Make start/end step work with vid2vid

nodes.py

@@ -1336,8 +1336,6 @@ class WanVideoSampler:
         multitalk_sampling = image_embeds.get("multitalk_sampling", False)
         if not multitalk_sampling and scheduler == "multitalk":
             raise Exception("multitalk scheduler is only for multitalk sampling when using ImagetoVideoMultiTalk -node")
-        
-        steps = int(steps/denoise_strength)
 
         if text_embeds == None:
             text_embeds = {
@@ -1347,30 +1345,42 @@ class WanVideoSampler:
         else:
             text_embeds = dict_to_device(text_embeds, device)
 
-        if isinstance(cfg, list):
-            if steps != len(cfg):
-                log.info(f"Received {len(cfg)} cfg values, but only {steps} steps. Setting step count to match.")
-                steps = len(cfg)
-        else:
-            cfg = [cfg] * (steps +1)
-
         seed_g = torch.Generator(device=torch.device("cpu"))
         seed_g.manual_seed(seed)
 
-        # Scheduler
+        #region Scheduler
         if scheduler != "multitalk":
             sample_scheduler, timesteps = get_scheduler(scheduler, steps, shift, device, transformer.dim, flowedit_args, denoise_strength, sigmas=sigmas)
         else:
             timesteps = torch.tensor([1000, 750, 500, 250], device=device)
         log.info(f"sigmas: {sample_scheduler.sigmas}")
+        
+        steps = len(timesteps)
 
-        if end_step != -1 or end_step >= steps:
+        if denoise_strength < 1.0:
+            start_step = steps - int(steps * denoise_strength) - 1
+
+        first_sampler = (end_step != -1 or end_step >= steps)
+        add_noise_to_samples = True if first_sampler else False
+
+        if isinstance(cfg, list):
+            if steps != len(cfg):
+                log.info(f"Received {len(cfg)} cfg values, but only {steps} steps. Setting step count to match.")
+                steps = len(cfg)
+        else:
+            cfg = [cfg] * (steps + 1)
+
+        if first_sampler:
             timesteps = timesteps[:end_step]
             sample_scheduler.sigmas = sample_scheduler.sigmas[:end_step+1]
-        elif start_step > 0:
+            log.info(f"Sampling until step {end_step}, timestep: {timesteps[-1]}")
+        if start_step > 0:
             timesteps = timesteps[start_step:]
             sample_scheduler.sigmas = sample_scheduler.sigmas[start_step:]
+            log.info(f"Skipping first {start_step} steps, starting from timestep {timesteps[0]}")
 
+        log.info(f"timesteps: {timesteps}")
+        
         if hasattr(sample_scheduler, 'timesteps'):
             sample_scheduler.timesteps = timesteps
 
@@ -1379,10 +1389,6 @@ class WanVideoSampler:
         for arg in list(scheduler_step_args.keys()):
             if arg not in step_sig.parameters:
                 scheduler_step_args.pop(arg)
-        
-        if denoise_strength < 1.0:
-            steps = int(steps * denoise_strength)
-            timesteps = timesteps[-(steps + 1):] 
        
         control_latents = control_camera_latents = clip_fea = clip_fea_neg = end_image = recammaster = camera_embed = unianim_data = None
         vace_data = vace_context = vace_scale = None
@@ -1698,7 +1704,7 @@ class WanVideoSampler:
             if input_samples.shape[1] != noise.shape[1]:
                input_samples = torch.cat([input_samples[:, :1].repeat(1, noise.shape[1] - input_samples.shape[1], 1, 1), input_samples], dim=1)
             
-            if denoise_strength < 1.0:
+            if add_noise_to_samples:
                latent_timestep = timesteps[:1].to(noise)
                noise = noise * latent_timestep / 1000 + (1 - latent_timestep / 1000) * input_samples
             else:

nodes_model_loading.py

@@ -661,14 +661,12 @@ class WanVideoSetLoRAs:
         
         patcher = model.clone()
         
-        lora_low_mem_load = merge_loras = False
+        merge_loras = False
         for l in lora:
-            lora_low_mem_load = l.get("low_mem_load", False)
             merge_loras = l.get("merge_loras", True)
-        if lora_low_mem_load is True or merge_loras is True:
-            raise ValueError("Set LoRA node does not use low_mem_load and can't merge LoRAs, disable low_mem_load when and merge_loras in the LoRA select node.")
+        if merge_loras is True:
+            raise ValueError("Set LoRA node does not use low_mem_load and can't merge LoRAs, disable 'merge_loras' in the LoRA select node.")
 
-        
         for l in lora:
             log.info(f"Loading LoRA: {l['name']} with strength: {l['strength']}")
             lora_path = l["path"]
@@ -1241,7 +1239,7 @@ class WanVideoModelLoader:
 
         for model in mm.current_loaded_models:
             if model._model() == patcher:
-                mm.current_loaded_models.remove(model)            
+                mm.current_loaded_models.remove(model)
 
         return (patcher,)