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>
279 lines
9.3 KiB
Python
279 lines
9.3 KiB
Python
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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"""
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Cubic-based ease modes:
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- Linear
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- Ease_In
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- Ease_Out
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- Ease_In_Out
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"""
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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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# ---------- NODE --------------------------------------------------------------
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class CameraMoveNode:
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"""
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Camera move / directional pan for a batched IMAGE (video/sequence).
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Canvas size stays fixed. To avoid black borders, the input is uniformly
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scaled up (keeping aspect ratio) so that along each movement axis there is
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at least `distance_px` extra pixels. Each frame is then a crop that slides
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across this larger image.
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Directions
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----------
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Horizontal:
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- None : no horizontal move
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- Left : crop moves from left to right
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- Right : crop moves from right to left
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- Random: randomly chooses Left or Right once per run
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Vertical:
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- None : no vertical move
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- Top : crop moves from top to bottom
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- Bottom: crop moves from bottom to top
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- Random: randomly chooses Top or Bottom once per run
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You can combine them (e.g. Left + Top) to get diagonal movement.
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Parameters
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----------
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distance_px (FLOAT):
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Total camera travel in pixels over the whole sequence, per active axis.
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duration_s (FLOAT):
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Duration in seconds that this camera move represents (for info only).
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ease: "Linear", "Ease_In", "Ease_Out", "Ease_In_Out"
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Easing of the movement over the frames.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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"horizontal_direction": (
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["None", "Left", "Right", "Random"],
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{"default": "None"},
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),
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"vertical_direction": (
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["None", "Top", "Bottom", "Random"],
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{"default": "None"},
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),
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"distance_px": ("FLOAT", {"default": 100.0, "min": 0.0, "step": 1.0}),
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"duration_s": ("FLOAT", {"default": 5.0, "min": 0.0, "step": 0.1}),
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"ease": (
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["Linear", "Ease_In", "Ease_Out", "Ease_In_Out"],
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{"default": "Ease_Out"},
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),
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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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# Tell Comfy when to re-run this node instead of using cache
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@classmethod
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def IS_CHANGED(cls,
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images,
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horizontal_direction,
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vertical_direction,
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distance_px,
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duration_s,
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ease):
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# If any direction is Random, force a change every execution
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if horizontal_direction == "Random" or vertical_direction == "Random":
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# NaN is never equal to itself, so cache can't match -> always recompute
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return float("nan")
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# Normal caching behavior otherwise
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return None
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# ---- main ---------------------------------------------------------------
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def run(self,
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images: torch.Tensor,
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horizontal_direction: str,
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vertical_direction: str,
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distance_px: float,
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duration_s: float,
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ease: str):
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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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dist = max(0.0, float(distance_px))
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dist_int = int(round(dist))
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# Resolve directions / randoms
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hdir_key = horizontal_direction.strip().lower()
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vdir_key = vertical_direction.strip().lower()
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# Resolve random horizontally
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if hdir_key == "random":
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# choose left or right once per run
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idx = int(torch.randint(0, 2, (1,)).item())
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hdir_eff = ["left", "right"][idx]
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else:
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hdir_eff = hdir_key
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# Resolve random vertically
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if vdir_key == "random":
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idx = int(torch.randint(0, 2, (1,)).item())
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vdir_eff = ["top", "bottom"][idx]
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else:
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vdir_eff = vdir_key
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move_x = hdir_eff in ("left", "right")
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move_y = vdir_eff in ("top", "bottom")
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# If no movement or zero distance, just return original
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if dist_int == 0 or (not move_x and not move_y):
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info = (
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f"Frames: {B}, Canvas: {W}x{H}\n"
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f"Horizontal: {horizontal_direction} (resolved: {hdir_eff.title() if hdir_eff != 'none' else 'None'}), "
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f"Vertical: {vertical_direction} (resolved: {vdir_eff.title() if vdir_eff != 'none' else 'None'}), "
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f"Distance: {dist_int}px, Ease: {ease}\n"
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f"Duration: {duration_s:.2f}s\n"
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"No movement applied (either distance_px = 0 or both directions are None)."
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)
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return (images, info)
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# Desired overscan in each axis
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target_w = W + dist_int if move_x else W
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target_h = H + dist_int if move_y else H
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# Uniform scale to cover both overscans (keep aspect ratio)
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scale = max(target_w / float(W), target_h / float(H))
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newW = int(round(W * scale))
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newH = int(round(H * scale))
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# Convert to BCHW for interpolation
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imgs_nchw = images.permute(0, 3, 1, 2)
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# Uniform scale up
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imgs_big = F.interpolate(
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imgs_nchw,
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size=(newH, newW),
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mode="bicubic",
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align_corners=False,
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)
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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, ease) for t in ts]
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max_off_x = max(0, newW - W)
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max_off_y = max(0, newH - H)
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crops = []
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for i in range(B):
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pbar.update(1)
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e = es[i]
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frame = imgs_big[i] # (C, newH, newW)
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# Horizontal offset
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if move_x and max_off_x > 0:
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if hdir_eff == "left":
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start_x = 0
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end_x = max_off_x
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else: # "right"
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start_x = max_off_x
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end_x = 0
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offset_x = start_x + (end_x - start_x) * e
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x0 = int(round(offset_x))
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x0 = max(0, min(max_off_x, x0))
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else:
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# center if no movement on X
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x0 = max_off_x // 2
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x1 = x0 + W
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if x1 > newW:
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x1 = newW
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x0 = newW - W
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# Vertical offset
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if move_y and max_off_y > 0:
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if vdir_eff == "top":
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start_y = 0
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end_y = max_off_y
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else: # "bottom"
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start_y = max_off_y
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end_y = 0
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offset_y = start_y + (end_y - start_y) * e
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y0 = int(round(offset_y))
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y0 = max(0, min(max_off_y, y0))
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else:
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# center if no movement on Y
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y0 = max_off_y // 2
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y1 = y0 + H
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if y1 > newH:
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y1 = newH
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y0 = newH - H
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crop = frame[:, y0:y1, x0:x1]
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# Safety: ensure crop size matches original canvas
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if crop.shape[1] != H or crop.shape[2] != W:
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crop = F.interpolate(
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crop.unsqueeze(0),
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size=(H, W),
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mode="bicubic",
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align_corners=False,
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).squeeze(0)
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crops.append(crop)
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out_nchw = torch.stack(crops, dim=0) # (B, C, H, W)
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out_bhwc = out_nchw.permute(0, 2, 3, 1) # back to (B, H, W, C)
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# Info / diagnostics
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fps = None
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if duration_s > 0.0 and B > 1:
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fps = (B - 1) / duration_s
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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(
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"Horizontal: "
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f"{horizontal_direction} (resolved: {hdir_eff.title() if hdir_eff != 'none' else 'None'}), "
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"Vertical: "
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f"{vertical_direction} (resolved: {vdir_eff.title() if vdir_eff != 'none' else 'None'})"
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)
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info_lines.append(f"Distance per axis: {dist_int}px, Ease: {ease}")
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info_lines.append(f"Duration: {duration_s:.2f}s")
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if fps is not None:
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info_lines.append(f"Implied FPS (based on duration): {fps:.3f}")
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info_lines.append(
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f"Image uniformly scaled to {newW}x{newH} then animated via sliding crop "
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"(no black borders, aspect ratio preserved)."
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)
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info = "\n".join(info_lines)
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return (out_bhwc, info) |