Save memory by storing text encoder weights in fp16 in most situations.

Do inference in fp32 to make sure quality stays the exact same.

comfy/model_management.py

@@ -433,7 +433,7 @@ def text_encoder_device():
         return get_torch_device()
     elif vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.NORMAL_VRAM:
         #NOTE: on a Ryzen 5 7600X with 4080 it's faster to shift to GPU
-        if torch.get_num_threads() < 8: #leaving the text encoder on the CPU is faster than shifting it if the CPU is fast enough.
+        if should_use_fp16() or torch.get_num_threads() < 8: #leaving the text encoder on the CPU is faster than shifting it if the CPU is fast enough.
             return get_torch_device()
         else:
             return torch.device("cpu")

comfy/sd.py

@@ -546,11 +546,8 @@ class CLIP:
         offload_device = model_management.text_encoder_offload_device()
         params['device'] = load_device
         self.cond_stage_model = clip(**(params))
-        #TODO: make sure this doesn't have a quality loss before enabling.
-        # if model_management.should_use_fp16(load_device):
-        #     self.cond_stage_model.half()
-
-        self.cond_stage_model = self.cond_stage_model.to()
+        if model_management.should_use_fp16(load_device):
+            self.cond_stage_model.half()
 
         self.tokenizer = tokenizer(embedding_directory=embedding_directory)
         self.patcher = ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device)

comfy/sd1_clip.py

@@ -137,9 +137,9 @@ class SD1ClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         if backup_embeds.weight.dtype != torch.float32:
             precision_scope = torch.autocast
         else:
-            precision_scope = contextlib.nullcontext
+            precision_scope = lambda a, b: contextlib.nullcontext(a)
 
-        with precision_scope(model_management.get_autocast_device(device)):
+        with precision_scope(model_management.get_autocast_device(device), torch.float32):
             outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer=="hidden")
             self.transformer.set_input_embeddings(backup_embeds)
 

web/extensions/core/uploadImage.js

@@ -5,7 +5,6 @@ import { app } from "../../scripts/app.js";
 app.registerExtension({
 	name: "Comfy.UploadImage",
 	async beforeRegisterNodeDef(nodeType, nodeData, app) {
-		console.log(nodeData);
 		if (nodeData?.input?.required?.image?.[1]?.image_upload === true) {
 			nodeData.input.required.upload = ["IMAGEUPLOAD"];
 		}