Use transformers CLIP instead of open_clip for SD2.x

This should make things a bit cleaner.

comfy/sd.py

@@ -40,6 +40,42 @@ def load_model_from_config(config, ckpt, verbose=False, load_state_dict_to=[]):
         if ids.dtype == torch.float32:
             sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()
 
+    keys_to_replace = {
+        "cond_stage_model.model.positional_embedding": "cond_stage_model.transformer.text_model.embeddings.position_embedding.weight",
+        "cond_stage_model.model.token_embedding.weight": "cond_stage_model.transformer.text_model.embeddings.token_embedding.weight",
+        "cond_stage_model.model.ln_final.weight": "cond_stage_model.transformer.text_model.final_layer_norm.weight",
+        "cond_stage_model.model.ln_final.bias": "cond_stage_model.transformer.text_model.final_layer_norm.bias",
+    }
+
+    for x in keys_to_replace:
+        if x in sd:
+            sd[keys_to_replace[x]] = sd.pop(x)
+
+    resblock_to_replace = {
+        "ln_1": "layer_norm1",
+        "ln_2": "layer_norm2",
+        "mlp.c_fc": "mlp.fc1",
+        "mlp.c_proj": "mlp.fc2",
+        "attn.out_proj": "self_attn.out_proj",
+    }
+
+    for resblock in range(24):
+        for x in resblock_to_replace:
+            for y in ["weight", "bias"]:
+                k = "cond_stage_model.model.transformer.resblocks.{}.{}.{}".format(resblock, x, y)
+                k_to = "cond_stage_model.transformer.text_model.encoder.layers.{}.{}.{}".format(resblock, resblock_to_replace[x], y)
+                if k in sd:
+                    sd[k_to] = sd.pop(k)
+
+        for y in ["weight", "bias"]:
+            k_from = "cond_stage_model.model.transformer.resblocks.{}.attn.in_proj_{}".format(resblock, y)
+            if k_from in sd:
+                weights = sd.pop(k_from)
+                for x in range(3):
+                    p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"]
+                    k_to = "cond_stage_model.transformer.text_model.encoder.layers.{}.{}.{}".format(resblock, p[x], y)
+                    sd[k_to] = weights[1024*x:1024*(x + 1)]
+
     for x in load_state_dict_to:
         x.load_state_dict(sd, strict=False)
 
@@ -62,12 +98,6 @@ LORA_CLIP_MAP = {
     "self_attn.out_proj": "self_attn_out_proj",
 }
 
-LORA_CLIP2_MAP = {
-    "mlp.c_fc": "mlp_fc1",
-    "mlp.c_proj": "mlp_fc2",
-    "attn.out_proj": "self_attn_out_proj",
-}
-
 LORA_UNET_MAP = {
     "proj_in": "proj_in",
     "proj_out": "proj_out",
@@ -116,7 +146,7 @@ def model_lora_keys(model, key_map={}):
             k = "{}.{}.weight".format(tk, c)
             if k in sdk:
                 lora_key = "lora_unet_down_blocks_{}_attentions_{}_{}".format(counter // 2, counter % 2, LORA_UNET_MAP[c])
-                key_map[lora_key] = (k, 0)
+                key_map[lora_key] = k
                 up_counter += 1
         if up_counter >= 4:
             counter += 1
@@ -124,7 +154,7 @@ def model_lora_keys(model, key_map={}):
         k = "model.diffusion_model.middle_block.1.{}.weight".format(c)
         if k in sdk:
             lora_key = "lora_unet_mid_block_attentions_0_{}".format(LORA_UNET_MAP[c])
-            key_map[lora_key] = (k, 0)
+            key_map[lora_key] = k
     counter = 3
     for b in range(12):
         tk = "model.diffusion_model.output_blocks.{}.1".format(b)
@@ -133,29 +163,18 @@ def model_lora_keys(model, key_map={}):
             k = "{}.{}.weight".format(tk, c)
             if k in sdk:
                 lora_key = "lora_unet_up_blocks_{}_attentions_{}_{}".format(counter // 3, counter % 3, LORA_UNET_MAP[c])
-                key_map[lora_key] = (k, 0)
+                key_map[lora_key] = k
                 up_counter += 1
         if up_counter >= 4:
             counter += 1
     counter = 0
     text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}"
-    for b in range(12):
+    for b in range(24):
         for c in LORA_CLIP_MAP:
             k = "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, 0)
+                key_map[lora_key] = k
-    for b in range(24):
-        for c in LORA_CLIP2_MAP:
-            k = "model.transformer.resblocks.{}.{}.weight".format(b, c)
-            if k in sdk:
-                lora_key = text_model_lora_key.format(b, LORA_CLIP2_MAP[c])
-                key_map[lora_key] = (k, 0)
-        k = "model.transformer.resblocks.{}.attn.in_proj_weight".format(b)
-        if k in sdk:
-            key_map[text_model_lora_key.format(b, "self_attn_q_proj")] = (k, 0)
-            key_map[text_model_lora_key.format(b, "self_attn_k_proj")] = (k, 1)
-            key_map[text_model_lora_key.format(b, "self_attn_v_proj")] = (k, 2)
 
     return key_map
 
@@ -174,7 +193,7 @@ class ModelPatcher:
         p = {}
         model_sd = self.model.state_dict()
         for k in patches:
-            if k[0] in model_sd:
+            if k in model_sd:
                 p[k] = patches[k]
         self.patches += [(strength, p)]
         return p.keys()
@@ -184,8 +203,7 @@ class ModelPatcher:
         for p in self.patches:
             for k in p[1]:
                 v = p[1][k]
-                key = k[0]
+                key = k
-                index = k[1]
                 if key not in model_sd:
                     print("could not patch. key doesn't exist in model:", k)
                     continue
@@ -199,10 +217,7 @@ class ModelPatcher:
                 mat2 = v[1]
                 if v[2] is not None:
                     alpha *= v[2] / mat2.shape[0]
-                calc = (alpha * torch.mm(mat1.flatten(start_dim=1).float(), mat2.flatten(start_dim=1).float()))
+                weight += (alpha * torch.mm(mat1.flatten(start_dim=1).float(), mat2.flatten(start_dim=1).float())).reshape(weight.shape).type(weight.dtype).to(weight.device)
-                if len(weight.shape) > 2:
-                    calc = calc.reshape(weight.shape)
-                weight[index * mat1.shape[0]:(index + 1) * mat1.shape[0]] += calc.type(weight.dtype).to(weight.device)
         return self.model
     def unpatch_model(self):
         model_sd = self.model.state_dict()

comfy/sd2_clip.py

@@ -1,86 +1,22 @@
 import sd1_clip
-import open_clip
 import torch
+import os
 
-class SD2ClipModel(torch.nn.Module, sd1_clip.ClipTokenWeightEncoder):
+class SD2ClipModel(sd1_clip.SD1ClipModel):
-    """
+    def __init__(self, arch="ViT-H-14", device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None):
-    Uses the OpenCLIP transformer encoder for text
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd2_clip_config.json")
-    """
+        super().__init__(device=device, freeze=freeze, textmodel_json_config=textmodel_json_config)
-    LAYERS = [
-        #"pooled",
-        "last",
-        "penultimate",
-        "hidden"
-    ]
-    #version="laion2b_s32b_b79k"
-    def __init__(self, arch="ViT-H-14", device="cpu", max_length=77,
-                 freeze=True, layer="penultimate", layer_idx=None):
-        super().__init__()
-        assert layer in self.LAYERS
-        model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'))
-        del model.visual
-        self.model = model
-
-        self.device = device
-        self.max_length = max_length
         self.empty_tokens = [[49406] + [49407] + [0] * 75]
-        if freeze:
+        if layer == "last":
-            self.freeze()
+            layer_idx = -1
-        self.layer = layer
+        elif layer == "penultimate":
-        if self.layer == "last":
+            layer_idx = -2
-            self.layer_idx = 0
-        elif self.layer == "penultimate":
-            self.layer_idx = 1
         elif self.layer == "hidden":
             assert layer_idx is not None
             assert abs(layer_idx) < 24
-            self.clip_layer(layer_idx)
         else:
             raise NotImplementedError()
-
+        self.clip_layer(layer_idx)
-    def freeze(self):
-        self.model = self.model.eval()
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def clip_layer(self, layer_idx):
-        #layer_idx should have the same logic as the one for SD1
-        if abs(layer_idx) >= 24:
-            self.layer_idx = 0
-        else:
-            if layer_idx < 0:
-                self.layer_idx = -(layer_idx + 1)
-            else:
-                self.layer_idx = 24 - (layer_idx + 1)
-
-    def forward(self, tokens):
-        tokens = torch.LongTensor(tokens).to(self.device)
-        z = self.encode_with_transformer(tokens)
-        return z
-
-    def encode_with_transformer(self, tokens):
-        x = self.model.token_embedding(tokens)  # [batch_size, n_ctx, d_model]
-        x = x + self.model.positional_embedding
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-        x = self.model.ln_final(x)
-        return x
-
-    def text_transformer_forward(self, x: torch.Tensor, attn_mask = None):
-        for i, r in enumerate(self.model.transformer.resblocks):
-            if i == len(self.model.transformer.resblocks) - self.layer_idx:
-                break
-            if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting():
-                x = checkpoint(r, x, attn_mask)
-            else:
-                x = r(x, attn_mask=attn_mask)
-        return x
-
-    def encode(self, tokens):
-        return self(tokens)
-
-
 
 class SD2Tokenizer(sd1_clip.SD1Tokenizer):
     def __init__(self, tokenizer_path=None):

comfy/sd2_clip_config.json

@@ -0,0 +1,23 @@
+{
+  "architectures": [
+    "CLIPTextModel"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_size": 1024,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 1,
+  "projection_dim": 512,
+  "torch_dtype": "float32",
+  "vocab_size": 49408
+}