mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-06-10 16:27:33 +08:00
Jukka Seppänen
GitHub
parent
07c53f8f0f
commit
184009c2f6
@ -1631,13 +1631,15 @@ class SCAILWanModel(WanModel):
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self.patch_embedding_pose = operations.Conv3d(in_dim, dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=torch.float32)
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def forward_orig(self, x, t, context, clip_fea=None, freqs=None, transformer_options={}, pose_latents=None, reference_latent=None, **kwargs):
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def forward_orig(self, x, t, context, clip_fea=None, freqs=None, transformer_options={}, pose_latents=None, reference_latent=None, ref_mask_latents=None, sam_latents=None, **kwargs):
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if reference_latent is not None:
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x = torch.cat((reference_latent, x), dim=2)
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# embeddings
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x = self.patch_embedding(x.float()).to(x.dtype)
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if ref_mask_latents is not None: # SCAIL-2 additive mask stream
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x = x + self.patch_embedding_mask(ref_mask_latents.float()).to(x.dtype)
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grid_sizes = x.shape[2:]
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transformer_options["grid_sizes"] = grid_sizes
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x = x.flatten(2).transpose(1, 2)
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@ -1645,6 +1647,8 @@ class SCAILWanModel(WanModel):
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scail_pose_seq_len = 0
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if pose_latents is not None:
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scail_x = self.patch_embedding_pose(pose_latents.float()).to(x.dtype)
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if sam_latents is not None: # SCAIL-2 additive mask stream
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scail_x = scail_x + self.patch_embedding_mask(sam_latents.float()).to(x.dtype)
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scail_x = scail_x.flatten(2).transpose(1, 2)
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scail_pose_seq_len = scail_x.shape[1]
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x = torch.cat([x, scail_x], dim=1)
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@ -1695,7 +1699,36 @@ class SCAILWanModel(WanModel):
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return x
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def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, pose_latents=None, reference_latent=None, transformer_options={}):
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# ref_mask_flag is a scalar bool (CONDConstant, SCAIL-2 only). False => replacement mode,
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# which places ref/pose via H/W rope shifts instead of the animation-mode temporal offset.
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def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, steps_w=None, device=None, dtype=None, pose_latents=None, reference_latent=None, ref_mask_flag=None, transformer_options={}):
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if ref_mask_flag is not None and not bool(ref_mask_flag):
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REF_ROPE_H = 120.0
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POSE_ROPE_W = 120.0
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ref_t_patches = 0
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if reference_latent is not None:
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ref_t_patches = (reference_latent.shape[2] + (self.patch_size[0] // 2)) // self.patch_size[0]
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main_t_patches = t - ref_t_patches
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parts = []
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if ref_t_patches > 0:
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ref_tf = {"rope_options": {"shift_y": REF_ROPE_H, "shift_x": 0.0, "scale_y": 1.0, "scale_x": 1.0}}
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parts.append(super().rope_encode(ref_t_patches, h, w, t_start=0, device=device, dtype=dtype, transformer_options=ref_tf))
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if main_t_patches > 0:
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parts.append(super().rope_encode(main_t_patches, h, w, t_start=0, device=device, dtype=dtype, transformer_options=transformer_options))
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if pose_latents is not None:
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F_pose, H_pose, W_pose = pose_latents.shape[-3], pose_latents.shape[-2], pose_latents.shape[-1]
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h_scale = h / H_pose
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w_scale = w / W_pose
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h_shift = (h_scale - 1) / 2
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w_shift = (w_scale - 1) / 2
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pose_tf = {"rope_options": {"shift_y": h_shift, "shift_x": POSE_ROPE_W + w_shift, "scale_y": h_scale, "scale_x": w_scale}}
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parts.append(super().rope_encode(F_pose, H_pose, W_pose, t_start=0, device=device, dtype=dtype, transformer_options=pose_tf))
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return torch.cat(parts, dim=1)
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main_freqs = super().rope_encode(t, h, w, t_start=t_start, steps_t=steps_t, steps_h=steps_h, steps_w=steps_w, device=device, dtype=dtype, transformer_options=transformer_options)
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if pose_latents is None:
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@ -1719,12 +1752,16 @@ class SCAILWanModel(WanModel):
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return torch.cat([main_freqs, pose_freqs], dim=1)
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def _forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, pose_latents=None, **kwargs):
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def _forward(self, x, timestep, context, clip_fea=None, time_dim_concat=None, transformer_options={}, pose_latents=None, ref_mask_latents=None, sam_latents=None, **kwargs):
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bs, c, t, h, w = x.shape
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x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size)
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if pose_latents is not None:
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pose_latents = comfy.ldm.common_dit.pad_to_patch_size(pose_latents, self.patch_size)
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if ref_mask_latents is not None: # SCAIL-2
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ref_mask_latents = comfy.ldm.common_dit.pad_to_patch_size(ref_mask_latents, self.patch_size)
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if sam_latents is not None: # SCAIL-2
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sam_latents = comfy.ldm.common_dit.pad_to_patch_size(sam_latents, self.patch_size)
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t_len = t
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if time_dim_concat is not None:
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@ -1737,5 +1774,15 @@ class SCAILWanModel(WanModel):
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reference_latent = comfy.ldm.common_dit.pad_to_patch_size(kwargs.pop("reference_latent"), self.patch_size)
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t_len += reference_latent.shape[2]
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freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options, pose_latents=pose_latents, reference_latent=reference_latent)
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return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options, pose_latents=pose_latents, reference_latent=reference_latent, **kwargs)[:, :, :t, :h, :w]
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ref_mask_flag = kwargs.pop("ref_mask_flag", None) # SCAIL-2
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freqs = self.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options, pose_latents=pose_latents, reference_latent=reference_latent, ref_mask_flag=ref_mask_flag)
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return self.forward_orig(x, timestep, context, clip_fea=clip_fea, freqs=freqs, transformer_options=transformer_options, pose_latents=pose_latents, reference_latent=reference_latent, ref_mask_latents=ref_mask_latents, sam_latents=sam_latents, **kwargs)[:, :, :t, :h, :w]
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class SCAIL2WanModel(SCAILWanModel):
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"""SCAIL-2: SCAIL-Preview + an additive binary multi-identity mask stream."""
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def __init__(self, model_type="scail2", patch_size=(1, 2, 2), in_dim=20, mask_in_dim=28, dim=5120, operations=None, device=None, dtype=None, **kwargs):
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super().__init__(model_type=model_type, patch_size=patch_size, in_dim=in_dim, dim=dim, operations=operations, device=device, dtype=dtype, **kwargs)
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self.patch_embedding_mask = operations.Conv3d(mask_in_dim, dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=torch.float32)
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@ -1754,6 +1754,80 @@ class WAN21_SCAIL(WAN21):
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return out
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class WAN21_SCAIL2(WAN21_SCAIL):
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"""SCAIL-2: SCAIL-Preview + an additive binary multi-identity mask stream."""
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def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
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super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.SCAIL2WanModel)
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self.memory_usage_factor_conds = ("reference_latent", "pose_latents", "ref_mask_latents", "sam_latents")
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self.memory_usage_shape_process = {
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"pose_latents": lambda shape: [shape[0], shape[1], 1.5, shape[-2], shape[-1]],
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"sam_latents": lambda shape: [shape[0], shape[1], 1.5, shape[-2], shape[-1]],
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}
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self.image_to_video = image_to_video
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def extra_conds(self, **kwargs):
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out = super().extra_conds(**kwargs)
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driving_mask_28ch = kwargs.get("driving_mask_28ch", None)
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if driving_mask_28ch is not None:
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out['sam_latents'] = comfy.conds.CONDRegular(driving_mask_28ch.movedim(1, 2).contiguous())
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ref_mask_28ch = kwargs.get("ref_mask_28ch", None)
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if ref_mask_28ch is not None:
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out['ref_mask_latents'] = comfy.conds.CONDRegular(ref_mask_28ch.movedim(1, 2).contiguous())
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ref_mask_flag = kwargs.get("ref_mask_flag", None)
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if ref_mask_flag is not None:
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out['ref_mask_flag'] = comfy.conds.CONDConstant(ref_mask_flag)
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return out
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def extra_conds_shapes(self, **kwargs):
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out = super().extra_conds_shapes(**kwargs)
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driving_mask_28ch = kwargs.get("driving_mask_28ch", None)
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if driving_mask_28ch is not None:
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s = driving_mask_28ch.shape
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out['sam_latents'] = [s[0], 28, s[1], s[3], s[4]]
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ref_mask_28ch = kwargs.get("ref_mask_28ch", None)
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if ref_mask_28ch is not None:
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s = ref_mask_28ch.shape
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out['ref_mask_latents'] = [s[0], 28, s[1], s[3], s[4]]
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return out
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def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):
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if cond_key in ("sam_latents", "pose_latents"):
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return comfy.context_windows.slice_cond(cond_value, window, x_in, device, temporal_dim=2, temporal_offset=1)
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return super().resize_cond_for_context_window(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list)
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def concat_cond(self, **kwargs):
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# The 4 extra channels are the history_mask (1 at clean-anchor frames).
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noise = kwargs.get("noise", None)
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extra_channels = self.diffusion_model.patch_embedding.weight.shape[1] - noise.shape[1]
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if extra_channels != 4:
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return super().concat_cond(**kwargs)
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mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
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if mask is None:
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return torch.zeros_like(noise)[:, :4]
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device = kwargs["device"]
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if mask.shape[1] != 4:
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mask = torch.mean(mask, dim=1, keepdim=True)
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mask = 1.0 - mask
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mask = utils.common_upscale(mask.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
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if mask.shape[-3] < noise.shape[-3]:
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mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode='constant', value=0)
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if mask.shape[1] == 1:
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mask = mask.repeat(1, 4, 1, 1, 1)
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mask = utils.resize_to_batch_size(mask, noise.shape[0])
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return mask
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def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
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# Hold anchor constant across all sigmas instead of base sigma*noise + (1-sigma)*latent_image.
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return latent_image
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class WAN22_WanDancer(WAN21):
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def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=True, device=None):
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super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model_wandancer.WanDancerModel)
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@ -630,6 +630,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
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dit_config["model_type"] = "humo"
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elif '{}face_adapter.fuser_blocks.0.k_norm.weight'.format(key_prefix) in state_dict_keys:
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dit_config["model_type"] = "animate"
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elif '{}patch_embedding_mask.weight'.format(key_prefix) in state_dict_keys:
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dit_config["model_type"] = "scail2"
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elif '{}patch_embedding_pose.weight'.format(key_prefix) in state_dict_keys:
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dit_config["model_type"] = "scail"
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elif '{}patch_embedding_global.weight'.format(key_prefix) in state_dict_keys:
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@ -1450,6 +1450,17 @@ class WAN21_SCAIL(WAN21_T2V):
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out = model_base.WAN21_SCAIL(self, image_to_video=False, device=device)
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return out
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class WAN21_SCAIL2(WAN21_T2V):
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unet_config = {
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"image_model": "wan2.1",
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"model_type": "scail2",
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}
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def get_model(self, state_dict, prefix="", device=None):
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out = model_base.WAN21_SCAIL2(self, image_to_video=False, device=device)
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return out
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class WAN22_WanDancer(WAN21_T2V):
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unet_config = {
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"image_model": "wan2.1",
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@ -2259,6 +2270,7 @@ models = [
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WAN22_Animate,
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WAN21_FlowRVS,
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WAN21_SCAIL,
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WAN21_SCAIL2,
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WAN22_WanDancer,
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Hunyuan3Dv2mini,
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Hunyuan3Dv2,
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321
comfy_extras/nodes_scail.py
Normal file
321
comfy_extras/nodes_scail.py
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@ -0,0 +1,321 @@
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"""SCAIL / SCAIL-2 nodes: the WanSCAILToVideo conditioning node and the SAM3
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preprocessing that turns video tracks into the bundle the SCAIL-2 model consumes."""
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from typing_extensions import override
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import torch
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import torch.nn.functional as F
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import nodes
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import node_helpers
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import comfy.model_management
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import comfy.utils
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from comfy_api.latest import ComfyExtension, io
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from comfy.ldm.sam3.tracker import unpack_masks
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SAM3TrackData = io.Custom("SAM3_TRACK_DATA")
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# Model was trained on these exact colors; deviating degrades multi-identity quality.
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DEFAULT_PALETTE = [
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(0.0, 0.0, 1.0), # Blue
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(1.0, 0.0, 0.0), # Red
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(0.0, 1.0, 0.0), # Green
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(1.0, 0.0, 1.0), # Magenta
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(0.0, 1.0, 1.0), # Cyan
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(1.0, 1.0, 0.0), # Yellow
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]
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def _unpack(track_data):
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packed = track_data["packed_masks"]
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if packed is None or packed.shape[1] == 0:
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return None
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return unpack_masks(packed)
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def _first_frame_cx_area(masks_bool):
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first = masks_bool[0].float()
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H, W = first.shape[-2], first.shape[-1]
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n_pixels = H * W
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grid_x = torch.arange(W, device=first.device, dtype=first.dtype).view(1, W)
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area = first.sum(dim=(-1, -2)).clamp_(min=1)
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cx = (first * grid_x).sum(dim=(-1, -2)) / area
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return (cx / W).tolist(), (area / n_pixels).tolist()
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def _subset_track_data(track_data, obj_indices):
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out = dict(track_data)
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packed = track_data["packed_masks"]
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if packed is None or not obj_indices:
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out["packed_masks"] = None
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if "scores" in out:
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out["scores"] = []
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return out
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out["packed_masks"] = packed[:, obj_indices].contiguous()
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scores = track_data.get("scores")
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if scores is not None:
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out["scores"] = [scores[i] for i in obj_indices if i < len(scores)]
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return out
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def _render_colored_masks(track_data, background="black"):
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packed = track_data["packed_masks"]
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H, W = track_data["orig_size"]
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device = comfy.model_management.intermediate_device()
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dtype = comfy.model_management.intermediate_dtype()
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bg_rgb = (1.0, 1.0, 1.0) if background.startswith("white") else (0.0, 0.0, 0.0)
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if packed is None or packed.shape[1] == 0:
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T = track_data.get("n_frames", 1) if packed is None else packed.shape[0]
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out = torch.empty(T, H, W, 3, device=device, dtype=dtype)
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out[..., 0], out[..., 1], out[..., 2] = bg_rgb[0], bg_rgb[1], bg_rgb[2]
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return out
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T, N_obj = packed.shape[0], packed.shape[1]
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colors = torch.tensor(
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[DEFAULT_PALETTE[i % len(DEFAULT_PALETTE)] for i in range(N_obj)],
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device=device, dtype=dtype,
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)
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masks_full = unpack_masks(packed.to(device)).float()
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Hm, Wm = masks_full.shape[-2], masks_full.shape[-1]
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masks_full = F.interpolate(
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masks_full.view(T * N_obj, 1, Hm, Wm), size=(H, W), mode="nearest"
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).view(T, N_obj, H, W) > 0.5
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any_mask = masks_full.any(dim=1)
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obj_idx_map = masks_full.to(torch.uint8).argmax(dim=1)
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color_overlay = colors[obj_idx_map]
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bg_tensor = torch.tensor(bg_rgb, device=device, dtype=color_overlay.dtype).view(1, 1, 1, 3)
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return torch.where(any_mask.unsqueeze(-1), color_overlay, bg_tensor.expand_as(color_overlay))
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def _extract_mask_to_28ch(rgb_video):
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"""Colored RGB mask (T, H, W, 3) in [0, 1] -> SCAIL-2 28-channel binary latent
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(1, T_lat, 28, H_lat, W_lat). 7 per-color binary channels (white/r/g/b/y/m/c)
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threshold-extracted at 225/255, 8x spatial downsample, 4-frame temporal stacking."""
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T, H, W, _ = rgb_video.shape
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_ON_THRESH = 225.0 / 255.0
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mask = rgb_video.movedim(-1, 1).float()
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R = (mask[:, 0:1] > _ON_THRESH).float()
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G = (mask[:, 1:2] > _ON_THRESH).float()
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B = (mask[:, 2:3] > _ON_THRESH).float()
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nR, nG, nB = 1 - R, 1 - G, 1 - B
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binary_7ch = torch.cat([
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R * G * B, # white
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R * nG * nB, # red
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nR * G * nB, # green
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nR * nG * B, # blue
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R * G * nB, # yellow
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R * nG * B, # magenta
|
||||
nR * G * B, # cyan
|
||||
], dim=1)
|
||||
H_lat, W_lat = H, W
|
||||
for _ in range(3):
|
||||
H_lat = (H_lat + 1) // 2
|
||||
W_lat = (W_lat + 1) // 2
|
||||
binary_7ch = torch.nn.functional.interpolate(binary_7ch, size=(H_lat, W_lat), mode='area')
|
||||
T_latent = (T - 1) // 4 + 1
|
||||
padded = torch.cat([binary_7ch[:1].repeat(4, 1, 1, 1), binary_7ch[1:]], dim=0)
|
||||
out = padded.view(T_latent, 28, H_lat, W_lat)
|
||||
return out.unsqueeze(0)
|
||||
|
||||
|
||||
class WanSCAILToVideo(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanSCAILToVideo",
|
||||
category="model/conditioning/video_models",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
io.Vae.Input("vae"),
|
||||
io.Int.Input("width", default=512, min=32, max=nodes.MAX_RESOLUTION, step=32),
|
||||
io.Int.Input("height", default=896, min=32, max=nodes.MAX_RESOLUTION, step=32),
|
||||
io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
|
||||
io.Int.Input("batch_size", default=1, min=1, max=4096),
|
||||
io.Image.Input("pose_video", optional=True, tooltip="Video used for pose conditioning. Will be downscaled to half the resolution of the main video."),
|
||||
io.Image.Input("pose_video_mask", optional=True, tooltip="SCAIL-2 only. Colored per-identity SAM3 mask video at the same resolution as pose_video."),
|
||||
io.Boolean.Input("replacement_mode", default=False, optional=True, tooltip="SCAIL-2 only. False = Animation Mode (pose_video_mask should have black background). True = Replacement Mode (pose_video_mask should have white background)."),
|
||||
io.Float.Input("pose_strength", default=1.0, min=0.0, max=10.0, step=0.01, tooltip="Strength of the pose latent."),
|
||||
io.Float.Input("pose_start", default=0.0, min=0.0, max=1.0, step=0.01, tooltip="Start step of the pose conditioning."),
|
||||
io.Float.Input("pose_end", default=1.0, min=0.0, max=1.0, step=0.01, tooltip="End step of the pose conditioning."),
|
||||
io.Image.Input("reference_image", optional=True, tooltip="Reference image, for multiple references composite all on single image."),
|
||||
io.Image.Input("reference_image_mask", optional=True, tooltip="SCAIL-2 only. Colored reference mask at the same resolution as reference_image."),
|
||||
io.ClipVisionOutput.Input("clip_vision_output", optional=True, tooltip="CLIP vision features for conditioning. Model is trained with stretch resize to aspect ratio."),
|
||||
io.Int.Input("video_frame_offset", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1, tooltip="Cumulative output frame this chunk begins at. Wire from the previous chunk's video_frame_offset output."),
|
||||
io.Int.Input("previous_frame_count", default=5, min=1, max=nodes.MAX_RESOLUTION, step=4, tooltip="Tail frames of previous_frames to anchor. SCAIL-2 trained at 5 (81-frame chunks, 76-frame step)."),
|
||||
io.Image.Input("previous_frames", optional=True, tooltip="SCAIL-2 only. Full decoded output of the previous chunk. Only the last previous_frame_count are used as the extension anchor."),
|
||||
],
|
||||
outputs=[
|
||||
io.Conditioning.Output(display_name="positive"),
|
||||
io.Conditioning.Output(display_name="negative"),
|
||||
io.Latent.Output(display_name="latent", tooltip="Empty latent of the generation size."),
|
||||
io.Int.Output(display_name="video_frame_offset", tooltip="Adjusted offset + length. Wire into the next chunk."),
|
||||
],
|
||||
is_experimental=True,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, positive, negative, vae, width, height, length, batch_size, pose_strength, pose_start, pose_end,
|
||||
video_frame_offset, previous_frame_count, replacement_mode=False, reference_image=None, clip_vision_output=None, pose_video=None,
|
||||
pose_video_mask=None, reference_image_mask=None, previous_frames=None) -> io.NodeOutput:
|
||||
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
|
||||
noise_mask = None
|
||||
|
||||
ref_mask_flag = not replacement_mode
|
||||
positive = node_helpers.conditioning_set_values(positive, {"ref_mask_flag": ref_mask_flag})
|
||||
negative = node_helpers.conditioning_set_values(negative, {"ref_mask_flag": ref_mask_flag})
|
||||
|
||||
prev_trimmed = None
|
||||
if previous_frames is not None and previous_frames.shape[0] > 0:
|
||||
prev_trimmed = previous_frames[-previous_frame_count:]
|
||||
video_frame_offset -= prev_trimmed.shape[0]
|
||||
video_frame_offset = max(0, video_frame_offset)
|
||||
|
||||
ref_latent = None
|
||||
if reference_image is not None:
|
||||
reference_image = comfy.utils.common_upscale(reference_image[:1].movedim(-1, 1), width, height, "bicubic", "center").movedim(1, -1)
|
||||
# Replacement Mode: composite ref on black bg using reference_image_mask as alpha matte
|
||||
if replacement_mode and reference_image_mask is not None:
|
||||
rm = comfy.utils.common_upscale(reference_image_mask[:1].movedim(-1, 1), width, height, "nearest-exact", "center").movedim(1, -1)
|
||||
is_char = (rm[..., :3].max(dim=-1, keepdim=True).values > 0.1).to(reference_image.dtype)
|
||||
reference_image = reference_image * is_char
|
||||
ref_latent = vae.encode(reference_image[:, :, :, :3])
|
||||
|
||||
if ref_latent is not None:
|
||||
positive = node_helpers.conditioning_set_values(positive, {"reference_latents": [ref_latent]}, append=True)
|
||||
negative = node_helpers.conditioning_set_values(negative, {"reference_latents": [ref_latent]}, append=True)
|
||||
|
||||
if clip_vision_output is not None:
|
||||
positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
|
||||
negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
|
||||
|
||||
if pose_video is not None:
|
||||
if pose_video.shape[0] <= video_frame_offset:
|
||||
pose_video = None
|
||||
else:
|
||||
pose_video = pose_video[video_frame_offset:]
|
||||
if pose_video_mask is not None:
|
||||
if pose_video_mask.shape[0] <= video_frame_offset:
|
||||
pose_video_mask = None
|
||||
else:
|
||||
pose_video_mask = pose_video_mask[video_frame_offset:]
|
||||
|
||||
# Truncate pose+mask jointly to the shorter of the two, capped at length.
|
||||
ts = [v.shape[0] for v in (pose_video, pose_video_mask) if v is not None]
|
||||
if ts:
|
||||
T_kept = ((min(min(ts), length) - 1) // 4) * 4 + 1
|
||||
if pose_video is not None:
|
||||
pose_video = pose_video[:T_kept]
|
||||
if pose_video_mask is not None:
|
||||
pose_video_mask = pose_video_mask[:T_kept]
|
||||
|
||||
if pose_video is not None:
|
||||
pose_video = comfy.utils.common_upscale(pose_video[:length].movedim(-1, 1), width // 2, height // 2, "area", "center").movedim(1, -1)
|
||||
pose_video_latent = vae.encode(pose_video[:, :, :, :3]) * pose_strength
|
||||
positive = node_helpers.conditioning_set_values_with_timestep_range(positive, {"pose_video_latent": pose_video_latent}, pose_start, pose_end)
|
||||
negative = node_helpers.conditioning_set_values_with_timestep_range(negative, {"pose_video_latent": pose_video_latent}, pose_start, pose_end)
|
||||
|
||||
if pose_video_mask is not None:
|
||||
mask_video_hw = comfy.utils.common_upscale(pose_video_mask[:length].movedim(-1, 1), width // 2, height // 2, "area", "center").movedim(1, -1)
|
||||
driving_mask_28ch = _extract_mask_to_28ch(mask_video_hw)
|
||||
positive = node_helpers.conditioning_set_values(positive, {"driving_mask_28ch": driving_mask_28ch})
|
||||
negative = node_helpers.conditioning_set_values(negative, {"driving_mask_28ch": driving_mask_28ch})
|
||||
|
||||
if reference_image_mask is not None:
|
||||
ref_mask_hw = comfy.utils.common_upscale(reference_image_mask[:1].movedim(-1, 1), width, height, "bicubic", "center").movedim(1, -1)
|
||||
ref_mask_1f = _extract_mask_to_28ch(ref_mask_hw)
|
||||
zeros = torch.zeros((1, latent.shape[2], 28, ref_mask_1f.shape[-2], ref_mask_1f.shape[-1]), device=ref_mask_1f.device, dtype=ref_mask_1f.dtype)
|
||||
ref_mask_28ch = torch.cat([ref_mask_1f, zeros], dim=1)
|
||||
positive = node_helpers.conditioning_set_values(positive, {"ref_mask_28ch": ref_mask_28ch})
|
||||
negative = node_helpers.conditioning_set_values(negative, {"ref_mask_28ch": ref_mask_28ch})
|
||||
|
||||
if prev_trimmed is not None:
|
||||
pf = comfy.utils.common_upscale(prev_trimmed.movedim(-1, 1), width, height, "bicubic", "center").movedim(1, -1)
|
||||
prev_latent = vae.encode(pf[:, :, :, :3])
|
||||
prev_latent_frames = min(prev_latent.shape[2], latent.shape[2])
|
||||
latent[:, :, :prev_latent_frames] = prev_latent[:, :, :prev_latent_frames].to(latent.dtype)
|
||||
noise_mask = torch.ones((1, 1, latent.shape[2], latent.shape[-2], latent.shape[-1]), device=latent.device, dtype=latent.dtype)
|
||||
noise_mask[:, :, :prev_latent_frames] = 0.0
|
||||
|
||||
out_latent = {"samples": latent}
|
||||
if noise_mask is not None:
|
||||
out_latent["noise_mask"] = noise_mask
|
||||
return io.NodeOutput(positive, negative, out_latent, video_frame_offset + length)
|
||||
|
||||
|
||||
class SCAIL2ColoredMask(io.ComfyNode):
|
||||
"""Render SAM3 tracks for the driving pose video and (optionally) the reference
|
||||
image into the two colored masks WanSCAILToVideo consumes. Shared `sort_by`
|
||||
across both outputs guarantees identity K maps to the same color on both
|
||||
sides, for multi-person workflow consistency.
|
||||
reference_image_mask is always rendered black-bg (model convention)
|
||||
pose_video_mask bg follows replacement_mode: black = Animation Mode, white = Replacement Mode
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="SCAIL2ColoredMask",
|
||||
display_name="Create SCAIL-2 Colored Mask",
|
||||
category="conditioning/video_models/scail",
|
||||
inputs=[
|
||||
SAM3TrackData.Input("driving_track_data", tooltip="SAM3 track of the driving pose video. Will be rendered into the pose_video_mask output."),
|
||||
SAM3TrackData.Input("ref_track_data", optional=True,
|
||||
tooltip="SAM3 track of the reference image."),
|
||||
io.String.Input("object_indices", default="",
|
||||
tooltip="Comma-separated list of person indices to include (e.g. '0,2,3'). Applied to both reference and pose video masks. Empty = all."),
|
||||
io.Combo.Input("sort_by", options=["none", "left_to_right", "area"], default="left_to_right",
|
||||
tooltip="Order in which palette colors are assigned to the tracked objects (applied to both reference and pose video so each identity keeps the same color). left_to_right = leftmost object (by first-frame centroid) gets the first color; area = biggest object (by first-frame mask area) gets the first color; none = keep SAM3's order."),
|
||||
io.Boolean.Input("replacement_mode", default=False,
|
||||
tooltip="False = mask_video has black bg (Animation Mode). True = white bg (Replacement Mode). Set the matching replacement_mode on WanSCAILToVideo. reference_image_mask is always black-bg regardless."),
|
||||
],
|
||||
outputs=[
|
||||
io.Image.Output("pose_video_mask"),
|
||||
io.Image.Output("reference_image_mask"),
|
||||
],
|
||||
is_experimental=True,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, driving_track_data, object_indices, sort_by, replacement_mode, ref_track_data=None):
|
||||
def _prep(td):
|
||||
masks_bool = _unpack(td)
|
||||
if sort_by != "none" and masks_bool is not None:
|
||||
cx, area = _first_frame_cx_area(masks_bool)
|
||||
if sort_by == "left_to_right":
|
||||
order = sorted(range(len(cx)), key=lambda i: cx[i])
|
||||
else: # "area"
|
||||
order = sorted(range(len(area)), key=lambda i: -area[i])
|
||||
td = _subset_track_data(td, order)
|
||||
if object_indices.strip():
|
||||
indices = [int(i.strip()) for i in object_indices.split(",") if i.strip().isdigit()]
|
||||
packed = td.get("packed_masks")
|
||||
n_obj = packed.shape[1] if packed is not None else 0
|
||||
indices = [i for i in indices if 0 <= i < n_obj]
|
||||
td = _subset_track_data(td, indices)
|
||||
return td
|
||||
|
||||
drv = _prep(driving_track_data)
|
||||
mask_video = _render_colored_masks(drv, "white" if replacement_mode else "black")
|
||||
|
||||
if ref_track_data is not None:
|
||||
ref = _prep(ref_track_data)
|
||||
reference_image_mask = _render_colored_masks(ref, "black")
|
||||
else:
|
||||
H, W = drv["orig_size"]
|
||||
reference_image_mask = torch.zeros(1, H, W, 3, device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
|
||||
|
||||
return io.NodeOutput(mask_video, reference_image_mask)
|
||||
|
||||
|
||||
class SCAILExtension(ComfyExtension):
|
||||
@override
|
||||
async def get_node_list(self) -> list[type[io.ComfyNode]]:
|
||||
return [
|
||||
WanSCAILToVideo,
|
||||
SCAIL2ColoredMask,
|
||||
]
|
||||
|
||||
|
||||
async def comfy_entrypoint() -> SCAILExtension:
|
||||
return SCAILExtension()
|
||||
@ -1456,63 +1456,6 @@ class WanInfiniteTalkToVideo(io.ComfyNode):
|
||||
return io.NodeOutput(model_patched, positive, negative, out_latent, trim_image)
|
||||
|
||||
|
||||
class WanSCAILToVideo(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return io.Schema(
|
||||
node_id="WanSCAILToVideo",
|
||||
category="model/conditioning/video_models",
|
||||
inputs=[
|
||||
io.Conditioning.Input("positive"),
|
||||
io.Conditioning.Input("negative"),
|
||||
io.Vae.Input("vae"),
|
||||
io.Int.Input("width", default=512, min=32, max=nodes.MAX_RESOLUTION, step=32),
|
||||
io.Int.Input("height", default=896, min=32, max=nodes.MAX_RESOLUTION, step=32),
|
||||
io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
|
||||
io.Int.Input("batch_size", default=1, min=1, max=4096),
|
||||
io.ClipVisionOutput.Input("clip_vision_output", optional=True),
|
||||
io.Image.Input("reference_image", optional=True),
|
||||
io.Image.Input("pose_video", optional=True, tooltip="Video used for pose conditioning. Will be downscaled to half the resolution of the main video."),
|
||||
io.Float.Input("pose_strength", default=1.0, min=0.0, max=10.0, step=0.01, tooltip="Strength of the pose latent."),
|
||||
io.Float.Input("pose_start", default=0.0, min=0.0, max=1.0, step=0.01, tooltip="Start step to use pose conditioning."),
|
||||
io.Float.Input("pose_end", default=1.0, min=0.0, max=1.0, step=0.01, tooltip="End step to use pose conditioning."),
|
||||
],
|
||||
outputs=[
|
||||
io.Conditioning.Output(display_name="positive"),
|
||||
io.Conditioning.Output(display_name="negative"),
|
||||
io.Latent.Output(display_name="latent", tooltip="Empty latent of the generation size."),
|
||||
],
|
||||
is_experimental=True,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, positive, negative, vae, width, height, length, batch_size, pose_strength, pose_start, pose_end, reference_image=None, clip_vision_output=None, pose_video=None) -> io.NodeOutput:
|
||||
latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
|
||||
|
||||
ref_latent = None
|
||||
if reference_image is not None:
|
||||
reference_image = comfy.utils.common_upscale(reference_image[:1].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
|
||||
ref_latent = vae.encode(reference_image[:, :, :, :3])
|
||||
|
||||
if ref_latent is not None:
|
||||
positive = node_helpers.conditioning_set_values(positive, {"reference_latents": [ref_latent]}, append=True)
|
||||
negative = node_helpers.conditioning_set_values(negative, {"reference_latents": [torch.zeros_like(ref_latent)]}, append=True)
|
||||
|
||||
if clip_vision_output is not None:
|
||||
positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
|
||||
negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
|
||||
|
||||
if pose_video is not None:
|
||||
pose_video = comfy.utils.common_upscale(pose_video[:length].movedim(-1, 1), width // 2, height // 2, "area", "center").movedim(1, -1)
|
||||
pose_video_latent = vae.encode(pose_video[:, :, :, :3]) * pose_strength
|
||||
positive = node_helpers.conditioning_set_values_with_timestep_range(positive, {"pose_video_latent": pose_video_latent}, pose_start, pose_end)
|
||||
negative = node_helpers.conditioning_set_values_with_timestep_range(negative, {"pose_video_latent": pose_video_latent}, pose_start, pose_end)
|
||||
|
||||
out_latent = {}
|
||||
out_latent["samples"] = latent
|
||||
return io.NodeOutput(positive, negative, out_latent)
|
||||
|
||||
|
||||
class WanExtension(ComfyExtension):
|
||||
@override
|
||||
async def get_node_list(self) -> list[type[io.ComfyNode]]:
|
||||
@ -1533,7 +1476,6 @@ class WanExtension(ComfyExtension):
|
||||
WanAnimateToVideo,
|
||||
Wan22ImageToVideoLatent,
|
||||
WanInfiniteTalkToVideo,
|
||||
WanSCAILToVideo,
|
||||
]
|
||||
|
||||
async def comfy_entrypoint() -> WanExtension:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user