Fix FlashVSR ghosting: streaming TCDecoder decode + Causal LQ projection

Root cause: three critical differences from naxci1 reference implementation:

1. Batch decode after loop → streaming per-chunk TCDecoder decode with LQ
   conditioning inside the loop. The TCDecoder uses causal convolutions with
   temporal memory that must be built incrementally per-chunk. Batch decode
   breaks this design and loses LQ frame conditioning, causing ghosting.

2. Buffer_LQ4x_Proj → Causal_LQ4x_Proj for FlashVSR v1.1. The causal
   variant reads the OLD cache before writing the new one (truly causal),
   while Buffer writes cache BEFORE the conv call. Using the wrong variant
   misaligns temporal LQ conditioning features.

3. Temporal padding formula: changed from round-up to largest_8n1_leq(N+4)
   matching the naxci1 reference approach.

Changes:
- flashvsr_full.py: streaming TCDecoder decode per-chunk with LQ conditioning
  and per-chunk color correction (was: batch VAE decode after loop)
- flashvsr_tiny.py: streaming TCDecoder decode per-chunk (was: batch decode)
- inference.py: use Causal_LQ4x_Proj, build TCDecoder for ALL modes (including
  full), fix temporal padding to largest_8n1_leq(N+4), clear TCDecoder in
  clear_caches()
- utils.py: add Causal_LQ4x_Proj class
- nodes.py: update progress bar estimation for new padding formula

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

inference.py

@@ -648,7 +648,7 @@ class FlashVSRModel:
             ModelManager, FlashVSRFullPipeline,
             FlashVSRTinyPipeline, FlashVSRTinyLongPipeline,
         )
-        from .flashvsr_arch.models.utils import Buffer_LQ4x_Proj
+        from .flashvsr_arch.models.utils import Causal_LQ4x_Proj
         from .flashvsr_arch.models.TCDecoder import build_tcdecoder
 
         self.mode = mode
@@ -672,16 +672,18 @@ class FlashVSRModel:
             mm.load_models([dit_path])
             Pipeline = FlashVSRTinyLongPipeline if mode == "tiny-long" else FlashVSRTinyPipeline
             self.pipe = Pipeline.from_model_manager(mm, device=device)
-            self.pipe.TCDecoder = build_tcdecoder(
-                [512, 256, 128, 128], device, dtype, 16 + 768,
-            )
-            self.pipe.TCDecoder.load_state_dict(
-                load_file(tcd_path, device=device), strict=False,
-            )
-            self.pipe.TCDecoder.clean_mem()
 
-        # LQ frame projection
-        self.pipe.denoising_model().LQ_proj_in = Buffer_LQ4x_Proj(3, 1536, 1).to(device, dtype)
+        # TCDecoder for ALL modes (streaming per-chunk decode with LQ conditioning)
+        self.pipe.TCDecoder = build_tcdecoder(
+            [512, 256, 128, 128], device, dtype, 16 + 768,
+        )
+        self.pipe.TCDecoder.load_state_dict(
+            load_file(tcd_path, device=device), strict=False,
+        )
+        self.pipe.TCDecoder.clean_mem()
+
+        # LQ frame projection — Causal variant for FlashVSR v1.1
+        self.pipe.denoising_model().LQ_proj_in = Causal_LQ4x_Proj(3, 1536, 1).to(device, dtype)
         if os.path.exists(lq_path):
             lq_sd = load_file(lq_path, device="cpu")
             cleaned = {}
@@ -714,6 +716,8 @@ class FlashVSRModel:
             self.pipe.denoising_model().LQ_proj_in.clear_cache()
         if hasattr(self.pipe, "vae") and self.pipe.vae is not None:
             self.pipe.vae.clear_cache()
+        if hasattr(self.pipe, "TCDecoder") and self.pipe.TCDecoder is not None:
+            self.pipe.TCDecoder.clean_mem()
 
     # ------------------------------------------------------------------
     # Frame preprocessing / postprocessing helpers
@@ -743,7 +747,7 @@ class FlashVSRModel:
         1. Bicubic-upscale each frame to target resolution
         2. Centered symmetric padding to 128-pixel alignment (reflect mode)
         3. Normalize to [-1, 1]
-        4. Temporal padding: repeat last frame to reach 8k+1 count
+        4. Temporal padding: N+4 then floor to largest 8k+1 (matches naxci1 reference)
 
         No front dummy frames — the pipeline handles LQ indexing correctly
         starting from frame 0.
@@ -780,14 +784,16 @@ class FlashVSRModel:
 
         video = torch.stack(processed, 0).permute(1, 0, 2, 3).unsqueeze(0)
 
-        # Temporal padding: repeat last frame to reach 8k+1 (pipeline requirement)
-        target = max(N, 25)  # minimum 25 for streaming loop (P >= 1)
-        remainder = (target - 1) % 8
-        if remainder != 0:
-            target += 8 - remainder
+        # Temporal padding: N+4 then floor to largest 8k+1 (matches naxci1 reference)
+        num_with_pad = N + 4
+        target = ((num_with_pad - 1) // 8) * 8 + 1  # largest_8n1_leq
+        if target < 1:
+            target = 1
         if target > N:
             pad = video[:, :, -1:].repeat(1, 1, target - N, 1, 1)
             video = torch.cat([video, pad], dim=2)
+        elif target < N:
+            video = video[:, :, :target, :, :]
         nf = video.shape[2]
 
         return video, th, tw, nf, sh, sw, pad_top, pad_left