SD1 and SD2 clip and tokenizer code is now more similar to the SDXL one.

comfy/lora.py

@@ -141,9 +141,9 @@ def model_lora_keys_clip(model, key_map={}):
 
     text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}"
     clip_l_present = False
-    for b in range(32):
+    for b in range(32): #TODO: clean up
         for c in LORA_CLIP_MAP:
-            k = "transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
+            k = "clip_h.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
             if k in sdk:
                 lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c])
                 key_map[lora_key] = k
@@ -154,6 +154,8 @@ def model_lora_keys_clip(model, key_map={}):
 
             k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
             if k in sdk:
+                lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c])
+                key_map[lora_key] = k
                 lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base
                 key_map[lora_key] = k
                 clip_l_present = True

comfy/sd1_clip.py

@@ -35,7 +35,7 @@ class ClipTokenWeightEncoder:
             return z_empty.cpu(), first_pooled.cpu()
         return torch.cat(output, dim=-2).cpu(), first_pooled.cpu()
 
-class SD1ClipModel(torch.nn.Module, ClipTokenWeightEncoder):
+class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     """Uses the CLIP transformer encoder for text (from huggingface)"""
     LAYERS = [
         "last",
@@ -342,7 +342,7 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
             embed_out = next(iter(values))
     return embed_out
 
-class SD1Tokenizer:
+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'):
         if tokenizer_path is None:
             tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
@@ -454,3 +454,40 @@ class SD1Tokenizer:
 
     def untokenize(self, token_weight_pair):
         return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair))
+
+
+class SD1Tokenizer:
+    def __init__(self, embedding_directory=None, clip_name="l", tokenizer=SDTokenizer):
+        self.clip_name = clip_name
+        self.clip = "clip_{}".format(self.clip_name)
+        setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory))
+
+    def tokenize_with_weights(self, text:str, return_word_ids=False):
+        out = {}
+        out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids)
+        return out
+
+    def untokenize(self, token_weight_pair):
+        return getattr(self, self.clip).untokenize(token_weight_pair)
+
+
+class SD1ClipModel(torch.nn.Module):
+    def __init__(self, device="cpu", dtype=None, clip_name="l", clip_model=SDClipModel):
+        super().__init__()
+        self.clip_name = clip_name
+        self.clip = "clip_{}".format(self.clip_name)
+        setattr(self, self.clip, clip_model(device=device, dtype=dtype))
+
+    def clip_layer(self, layer_idx):
+        getattr(self, self.clip).clip_layer(layer_idx)
+
+    def reset_clip_layer(self):
+        getattr(self, self.clip).reset_clip_layer()
+
+    def encode_token_weights(self, token_weight_pairs):
+        token_weight_pairs = token_weight_pairs[self.clip_name]
+        out, pooled = getattr(self, self.clip).encode_token_weights(token_weight_pairs)
+        return out, pooled
+
+    def load_sd(self, sd):
+        return getattr(self, self.clip).load_sd(sd)

comfy/sd2_clip.py

@@ -2,7 +2,7 @@ from comfy import sd1_clip
 import torch
 import os
 
-class SD2ClipModel(sd1_clip.SD1ClipModel):
+class SD2ClipHModel(sd1_clip.SDClipModel):
     def __init__(self, arch="ViT-H-14", device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, textmodel_path=None, dtype=None):
         if layer == "penultimate":
             layer="hidden"
@@ -12,6 +12,14 @@ class SD2ClipModel(sd1_clip.SD1ClipModel):
         super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, textmodel_path=textmodel_path, dtype=dtype)
         self.empty_tokens = [[49406] + [49407] + [0] * 75]
 
-class SD2Tokenizer(sd1_clip.SD1Tokenizer):
+class SD2ClipHTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, tokenizer_path=None, embedding_directory=None):
         super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1024)
+
+class SD2Tokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None):
+        super().__init__(embedding_directory=embedding_directory, clip_name="h", tokenizer=SD2ClipHTokenizer)
+
+class SD2ClipModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None):
+        super().__init__(device=device, dtype=dtype, clip_name="h", clip_model=SD2ClipHModel)

comfy/sdxl_clip.py

@@ -2,7 +2,7 @@ from comfy import sd1_clip
 import torch
 import os
 
-class SDXLClipG(sd1_clip.SD1ClipModel):
+class SDXLClipG(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, textmodel_path=None, dtype=None):
         if layer == "penultimate":
             layer="hidden"
@@ -16,14 +16,14 @@ class SDXLClipG(sd1_clip.SD1ClipModel):
     def load_sd(self, sd):
         return super().load_sd(sd)
 
-class SDXLClipGTokenizer(sd1_clip.SD1Tokenizer):
+class SDXLClipGTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, tokenizer_path=None, embedding_directory=None):
         super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g')
 
 
-class SDXLTokenizer(sd1_clip.SD1Tokenizer):
+class SDXLTokenizer:
     def __init__(self, embedding_directory=None):
-        self.clip_l = sd1_clip.SD1Tokenizer(embedding_directory=embedding_directory)
+        self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
         self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory)
 
     def tokenize_with_weights(self, text:str, return_word_ids=False):
@@ -38,7 +38,7 @@ class SDXLTokenizer(sd1_clip.SD1Tokenizer):
 class SDXLClipModel(torch.nn.Module):
     def __init__(self, device="cpu", dtype=None):
         super().__init__()
-        self.clip_l = sd1_clip.SD1ClipModel(layer="hidden", layer_idx=11, device=device, dtype=dtype)
+        self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=11, device=device, dtype=dtype)
         self.clip_l.layer_norm_hidden_state = False
         self.clip_g = SDXLClipG(device=device, dtype=dtype)
 
@@ -63,21 +63,6 @@ class SDXLClipModel(torch.nn.Module):
         else:
             return self.clip_l.load_sd(sd)
 
-class SDXLRefinerClipModel(torch.nn.Module):
+class SDXLRefinerClipModel(sd1_clip.SD1ClipModel):
     def __init__(self, device="cpu", dtype=None):
-        super().__init__()
-        self.clip_g = SDXLClipG(device=device, dtype=dtype)
-
-    def clip_layer(self, layer_idx):
-        self.clip_g.clip_layer(layer_idx)
-
-    def reset_clip_layer(self):
-        self.clip_g.reset_clip_layer()
-
-    def encode_token_weights(self, token_weight_pairs):
-        token_weight_pairs_g = token_weight_pairs["g"]
-        g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
-        return g_out, g_pooled
-
-    def load_sd(self, sd):
-        return self.clip_g.load_sd(sd)
+        super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=SDXLClipG)

comfy/supported_models.py

@@ -38,8 +38,15 @@ class SD15(supported_models_base.BASE):
             if ids.dtype == torch.float32:
                 state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()
 
+        replace_prefix = {}
+        replace_prefix["cond_stage_model."] = "cond_stage_model.clip_l."
+        state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix)
         return state_dict
 
+    def process_clip_state_dict_for_saving(self, state_dict):
+        replace_prefix = {"clip_l.": "cond_stage_model."}
+        return utils.state_dict_prefix_replace(state_dict, replace_prefix)
+
     def clip_target(self):
         return supported_models_base.ClipTarget(sd1_clip.SD1Tokenizer, sd1_clip.SD1ClipModel)
 
@@ -62,12 +69,12 @@ class SD20(supported_models_base.BASE):
         return model_base.ModelType.EPS
 
     def process_clip_state_dict(self, state_dict):
-        state_dict = utils.transformers_convert(state_dict, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24)
+        state_dict = utils.transformers_convert(state_dict, "cond_stage_model.model.", "cond_stage_model.clip_h.transformer.text_model.", 24)
         return state_dict
 
     def process_clip_state_dict_for_saving(self, state_dict):
         replace_prefix = {}
-        replace_prefix[""] = "cond_stage_model.model."
+        replace_prefix["clip_h"] = "cond_stage_model.model"
         state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix)
         state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict)
         return state_dict