refactor: update Client initialization to use 'token' instead of 'hf_token' for consistency across models

app.py

@@ -113,7 +113,7 @@ def run_upscaler(image, model_selection="4xBHI_dat2_real"):
     except Exception as e:
         raise RuntimeError(f"Upscaler: could not load image from path: {e}") from e
     try:
-        client = Client("Phips/Upscaler", hf_token=_hf_token())
+        client = Client("Phips/Upscaler", token=_hf_token())
         result = client.predict(image_arg, model_selection, api_name="/upscale_image")
     except Exception as e:
         raise RuntimeError(f"Upscaler API error: {e}") from e
@@ -153,7 +153,7 @@ def run_z_image_turbo(prompt, height=1024, width=1024, seed=42):
     if not prompt or not isinstance(prompt, str) or not prompt.strip():
         return None
     try:
-        client = Client("hf-applications/Z-Image-Turbo", hf_token=_hf_token())
+        client = Client("hf-applications/Z-Image-Turbo", token=_hf_token())
         result = client.predict(
             prompt=prompt.strip(),
             height=float(height),
@@ -203,7 +203,7 @@ def run_flux_klein(
     if not prompt or not isinstance(prompt, str) or not prompt.strip():
         return None
     try:
-        client = Client("black-forest-labs/FLUX.2-klein-9B", hf_token=_hf_token())
+        client = Client("black-forest-labs/FLUX.2-klein-9B", token=_hf_token())
         result = client.predict(
             prompt=prompt.strip(),
             input_images=[],