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https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2026-09-04 08:47:08 +08:00
support wan camera models
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Qirui Sun
parent
924d771e18
commit
3a59a6e28b
@ -247,6 +247,60 @@ class VaceWanAttentionBlock(WanAttentionBlock):
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return c_skip, c
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class WanCamAdapter(nn.Module):
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def __init__(self, in_dim, out_dim, kernel_size, stride, num_residual_blocks=1, operation_settings={}):
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super(WanCamAdapter, self).__init__()
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# Pixel Unshuffle: reduce spatial dimensions by a factor of 8
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self.pixel_unshuffle = nn.PixelUnshuffle(downscale_factor=8)
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# Convolution: reduce spatial dimensions by a factor
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# of 2 (without overlap)
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self.conv = operation_settings.get("operations").Conv2d(in_dim * 64, out_dim, kernel_size=kernel_size, stride=stride, padding=0, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
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# Residual blocks for feature extraction
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self.residual_blocks = nn.Sequential(
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*[WanCamResidualBlock(out_dim, operation_settings = operation_settings) for _ in range(num_residual_blocks)]
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)
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def forward(self, x):
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# Reshape to merge the frame dimension into batch
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bs, c, f, h, w = x.size()
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x = x.permute(0, 2, 1, 3, 4).contiguous().view(bs * f, c, h, w)
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# Pixel Unshuffle operation
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x_unshuffled = self.pixel_unshuffle(x)
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# Convolution operation
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x_conv = self.conv(x_unshuffled)
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# Feature extraction with residual blocks
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out = self.residual_blocks(x_conv)
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# Reshape to restore original bf dimension
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out = out.view(bs, f, out.size(1), out.size(2), out.size(3))
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# Permute dimensions to reorder (if needed), e.g., swap channels and feature frames
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out = out.permute(0, 2, 1, 3, 4)
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return out
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class WanCamResidualBlock(nn.Module):
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def __init__(self, dim, operation_settings={}):
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super(WanCamResidualBlock, self).__init__()
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self.conv1 = operation_settings.get("operations").Conv2d(dim, dim, kernel_size=3, padding=1, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = operation_settings.get("operations").Conv2d(dim, dim, kernel_size=3, padding=1, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
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def forward(self, x):
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residual = x
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out = self.relu(self.conv1(x))
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out = self.conv2(out)
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out += residual
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return out
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class Head(nn.Module):
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def __init__(self, dim, out_dim, patch_size, eps=1e-6, operation_settings={}):
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@ -637,3 +691,92 @@ class VaceWanModel(WanModel):
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# unpatchify
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x = self.unpatchify(x, grid_sizes)
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return x
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class CameraWanModel(WanModel):
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r"""
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Wan diffusion backbone supporting both text-to-video and image-to-video.
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"""
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def __init__(self,
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model_type='camera',
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patch_size=(1, 2, 2),
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text_len=512,
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in_dim=16,
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dim=2048,
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ffn_dim=8192,
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freq_dim=256,
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text_dim=4096,
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out_dim=16,
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num_heads=16,
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num_layers=32,
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window_size=(-1, -1),
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qk_norm=True,
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cross_attn_norm=True,
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eps=1e-6,
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flf_pos_embed_token_number=None,
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image_model=None,
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in_dim_control_adapter=24,
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device=None,
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dtype=None,
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operations=None,
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):
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super().__init__(model_type='i2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations)
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operation_settings = {"operations": operations, "device": device, "dtype": dtype}
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self.control_adapter = WanCamAdapter(in_dim_control_adapter, dim, kernel_size=patch_size[1:], stride=patch_size[1:], operation_settings=operation_settings)
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def forward_orig(
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self,
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x,
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t,
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context,
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clip_fea=None,
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freqs=None,
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camera_conditions = None,
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transformer_options={},
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**kwargs,
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):
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# embeddings
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x = self.patch_embedding(x.float()).to(x.dtype)
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if self.control_adapter is not None and camera_conditions is not None:
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x_camera = self.control_adapter(camera_conditions).to(x.dtype)
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x = x + x_camera
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grid_sizes = x.shape[2:]
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x = x.flatten(2).transpose(1, 2)
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# time embeddings
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e = self.time_embedding(
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sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype))
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e0 = self.time_projection(e).unflatten(1, (6, self.dim))
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# context
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context = self.text_embedding(context)
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context_img_len = None
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if clip_fea is not None:
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if self.img_emb is not None:
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context_clip = self.img_emb(clip_fea) # bs x 257 x dim
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context = torch.concat([context_clip, context], dim=1)
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context_img_len = clip_fea.shape[-2]
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patches_replace = transformer_options.get("patches_replace", {})
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blocks_replace = patches_replace.get("dit", {})
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for i, block in enumerate(self.blocks):
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if ("double_block", i) in blocks_replace:
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def block_wrap(args):
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out = {}
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out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len)
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return out
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out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap})
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x = out["img"]
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else:
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x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
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# head
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x = self.head(x, e)
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# unpatchify
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x = self.unpatchify(x, grid_sizes)
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return x
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@ -1075,6 +1075,16 @@ class WAN21_Vace(WAN21):
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out['vace_strength'] = comfy.conds.CONDConstant(vace_strength)
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return out
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class WAN21_Camera(WAN21):
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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.CameraWanModel)
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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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camera_conditions = kwargs.get("camera_conditions", None)
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out['camera_conditions'] = comfy.conds.CONDRegular(camera_conditions)
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return out
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class Hunyuan3Dv2(BaseModel):
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def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
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@ -992,6 +992,16 @@ class WAN21_FunControl2V(WAN21_T2V):
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out = model_base.WAN21(self, image_to_video=False, device=device)
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return out
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class WAN21_Camera(WAN21_T2V):
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unet_config = {
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"image_model": "wan2.1",
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"model_type": "i2v",
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"in_dim": 32,
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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_Camera(self, image_to_video=False, device=device)
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return out
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class WAN21_Vace(WAN21_T2V):
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unet_config = {
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"image_model": "wan2.1",
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@ -1129,6 +1139,6 @@ class ACEStep(supported_models_base.BASE):
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def clip_target(self, state_dict={}):
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return supported_models_base.ClipTarget(comfy.text_encoders.ace.AceT5Tokenizer, comfy.text_encoders.ace.AceT5Model)
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models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma, ACEStep]
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models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, Lumina2, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma, ACEStep]
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models += [SVD_img2vid]
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@ -297,6 +297,48 @@ class TrimVideoLatent:
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samples_out["samples"] = s1[:, :, trim_amount:]
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return (samples_out,)
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class WanCameraImageToVideo:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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"vae": ("VAE", ),
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"camera_conditions": ("LATENT", ),
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"width": ("INT", {"default": 832, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
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"height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
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"length": ("INT", {"default": 81, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
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"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
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},
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"optional": {"clip_vision_output": ("CLIP_VISION_OUTPUT", ),
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"start_image": ("IMAGE", ),
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}}
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RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
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RETURN_NAMES = ("positive", "negative", "latent")
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FUNCTION = "encode"
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CATEGORY = "conditioning/video_models"
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def encode(self, positive, negative, vae, camera_conditions, width, height, length, batch_size, start_image=None, clip_vision_output=None):
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latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
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concat_latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
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concat_latent = comfy.latent_formats.Wan21().process_out(concat_latent)
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if start_image is not None:
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start_image = comfy.utils.common_upscale(start_image[:length].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
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concat_latent_image = vae.encode(start_image[:, :, :, :3])
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concat_latent[:,:,:concat_latent_image.shape[2]] = concat_latent_image[:,:,:concat_latent.shape[2]]
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positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": concat_latent, 'camera_conditions': camera_conditions})
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negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": concat_latent, 'camera_conditions': camera_conditions})
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if clip_vision_output is not None:
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positive = node_helpers.conditioning_set_values(positive, {"clip_vision_output": clip_vision_output})
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negative = node_helpers.conditioning_set_values(negative, {"clip_vision_output": clip_vision_output})
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out_latent = {}
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out_latent["samples"] = latent
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return (positive, negative, out_latent)
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NODE_CLASS_MAPPINGS = {
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"WanImageToVideo": WanImageToVideo,
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@ -305,4 +347,5 @@ NODE_CLASS_MAPPINGS = {
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"WanFirstLastFrameToVideo": WanFirstLastFrameToVideo,
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"WanVaceToVideo": WanVaceToVideo,
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"TrimVideoLatent": TrimVideoLatent,
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"WanCameraImageToVideo": WanCameraImageToVideo,
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}
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