Add a T5TokenizerOptions node to set options for the T5 tokenizer. (#7803)

comfy/sd.py

@@ -120,6 +120,7 @@ class CLIP:
         self.layer_idx = None
         self.use_clip_schedule = False
         logging.info("CLIP/text encoder model load device: {}, offload device: {}, current: {}, dtype: {}".format(load_device, offload_device, params['device'], dtype))
+        self.tokenizer_options = {}
 
     def clone(self):
         n = CLIP(no_init=True)
@@ -127,6 +128,7 @@ class CLIP:
         n.cond_stage_model = self.cond_stage_model
         n.tokenizer = self.tokenizer
         n.layer_idx = self.layer_idx
+        n.tokenizer_options = self.tokenizer_options.copy()
         n.use_clip_schedule = self.use_clip_schedule
         n.apply_hooks_to_conds = self.apply_hooks_to_conds
         return n
@@ -134,10 +136,18 @@ class CLIP:
     def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
         return self.patcher.add_patches(patches, strength_patch, strength_model)
 
+    def set_tokenizer_option(self, option_name, value):
+        self.tokenizer_options[option_name] = value
+
     def clip_layer(self, layer_idx):
         self.layer_idx = layer_idx
 
     def tokenize(self, text, return_word_ids=False, **kwargs):
+        tokenizer_options = kwargs.get("tokenizer_options", {})
+        if len(self.tokenizer_options) > 0:
+            tokenizer_options = {**self.tokenizer_options, **tokenizer_options}
+        if len(tokenizer_options) > 0:
+            kwargs["tokenizer_options"] = tokenizer_options
         return self.tokenizer.tokenize_with_weights(text, return_word_ids, **kwargs)
 
     def add_hooks_to_dict(self, pooled_dict: dict[str]):

comfy/sd1_clip.py

@@ -457,13 +457,14 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
     return embed_out
 
 class SDTokenizer:
-    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, tokenizer_data={}, tokenizer_args={}):
+    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, tokenizer_data={}, tokenizer_args={}):
         if tokenizer_path is None:
             tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
         self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path, **tokenizer_args)
         self.max_length = tokenizer_data.get("{}_max_length".format(embedding_key), max_length)
         self.min_length = min_length
         self.end_token = None
+        self.min_padding = min_padding
 
         empty = self.tokenizer('')["input_ids"]
         self.tokenizer_adds_end_token = has_end_token
@@ -518,13 +519,15 @@ class SDTokenizer:
         return (embed, leftover)
 
 
-    def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
+    def tokenize_with_weights(self, text:str, return_word_ids=False, tokenizer_options={}, **kwargs):
         '''
         Takes a prompt and converts it to a list of (token, weight, word id) elements.
         Tokens can both be integer tokens and pre computed CLIP tensors.
         Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens.
         Returned list has the dimensions NxM where M is the input size of CLIP
         '''
+        min_length = tokenizer_options.get("{}_min_length".format(self.embedding_key), self.min_length)
+        min_padding = tokenizer_options.get("{}_min_padding".format(self.embedding_key), self.min_padding)
 
         text = escape_important(text)
         parsed_weights = token_weights(text, 1.0)
@@ -603,10 +606,12 @@ class SDTokenizer:
         #fill last batch
         if self.end_token is not None:
             batch.append((self.end_token, 1.0, 0))
-        if self.pad_to_max_length:
+        if min_padding is not None:
+            batch.extend([(self.pad_token, 1.0, 0)] * min_padding)
+        if self.pad_to_max_length and len(batch) < self.max_length:
             batch.extend([(self.pad_token, 1.0, 0)] * (self.max_length - len(batch)))
-        if self.min_length is not None and len(batch) < self.min_length:
+        if min_length is not None and len(batch) < min_length:
-            batch.extend([(self.pad_token, 1.0, 0)] * (self.min_length - len(batch)))
+            batch.extend([(self.pad_token, 1.0, 0)] * (min_length - len(batch)))
 
         if not return_word_ids:
             batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens]
@@ -634,7 +639,7 @@ class SD1Tokenizer:
 
     def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
         out = {}
-        out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids)
+        out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids, **kwargs)
         return out
 
     def untokenize(self, token_weight_pair):

comfy/sdxl_clip.py

@@ -28,8 +28,8 @@ class SDXLTokenizer:
 
     def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
         out = {}
-        out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids)
+        out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs)
-        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
+        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs)
         return out
 
     def untokenize(self, token_weight_pair):

comfy/text_encoders/flux.py

@@ -19,8 +19,8 @@ class FluxTokenizer:
 
     def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
         out = {}
-        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
+        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs)
-        out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids)
+        out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs)
         return out
 
     def untokenize(self, token_weight_pair):

comfy/text_encoders/hidream.py

@@ -16,11 +16,11 @@ class HiDreamTokenizer:
 
     def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
         out = {}
-        out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids)
+        out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs)
-        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
+        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs)
-        t5xxl = self.t5xxl.tokenize_with_weights(text, return_word_ids)
+        t5xxl = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs)
         out["t5xxl"] = [t5xxl[0]]  # Use only first 128 tokens
-        out["llama"] = self.llama.tokenize_with_weights(text, return_word_ids)
+        out["llama"] = self.llama.tokenize_with_weights(text, return_word_ids, **kwargs)
         return out
 
     def untokenize(self, token_weight_pair):

comfy/text_encoders/hunyuan_video.py

@@ -49,13 +49,13 @@ class HunyuanVideoTokenizer:
 
     def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, image_embeds=None, image_interleave=1, **kwargs):
         out = {}
-        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
+        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs)
 
         if llama_template is None:
             llama_text = self.llama_template.format(text)
         else:
             llama_text = llama_template.format(text)
-        llama_text_tokens = self.llama.tokenize_with_weights(llama_text, return_word_ids)
+        llama_text_tokens = self.llama.tokenize_with_weights(llama_text, return_word_ids, **kwargs)
         embed_count = 0
         for r in llama_text_tokens:
             for i in range(len(r)):

comfy/text_encoders/hydit.py

@@ -41,8 +41,8 @@ class HyditTokenizer:
 
     def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
         out = {}
-        out["hydit_clip"] = self.hydit_clip.tokenize_with_weights(text, return_word_ids)
+        out["hydit_clip"] = self.hydit_clip.tokenize_with_weights(text, return_word_ids, **kwargs)
-        out["mt5xl"] = self.mt5xl.tokenize_with_weights(text, return_word_ids)
+        out["mt5xl"] = self.mt5xl.tokenize_with_weights(text, return_word_ids, **kwargs)
         return out
 
     def untokenize(self, token_weight_pair):

comfy/text_encoders/sd3_clip.py

@@ -45,9 +45,9 @@ class SD3Tokenizer:
 
     def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
         out = {}
-        out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids)
+        out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs)
-        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
+        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs)
-        out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids)
+        out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs)
         return out
 
     def untokenize(self, token_weight_pair):

comfy_extras/nodes_cond.py

@@ -20,6 +20,29 @@ class CLIPTextEncodeControlnet:
             c.append(n)
         return (c, )
 
+class T5TokenizerOptions:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "clip": ("CLIP", ),
+                "min_padding": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}),
+                "min_length": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "set_options"
+
+    def set_options(self, clip, min_padding, min_length):
+        clip = clip.clone()
+        for t5_type in ["t5xxl", "pile_t5xl", "t5base", "mt5xl", "umt5xxl"]:
+            clip.set_tokenizer_option("{}_min_padding".format(t5_type), min_padding)
+            clip.set_tokenizer_option("{}_min_length".format(t5_type), min_length)
+
+        return (clip, )
+
 NODE_CLASS_MAPPINGS = {
-    "CLIPTextEncodeControlnet": CLIPTextEncodeControlnet
+    "CLIPTextEncodeControlnet": CLIPTextEncodeControlnet,
+    "T5TokenizerOptions": T5TokenizerOptions,
 }