more comments

modules/sd_hijack_clip.py

@@ -3,7 +3,7 @@ from collections import namedtuple
 
 import torch
 
-from modules import prompt_parser, devices
+from modules import prompt_parser, devices, sd_hijack
 from modules.shared import opts
 
 
@@ -22,14 +22,24 @@ class PromptChunk:
 
 
 PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding'])
-"""This is a marker showing that textual inversion embedding's vectors have to placed at offset in the prompt chunk"""
+"""An object of this type is a marker showing that textual inversion embedding's vectors have to placed at offset in the prompt
+chunk. Thos objects are found in PromptChunk.fixes and, are placed into FrozenCLIPEmbedderWithCustomWordsBase.hijack.fixes, and finally
+are applied by sd_hijack.EmbeddingsWithFixes's forward function."""
 
 
 class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
+    """A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to
+    have unlimited prompt length and assign weights to tokens in prompt.
+    """
+
     def __init__(self, wrapped, hijack):
         super().__init__()
+
         self.wrapped = wrapped
-        self.hijack = hijack
+        """Original FrozenCLIPEmbedder module; can also be FrozenOpenCLIPEmbedder or xlmr.BertSeriesModelWithTransformation,
+        depending on model."""
+
+        self.hijack: sd_hijack.StableDiffusionModelHijack = hijack
         self.chunk_length = 75
 
     def empty_chunk(self):
@@ -55,7 +65,8 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
         converts a batch of token ids (in python lists) into a single tensor with numeric respresentation of those tokens;
         All python lists with tokens are assumed to have same length, usually 77.
         if input is a list with B elements and each element has T tokens, expected output shape is (B, T, C), where C depends on
-        model - can be 768 and 1024
+        model - can be 768 and 1024.
+        Among other things, this call will read self.hijack.fixes, apply it to its inputs, and clear it (setting it to None).
         """
 
         raise NotImplementedError
@@ -113,7 +124,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
                     last_comma = len(chunk.tokens)
 
                 # this is when we are at the end of alloted 75 tokens for the current chunk, and the current token is not a comma. opts.comma_padding_backtrack
-                # is a setting that specifies that is there is a comma nearby, the text after comma should be moved out of this chunk and into the next.
+                # is a setting that specifies that if there is a comma nearby, the text after the comma should be moved out of this chunk and into the next.
                 elif opts.comma_padding_backtrack != 0 and len(chunk.tokens) == self.chunk_length and last_comma != -1 and len(chunk.tokens) - last_comma <= opts.comma_padding_backtrack:
                     break_location = last_comma + 1