lint and safeguard glm

Signed-off-by: vladmandic <mandic00@live.com>

CHANGELOG.md

@@ -1,22 +1,36 @@
 # Change Log for SD.Next
 
-## Update for 2025-01-15
+## Update for 2025-01-16
+
+### Highlights for 2025-01-16
+
+First release of 2026 brings quite a few new models: **Flux.2-Klein, Qwen-Image-2512, LTX-2-Dev, GLM-Image**  
+There are also improvements to SDNQ quantization engine, updated Prompt Enhance and many others  
+Plus some significant under-the-hood changes to improve code coverage and quality which resulted in more than usual levels of bug-fixes!  
+
+[ReadMe](https://github.com/vladmandic/automatic/blob/master/README.md) | [ChangeLog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) | [Docs](https://vladmandic.github.io/sdnext-docs/) | [WiKi](https://github.com/vladmandic/automatic/wiki) | [Discord](https://discord.com/invite/sd-next-federal-batch-inspectors-1101998836328697867) | [Sponsor](https://github.com/sponsors/vladmandic)  
+
+### Details for 2025-01-16
 
 - **Models**
-  - [Qwen-Image-2512](https://huggingface.co/Qwen/Qwen-Image-2512)  
+  - [Flux.2 Klein](https://bfl.ai/blog/flux2-klein-towards-interactive-visual-intelligence)  
+    Flux.2-Klein is a new family of compact models from BFL in *4B and 9B sizes* and avaialable as *destilled and base* variants  
+    also includes are *SDNQ prequantized variants*  
+  - [Qwen-Image-2512](https://qwen.ai/blog?id=qwen-image-2512)  
     Qwen-Image successor, significantly reduces the AI-generated look and adds finer natural detailils and improved text rendering  
     available in both *original*, *sdnq-svd prequantized* and *sdnq-dynamic prequantized*  variants  
-  - [LTX-2 19B Dev](https://huggingface.co/Lightricks/LTX-2)  
+  - [LTX-2 19B Dev](https://ltx.io/model/ltx-2)  
     LTX-2 is a new very large 19B parameter video generation model from Lightricks using Gemma-3 text encoder  
     available for T2I/I2I workflows in original and SDNQ prequantized variants  
     *note*: audio generation and upsampling are not yet supported (soon)  
-  - [GLM-Image](https://huggingface.co/zai-org/GLM-Image)  
+  - [GLM-Image](https://z.ai/blog/glm-image)  
     GLM-image is a new image generation model that adopts a hybrid autoregressive with diffusion decoder architecture  
     available in both *original* and *sdnq-dynamic prequantized* variants, thanks @CalamitousFelicitousness  
     *note*: model requires pre-release versions of `transformers` package:  
     > pip install --upgrade git+https://github.com/huggingface/transformers.git  
     > ./webui.sh --experimental  
   - [Nunchaku Z-Image Turbo](https://huggingface.co/nunchaku-tech/nunchaku-z-image-turbo)  
+    nunchaku optimized z-image turbo  
 - **Feaures**
   - **SDNQ**: add *dynamic* quantization method  
     sdnq can dynamically determine best quantization method for each module layer  

cli/hf-search.py

@@ -9,10 +9,19 @@ if __name__ == "__main__":
     keyword = sys.argv[0] if len(sys.argv) > 0 else ''
     hf.logging.set_verbosity_info()
     hf_api = hf.HfApi()
-    res = hf_api.list_models(model_name=keyword, full=True, limit=100, sort="downloads", direction=-1)
+    res = hf_api.list_models(
+        model_name=keyword,
+        full=True,
+        limit=100,
+        sort="downloads",
+        direction=-1,
+    )
     res = sorted(res, key=lambda x: x.id)
+    exact = [m for m in res if keyword.lower() in m.id.lower()]
+    if len(exact) > 0:
+        res = exact
     for m in res:
         meta = hf_api.model_info(m.id, files_metadata=True)
         m.files = [f.rfilename for f in meta.siblings if f.rfilename.endswith('.bin') or f.rfilename.endswith('.safetensors')]
-        m.size = sum([f.size for f in meta.siblings]) / 1024 / 1024 / 1024 # in GB
-        print({ 'name': m.id, 'files': len(m.files), 'size': m.size, 'downloads': m.downloads, 'mtime': m.lastModified, 'url': f'https://huggingface.co/{m.id}', 'pipeline': m.pipeline_tag })
+        m.size = round(sum([f.size for f in meta.siblings]) / 1024 / 1024 / 1024, 2)
+        print({ 'name': m.id, 'files': len(m.files), 'size': m.size, 'downloads': m.downloads, 'ctime': m.created_at.isoformat(), 'url': f'https://huggingface.co/{m.id}', 'pipeline': m.pipeline_tag })

html/reference-quant.json

@@ -56,7 +56,7 @@
     "skip": true,
     "extras": "sampler: Default, cfg_scale: 4.0, steps: 50",
     "tags": "quantized",
-    "size": 8.5,
+    "size": 5.1,
     "date": "2025 January"
   },
   "Black Forest Labs FLUX.2 Klein Base 9B sdnq-uint4-dynamic-svd": {
@@ -66,10 +66,9 @@
     "skip": true,
     "extras": "sampler: Default, cfg_scale: 4.0, steps: 50",
     "tags": "quantized",
-    "size": 18.5,
+    "size": 11.7,
     "date": "2025 January"
   },
-
   "Chroma1-HD sdnq-svd-uint4": {
     "path": "Disty0/Chroma1-HD-SDNQ-uint4-svd-r32",
     "preview": "Disty0--Chroma1-HD-SDNQ-uint4-svd-r32.jpg",
@@ -137,7 +136,7 @@
     "skip": true,
     "tags": "quantized",
     "extras": "",
-    "size": 16.10,
+    "size": 17.2,
     "date": "2026 January"
   },
   "Qwen-Image-Edit sdnq-svd-uint4": {
@@ -233,7 +232,7 @@
     "desc": "Quantization of ZAI GLM-Image using SDNQ: sdnq-dynamic 4-bit uint",
     "skip": true,
     "extras": "sampler: Default, cfg_scale: 1.5, steps: 50",
-    "size": 15.3,
+    "size": 11.6,
     "tags": "quantized",
     "date": "2025 January"
   }

modules/framepack/framepack_wrappers.py

@@ -1,7 +1,6 @@
 import os
 import re
 import random
-import threading
 import numpy as np
 import torch
 import gradio as gr

modules/ltx/ltx_process.py

@@ -1,6 +1,5 @@
 import os
 import time
-import threading
 import torch
 from PIL import Image
 

modules/ui_control.py

@@ -314,7 +314,6 @@ def create_ui(_blocks: gr.Blocks=None):
                 result_txt,
                 output_html_log,
             ]
-            global control_dict # pylint: disable=global-statement
             control_dict = dict(
                 fn=generate_click,
                 _js="submit_control",

modules/ui_loadsave.py

@@ -1,6 +1,6 @@
+from typing import TYPE_CHECKING, cast
 import os
 import gradio as gr
-from typing import TYPE_CHECKING, cast
 from modules import errors
 from modules.ui_components import ToolButton
 

pipelines/model_glm.py

@@ -86,6 +86,10 @@ def load_glm_image(checkpoint_info, diffusers_load_config=None):
     repo_id = sd_models.path_to_repo(checkpoint_info)
     sd_models.hf_auth_check(checkpoint_info)
 
+    if not hasattr(transformers, 'GlmImageForConditionalGeneration'):
+        shared.log.error(f'Load model: type=GLM-Image repo="{repo_id}" transformers={transformers.__version__} not supported')
+        return None
+
     load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
     shared.log.debug(f'Load model: type=GLM-Image repo="{repo_id}" offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
 
@@ -108,7 +112,7 @@ def load_glm_image(checkpoint_info, diffusers_load_config=None):
     # Note: This is a conditional generation model, different from typical text encoders
     vision_language_encoder = generic.load_text_encoder(
         repo_id,
-        cls_name=transformers.GlmImageForConditionalGeneration,
+        cls_name=transformers.GlmImageForConditionalGeneration, # pylint: disable=no-member
         subfolder="vision_language_encoder",
         load_config=diffusers_load_config,
         allow_shared=False