update lora adapter

configs/distill/wan_i2v_distill_4step_cfg_lora_rank32.json

+{
+    "infer_steps": 4,
+    "target_video_length": 81,
+    "target_height": 480,
+    "target_width": 832,
+    "attention_type": "flash_attn3",
+    "seed": 42,
+    "sample_guide_scale": 5,
+    "sample_shift": 5,
+    "enable_cfg": false,
+    "cpu_offload": false,
+    "denoising_step_list": [999, 750, 500, 250],
+    "lora_path": [
+        "Wan2.1-T2V-14B/loras/Wan21_T2V_14B_lightx2v_cfg_step_distill_lora_rank32.safetensors"
+    ]
+}

configs/distill/wan_t2v_distill_4step_cfg_lora_rank32.json

+{
+    "infer_steps": 4,
+    "target_video_length": 81,
+    "text_len": 512,
+    "target_height": 480,
+    "target_width": 832,
+    "attention_type": "flash_attn3",
+    "seed": 42,
+    "sample_guide_scale": 6,
+    "sample_shift": 8,
+    "enable_cfg": false,
+    "cpu_offload": false,
+    "denoising_step_list": [999, 750, 500, 250],
+    "lora_path": [
+        "Wan2.1-T2V-14B/loras/Wan21_T2V_14B_lightx2v_cfg_step_distill_lora_rank32.safetensors"
+    ]
+}

lightx2v/models/networks/wan/causvid_model.py

@@ -31,7 +31,6 @@ class WanCausVidModel(WanModel):
         use_bfloat16 = GET_DTYPE() == "BF16"
         ckpt_path = os.path.join(self.model_path, "causal_model.pt")
         if not os.path.exists(ckpt_path):
-            # 文件不存在，调用父类的 _load_ckpt 方法
             return super()._load_ckpt(use_bf16, skip_bf16)
 
         weight_dict = torch.load(ckpt_path, map_location="cpu", weights_only=True)

lightx2v/models/networks/wan/distill_model.py

@@ -23,7 +23,6 @@ class WanDistillModel(WanModel):
     def _load_ckpt(self, use_bf16, skip_bf16):
         ckpt_path = os.path.join(self.model_path, "distill_model.pt")
         if not os.path.exists(ckpt_path):
-            # 文件不存在，调用父类的 _load_ckpt 方法
             return super()._load_ckpt(use_bf16, skip_bf16)
 
         weight_dict = torch.load(ckpt_path, map_location="cpu", weights_only=True)

lightx2v/models/networks/wan/lora_adapter.py

@@ -40,9 +40,6 @@ class WanLoraWrapper:
         if lora_name not in self.lora_metadata:
             logger.info(f"LoRA {lora_name} not found. Please load it first.")
 
-        if hasattr(self.model, "current_lora") and self.model.current_lora:
-            self.remove_lora()
-
         if not hasattr(self.model, "original_weight_dict"):
             logger.error("Model does not have 'original_weight_dict'. Cannot apply LoRA.")
             return False
@@ -52,21 +49,35 @@ class WanLoraWrapper:
         self._apply_lora_weights(weight_dict, lora_weights, alpha)
         self.model._init_weights(weight_dict)
 
-        self.model.current_lora = lora_name
         logger.info(f"Applied LoRA: {lora_name} with alpha={alpha}")
         return True
 
     @torch.no_grad()
     def _apply_lora_weights(self, weight_dict, lora_weights, alpha):
         lora_pairs = {}
+        lora_diffs = {}
         prefix = "diffusion_model."
 
+        def try_lora_pair(key, suffix_a, suffix_b, target_suffix):
+            if key.endswith(suffix_a):
+                base_name = key[len(prefix) :].replace(suffix_a, target_suffix)
+                pair_key = key.replace(suffix_a, suffix_b)
+                if pair_key in lora_weights:
+                    lora_pairs[base_name] = (key, pair_key)
+
+        def try_lora_diff(key, suffix, target_suffix):
+            if key.endswith(suffix):
+                base_name = key[len(prefix) :].replace(suffix, target_suffix)
+                lora_diffs[base_name] = key
+
         for key in lora_weights.keys():
-            if key.endswith("lora_A.weight") and key.startswith(prefix):
-                base_name = key[len(prefix) :].replace("lora_A.weight", "weight")
-                b_key = key.replace("lora_A.weight", "lora_B.weight")
-                if b_key in lora_weights:
-                    lora_pairs[base_name] = (key, b_key)
+            if not key.startswith(prefix):
+                continue
+
+            try_lora_pair(key, "lora_A.weight", "lora_B.weight", "weight")
+            try_lora_pair(key, "lora_down.weight", "lora_up.weight", "weight")
+            try_lora_diff(key, "diff", "weight")
+            try_lora_diff(key, "diff_b", "bias")
 
         applied_count = 0
         for name, param in weight_dict.items():
@@ -79,6 +90,14 @@ class WanLoraWrapper:
                 lora_B = lora_weights[name_lora_B].to(param.device, param.dtype)
                 param += torch.matmul(lora_B, lora_A) * alpha
                 applied_count += 1
+            elif name in lora_diffs:
+                if name not in self.override_dict:
+                    self.override_dict[name] = param.clone().cpu()
+
+                name_diff = lora_diffs[name]
+                lora_diff = lora_weights[name_diff].to(param.device, param.dtype)
+                param += lora_diff
+                applied_count += 1
 
         logger.info(f"Applied {applied_count} LoRA weight adjustments")
         if applied_count == 0:
@@ -88,30 +107,22 @@ class WanLoraWrapper:
 
     @torch.no_grad()
     def remove_lora(self):
-        if not self.model.current_lora:
-            logger.info("No LoRA currently applied")
-            return
-        logger.info(f"Removing LoRA {self.model.current_lora}...")
+        logger.info(f"Removing LoRA ...")
 
         restored_count = 0
         for k, v in self.override_dict.items():
             self.model.original_weight_dict[k] = v.to(self.model.device)
             restored_count += 1
 
-        logger.info(f"LoRA {self.model.current_lora} removed, restored {restored_count} weights")
+        logger.info(f"LoRA removed, restored {restored_count} weights")
 
         self.model._init_weights(self.model.original_weight_dict)
 
         torch.cuda.empty_cache()
         gc.collect()
 
-        if self.model.current_lora and self.model.current_lora in self.lora_metadata:
-            del self.lora_metadata[self.model.current_lora]
+        self.lora_metadata = {}
         self.override_dict = {}
-        self.model.current_lora = None
 
     def list_loaded_loras(self):
         return list(self.lora_metadata.keys())
-
-    def get_current_lora(self):
-        return self.model.current_lora

lightx2v/models/networks/wan/model.py

@@ -46,7 +46,6 @@ class WanModel:
         self._init_infer_class()
         self._init_weights()
         self._init_infer()
-        self.current_lora = None
 
         if config["parallel_attn_type"]:
             if config["parallel_attn_type"] == "ulysses":

lightx2v/models/runners/wan/wan_causvid_runner.py

@@ -12,6 +12,7 @@ from lightx2v.models.schedulers.wan.step_distill.scheduler import WanStepDistill
 from lightx2v.utils.profiler import ProfilingContext4Debug, ProfilingContext
 from lightx2v.models.input_encoders.hf.t5.model import T5EncoderModel
 from lightx2v.models.input_encoders.hf.xlm_roberta.model import CLIPModel
+from lightx2v.models.networks.wan.model import WanModel
 from lightx2v.models.networks.wan.causvid_model import WanCausVidModel
 from lightx2v.models.networks.wan.lora_adapter import WanLoraWrapper
 from lightx2v.models.video_encoders.hf.wan.vae import WanVAE
@@ -30,7 +31,20 @@ class WanCausVidRunner(WanRunner):
         self.num_fragments = self.model.config.num_fragments
 
     def load_transformer(self):
-        return WanCausVidModel(self.config.model_path, self.config, self.init_device)
+        if self.config.lora_path:
+            model = WanModel(
+                self.config.model_path,
+                self.config,
+                self.init_device,
+            )
+            lora_wrapper = WanLoraWrapper(model)
+            for lora_path in self.config.lora_path:
+                lora_name = lora_wrapper.load_lora(lora_path)
+                lora_wrapper.apply_lora(lora_name, self.config.strength_model)
+                logger.info(f"Loaded LoRA: {lora_name}")
+        else:
+            model = WanCausVidModel(self.config.model_path, self.config, self.init_device)
+        return model
 
     def set_inputs(self, inputs):
         super().set_inputs(inputs)

lightx2v/models/runners/wan/wan_distill_runner.py

@@ -24,12 +24,19 @@ class WanDistillRunner(WanRunner):
         super().__init__(config)
 
     def load_transformer(self):
-        model = WanDistillModel(self.config.model_path, self.config, self.init_device)
         if self.config.lora_path:
+            model = WanModel(
+                self.config.model_path,
+                self.config,
+                self.init_device,
+            )
             lora_wrapper = WanLoraWrapper(model)
-            lora_name = lora_wrapper.load_lora(self.config.lora_path)
-            lora_wrapper.apply_lora(lora_name, self.config.strength_model)
-            logger.info(f"Loaded LoRA: {lora_name}")
+            for lora_path in self.config.lora_path:
+                lora_name = lora_wrapper.load_lora(lora_path)
+                lora_wrapper.apply_lora(lora_name, self.config.strength_model)
+                logger.info(f"Loaded LoRA: {lora_name}")
+        else:
+            model = WanDistillModel(self.config.model_path, self.config, self.init_device)
         return model
 
     def init_scheduler(self):

lightx2v/models/runners/wan/wan_runner.py

@@ -38,9 +38,10 @@ class WanRunner(DefaultRunner):
         if self.config.lora_path:
             assert not self.config.get("dit_quantized", False) or self.config.mm_config.get("weight_auto_quant", False)
             lora_wrapper = WanLoraWrapper(model)
-            lora_name = lora_wrapper.load_lora(self.config.lora_path)
-            lora_wrapper.apply_lora(lora_name, self.config.strength_model)
-            logger.info(f"Loaded LoRA: {lora_name}")
+            for lora_path in self.config.lora_path:
+                lora_name = lora_wrapper.load_lora(lora_path)
+                lora_wrapper.apply_lora(lora_name, self.config.strength_model)
+                logger.info(f"Loaded LoRA: {lora_name}")
         return model
 
     def load_image_encoder(self):

scripts/wan/run_wan_i2v_distill.sh → scripts/wan/run_wan_i2v_distill_4step_cfg.sh

@@ -33,7 +33,7 @@ python -m lightx2v.infer \
 --model_cls wan2.1_distill \
 --task i2v \
 --model_path $model_path \
---config_json ${lightx2v_path}/configs/distill/wan_i2v_distill.json \
+--config_json ${lightx2v_path}/configs/distill/wan_i2v_distill_4step_cfg.json \
 --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
 --negative_prompt "镜头晃动，色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" \
 --image_path ${lightx2v_path}/assets/inputs/imgs/img_0.jpg \

scripts/wan/run_wan_i2v_distill_4step_cfg_lora_rank32.sh

+#!/bin/bash
+
+# set path and first
+lightx2v_path=
+model_path=
+
+# check section
+if [ -z "${CUDA_VISIBLE_DEVICES}" ]; then
+    cuda_devices=0
+    echo "Warn: CUDA_VISIBLE_DEVICES is not set, using default value: ${cuda_devices}, change at shell script or set env variable."
+    export CUDA_VISIBLE_DEVICES=${cuda_devices}
+fi
+
+if [ -z "${lightx2v_path}" ]; then
+    echo "Error: lightx2v_path is not set. Please set this variable first."
+    exit 1
+fi
+
+if [ -z "${model_path}" ]; then
+    echo "Error: model_path is not set. Please set this variable first."
+    exit 1
+fi
+
+export TOKENIZERS_PARALLELISM=false
+
+export PYTHONPATH=${lightx2v_path}:$PYTHONPATH
+
+export ENABLE_PROFILING_DEBUG=true
+export ENABLE_GRAPH_MODE=false
+export DTYPE=BF16
+
+python -m lightx2v.infer \
+--model_cls wan2.1_distill \
+--task i2v \
+--model_path $model_path \
+--config_json ${lightx2v_path}/configs/distill/wan_i2v_distill_4step_cfg_lora_rank32.json \
+--prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
+--negative_prompt "镜头晃动，色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" \
+--image_path ${lightx2v_path}/assets/inputs/imgs/img_0.jpg \
+--save_video_path ${lightx2v_path}/save_results/output_lightx2v_wan_i2v_distill.mp4

scripts/wan/run_wan_t2v_distill.sh → scripts/wan/run_wan_t2v_distill_4step_cfg.sh

 #!/bin/bash
 
 # set path and first
-lightx2v_path=
-model_path=
+lightx2v_path="/data/lightx2v-dev"
+model_path="/data/lightx2v-dev/Wan2.1-T2V-14B/"
 
 # check section
 if [ -z "${CUDA_VISIBLE_DEVICES}" ]; then
@@ -32,7 +32,7 @@ python -m lightx2v.infer \
 --model_cls wan2.1_distill \
 --task t2v \
 --model_path $model_path \
---config_json ${lightx2v_path}/configs/distill/wan_t2v_distill.json \
+--config_json ${lightx2v_path}/configs/distill/wan_t2v_distill_4step_cfg.json \
 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." \
 --use_prompt_enhancer \
 --negative_prompt "镜头晃动，色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" \

scripts/wan/run_wan_t2v_distill_4step_cfg_lora_rank32.sh

+#!/bin/bash
+
+# set path and first
+lightx2v_path="/data/lightx2v-dev"
+model_path="/data/lightx2v-dev/Wan2.1-T2V-14B/"
+
+# check section
+if [ -z "${CUDA_VISIBLE_DEVICES}" ]; then
+    cuda_devices=0
+    echo "Warn: CUDA_VISIBLE_DEVICES is not set, using default value: ${cuda_devices}, change at shell script or set env variable."
+    export CUDA_VISIBLE_DEVICES=${cuda_devices}
+fi
+
+if [ -z "${lightx2v_path}" ]; then
+    echo "Error: lightx2v_path is not set. Please set this variable first."
+    exit 1
+fi
+
+if [ -z "${model_path}" ]; then
+    echo "Error: model_path is not set. Please set this variable first."
+    exit 1
+fi
+
+export TOKENIZERS_PARALLELISM=false
+
+export PYTHONPATH=${lightx2v_path}:$PYTHONPATH
+export DTYPE=BF16
+export ENABLE_PROFILING_DEBUG=true
+export ENABLE_GRAPH_MODE=false
+
+python -m lightx2v.infer \
+--model_cls wan2.1_distill \
+--task t2v \
+--model_path $model_path \
+--config_json ${lightx2v_path}/configs/distill/wan_t2v_distill_4step_cfg_lora_rank32.json \
+--prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." \
+--use_prompt_enhancer \
+--negative_prompt "镜头晃动，色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" \
+--save_video_path ${lightx2v_path}/save_results/output_lightx2v_wan_t2v.mp4