Add diagnostic debug logging to pipeline stages

Prints tensor stats (shape, dtype, min, max, mean, std) at each stage
to help diagnose quality issues.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

star_pipeline.py

@@ -271,8 +271,14 @@ 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()
@@ -280,6 +286,8 @@ 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)
@@ -288,6 +296,7 @@ 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]
 
@@ -296,7 +305,9 @@ def run_star_inference(
         text_encoder.model.to(device)
         text_encoder.device = device
     text = (prompt 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"
@@ -306,6 +317,7 @@ 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.
@@ -316,6 +328,7 @@ def run_star_inference(
     with torch.amp.autocast("cuda"):
         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}]
 
@@ -367,6 +380,7 @@ def run_star_inference(
             )
         finally:
             _solvers_mod.trange = _orig_trange
+        _dbg("diffusion_output", gen_vid)
         if offload != "disabled":
             _move(generator, "cpu")
 
@@ -378,6 +392,7 @@ 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")
 
@@ -388,6 +403,7 @@ 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]