update wan input params

diffsynth/pipelines/wan_video.py

@@ -30,6 +30,8 @@ class WanVideoPipeline(BasePipeline):
         self.dit: WanModel = None
         self.vae: WanVideoVAE = None
         self.model_names = ['text_encoder', 'dit', 'vae']
+        self.height_division_factor = 16
+        self.width_division_factor = 16
 
 
     def enable_vram_management(self, num_persistent_param_in_dit=None):
@@ -202,17 +204,24 @@ class WanVideoPipeline(BasePipeline):
         num_frames=81,
         cfg_scale=5.0,
         num_inference_steps=50,
+        sigma_shift=5.0,
         tiled=True,
         tile_size=(30, 52),
         tile_stride=(15, 26),
         progress_bar_cmd=tqdm,
         progress_bar_st=None,
     ):
+        # Parameter check
+        height, width = self.check_resize_height_width(height, width)
+        if num_frames % 4 != 1:
+            num_frames = (num_frames + 2) // 4 * 4 + 1
+            print(f"Only `num_frames % 4 != 1` is acceptable. We round it up to {num_frames}.")
+        
         # Tiler parameters
         tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride}
 
         # Scheduler
-        self.scheduler.set_timesteps(num_inference_steps, denoising_strength)
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength, shift=sigma_shift)
 
         # Initialize noise
         noise = self.generate_noise((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), seed=seed, device=rand_device, dtype=torch.float32).to(self.device)

diffsynth/schedulers/flow_match.py

@@ -15,7 +15,9 @@ class FlowMatchScheduler():
         self.set_timesteps(num_inference_steps)
 
 
-    def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0, training=False):
+    def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0, training=False, shift=None):
+        if shift is not None:
+            self.shift = shift
         sigma_start = self.sigma_min + (self.sigma_max - self.sigma_min) * denoising_strength
         if self.extra_one_step:
             self.sigmas = torch.linspace(sigma_start, self.sigma_min, num_inference_steps + 1)[:-1]

diffsynth/trainers/text_to_image.py

@@ -278,7 +278,7 @@ def launch_training_task(model, args):
         strategy=args.training_strategy,
         default_root_dir=args.output_path,
         accumulate_grad_batches=args.accumulate_grad_batches,
-        callbacks=[pl.pytorch.callbacks.ModelCheckpoint(save_top_k=-1)],
+        callbacks=[pl.pytorch.callbacks.ModelCheckpoint(save_top_k=-1)]
     )
     trainer.fit(model=model, train_dataloaders=train_loader)