Fix --use-flash-attention ignored when xformers installed. (#14083)

* openapi: add enum values + FeedbackRequest schema for cloud cutover (PR E)

Adds missing cloud-runtime enum values to vendor schemas that the
cloud runtime emits but vendor declared as plain strings.

Changes:
  - JobEntry.status: enum [pending, in_progress, completed, failed, cancelled]
  - JobDetailResponse.status: same enum
  - BillingStatus: enum [awaiting_payment_method, pending_payment, paid,
      payment_failed, inactive]
  - FeedbackRequest schema added (with type enum)
  - /api/feedback POST: requestBody now $refs FeedbackRequest

All cloud-runtime-emitted; no impact on OSS-local semantics.

Identified via Comfy-Org/cloud's TestCutoverSafe gate (BE-1106) as
the remaining schema-level divergences after PRs A-D landed and got
synced.

* openapi: add type enum to Workspace schema (cutover follow-up)

Cloud's Workspace runtime shape includes a 'type' field with enum
[personal, team] that vendor's Workspace was missing. Cloud handlers
reference the generated ingest.WorkspaceType Go enum.

Same kind of surgical addition as JobEntry.status / BillingStatus /
JobDetailResponse.status in this PR — adds cloud-runtime field to
existing vendor schema.

comfy/ldm/modules/attention.py

@@ -741,12 +741,12 @@ optimized_attention = attention_basic
 if model_management.sage_attention_enabled():
     logging.info("Using sage attention")
     optimized_attention = attention_sage
-elif model_management.xformers_enabled():
-    logging.info("Using xformers attention")
-    optimized_attention = attention_xformers
 elif model_management.flash_attention_enabled():
     logging.info("Using Flash Attention")
     optimized_attention = attention_flash
+elif model_management.xformers_enabled():
+    logging.info("Using xformers attention")
+    optimized_attention = attention_xformers
 elif model_management.pytorch_attention_enabled():
     logging.info("Using pytorch attention")
     optimized_attention = attention_pytorch

comfy/samplers.py

@@ -265,7 +265,6 @@ def _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: torch.Tens
                 input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:]
                 cond_shapes = collections.defaultdict(list)
                 for tt in batch_amount:
-                    cond = {k: v.size() for k, v in to_run[tt][0].conditioning.items()}
                     for k, v in to_run[tt][0].conditioning.items():
                         cond_shapes[k].append(v.size())
 

requirements.txt

@@ -1,4 +1,4 @@
-comfyui-frontend-package==1.43.18
+comfyui-frontend-package==1.44.19
 comfyui-workflow-templates==0.9.82
 comfyui-embedded-docs==0.5.0
 torch