diff --git a/leco.py b/leco.py
index 87e295d..7f46827 100644
--- a/leco.py
+++ b/leco.py
@@ -131,7 +131,8 @@ def main(config):
 
     sampling_step = config.leco.sampling_step
     num_samples = config.leco.num_samples
-    noise_scheduler.set_timesteps(sampling_step, device=device)
+    if sampling_step > 0:
+        noise_scheduler.set_timesteps(sampling_step, device=device)
 
     generate_guidance_scale = config.leco.generate_guidance_scale
 
@@ -142,6 +143,7 @@ def main(config):
     dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=True, num_workers=0, collate_fn=collate_fn)
     
     del text_model
+    torch.cuda.empty_cache()
     
     total_steps = config.leco.epochs * len(dataloader)
     save_steps = config.leco.save_steps
@@ -178,19 +180,22 @@ def main(config):
                 if len(latents_and_times[idx]) == 0:
                     # x_T
                     latents = torch.randn(batch_size, 4, resolution, resolution, device=device, dtype=weight_dtype)
-    
-                    timestep_to = random.sample(range(sampling_step), num_samples) # tをnum_samples個サンプリング
-                    timestep_to.sort()
-                    timedelta = random.choice(range(1000//sampling_step-1)) # 全ステップ学習できるようちょっとずらす
-                    target_index = 0
-                    for i, t in tqdm(enumerate(noise_scheduler.timesteps[0:timestep_to[-1]+1])):
-                        timestep = t + timedelta
-                        latents_input = noise_scheduler.scale_model_input(latents, timestep)
-                        noise_pred = cfg(unet, latents_input, timestep, torch.cat([target, negative],dim=0), generate_guidance_scale, target_proj, negative_proj, size_condition)
-                        latents = noise_scheduler.step(noise_pred, timestep, latents, return_dict=False)[0]
-                        if i == timestep_to[target_index]:
-                            target_index += 1
-                            latents_and_times[idx].append((latents, timestep))
+                    if sampling_step > 0:
+                        timestep_to = random.sample(range(sampling_step), num_samples) # tをnum_samples個サンプリング
+                        timestep_to.sort()
+                        timedelta = random.choice(range(1000//sampling_step-1)) # 全ステップ学習できるようちょっとずらす
+                        target_index = 0
+                        for i, t in tqdm(enumerate(noise_scheduler.timesteps[0:timestep_to[-1]+1])):
+                            timestep = t + timedelta
+                            latents_input = noise_scheduler.scale_model_input(latents, timestep)
+                            noise_pred = cfg(unet, latents_input, timestep, torch.cat([target, negative],dim=0), generate_guidance_scale, target_proj, negative_proj, size_condition)
+                            latents = noise_scheduler.step(noise_pred, timestep, latents, return_dict=False)[0]
+                            if i == timestep_to[target_index]:
+                                target_index += 1
+                                latents_and_times[idx].append((latents, timestep))
+                    else:
+                        timestep = torch.tensor([999]).to(latents).repeat(batch_size)
+                        latents_and_times[idx].append((latents, timestep))
                             
             with torch.autocast("cuda", enabled=not config.train.amp == False): 
                 latents, timesteps = latents_and_times[idx].pop()