Convert wan lora weights on save to be something comfy can handle

toolkit/lora_special.py

@@ -20,9 +20,13 @@ sys.path.append(SD_SCRIPTS_ROOT)
 
 from networks.lora import LoRANetwork, get_block_index
 from toolkit.models.DoRA import DoRAModule
+from typing import TYPE_CHECKING
 
 from torch.utils.checkpoint import checkpoint
 
+if TYPE_CHECKING:
+    from toolkit.stable_diffusion_model import StableDiffusion
+
 RE_UPDOWN = re.compile(r"(up|down)_blocks_(\d+)_(resnets|upsamplers|downsamplers|attentions)_(\d+)_")
 
 
@@ -179,6 +183,7 @@ class LoRASpecialNetwork(ToolkitNetworkMixin, LoRANetwork):
             peft_format: bool = False,
             is_assistant_adapter: bool = False,
             is_transformer: bool = False,
+            base_model: 'StableDiffusion' = None,
             **kwargs
     ) -> None:
         """
@@ -204,6 +209,7 @@ class LoRASpecialNetwork(ToolkitNetworkMixin, LoRANetwork):
             ignore_if_contains = []
         self.ignore_if_contains = ignore_if_contains
         self.transformer_only = transformer_only
+        self.base_model_ref = weakref.ref(base_model)
 
         self.only_if_contains: Union[List, None] = only_if_contains
 

toolkit/models/base_model.py

@@ -1433,3 +1433,11 @@ class BaseModel:
                 encoder.to(*args, **kwargs)
         else:
             self.text_encoder.to(*args, **kwargs)
+    
+    def convert_lora_weights_before_save(self, state_dict):
+        # can be overridden in child classes to convert weights before saving
+        return state_dict
+    
+    def convert_lora_weights_before_load(self, state_dict):
+        # can be overridden in child classes to convert weights before loading
+        return state_dict

toolkit/models/wan21/__init__.py

@@ -0,0 +1 @@
+from .wan21 import Wan21

toolkit/models/wan21/wan21.py

@@ -42,6 +42,7 @@ from diffusers.pipelines.wan.pipeline_wan import XLA_AVAILABLE
 # from ...callbacks import MultiPipelineCallbacks, PipelineCallback
 from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
 from typing import Any, Callable, Dict, List, Optional, Union
+from toolkit.models.wan21.wan_lora_convert import convert_to_diffusers, convert_to_original
 
 # for generation only?
 scheduler_configUniPC = {
@@ -160,14 +161,14 @@ class AggressiveWanUnloadPipeline(WanPipeline):
         # unload text encoder
         print("Unloading text encoder")
         self.text_encoder.to("cpu")
-        
-        self.transformer.to(self._execution_device)
+
+        self.transformer.to(device)
 
         transformer_dtype = self.transformer.dtype
-        prompt_embeds = prompt_embeds.to(transformer_dtype)
+        prompt_embeds = prompt_embeds.to(device, transformer_dtype)
         if negative_prompt_embeds is not None:
             negative_prompt_embeds = negative_prompt_embeds.to(
-                transformer_dtype)
+                device, transformer_dtype)
 
         # 4. Prepare timesteps
         self.scheduler.set_timesteps(num_inference_steps, device=device)
@@ -198,7 +199,7 @@ class AggressiveWanUnloadPipeline(WanPipeline):
                     continue
 
                 self._current_timestep = t
-                latent_model_input = latents.to(transformer_dtype)
+                latent_model_input = latents.to(device, transformer_dtype)
                 timestep = t.expand(latents.shape[0])
 
                 noise_pred = self.transformer(
@@ -468,6 +469,8 @@ class Wan21(BaseModel):
                 scheduler=scheduler,
             )
 
+        pipeline = pipeline.to(self.device_torch)
+
         return pipeline
 
     def generate_single_image(
@@ -481,6 +484,7 @@ class Wan21(BaseModel):
     ):
         # reactivate progress bar since this is slooooow
         pipeline.set_progress_bar_config(disable=False)
+        pipeline = pipeline.to(self.device_torch)
         # todo, figure out how to do video
         output = pipeline(
             prompt_embeds=conditional_embeds.text_embeds.to(
@@ -619,3 +623,9 @@ class Wan21(BaseModel):
         if noise is None:
             raise ValueError("Noise is not provided")
         return (noise - batch.latents).detach()
+
+    def convert_lora_weights_before_save(self, state_dict):
+        return convert_to_original(state_dict)
+
+    def convert_lora_weights_before_load(self, state_dict):
+        return convert_to_diffusers(state_dict)

toolkit/models/wan21/wan_lora_convert.py

@@ -0,0 +1,65 @@
+def convert_to_diffusers(state_dict):
+    new_state_dict = {}
+    for key in state_dict:
+        new_key = key
+        # Base model name change
+        if key.startswith("diffusion_model."):
+            new_key = key.replace("diffusion_model.", "transformer.")
+
+        # Attention blocks conversion
+        if "self_attn" in new_key:
+            new_key = new_key.replace("self_attn", "attn1")
+        elif "cross_attn" in new_key:
+            new_key = new_key.replace("cross_attn", "attn2")
+
+        # Attention components conversion
+        parts = new_key.split(".")
+        for i, part in enumerate(parts):
+            if part in ["q", "k", "v"]:
+                parts[i] = f"to_{part}"
+            elif part == "o":
+                parts[i] = "to_out.0"
+        new_key = ".".join(parts)
+
+        # FFN conversion
+        if "ffn.0" in new_key:
+            new_key = new_key.replace("ffn.0", "ffn.net.0.proj")
+        elif "ffn.2" in new_key:
+            new_key = new_key.replace("ffn.2", "ffn.net.2")
+
+        new_state_dict[new_key] = state_dict[key]
+    return new_state_dict
+
+
+def convert_to_original(state_dict):
+    new_state_dict = {}
+    for key in state_dict:
+        new_key = key
+        # Base model name change
+        if key.startswith("transformer."):
+            new_key = key.replace("transformer.", "diffusion_model.")
+
+        # Attention blocks conversion
+        if "attn1" in new_key:
+            new_key = new_key.replace("attn1", "self_attn")
+        elif "attn2" in new_key:
+            new_key = new_key.replace("attn2", "cross_attn")
+
+        # Attention components conversion
+        if "to_out.0" in new_key:
+            new_key = new_key.replace("to_out.0", "o")
+        elif "to_q" in new_key:
+            new_key = new_key.replace("to_q", "q")
+        elif "to_k" in new_key:
+            new_key = new_key.replace("to_k", "k")
+        elif "to_v" in new_key:
+            new_key = new_key.replace("to_v", "v")
+
+        # FFN conversion
+        if "ffn.net.0.proj" in new_key:
+            new_key = new_key.replace("ffn.net.0.proj", "ffn.0")
+        elif "ffn.net.2" in new_key:
+            new_key = new_key.replace("ffn.net.2", "ffn.2")
+
+        new_state_dict[new_key] = state_dict[key]
+    return new_state_dict

toolkit/network_mixins.py

@@ -554,6 +554,8 @@ class ToolkitNetworkMixin:
                 new_save_dict[new_key] = value
 
             save_dict = new_save_dict
+        
+        save_dict = self.base_model_ref().convert_lora_weights_before_save(save_dict)
 
         if metadata is None:
             metadata = OrderedDict()
@@ -579,6 +581,8 @@ class ToolkitNetworkMixin:
         else:
             # probably a state dict
             weights_sd = file
+        
+        weights_sd = self.base_model_ref().convert_lora_weights_before_load(weights_sd)
 
         load_sd = OrderedDict()
         for key, value in weights_sd.items():

toolkit/stable_diffusion_model.py

@@ -3061,3 +3061,11 @@ class StableDiffusion:
                 encoder.to(*args, **kwargs)
         else:
             self.text_encoder.to(*args, **kwargs)
+            
+    def convert_lora_weights_before_save(self, state_dict):
+        # can be overridden in child classes to convert weights before saving
+        return state_dict
+    
+    def convert_lora_weights_before_load(self, state_dict):
+        # can be overridden in child classes to convert weights before loading
+        return state_dict