has_weights or sections == 0:
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to_encode.append(ldm_patched.modules.sd1_clip.gen_empty_tokens(self.special_tokens, max_token_len))
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out, pooled = self.encode(to_encode)
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if pooled is not None:
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first_pooled = pooled[0:1].to(ldm_patched.modules.model_management.intermediate_device())
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else:
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first_pooled = pooled
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output = []
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for k in range(0, sections):
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z = out[k:k + 1]
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if has_weights:
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original_mean = z.mean()
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z_empty = out[-1]
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for i in range(len(z)):
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for j in range(len(z[i])):
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weight = token_weight_pairs[k][j][1]
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if weight != 1.0:
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z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j]
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new_mean = z.mean()
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z = z * (original_mean / new_mean)
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output.append(z)
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if len(output) == 0:
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return out[-1:].to(ldm_patched.modules.model_management.intermediate_device()), first_pooled
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return torch.cat(output, dim=-2).to(ldm_patched.modules.model_management.intermediate_device()), first_pooled
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class SDClipModelFooocus(torch.nn.Module, ldm_patched.modules.sd1_clip.ClipTokenWeightEncoder):
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"""Uses the CLIP transformer encoder for text (from huggingface)"""
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LAYERS = [
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"last",
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"pooled",
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"hidden"
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]
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def __init__(self,
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max_length=77,
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freeze=True,
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layer="last",
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layer_idx=None,
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textmodel_json_config=None,
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dtype=None,
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special_tokens=None,
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layer_norm_hidden_state=True,
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**kwargs):
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super().__init__()
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assert layer in self.LAYERS
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if special_tokens is None:
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special_tokens = {"start": 49406, "end": 49407, "pad": 49407}
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if textmodel_json_config is None:
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textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(ldm_patched.modules.sd1_clip.__file__)), "sd1_clip_config.json")
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config = CLIPTextConfig.from_json_file(textmodel_json_config)
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self.num_layers = config.num_hidden_layers
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with modeling_utils.no_init_weights():
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self.transformer = CLIPTextModel(config)
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if 'cuda' not in model_management.text_encoder_device().type:
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dtype = torch.float32
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if dtype is not None:
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self.transformer.to(dtype)
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self.transformer.text_model.embeddings.to(torch.float32)
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if freeze:
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self.freeze()
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self.max_length = max_length
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self.layer = layer
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self.layer_idx = None
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self.special_tokens = special_tokens
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self.text_projection = torch.nn.Parameter(torch.eye(self.transformer.get_input_embeddings().weight.shape[1]))
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self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))
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self.enable_attention_masks = False
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self.layer_norm_hidden_state = layer_norm_hidden_state
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if layer == "hidden":
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assert layer_idx is not None
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self.clip_layer(layer_idx)
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self.layer_default = (self.layer, self.layer_idx)
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def freeze(self):
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self.transformer = self.transformer.eval()
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# self.train = disabled_train
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for param in self.parameters():
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param.requires_grad = False
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def clip_layer(self, layer_idx):
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self.layer = "hidden"
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self.layer_idx = layer_idx
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def reset_clip_layer(self):
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self.layer = self.layer_default[0]
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self.layer_idx = self.layer_default[1]
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def set_up_textual_embeddings(self, tokens, current_embeds):
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out_tokens = []
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next_new_token = token_dict_size = current_embeds.weight.shape[0] - 1
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embedding_weights = []
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for x in tokens:
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tokens_temp = []
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for y in x:
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if isinstance(y, int):
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if y == token_dict_size: # EOS token
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y = -1
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tokens_temp += [y]
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else:
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if y.shape[0] == current_embeds.weight.shape[1]:
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embedding_weights += [y]
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tokens_temp += [next_new_token]
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next_new_token += 1
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else:
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print("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored",
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y.shape[0], current_embeds.weight.shape[1])
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while len(tokens_temp) < len(x):
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tokens_temp += [self.special_tokens["pad"]]
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out_tokens += [tokens_temp]
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n = token_dict_size
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if len(embedding_weights) > 0:
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new_embedding = torch.nn.Embedding(next_new_token + 1, current_embeds.weight.shape[1],
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device=current_embeds.weight.device, dtype=current_embeds.weight.dtype)
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new_embedding.weight[:token_dict_size] = current_embeds.weight[:-1]
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for x in embedding_weights:
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new_embedding.weight[n] = x
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n += 1
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new_embedding.weight[n] = current_embeds.weight[-1] # EOS embedding
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self.transformer.set_input_embeddings(new_embedding)
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processed_tokens = []
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for x in out_tokens:
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processed_tokens += [
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list(map(lambda a: n if a == -1 else a, x))] # The EOS token should always be the largest one
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return processed_tokens
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def forward(self, tokens):
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backup_embeds = self.transformer.get_input_embeddings()
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device = backup_embeds.weight.device
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tokens = self.set_up_textual_embeddings(tokens, backup_embeds)
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tokens = torch.LongTensor(tokens).to(device)
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if self.transformer.text_model.final_layer_norm.weight.dtype != torch.float32:
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precision_scope = torch.autocast
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else:
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precision_scope = lambda a, dtype: contextlib.nullcontext(a)
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with precision_scope(model_management.get_autocast_device(device), dtype=torch.float32):
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attention_mask = None
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if self.enable_attention_masks:
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attention_mask = torch.zeros_like(tokens)
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max_token = self.transformer.get_input_embeddings().weight.shape[0] - 1
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for x in range(attention_mask.shape[0]):
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for y in range(attention_mask.shape[1]):
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attention_mask[x, y] = 1
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if tokens[x, y] == max_token:
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break
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outputs = self.transformer(input_ids=tokens, attention_mask=attention_mask,
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output_hidden_states=self.layer == "hidden")
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self.transformer.set_input_embeddings(backup_embeds)
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if self.layer == "last":
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z = outputs.last_hidden_state
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elif self.layer == "pooled":
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z = outputs.pooler_output[:, None, :]
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else:
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z = outputs.hidden_states[self.layer_idx]
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if self.layer_norm_hidden_state:
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z = self.transformer.text_model.final_layer_norm(z)
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if hasattr(outputs, "pooler_output"):
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pooled_output = outputs.pooler_output.float()
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else:
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pooled_output = None
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if self.text_projection is not None and pooled_output is not None:
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pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float()
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return z.float(), pooled_output
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def encode(self, tokens):
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return self(tokens)
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def load_sd(self, sd):
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if "text_projection" in sd:
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self.text_projection[:] = sd.pop("text_projection")
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if "text_projection.weight" in sd:
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self.text_projection[:] = sd.pop("text_projection.weight").transpose(0, 1)
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return self.transformer.load_state_dict(sd, strict=False)
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class ClipVisionModelFooocus:
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def __init__(self, json_config):
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config = CLIPVisionConfig.from_json_file(json_config)
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self.load_device = ldm_patched.modules.model_management.text_encoder_device()
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self.offload_device = ldm_patched.modules.model_management.text_encoder_offload_device()
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if ldm_patched.modules.model_management.should_use_fp16(self.load_device, prioritize_performance=False):
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self.dtype = torch.float16
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else:
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self.dtype = torch.float32
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if 'cuda' not in self.load_device.type:
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self.dtype = torch.float32
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with modeling_utils.no_init_weights():
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self.model = CLIPVisionModelWithProjection(config)
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self.model.to(self.dtype)
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self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(
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self.model,
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load_device=self.load_device,
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offload_device=self.offload_device
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)
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def load_sd(self, sd):
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return self.model.load_state_dict(sd, strict=False)
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def patch_all_clip():
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ldm_patched.modules.sd1_clip.ClipTokenWeightEncoder.encode_token_weights = encode_token_weights_fooocus
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ldm_patched.modules.sd1_clip.SDClipModel = SDClipModelFooocus
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ldm_patched.modules.clip_vision.ClipVisionModel = ClipVisionModelFooocus
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return
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