Fix: Resolve ValueError and GatedRepoError issues

- Fix ValueError in selected_payload: return 10 values (was 9) when selected_repo is None
- Add HF_TOKEN authentication for gated models (FLUX, etc.)
- Pass token to DiffusionPipeline.from_pretrained() and load_lora_weights()

app.py

@@ -1,6 +1,7 @@
 import hashlib
 import inspect
 import json
+import os
 from pathlib import Path
 from typing import Any
 
@@ -27,6 +28,7 @@ COVER_CACHE_DIR = Path("images/auto-covers")
 COVER_CACHE_DIR.mkdir(parents=True, exist_ok=True)
 
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+HF_TOKEN = os.environ.get("HF_TOKEN")
 DEFAULT_NEGATIVE = (
     "low quality, bad anatomy, bad hands, text, watermark, blurry, jpeg artifacts"
 )
@@ -196,6 +198,7 @@ def selected_payload(selected_repo: str):
             gr.update(),
             gr.update(),
             gr.update(),
+            gr.update(),
             gr.update(value="Auto", interactive=False),
             gr.update(value=1.0),
         )
@@ -308,7 +311,11 @@ def load_pipeline(base_model: str, family: str) -> DiffusionPipeline:
             torch.cuda.empty_cache()
 
     dtype = pick_dtype_for_family(family)
-    pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype)
+    pipe = DiffusionPipeline.from_pretrained(
+        base_model,
+        torch_dtype=dtype,
+        token=HF_TOKEN,
+    )
 
     if DEVICE == "cuda":
         pipe = pipe.to("cuda")
@@ -392,6 +399,8 @@ def run_lora(
     weight_name = (selected.get("weight_name") or "").strip()
     if weight_name:
         load_kwargs["weight_name"] = weight_name
+    if HF_TOKEN:
+        load_kwargs["token"] = HF_TOKEN
     pipe.load_lora_weights(selected_repo, **load_kwargs)
     CURRENT_LOADED_REPO = selected_repo