Fix GGUF with LoRAs and support GGUF witgh SetLoras -node

gguf/gguf.py

@@ -33,15 +33,6 @@ def _replace_with_gguf_linear(model, compute_dtype, state_dict, prefix="", modul
         ):
             key = "diffusion_model." + module_prefix + "weight"
             patch = patches.get(key, [])
-            
-            lora_diffs = lora_strengths = None
-            if len(patch) != 0:
-                lora_diffs = [p[1].weights for p in patch]
-                lora_strengths = [p[0] for p in patch]
-            
-            #print("lora_diff", lora_diff)
-
-            #print(state_dict[module_prefix + "weight"].shape)
             in_features = state_dict[module_prefix + "weight"].shape[1]
             out_features = state_dict[module_prefix + "weight"].shape[0]
 
@@ -51,16 +42,30 @@ def _replace_with_gguf_linear(model, compute_dtype, state_dict, prefix="", modul
                     in_features,
                     out_features,
                     module.bias is not None,
-                    compute_dtype=compute_dtype,
-                    lora_diffs=lora_diffs,
-                    lora_strengths = lora_strengths
+                    compute_dtype=compute_dtype
                 )
+            set_lora_params(model._modules[name], patches, module_prefix)
             model._modules[name].source_cls = type(module)
             # Force requires_grad to False to avoid unexpected errors
             model._modules[name].requires_grad_(False)
 
     return model
 
+def set_lora_params(module, patches, module_prefix=""):
+    # Recursively set lora_diffs and lora_strengths for all GGUFLinear layers
+    for name, child in module.named_children():
+        child_prefix = module_prefix + name + "."
+        set_lora_params(child, patches, child_prefix)
+    if isinstance(module, GGUFLinear):
+        key = "diffusion_model." + module_prefix + "weight"
+        patch = patches.get(key, [])
+        lora_diffs = lora_strengths = None
+        if len(patch) != 0:
+            lora_diffs = [p[1].weights for p in patch]
+            lora_strengths = [p[0] for p in patch]
+        module.lora_diffs = lora_diffs
+        module.lora_strengths = lora_strengths
+
 class GGUFLinear(nn.Linear):
     def __init__(
         self,

nodes.py

@@ -10,7 +10,7 @@ from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
 from .wanvideo.modules.model import rope_params
 from .fp8_optimization import convert_linear_with_lora_and_scale, remove_lora_from_module
 from .wanvideo.schedulers import get_scheduler, get_sampling_sigmas, retrieve_timesteps, scheduler_list
-
+from .gguf.gguf import set_lora_params
 from .multitalk.multitalk import timestep_transform, add_noise
 from .utils import log, print_memory, apply_lora, clip_encode_image_tiled, fourier_filter, is_image_black, add_noise_to_reference_video, optimized_scale, find_closest_valid_dim
 from .cache_methods.cache_methods import cache_report
@@ -1278,12 +1278,16 @@ class WanVideoSampler:
         model = model.model
         transformer = model.diffusion_model
         dtype = model["dtype"]
+        gguf = model["gguf"]
         control_lora = model["control_lora"]
         transformer_options = patcher.model_options.get("transformer_options", None)
 
         if len(patcher.patches) != 0 and transformer_options.get("linear_with_lora", False) is True:
             log.info(f"Using {len(patcher.patches)} LoRA weight patches for WanVideo model")
-            convert_linear_with_lora_and_scale(transformer, patches=patcher.patches)
+            if not gguf:
+                convert_linear_with_lora_and_scale(transformer, patches=patcher.patches)
+            else:
+                set_lora_params(transformer, patcher.patches)
         else:
             log.info("Unloading all LoRAs")
             remove_lora_from_module(transformer)

nodes_model_loading.py

@@ -755,7 +755,8 @@ class WanVideoModelLoader:
                 raise ValueError("Quantization should be disabled when loading GGUF models.")
             quantization = "gguf"
             gguf = True
-            merge_loras = False
+            if merge_loras is True:
+                raise ValueError("GGUF models do not support LoRA merging, please disable merge_loras in the LoRA select node.")
 
                 
         manual_offloading = True
@@ -1223,6 +1224,7 @@ class WanVideoModelLoader:
         patcher.model["auto_cpu_offload"] = True if vram_management_args is not None else False
         patcher.model["control_lora"] = control_lora
         patcher.model["compile_args"] = compile_args
+        patcher.model["gguf"] = gguf
 
         if 'transformer_options' not in patcher.model_options:
             patcher.model_options['transformer_options'] = {}