update dynamic-cfg settings

lightx2v/models/networks/wan/infer/pre_infer.py

 import torch
+from diffusers.models.embeddings import TimestepEmbedding
 from .utils import rope_params, sinusoidal_embedding_1d, guidance_scale_embedding
 from lightx2v.utils.envs import *
 
@@ -64,8 +65,10 @@ class WanPreInfer:
         embed = sinusoidal_embedding_1d(self.freq_dim, t.flatten())
         if self.enable_dynamic_cfg:
             s = torch.tensor([self.cfg_scale], dtype=torch.float32).to(x.device)
-            cfg_embed = guidance_scale_embedding(s, embedding_dim=256, cfg_range=(1.0, 8.0), target_range=1000.0, dtype=torch.float32).type_as(x)
-            cfg_embed = weights.cfg_cond_proj.apply(cfg_embed)
+            cfg_embed = guidance_scale_embedding(s, embedding_dim=256, cfg_range=(1.0, 6.0), target_range=1000.0, dtype=torch.float32).type_as(x)
+            cfg_embed = weights.cfg_cond_proj_1.apply(cfg_embed)
+            cfg_embed = torch.nn.functional.silu(cfg_embed)
+            cfg_embed = weights.cfg_cond_proj_2.apply(cfg_embed)
             embed = embed + cfg_embed
         if GET_DTYPE() != "BF16":
             embed = weights.time_embedding_0.apply(embed.float())

lightx2v/models/networks/wan/infer/utils.py

@@ -172,7 +172,7 @@ def sinusoidal_embedding_1d(dim, position):
     return x
 
 
-def guidance_scale_embedding(w, embedding_dim=256, cfg_range=(0.0, 8.0), target_range=1000.0, dtype=torch.float32):
+def guidance_scale_embedding(w, embedding_dim=256, cfg_range=(1.0, 6.0), target_range=1000.0, dtype=torch.float32):
     """
     Args:
     timesteps: torch.Tensor: generate embedding vectors at these timesteps
@@ -184,6 +184,8 @@ def guidance_scale_embedding(w, embedding_dim=256, cfg_range=(0.0, 8.0), target_
     """
     assert len(w.shape) == 1
     cfg_min, cfg_max = cfg_range
+    w = torch.round(w)
+    w = torch.clamp(w, min=cfg_min, max=cfg_max)
     w = (w - cfg_min) / (cfg_max - cfg_min)  # [0, 1]
     w = w * target_range
     half_dim = embedding_dim // 2

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

@@ -59,6 +59,10 @@ class WanPreWeights(WeightModule):
 
         if config.model_cls == "wan2.1_distill" and config.get("enable_dynamic_cfg", False):
             self.add_module(
-                "cfg_cond_proj",
-                MM_WEIGHT_REGISTER["Default"]("cfg_cond_proj.weight", "cfg_cond_proj.bias"),
+                "cfg_cond_proj_1",
+                MM_WEIGHT_REGISTER["Default"]("guidance_embedding.linear_1.weight", "guidance_embedding.linear_1.bias"),
+            )
+            self.add_module(
+                "cfg_cond_proj_2",
+                MM_WEIGHT_REGISTER["Default"]("guidance_embedding.linear_2.weight", "guidance_embedding.linear_2.bias"),
             )

scripts/wan/run_wan_t2v_distill_4step_cfg_dynamic.sh

 #!/bin/bash
 
 # set path and first
-lightx2v_path="/data/lightx2v-dev/"
-model_path="/data/lightx2v-dev/Wan2.1-T2V-14B/"
+lightx2v_path=
+model_path=
 
 # check section
 if [ -z "${CUDA_VISIBLE_DEVICES}" ]; then