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Improve SAM3 large input handling (#13767)

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This commit is contained in:
Jukka Seppänen
2026-05-08 03:18:28 +03:00
committed by GitHub
parent c011fb520c
commit 8dc3f3f209
3 changed files with 53 additions and 29 deletions
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24
comfy_extras/nodes_sam3.py
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@@ -272,8 +272,8 @@ class SAM3_VideoTrack(io.ComfyNode):
io.Model.Input("model", display_name="model"),
io.Mask.Input("initial_mask", display_name="initial_mask", optional=True, tooltip="Mask(s) for the first frame to track (one per object)"),
io.Conditioning.Input("conditioning", display_name="conditioning", optional=True, tooltip="Text conditioning for detecting new objects during tracking"),
io.Float.Input("detection_threshold", display_name="detection_threshold", default=0.5, min=0.0, max=1.0, step=0.01, tooltip="Score threshold for text-prompted detection"),
io.Int.Input("max_objects", display_name="max_objects", default=0, min=0, tooltip="Max tracked objects (0=unlimited). Initial masks count toward this limit."),
io.Float.Input("detection_threshold", display_name="detection_threshold", default=0.5, min=0.0, max=1.0, step=0.01, tooltip="Score threshold for text-prompted detection."),
io.Int.Input("max_objects", display_name="max_objects", default=4, min=0, max=64, tooltip="Max tracked objects. Initial masks count toward this limit. 0 uses the internal cap of 64."),
io.Int.Input("detect_interval", display_name="detect_interval", default=1, min=1, tooltip="Run detection every N frames (1=every frame). Higher values save compute."),
],
outputs=[
@@ -290,8 +290,7 @@ class SAM3_VideoTrack(io.ComfyNode):
dtype = model.model.get_dtype()
sam3_model = model.model.diffusion_model
frames = images[..., :3].movedim(-1, 1)
frames_in = comfy.utils.common_upscale(frames, 1008, 1008, "bilinear", crop="disabled").to(device=device, dtype=dtype)
frames_in = images[..., :3].movedim(-1, 1)
init_masks = None
if initial_mask is not None:
@@ -308,7 +307,7 @@ class SAM3_VideoTrack(io.ComfyNode):
result = sam3_model.forward_video(
images=frames_in, initial_masks=init_masks, pbar=pbar, text_prompts=text_prompts,
new_det_thresh=detection_threshold, max_objects=max_objects,
detect_interval=detect_interval)
detect_interval=detect_interval, target_device=device, target_dtype=dtype)
result["orig_size"] = (H, W)
return io.NodeOutput(result)
@@ -449,14 +448,18 @@ class SAM3_TrackPreview(io.ComfyNode):
cx = (bool_masks * grid_x).sum(dim=(-1, -2)) // area
has = area > 1
scores = track_data.get("scores", [])
label_scale = max(3, H // 240) # Scale font with resolutio
size_caps = (area.float().sqrt() / 15).clamp_(min=1).long().tolist() #cap per-object so the number doesn't dwarf small masks
for obj_idx in range(N_obj):
if has[obj_idx]:
_cx, _cy = int(cx[obj_idx]), int(cy[obj_idx])
color = cls.COLORS[obj_idx % len(cls.COLORS)]
SAM3_TrackPreview._draw_number_gpu(frame_gpu, obj_idx, _cx, _cy, color)
obj_scale = min(label_scale, size_caps[obj_idx])
score_scale = max(1, obj_scale * 2 // 3)
SAM3_TrackPreview._draw_number_gpu(frame_gpu, obj_idx, _cx, _cy, color, scale=obj_scale)
if obj_idx < len(scores) and scores[obj_idx] < 1.0:
SAM3_TrackPreview._draw_number_gpu(frame_gpu, int(scores[obj_idx] * 100),
_cx, _cy + 5 * 3 + 3, color, scale=2)
_cx, _cy + 5 * obj_scale + 3, color, scale=score_scale)
frame_cpu.copy_(frame_gpu.clamp_(0, 1).mul_(255).byte())
else:
frame_cpu.copy_(frame.clamp_(0, 1).mul_(255).byte())
@@ -507,9 +510,10 @@ class SAM3_TrackToMask(io.ComfyNode):
if not indices:
return io.NodeOutput(torch.zeros(N, H, W, device=comfy.model_management.intermediate_device()))
selected = packed[:, indices]
binary = unpack_masks(selected) # [N, len(indices), Hm, Wm] bool
union = binary.any(dim=1, keepdim=True).float()
union_packed = packed[:, indices[0]].clone()
for i in indices[1:]:
union_packed |= packed[:, i]
union = unpack_masks(union_packed).unsqueeze(1).float() # [N, 1, Hm, Wm]
mask_out = F.interpolate(union, size=(H, W), mode="bilinear", align_corners=False)[:, 0]
return io.NodeOutput(mask_out)