Try to keep text encoders loaded and patched to increase speed.

load_model_gpu() is now used with the text encoder models instead of just
the unet.

comfy/diffusers_load.py

@@ -3,7 +3,7 @@ import os
 import yaml
 
 import folder_paths
-from comfy.sd import ModelPatcher, load_model_weights, CLIP, VAE, load_checkpoint
+from comfy.sd import load_checkpoint
 import os.path as osp
 import re
 import torch

comfy/model_management.py

@@ -216,11 +216,6 @@ current_gpu_controlnets = []
 
 model_accelerated = False
 
-def unet_offload_device():
-    if vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.SHARED:
-        return get_torch_device()
-    else:
-        return torch.device("cpu")
 
 def unload_model():
     global current_loaded_model
@@ -234,8 +229,8 @@ def unload_model():
             model_accelerated = False
 
 
-        current_loaded_model.model.to(unet_offload_device())
-        current_loaded_model.model_patches_to(unet_offload_device())
+        current_loaded_model.model.to(current_loaded_model.offload_device)
+        current_loaded_model.model_patches_to(current_loaded_model.offload_device)
         current_loaded_model.unpatch_model()
         current_loaded_model = None
 
@@ -260,10 +255,14 @@ def load_model_gpu(model):
         model.unpatch_model()
         raise e
 
-    torch_dev = get_torch_device()
+    torch_dev = model.load_device
     model.model_patches_to(torch_dev)
 
-    vram_set_state = vram_state
+    if is_device_cpu(torch_dev):
+        vram_set_state = VRAMState.DISABLED
+    else:
+        vram_set_state = vram_state
+
     if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM):
         model_size = model.model_size()
         current_free_mem = get_free_memory(torch_dev)
@@ -277,14 +276,14 @@ def load_model_gpu(model):
         pass
     elif vram_set_state == VRAMState.NORMAL_VRAM or vram_set_state == VRAMState.HIGH_VRAM or vram_set_state == VRAMState.SHARED:
         model_accelerated = False
-        real_model.to(get_torch_device())
+        real_model.to(torch_dev)
     else:
         if vram_set_state == VRAMState.NO_VRAM:
             device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "256MiB", "cpu": "16GiB"})
         elif vram_set_state == VRAMState.LOW_VRAM:
             device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "{}MiB".format(lowvram_model_memory // (1024 * 1024)), "cpu": "16GiB"})
 
-        accelerate.dispatch_model(real_model, device_map=device_map, main_device=get_torch_device())
+        accelerate.dispatch_model(real_model, device_map=device_map, main_device=torch_dev)
         model_accelerated = True
     return current_loaded_model
 
@@ -327,6 +326,12 @@ def unload_if_low_vram(model):
         return model.cpu()
     return model
 
+def unet_offload_device():
+    if vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.SHARED:
+        return get_torch_device()
+    else:
+        return torch.device("cpu")
+
 def text_encoder_offload_device():
     if args.gpu_only:
         return get_torch_device()
@@ -428,14 +433,19 @@ def mps_mode():
     global cpu_state
     return cpu_state == CPUState.MPS
 
+def is_device_cpu(device):
+    if hasattr(device, 'type'):
+        if (device.type == 'cpu' or device.type == 'mps'):
+            return True
+    return False
+
 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 is_device_cpu(device):
+            return False
 
     if FORCE_FP32:
         return False

comfy/sd.py

@@ -308,13 +308,15 @@ def model_lora_keys(model, key_map={}):
 
 
 class ModelPatcher:
-    def __init__(self, model, size=0):
+    def __init__(self, model, load_device, offload_device, size=0):
         self.size = size
         self.model = model
         self.patches = []
         self.backup = {}
         self.model_options = {"transformer_options":{}}
         self.model_size()
+        self.load_device = load_device
+        self.offload_device = offload_device
 
     def model_size(self):
         if self.size > 0:
@@ -329,7 +331,7 @@ class ModelPatcher:
         return size
 
     def clone(self):
-        n = ModelPatcher(self.model, self.size)
+        n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size)
         n.patches = self.patches[:]
         n.model_options = copy.deepcopy(self.model_options)
         n.model_keys = self.model_keys
@@ -341,6 +343,9 @@ class ModelPatcher:
         else:
             self.model_options["sampler_cfg_function"] = sampler_cfg_function
 
+    def set_model_unet_function_wrapper(self, unet_wrapper_function):
+        self.model_options["model_function_wrapper"] = unet_wrapper_function
+
     def set_model_patch(self, patch, name):
         to = self.model_options["transformer_options"]
         if "patches" not in to:
@@ -525,14 +530,16 @@ class CLIP:
         clip = target.clip
         tokenizer = target.tokenizer
 
-        self.device = model_management.text_encoder_device()
+        load_device = model_management.text_encoder_device()
+        offload_device = model_management.text_encoder_offload_device()
         self.cond_stage_model = clip(**(params))
-        if model_management.should_use_fp16(self.device):
+        if model_management.should_use_fp16(load_device):
             self.cond_stage_model.half()
-        self.cond_stage_model = self.cond_stage_model.to(model_management.text_encoder_offload_device())
+
+        self.cond_stage_model = self.cond_stage_model.to()
 
         self.tokenizer = tokenizer(embedding_directory=embedding_directory)
-        self.patcher = ModelPatcher(self.cond_stage_model)
+        self.patcher = ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device)
         self.layer_idx = None
 
     def clone(self):
@@ -541,7 +548,6 @@ class CLIP:
         n.cond_stage_model = self.cond_stage_model
         n.tokenizer = self.tokenizer
         n.layer_idx = self.layer_idx
-        n.device = self.device
         return n
 
     def load_from_state_dict(self, sd):
@@ -559,21 +565,12 @@ class CLIP:
     def encode_from_tokens(self, tokens, return_pooled=False):
         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
+
+        model_management.load_model_gpu(self.patcher)
+        cond, pooled = self.cond_stage_model.encode_token_weights(tokens)
         if return_pooled:
-            return cond_out, pooled
-        return cond_out
+            return cond, pooled
+        return cond
 
     def encode(self, text):
         tokens = self.tokenize(text)
@@ -1097,6 +1094,8 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
     if fp16:
         model = model.half()
 
+    offload_device = model_management.unet_offload_device()
+    model = model.to(offload_device)
     model.load_model_weights(state_dict, "model.diffusion_model.")
 
     if output_vae:
@@ -1119,7 +1118,7 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
         w.cond_stage_model = clip.cond_stage_model
         load_clip_weights(w, state_dict)
 
-    return (ModelPatcher(model), clip, vae)
+    return (ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae)
 
 
 def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None):
@@ -1144,8 +1143,9 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
         if output_clipvision:
             clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True)
 
+    offload_device = model_management.unet_offload_device()
     model = model_config.get_model(sd)
-    model = model.to(model_management.unet_offload_device())
+    model = model.to(offload_device)
     model.load_model_weights(sd, "model.diffusion_model.")
 
     if output_vae:
@@ -1166,7 +1166,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
     if len(left_over) > 0:
         print("left over keys:", left_over)
 
-    return (ModelPatcher(model), clip, vae, clipvision)
+    return (ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae, clipvision)
 
 def save_checkpoint(output_path, model, clip, vae, metadata=None):
     try:

comfy/sd1_clip.py

@@ -112,11 +112,9 @@ class SD1ClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         tokens = torch.LongTensor(tokens).to(device)
 
         if backup_embeds.weight.dtype != torch.float32:
-            print("autocast clip")
             precision_scope = torch.autocast
         else:
             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")