fix sdnq svd

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

modules/api/gpu.py

@@ -21,7 +21,7 @@ def get_gpu_smi():
     if device is None:
         try:
             device = torch.cuda.get_device_name(torch.cuda.current_device())
-            log.info(f'GPU monitoring: device={device}')
+            log.info(f'GPU monitoring: device="{device}"')
         except Exception:
             device = ''
     # per vendor modules

modules/sdnq/quantizer.py

@@ -287,7 +287,7 @@ def sdnq_quantize_layer_weight_dynamic(
             svd_up, svd_down = prepare_svd_for_matmul(svd_up, svd_down, False)
             if use_quantized_matmul:
                 svd_up_t, svd_down_t = svd_up.clone().t_(), svd_down.clone().t_()
-                svd_up_t, svd_down_t = prepare_svd_for_matmul(svd_up, svd_down, True)
+                svd_up_t, svd_down_t = prepare_svd_for_matmul(svd_up_t, svd_down_t, True)
             else:
                 svd_up_t, svd_down_t = None, None
         except Exception:

pipelines/ideogram/ideogram4.py

@@ -499,7 +499,7 @@ class Ideogram4Pipeline(DiffusionPipeline):
         width: int = 2048,
         num_inference_steps: int = 48,
         guidance_scale: float | None = None,
-        guidance_schedule: list[float] | torch.Tensor | None = (7.0,) * 45 + (3.0,) * 3,
+        guidance_schedule: list[float] | torch.Tensor | None = None,
         mu: float = 0.0,
         std: float = 1.5,
         prompt_upsampling: bool = False,

pipelines/model_ideogram4.py

@@ -14,7 +14,9 @@ def pin_transformers(transformer, unconditional_transformer) -> bool:
     """
     if shared.opts.diffusers_offload_mode != 'balanced' or shared.gpu_memory <= 0 or not shared.opts.model_ideogram4_pin:
         return False
-    if transformer is None or unconditional_transformer is None:
+    if transformer is None or unconditional_transformer is None: # if cg is disabled we dont need to pin
+        return False
+    if id(transformer) == id(unconditional_transformer): # if we're using the same transformer for both, no need to pin
         return False
     size_gb = sum(p.numel() * p.element_size() for m in (transformer, unconditional_transformer) for p in m.parameters()) / (1024 ** 3)
     budget_gb = shared.gpu_memory * shared.opts.diffusers_offload_max_gpu_memory
@@ -57,12 +59,14 @@ def load_ideogram4(checkpoint_info, diffusers_load_config=None):
     prompt_enhancer_head = None
     if shared.opts.model_ideogram4_enable_pe:
         enhancer_repo_id = "diffusers/qwen3-vl-8b-instruct-lm-head"
+        pe_load_args, pe_quant_args = model_quant.get_dit_args(diffusers_load_config, module='TE', device_map=True)
         enhancer_cls = diffusers.Ideogram4PromptEnhancerHead
         log.debug(f'Load model: enhancer="{enhancer_repo_id}" cls={enhancer_cls.__name__}')
         prompt_enhancer_head = enhancer_cls.from_pretrained(
             enhancer_repo_id,
-            torch_dtype=devices.dtype,
-            cache_dir=shared.opts.hfcache_dir
+            cache_dir=shared.opts.hfcache_dir,
+            **pe_load_args,
+            **pe_quant_args,
         )
         components['prompt_enhancer_head'] = prompt_enhancer_head