Merge pull request #95 from ModelTC/dev_FIX

Fix bugs

app/gradio_demo.py

@@ -111,6 +111,12 @@ def is_fp8_supported_gpu():
     return (major == 8 and minor == 9) or (major >= 9)
 
 
+def update_precision_mode(dit_quant_scheme):
+    if dit_quant_scheme != "bf16":
+        return "bf16"
+    return "fp32"
+
+
 global_runner = None
 current_config = None
 
@@ -261,19 +267,15 @@ def run_inference(
     is_dit_quant = dit_quant_scheme != "bf16"
     is_t5_quant = t5_quant_scheme != "bf16"
     if is_t5_quant:
-        if t5_quant_scheme == "int8":
-            t5_quant_ckpt = os.path.join(model_path, "models_t5_umt5-xxl-enc-int8.pth")
-        else:
-            t5_quant_ckpt = os.path.join(model_path, "models_t5_umt5-xxl-enc-fp8.pth")
+        t5_path = os.path.join(model_path, t5_quant_scheme)
+        t5_quant_ckpt = os.path.join(t5_path, f"models_t5_umt5-xxl-enc-{t5_quant_scheme}.pth")
     else:
         t5_quant_ckpt = None
 
     is_clip_quant = clip_quant_scheme != "fp16"
     if is_clip_quant:
-        if clip_quant_scheme == "int8":
-            clip_quant_ckpt = os.path.join(model_path, "clip-int8.pth")
-        else:
-            clip_quant_ckpt = os.path.join(model_path, "clip-fp8.pth")
+        clip_path = os.path.join(model_path, clip_quant_scheme)
+        clip_quant_ckpt = os.path.join(clip_path, f"clip-{clip_quant_scheme}.pth")
     else:
         clip_quant_ckpt = None
 
@@ -297,16 +299,22 @@ def run_inference(
             mm_type = f"W-{dit_quant_scheme}-channel-sym-A-{dit_quant_scheme}-channel-sym-dynamic-Q8F"
 
         dit_quantized_ckpt = os.path.join(model_path, dit_quant_scheme)
+        if os.path.exists(os.path.join(dit_quantized_ckpt, "config.json")):
+            with open(os.path.join(dit_quantized_ckpt, "config.json"), "r") as f:
+                quant_model_config = json.load(f)
     else:
         mm_type = "Default"
         dit_quantized_ckpt = None
+        quant_model_config = {}
 
     config = {
         "infer_steps": infer_steps,
         "target_video_length": num_frames,
         "target_width": int(resolution.split("x")[0]),
         "target_height": int(resolution.split("x")[1]),
-        "attention_type": attention_type,
+        "self_attn_1_type": attention_type,
+        "cross_attn_1_type": attention_type,
+        "cross_attn_2_type": attention_type,
         "seed": seed,
         "enable_cfg": enable_cfg,
         "sample_guide_scale": cfg_scale,
@@ -364,6 +372,7 @@ def run_inference(
     config = EasyDict(config)
     config["mode"] = "infer"
     config.update(model_config)
+    config.update(quant_model_config)
 
     logger.info(f"Using model: {model_path}")
     logger.info(f"Inference configuration:\n{json.dumps(config, indent=4, ensure_ascii=False)}")
@@ -588,13 +597,7 @@ def auto_configure(enable_auto_config, model_type, resolution):
             (32, {"dit_quant_scheme_val": quant_type, "lazy_load_val": True}),
             (
                 16,
-                {
-                    "dit_quant_scheme_val": quant_type,
-                    "t5_quant_scheme_val": quant_type,
-                    "clip_quant_scheme_val": quant_type,
-                    "lazy_load_val": True,
-                    "dit_quant_scheme_val": quant_type,
-                },
+                {"dit_quant_scheme_val": quant_type, "t5_quant_scheme_val": quant_type, "clip_quant_scheme_val": quant_type, "lazy_load_val": True},
             ),
         ]
 
@@ -784,7 +787,7 @@ def main():
                             elem_classes=["output-video"],
                         )
 
-                infer_btn = gr.Button("Generate Video", variant="primary", size="lg")
+                        infer_btn = gr.Button("Generate Video", variant="primary", size="lg")
 
             with gr.Tab("⚙️ Advanced Options", id=2):
                 with gr.Group(elem_classes="advanced-options"):
@@ -894,10 +897,10 @@ def main():
                             info="Quantization precision for the Clip Encoder",
                         )
                         precision_mode = gr.Dropdown(
-                            label="Precision Mode",
+                            label="Precision Mode for Sensitive Layers",
                             choices=["fp32", "bf16"],
                             value="fp32",
-                            info="Select the numerical precision used for sensitive layers.",
+                            info="Select the numerical precision for critical model components like normalization and embedding layers. FP32 offers higher accuracy, while BF16 improves performance on compatible hardware.",
                         )
 
                     gr.Markdown("### Variational Autoencoder (VAE)")
@@ -960,6 +963,8 @@ def main():
                     ],
                 )
 
+                dit_quant_scheme.change(fn=update_precision_mode, inputs=[dit_quant_scheme], outputs=[precision_mode])
+
         infer_btn.click(
             fn=run_inference,
             inputs=[

app/gradio_demo_zh.py

@@ -112,6 +112,12 @@ def is_fp8_supported_gpu():
     return (major == 8 and minor == 9) or (major >= 9)
 
 
+def update_precision_mode(dit_quant_scheme):
+    if dit_quant_scheme != "bf16":
+        return "bf16"
+    return "fp32"
+
+
 global_runner = None
 current_config = None
 
@@ -262,19 +268,15 @@ def run_inference(
     is_dit_quant = dit_quant_scheme != "bf16"
     is_t5_quant = t5_quant_scheme != "bf16"
     if is_t5_quant:
-        if t5_quant_scheme == "int8":
-            t5_quant_ckpt = os.path.join(model_path, "models_t5_umt5-xxl-enc-int8.pth")
-        else:
-            t5_quant_ckpt = os.path.join(model_path, "models_t5_umt5-xxl-enc-fp8.pth")
+        t5_path = os.path.join(model_path, t5_quant_scheme)
+        t5_quant_ckpt = os.path.join(t5_path, f"models_t5_umt5-xxl-enc-{t5_quant_scheme}.pth")
     else:
         t5_quant_ckpt = None
 
     is_clip_quant = clip_quant_scheme != "fp16"
     if is_clip_quant:
-        if clip_quant_scheme == "int8":
-            clip_quant_ckpt = os.path.join(model_path, "clip-int8.pth")
-        else:
-            clip_quant_ckpt = os.path.join(model_path, "clip-fp8.pth")
+        clip_path = os.path.join(model_path, clip_quant_scheme)
+        clip_quant_ckpt = os.path.join(clip_path, f"clip-{clip_quant_scheme}.pth")
     else:
         clip_quant_ckpt = None
 
@@ -298,16 +300,22 @@ def run_inference(
             mm_type = f"W-{dit_quant_scheme}-channel-sym-A-{dit_quant_scheme}-channel-sym-dynamic-Q8F"
 
         dit_quantized_ckpt = os.path.join(model_path, dit_quant_scheme)
+        if os.path.exists(os.path.join(dit_quantized_ckpt, "config.json")):
+            with open(os.path.join(dit_quantized_ckpt, "config.json"), "r") as f:
+                quant_model_config = json.load(f)
     else:
         mm_type = "Default"
         dit_quantized_ckpt = None
+        quant_model_config = {}
 
     config = {
         "infer_steps": infer_steps,
         "target_video_length": num_frames,
         "target_width": int(resolution.split("x")[0]),
         "target_height": int(resolution.split("x")[1]),
-        "attention_type": attention_type,
+        "self_attn_1_type": attention_type,
+        "cross_attn_1_type": attention_type,
+        "cross_attn_2_type": attention_type,
         "seed": seed,
         "enable_cfg": enable_cfg,
         "sample_guide_scale": cfg_scale,
@@ -365,6 +373,7 @@ def run_inference(
     config = EasyDict(config)
     config["mode"] = "infer"
     config.update(model_config)
+    config.update(quant_model_config)
 
     logger.info(f"使用模型: {model_path}")
     logger.info(f"推理配置:\n{json.dumps(config, indent=4, ensure_ascii=False)}")
@@ -588,13 +597,7 @@ def auto_configure(enable_auto_config, model_type, resolution):
             (32, {"dit_quant_scheme_val": quant_type, "lazy_load_val": True}),
             (
                 16,
-                {
-                    "dit_quant_scheme_val": quant_type,
-                    "t5_quant_scheme_val": quant_type,
-                    "clip_quant_scheme_val": quant_type,
-                    "lazy_load_val": True,
-                    "dit_quant_scheme_val": quant_type,
-                },
+                {"dit_quant_scheme_val": quant_type, "t5_quant_scheme_val": quant_type, "clip_quant_scheme_val": quant_type, "lazy_load_val": True},
             ),
         ]
 
@@ -784,7 +787,7 @@ def main():
                             elem_classes=["output-video"],
                         )
 
-                infer_btn = gr.Button("生成视频", variant="primary", size="lg")
+                        infer_btn = gr.Button("生成视频", variant="primary", size="lg")
 
             with gr.Tab("⚙️ 高级选项", id=2):
                 with gr.Group(elem_classes="advanced-options"):
@@ -894,10 +897,10 @@ def main():
                             info="Clip编码器的推理精度",
                         )
                         precision_mode = gr.Dropdown(
-                            label="精度模式",
+                            label="敏感层精度",
                             choices=["fp32", "bf16"],
                             value="fp32",
-                            info="部分敏感层的推理精度。",
+                            info="选择用于敏感层（如norm层和embedding层）的数值精度",
                         )
 
                     gr.Markdown("### 变分自编码器(VAE)")
@@ -960,6 +963,8 @@ def main():
                     ],
                 )
 
+                dit_quant_scheme.change(fn=update_precision_mode, inputs=[dit_quant_scheme], outputs=[precision_mode])
+
         infer_btn.click(
             fn=run_inference,
             inputs=[

app/run_gradio.sh

 #!/bin/bash
 
-lightx2v_path=/path/to/lightx2v
-model_path=/path/to/wan
+lightx2v_path=/mtc/gushiqiao/llmc_workspace/lightx2v_new/lightx2v
+model_path=/data/nvme0/gushiqiao/models/Wan2.1-I2V-14B-480P-Lightx2v
 
-export CUDA_VISIBLE_DEVICES=0
+export CUDA_VISIBLE_DEVICES=7
 export CUDA_LAUNCH_BLOCKING=1
 export PYTHONPATH=${lightx2v_path}:$PYTHONPATH
 export ENABLE_PROFILING_DEBUG=true

configs/quantization/advanced_ptq/wan_i2v.json

-{
-    "infer_steps": 50,
-    "target_video_length": 81,
-    "text_len": 512,
-    "target_height": 480,
-    "target_width": 832,
-    "self_attn_1_type": "flash_attn3",
-    "cross_attn_1_type": "flash_attn3",
-    "cross_attn_2_type": "flash_attn3",
-    "seed": 42,
-    "sample_guide_scale": 5,
-    "sample_shift": 5,
-    "enable_cfg": true,
-    "cpu_offload": false,
-    "mm_config": {
-        "mm_type": "W-int8-channel-sym-A-int8-channel-sym-dynamic-Vllm",
-        "quant_method": "smoothquant"
-    },
-    "dit_quantized_ckpt": "/path/to/dit_int8"
-}

lightx2v/attentions/common/radial_attn.py

 import torch
-import flashinfer
+
+try:
+    import flashinfer
+except ImportError:
+    flashinfer = None
 
 ###
 ###  Code from radial-attention

lightx2v/common/ops/attn/attn_weight.py

@@ -131,7 +131,18 @@ class SageAttn2Weight(AttnWeightTemplate):
     def __init__(self):
         self.config = {}
 
-    def apply(self, q, k, v, cu_seqlens_q=None, cu_seqlens_kv=None, max_seqlen_q=None, max_seqlen_kv=None, model_cls=None):
+    def apply(
+        self,
+        q,
+        k,
+        v,
+        cu_seqlens_q=None,
+        cu_seqlens_kv=None,
+        max_seqlen_q=None,
+        max_seqlen_kv=None,
+        model_cls=None,
+        mask_map=None,
+    ):
         q, k, v = q.contiguous(), k.contiguous(), v.contiguous()
         if model_cls == "hunyuan":
             x1 = sageattn(

lightx2v/models/networks/wan/model.py

@@ -120,7 +120,7 @@ class WanModel:
     def _load_quant_split_ckpt(self, use_bf16, skip_bf16):
         lazy_load_model_path = self.config.dit_quantized_ckpt
         logger.info(f"Loading splited quant model from {lazy_load_model_path}")
-        pre_post_weight_dict, transformer_weight_dict = {}, {}
+        pre_post_weight_dict = {}
 
         safetensor_path = os.path.join(lazy_load_model_path, "non_block.safetensors")
         with safe_open(safetensor_path, framework="pt", device="cpu") as f:

lightx2v/models/networks/wan/weights/transformer_weights.py

@@ -32,7 +32,7 @@ class WanTransformerAttentionBlock(WeightModule):
         self.mm_type = mm_type
         self.task = task
         self.config = config
-        self.quant_method = config["mm_config"].get("quant_method", None)
+        self.quant_method = config.get("quant_method", None)
         self.sparge = config.get("sparge", False)
 
         self.lazy_load = self.config.get("lazy_load", False)
@@ -89,7 +89,7 @@ class WanModulation(WeightModule):
         self.mm_type = mm_type
         self.task = task
         self.config = config
-        self.quant_method = config["mm_config"].get("quant_method", None)
+        self.quant_method = config.get("quant_method", None)
         self.sparge = config.get("sparge", False)
 
         self.lazy_load = lazy_load
@@ -112,7 +112,7 @@ class WanSelfAttention(WeightModule):
         self.mm_type = mm_type
         self.task = task
         self.config = config
-        self.quant_method = config["mm_config"].get("quant_method", None)
+        self.quant_method = config.get("quant_method", None)
         self.sparge = config.get("sparge", False)
 
         self.lazy_load = lazy_load
@@ -185,7 +185,7 @@ class WanSelfAttention(WeightModule):
             self.self_attn_1.load(sparge_ckpt)
         else:
             self.add_module("self_attn_1", ATTN_WEIGHT_REGISTER[self.config["self_attn_1_type"]]())
-        if self.quant_method in ["smoothquant", "awq"]:
+        if self.quant_method in ["advanced_ptq"]:
             self.add_module(
                 "smooth_norm1_weight",
                 TENSOR_REGISTER["Default"](
@@ -314,7 +314,7 @@ class WanFFN(WeightModule):
         self.mm_type = mm_type
         self.task = task
         self.config = config
-        self.quant_method = config["mm_config"].get("quant_method", None)
+        self.quant_method = config.get("quant_method", None)
         self.lazy_load = lazy_load
         self.lazy_load_file = lazy_load_file
 
@@ -342,7 +342,7 @@ class WanFFN(WeightModule):
             ),
         )
 
-        if self.quant_method in ["smoothquant", "awq"]:
+        if self.quant_method in ["advanced_ptq"]:
             self.add_module(
                 "smooth_norm2_weight",
                 TENSOR_REGISTER["Default"](

tools/convert/readme.md

@@ -10,7 +10,8 @@ Facilitates mutual conversion between diffusers architecture and lightx2v archit
 python converter.py \
        --source /Path/To/Wan-AI/Wan2.1-I2V-14B-480P \
        --output /Path/To/Wan2.1-I2V-14B-480P-Diffusers \
-       --direction forward
+       --direction forward \
+       --save_by_block
 ```
 
 ### Diffusers->Lightx2v
@@ -18,7 +19,8 @@ python converter.py \
 python converter.py \
        --source /Path/To/Wan-AI/Wan2.1-I2V-14B-480P-Diffusers \
        --output /Path/To/Wan2.1-I2V-14B-480P \
-       --direction backward
+       --direction backward \
+       --save_by_block
 ```
 
 
@@ -30,31 +32,32 @@ This tool supports converting fp32/fp16/bf16 model weights to INT8、FP8 type.
 
 ```bash
 python converter.py \
-    --quantized \
     --source /Path/To/Wan-AI/Wan2.1-I2V-14B-480P/ \
     --output /Path/To/output \
     --output_ext .safetensors \
     --output_name wan_int8 \
     --dtype torch.int8 \
-    --model_type wan_dit
+    --model_type wan_dit \
+    --quantized \
+    --save_by_block
 ```
 
 ```bash
 python converter.py \
-    --quantized \
     --source /Path/To/Wan-AI/Wan2.1-I2V-14B-480P/ \
     --output /Path/To/output \
     --output_ext .safetensors \
     --output_name wan_fp8 \
     --dtype torch.float8_e4m3fn \
-    --model_type wan_dit
+    --model_type wan_dit \
+    --quantized \
+    --save_by_block
 ```
 
 ### Wan DiT + LoRA
 
 ```bash
 python converter.py \
-    --quantized \
     --source /Path/To/Wan-AI/Wan2.1-T2V-14B/ \
     --output /Path/To/output \
     --output_ext .safetensors \
@@ -62,31 +65,33 @@ python converter.py \
     --dtype torch.int8 \
     --model_type wan_dit \
     --lora_path /Path/To/LoRA1/ /Path/To/LoRA2/ \
-    --lora_alpha 1.0 1.0
+    --lora_alpha 1.0 1.0 \
+    --quantized \
+    --save_by_block
 ```
 
 ### Hunyuan DIT
 
 ```bash
 python converter.py \
-    --quantized \
     --source /Path/To/hunyuan/lightx2v_format/i2v/ \
     --output /Path/To/output \
-    --output_ext .safetensors \
+    --output_ext ..safetensors \
     --output_name hunyuan_int8 \
     --dtype torch.int8 \
-    --model_type hunyuan_dit
+    --model_type hunyuan_dit \
+    --quantized
 ```
 
 ```bash
 python converter.py \
-    --quantized \
     --source /Path/To/hunyuan/lightx2v_format/i2v/ \
     --output /Path/To/output \
     --output_ext .safetensors \
     --output_name hunyuan_fp8 \
     --dtype torch.float8_e4m3fn \
-    --model_type hunyuan_dit
+    --model_type hunyuan_dit \
+    --quantized
 ```
 
 
@@ -94,24 +99,24 @@ python converter.py \
 
 ```bash
 python converter.py \
-    --quantized \
     --source /Path/To/Wan-AI/Wan2.1-I2V-14B-480P/models_t5_umt5-xxl-enc-bf16.pth \
     --output /Path/To/output \
     --output_ext .pth\
     --output_name models_t5_umt5-xxl-enc-int8 \
     --dtype torch.int8 \
-    --model_type wan_t5
+    --model_type wan_t5 \
+    --quantized
 ```
 
 ```bash
 python converter.py \
-    --quantized \
     --source /Path/To/Wan-AI/Wan2.1-I2V-14B-480P/models_t5_umt5-xxl-enc-bf16.pth \
     --output /Path/To/output \
     --output_ext .pth\
     --output_name models_t5_umt5-xxl-enc-fp8 \
     --dtype torch.float8_e4m3fn \
-    --model_type wan_t5
+    --model_type wan_t5 \
+    --quantized
 ```
 
 
@@ -120,21 +125,21 @@ python converter.py \
 ```bash
 python converter.py \
   --source /Path/To/Wan-AI/Wan2.1-I2V-14B-480P/models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth \
-  --quantized \
   --output /Path/To/output \
   --output_ext .pth \
-  --output_name clip_int8 \
+  --output_name clip-int8 \
   --dtype torch.int8 \
-  --model_type wan_clip
+  --model_type wan_clip \
+  --quantized
 
 ```
 ```bash
 python converter.py \
   --source /Path/To/Wan-AI/Wan2.1-I2V-14B-480P/models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth \
-  --quantized \
   --output /Path/To/output \
   --output_ext .pth \
-  --output_name clip_fp8 \
+  --output_name clip-fp8 \
   --dtype torch.float8_e4m3fn \
-  --model_type wan_clip
+  --model_type wan_clip \
+  --quantized
 ```

tools/convert/readme_zh.md

@@ -10,7 +10,8 @@
 python converter.py \
        --source /Path/To/Wan-AI/Wan2.1-I2V-14B-480P \
        --output /Path/To/Wan2.1-I2V-14B-480P-Diffusers \
-       --direction forward
+       --direction forward \
+       --save_by_block
 ```
 
 ### Diffusers->Lightx2v
@@ -18,7 +19,8 @@ python converter.py \
 python converter.py \
        --source /Path/To/Wan-AI/Wan2.1-I2V-14B-480P-Diffusers \
        --output /Path/To/Wan2.1-I2V-14B-480P \
-       --direction backward
+       --direction backward \
+       --save_by_block
 ```
 
 
@@ -30,31 +32,32 @@ python converter.py \
 
 ```bash
 python converter.py \
-    --quantized \
     --source /Path/To/Wan-AI/Wan2.1-I2V-14B-480P/ \
     --output /Path/To/output \
     --output_ext .safetensors \
     --output_name wan_int8 \
     --dtype torch.int8 \
-    --model_type wan_dit
+    --model_type wan_dit \
+    --quantized \
+    --save_by_block
 ```
 
 ```bash
 python converter.py \
-    --quantized \
     --source /Path/To/Wan-AI/Wan2.1-I2V-14B-480P/ \
     --output /Path/To/output \
     --output_ext .safetensors \
     --output_name wan_fp8 \
     --dtype torch.float8_e4m3fn \
-    --model_type wan_dit
+    --model_type wan_dit \
+    --quantized \
+    --save_by_block
 ```
 
 ### Wan DiT + LoRA
 
 ```bash
 python converter.py \
-    --quantized \
     --source /Path/To/Wan-AI/Wan2.1-T2V-14B/ \
     --output /Path/To/output \
     --output_ext .safetensors \
@@ -62,31 +65,33 @@ python converter.py \
     --dtype torch.int8 \
     --model_type wan_dit \
     --lora_path /Path/To/LoRA1/ /Path/To/LoRA2/ \
-    --lora_alpha 1.0 1.0
+    --lora_alpha 1.0 1.0 \
+    --quantized \
+    --save_by_block
 ```
 
 ### Hunyuan DIT
 
 ```bash
 python converter.py \
-    --quantized \
     --source /Path/To/hunyuan/lightx2v_format/i2v/ \
     --output /Path/To/output \
     --output_ext ..safetensors \
     --output_name hunyuan_int8 \
     --dtype torch.int8 \
-    --model_type hunyuan_dit
+    --model_type hunyuan_dit \
+    --quantized
 ```
 
 ```bash
 python converter.py \
-    --quantized \
     --source /Path/To/hunyuan/lightx2v_format/i2v/ \
     --output /Path/To/output \
     --output_ext .safetensors \
     --output_name hunyuan_fp8 \
     --dtype torch.float8_e4m3fn \
-    --model_type hunyuan_dit
+    --model_type hunyuan_dit \
+    --quantized
 ```
 
 
@@ -94,24 +99,24 @@ python converter.py \
 
 ```bash
 python converter.py \
-    --quantized \
     --source /Path/To/Wan-AI/Wan2.1-I2V-14B-480P/models_t5_umt5-xxl-enc-bf16.pth \
     --output /Path/To/output \
     --output_ext .pth\
     --output_name models_t5_umt5-xxl-enc-int8 \
     --dtype torch.int8 \
-    --model_type wan_t5
+    --model_type wan_t5 \
+    --quantized
 ```
 
 ```bash
 python converter.py \
-    --quantized \
     --source /Path/To/Wan-AI/Wan2.1-I2V-14B-480P/models_t5_umt5-xxl-enc-bf16.pth \
     --output /Path/To/output \
     --output_ext .pth\
     --output_name models_t5_umt5-xxl-enc-fp8 \
     --dtype torch.float8_e4m3fn \
-    --model_type wan_t5
+    --model_type wan_t5 \
+    --quantized
 ```
 
 
@@ -120,21 +125,21 @@ python converter.py \
 ```bash
 python converter.py \
   --source /Path/To/Wan-AI/Wan2.1-I2V-14B-480P/models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth \
-  --quantized \
   --output /Path/To/output \
   --output_ext .pth \
-  --output_name clip_int8 \
+  --output_name clip-int8 \
   --dtype torch.int8 \
-  --model_type wan_clip
+  --model_type wan_clip \
+  --quantized
 
 ```
 ```bash
 python converter.py \
   --source /Path/To/Wan-AI/Wan2.1-I2V-14B-480P/models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth \
-  --quantized \
   --output /Path/To/output \
   --output_ext .pth \
-  --output_name clip_fp8 \
+  --output_name clip-fp8 \
   --dtype torch.float8_e4m3fn \
-  --model_type wan_clip
+  --model_type wan_clip \
+  --quantized
 ```