Support base SDXL and SDXL refiner models.

Large refactor of the model detection and loading code.

comfy/sdxl_clip.py

@@ -0,0 +1,83 @@
+from comfy import sd1_clip
+import torch
+import os
+
+class SDXLClipG(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json")
+        super().__init__(device=device, freeze=freeze, textmodel_json_config=textmodel_json_config)
+        self.empty_tokens = [[49406] + [49407] + [0] * 75]
+        self.text_projection = torch.nn.Parameter(torch.empty(1280, 1280))
+        self.layer_norm_hidden_state = False
+        if layer == "last":
+            pass
+        elif layer == "penultimate":
+            layer_idx = -1
+            self.clip_layer(layer_idx)
+        elif self.layer == "hidden":
+            assert layer_idx is not None
+            assert abs(layer_idx) < 32
+            self.clip_layer(layer_idx)
+        else:
+            raise NotImplementedError()
+
+    def clip_layer(self, layer_idx):
+        if layer_idx < 0:
+            layer_idx -= 1 #The real last layer of SD2.x clip is the penultimate one. The last one might contain garbage.
+        if abs(layer_idx) >= 32:
+            self.layer = "hidden"
+            self.layer_idx = -2
+        else:
+            self.layer = "hidden"
+            self.layer_idx = layer_idx
+
+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)
+
+
+class SDXLTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None):
+        self.clip_l = sd1_clip.SD1Tokenizer(embedding_directory=embedding_directory)
+        self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory)
+
+    def tokenize_with_weights(self, text:str, return_word_ids=False):
+        out = {}
+        out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids)
+        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
+        return out
+
+    def untokenize(self, token_weight_pair):
+        return self.clip_g.untokenize(token_weight_pair)
+
+class SDXLClipModel(torch.nn.Module):
+    def __init__(self, device="cpu"):
+        super().__init__()
+        self.clip_l = sd1_clip.SD1ClipModel(layer="hidden", layer_idx=11, device=device)
+        self.clip_l.layer_norm_hidden_state = False
+        self.clip_g = SDXLClipG(device=device)
+
+    def clip_layer(self, layer_idx):
+        self.clip_l.clip_layer(layer_idx)
+        self.clip_g.clip_layer(layer_idx)
+
+    def encode_token_weights(self, token_weight_pairs):
+        token_weight_pairs_g = token_weight_pairs["g"]
+        token_weight_pairs_l = token_weight_pairs["l"]
+        g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
+        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
+
+class SDXLRefinerClipModel(torch.nn.Module):
+    def __init__(self, device="cpu"):
+        super().__init__()
+        self.clip_g = SDXLClipG(device=device)
+
+    def clip_layer(self, layer_idx):
+        self.clip_g.clip_layer(layer_idx)
+
+    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
+