Add DualClipLoader to load clip models for SDXL.

Update LoadClip to load clip models for SDXL refiner.

comfy/sd.py

@@ -19,6 +19,7 @@ from . import model_detection
 
 from . import sd1_clip
 from . import sd2_clip
+from . import sdxl_clip
 
 def load_model_weights(model, sd):
     m, u = model.load_state_dict(sd, strict=False)
@@ -524,7 +525,7 @@ class CLIP:
         return n
 
     def load_from_state_dict(self, sd):
-        self.cond_stage_model.transformer.load_state_dict(sd, strict=False)
+        self.cond_stage_model.load_sd(sd)
 
     def add_patches(self, patches, strength=1.0):
         return self.patcher.add_patches(patches, strength)
@@ -555,6 +556,8 @@ class CLIP:
         tokens = self.tokenize(text)
         return self.encode_from_tokens(tokens)
 
+    def load_sd(self, sd):
+        return self.cond_stage_model.load_sd(sd)
 
 class VAE:
     def __init__(self, ckpt_path=None, device=None, config=None):
@@ -959,22 +962,42 @@ def load_style_model(ckpt_path):
     return StyleModel(model)
 
 
-def load_clip(ckpt_path, embedding_directory=None):
-    clip_data = utils.load_torch_file(ckpt_path, safe_load=True)
+def load_clip(ckpt_paths, embedding_directory=None):
+    clip_data = []
+    for p in ckpt_paths:
+        clip_data.append(utils.load_torch_file(p, safe_load=True))
+
     class EmptyClass:
         pass
 
+    for i in range(len(clip_data)):
+        if "transformer.resblocks.0.ln_1.weight" in clip_data[i]:
+            clip_data[i] = utils.transformers_convert(clip_data[i], "", "text_model.", 32)
+
     clip_target = EmptyClass()
     clip_target.params = {}
-    if "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data:
-        clip_target.clip = sd2_clip.SD2ClipModel
-        clip_target.tokenizer = sd2_clip.SD2Tokenizer
+    if len(clip_data) == 1:
+        if "text_model.encoder.layers.30.mlp.fc1.weight" in clip_data[0]:
+            clip_target.clip = sdxl_clip.SDXLRefinerClipModel
+            clip_target.tokenizer = sdxl_clip.SDXLTokenizer
+        elif "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data[0]:
+            clip_target.clip = sd2_clip.SD2ClipModel
+            clip_target.tokenizer = sd2_clip.SD2Tokenizer
+        else:
+            clip_target.clip = sd1_clip.SD1ClipModel
+            clip_target.tokenizer = sd1_clip.SD1Tokenizer
     else:
-        clip_target.clip = sd1_clip.SD1ClipModel
-        clip_target.tokenizer = sd1_clip.SD1Tokenizer
+        clip_target.clip = sdxl_clip.SDXLClipModel
+        clip_target.tokenizer = sdxl_clip.SDXLTokenizer
 
     clip = CLIP(clip_target, embedding_directory=embedding_directory)
-    clip.load_from_state_dict(clip_data)
+    for c in clip_data:
+        m, u = clip.load_sd(c)
+        if len(m) > 0:
+            print("clip missing:", m)
+
+        if len(u) > 0:
+            print("clip unexpected:", u)
     return clip
 
 def load_gligen(ckpt_path):

comfy/sd1_clip.py

@@ -128,6 +128,9 @@ class SD1ClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     def encode(self, tokens):
         return self(tokens)
 
+    def load_sd(self, sd):
+        return self.transformer.load_state_dict(sd, strict=False)
+
 def parse_parentheses(string):
     result = []
     current_item = ""

comfy/sdxl_clip.py

@@ -31,6 +31,11 @@ class SDXLClipG(sd1_clip.SD1ClipModel):
             self.layer = "hidden"
             self.layer_idx = layer_idx
 
+    def load_sd(self, sd):
+        if "text_projection" in sd:
+            self.text_projection[:] = sd.pop("text_projection")
+        return super().load_sd(sd)
+
 class SDXLClipGTokenizer(sd1_clip.SD1Tokenizer):
     def __init__(self, tokenizer_path=None, embedding_directory=None):
         super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280)
@@ -68,6 +73,12 @@ class SDXLClipModel(torch.nn.Module):
         l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
         return torch.cat([l_out, g_out], dim=-1), g_pooled
 
+    def load_sd(self, sd):
+        if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
+            return self.clip_g.load_sd(sd)
+        else:
+            return self.clip_l.load_sd(sd)
+
 class SDXLRefinerClipModel(torch.nn.Module):
     def __init__(self, device="cpu"):
         super().__init__()
@@ -81,3 +92,5 @@ class SDXLRefinerClipModel(torch.nn.Module):
         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)

nodes.py

@@ -520,11 +520,27 @@ class CLIPLoader:
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
 
-    CATEGORY = "loaders"
+    CATEGORY = "advanced/loaders"
 
     def load_clip(self, clip_name):
         clip_path = folder_paths.get_full_path("clip", clip_name)
-        clip = comfy.sd.load_clip(ckpt_path=clip_path, embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        return (clip,)
+
+class DualCLIPLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ),
+                             }}
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "load_clip"
+
+    CATEGORY = "advanced/loaders"
+
+    def load_clip(self, clip_name1, clip_name2):
+        clip_path1 = folder_paths.get_full_path("clip", clip_name1)
+        clip_path2 = folder_paths.get_full_path("clip", clip_name2)
+        clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"))
         return (clip,)
 
 class CLIPVisionLoader:
@@ -1315,6 +1331,7 @@ NODE_CLASS_MAPPINGS = {
     "LatentCrop": LatentCrop,
     "LoraLoader": LoraLoader,
     "CLIPLoader": CLIPLoader,
+    "DualCLIPLoader": DualCLIPLoader,
     "CLIPVisionEncode": CLIPVisionEncode,
     "StyleModelApply": StyleModelApply,
     "unCLIPConditioning": unCLIPConditioning,