# comfy_zoom_sequence.py import torch import torch.nn.functional as F # ---------- EASING ------------------------------------------------------------ def ease_value(t: float, mode: str) -> float: key = mode.strip().upper().replace("-", "_") if key == "LINEAR": return t elif key == "EASE_IN": return t * t * t elif key == "EASE_OUT": u = 1.0 - t return 1.0 - u * u * u elif key == "EASE_IN_OUT": return t * t * (3 - 2 * t) # smoothstep return t # fallback def normalize_mode(mode: str) -> str: m = mode.strip().lower() if m in ("zoom in", "in"): return "IN" if m in ("zoom out", "out"): return "OUT" return "IN" # ---------- NODE -------------------------------------------------------------- class ZoomSequenceNode: """ Zooms IN or OUT across a batched IMAGE (video/sequence). The canvas size stays fixed and the original aspect ratio is preserved for every frame. Parameters ---------- mode: "Zoom In" or "Zoom Out" pixels_per_frame (FLOAT): zoom speed, defined on the SMALLER canvas dimension (per-side). We compute a scalar inset on the smaller dimension and convert it proportionally to per-axis margins so aspect stays constant. ease: "Linear", "Ease_In", "Ease_Out", "Ease_In_Out" """ @classmethod def INPUT_TYPES(s): return { "required": { "images": ("IMAGE",), "mode": (["Zoom In", "Zoom Out"], {"default": "Zoom In"}), "pixels_per_frame": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.1}), "ease": (["Linear", "Ease_In", "Ease_Out", "Ease_In_Out"], {"default": "Linear"}), } } RETURN_TYPES = ("IMAGE", "STRING",) RETURN_NAMES = ("images", "info",) FUNCTION = "run" CATEGORY = "Simple Video Effects/Image Processing" # ---- helpers ------------------------------------------------------------- def _resize_to(self, frame_hwc: torch.Tensor, size_hw): H, W = size_hw x = frame_hwc.permute(2, 0, 1).unsqueeze(0) # 1,C,H,W x = F.interpolate(x, size=(H, W), mode="bilinear", align_corners=False) return x.squeeze(0).permute(1, 2, 0) def _aspect_corrected_crop_box(self, W: int, H: int, m_small_int: int): """ Given an integer scalar margin on the smaller dimension (per-side), compute integer crop box (x0,y0,x1,y1) that: - preserves aspect (within ±1 px due to rounding), - never exceeds bounds. """ aspect = W / H if H > 0 else 1.0 # Which side is smaller? small = min(W, H) if small <= 2: return 0, 0, W, H, 0, 0 # Safe guard: ensure at least 1 px interior max_small_margin = max(0, (small // 2) - 1) m_small = max(0, min(int(m_small_int), max_small_margin)) # Fractional zoom level based on smaller half-size half_small = small / 2.0 frac = 0.0 if half_small <= 0 else (m_small / half_small) # 0..~1 # Proportional margins per axis to keep aspect (before integer correction) m_x = int(round(frac * (W / 2.0))) m_y = int(round(frac * (H / 2.0))) # Compute crop size cw = W - 2 * m_x ch = H - 2 * m_y # Enforce minimum interior cw = max(1, cw) ch = max(1, ch) # ---- Aspect correction (integer) ------------------------------------ # target: cw / ch == W / H => cw == round(ch * aspect) ideal_cw = int(round(ch * aspect)) ideal_cw = max(1, min(ideal_cw, W - 2)) # keep some border to be safe in edge cases if ideal_cw != cw: # Recompute m_x from ideal_cw, keep m_y if possible cw = ideal_cw m_x = (W - cw) // 2 # ensure symmetric crop; parity fix if needed if W - 2 * m_x != cw: m_x2 = max(0, min(m_x + 1, (W - 1) // 2)) if W - 2 * m_x2 == cw: m_x = m_x2 else: # else recompute ch from cw to restore symmetry ch = max(1, int(round(cw / aspect))) m_y = (H - ch) // 2 # Final clamp to safe range m_x = max(0, min(m_x, (W - 1) // 2)) m_y = max(0, min(m_y, (H - 1) // 2)) x0, y0 = m_x, m_y x1, y1 = W - m_x, H - m_y return x0, y0, x1, y1, m_x, m_y # ---- main --------------------------------------------------------------- def run(self, images: torch.Tensor, mode: str, pixels_per_frame: float, ease: str): if images.ndim != 4: return (images, "Input is not a batched IMAGE (B,H,W,C).") B, H, W, C = images.shape if B <= 0: return (images, "Empty batch; nothing to do.") # Normalize i_mode = normalize_mode(mode) i_ease = ease # Compute max scalar margin ON THE SMALLER DIMENSION (per-side) small = min(W, H) max_safe_small_margin = max(0, (small // 2) - 1) # keep as float (allows fractional speed), clamp later when actually cropping requested_small_margin_max_f = pixels_per_frame * max(0, B - 1) small_margin_max_f = min(requested_small_margin_max_f, float(max_safe_small_margin)) # Eased progress 0..1 across frames ts = [0.0] if B == 1 else [i / (B - 1) for i in range(B)] es = [ease_value(t, i_ease) for t in ts] # Per-frame scalar margins (float, on the smaller dimension) if i_mode == "IN": m_smalls_f = [small_margin_max_f * e for e in es] else: # OUT m_smalls_f = [small_margin_max_f * (1.0 - e) for e in es] # Apply proportional, aspect-corrected crops out_frames = [] clamped = False used_mx = [] used_my = [] for i in range(B): # Round to integer ONLY where slicing happens m_small_int = int(round(m_smalls_f[i])) # Detect clamping relative to theoretical safe limit if m_small_int != max(0, min(m_small_int, (small // 2) - 1)): clamped = True x0, y0, x1, y1, m_x, m_y = self._aspect_corrected_crop_box(W, H, m_small_int) if x1 <= x0 or y1 <= y0: out_frames.append(images[i]) continue cropped = images[i, y0:y1, x0:x1, :] resized = self._resize_to(cropped, (H, W)) out_frames.append(resized) used_mx.append(m_x) used_my.append(m_y) out = torch.stack(out_frames, dim=0) info_lines = [] info_lines.append(f"Frames: {B}, Canvas: {W}x{H}, Mode: {mode}, Ease: {ease}") info_lines.append(f"Requested small-dim max margin: {requested_small_margin_max_f:.2f} px") info_lines.append(f"Applied small-dim max margin: {small_margin_max_f:.2f} px (safe limit: {max_safe_small_margin} px)") info_lines.append("Note: margins are proportional per axis to preserve aspect; integer crop indices are used.") if clamped: info_lines.append("Warning: requested margins exceeded safe bounds and were clamped.") info = "\n".join(info_lines) return (out, info)