Add logit scale parameter so it's present when saving the checkpoint.

comfy/sdxl_clip.py

@@ -8,6 +8,7 @@ class SDXLClipG(sd1_clip.SD1ClipModel):
         super().__init__(device=device, freeze=freeze, textmodel_json_config=textmodel_json_config, textmodel_path=textmodel_path)
         self.empty_tokens = [[49406] + [49407] + [0] * 75]
         self.text_projection = torch.nn.Parameter(torch.empty(1280, 1280))
+        self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))
         self.layer_norm_hidden_state = False
         if layer == "last":
             pass

comfy/supported_models.py

@@ -118,6 +118,7 @@ class SDXLRefiner(supported_models_base.BASE):
 
         state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.0.model.", "cond_stage_model.clip_g.transformer.text_model.", 32)
         keys_to_replace["conditioner.embedders.0.model.text_projection"] = "cond_stage_model.clip_g.text_projection"
+        keys_to_replace["conditioner.embedders.0.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale"
 
         state_dict = supported_models_base.state_dict_key_replace(state_dict, keys_to_replace)
         return state_dict
@@ -153,6 +154,7 @@ class SDXL(supported_models_base.BASE):
         replace_prefix["conditioner.embedders.0.transformer.text_model"] = "cond_stage_model.clip_l.transformer.text_model"
         state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.1.model.", "cond_stage_model.clip_g.transformer.text_model.", 32)
         keys_to_replace["conditioner.embedders.1.model.text_projection"] = "cond_stage_model.clip_g.text_projection"
+        keys_to_replace["conditioner.embedders.1.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale"
 
         state_dict = supported_models_base.state_dict_prefix_replace(state_dict, replace_prefix)
         state_dict = supported_models_base.state_dict_key_replace(state_dict, keys_to_replace)