diff --git a/CHANGELOG.md b/CHANGELOG.md
index 9259d02ee..3ea02ae4a 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -139,7 +139,7 @@ Feature highlights include:
 - [ModernUI](https://github.com/user-attachments/assets/6f156154-0b0a-4be2-94f0-979e9f679501) has quite some redesign which should make it more user friendly and easier to navigate plus several new UI themes  
   If you're still using **StandardUI**, give [ModernUI](https://vladmandic.github.io/sdnext-docs/Themes/) a try!  
 - New models such as [WanAI 2.2](https://wan.video/) in 5B and A14B variants for both *text-to-video* and *image-to-video* workflows as well as *text-to-image* workflow!  
-  and also [FreePix F-Lite](https://huggingface.co/Freepik/F-Lite), [Bria 3.2](https://huggingface.co/briaai/BRIA-3.2) and [bigASP 2.5](https://civitai.com/models/1789765?modelVersionId=2025412)  
+  and also [FreePik F-Lite](https://huggingface.co/Freepik/F-Lite), [Bria 3.2](https://huggingface.co/briaai/BRIA-3.2) and [bigASP 2.5](https://civitai.com/models/1789765?modelVersionId=2025412)  
 - Redesigned [Video](https://vladmandic.github.io/sdnext-docs/Video) interface with support for general video models plus optimized [FramePack](https://vladmandic.github.io/sdnext-docs/FramePack) and [LTXVideo](https://vladmandic.github.io/sdnext-docs/LTX) support  
 - Fully integrated nudity detection and optional censorship with [NudeNet](https://vladmandic.github.io/sdnext-docs/NudeNet)  
 - New background replacement and relightning methods using **Latent Bridge Matching** and new **PixelArt** processing filter  
@@ -184,7 +184,7 @@ For details, see [ChangeLog](https://github.com/vladmandic/automatic/blob/master
     can also load unet/transformer fine-tunes in safetensors format using UNET loader  
     simply select in *networks -> models -> reference*  
     *note* 1.3B model is a bit too small for good results and 14B is very large at 78GB even without second-stage so aggressive quantization and offloading are recommended  
-  - [FreePix F-Lite](https://huggingface.co/Freepik/F-Lite) in *7B, 10B and Texture* variants  
+  - [FreePik F-Lite](https://huggingface.co/Freepik/F-Lite) in *7B, 10B and Texture* variants  
     F-Lite is a 7B/10B model trained exclusively on copyright-safe and SFW content, trained on internal dataset comprising approximately 80 million copyright-safe images  
     available via *networks -> models -> reference*  
   - [Bria 3.2](https://huggingface.co/briaai/BRIA-3.2)  
diff --git a/TODO.md b/TODO.md
index da9883bd8..6116f4b45 100644
--- a/TODO.md
+++ b/TODO.md
@@ -4,6 +4,7 @@ Main ToDo list can be found at [GitHub projects](https://github.com/users/vladma
 
 ## Future Candidates
 
+- Unified `CLIPTextModelWithProjection` loader  
 - [Modular pipelines and guiders](https://github.com/huggingface/diffusers/issues/11915)  
 - Refactor: Sampler options  
 - Feature: Diffusers [group offloading](https://github.com/vladmandic/sdnext/issues/4049)  
diff --git a/cli/test-all-models.py b/cli/test-all-models.py
index 7115bc8f2..17559e430 100755
--- a/cli/test-all-models.py
+++ b/cli/test-all-models.py
@@ -1,4 +1,9 @@
 #!/usr/bin/env python
+"""
+fal/AuraFlow-v0.3: sdnq unusupported
+
+"""
+
 import io
 import os
 import time
@@ -27,8 +32,8 @@ models = [
     "stabilityai/stable-diffusion-3.5-medium",
     "stabilityai/stable-diffusion-3.5-large",
     "fal/AuraFlow-v0.3",
-    "THUDM/CogView3-Plus-3B",
-    "THUDM/CogView4-6B",
+    "zai-org/CogView4-6B",
+    "zai-org/CogView3-Plus-3B",
     "nvidia/Cosmos-Predict2-2B-Text2Image",
     "nvidia/Cosmos-Predict2-14B-Text2Image",
     "Qwen/Qwen-Image",
@@ -70,8 +75,11 @@ styles_tbd = [
     'Fixed Kneeling on Bed',
     'Fixed Girl in Sin City',
     'Fixed Girl in a city',
+    'Fixed Girl in Lace',
     'Fixed Lady in Tokyo',
     'Fixed MadMax selfie',
+    'Fixed Party Yacht',
+    'Fixed Yoga Girls',
     'Fixed SDNext Neon',
 ]
 
@@ -116,8 +124,10 @@ def generate(): # pylint: disable=redefined-outer-name
                     b64 = data['images'][0].split(',',1)[0]
                     image = Image.open(io.BytesIO(base64.b64decode(b64)))
                     info = data['info']
-                    log.info(f' image: size={image.size} time={t1-t0:.2f} info="{len(info)}" fn="{fn}"')
+                    log.info(f' image: size={image.width}x{image.height} time={t1-t0:.2f} info={len(info)}')
                     image.save(fn)
+                else:
+                    log.error(f' model: error="{model}" style="{style}" no image')
             except Exception as e:
                 log.error(f' model: error="{model}" style="{style}" exception="{e}"')
 
diff --git a/html/reference.json b/html/reference.json
index 63cdbbdda..21d42a81f 100644
--- a/html/reference.json
+++ b/html/reference.json
@@ -520,13 +520,13 @@
   },
 
   "CogView 4": {
-    "path": "THUDM/CogView4-6B",
+    "path": "zai-org/CogView4-6B",
     "desc": "An innovative cascaded framework that enhances the performance of text-to-image diffusion. CogView is the first model implementing relay diffusion in the realm of text-to-image generation, executing the task by first creating low-resolution images and subsequently applying relay-based super-resolution.",
     "preview": "THUDM--CogView4-6B.jpg",
     "skip": true
   },
   "CogView 3 Plus": {
-    "path": "THUDM/CogView3-Plus-3B",
+    "path": "zai-org/CogView3-Plus-3B",
     "desc": "An innovative cascaded framework that enhances the performance of text-to-image diffusion. CogView is the first model implementing relay diffusion in the realm of text-to-image generation, executing the task by first creating low-resolution images and subsequently applying relay-based super-resolution.",
     "preview": "THUDM--CogView3-Plus-3B.jpg",
     "skip": true
diff --git a/modules/shared.py b/modules/shared.py
index 55fc32010..25ca83b92 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -248,7 +248,7 @@ options_templates.update(options_section(('text_encoder', "Text Encoder"), {
     "diffusers_zeros_prompt_pad": OptionInfo(False, "Use zeros for prompt padding", gr.Checkbox),
     "te_hijack": OptionInfo(True, "Offload after prompt encode", gr.Checkbox),
     "te_optional_sep": OptionInfo("<h2>Optional</h2>", "", gr.HTML),
-    "te_shared_t5": OptionInfo(False, "T5: Use shared instance of text encoder"),
+    "te_shared_t5": OptionInfo(True, "T5: Use shared instance of text encoder"),
     "te_pooled_embeds": OptionInfo(False, "SDXL: Use weighted pooled embeds"),
     "te_complex_human_instruction": OptionInfo(True, "Sana: Use complex human instructions"),
     "te_use_mask": OptionInfo(True, "Lumina: Use mask in transformers"),
diff --git a/pipelines/generic.py b/pipelines/generic.py
index 08fe00275..7b9039413 100644
--- a/pipelines/generic.py
+++ b/pipelines/generic.py
@@ -8,8 +8,8 @@ from modules import shared, devices, sd_models, model_quant
 debug = shared.log.trace if os.environ.get('SD_LOAD_DEBUG', None) is not None else lambda *args, **kwargs: None
 
 
-def load_transformer(repo_id, cls_name, load_config={}, subfolder="transformer"):
-    load_args, quant_args = model_quant.get_dit_args(load_config, module='Model', device_map=True)
+def load_transformer(repo_id, cls_name, load_config={}, subfolder="transformer", allow_quant=True):
+    load_args, quant_args = model_quant.get_dit_args(load_config, module='Model', device_map=True, allow_quant=allow_quant)
     quant_type = model_quant.get_quant_type(quant_args)
 
     local_file = None
@@ -56,8 +56,8 @@ def load_transformer(repo_id, cls_name, load_config={}, subfolder="transformer")
     return transformer
 
 
-def load_text_encoder(repo_id, cls_name, load_config={}, subfolder="text_encoder"):
-    load_args, quant_args = model_quant.get_dit_args(load_config, module='TE', device_map=True)
+def load_text_encoder(repo_id, cls_name, load_config={}, subfolder="text_encoder", allow_quant=True, allow_shared=True):
+    load_args, quant_args = model_quant.get_dit_args(load_config, module='TE', device_map=True, allow_quant=allow_quant)
     quant_type = model_quant.get_quant_type(quant_args)
     text_encoder = None
 
@@ -92,7 +92,7 @@ def load_text_encoder(repo_id, cls_name, load_config={}, subfolder="text_encoder
         )
         text_encoder = model_quant.do_post_load_quant(text_encoder, allow=quant_type is not None)
     # use shared t5 if possible
-    elif cls_name == transformers.T5EncoderModel:
+    elif cls_name == transformers.T5EncoderModel and allow_shared:
         with open(os.path.join('configs', 'flux', 'text_encoder_2', 'config.json'), encoding='utf8') as f:
             load_args['config'] = transformers.T5Config(**json.load(f))
         if model_quant.check_nunchaku('TE'):
@@ -114,7 +114,7 @@ def load_text_encoder(repo_id, cls_name, load_config={}, subfolder="text_encoder
                 **load_args,
                 **quant_args,
             )
-    
+
     # load from repo
     if text_encoder is None:
         shared.log.debug(f'Load model: text_encoder="{repo_id}" cls={cls_name.__name__} quant="{quant_type}" shared={shared.opts.te_shared_t5}')
diff --git a/pipelines/model_auraflow.py b/pipelines/model_auraflow.py
index c6f2ade77..06cc978ce 100644
--- a/pipelines/model_auraflow.py
+++ b/pipelines/model_auraflow.py
@@ -1,6 +1,6 @@
 import transformers
 import diffusers
-from modules import shared, sd_models, devices, model_quant
+from modules import shared, sd_models, devices, model_quant, sd_hijack_te
 from pipelines import generic
 
 
@@ -8,21 +8,22 @@ def load_auraflow(checkpoint_info, diffusers_load_config={}):
     repo_id = sd_models.path_to_repo(checkpoint_info)
     sd_models.hf_auth_check(checkpoint_info)
 
-    load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
+    load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config)
     shared.log.debug(f'Load model: type=AuraFlow repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
 
     transformer = generic.load_transformer(repo_id, cls_name=diffusers.AuraFlowTransformer2DModel, load_config=diffusers_load_config)
-    text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.T5EncoderModel, load_config=diffusers_load_config)
+    text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.UMT5EncoderModel, load_config=diffusers_load_config, allow_quant=False) # auraflow uses EleutherAI/pile-t5-xl
 
     pipe = diffusers.AuraFlowPipeline.from_pretrained(
         repo_id,
         transformer=transformer,
-        text_encoder=text_encoder,
+        # text_encoder=text_encoder,
         cache_dir=shared.opts.diffusers_dir,
         **load_args,
     )
 
     del text_encoder
     del transformer
+    sd_hijack_te.init_hijack(pipe)
     devices.torch_gc(force=True, reason='load')
     return pipe
diff --git a/pipelines/model_hidream.py b/pipelines/model_hidream.py
index a5c18d3bc..6d1a716b8 100644
--- a/pipelines/model_hidream.py
+++ b/pipelines/model_hidream.py
@@ -2,6 +2,7 @@ import os
 import transformers
 import diffusers
 from modules import shared, devices, sd_models, model_quant, sd_hijack_te
+from pipelines import generic
 
 
 def load_llama(repo_id, diffusers_load_config={}):
@@ -37,7 +38,7 @@ def load_hidream(checkpoint_info, diffusers_load_config={}):
 
     transformer = generic.load_transformer(repo_id, cls_name=diffusers.HiDreamImageTransformer2DModel, load_config=diffusers_load_config, subfolder="transformer")
     text_encoder_3 = generic.load_text_encoder(repo_id, cls_name=transformers.T5EncoderModel, load_config=diffusers_load_config, subfolder="text_encoder_3")
-    text_encoder_4, tokenizer_4 = load_text_encoders(repo_id, diffusers_load_config)
+    text_encoder_4, tokenizer_4 = load_llama(repo_id, diffusers_load_config)
 
     if shared.opts.teacache_enabled:
         from modules import teacache
diff --git a/wiki b/wiki
index 96e3932bf..0b658ebad 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 96e3932bffee9117951956074956e943f254702b
+Subproject commit 0b658ebad7ad81ebb9cd131abf409878d9c8744f