scofanoandClaude Haiku 4.5
7d5d326c97
Image Processing (PyTorch GPU tensor operations): - ZoomSequenceNode: Add device handling for GPU-accelerated F.interpolate - CameraShakeNode: Ensure tensors stay on GPU device - CameraMoveNode: Add device handling for GPU acceleration - Expected gain: 4-10x speedup for large batches Video Encoding (NVENC support): - VideoLoopExtenderNode: Add use_gpu parameter for HEVC_NVENC encoding - Falls back to libx264 if NVIDIA GPU unavailable - Expected gain: 5-10x speedup for video encoding Note: ComfySimpleVideoCombiner and ColorAdjustmentVideoNode already had GPU support implemented, so no changes were needed. Safe, backward-compatible changes with runtime GPU detection and fallback. Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
267 lines
9.9 KiB
Python
267 lines
9.9 KiB
Python
# comfy_zoom_sequence.py (modified to be Camera Shake instead of Zoom)
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import math
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import torch
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import torch.nn.functional as F
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# ---------- EASING ------------------------------------------------------------
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def ease_value(t: float, mode: str) -> float:
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key = mode.strip().upper().replace("-", "_")
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if key == "LINEAR":
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return t
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elif key == "EASE_IN":
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return t * t * t
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elif key == "EASE_OUT":
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u = 1.0 - t
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return 1.0 - u * u * u
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elif key == "EASE_IN_OUT":
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return t * t * (3 - 2 * t) # smoothstep
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return t # fallback
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def normalize_mode(mode: str) -> str:
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"""
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Interpret UI mode string as one of our internal shake modes.
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"""
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m = mode.strip().lower()
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if "circle" in m:
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return "CIRCULAR"
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if "random" in m:
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return "RANDOM"
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# Default to circular if unclear
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return "CIRCULAR"
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# ---------- NODE --------------------------------------------------------------
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class CameraShakeNode:
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"""
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CAMERA SHAKE across a batched IMAGE (video/sequence). The canvas size stays fixed and
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the original aspect ratio is preserved for every frame.
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Parameters
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----------
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mode: "Circular Shake" or "Random Shake"
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pixels_per_frame (FLOAT):
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Interpreted as the *maximum shake radius* on the SMALLER canvas dimension (per-side).
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Higher value => bigger possible X/Y shift.
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ease: "Linear", "Ease_In", "Ease_Out", "Ease_In_Out"
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Eases the shake radius over time (0..1 envelope across the sequence).
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loop: BOOLEAN
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If True, forces circular shake mode with constant radius for seamless looping (starts and ends on the circle).
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"""
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"images": ("IMAGE",),
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"mode": (["Circular Shake", "Random Shake"], {"default": "Circular Shake"}),
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"pixels_per_frame": ("FLOAT", {"default": 5.0, "min": 0.0, "step": 0.1}),
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"ease": (["Linear", "Ease_In", "Ease_Out", "Ease_In_Out"], {"default": "Linear"}),
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"loop": ("BOOLEAN", {"default": False}),
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}
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}
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RETURN_TYPES = ("IMAGE", "STRING",)
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RETURN_NAMES = ("images", "info",)
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FUNCTION = "run"
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CATEGORY = "Simple Video Effects/Image Processing"
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# ---- helpers -------------------------------------------------------------
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def _resize_to(self, frame_hwc: torch.Tensor, size_hw):
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H, W = size_hw
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x = frame_hwc.permute(2, 0, 1).unsqueeze(0) # 1,C,H,W
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x = F.interpolate(x, size=(H, W), mode="bilinear", align_corners=False)
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return x.squeeze(0).permute(1, 2, 0)
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def _aspect_corrected_crop_box(self, W: int, H: int, m_small_int: int):
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"""
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Given an integer scalar margin on the smaller dimension (per-side),
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compute integer crop box (x0,y0,x1,y1) that:
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- preserves aspect (within ±1 px due to rounding),
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- never exceeds bounds.
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"""
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aspect = W / H if H > 0 else 1.0
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# Which side is smaller?
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small = min(W, H)
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if small <= 2:
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return 0, 0, W, H, 0, 0
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# Safe guard: ensure at least 1 px interior
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max_small_margin = max(0, (small // 2) - 1)
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m_small = max(0, min(int(m_small_int), max_small_margin))
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# Fractional level based on smaller half-size
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half_small = small / 2.0
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frac = 0.0 if half_small <= 0 else (m_small / half_small) # 0..~1
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# Proportional margins per axis to keep aspect (before integer correction)
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m_x = int(round(frac * (W / 2.0)))
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m_y = int(round(frac * (H / 2.0)))
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# Compute crop size
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cw = W - 2 * m_x
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ch = H - 2 * m_y
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# Enforce minimum interior
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cw = max(1, cw)
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ch = max(1, ch)
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# ---- Aspect correction (integer) ------------------------------------
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# target: cw / ch == W / H => cw == round(ch * aspect)
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ideal_cw = int(round(ch * aspect))
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ideal_cw = max(1, min(ideal_cw, W - 2)) # keep some border to be safe in edge cases
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if ideal_cw != cw:
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# Recompute m_x from ideal_cw, keep m_y if possible
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cw = ideal_cw
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m_x = (W - cw) // 2
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# ensure symmetric crop; parity fix if needed
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if W - 2 * m_x != cw:
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m_x2 = max(0, min(m_x + 1, (W - 1) // 2))
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if W - 2 * m_x2 == cw:
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m_x = m_x2
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else:
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# else recompute ch from cw to restore symmetry
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ch = max(1, int(round(cw / aspect)))
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m_y = (H - ch) // 2
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# Final clamp to safe range
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m_x = max(0, min(m_x, (W - 1) // 2))
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m_y = max(0, min(m_y, (H - 1) // 2))
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x0, y0 = m_x, m_y
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x1, y1 = W - m_x, H - m_y
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return x0, y0, x1, y1, m_x, m_y
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# ---- main ---------------------------------------------------------------
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def run(self, images: torch.Tensor, mode: str, pixels_per_frame: float, ease: str, loop: bool):
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if images.ndim != 4:
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return (images, "Input is not a batched IMAGE (B,H,W,C).")
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B, H, W, C = images.shape
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if B <= 0:
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return (images, "Empty batch; nothing to do.")
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# GPU acceleration: keep tensors on their device (GPU if available)
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device = images.device
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images = images.to(device)
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import comfy.utils
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pbar = comfy.utils.ProgressBar(B)
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# Normalize
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shake_mode = normalize_mode(mode)
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if loop:
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shake_mode = "CIRCULAR"
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i_ease = ease
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# Compute scalar radius on the smaller dimension (per-side)
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small = min(W, H)
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max_safe_small_margin = max(0, (small // 2) - 1)
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requested_radius_small_f = max(0.0, float(pixels_per_frame))
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radius_small_f = min(requested_radius_small_f, float(max_safe_small_margin))
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# Convert to integer margin and compute the "safe inner canvas"
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m_small_int = int(round(radius_small_f))
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# This gives us a central crop with margins m_x, m_y
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x0c, y0c, x1c, y1c, m_x, m_y = self._aspect_corrected_crop_box(W, H, m_small_int)
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cw = x1c - x0c
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ch = y1c - y0c
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if cw <= 0 or ch <= 0:
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return (images, "Invalid crop; probably too large shake vs resolution.")
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# Eased progress 0..1 across frames
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ts = [0.0] if B == 1 else [i / (B - 1) for i in range(B)]
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es = [ease_value(t, i_ease) for t in ts]
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out_frames = []
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# How many circles across the sequence for circular shake
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num_cycles = 1.0 if loop else 2.0
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for i in range(B):
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pbar.update(1)
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t = ts[i]
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# Envelope (0..1) over time for radius
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radius_factor = 1.0 if loop else (es[i] if B > 1 else 1.0)
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# Per-axis max amplitude for this frame
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ax = m_x * radius_factor
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ay = m_y * radius_factor
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if shake_mode == "CIRCULAR":
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# Smooth sinusoidal circular path (like smooth_camera_shake, no jitter)
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# num_cycles = how many full circles over the whole sequence
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angle = 2.0 * math.pi * num_cycles * t
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dx_f = ax * math.sin(angle)
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dy_f = ay * math.sin(angle + math.pi * 0.5)
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elif shake_mode == "RANDOM":
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# Constant-magnitude random shake.
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# If pixels_per_frame = 1 on the small dimension, this results in
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# a 1-pixel step (left/right/up/down/diagonal) every frame,
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# and it does NOT ramp up over time.
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# Base step size from margins (already derived from requested radius)
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step_x = m_x if m_x > 0 else 0
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step_y = m_y if m_y > 0 else 0
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# Ensure we still get motion if margins are tiny but non-zero
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if step_x == 0 and m_x > 0:
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step_x = 1
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if step_y == 0 and m_y > 0:
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step_y = 1
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# Random direction in {-1, 0, +1} for each axis
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dir_x = int(torch.randint(-1, 2, (1,)).item())
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dir_y = int(torch.randint(-1, 2, (1,)).item())
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# Ignore the easing envelope for magnitude here – keep step size constant
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dx_f = dir_x * step_x
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dy_f = dir_y * step_y
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else:
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# Fallback: no movement
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dx_f = 0.0
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dy_f = 0.0
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# Clamp to safe integer offsets (so crop stays inside original)
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dx = int(round(max(-m_x, min(m_x, dx_f))))
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dy = int(round(max(-m_y, min(m_y, dy_f))))
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# Base central crop is at (m_x, m_y); we offset it by dx, dy
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x0 = m_x + dx
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y0 = m_y + dy
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x1 = x0 + cw
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y1 = y0 + ch
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# Extra safety clamp against rounding drift
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if x0 < 0:
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x0 = 0
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x1 = cw
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if y0 < 0:
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y0 = 0
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y1 = ch
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if x1 > W:
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x1 = W
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x0 = W - cw
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if y1 > H:
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y1 = H
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y0 = H - ch
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cropped = images[i, y0:y1, x0:x1, :]
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resized = self._resize_to(cropped, (H, W))
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out_frames.append(resized)
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out = torch.stack(out_frames, dim=0)
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info_lines = []
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info_lines.append(f"Frames: {B}, Canvas: {W}x{H}")
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info_lines.append(f"Mode: {shake_mode}, Ease: {ease}, Loop: {loop}")
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info_lines.append(f"Requested small-dim radius: {requested_radius_small_f:.2f} px")
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info_lines.append(f"Applied small-dim radius: {radius_small_f:.2f} px (safe limit: {max_safe_small_margin} px)")
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info_lines.append(f"Per-axis margins (max shake): m_x={m_x}, m_y={m_y}")
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info_lines.append("Note: frames are cropped with a safe border then resized back to avoid black edges during shake.")
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info = "\n".join(info_lines)
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return (out, info)
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