Make highvram and normalvram shift the text encoders to vram and back.

This is faster on big text encoder models than running it on the CPU.

comfy/model_management.py

@@ -327,12 +327,18 @@ def unload_if_low_vram(model):
         return model.cpu()
     return model
 
-def text_encoder_device():
+def text_encoder_offload_device():
     if args.gpu_only:
         return get_torch_device()
     else:
         return torch.device("cpu")
 
+def text_encoder_device():
+    if vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.SHARED or vram_state == VRAMState.NORMAL_VRAM:
+        return get_torch_device()
+    else:
+        return torch.device("cpu")
+
 def get_autocast_device(dev):
     if hasattr(dev, 'type'):
         return dev.type
@@ -422,10 +428,15 @@ def mps_mode():
     global cpu_state
     return cpu_state == CPUState.MPS
 
-def should_use_fp16():
+def should_use_fp16(device=None):
     global xpu_available
     global directml_enabled
 
+    if device is not None: #TODO
+        if hasattr(device, 'type'):
+            if (device.type == 'cpu' or device.type == 'mps'):
+                return False
+
     if FORCE_FP32:
         return False
 

comfy/sd.py

@@ -526,9 +526,10 @@ class CLIP:
         tokenizer = target.tokenizer
 
         self.device = model_management.text_encoder_device()
-        params["device"] = self.device
         self.cond_stage_model = clip(**(params))
-        self.cond_stage_model = self.cond_stage_model.to(self.device)
+        if model_management.should_use_fp16(self.device):
+            self.cond_stage_model.half()
+        self.cond_stage_model = self.cond_stage_model.to(model_management.text_encoder_offload_device())
 
         self.tokenizer = tokenizer(embedding_directory=embedding_directory)
         self.patcher = ModelPatcher(self.cond_stage_model)
@@ -559,11 +560,14 @@ class CLIP:
         if self.layer_idx is not None:
             self.cond_stage_model.clip_layer(self.layer_idx)
         try:
+            self.cond_stage_model.to(self.device)
             self.patch_model()
             cond, pooled = self.cond_stage_model.encode_token_weights(tokens)
             self.unpatch_model()
+            self.cond_stage_model.to(model_management.text_encoder_offload_device())
         except Exception as e:
             self.unpatch_model()
+            self.cond_stage_model.to(model_management.text_encoder_offload_device())
             raise e
 
         cond_out = cond

comfy/sd1_clip.py

@@ -5,6 +5,8 @@ import comfy.ops
 import torch
 import traceback
 import zipfile
+from . import model_management
+import contextlib
 
 class ClipTokenWeightEncoder:
     def encode_token_weights(self, token_weight_pairs):
@@ -46,7 +48,6 @@ class SD1ClipModel(torch.nn.Module, ClipTokenWeightEncoder):
                 with modeling_utils.no_init_weights():
                     self.transformer = CLIPTextModel(config)
 
-        self.device = device
         self.max_length = max_length
         if freeze:
             self.freeze()
@@ -95,7 +96,7 @@ class SD1ClipModel(torch.nn.Module, ClipTokenWeightEncoder):
             out_tokens += [tokens_temp]
 
         if len(embedding_weights) > 0:
-            new_embedding = torch.nn.Embedding(next_new_token, current_embeds.weight.shape[1], device=self.device)
+            new_embedding = torch.nn.Embedding(next_new_token, current_embeds.weight.shape[1], device=current_embeds.weight.device, dtype=current_embeds.weight.dtype)
             new_embedding.weight[:token_dict_size] = current_embeds.weight[:]
             n = token_dict_size
             for x in embedding_weights:
@@ -106,24 +107,34 @@ class SD1ClipModel(torch.nn.Module, ClipTokenWeightEncoder):
 
     def forward(self, tokens):
         backup_embeds = self.transformer.get_input_embeddings()
+        device = backup_embeds.weight.device
         tokens = self.set_up_textual_embeddings(tokens, backup_embeds)
-        tokens = torch.LongTensor(tokens).to(self.device)
-        outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer=="hidden")
-        self.transformer.set_input_embeddings(backup_embeds)
-
-        if self.layer == "last":
-            z = outputs.last_hidden_state
-        elif self.layer == "pooled":
-            z = outputs.pooler_output[:, None, :]
+        tokens = torch.LongTensor(tokens).to(device)
+
+        if backup_embeds.weight.dtype != torch.float32:
+            print("autocast clip")
+            precision_scope = torch.autocast
         else:
-            z = outputs.hidden_states[self.layer_idx]
-            if self.layer_norm_hidden_state:
-                z = self.transformer.text_model.final_layer_norm(z)
-
-        pooled_output = outputs.pooler_output
-        if self.text_projection is not None:
-            pooled_output = pooled_output @ self.text_projection
-        return z, pooled_output
+            precision_scope = contextlib.nullcontext
+            print("no autocast clip")
+
+        with precision_scope(model_management.get_autocast_device(device)):
+            outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer=="hidden")
+            self.transformer.set_input_embeddings(backup_embeds)
+
+            if self.layer == "last":
+                z = outputs.last_hidden_state
+            elif self.layer == "pooled":
+                z = outputs.pooler_output[:, None, :]
+            else:
+                z = outputs.hidden_states[self.layer_idx]
+                if self.layer_norm_hidden_state:
+                    z = self.transformer.text_model.final_layer_norm(z)
+
+            pooled_output = outputs.pooler_output
+            if self.text_projection is not None:
+                pooled_output = pooled_output @ self.text_projection
+        return z.float(), pooled_output.float()
 
     def encode(self, tokens):
         return self(tokens)