Fix Issue #43: Prevent ComfyUI-nunchaku bug by forcing guidance=None after conversion to additional_t_cond

- Force guidance to None in forward() and _execute_model() to prevent ComfyUI-nunchaku's buggy code path (line 726) from executing
- Preserve legacy guidance values from kwargs/transformer_options and convert to additional_t_cond when needed (backward compatibility)
- This prevents TypeError: QwenTimestepProjEmbeddings.forward() takes 3 positional arguments but 4 were given
- Similar defensive approach to v2.0.2-v2.0.8 guidance duplication fixes

wrappers/qwenimage.py

@@ -96,7 +96,10 @@ class ComfyQwenImageWrapper(nn.Module):
         # which could cause issues when passing to _execute_model
         # Note: ref_latents is NOT a parameter of forward(), so we keep it in kwargs for _execute_model to use
         if "guidance" in kwargs:
-            # Remove guidance from kwargs to avoid duplication (explicit parameter takes precedence)
+            # Preserve legacy guidance if it only arrives via kwargs (environment-dependent),
+            # then remove it to avoid duplication downstream.
+            if guidance is None:
+                guidance = kwargs.get("guidance")
             kwargs.pop("guidance")
         if "transformer_options" in kwargs:
             # Merge transformer_options from kwargs into the parameter if needed
@@ -104,6 +107,10 @@ class ComfyQwenImageWrapper(nn.Module):
                 transformer_options = {**transformer_options, **kwargs.pop("transformer_options")}
             else:
                 kwargs.pop("transformer_options")
+        # Preserve legacy guidance if it only arrives via transformer_options (environment-dependent).
+        # We will later remove it from transformer_options when actually calling the model.
+        if guidance is None and isinstance(transformer_options, dict) and "guidance" in transformer_options:
+            guidance = transformer_options.get("guidance")
         if "attention_mask" in kwargs:
             kwargs.pop("attention_mask")
         if isinstance(timestep, torch.Tensor):