fix VAE scaling (again)

nodes.py

@@ -298,7 +298,7 @@ class CogVideoTextEncode:
         
         embeds = clip.encode_from_tokens(tokens, return_pooled=False, return_dict=False)
 
-        if embeds.shape[1] > 226:
+        if embeds.shape[1] > max_tokens:
             raise ValueError(f"Prompt is too long, max tokens supported is {max_tokens} or less, got {embeds.shape[1]}")
         embeds *= strength
         if force_offload:
@@ -371,7 +371,7 @@ class CogVideoImageEncode:
         model_name = pipeline.get("model_name", "")
 
         if ("1.5" in model_name or "1_5" in model_name) and image.shape[0] == 1:
-            vae_scaling_factor = 1 / vae.config.scaling_factor
+            vae_scaling_factor = 1 #/ vae.config.scaling_factor
         else:
             vae_scaling_factor = vae.config.scaling_factor
         
@@ -599,16 +599,18 @@ class ToraEncodeTrajectory:
             vae.to(device)
 
         video_flow = vae.encode(video_flow).latent_dist.sample(generator) * vae.config.scaling_factor
+        log.info(f"video_flow shape after encoding: {video_flow.shape}") #torch.Size([1, 16, 4, 80, 80])
 
         if not pipeline["cpu_offloading"]:
             vae.to(offload_device)
-
+        #print("video_flow shape before traj_extractor: ", video_flow.shape) #torch.Size([1, 16, 4, 80, 80])
         video_flow_features = tora_model["traj_extractor"](video_flow.to(torch.float32))
         video_flow_features = torch.stack(video_flow_features)
+        #print("video_flow_features after traj_extractor: ", video_flow_features.shape) #torch.Size([42, 4, 128, 40, 40])
 
         video_flow_features = video_flow_features * strength
 
-        log.info(f"video_flow shape: {video_flow.shape}")
+        
 
         tora = {
             "video_flow_features" : video_flow_features,