logging cleanup

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

CHANGELOG.md

@@ -16,6 +16,7 @@ Plus some significant under-the-hood changes to improve code coverage and qualit
   - [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*  
+    *note*: 9B variant is [gated](https://vladmandic.github.io/sdnext-docs/Gated/)  
   - [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  

modules/model_quant.py

@@ -236,7 +236,8 @@ def create_sdnq_config(kwargs = None, allow: bool = True, module: str = 'Model',
         )
         if quantized_matmul_dtype is None:
             quantized_matmul_dtype = "auto" # set for logging
-        log.debug(f'Quantization: module="{module}" type=sdnq mode=pre dtype={weights_dtype} matmul_dtype={quantized_matmul_dtype} matmul={shared.opts.sdnq_use_quantized_matmul} group_size={shared.opts.sdnq_quantize_weights_group_size} svd_rank={shared.opts.sdnq_svd_rank} svd_steps={shared.opts.sdnq_svd_steps} use_svd={shared.opts.sdnq_use_svd} dynamic={shared.opts.sdnq_use_dynamic_quantization} loss={shared.opts.sdnq_dynamic_loss_threshold} quant_conv={shared.opts.sdnq_quantize_conv_layers} matmul_conv={shared.opts.sdnq_use_quantized_matmul_conv} dequantize_fp32={shared.opts.sdnq_dequantize_fp32} quantize_with_gpu={shared.opts.sdnq_quantize_with_gpu} quantization_device={quantization_device} return_device={return_device} device_map={shared.opts.device_map} offload_mode={shared.opts.diffusers_offload_mode} non_blocking={shared.opts.diffusers_offload_nonblocking} modules_to_not_convert={modules_to_not_convert} modules_dtype_dict={modules_dtype_dict}')
+        svd = f'{shared.opts.sdnq_use_svd} rank={shared.opts.sdnq_svd_rank} steps={shared.opts.sdnq_svd_steps}' if shared.opts.sdnq_use_svd else f'{shared.opts.sdnq_use_svd}'
+        log.debug(f'Quantization: module="{module}" type=sdnq mode=pre dtype={weights_dtype} svd={svd} dynamic={shared.opts.sdnq_use_dynamic_quantization} group={shared.opts.sdnq_quantize_weights_group_size} loss={shared.opts.sdnq_dynamic_loss_threshold} matmul_dtype={quantized_matmul_dtype} matmul_quant={shared.opts.sdnq_use_quantized_matmul} matmul_conv={shared.opts.sdnq_use_quantized_matmul_conv}  quant_conv={shared.opts.sdnq_quantize_conv_layers} fp32={shared.opts.sdnq_dequantize_fp32} device={quantization_device} return={return_device} use_gpu={shared.opts.sdnq_quantize_with_gpu} map={shared.opts.device_map} offload={shared.opts.diffusers_offload_mode} non_blocking={shared.opts.diffusers_offload_nonblocking} skip_modules={modules_to_not_convert} dict={modules_dtype_dict}')
         if kwargs is None:
             return sdnq_config
         else:

modules/sd_models.py

@@ -493,7 +493,8 @@ def load_diffuser_force(detected_model_type, checkpoint_info, diffusers_load_con
             allow_post_quant = False
     except Exception as e:
         shared.log.error(f'Load {op}: path="{checkpoint_info.path}" {e}')
-        errors.display(e, 'Load')
+        if debug_load:
+            errors.display(e, 'Load')
         return None, True
     if sd_model is not None:
         return sd_model, True

pipelines/generic.py

@@ -92,7 +92,7 @@ def load_transformer(repo_id, cls_name, load_config=None, subfolder="transformer
                 transformer.quantization_config = quant_args.get('quantization_config', None)
     except Exception as e:
         shared.log.error(f'Load model: transformer="{repo_id}" cls={cls_name.__name__} {e}')
-        errors.display(e, 'Load:')
+        errors.display(e, 'Load')
         raise
     devices.torch_gc()
     shared.state.end(jobid)
@@ -243,7 +243,7 @@ def load_text_encoder(repo_id, cls_name, load_config=None, subfolder="text_encod
                 text_encoder.quantization_config = quant_args.get('quantization_config', None)
     except Exception as e:
         shared.log.error(f'Load model: text_encoder="{repo_id}" cls={cls_name.__name__} {e}')
-        errors.display(e, 'Load:')
+        errors.display(e, 'Load')
         raise
     devices.torch_gc()
     shared.state.end(jobid)