diff --git a/.github/workflows/build-swebenchmultimodal-images.yml b/.github/workflows/build-swebenchmultimodal-images.yml
index b0fc1f92..c2dd6265 100644
--- a/.github/workflows/build-swebenchmultimodal-images.yml
+++ b/.github/workflows/build-swebenchmultimodal-images.yml
@@ -161,8 +161,12 @@ jobs:
         if: ${{ github.event_name == 'workflow_dispatch' && inputs.sdk-commit != '' }}
         run: |
           cd vendor/software-agent-sdk
-          git fetch origin ${{ inputs.sdk-commit }}
-          git checkout FETCH_HEAD
+          # Fetch from origin (handles both branch names and commit SHAs)
+          git fetch origin
+          # Try to fetch the specific ref (works for branches/tags)
+          git fetch origin ${{ inputs.sdk-commit }} 2>/dev/null || true
+          # Checkout the commit/ref (works for both branches and commit SHAs)
+          git checkout ${{ inputs.sdk-commit }}
           SDK_SHA=$(git rev-parse HEAD)
           cd ../..
           # Stage the submodule reference update so make build uses it
diff --git a/benchmarks/swebenchmultimodal/build_images.py b/benchmarks/swebenchmultimodal/build_images.py
index d32b5dc6..4bc43cc2 100644
--- a/benchmarks/swebenchmultimodal/build_images.py
+++ b/benchmarks/swebenchmultimodal/build_images.py
@@ -57,9 +57,17 @@ def extract_custom_tag(base_image: str) -> str:
     return name
 
 
-def collect_unique_base_images(dataset, split, n_limit):
+def collect_unique_base_images(
+    dataset,
+    split,
+    n_limit,
+    selected_instances_file: str | None = None,
+):
     df = get_dataset(
-        dataset_name=dataset, split=split, eval_limit=n_limit if n_limit else None
+        dataset_name=dataset,
+        split=split,
+        eval_limit=n_limit if n_limit else None,
+        selected_instances_file=selected_instances_file,
     )
     return sorted(
         {get_official_docker_image(str(row["instance_id"])) for _, row in df.iterrows()}
@@ -71,7 +79,10 @@ def main(argv: list[str]) -> int:
     args = parser.parse_args(argv)
 
     base_images: list[str] = collect_unique_base_images(
-        args.dataset, args.split, args.n_limit
+        args.dataset,
+        args.split,
+        args.n_limit,
+        args.select,
     )
     build_dir = default_build_output_dir(args.dataset, args.split)
     return build_all_images(