diff --git a/modules/sd_checkpoint.py b/modules/sd_checkpoint.py
index 45834a1f0..e1787246f 100644
--- a/modules/sd_checkpoint.py
+++ b/modules/sd_checkpoint.py
@@ -96,6 +96,9 @@ class CheckpointInfo:
         self.register()
         return self.shorthash
 
+    def __str__(self):
+        return f'checkpoint: type={self.type} title="{self.title}" path="{self.path}"'
+
 
 def setup_model():
     list_models()
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 5be48e772..7aa70b90f 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -634,65 +634,65 @@ def load_diffuser_folder(model_type, pipeline, checkpoint_info, diffusers_load_c
     files = shared.walk_files(checkpoint_info.path, ['.safetensors', '.bin', '.ckpt'])
     if 'variant' not in diffusers_load_config and any('diffusion_pytorch_model.fp16' in f for f in files): # deal with diffusers lack of variant fallback when loading
         diffusers_load_config['variant'] = 'fp16'
-        if model_type is not None and pipeline is not None and 'ONNX' in model_type: # forced pipeline
-            try:
-                sd_model = pipeline.from_pretrained(checkpoint_info.path)
-            except Exception as e:
-                shared.log.error(f'Load {op}: type=ONNX path="{checkpoint_info.path}" {e}')
-                if debug_load:
-                    errors.display(e, 'Load')
-                return None
-        else:
-            err1, err2, err3 = None, None, None
-            if os.path.exists(checkpoint_info.path) and os.path.isdir(checkpoint_info.path):
-                if os.path.exists(os.path.join(checkpoint_info.path, 'unet', 'diffusion_pytorch_model.bin')):
-                    shared.log.debug(f'Load {op}: type=pickle')
-                    diffusers_load_config['use_safetensors'] = False
+    if model_type is not None and pipeline is not None and 'ONNX' in model_type: # forced pipeline
+        try:
+            sd_model = pipeline.from_pretrained(checkpoint_info.path)
+        except Exception as e:
+            shared.log.error(f'Load {op}: type=ONNX path="{checkpoint_info.path}" {e}')
             if debug_load:
-                shared.log.debug(f'Load {op}: args={diffusers_load_config}')
-            try: # 1 - autopipeline, best choice but not all pipelines are available
-                try:
+                errors.display(e, 'Load')
+            return None
+    else:
+        err1, err2, err3 = None, None, None
+        if os.path.exists(checkpoint_info.path) and os.path.isdir(checkpoint_info.path):
+            if os.path.exists(os.path.join(checkpoint_info.path, 'unet', 'diffusion_pytorch_model.bin')):
+                shared.log.debug(f'Load {op}: type=pickle')
+                diffusers_load_config['use_safetensors'] = False
+        if debug_load:
+            shared.log.debug(f'Load {op}: args={diffusers_load_config}')
+        try: # 1 - autopipeline, best choice but not all pipelines are available
+            try:
+                sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
+                sd_model.model_type = sd_model.__class__.__name__
+            except ValueError as e:
+                if 'no variant default' in str(e):
+                    shared.log.warning(f'Load {op}: variant={diffusers_load_config["variant"]} model="{checkpoint_info.path}" using default variant')
+                    diffusers_load_config.pop('variant', None)
                     sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
                     sd_model.model_type = sd_model.__class__.__name__
-                except ValueError as e:
-                    if 'no variant default' in str(e):
-                        shared.log.warning(f'Load {op}: variant={diffusers_load_config["variant"]} model="{checkpoint_info.path}" using default variant')
-                        diffusers_load_config.pop('variant', None)
-                        sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
-                        sd_model.model_type = sd_model.__class__.__name__
-                    elif 'safetensors found in directory' in str(err1):
-                        shared.log.warning(f'Load {op}: type=pickle')
-                        diffusers_load_config['use_safetensors'] = False
-                        sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
-                        sd_model.model_type = sd_model.__class__.__name__
-                    else:
-                        raise ValueError from e # reraise
-            except Exception as e:
-                err1 = e
-                if debug_load:
-                    errors.display(e, 'Load AutoPipeline')
-                # shared.log.error(f'AutoPipeline: {e}')
-            try: # 2 - diffusion pipeline, works for most non-linked pipelines
-                if err1 is not None:
-                    sd_model = diffusers.DiffusionPipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
+                elif 'safetensors found in directory' in str(err1):
+                    shared.log.warning(f'Load {op}: type=pickle')
+                    diffusers_load_config['use_safetensors'] = False
+                    sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
                     sd_model.model_type = sd_model.__class__.__name__
-            except Exception as e:
-                err2 = e
-                if debug_load:
-                    errors.display(e, "Load DiffusionPipeline")
-                # shared.log.error(f'DiffusionPipeline: {e}')
-            try: # 3 - try basic pipeline just in case
-                if err2 is not None:
-                    sd_model = diffusers.StableDiffusionPipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
-                    sd_model.model_type = sd_model.__class__.__name__
-            except Exception as e:
-                err3 = e  # ignore last error
-                shared.log.error(f"StableDiffusionPipeline: {e}")
-                if debug_load:
-                    errors.display(e, "Load StableDiffusionPipeline")
-            if err3 is not None:
-                shared.log.error(f'Load {op}: {checkpoint_info.path} auto={err1} diffusion={err2}')
-                return None
+                else:
+                    raise ValueError from e # reraise
+        except Exception as e:
+            err1 = e
+            if debug_load:
+                errors.display(e, 'Load AutoPipeline')
+            # shared.log.error(f'AutoPipeline: {e}')
+        try: # 2 - diffusion pipeline, works for most non-linked pipelines
+            if err1 is not None:
+                sd_model = diffusers.DiffusionPipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
+                sd_model.model_type = sd_model.__class__.__name__
+        except Exception as e:
+            err2 = e
+            if debug_load:
+                errors.display(e, "Load DiffusionPipeline")
+            # shared.log.error(f'DiffusionPipeline: {e}')
+        try: # 3 - try basic pipeline just in case
+            if err2 is not None:
+                sd_model = diffusers.StableDiffusionPipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
+                sd_model.model_type = sd_model.__class__.__name__
+        except Exception as e:
+            err3 = e  # ignore last error
+            shared.log.error(f"StableDiffusionPipeline: {e}")
+            if debug_load:
+                errors.display(e, "Load StableDiffusionPipeline")
+        if err3 is not None:
+            shared.log.error(f'Load {op}: {checkpoint_info.path} auto={err1} diffusion={err2}')
+            return None
     return sd_model