diff --git a/CHANGELOG.md b/CHANGELOG.md
index 1cd2860ee..d49041554 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,8 @@
   - **Captioning** new feature: analyze existing images for prompt adherence  
     *tip*: image analysis requires larger VLM model to produce quality output  
     new api endpoint: `/sdapi/v1/analyze`  
+  - **HF download** use `XET` by default  
+    see *settings -> huggingface -> download method* for options  
 - **AI**
   - Cognitive analysis and improvements to *all* AI prompts
   - Automated fixes using `/check-` skills
@@ -18,6 +20,7 @@
   - `gradio` initial hijack
   - `SmolVLM` captioning
   - `gradio` temp files guard against large image
+  - `diffusers` patch custom pipelines for `qk_norm`
 
 ## Update for 2026-05-13
 
diff --git a/modules/models_hf.py b/modules/models_hf.py
index c3e643b06..a5db96191 100644
--- a/modules/models_hf.py
+++ b/modules/models_hf.py
@@ -1,7 +1,6 @@
 import os
 import time
 import gradio as gr
-from installer import install
 from modules.logger import log
 from modules.shared import opts
 
@@ -19,22 +18,20 @@ def hf_init():
     os.environ.setdefault('HF_ENABLE_PARALLEL_LOADING', 'true' if opts.sd_parallel_load else 'false')
     os.environ.setdefault('HF_HUB_CACHE', opts.hfcache_dir)
     os.environ.setdefault('HF_XET_CACHE', opts.xetcache_dir)
-    if opts.hf_transfer_mode == 'requests':
+    if opts.hf_transfer_mode == 'HTTP':
         os.environ.setdefault('HF_XET_HIGH_PERFORMANCE', 'false')
-        os.environ.setdefault('HF_HUB_ENABLE_HF_TRANSFER', 'false')
         os.environ.setdefault('HF_HUB_DISABLE_XET', 'true')
-    elif opts.hf_transfer_mode == 'rust':
-        install('hf_transfer')
+    elif opts.hf_transfer_mode == 'XET':
         os.environ.setdefault('HF_XET_HIGH_PERFORMANCE', 'false')
-        os.environ.setdefault('HF_HUB_ENABLE_HF_TRANSFER', 'true')
-        os.environ.setdefault('HF_HUB_DISABLE_XET', 'true')
-    elif opts.hf_transfer_mode == 'xet':
-        install('hf_xet')
-        import huggingface_hub
-        huggingface_hub.utils._runtime.is_xet_available = lambda: True  # pylint: disable=protected-access
-        os.environ.setdefault('HF_XET_HIGH_PERFORMANCE', 'true')
-        os.environ.setdefault('HF_HUB_ENABLE_HF_TRANSFER', 'true')
         os.environ.setdefault('HF_HUB_DISABLE_XET', 'false')
+    elif opts.hf_transfer_mode == 'XET HighPerformance':
+        os.environ.setdefault('HF_XET_HIGH_PERFORMANCE', 'true')
+        os.environ.setdefault('HF_HUB_DISABLE_XET', 'false')
+        os.environ.setdefault('HF_XET_RECONSTRUCT_WRITE_SEQUENTIALLY', 'false')
+    elif opts.hf_transfer_mode == 'XET Sequential':
+        os.environ.setdefault('HF_XET_HIGH_PERFORMANCE', 'false')
+        os.environ.setdefault('HF_HUB_DISABLE_XET', 'false')
+        os.environ.setdefault('HF_XET_RECONSTRUCT_WRITE_SEQUENTIALLY', 'true')
 
     obfuscated_token = None
     if len(opts.huggingface_token) > 0 and opts.huggingface_token.startswith('hf_'):
diff --git a/modules/sd_hijack_hfhub.py b/modules/sd_hijack_hfhub.py
index 17be5a172..e200bfb63 100644
--- a/modules/sd_hijack_hfhub.py
+++ b/modules/sd_hijack_hfhub.py
@@ -9,14 +9,14 @@ orig_xet_get = None
 
 
 def http_get_hijack(*args, **kwargs):
-    from modules.shared import state
+    from modules.shared import state, opts
     if len(args) > 0 and isinstance(args[0], str) and args[0].endswith(".json"):
         return orig_http_get(*args, **kwargs)
     jobid = state.begin('Download')
     fn = kwargs.get("displayed_filename", None)
     size = kwargs.get("expected_size", None)
     if fn and not fn.endswith(".json") and size is not None and size > 10240:
-        log.debug(f'Download: type=http fn="{fn}" size={size}')
+        log.debug(f'Download: type=http mode="{opts.hf_transfer_mode}" fn="{fn}" size={size}')
     debug(f'Download start: type=http args={args} kwargs={kwargs}')
     t0 = time.time()
     res = orig_http_get(*args, **kwargs)
@@ -27,14 +27,14 @@ def http_get_hijack(*args, **kwargs):
 
 
 def xet_get_hijack(*args, **kwargs):
-    from modules.shared import state
+    from modules.shared import state, opts
     if len(args) > 0 and isinstance(args[0], str) and args[0].endswith(".json"):
         return orig_xet_get(*args, **kwargs)
     jobid = state.begin('Download')
     fn = kwargs.get("displayed_filename", None)
     size = kwargs.get("expected_size", None)
     if fn and not fn.endswith(".json"):
-        log.debug(f'Download: type=xet fn="{fn}" size={size}')
+        log.debug(f'Download: type=xet mode="{opts.hf_transfer_mode}" fn="{fn}" size={size}')
     debug(f'Download start: type=xet args={args} kwargs={kwargs}')
     res = orig_xet_get(*args, **kwargs)
     debug(f'Download end: type=xet res={res}')
diff --git a/modules/ui_definitions.py b/modules/ui_definitions.py
index 889901119..86607bdf5 100644
--- a/modules/ui_definitions.py
+++ b/modules/ui_definitions.py
@@ -606,7 +606,7 @@ def create_settings(cmd_opts):
         "huggingface_sep": OptionInfo("<h2>Huggingface</h2>", "", gr.HTML),
         "diffuser_cache_config": OptionInfo(True, "Use cached model config when available"),
         "huggingface_token": OptionInfo('', 'HuggingFace token', gr.Textbox, {"lines": 2}, secret=True, env_var='HF_TOKEN'),
-        "hf_transfer_mode": OptionInfo("rust", "HuggingFace download method", gr.Radio, {"choices": ['requests', 'rust', 'xet']}),
+        "hf_transfer_mode": OptionInfo("XET", "HuggingFace download method", gr.Dropdown, {"choices": ['HTTP', 'XET', 'XET HighPerformance', 'XET Sequential']}),
         "huggingface_mirror": OptionInfo('', 'HuggingFace mirror', gr.Textbox),
         "offline_mode": OptionInfo(False, 'Force offline mode', gr.Checkbox),
 
diff --git a/package.json b/package.json
index b280e6231..38520a29b 100644
--- a/package.json
+++ b/package.json
@@ -18,7 +18,7 @@
     "venv": ". venv/bin/activate",
     "start": ". venv/bin/activate; python launch.py --debug",
     "localize": "node cli/localize.js",
-    "packages": ". venv/bin/activate && pip install --upgrade accelerate huggingface_hub safetensors tokenizers peft pytorch_lightning pylint ruff",
+    "packages": ". venv/bin/activate && pip install --upgrade accelerate huggingface_hub hf_xet safetensors tokenizers peft pytorch_lightning pylint ruff",
     "format": ". venv/bin/activate && pre-commit run --all-files",
     "format-win": "venv\\scripts\\activate && pre-commit run --all-files",
     "eslint": "eslint javascript/",
diff --git a/pipelines/bria/transformer_block.py b/pipelines/bria/transformer_block.py
index 78dc3bc58..d909b6d83 100644
--- a/pipelines/bria/transformer_block.py
+++ b/pipelines/bria/transformer_block.py
@@ -106,9 +106,11 @@ class FluxSingleTransformerBlock(nn.Module):
 @maybe_allow_in_graph
 class FluxTransformerBlock(nn.Module):
     def __init__(
-        self, dim: int, num_attention_heads: int, attention_head_dim: int, qk_norm: str = "rms_norm", eps: float = 1e-6
+        self, dim: int, num_attention_heads: int, attention_head_dim: int, qk_norm: str | bool | None = "rms_norm", eps: float = 1e-6
     ):
         super().__init__()
+        if isinstance(qk_norm, bool):
+            qk_norm = "rms_norm" if qk_norm else None
 
         self.norm1 = AdaLayerNormZero(dim)
         self.norm1_context = AdaLayerNormZero(dim)
diff --git a/pipelines/meissonic/transformer.py b/pipelines/meissonic/transformer.py
index c2336d323..274cbb7cb 100644
--- a/pipelines/meissonic/transformer.py
+++ b/pipelines/meissonic/transformer.py
@@ -440,8 +440,10 @@ class TransformerBlock(nn.Module):
             processing of `context` conditions.
     """
 
-    def __init__(self, dim, num_attention_heads, attention_head_dim, qk_norm="rms_norm", eps=1e-6):
+    def __init__(self, dim, num_attention_heads, attention_head_dim, qk_norm: str | bool | None = "rms_norm", eps=1e-6):
         super().__init__()
+        if isinstance(qk_norm, bool):
+            qk_norm = "rms_norm" if qk_norm else None
 
         self.norm1 = AdaLayerNormZero(dim)
 
diff --git a/pipelines/vibe/vibe_sana_editing.py b/pipelines/vibe/vibe_sana_editing.py
index 3b88bed2e..4f013a3dc 100644
--- a/pipelines/vibe/vibe_sana_editing.py
+++ b/pipelines/vibe/vibe_sana_editing.py
@@ -23,6 +23,14 @@ from .edit_head import MetaConnector
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
+def _normalize_qk_norm(qk_norm: str | bool | None) -> str | None:
+    """Normalize legacy boolean qk_norm values to Diffusers-compatible strings."""
+    if isinstance(qk_norm, bool):
+        # Legacy checkpoints can store qk_norm as bool while current diffusers expects a string.
+        return "rms_norm" if qk_norm else None
+    return qk_norm
+
+
 class SanaLinearAttnProcessor2_0:
     r"""Processor for implementing scaled dot-product linear attention."""
 
@@ -112,7 +120,7 @@ class SanaTransformerBlock(nn.Module):
         cross_attention_dim: int = 2240,
         norm_eps: float = 1e-6,
         mlp_ratio: float = 2.5,
-        qk_norm: str | None = None,
+        qk_norm: str | bool | None = None,
         *,
         attention_out_bias: bool = True,
         attention_bias: bool = True,
@@ -130,12 +138,13 @@ class SanaTransformerBlock(nn.Module):
             cross_attention_dim (int): The dimension of the cross-attention context.
             norm_eps (float): Epsilon value for layer normalization stability.
             mlp_ratio (float): Expansion ratio for the feed-forward network hidden dimension.
-            qk_norm (str | None): Normalization method for Query/Key vectors.
+            qk_norm (str | bool | None): Normalization method for Query/Key vectors.
             attention_out_bias (bool): Whether to include bias in the attention output projection.
             attention_bias (bool): Whether to include bias in the attention Q/K/V projections.
             norm_elementwise_affine (bool): Whether to learn affine parameters for normalization.
         """
         super().__init__()
+        qk_norm = _normalize_qk_norm(qk_norm)
 
         # 1. Self Attention
         self.norm1 = nn.LayerNorm(dim, elementwise_affine=False, eps=norm_eps)
@@ -269,7 +278,7 @@ class VIBESanaEditingModel(SanaTransformer2DModel):
         patch_size: int = 1,
         norm_eps: float = 1e-6,
         interpolation_scale: int | None = None,
-        qk_norm: str | None = None,
+        qk_norm: str | bool | None = None,
         timestep_scale: float = 1.0,
         input_condition_type: str = "channel_cat",
         edit_head_input_dim: int = 2048,
@@ -299,7 +308,7 @@ class VIBESanaEditingModel(SanaTransformer2DModel):
             patch_size (int): Size of the patches extracted from the input latent.
             norm_eps (float): Epsilon for layer normalization.
             interpolation_scale (int | None): Scale factor for positional embedding interpolation.
-            qk_norm (str | None): Normalization type for Query/Key (e.g., 'rms_norm').
+            qk_norm (str | bool | None): Normalization type for Query/Key (e.g., 'rms_norm').
             timestep_scale (float): Scale factor for the timestep.
             input_condition_type (str): Method for conditioning on the input image. Options: 'channel_cat', 'seq_cat'.
             edit_head_input_dim (int): Input dimension for the `MetaConnector` edit head.
@@ -311,6 +320,7 @@ class VIBESanaEditingModel(SanaTransformer2DModel):
             guidance_embeds (bool): Whether to use additional guidance embeddings.
         """
         super(SanaTransformer2DModel, self).__init__()
+        qk_norm = _normalize_qk_norm(qk_norm)
 
         out_channels = out_channels or in_channels
         inner_dim = num_attention_heads * attention_head_dim
diff --git a/requirements.txt b/requirements.txt
index f1391e86a..c315c9e86 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -30,7 +30,8 @@ requests==2.32.3
 tqdm==4.67.3
 accelerate==1.13.0
 einops==0.8.1
-huggingface_hub==1.14.0
+huggingface_hub==1.15.0
+hf_xet==1.5.0
 numpy==2.1.2
 pandas==2.3.1
 protobuf==6.33.5