Add skip layer guidance

i2v_inference.py

@@ -413,6 +413,9 @@ def parse_args():
     parser.add_argument("--teacache", type=float, default=0.25, help="TeaCache multiplier, e.g. 0.5, 2.0, etc.")
     parser.add_argument("--teacache-start", type=float, default=0.1, help="Teacache start step percentage [0..100]")
     parser.add_argument("--seed", type=int, default=-1, help="Random seed. -1 means random each time.")
+    parser.add_argument("--slg-layers", type=str, default=None, help="Which layers to use for skip layer guidance")
+    parser.add_argument("--slg-start", type=float, default=0.0, help="Percentage in to start SLG")
+    parser.add_argument("--slg-end", type=float, default=1.0, help="Percentage in to end SLG")
 
     # LoRA usage
     parser.add_argument("--loras-choices", type=str, default="", help="Comma-separated list of chosen LoRA indices or preset names to load. Usually you only use the preset.")
@@ -540,6 +543,12 @@ def main():
     except:
         raise ValueError(f"Invalid resolution: '{resolution_str}'")
 
+    # Parse slg_layers from comma-separated string to a Python list of ints (or None if not provided)
+    if args.slg_layers:
+        slg_list = [int(x) for x in args.slg_layers.split(",")]
+    else:
+        slg_list = None
+
     # Additional checks (from your original code).
     if "480p" in args.transformer_file:
         # Then we cannot exceed certain area for 480p model
@@ -628,6 +637,10 @@ def main():
             callback=None,  # or define your own callback if you want
             enable_RIFLEx=enable_riflex,
             VAE_tile_size=VAE_tile_size,
+            joint_pass=slg_list is None,  # set if you want a small speed improvement without SLG
+            slg_layers=slg_list,
+            slg_start=args.slg_start,
+            slg_end=args.slg_end,
         )
     except Exception as e:
         offloadobj.unload_all()

wan/image2video.py

@@ -132,22 +132,25 @@ class WanI2V:
         self.sample_neg_prompt = config.sample_neg_prompt
 
     def generate(self,
-                input_prompt,
-                img,
-                max_area=720 * 1280,
-                frame_num=81,
-                shift=5.0,
-                sample_solver='unipc',
-                sampling_steps=40,
-                guide_scale=5.0,
-                n_prompt="",
-                seed=-1,
-                offload_model=True,
-                callback = None,
-                enable_RIFLEx = False,
-                VAE_tile_size= 0,
-                joint_pass = False,
-                ):
+        input_prompt,
+        img,
+        max_area=720 * 1280,
+        frame_num=81,
+        shift=5.0,
+        sample_solver='unipc',
+        sampling_steps=40,
+        guide_scale=5.0,
+        n_prompt="",
+        seed=-1,
+        offload_model=True,
+        callback = None,
+        enable_RIFLEx = False,
+        VAE_tile_size= 0,
+        joint_pass = False,
+        slg_layers = None,
+        slg_start = 0.0,
+        slg_end = 1.0,
+    ):
         r"""
         Generates video frames from input image and text prompt using diffusion process.
 
@@ -331,25 +334,42 @@ class WanI2V:
             callback(-1, None)
 
         for i, t in enumerate(tqdm(timesteps)):
+            slg_layers_local = None
+            if int(slg_start * sampling_steps) <= i < int(slg_end * sampling_steps):
+                slg_layers_local = slg_layers
+
             offload.set_step_no_for_lora(i)
             latent_model_input = [latent.to(self.device)]
             timestep = [t]
 
             timestep = torch.stack(timestep).to(self.device)
             if joint_pass:
+                if slg_layers is not None:
+                    raise ValueError('Can not use SLG and joint-pass')
                 noise_pred_cond, noise_pred_uncond = self.model(
                     latent_model_input, t=timestep, current_step=i, **arg_both)
                 if self._interrupt:
                     return None                
             else:
                 noise_pred_cond = self.model(
-                    latent_model_input, t=timestep, current_step=i, is_uncond = False, **arg_c)[0]
+                    latent_model_input,
+                    t=timestep,
+                    current_step=i,
+                    is_uncond=False,
+                    **arg_c,
+                )[0]
                 if self._interrupt:
                     return None                
                 if offload_model:
                     torch.cuda.empty_cache()
                 noise_pred_uncond = self.model(
-                    latent_model_input, t=timestep, current_step=i, is_uncond = True, **arg_null)[0]
+                    latent_model_input,
+                    t=timestep,
+                    current_step=i,
+                    is_uncond=True,
+                    slg_layers=slg_layers_local,
+                    **arg_null,
+                )[0]
                 if self._interrupt:
                     return None                
             del latent_model_input

wan/modules/model.py

@@ -716,7 +716,8 @@ class WanModel(ModelMixin, ConfigMixin):
         pipeline = None,
         current_step = 0,
         context2 = None,
-        is_uncond=False        
+        is_uncond=False,
+        slg_layers=None,
     ):
         r"""
         Forward pass through the diffusion model
@@ -843,7 +844,9 @@ class WanModel(ModelMixin, ConfigMixin):
                 # context=context,
                 context_lens=context_lens)
 
-            for block in self.blocks:
+            for block_idx, block in enumerate(self.blocks):
+                if slg_layers is not None and block_idx in slg_layers and is_uncond:
+                    continue
                 if pipeline._interrupt:
                     if joint_pass:
                         return None, None