Installer should download fp16 models if user has specified 'auto' in config

invokeai/backend/install/model_install_backend.py

@@ -13,6 +13,7 @@
 from diffusers import DiffusionPipeline
 from diffusers import logging as dlogging
 import onnx
+import torch
 from huggingface_hub import hf_hub_url, HfFolder, HfApi
 from omegaconf import OmegaConf
 from tqdm import tqdm
@@ -23,6 +24,7 @@
 from invokeai.backend.model_management import ModelManager, ModelType, BaseModelType, ModelVariantType, AddModelResult
 from invokeai.backend.model_management.model_probe import ModelProbe, SchedulerPredictionType, ModelProbeInfo
 from invokeai.backend.util import download_with_resume
+from invokeai.backend.util.devices import torch_dtype, choose_torch_device
 from ..util.logging import InvokeAILogger
 
 warnings.filterwarnings("ignore")
@@ -416,15 +418,25 @@ def _download_hf_pipeline(self, repo_id: str, staging: Path) -> Path:
         does a save_pretrained() to the indicated staging area.
         """
         _, name = repo_id.split("/")
-        revisions = ["fp16", "main"] if self.config.precision == "float16" else ["main"]
+        precision = torch_dtype(choose_torch_device())
+        variants = ["fp16", None] if precision == torch.float16 else [None, "fp16"]
+
         model = None
-        for revision in revisions:
+        for variant in variants:
             try:
-                model = DiffusionPipeline.from_pretrained(repo_id, revision=revision, safety_checker=None)
-            except:  # most errors are due to fp16 not being present. Fix this to catch other errors
-                pass
+                model = DiffusionPipeline.from_pretrained(
+                    repo_id,
+                    variant=variant,
+                    torch_dtype=precision,
+                    safety_checker=None,
+                )
+            except Exception as e:  # most errors are due to fp16 not being present. Fix this to catch other errors
+                if "fp16" not in str(e):
+                    print(e)
+
             if model:
                 break
+
         if not model:
             logger.error(f"Diffusers model {repo_id} could not be downloaded. Skipping.")
             return None