Fix FlashVSR quality: two-stage temporal padding, kv_ratio=3, float64 precision
Root cause of remaining ghosting: our single-stage temporal padding (N+4 → floor to 8k+1) TRUNCATED frames when N+4 wasn't already 8k+1. For 50 frames: 50+4=54 → floor to 49, LOSING the last input frame. The pipeline then processed misaligned LQ→output frame mapping. Fix matches naxci1/ComfyUI-FlashVSR_Stable two-stage approach: 1. Pad to next_8n5(N) (next integer >= N of form 8k+5, minimum 21) 2. Add 4 → result is always 8(k+1)+1, a valid 8k+1 — NEVER truncates Also: - kv_ratio default 2.0→3.0 (matches naxci1, max quality KV cache) - local_range default 9→11 (more stable temporal consistency) - sinusoidal_embedding_1d, precompute_freqs_cis, rope_apply: float32→float64 (matches naxci1 reference precision for embeddings and RoPE) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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39
inference.py
39
inference.py
@@ -740,17 +740,22 @@ class FlashVSRModel:
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result = torch.cat([result, pad], dim=0)
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return result
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@staticmethod
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def _next_8n5(n, minimum=21):
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"""Next integer >= n of the form 8k+5 (minimum 21)."""
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if n < minimum:
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return minimum
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return ((n - 5 + 7) // 8) * 8 + 5
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def _prepare_video(self, frames, scale):
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"""Convert [F, H, W, C] [0,1] frames to padded [1, C, F_padded, H, W] [-1,1].
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Matches naxci1/ComfyUI-FlashVSR_Stable preprocessing:
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Matches naxci1/ComfyUI-FlashVSR_Stable two-stage temporal padding:
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1. Bicubic-upscale each frame to target resolution
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2. Centered symmetric padding to 128-pixel alignment (reflect mode)
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3. Normalize to [-1, 1]
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4. Temporal padding: N+4 then floor to largest 8k+1 (matches naxci1 reference)
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No front dummy frames — the pipeline handles LQ indexing correctly
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starting from frame 0.
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4. Stage 1: Pad frame count to next 8n+5 (min 21) by repeating last frame
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5. Stage 2: Add 4 → result is always 8k+1 (since 8n+5+4 = 8(n+1)+1)
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Returns:
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video: [1, C, F_padded, H, W] tensor
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@@ -762,6 +767,12 @@ class FlashVSRModel:
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N, H, W, C = frames.shape
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sw, sh, tw, th = self._compute_dims(W, H, scale)
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# Stage 1: pad frame count to next 8n+5 (matches naxci1 process_chunk)
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N_padded = self._next_8n5(N)
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# Stage 2: add 4 → gives 8(n+1)+1, always a valid 8k+1
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target = N_padded + 4
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# Centered spatial padding offsets
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pad_top = (th - sh) // 2
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pad_bottom = th - sh - pad_top
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@@ -769,8 +780,9 @@ class FlashVSRModel:
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pad_right = tw - sw - pad_left
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processed = []
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for i in range(N):
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frame = frames[i].permute(2, 0, 1).unsqueeze(0) # [1, C, H, W]
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for i in range(target):
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frame_idx = min(i, N - 1) # clamp to last real frame
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frame = frames[frame_idx].permute(2, 0, 1).unsqueeze(0) # [1, C, H, W]
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upscaled = F.interpolate(frame, size=(sh, sw), mode='bicubic', align_corners=False)
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if pad_top > 0 or pad_bottom > 0 or pad_left > 0 or pad_right > 0:
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# Centered reflect padding (matches naxci1 reference)
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@@ -783,17 +795,6 @@ class FlashVSRModel:
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processed.append(normalized.squeeze(0).cpu().to(self.dtype))
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video = torch.stack(processed, 0).permute(1, 0, 2, 3).unsqueeze(0)
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# Temporal padding: N+4 then floor to largest 8k+1 (matches naxci1 reference)
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num_with_pad = N + 4
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target = ((num_with_pad - 1) // 8) * 8 + 1 # largest_8n1_leq
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if target < 1:
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target = 1
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if target > N:
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pad = video[:, :, -1:].repeat(1, 1, target - N, 1, 1)
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video = torch.cat([video, pad], dim=2)
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elif target < N:
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video = video[:, :, :target, :, :]
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nf = video.shape[2]
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return video, th, tw, nf, sh, sw, pad_top, pad_left
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@@ -812,7 +813,7 @@ class FlashVSRModel:
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@torch.no_grad()
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def upscale(self, frames, scale=4, tiled=True, tile_size=(60, 104),
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topk_ratio=2.0, kv_ratio=2.0, local_range=9,
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topk_ratio=2.0, kv_ratio=3.0, local_range=11,
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color_fix=True, unload_dit=False, seed=1,
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progress_bar_cmd=None):
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"""Upscale video frames with FlashVSR.
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