Improve docs related to dynamic T5 sequence length selection.

invokeai/app/invocations/flux_text_encoder.py

@@ -81,7 +81,9 @@ def _t5_encode(self, context: InvocationContext) -> torch.Tensor:
 
         valid_seq_lens = [self.t5_max_seq_len]
         if self.use_short_t5_seq_len:
-            valid_seq_lens = [128, 256, 512]
+            # We allow a minimum sequence length of 128. Going too short results in more significant image chagnes.
+            valid_seq_lens = list(range(128, self.t5_max_seq_len, 128))
+            valid_seq_lens.append(self.t5_max_seq_len)
 
         with (
             t5_text_encoder_info as t5_text_encoder,

invokeai/backend/flux/modules/conditioner.py

@@ -15,7 +15,17 @@ def __init__(self, encoder: PreTrainedModel, tokenizer: PreTrainedTokenizer, is_
         self.hf_module = self.hf_module.eval().requires_grad_(False)
 
     def forward(self, text: list[str], valid_seq_lens: list[int]) -> Tensor:
+        """Encode text into a tensor.
+
+        Args:
+            text: A list of text prompts to encode.
+            valid_seq_lens: A list of valid sequence lengths. The shortest valid sequence length that can contain the
+                text will be used. If the largest valid sequence length cannot contain the text, the encoding will be
+                truncated.
+        """
         valid_seq_lens = sorted(valid_seq_lens)
+
+        # Perform initial encoding with the maximum valid sequence length.
         batch_encoding = self.tokenizer(
             text,
             truncation=True,
@@ -26,8 +36,8 @@ def forward(self, text: list[str], valid_seq_lens: list[int]) -> Tensor:
             return_tensors="pt",
         )
 
-        seq_len: int = batch_encoding["length"][0].item()
         # Find selected_seq_len, the minimum valid sequence length that can contain all of the input tokens.
+        seq_len: int = batch_encoding["length"][0].item()
         selected_seq_len = valid_seq_lens[-1]
         for len in valid_seq_lens:
             if len >= seq_len: