Support for CosXL models.

comfy/model_base.py

@@ -503,8 +503,10 @@ class SD15_instructpix2pix(IP2P, BaseModel):
 class SDXL_instructpix2pix(IP2P, SDXL):
     def __init__(self, model_config, model_type=ModelType.EPS, device=None):
         super().__init__(model_config, model_type, device=device)
-        # self.process_ip2p_image_in = lambda image: comfy.latent_formats.SDXL().process_in(image)
-        self.process_ip2p_image_in = lambda image: image
+        if model_type == ModelType.V_PREDICTION_EDM:
+            self.process_ip2p_image_in = lambda image: comfy.latent_formats.SDXL().process_in(image) #cosxl ip2p
+        else:
+            self.process_ip2p_image_in = lambda image: image #diffusers ip2p
 
 
 class StableCascade_C(BaseModel):

comfy/supported_models.py

@@ -174,6 +174,11 @@ class SDXL(supported_models_base.BASE):
             self.sampling_settings["sigma_max"] = 80.0
             self.sampling_settings["sigma_min"] = 0.002
             return model_base.ModelType.EDM
+        elif "edm_vpred.sigma_max" in state_dict:
+            self.sampling_settings["sigma_max"] = float(state_dict["edm_vpred.sigma_max"].item())
+            if "edm_vpred.sigma_min" in state_dict:
+                self.sampling_settings["sigma_min"] = float(state_dict["edm_vpred.sigma_min"].item())
+            return model_base.ModelType.V_PREDICTION_EDM
         elif "v_pred" in state_dict:
             return model_base.ModelType.V_PREDICTION
         else:
@@ -469,7 +474,7 @@ class SDXL_instructpix2pix(SDXL):
     }
 
     def get_model(self, state_dict, prefix="", device=None):
-        return model_base.SDXL_instructpix2pix(self, device=device)
+        return model_base.SDXL_instructpix2pix(self, model_type=self.model_type(state_dict, prefix), device=device)
 
 models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p]