diff --git a/nodes.py b/nodes.py
index 3740cf3..4bc3aa1 100644
--- a/nodes.py
+++ b/nodes.py
@@ -186,7 +186,7 @@ class STARModelLoader:
                 }),
                 "precision": (["fp16", "bf16", "fp32"], {
                     "default": "fp16",
-                    "tooltip": "Weight precision. fp16 is recommended (fastest, lowest VRAM). bf16 for newer GPUs. fp32 for maximum quality at 2x VRAM cost.",
+                    "tooltip": "Inference precision. fp16 is recommended (fastest, lowest VRAM). bf16 for newer GPUs. fp32 disables autocast for maximum quality at 2x VRAM cost.",
                 }),
                 "offload": (["disabled", "model", "aggressive"], {
                     "default": "disabled",
diff --git a/star_pipeline.py b/star_pipeline.py
index 953741f..48a039b 100644
--- a/star_pipeline.py
+++ b/star_pipeline.py
@@ -271,14 +271,8 @@ def run_star_inference(
 
     total_noise_levels = int(round(denoise * 1000))
 
-    def _dbg(name, t):
-        print(f"[STAR DEBUG] {name}: shape={list(t.shape)} dtype={t.dtype} "
-              f"min={t.min().item():.4f} max={t.max().item():.4f} "
-              f"mean={t.float().mean().item():.4f} std={t.float().std().item():.4f}")
-
     # -- Convert ComfyUI frames to STAR format --
     video_data = comfyui_to_star_frames(images)  # [F, 3, H, W]
-    _dbg("input_frames", video_data)
 
     # Keep a copy at input resolution (on CPU) for colour correction later
     input_frames_star = video_data.clone().cpu()
@@ -286,8 +280,6 @@ def run_star_inference(
     frames_num, _, orig_h, orig_w = video_data.shape
     target_h = orig_h * upscale
     target_w = orig_w * upscale
-    print(f"[STAR DEBUG] orig={orig_h}x{orig_w} target={target_h}x{target_w} "
-          f"frames={frames_num} noise_levels={total_noise_levels}")
 
     # -- Bilinear upscale to target resolution --
     video_data = F.interpolate(video_data, size=(target_h, target_w), mode="bilinear", align_corners=False)
@@ -296,7 +288,6 @@ def run_star_inference(
     # -- Pad to model-friendly resolution --
     padding = pad_to_fit(h, w)
     video_data = F.pad(video_data, padding, "constant", 1)
-    print(f"[STAR DEBUG] padded={video_data.shape[2]}x{video_data.shape[3]} padding={padding}")
 
     video_data = video_data.unsqueeze(0).to(device)  # [1, F, 3, H_pad, W_pad]
 
@@ -305,9 +296,7 @@ def run_star_inference(
         text_encoder.model.to(device)
         text_encoder.device = device
     text = ((prompt.strip() + " ") if prompt.strip() else "") + cfg.positive_prompt
-    print(f"[STAR DEBUG] prompt: {text[:80]}...")
     y = text_encoder(text).detach()
-    _dbg("text_embedding", y)
     if offload != "disabled":
         text_encoder.model.to("cpu")
         text_encoder.device = "cpu"
@@ -317,7 +306,6 @@ def run_star_inference(
     if offload != "disabled":
         _move(vae, device)
     video_data_feature = vae_encode(vae, video_data, chunk_size=vae_enc_chunk)
-    _dbg("vae_latent", video_data_feature)
     if offload != "disabled":
         _move(vae, "cpu")
     # Free the full-res pixel tensor — only latents needed from here.
@@ -325,10 +313,11 @@ def run_star_inference(
     torch.cuda.empty_cache()
 
     # -- Diffusion sampling + VAE decode (under autocast) --
-    with torch.amp.autocast("cuda"):
+    # Use the selected precision for autocast (fp16/bf16), or disable for fp32.
+    use_autocast = dtype != torch.float32
+    with torch.amp.autocast("cuda", dtype=dtype, enabled=use_autocast):
         t = torch.LongTensor([total_noise_levels - 1]).to(device)
         noised_lr = diffusion.diffuse(video_data_feature, t)
-        _dbg("noised_lr", noised_lr)
 
         model_kwargs = [{"y": y}, {"y": negative_y}, {"hint": video_data_feature}]
 
@@ -380,7 +369,6 @@ def run_star_inference(
             )
         finally:
             _solvers_mod.trange = _orig_trange
-        _dbg("diffusion_output", gen_vid)
         if offload != "disabled":
             _move(generator, "cpu")
 
@@ -392,7 +380,6 @@ def run_star_inference(
         if offload != "disabled":
             _move(vae, device)
         vid_tensor_gen = vae_decode_chunk(vae, gen_vid, chunk_size=vae_dec_chunk)
-        _dbg("vae_decoded", vid_tensor_gen)
         if offload != "disabled":
             _move(vae, "cpu")
 
@@ -403,7 +390,6 @@ def run_star_inference(
     # -- Reshape to [B, C, F, H, W] then convert to ComfyUI format --
     gen_video = rearrange(vid_tensor_gen, "(b f) c h w -> b c f h w", b=1)
     gen_video = gen_video.float().cpu()
-    _dbg("final_output", gen_video)
 
     result = star_output_to_comfyui(gen_video)  # [F, H, W, 3]