Initial sketch controlnet support

lvdm/models/ddpm3d.py

@@ -387,6 +387,7 @@ class LatentDiffusion(DDPM):
                  logdir=None,
                  rand_cond_frame=False,
                  en_and_decode_n_samples_a_time=None,
+                 control_scale=1.0,
                  *args, **kwargs):
         self.num_timesteps_cond = default(num_timesteps_cond, 1)
         self.scale_by_std = scale_by_std
@@ -404,6 +405,7 @@ class LatentDiffusion(DDPM):
         self.loop_video = loop_video
         self.fps_condition_type = fps_condition_type
         self.perframe_ae = perframe_ae
+        self.control_scale = control_scale
 
         self.logdir = logdir
         self.rand_cond_frame = rand_cond_frame
@@ -572,9 +574,20 @@ class LatentDiffusion(DDPM):
             if not isinstance(cond, list):
                 cond = [cond]
             key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
-            cond = {key: cond}
+            cond = {key: [cond[0]]}
 
-        x_recon = self.model(x_noisy, t, **cond, **kwargs)
+        control_cond = cond["control_cond"]
+        
+        if control_cond is not None:
+            control_cond = rearrange(control_cond, 'b c t h w-> (b t) c h w')
+            control_x = rearrange(x_noisy, 'b c t h w-> (b t) c h w')
+            control_context = repeat(cond["c_crossattn"][0], "b c l-> (repeat b) c l", repeat=16)
+            control = self.control_model(x=control_x, hint=control_cond, timesteps=t, context=control_context)
+            control = [c * self.control_model.control_scale for c in control]
+        else:
+            control = None
+
+        x_recon = self.model(x_noisy, t, c_crossattn=cond["c_crossattn"], c_concat=cond["c_concat"], control=control, **kwargs)
 
         if isinstance(x_recon, tuple):
             return x_recon[0]
@@ -722,7 +735,7 @@ class DiffusionWrapper(pl.LightningModule):
         self.diffusion_model = instantiate_from_config(diff_model_config)
         self.conditioning_key = conditioning_key
 
-    def forward(self, x, t, c_concat: list = None, c_crossattn: list = None,
+    def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, control = None,
                 c_adm=None, s=None, mask=None, **kwargs):
         # temporal_context = fps is foNone
         if self.conditioning_key is None:
@@ -737,7 +750,7 @@ class DiffusionWrapper(pl.LightningModule):
             ## it is just right [b,c,t,h,w]: concatenate in channel dim
             xc = torch.cat([x] + c_concat, dim=1)
             cc = torch.cat(c_crossattn, 1)
-            out = self.diffusion_model(xc, t, context=cc, **kwargs)
+            out = self.diffusion_model(xc, t, context=cc, control=control, **kwargs)
         elif self.conditioning_key == 'resblockcond':
             cc = c_crossattn[0]
             out = self.diffusion_model(x, t, context=cc)

lvdm/modules/networks/openaimodel3d.py

@@ -2,7 +2,9 @@ from functools import partial
 from abc import abstractmethod
 import torch
 import torch.nn as nn
+import numpy as np
 from einops import rearrange
+import math
 import torch.nn.functional as F
 from ....lvdm.models.utils_diffusion import timestep_embedding
 from ....lvdm.common import checkpoint
@@ -18,6 +20,9 @@ from ....lvdm.modules.attention import SpatialTransformer, TemporalTransformer
 import comfy.ops
 ops = comfy.ops.manual_cast
 
+def exists(x):
+    return x is not None
+
 class TimestepBlock(nn.Module):
     """
     Any module where forward() takes timestep embeddings as a second argument.
@@ -547,7 +552,7 @@ class UNetModel(nn.Module):
             zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
         )
 
-    def forward(self, x, timesteps, context=None, features_adapter=None, fs=None, frame_window_size=None, frame_window_stride=None, **kwargs):
+    def forward(self, x, timesteps, context=None, features_adapter=None, fs=None, frame_window_size=None, frame_window_stride=None, control=None, **kwargs):
         b,_,t,_,_ = x.shape
         t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).type(x.dtype)
         emb = self.time_embed(t_emb)
@@ -594,8 +599,15 @@ class UNetModel(nn.Module):
             assert len(features_adapter)==adapter_idx, 'Wrong features_adapter'
 
         h = self.middle_block(h, emb, context=context, batch_size=b, frame_window_size=frame_window_size, frame_window_stride=frame_window_stride)
+        
+        if control is not None:
+            h += control.pop()
+        
         for module in self.output_blocks:
+            if control is None:
                 h = torch.cat([h, hs.pop()], dim=1)
+            else:
+                h = torch.cat([h, hs.pop() + control.pop()], dim=1)
             h = module(h, emb, context=context, batch_size=b, frame_window_size=frame_window_size, frame_window_stride=frame_window_stride)
         h = h.type(x.dtype)
         y = self.out(h)
@@ -603,3 +615,394 @@ class UNetModel(nn.Module):
         # reshape back to (b c t h w)
         y = rearrange(y, '(b t) c h w -> b c t h w', b=b)
         return y
+
+class ControlNet(nn.Module):
+    def __init__(
+            self,
+            image_size,
+            in_channels,
+            model_channels,
+            hint_channels,
+            num_res_blocks,
+            attention_resolutions,
+            dropout=0,
+            channel_mult=(1, 2, 4, 8),
+            conv_resample=True,
+            dims=2,
+            use_checkpoint=False,
+            use_fp16=False,
+            num_heads=-1,
+            num_head_channels=-1,
+            num_heads_upsample=-1,
+            use_scale_shift_norm=False,
+            resblock_updown=False,
+            use_new_attention_order=False,
+            use_spatial_transformer=False,  # custom transformer support
+            transformer_depth=1,  # custom transformer support
+            context_dim=None,  # custom transformer support
+            n_embed=None,  # custom support for prediction of discrete ids into codebook of first stage vq model
+            legacy=True,
+            disable_self_attentions=None,
+            num_attention_blocks=None,
+            disable_middle_self_attn=False,
+            use_linear_in_transformer=False,
+    ):
+        super().__init__()
+        if use_spatial_transformer:
+            assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
+
+        if context_dim is not None:
+            assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+            from omegaconf.listconfig import ListConfig
+            if type(context_dim) == ListConfig:
+                context_dim = list(context_dim)
+
+        if num_heads_upsample == -1:
+            num_heads_upsample = num_heads
+
+        if num_heads == -1:
+            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+
+        if num_head_channels == -1:
+            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+
+        self.dims = dims
+        self.image_size = image_size
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        if isinstance(num_res_blocks, int):
+            self.num_res_blocks = len(channel_mult) * [num_res_blocks]
+        else:
+            if len(num_res_blocks) != len(channel_mult):
+                raise ValueError("provide num_res_blocks either as an int (globally constant) or "
+                                 "as a list/tuple (per-level) with the same length as channel_mult")
+            self.num_res_blocks = num_res_blocks
+        if disable_self_attentions is not None:
+            # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
+            assert len(disable_self_attentions) == len(channel_mult)
+        if num_attention_blocks is not None:
+            assert len(num_attention_blocks) == len(self.num_res_blocks)
+            assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
+            print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
+                  f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
+                  f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
+                  f"attention will still not be set.")
+
+        self.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.use_checkpoint = use_checkpoint
+        self.dtype = torch.float16 if use_fp16 else torch.float32
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+        self.predict_codebook_ids = n_embed is not None
+
+        time_embed_dim = model_channels * 4
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        self.input_blocks = nn.ModuleList(
+            [
+                TimestepEmbedSequential(
+                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
+                )
+            ]
+        )
+        self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)])
+
+        self.input_hint_block = TimestepEmbedSequential(
+            conv_nd(dims, hint_channels, 16, 3, padding=1),
+            nn.SiLU(),
+            conv_nd(dims, 16, 16, 3, padding=1),
+            nn.SiLU(),
+            conv_nd(dims, 16, 32, 3, padding=1, stride=2),
+            nn.SiLU(),
+            conv_nd(dims, 32, 32, 3, padding=1),
+            nn.SiLU(),
+            conv_nd(dims, 32, 96, 3, padding=1, stride=2),
+            nn.SiLU(),
+            conv_nd(dims, 96, 96, 3, padding=1),
+            nn.SiLU(),
+            conv_nd(dims, 96, 256, 3, padding=1, stride=2),
+            nn.SiLU(),
+            zero_module(conv_nd(dims, 256, model_channels, 3, padding=1))
+        )
+
+        self._feature_size = model_channels
+        input_block_chans = [model_channels]
+        ch = model_channels
+        ds = 1
+        for level, mult in enumerate(channel_mult):
+            for nr in range(self.num_res_blocks[level]):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=mult * model_channels,
+                        dims=dims,
+                        use_checkpoint=use_checkpoint,
+                        use_scale_shift_norm=use_scale_shift_norm,
+                    )
+                ]
+                ch = mult * model_channels
+                if ds in attention_resolutions:
+                    if num_head_channels == -1:
+                        dim_head = ch // num_heads
+                    else:
+                        num_heads = ch // num_head_channels
+                        dim_head = num_head_channels
+                    if legacy:
+                        # num_heads = 1
+                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+                    if exists(disable_self_attentions):
+                        disabled_sa = disable_self_attentions[level]
+                    else:
+                        disabled_sa = False
+
+                    if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
+                        layers.append(
+                            AttentionBlock(
+                                ch,
+                                use_checkpoint=use_checkpoint,
+                                num_heads=num_heads,
+                                num_head_channels=dim_head,
+                                use_new_attention_order=use_new_attention_order,
+                            ) if not use_spatial_transformer else SpatialTransformer(
+                                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+                                disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+                                use_checkpoint=use_checkpoint
+                            )
+                        )
+                self.input_blocks.append(TimestepEmbedSequential(*layers))
+                self.zero_convs.append(self.make_zero_conv(ch))
+                self._feature_size += ch
+                input_block_chans.append(ch)
+            if level != len(channel_mult) - 1:
+                out_ch = ch
+                self.input_blocks.append(
+                    TimestepEmbedSequential(
+                        ResBlock(
+                            ch,
+                            time_embed_dim,
+                            dropout,
+                            out_channels=out_ch,
+                            dims=dims,
+                            use_checkpoint=use_checkpoint,
+                            use_scale_shift_norm=use_scale_shift_norm,
+                            down=True,
+                        )
+                        if resblock_updown
+                        else Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch
+                        )
+                    )
+                )
+                ch = out_ch
+                input_block_chans.append(ch)
+                self.zero_convs.append(self.make_zero_conv(ch))
+                ds *= 2
+                self._feature_size += ch
+
+        if num_head_channels == -1:
+            dim_head = ch // num_heads
+        else:
+            num_heads = ch // num_head_channels
+            dim_head = num_head_channels
+        if legacy:
+            # num_heads = 1
+            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+        self.middle_block = TimestepEmbedSequential(
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+            AttentionBlock(
+                ch,
+                use_checkpoint=use_checkpoint,
+                num_heads=num_heads,
+                num_head_channels=dim_head,
+                use_new_attention_order=use_new_attention_order,
+            ) if not use_spatial_transformer else SpatialTransformer(  # always uses a self-attn
+                ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+                disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
+                use_checkpoint=use_checkpoint
+            ),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                use_checkpoint=use_checkpoint,
+                use_scale_shift_norm=use_scale_shift_norm,
+            ),
+        )
+        self.middle_block_out = self.make_zero_conv(ch)
+        self._feature_size += ch
+
+    def make_zero_conv(self, channels):
+        return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0)))
+
+    def forward(self, x, hint, timesteps, context, **kwargs):
+        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+        emb = self.time_embed(t_emb)
+
+        guided_hint = self.input_hint_block(hint, emb, context)
+
+        outs = []
+
+        h = x.type(self.dtype)
+     
+        for module, zero_conv in zip(self.input_blocks, self.zero_convs):
+            if guided_hint is not None:
+                h = module(h, emb, context)
+                h += guided_hint
+                guided_hint = None
+            else:
+                h = module(h, emb, context)
+            outs.append(zero_conv(h, emb, context, True))
+
+        h = self.middle_block(h, emb, context)
+        outs.append(self.middle_block_out(h, emb, context))
+
+        return outs
+
+class AttentionBlock(nn.Module):
+    """
+    An attention block that allows spatial positions to attend to each other.
+    Originally ported from here, but adapted to the N-d case.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+    """
+
+    def __init__(
+        self,
+        channels,
+        num_heads=1,
+        num_head_channels=-1,
+        use_checkpoint=False,
+        use_new_attention_order=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        if num_head_channels == -1:
+            self.num_heads = num_heads
+        else:
+            assert (
+                channels % num_head_channels == 0
+            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+            self.num_heads = channels // num_head_channels
+        self.use_checkpoint = use_checkpoint
+        self.norm = normalization(channels)
+        self.qkv = conv_nd(1, channels, channels * 3, 1)
+        if use_new_attention_order:
+            # split qkv before split heads
+            self.attention = QKVAttention(self.num_heads)
+        else:
+            # split heads before split qkv
+            self.attention = QKVAttentionLegacy(self.num_heads)
+
+        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
+
+    def forward(self, x):
+        return checkpoint(self._forward, (x,), self.parameters(), True)   # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
+        #return pt_checkpoint(self._forward, x)  # pytorch
+
+    def _forward(self, x):
+        b, c, *spatial = x.shape
+        x = x.reshape(b, c, -1)
+        qkv = self.qkv(self.norm(x))
+        h = self.attention(qkv)
+        h = self.proj_out(h)
+        return (x + h).reshape(b, c, *spatial)
+    
+class QKVAttention(nn.Module):
+    """
+    A module which performs QKV attention and splits in a different order.
+    """
+
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv):
+        """
+        Apply QKV attention.
+        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.chunk(3, dim=1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = torch.einsum(
+            "bct,bcs->bts",
+            (q * scale).view(bs * self.n_heads, ch, length),
+            (k * scale).view(bs * self.n_heads, ch, length),
+        )  # More stable with f16 than dividing afterwards
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        a = torch.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
+        return a.reshape(bs, -1, length)
+
+    @staticmethod
+    def count_flops(model, _x, y):
+        return count_flops_attn(model, _x, y)
+
+class QKVAttentionLegacy(nn.Module):
+    """
+    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
+    """
+
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv):
+        """
+        Apply QKV attention.
+        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = torch.einsum(
+            "bct,bcs->bts", q * scale, k * scale
+        )  # More stable with f16 than dividing afterwards
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        a = torch.einsum("bts,bcs->bct", weight, v)
+        return a.reshape(bs, -1, length)
+
+    @staticmethod
+    def count_flops(model, _x, y):
+        return count_flops_attn(model, _x, y)
+    
+def count_flops_attn(model, _x, y):
+    """
+    A counter for the `thop` package to count the operations in an
+    attention operation.
+    Meant to be used like:
+        macs, params = thop.profile(
+            model,
+            inputs=(inputs, timestamps),
+            custom_ops={QKVAttention: QKVAttention.count_flops},
+        )
+    """
+    b, c, *spatial = y[0].shape
+    num_spatial = int(np.prod(spatial))
+    # We perform two matmuls with the same number of ops.
+    # The first computes the weight matrix, the second computes
+    # the combination of the value vectors.
+    matmul_ops = 2 * b * (num_spatial ** 2) * c
+    model.total_ops += torch.DoubleTensor([matmul_ops])

nodes.py

@@ -10,6 +10,7 @@ import comfy.model_management as mm
 import comfy.utils
 from contextlib import nullcontext
 from .lvdm.models.samplers.ddim import DDIMSampler
+from. lvdm.modules.networks.openaimodel3d import ControlNet
 
 from contextlib import nullcontext
 try:
@@ -140,9 +141,70 @@ class DownloadAndLoadDynamiCrafterModel:
             dcmodel = {
                 'model': self.model,
                 'model_name': model,
+                'dtype': precision
             }
         return (dcmodel,)
         
+class DownloadAndLoadDynamiCrafterCNModel:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "model": (
+                    [   
+                        'sketch_encoder-fp16.safetensors',
+                    ],
+                    ),
+            },
+        }
+
+    RETURN_TYPES = ("DC_CN_MODEL",)
+    RETURN_NAMES = ("DynCraft_CN_model",)
+    FUNCTION = "loadmodel"
+    CATEGORY = "DynamiCrafterWrapper"
+
+    def loadmodel(self, model):
+        device = mm.get_torch_device()
+        mm.soft_empty_cache()
+        custom_config = {
+            'ckpt_name': model,
+        }
+        if not hasattr(self, 'model') or self.model == None or custom_config != self.current_config:
+
+            download_path = os.path.join(folder_paths.models_dir, "checkpoints", "dynamicrafter", "controlnet")
+            cn_model_path = os.path.join(download_path, model)
+
+            if not os.path.exists(cn_model_path):
+                print(f"Downloading model to: {cn_model_path}")
+                from huggingface_hub import snapshot_download
+                snapshot_download(repo_id="Kijai/DynamiCrafter_pruned", 
+                                  allow_patterns=[f"*{model}*"],
+                                  local_dir=download_path, 
+                                  local_dir_use_symlinks=False)
+            cn_config = {
+                "use_checkpoint": True,
+                "image_size": 32,  # unused
+                "in_channels": 4,
+                "hint_channels": 1,
+                "model_channels": 320,
+                "attention_resolutions": [4, 2, 1],
+                "num_res_blocks": 2,
+                "channel_mult": [1, 2, 4, 4],
+                "num_head_channels": 64,  # need to fix for flash-attn
+                "use_spatial_transformer": True,
+                "use_linear_in_transformer": True,
+                "transformer_depth": 1,
+                "context_dim": 1024,
+                "legacy": False
+            }
+
+            cn_model = ControlNet(**cn_config)
+            print("Loading ControlNet")
+            cn_sd = comfy.utils.load_torch_file(cn_model_path)
+            cn_model.load_state_dict(cn_sd, strict=True)
+            print("ControlNet loaded")
+
+        return (cn_model,)
+    
 class DownloadAndLoadCLIPModel:
     @classmethod
     def INPUT_TYPES(s):
@@ -633,6 +695,7 @@ class ToonCrafterInterpolation:
                 "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
                 "optional_latents": ("LATENT",),
                 "ddpm_from": ("INT", {"default": 1000, "min": 1, "max": 1000, "step": 1}),
+                "controlnet": ("DC_CONTROL",),
             }
         }
 
@@ -641,7 +704,8 @@ class ToonCrafterInterpolation:
     FUNCTION = "process"
     CATEGORY = "DynamiCrafterWrapper"
 
-    def process(self, model, clip_vision, images, positive, negative, cfg, steps, eta, seed, fs, frames, vae_dtype, image_embed_ratio=1.0, augmentation_level=0, optional_latents=None, ddpm_from=1000):
+    def process(self, model, clip_vision, images, positive, negative, cfg, steps, eta, seed, fs, frames, 
+                vae_dtype, image_embed_ratio=1.0, augmentation_level=0, optional_latents=None, ddpm_from=1000, controlnet=None):
         device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()
         mm.unload_all_models()
@@ -651,6 +715,11 @@ class ToonCrafterInterpolation:
 
         self.model = model['model']
 
+        if controlnet is not None:
+            self.model.control_model = controlnet["model"]
+        else:
+            self.model.control_model = None
+
         dtype = self.model.dtype
         if vae_dtype == "auto":
             try:
@@ -737,7 +806,14 @@ class ToonCrafterInterpolation:
                     fs = torch.tensor([fs], dtype=torch.float32, device=self.model.device)
                 else:
                     fs = torch.tensor([fs], dtype=torch.float64, device=self.model.device)
-                cond = {"c_crossattn": [imtext_cond], "c_concat": [img_tensor_repeat]}
+
+                if controlnet is not None:
+                    cn_videos = controlnet["cn_videos"]
+                    cn_videos = cn_videos.to(dtype).to(device)
+                else:
+                    cn_videos = None
+
+                cond = {"c_crossattn": [imtext_cond], "fs": fs, "c_concat": [img_tensor_repeat], "control_cond": cn_videos}
 
                 if noise_shape[-1] == 32:
                     timestep_spacing = "uniform"
@@ -820,6 +896,39 @@ class ToonCrafterInterpolation:
 
             return (latent,)
         
+class DynamiCrafterControlnetApply:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "model": ("DC_CN_MODEL",),
+            "images": ("IMAGE",),
+            "control_scale": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 1.0, "step": 0.01}),
+        }
+        }
+
+    RETURN_TYPES = ("DC_CONTROL",)
+    RETURN_NAMES = ("controlnet",)
+    FUNCTION = "process"
+    CATEGORY = "DynamiCrafterWrapper"
+
+    def process(self, model, images, control_scale):
+
+        model.control_scale = control_scale
+
+        #images = images * 2.0 - 1.0
+   
+        cn_tensor = images.permute(3, 0, 1, 2).unsqueeze(0)
+        print("control frame: ", cn_tensor.shape) # b c t h w
+        cn_tensor = cn_tensor[:, :1, :, :, :]
+        print("control frame: ", cn_tensor.shape) # b c t h w
+
+        controlnet = {
+            "model": model,
+            "cn_videos": cn_tensor,
+        }
+
+        return (controlnet,)
+
 class ToonCrafterDecode:
     @classmethod
     def INPUT_TYPES(s):
@@ -1120,7 +1229,9 @@ NODE_CLASS_MAPPINGS = {
     "DownloadAndLoadDynamiCrafterModel": DownloadAndLoadDynamiCrafterModel,
     "DownloadAndLoadCLIPModel": DownloadAndLoadCLIPModel,
     "DownloadAndLoadCLIPVisionModel": DownloadAndLoadCLIPVisionModel,
-    "DynamiCrafterLoadInitNoise": DynamiCrafterLoadInitNoise
+    "DynamiCrafterLoadInitNoise": DynamiCrafterLoadInitNoise,
+    "DownloadAndLoadDynamiCrafterCNModel": DownloadAndLoadDynamiCrafterCNModel,
+    "DynamiCrafterControlnetApply": DynamiCrafterControlnetApply
 
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -1129,8 +1240,10 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "DynamiCrafterBatchInterpolation": "DynamiCrafter BatchInterpolation",
     "ToonCrafterInterpolation": "ToonCrafter Interpolation",
     "ToonCrafterDecode": "ToonCrafter Decode",
-    "DownloadAndLoadDynamiCrafterModel": "DownloadAndLoadDynamiCrafterModel",
-    "DownloadAndLoadCLIPModel": "DownloadAndLoadCLIPModel",
-    "DownloadAndLoadCLIPVisionModel": "DownloadAndLoadCLIPVisionModel",
-    "DynamiCrafterLoadInitNoise": "DynamiCrafterLoadInitNoise"
+    "DownloadAndLoadDynamiCrafterModel": "(Down)Load DynamiCrafterModel",
+    "DownloadAndLoadCLIPModel": "(Down)Load CLIPModel",
+    "DownloadAndLoadCLIPVisionModel": "(Down)Load CLIPVisionModel",
+    "DynamiCrafterLoadInitNoise": "DynamiCrafter LoadInitNoise",
+    "DownloadAndLoadDynamiCrafterCNModel": "(Down)Load DynamiCrafter CNModel",
+    "DynamiCrafterControlnetApply": "DynamiCrafter ControlnetApply"
 }