Upload modeling_text_encoder.py

modeling_text_encoder.py

@@ -0,0 +1,142 @@
+import torch
+import torch.nn as nn
+import os
+from transformers import (
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    T5EncoderModel,
+    T5TokenizerFast,
+)
+from typing import Union, List, Optional
+
+class SD3TextEncoderWithMask(nn.Module):
+    def __init__(self, model_path, torch_dtype):
+        super().__init__()
+
+        # Initialization of models and tokenizers, but delay moving to device
+        self.tokenizer = CLIPTokenizer.from_pretrained(os.path.join(model_path, 'tokenizer'))
+        self.tokenizer_2 = CLIPTokenizer.from_pretrained(os.path.join(model_path, 'tokenizer_2'))
+        self.tokenizer_3 = T5TokenizerFast.from_pretrained(os.path.join(model_path, 'tokenizer_3'))
+
+        # Lazy loading of models for memory efficiency
+        self.text_encoder = None
+        self.text_encoder_2 = None
+        self.text_encoder_3 = None
+        self.model_path = model_path
+        self.torch_dtype = torch_dtype
+        self.tokenizer_max_length = self.tokenizer.model_max_length
+
+        # Freeze parameters to avoid unnecessary gradient computation
+        self._freeze()
+
+    def _freeze(self):
+        """ Freeze all model parameters to avoid training overhead. """
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def _load_models_if_needed(self):
+        """ Load models only if they haven't been loaded already. """
+        if self.text_encoder is None:
+            self.text_encoder = CLIPTextModelWithProjection.from_pretrained(
+                os.path.join(self.model_path, 'text_encoder'), torch_dtype=self.torch_dtype
+            ).to("cuda")
+
+        if self.text_encoder_2 is None:
+            self.text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(
+                os.path.join(self.model_path, 'text_encoder_2'), torch_dtype=self.torch_dtype
+            ).to("cuda")
+
+        if self.text_encoder_3 is None:
+            self.text_encoder_3 = T5EncoderModel.from_pretrained(
+                os.path.join(self.model_path, 'text_encoder_3'), torch_dtype=self.torch_dtype
+            ).to("cuda")
+
+    def _get_t5_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]] = None,
+        num_images_per_prompt: int = 1,
+        device: Optional[torch.device] = None,
+        max_sequence_length: int = 128,
+    ):
+        """ Get embeddings from T5 model. """
+        self._load_models_if_needed()  # Lazy loading
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        text_inputs = self.tokenizer_3(
+            prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            add_special_tokens=True,
+            return_tensors="pt",
+        )
+
+        text_input_ids = text_inputs.input_ids.to(device)
+        prompt_attention_mask = text_inputs.attention_mask.to(device)
+        prompt_embeds = self.text_encoder_3(text_input_ids, attention_mask=prompt_attention_mask)[0]
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_3.dtype, device=device)
+
+        # Duplicate embeddings for each image generation
+        batch_size = len(prompt)
+        _, seq_len, _ = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1).view(batch_size * num_images_per_prompt, seq_len, -1)
+        prompt_attention_mask = prompt_attention_mask.view(batch_size, -1).repeat(num_images_per_prompt, 1)
+
+        return prompt_embeds, prompt_attention_mask
+
+    def _get_clip_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]],
+        num_images_per_prompt: int = 1,
+        device: Optional[torch.device] = None,
+        clip_model_index: int = 0,
+    ):
+        """ Get embeddings from CLIP model. """
+        self._load_models_if_needed()  # Lazy loading
+
+        clip_tokenizers = [self.tokenizer, self.tokenizer_2]
+        clip_text_encoders = [self.text_encoder, self.text_encoder_2]
+
+        tokenizer = clip_tokenizers[clip_model_index]
+        text_encoder = clip_text_encoders[clip_model_index]
+
+        text_inputs = tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+        text_input_ids = text_inputs.input_ids.to(device)
+        prompt_embeds = text_encoder(text_input_ids, output_hidden_states=True)[0]
+
+        # Duplicate embeddings for each image generation
+        batch_size = len(prompt)
+        pooled_prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1).view(batch_size * num_images_per_prompt, -1)
+
+        return pooled_prompt_embeds
+
+    def encode_prompt(self, 
+        prompt, 
+        num_images_per_prompt=1, 
+        device=None
+    ):
+        """ Encode the prompt using both CLIP and T5 models. """
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        # Get embeddings from both CLIP models
+        pooled_prompt_embed = self._get_clip_prompt_embeds(prompt, num_images_per_prompt=num_images_per_prompt, device=device, clip_model_index=0)
+        pooled_prompt_2_embed = self._get_clip_prompt_embeds(prompt, num_images_per_prompt=num_images_per_prompt, device=device, clip_model_index=1)
+        pooled_prompt_embeds = torch.cat([pooled_prompt_embed, pooled_prompt_2_embed], dim=-1)
+
+        # Get T5 embeddings
+        prompt_embeds, prompt_attention_mask = self._get_t5_prompt_embeds(prompt, num_images_per_prompt=num_images_per_prompt, device=device)
+
+        return prompt_embeds, prompt_attention_mask, pooled_prompt_embeds
+
+    def forward(self, input_prompts, device):
+        """ Forward pass for encoding prompts. """
+        with torch.no_grad():
+            prompt_embeds, prompt_attention_mask, pooled_prompt_embeds = self.encode_prompt(input_prompts, num_images_per_prompt=1, device=device)
+
+        return prompt_embeds, prompt_attention_mask, pooled_prompt_embeds