cleanup

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

CHANGELOG.md

@@ -29,6 +29,8 @@
     note that rocm for windows is still in preview and has limited gpu support, please check rocm docs for details  
   - **DirectML** warn as end-of-life  
     `torch-directml` received no updates in over 1 year and its currently superceded by `rocm` or `zluda`  
+  - command line params `--use-zluda` and `--use-rocm` will attempt desired operation or fail if not possible  
+    previously sdnext was performing a fallback to `torch-cpu` which is not desired  
 - **Extensions**  
   - [Agent-Scheduler](https://github.com/SipherAGI/sd-webui-agent-scheduler)  
     was a high-value built-in extension, but it has not been maintained for 1.5 years  

html/reference.json

@@ -666,7 +666,8 @@
     "desc": "HiDream-E1 is an image editing model built on HiDream-I1.",
     "preview": "HiDream-ai--HiDream-E1-Full.jpg",
     "skip": true,
-    "extras": "sampler: Default"
+    "extras": "sampler: Default",
+    "experimental": true
   },
 
   "Kwai Kolors": {

modules/lora/lora_nunchaku.py

@@ -8,9 +8,6 @@ previously_loaded = [] # we maintain private state here
 
 def load_nunchaku(names, strengths):
     global previously_loaded # pylint: disable=global-statement
-    if not hasattr(shared.sd_model, 'transformer') or not hasattr(shared.sd_model.transformer, 'update_lora_params'):
-        shared.log.error(f'Network load: type=LoRA method=nunchaku model={shared.sd_model.__class__.__name__} unsupported')
-        return False
     strengths = [s[0] if isinstance(s, list) else s for s in strengths]
     networks = lora_load.gather_networks(names)
     networks = [(network, strength) for network, strength in zip(networks, strengths) if network is not None and strength > 0]
@@ -18,6 +15,9 @@ def load_nunchaku(names, strengths):
     is_changed = loras != previously_loaded
     if not is_changed:
         return False
+    if not hasattr(shared.sd_model, 'transformer') or not hasattr(shared.sd_model.transformer, 'update_lora_params'):
+        shared.log.error(f'Network load: type=LoRA method=nunchaku model={shared.sd_model.__class__.__name__} unsupported')
+        return False
 
     previously_loaded = loras
     try:

modules/model_quant.py

@@ -501,11 +501,6 @@ def sdnq_quantize_model(model, op=None, sd_model=None, do_gc: bool = True, weigh
         modules_to_not_convert.extend(model._skip_layerwise_casting_patterns) # pylint: disable=protected-access
     if model.__class__.__name__ == "ChromaTransformer2DModel":
         modules_to_not_convert.append("distilled_guidance_layer")
-    if model.__class__.__name__ == "QwenImageTransformer2DModel":
-        if "minimum_6bit" not in modules_dtype_dict.keys():
-            modules_dtype_dict["minimum_6bit"] = ["img_mod", "pos_embed", "time_text_embed", "img_in", "txt_in", "norm_out"]
-        else:
-            modules_dtype_dict["minimum_6bit"].extend(["img_mod", "pos_embed", "time_text_embed", "img_in", "txt_in", "norm_out"])
 
     sdnq_modules_to_not_convert = [m.strip() for m in re.split(';|,| ', shared.opts.sdnq_modules_to_not_convert) if len(m.strip()) > 1]
     if len(sdnq_modules_to_not_convert) > 0:

pipelines/model_qwen.py

@@ -46,7 +46,7 @@ def load_qwen(checkpoint_info, diffusers_load_config={}):
             subfolder=transformer_subfolder,
             cls_name=diffusers.QwenImageTransformer2DModel,
             load_config=diffusers_load_config,
-            modules_dtype_dict={"minimum_6bit": ["pos_embed", "time_text_embed", "img_in", "txt_in", "norm_out", "transformer_blocks.0.img_mod.1.weight"]},
+            modules_dtype_dict={"minimum_6bit": ["pos_embed", "time_text_embed", "img_in", "txt_in", "norm_out", "img_mod", "transformer_blocks.0.img_mod.1.weight"]},
         )
 
     repo_te = 'Qwen/Qwen-Image'