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src/hook.py

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+import os.path
+
+import ipdb
+from peft import set_peft_model_state_dict,get_peft_model_state_dict
+from diffusers import FluxPipeline
+from diffusers.training_utils import cast_training_params
+
+def save_model_hook(models, weights, output_dir,wanted_model, accelerator,adapter_names):
+    if accelerator.is_main_process:
+        transformer_lora_layers_to_save = None
+        for model in models:
+            if isinstance(model, type(accelerator.unwrap_model(wanted_model))):
+                transformer_lora_layers_to_save = {adapter_name: get_peft_model_state_dict(model,adapter_name=adapter_name) for adapter_name in adapter_names}
+            else:
+                raise ValueError(f"unexpected save model: {model.__class__}")
+
+            # make sure to pop weight so that corresponding model is not saved again
+            if weights:
+                weights.pop()
+        for adapter_name,lora in transformer_lora_layers_to_save.items():
+            FluxPipeline.save_lora_weights(
+                os.path.join(output_dir,adapter_name),
+                transformer_lora_layers=lora,
+            )
+
+
+def load_model_hook(models, input_dir,wanted_model, accelerator,adapter_names):
+    transformer_ = None
+    while len(models) > 0:
+        model = models.pop()
+        if isinstance(model, type(accelerator.unwrap_model(wanted_model))):
+            transformer_ = model
+        else:
+            raise ValueError(f"unexpected save model: {model.__class__}")
+
+    lora_state_dict_list = []
+    for adapter_name in adapter_names:
+        lora_path = os.path.join(input_dir,adapter_name)
+        lora_state_dict_list.append(FluxPipeline.lora_state_dict(lora_path))
+    transformer_lora_state_dict_list = []
+    for lora_state_dict in lora_state_dict_list:
+        transformer_lora_state_dict_list.append({
+            f'{k.replace("transformer.", "")}': v
+            for k, v in lora_state_dict.items()
+            if k.startswith("transformer.") and "lora" in k
+        })
+    incompatible_keys = [set_peft_model_state_dict(transformer_, transformer_lora_state_dict_list[i], adapter_name=adapter_name) for i,adapter_name in enumerate(adapter_names)]
+    if incompatible_keys is not None:
+        # check only for unexpected keys
+        unexpected_keys = getattr(incompatible_keys, "unexpected_keys", None)
+        if unexpected_keys:
+            accelerator.warning(
+                f"Loading adapter weights from state_dict led to unexpected keys not found in the model: "
+                f" {unexpected_keys}. "
+            )
+
+    # Make sure the trainable params are in float32. This is again needed since the base models
+    # are in `weight_dtype`. More details:
+    # https://github.com/huggingface/diffusers/pull/6514#discussion_r1449796804
+    if accelerator.mixed_precision == "fp16":
+        models = [transformer_]
+        # only upcast trainable parameters (LoRA) into fp32
+        cast_training_params(models)