Update nodes.py

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
@@ -565,7 +567,6 @@ class LatentDiffusion(DDPM):
         return self.p_losses(x, c, t, **kwargs)
 
     def apply_model(self, x_noisy, t, cond, **kwargs):
-        print("apply_model:", kwargs)
         if isinstance(cond, dict):
             # hybrid case, cond is exptected to be a dict
             pass
@@ -573,9 +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]
@@ -723,7 +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:
@@ -738,7 +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/models/samplers/ddim.py

@@ -197,7 +197,7 @@ class DDIMSampler(object):
                                       corrector_kwargs=corrector_kwargs,
                                       unconditional_guidance_scale=unconditional_guidance_scale,
                                       unconditional_conditioning=unconditional_conditioning,
-                                      mask=mask,x0=x0,step=i,fs=fs,guidance_rescale=guidance_rescale,
+                                      mask=mask,x0=x0,fs=fs,guidance_rescale=guidance_rescale,
                                       **kwargs)
             
 
@@ -214,7 +214,7 @@ class DDIMSampler(object):
     @torch.no_grad()
     def p_sample_ddim(self, x, c, t, sigmas, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
                       temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                      unconditional_guidance_scale=1., unconditional_conditioning=None, step=0, ddim_edit=0,
+                      unconditional_guidance_scale=1., unconditional_conditioning=None,
                       uc_type=None, conditional_guidance_scale_temporal=None,mask=None,x0=None,guidance_rescale=0.0,**kwargs):
         b, *_, device = *x.shape, x.device
         if x.dim() == 5:
@@ -225,13 +225,8 @@ class DDIMSampler(object):
         if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
             model_output = self.model.apply_model(x, t, c, **kwargs) # unet denoiser
         else:
-            if step < ddim_edit:
-                print("applying ddim_edit step", step)
-                print(kwargs)
-                e_t_cond = self.model.apply_model(x, t, c, use_freetraj=True, **kwargs)
-                e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, use_freetraj=True, **kwargs)
             ### do_classifier_free_guidance
-            elif isinstance(c, torch.Tensor) or isinstance(c, dict):
+            if isinstance(c, torch.Tensor) or isinstance(c, dict):
                 e_t_cond = self.model.apply_model(x, t, c, **kwargs)
                 e_t_uncond = self.model.apply_model(x, t, unconditional_conditioning, **kwargs)
             else:

lvdm/modules/attention.py

@@ -18,36 +18,6 @@ from ...lvdm.basics import zero_module
 
 import comfy.ops
 ops = comfy.ops.manual_cast
-from ...utils.utils_freetraj import get_path, plan_path
-import math
-
-def gaussian_2d(x=0, y=0, mx=0, my=0, sx=1, sy=1):
-    """ 2d Gaussian weight function
-    """
-    gaussian_map = (
-        1
-        / (2 * math.pi * sx * sy)
-        * torch.exp(-((x - mx) ** 2 / (2 * sx**2) + (y - my) ** 2 / (2 * sy**2)))
-    )
-    gaussian_map.div_(gaussian_map.max())
-    return gaussian_map
-
-def gaussian_weight(height=32, width=32, KERNEL_DIVISION=3.0):
-
-    x = torch.linspace(0, height, height)
-    y = torch.linspace(0, width, width)
-    x, y = torch.meshgrid(x, y, indexing="ij")
-    noise_patch = (
-                    gaussian_2d(
-                        x,
-                        y,
-                        mx=int(height / 2),
-                        my=int(width / 2),
-                        sx=float(height / KERNEL_DIVISION),
-                        sy=float(width / KERNEL_DIVISION),
-                    )
-                ).half()
-    return noise_patch
 
 class RelativePosition(nn.Module):
     """ https://github.com/evelinehong/Transformer_Relative_Position_PyTorch/blob/master/relative_position.py """
@@ -110,8 +80,7 @@ class CrossAttention(nn.Module):
                 self.register_parameter('alpha', nn.Parameter(torch.tensor(0.)) )
 
 
-    def forward(self, x, context=None, mask=None, use_freetraj=False, idx_list=[], input_traj=[]):
-        print("forward input_traj: ", input_traj)
+    def forward(self, x, context=None, mask=None):
         spatial_self_attn = (context is None)
         k_ip, v_ip, out_ip = None, None, None
 
@@ -130,18 +99,9 @@ class CrossAttention(nn.Module):
                 context = context[:,:self.text_context_len,:]
             k = self.to_k(context)
             v = self.to_v(context)
-        hw = q.shape[0]
-        w_base = 64
-        h_base = 40
-        w_len = int((hw / w_base / h_base) ** 0.5 * h_base)
-        h_len = int(hw / w_len)
-        BOX_SIZE_H = input_traj[0][2] - input_traj[0][1]
-        BOX_SIZE_W = input_traj[0][4] - input_traj[0][3]
-        PATHS = plan_path(input_traj)
-        sub_h = int(BOX_SIZE_H * h_len) 
-        sub_w = int(BOX_SIZE_W * w_len)
 
         q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
         sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale
         if self.relative_position:
             len_q, len_k, len_v = q.shape[1], k.shape[1], v.shape[1]
@@ -150,50 +110,6 @@ class CrossAttention(nn.Module):
             sim += sim2
         del k
 
-        if use_freetraj:
-            sim = rearrange(sim, '(y x h) i j -> y x h i j', h=h, y=h_len)
-            sim_mask = torch.zeros_like(sim)
-            for i in range(sim.shape[3]):
-                h_start1 = int(PATHS[i][0] * h_len)
-                h_end1 = h_start1 + sub_h
-                w_start1 = int(PATHS[i][2] * w_len)
-                w_end1 = w_start1 + sub_w
-
-                h_fg1 = list(range(h_start1, h_end1))
-                h_fg_tensor1 = torch.zeros(h_len, device=sim.device)
-                h_fg_tensor1[h_fg1] = 1
-                w_fg1 = list(range(w_start1, w_end1))
-                w_fg_tensor1 = torch.zeros(w_len, device=sim.device)
-                w_fg_tensor1[w_fg1] = 1
-                fg_tensor1 = h_fg_tensor1.view(-1, 1) * w_fg_tensor1.view(1, -1)
-                bg_tensor1 = 1 - fg_tensor1
-
-                for j in range(sim.shape[4]):
-                    h_start2 = int(PATHS[j][0] * h_len)
-                    h_end2 = h_start2 + sub_h
-                    w_start2 = int(PATHS[j][2] * w_len)
-                    w_end2 = w_start2 + sub_w
-
-                    h_fg2 = list(range(h_start2, h_end2))
-                    h_fg_tensor2 = torch.zeros(h_len, device=sim.device)
-                    h_fg_tensor2[h_fg2] = 1
-                    w_fg2 = list(range(w_start2, w_end2))
-                    w_fg_tensor2 = torch.zeros(w_len, device=sim.device)
-                    w_fg_tensor2[w_fg2] = 1
-                    fg_tensor2 = h_fg_tensor2.view(-1, 1) * w_fg_tensor2.view(1, -1)
-                    bg_tensor2 = 1 - fg_tensor2
-                    fg_tensor = fg_tensor1 * fg_tensor2
-                    bg_tensor = bg_tensor1 * bg_tensor2
-
-                    coef = 0.01
-                    sim_mask[:, :, :, i, j] = coef * torch.ones_like(sim_mask[:, :, :, i, j])
-                    sim_mask[:, :, :, i, j] += (1 - coef) * torch.ones_like(sim_mask[:, :, :, i, j]) * (fg_tensor.view(h_len, w_len, 1) + bg_tensor.view(h_len, w_len, 1))
-
-            sim *= sim_mask
-            sim = rearrange(sim, 'y x h i j -> (y x h) i j')
-
-            del sim_mask
-
         if exists(mask):
             ## feasible for causal attention mask only
             max_neg_value = -torch.finfo(sim.dtype).max
@@ -202,6 +118,7 @@ class CrossAttention(nn.Module):
 
         # attention, what we cannot get enough of
         sim = sim.softmax(dim=-1)
+
         out = torch.einsum('b i j, b j d -> b i d', sim, v)
         if self.relative_position:
             v2 = self.relative_position_v(len_q, len_v)
@@ -227,147 +144,6 @@ class CrossAttention(nn.Module):
                 out = out + self.image_cross_attention_scale * out_ip
         
         return self.to_out(out)
-    def space_forward(self, x, context=None, mask=None, use_freetraj=False, idx_list=[], input_traj=[]):
-        
-        if context is None:
-            SA_flag = True
-        else:
-            SA_flag = False
-
-        h = self.heads
-        
-        q = self.to_q(x)
-        context = default(context, x)
-
-        ## considering image token additionally
-        if context is not None and self.img_cross_attention:
-            context, context_img = context[:,:self.text_context_len,:], context[:,self.text_context_len:,:]
-            k = self.to_k(context)
-            v = self.to_v(context)
-            k_ip = self.to_k_ip(context_img)
-            v_ip = self.to_v_ip(context_img)
-        else:
-            k = self.to_k(context)
-            v = self.to_v(context)
-
-        hw = q.shape[1]
-        w_base = 64
-        h_base = 40
-        w_len = int((hw / h_base / w_base) ** 0.5 * w_base)
-        h_len = int(hw / w_len)
-        BOX_SIZE_H = input_traj[0][2] - input_traj[0][1]
-        BOX_SIZE_W = input_traj[0][4] - input_traj[0][3]
-        PATHS = plan_path(input_traj)
-        sub_h = int(BOX_SIZE_H * h_len) 
-        sub_w = int(BOX_SIZE_W * w_len)
-
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-        sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale
-        if self.relative_position:
-            len_q, len_k, len_v = q.shape[1], k.shape[1], v.shape[1]
-            k2 = self.relative_position_k(len_q, len_k)
-            sim2 = einsum('b t d, t s d -> b t s', q, k2) * self.scale # TODO check 
-            sim += sim2
-        del k
-
-        if use_freetraj:
-            coef_a = 0.25 / (BOX_SIZE_H * BOX_SIZE_W) / len(idx_list)
-            weight = gaussian_weight(sub_h, sub_w).to(x.device)
-            if SA_flag:
-                weight_add = 0
-
-                sim = rearrange(sim, '(t h) (y x) (y0 x0) -> t h y x y0 x0', h=h, y=h_len, y0=h_len)
-                sim_mask = torch.zeros_like(sim)
-                for i in range(sim.shape[0]):
-                    h_start = int(PATHS[i][0] * h_len)
-                    h_end = h_start + sub_h
-                    w_start = int(PATHS[i][2] * w_len)
-                    w_end = w_start + sub_w
-
-                    h_fg = list(range(h_start, h_end))
-                    h_fg_tensor = torch.zeros(h_len, device=sim.device)
-                    h_fg_tensor[h_fg] = 1
-                    w_fg = list(range(w_start, w_end))
-                    w_fg_tensor = torch.zeros(w_len, device=sim.device)
-                    w_fg_tensor[w_fg] = 1
-                    fg_tensor = h_fg_tensor.view(-1, 1) * w_fg_tensor.view(1, -1)
-                    bg_tensor = 1 - fg_tensor
-
-                    coef = 0.01
-                    sim_mask[i] = coef * torch.ones_like(sim_mask[i])
-                    sim_mask[i] += (1-coef) * (torch.ones_like(sim_mask[i]) * fg_tensor.view(1, h_len, w_len, 1, 1) * fg_tensor.view(1, 1, 1, h_len, w_len) + torch.ones_like(sim_mask[i]) * bg_tensor.view(1, h_len, w_len, 1, 1) * bg_tensor.view(1, 1, 1, h_len, w_len))
-                
-                sim *= sim_mask
-                sim = rearrange(sim, 't h y x y0 x0 -> (t h) (y x) (y0 x0)')    
-
-            else:
-                sim = rearrange(sim, '(t h) (y x) d -> t h y x d', h=h, y=h_len)
-                sim_mask = torch.zeros_like(sim)
-                weight_add = torch.zeros_like(sim)
-                weight_map = torch.zeros([sim.shape[0], h_len, w_len], device=sim.device)
-                for i in range(sim.shape[0]):
-                    h_start = int(PATHS[i][0] * h_len)
-                    h_end = h_start + sub_h
-                    w_start = int(PATHS[i][2] * w_len)
-                    w_end = w_start + sub_w
-
-                    h_fg = list(range(h_start, h_end))
-                    h_fg_tensor = torch.zeros(h_len, device=sim.device)
-                    h_fg_tensor[h_fg] = 1
-                    w_fg = list(range(w_start, w_end))
-                    w_fg_tensor = torch.zeros(w_len, device=sim.device)
-                    w_fg_tensor[w_fg] = 1
-                    fg_tensor = h_fg_tensor.view(-1, 1) * w_fg_tensor.view(1, -1)
-                    bg_tensor = 1 - fg_tensor
-                    if idx_list == []:
-                        p_fg = [2]
-                    else:
-                        p_fg = idx_list
-                    p_bg = list(range(77))
-                    for j in p_fg:
-                        p_bg.remove(j)
-
-                    weight_map[i, h_start:h_end, w_start:w_end] = weight * coef_a
-                    sim_mask[i, :, :, :, p_bg] = torch.ones_like(sim_mask[i, :, :, :, p_bg]) * bg_tensor.view(1, h_len, w_len, 1)
-                    weight_add[i, :, :, :, p_fg] = torch.ones_like(sim_mask[i, :, :, :, p_fg]) * weight_map[i].view(1, h_len, w_len, 1)
-
-                max_neg_value = -torch.finfo(sim.dtype).max
-                sim.masked_fill_(~(sim_mask>0.5), max_neg_value)
-                sim = rearrange(sim, 't h y x d -> (t h) (y x) d')
-                weight_add = rearrange(weight_add, 't h y x d -> (t h) (y x) d')
-
-            del sim_mask
-
-        if exists(mask):
-            ## feasible for causal attention mask only
-            max_neg_value = -torch.finfo(sim.dtype).max
-            mask = repeat(mask, 'b i j -> (b h) i j', h=h)
-            sim.masked_fill_(~(mask>0.5), max_neg_value)
-
-        # attention, what we cannot get enough of
-        sim = sim.softmax(dim=-1)
-        if use_freetraj:
-            sim += weight_add
-
-        out = torch.einsum('b i j, b j d -> b i d', sim, v)
-        if self.relative_position:
-            v2 = self.relative_position_v(len_q, len_v)
-            out2 = einsum('b t s, t s d -> b t d', sim, v2) # TODO check
-            out += out2
-        out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
-
-        ## considering image token additionally
-        if context is not None and self.img_cross_attention:
-            k_ip, v_ip = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (k_ip, v_ip))
-            sim_ip =  torch.einsum('b i d, b j d -> b i j', q, k_ip) * self.scale
-            del k_ip
-            sim_ip = sim_ip.softmax(dim=-1)
-            out_ip = torch.einsum('b i j, b j d -> b i d', sim_ip, v_ip)
-            out_ip = rearrange(out_ip, '(b h) n d -> b n (h d)', h=h)
-            out = out + self.image_cross_attention_scale * out_ip
-        del q
-
-        return self.to_out(out)
     
     def efficient_forward(self, x, context=None, mask=None):
         spatial_self_attn = (context is None)
@@ -454,7 +230,7 @@ class BasicTransformerBlock(nn.Module):
         self.checkpoint = checkpoint
 
 
-    def forward(self, x, context=None, mask=None, use_freetraj=False, idx_list=[], input_traj=[], **kwargs):
+    def forward(self, x, context=None, mask=None, **kwargs):
         ## implementation tricks: because checkpointing doesn't support non-tensor (e.g. None or scalar) arguments
         input_tuple = (x,)      ## should not be (x), otherwise *input_tuple will decouple x into multiple arguments
         if context is not None:
@@ -462,14 +238,12 @@ class BasicTransformerBlock(nn.Module):
         if mask is not None:
             forward_mask = partial(self._forward, mask=mask)
             return checkpoint(forward_mask, (x,), self.parameters(), self.checkpoint)
-        if context is not None and mask is not None:
-            input_tuple = (x, context, mask)
-        input_tuple = (x, context, mask, use_freetraj, idx_list, input_traj)
         return checkpoint(self._forward, input_tuple, self.parameters(), self.checkpoint)
 
-    def _forward(self, x, context=None, mask=None, use_freetraj=False, idx_list=[], input_traj=[]):
-        x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None, mask=mask, use_freetraj=use_freetraj, idx_list=idx_list, input_traj=input_traj) + x
-        x = self.attn2(self.norm2(x), context=context, mask=mask, use_freetraj=use_freetraj, idx_list=idx_list, input_traj=input_traj) + x
+
+    def _forward(self, x, context=None, mask=None):
+        x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None, mask=mask) + x
+        x = self.attn2(self.norm2(x), context=context, mask=mask) + x
         x = self.ff(self.norm3(x)) + x
         return x
 
@@ -591,7 +365,7 @@ class TemporalTransformer(nn.Module):
         self.use_linear = use_linear
 
 
-    def forward(self, x_in, context=None, frame_window_size=None, frame_window_stride=None, **kwargs):
+    def forward(self, x_in, context=None, frame_window_size=None, frame_window_stride=None):
         B, C, T, H, W = x_in.shape
         def process_slice(x, t_start=None, t_end=None):
             b, c, t, h, w = x.shape
@@ -620,7 +394,7 @@ class TemporalTransformer(nn.Module):
             if self.only_self_att:
                 ## note: if no context is given, cross-attention defaults to self-attention
                 for i, block in enumerate(self.transformer_blocks):
-                    x = block(x, mask=mask, **kwargs)
+                    x = block(x, mask=mask)
                 x = rearrange(x, "(b hw) t c -> b hw t c", b=b).contiguous()
             else:
                 x = rearrange(x, "(b hw) t c -> b hw t c", b=b).contiguous()
@@ -632,7 +406,7 @@ class TemporalTransformer(nn.Module):
                             context[j], "t l con -> (t r) l con", r=(h * w) // t, t=t
                         ).contiguous()
                         ## note: causal mask will not applied in cross-attention case
-                        x[j] = block(x[j], context=context_j, **kwargs)
+                        x[j] = block(x[j], context=context_j)
 
             if self.use_linear:
                 x = self.proj_out(x)

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.
@@ -35,15 +40,15 @@ class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
     support it as an extra input.
     """
 
-    def forward(self, x, emb, context=None, batch_size=None, use_freetraj=False, frame_window_size=None, frame_window_stride=None, **kwargs):
+    def forward(self, x, emb, context=None, batch_size=None, frame_window_size=None, frame_window_stride=None):
         for layer in self:
             if isinstance(layer, TimestepBlock):
                 x = layer(x, emb, batch_size=batch_size)
             elif isinstance(layer, SpatialTransformer):
-                x = layer(x, context, use_freetraj=use_freetraj, **kwargs)
+                x = layer(x, context)
             elif isinstance(layer, TemporalTransformer):
                 x = rearrange(x, '(b f) c h w -> b c f h w', b=batch_size)
-                x = layer(x, context, use_freetraj=use_freetraj, frame_window_size=frame_window_size, frame_window_stride=frame_window_stride, **kwargs)
+                x = layer(x, context, frame_window_size=frame_window_size, frame_window_stride=frame_window_stride)
                 x = rearrange(x, 'b c f h w -> (b f) c h w')
             else:
                 x = layer(x)
@@ -547,8 +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):
-        print("unet forward: ", 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)
@@ -583,9 +587,9 @@ class UNetModel(nn.Module):
         adapter_idx = 0
         hs = []
         for id, module in enumerate(self.input_blocks):
-            h = module(h, emb, context=context, batch_size=b, frame_window_size=frame_window_size, frame_window_stride=frame_window_stride, **kwargs)
+            h = module(h, emb, context=context, batch_size=b, frame_window_size=frame_window_size, frame_window_stride=frame_window_stride)
             if id ==0 and self.addition_attention:
-                h = self.init_attn(h, emb, context=context, batch_size=b, frame_window_size=frame_window_size, frame_window_stride=frame_window_stride, **kwargs)
+                h = self.init_attn(h, emb, context=context, batch_size=b, frame_window_size=frame_window_size, frame_window_stride=frame_window_stride)
             ## plug-in adapter features
             if ((id+1)%3 == 0) and features_adapter is not None:
                 h = h + features_adapter[adapter_idx]
@@ -594,13 +598,411 @@ class UNetModel(nn.Module):
         if features_adapter is not None:
             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, **kwargs)
+        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:
-            h = torch.cat([h, hs.pop()], dim=1)
-            h = module(h, emb, context=context, batch_size=b, frame_window_size=frame_window_size, frame_window_stride=frame_window_stride, **kwargs)
+            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)
         
         # 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

@@ -4,15 +4,13 @@ import torch
 import torch.nn.functional as F
 from .scripts.evaluation.funcs import load_model_checkpoint, get_latent_z, get_latent_z_with_hidden_states
 from .utils.utils import instantiate_from_config
-from .utils.utils_freetraj import get_freq_filter, freq_mix_3d, get_path, plan_path, load_idx, load_traj
 from einops import repeat
 import folder_paths
 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:
@@ -143,8 +141,130 @@ 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):
+        custom_config = {
+            'ckpt_name': model,
+        }
+        if not hasattr(self, 'cn_model') or self.cn_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": 3,
+                "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
+            }
+            if "sketch_encoder" in model:
+                cn_config["hint_channels"] = 1
+           
+            self.cn_model = ControlNet(**cn_config)
+            print("Loading ControlNet")
+            cn_sd = comfy.utils.load_torch_file(cn_model_path)
+            self.cn_model.load_state_dict(cn_sd, strict=True)
+            print("ControlNet loaded")
+
+        controlnet = {
+            'model': self.cn_model,
+            'config': cn_config,
+        }
+
+        return (controlnet,)
+
+class DynamiCrafterCNLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "ckpt_name": (folder_paths.get_filename_list("controlnet"), ),
+            },
+        }
+
+    RETURN_TYPES = ("DC_CN_MODEL",)
+    RETURN_NAMES = ("DynCraft_CN_model",)
+    FUNCTION = "loadmodel"
+    CATEGORY = "DynamiCrafterWrapper"
+
+    def loadmodel(self, ckpt_name):
+        custom_config = {
+            'ckpt_name': ckpt_name,
+        }
+        if not hasattr(self, 'cn_model') or self.cn_model == None or custom_config != self.current_config:
+            self.current_config = custom_config
+
+            model_path = folder_paths.get_full_path("controlnet", ckpt_name)
+            print(f"Loading ControlNet from: {model_path}")
+
+            cn_config = {
+                "use_checkpoint": True,
+                "image_size": 32,  # unused
+                "in_channels": 4,
+                "hint_channels": 3,
+                "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
+            }
+            if "sketch_encoder" in ckpt_name:
+                cn_config["hint_channels"] = 1
+            
+            self.cn_model = ControlNet(**cn_config)
+            print("Loading ControlNet")
+            cn_sd = comfy.utils.load_torch_file(model_path)
+            self.cn_model.load_state_dict(cn_sd, strict=True)
+            del cn_sd
+            print("ControlNet loaded")
+
+        controlnet = {
+            'model': self.cn_model,
+            'config': cn_config,
+        }
+
+        return (controlnet,)
     
 class DownloadAndLoadCLIPModel:
     @classmethod
@@ -392,6 +512,7 @@ class DynamiCrafterI2V:
         self.model.to(device)
         autocast_condition = (dtype != torch.float32) and not comfy.model_management.is_device_mps(device)
         with torch.autocast(comfy.model_management.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext():
+            
             image = image.permute(0, 3, 1, 2).to(dtype).to(device)
             if augmentation_level > 0:
                 image += torch.randn_like(image) * augmentation_level
@@ -403,7 +524,7 @@ class DynamiCrafterI2V:
             if H % 64 != 0:
                 H = H - (H % 64)
             if orig_H % 64 != 0 or orig_W % 64 != 0:
-                image = F.interpolate(image, size=(H, W), mode="bicubic")
+                image = F.interpolate(image, size=(H, W), mode="bilinear")
            
             B, C, H, W = image.shape
             noise_shape = [B, self.model.model.diffusion_model.out_channels, frames, H // 8, W // 8]
@@ -413,14 +534,13 @@ class DynamiCrafterI2V:
             z = get_latent_z(self.model, encode_pixels) #bc,1,hw
 
             if image2 is not None:
-                image2 = image2 * 2 - 1
                 image2 = image2.permute(0, 3, 1, 2).to(dtype).to(device)
 
                 if augmentation_level > 0:
                     image2 += torch.randn_like(image2) * augmentation_level
 
                 if image2.shape != image.shape:
-                    image2 = F.interpolate(image, size=(H, W), mode="bicubic")
+                    image2 = F.interpolate(image, size=(H, W), mode="bilinear")
 
                 encode_pixels = image2.unsqueeze(2) * 2 - 1
                 z2 = get_latent_z(self.model, encode_pixels) #bc,1,hw
@@ -437,6 +557,8 @@ class DynamiCrafterI2V:
             text_emb = positive[0][0].to(device)
 
             cond_images = clip_vision.encode_image(image.permute(0, 2, 3, 1))['last_hidden_state'].to(device)
+            cond_images = torch.sum(cond_images, dim=0).unsqueeze(0)
+            cond_images = torch.mean(cond_images, dim=0).unsqueeze(0)
 
             img_emb = self.model.image_proj_model(cond_images)
 
@@ -444,7 +566,9 @@ class DynamiCrafterI2V:
             del cond_images, img_emb, text_emb, encode_pixels
 
             fs = torch.tensor([fs], dtype=torch.long, device=self.model.device)
-            cond = {"c_crossattn": [imtext_cond], "c_concat": [img_tensor_repeat]}
+            cond = {"c_crossattn": [imtext_cond], "c_concat": [img_tensor_repeat], "control_cond": None}
+
+            self.model.control_model = None
 
             if noise_shape[-1] == 32:
                 timestep_spacing = "uniform"
@@ -636,6 +760,7 @@ 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",),
             }
         }
 
@@ -644,7 +769,8 @@ 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()
@@ -654,6 +780,11 @@ 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:
@@ -685,11 +816,12 @@ class ToonCrafterInterpolation:
         pbar = comfy.utils.ProgressBar(len(images) - 1)
         autocast_condition = (dtype != torch.float32) and not comfy.model_management.is_device_mps(device)
         with torch.autocast(comfy.model_management.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext():
-            for i in range(len(images) - 1):
+            for i in range(len(images) - 1) if len(images) > 1 else range(len(images)):
                 videos, videos2 = None, None
                 mm.soft_empty_cache()
                 image = images[i].unsqueeze(0)
-                image2 = images[i+1].unsqueeze(0)
+                if len(images) !=1:
+                    image2 = images[i+1].unsqueeze(0)
                 
                 B, C, H, W = image.shape
                 noise_shape = [B, self.model.model.diffusion_model.out_channels, frames, H // 8, W // 8]
@@ -701,12 +833,16 @@ class ToonCrafterInterpolation:
                     image2 += torch.randn_like(image) * augmentation_level
 
                 encode_pixels = image.unsqueeze(2) * 2 - 1
-                videos = encode_pixels # bc1hw
-                videos = repeat(videos, 'b c t h w -> b c (repeat t) h w', repeat=frames//2)
-                encode_pixels = image2.unsqueeze(2) * 2 - 1
-                videos2 = encode_pixels # bc1hw
-                videos2 = repeat(videos2, 'b c t h w -> b c (repeat t) h w', repeat=frames//2)
-                videos = torch.cat([videos, videos2], dim=2)                              
+                videos = encode_pixels  # bc1hw
+                videos = repeat(videos, 'b c t h w -> b c (repeat t) h w', repeat=frames // 2)
+
+                if len(images) == 1:
+                    videos = torch.cat([videos, videos], dim=2)
+                else:
+                    encode_pixels = image2.unsqueeze(2) * 2 - 1
+                    videos2 = encode_pixels  # bc1hw
+                    videos2 = repeat(videos2, 'b c t h w -> b c (repeat t) h w', repeat=frames // 2)
+                    videos = torch.cat([videos, videos2], dim=2)                          
 
                 try:
                     z, hs = get_latent_z_with_hidden_states(self.model, videos)
@@ -718,29 +854,38 @@ class ToonCrafterInterpolation:
 
                 img_tensor_repeat = torch.zeros_like(z)
                 img_tensor_repeat[:,:,:1,:,:] = z[:,:,:1,:,:]
-                img_tensor_repeat[:,:,-1:,:,:] = z[:,:,-1:,:,:]
+                if len(images) !=1:
+                    img_tensor_repeat[:,:,-1:,:,:] = z[:,:,-1:,:,:]
 
                 self.model.first_stage_model.to(offload_device)
 
                 text_emb = positive[0][0].to(device)
                 
-                cond_images = clip_vision.encode_image(image.permute(0, 2, 3, 1))["last_hidden_state"].to(device)
-                cond_images2 = clip_vision.encode_image(image2.permute(0, 2, 3, 1))["last_hidden_state"].to(device)
-
                 self.model.image_proj_model.to(device)
-
+                cond_images = clip_vision.encode_image(image.permute(0, 2, 3, 1))["last_hidden_state"].to(device)
                 img_emb = self.model.image_proj_model(cond_images)
-                img_emb2 = self.model.image_proj_model(cond_images2)
-                img_embeds = img_emb * image_embed_ratio + img_emb2 * (1.0 - image_embed_ratio)
+                if len(images) !=1:
+                    cond_images2 = clip_vision.encode_image(image2.permute(0, 2, 3, 1))["last_hidden_state"].to(device)
+                    img_emb2 = self.model.image_proj_model(cond_images2)
+                    img_embeds = img_emb * image_embed_ratio + img_emb2 * (1.0 - image_embed_ratio)
+                else:
+                    img_embeds = img_emb
 
                 imtext_cond = torch.cat([text_emb, img_embeds], dim=1)
-                del cond_images, img_emb, img_emb2, text_emb
+                del cond_images, img_emb, text_emb
 
                 if comfy.model_management.is_device_mps(device):
                     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"
@@ -822,6 +967,41 @@ class ToonCrafterInterpolation:
                 }
 
             return (latent,)
+        
+class DynamiCrafterControlnetApply:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "controlnet": ("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, controlnet, images, control_scale):
+
+        controlnet['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
+        print(controlnet["config"])
+        if controlnet["config"]["hint_channels"] == 1:
+            cn_tensor = cn_tensor[:, :1, :, :, :]
+        print("control frame: ", cn_tensor.shape) # b c t h w
+
+        controlnet = {
+            "model": controlnet['model'],
+            "cn_videos": cn_tensor,
+        }
+
+        return (controlnet,)
 
 class ToonCrafterDecode:
     @classmethod
@@ -955,8 +1135,9 @@ class DynamiCrafterBatchInterpolation:
         mm.soft_empty_cache()
 
         torch.manual_seed(seed)
-        dtype = model.dtype
+        
         self.model = model['model']
+        dtype = self.model.dtype
 
         if vae_dtype == "auto":
             try:
@@ -971,7 +1152,6 @@ class DynamiCrafterBatchInterpolation:
         print(f"VAE using dtype: {self.model.first_stage_model.dtype}")
       
         self.model.to(device)
-        images = images * 2 - 1
         images = images.permute(0, 3, 1, 2).to(dtype).to(device)
         B, C, H, W = images.shape
         orig_H, orig_W = H, W
@@ -994,8 +1174,11 @@ class DynamiCrafterBatchInterpolation:
 
                 self.model.first_stage_model.to(device)
 
-                z = get_latent_z(self.model, image.unsqueeze(2)) #bc,1,hw
-                z2 = get_latent_z(self.model, image2.unsqueeze(2)) #bc,1,hw
+                encode_pixels1 = image * 2 - 1
+                encode_pixels2 = image2 * 2 - 1
+
+                z = get_latent_z(self.model, encode_pixels1.unsqueeze(2)) #bc,1,hw
+                z2 = get_latent_z(self.model, encode_pixels2.unsqueeze(2)) #bc,1,hw
                 img_tensor_repeat = repeat(z, 'b c t h w -> b c (repeat t) h w', repeat=frames)
                 img_tensor_repeat = torch.zeros_like(img_tensor_repeat)
                 img_tensor_repeat[:,:,:1,:,:] = z
@@ -1012,7 +1195,9 @@ class DynamiCrafterBatchInterpolation:
                 imtext_cond = torch.cat([text_emb, img_emb], dim=1)
 
                 fs = torch.tensor([fs], dtype=torch.long, device=self.model.device)
-                cond = {"c_crossattn": [imtext_cond], "c_concat": [img_tensor_repeat]}
+                cond = {"c_crossattn": [imtext_cond], "c_concat": [img_tensor_repeat], "control_cond": None}
+
+                self.model.control_model = None
 
                 if noise_shape[-1] == 32:
                     timestep_spacing = "uniform"
@@ -1111,294 +1296,6 @@ class DynamiCrafterBatchInterpolation:
             last_image = out_video[-1].unsqueeze(0)
             return (out_video, last_image)
 
-class DynamiCrafterFreeTrajSampler:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "model": ("DCMODEL",),
-            "clip_vision": ("CLIP_VISION", ),
-            "positive": ("CONDITIONING", ),
-            "negative": ("CONDITIONING", ),
-            "image": ("IMAGE",),
-            "steps": ("INT", {"default": 50, "min": 1, "max": 200, "step": 1}),
-            "cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 20.0, "step": 0.01}),
-            "eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-            "frames": ("INT", {"default": 16, "min": 1, "max": 100, "step": 1}),
-            "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-            "fs": ("INT", {"default": 10, "min": 2, "max": 100, "step": 1}),
-            "n_samples": ("INT", {"default": 1, "min": 1, "max": 100, "step": 1}),
-            "ddim_edit": ("INT", {"default": 1, "min": 1, "max": 100, "step": 1}),
-            "keep_model_loaded": ("BOOLEAN", {"default": True}),
-            "vae_dtype": (
-                    [
-                        'fp32',
-                        'fp16',
-                        'bf16',
-                        'auto'
-                    ], {
-                        "default": 'auto'
-                    }),
-            
-            },
-            "optional": {
-                "image2": ("IMAGE",),
-                "mask": ("MASK",),
-                "frame_window_size": ("INT", {"default": 16, "min": 1, "max": 200, "step": 1}),
-                "frame_window_stride": ("INT", {"default": 4, "min": 1, "max": 200, "step": 1}),
-                "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.0001}),
-                "init_noise": ("DCNOISE",),
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE", "IMAGE",)
-    RETURN_NAMES = ("images", "last_image",)
-    FUNCTION = "process"
-    CATEGORY = "DynamiCrafterWrapper"
-
-    def process(self, model, image, clip_vision, positive, negative, cfg, steps, eta, seed, fs, n_samples, keep_model_loaded, ddim_edit, 
-                frames, vae_dtype, frame_window_size=16, frame_window_stride=4, mask=None, image2=None, augmentation_level=0, init_noise=None):
-        device = mm.get_torch_device()
-        offload_device = mm.unet_offload_device()
-        mm.unload_all_models()
-        mm.soft_empty_cache()
-
-        self.model = model['model']
-        channels = self.model.model.diffusion_model.out_channels
-
-        torch.manual_seed(seed)
-        dtype = self.model.dtype
-        if vae_dtype == "auto":
-            try:
-                if mm.should_use_bf16():
-                    self.model.first_stage_model.to(convert_dtype('bf16'))
-                else:
-                    self.model.first_stage_model.to(convert_dtype('fp32'))
-            except:
-                raise AttributeError("ComfyUI version too old, can't autodetect properly. Set your dtype manually.")
-        else:
-            self.model.first_stage_model.to(convert_dtype(vae_dtype))
-        print(f"VAE using dtype: {self.model.first_stage_model.dtype}")
-
-        
-        self.model.to(device)
-        autocast_condition = (dtype != torch.float32) and not comfy.model_management.is_device_mps(device)
-        with torch.autocast(comfy.model_management.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext():
-            image = image.permute(0, 3, 1, 2).to(dtype).to(device)
-            if augmentation_level > 0:
-                image += torch.randn_like(image) * augmentation_level
-
-            B, C, H, W = image.shape
-
-            orig_H, orig_W = H, W
-            if W % 64 != 0:
-                W = W - (W % 64)
-            if H % 64 != 0:
-                H = H - (H % 64)
-            if orig_H % 64 != 0 or orig_W % 64 != 0:
-                image = F.interpolate(image, size=(H, W), mode="bicubic")
-           
-            B, C, H, W = image.shape
-            noise_h, noise_w = H // 8, W // 8
-            noise_shape = [B, channels, frames, noise_h, noise_w]
-
-            self.model.first_stage_model.to(device)
-            encode_pixels = image.unsqueeze(2) * 2 - 1
-            z = get_latent_z(self.model, encode_pixels) #bc,1,hw
-
-            if image2 is not None:
-                image2 = image2 * 2 - 1
-                image2 = image2.permute(0, 3, 1, 2).to(dtype).to(device)
-
-                if augmentation_level > 0:
-                    image2 += torch.randn_like(image2) * augmentation_level
-
-                if image2.shape != image.shape:
-                    image2 = F.interpolate(image, size=(H, W), mode="bicubic")
-
-                encode_pixels = image2.unsqueeze(2) * 2 - 1
-                z2 = get_latent_z(self.model, encode_pixels) #bc,1,hw
-                img_tensor_repeat = repeat(z, 'b c t h w -> b c (repeat t) h w', repeat=frames)
-                img_tensor_repeat = torch.zeros_like(img_tensor_repeat)
-                img_tensor_repeat[:,:,:1,:,:] = z
-                img_tensor_repeat[:,:,-1:,:,:] = z2
-            else:
-                img_tensor_repeat = repeat(z, 'b c t h w -> b c (repeat t) h w', repeat=frames)
-
-            self.model.first_stage_model.to(offload_device)
-     
-            self.model.image_proj_model.to(device)
-            text_emb = positive[0][0].to(device)
-
-            cond_images = clip_vision.encode_image(image.permute(0, 2, 3, 1))['last_hidden_state'].to(device)
-
-            img_emb = self.model.image_proj_model(cond_images)
-
-            imtext_cond = torch.cat([text_emb, img_emb], dim=1)
-            del cond_images, img_emb, text_emb, encode_pixels
-
-            fs = torch.tensor([fs], dtype=torch.long, device=self.model.device)
-            cond = {"c_crossattn": [imtext_cond], "c_concat": [img_tensor_repeat]}
-
-            if noise_shape[-1] == 32:
-                timestep_spacing = "uniform"
-                guidance_rescale = 0.0
-            else:
-                timestep_spacing = "uniform_trailing"
-                guidance_rescale = 0.7
-
-            ## construct unconditional guidance
-            if cfg != 1.0: 
-                uc_emb = negative[0][0].to(device)
-                ## process image embedding token
-                if hasattr(self.model, 'embedder'):
-                    uc_img = torch.rand(noise_shape[0],3,224,224).to(self.model.device)
-                    ## img: b c h w >> b l c
-                    uc_img = clip_vision.encode_image(uc_img.permute(0, 2, 3, 1))['last_hidden_state'].to(self.model.device)
-                    uc_img = self.model.image_proj_model(uc_img)
-                    uc_emb = torch.cat([uc_emb, uc_img], dim=1)
-                if isinstance(cond, dict):
-                    uc = {key:cond[key] for key in cond.keys()}
-                    uc.update({'c_crossattn': [uc_emb]})
-                else:
-                    uc = uc_emb
-            else:
-                uc = None
-
-            self.model.image_proj_model.to(offload_device)
-
-            if mask is not None:     
-                mask = mask.to(dtype).to(device)
-                mask = F.interpolate(mask.unsqueeze(0), size=(H // 8, W // 8), mode="nearest").squeeze(0)
-                mask = (1 - mask)
-                mask = mask.unsqueeze(1)
-                B, C, H, W = mask.shape
-                if B < frames:
-                    mask = mask.unsqueeze(2)
-                    mask = mask.expand(-1, -1, frames, -1, -1)
-                else:
-                    mask = mask.unsqueeze(0)
-                    mask = mask.permute(0, 2, 1, 3, 4) 
-                mask = torch.where(mask < 1.0, torch.tensor(0.0, device=device, dtype=dtype), torch.tensor(1.0, device=device, dtype=dtype))
-
-            if init_noise is not None:
-                if init_noise['analytic_init']:
-                    eps=torch.randn_like(init_noise['mu_p'])
-                    sigma_p = init_noise['sigma_p']
-                    init = (init_noise['mu_p'] + sigma_p*eps).to(dtype).to(device)
-                    if noise_shape[2] % init.shape[2] == 0:
-                        init = init.repeat(1, 1, noise_shape[2] // init.shape[2], 1, 1)
-                    else:
-                        raise ValueError("The target dimension size is not an integral multiple of the original dimension size.")
-                else:
-                    init = None
-                timestep_spacing = "uniform_trailing"
-                guidance_rescale = 0.7
-                ddpm_from = init_noise['M']
-                
-               
-            else:
-                init = None
-                ddpm_from = 1000
-            
-            total_shape = [n_samples, 1, channels, frames, noise_h, noise_w]
-            print('total_shape', total_shape)
-
-            x_T_total = None
-
-            input_traj = [[0, 0, 0.3, 0, 0.3], [15, 0.7, 1, 0.7, 14]]
-            idx_list = [1, 2]
-
-            if x_T_total is None:
-                x_T_total = torch.randn(total_shape, device=device).repeat(1, B, 1, 1, 1, 1)
-
-                noise_flow = True
-                if noise_flow:
-                    print('noise_flow')
-                    BOX_SIZE_H = input_traj[0][2] - input_traj[0][1]
-                    BOX_SIZE_W = input_traj[0][4] - input_traj[0][3]
-                    print("box_size_h: ", BOX_SIZE_H, "box_size_h: ", BOX_SIZE_W)
-                    PATHS = plan_path(input_traj)
-                    print("PATHS: ",PATHS)
-                    sub_h = int(BOX_SIZE_H * noise_h) 
-                    sub_w = int(BOX_SIZE_W * noise_w)
-                    x_T_sub = torch.randn([n_samples, 1, channels, sub_h, sub_w], device=device)
-                    for i in range(frames):
-                        h_start = int(PATHS[i][0] * noise_h)
-                        h_end = h_start + sub_h
-                        w_start = int(PATHS[i][2] * noise_w)
-                        w_end = w_start + sub_w
-
-                        # no mix
-                        x_T_total[:, :, :, i, h_start:h_end, w_start:w_end] = x_T_sub
-
-                    filter_shape = [1, channels, frames, noise_h, noise_w]
-
-                    freq_filter = get_freq_filter(
-                        filter_shape, 
-                        device = device, 
-                        filter_type='butterworth',
-                        n=4,
-                        d_s=0.25,
-                        d_t=0.1
-                    )
-
-                    x_T_rand = torch.randn([1, 1, channels, frames, noise_h, noise_w], device=device)
-                    x_T_total = freq_mix_3d(x_T_total.to(dtype=torch.float32), x_T_rand, LPF=freq_filter).to(dtype=dtype).to(device)
-
-            #inference
-            ddim_sampler = DDIMSampler(self.model)
-            samples, _ = ddim_sampler.sample(
-                                            S=steps,
-                                            conditioning=cond,
-                                            batch_size=noise_shape[0],
-                                            shape=noise_shape[1:],
-                                            verbose=True,
-                                            unconditional_guidance_scale=cfg,
-                                            unconditional_conditioning=uc,
-                                            eta=eta,
-                                            temporal_length=noise_shape[2],
-                                            conditional_guidance_scale_temporal=None,
-                                            x_T=x_T_total[0],
-                                            fs=fs,
-                                            timestep_spacing=timestep_spacing,
-                                            guidance_rescale=guidance_rescale,
-                                            clean_cond=True,
-                                            mask=mask,
-                                            x0=img_tensor_repeat.clone() if mask is not None else None,
-                                            frame_window_size = frame_window_size,
-                                            frame_window_stride = frame_window_stride,
-                                            ddpm_from=ddpm_from,
-                                            idx_list=idx_list,
-                                            input_traj=input_traj,
-                                            ddim_edit = ddim_edit,
-                                            )
-            
-            assert not torch.isnan(samples).any().item(), "Resulting tensor containts NaNs. I'm unsure why this happens, changing step count and/or image dimensions might help."
-            
-            ## reconstruct from latent to pixel space
-            self.model.first_stage_model.to(device)
-            self.model.en_and_decode_n_samples_a_time = 1
-            decoded_images = self.model.decode_first_stage(samples) #b c t h w
-            self.model.first_stage_model.to(offload_device)
-        
-            video = decoded_images.detach().cpu()
-            video = torch.clamp(video.float(), -1., 1.)
-            video = (video + 1.0) / 2.0
-            video = video.squeeze(0).permute(1, 2, 3, 0)
-            del decoded_images, samples
-
-            if not keep_model_loaded:
-                self.model.to(offload_device)
-                mm.soft_empty_cache()
-            # Ensure the final dimensions are divisible by 2
-            final_H = (orig_H // 2) * 2
-            final_W = (orig_W // 2) * 2
-
-            if video.shape[1] != final_H or video.shape[2] != final_W:
-                video = F.interpolate(video.permute(0, 3, 1, 2), size=(final_H, final_W), mode="bicubic").permute(0, 2, 3, 1)
-            last_image = video[-1].unsqueeze(0)
-            return (video, last_image)
-
 NODE_CLASS_MAPPINGS = {
     "DynamiCrafterI2V": DynamiCrafterI2V,
     "DynamiCrafterModelLoader": DynamiCrafterModelLoader,
@@ -1409,18 +1306,22 @@ NODE_CLASS_MAPPINGS = {
     "DownloadAndLoadCLIPModel": DownloadAndLoadCLIPModel,
     "DownloadAndLoadCLIPVisionModel": DownloadAndLoadCLIPVisionModel,
     "DynamiCrafterLoadInitNoise": DynamiCrafterLoadInitNoise,
-    "DynamiCrafterFreeTrajSampler": DynamiCrafterFreeTrajSampler
+    "DownloadAndLoadDynamiCrafterCNModel": DownloadAndLoadDynamiCrafterCNModel,
+    "DynamiCrafterControlnetApply": DynamiCrafterControlnetApply,
+    "DynamiCrafterCNLoader": DynamiCrafterCNLoader
 
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
     "DynamiCrafterI2V": "DynamiCrafterI2V",
-    "DynamiCrafterModelLoader": "DynamiCrafterModelLoader",
-    "DynamiCrafterBatchInterpolation": "DynamiCrafterBatchInterpolation",
-    "ToonCrafterInterpolation": "ToonCrafterInterpolation",
-    "ToonCrafterDecode": "ToonCrafterDecode",
-    "DownloadAndLoadDynamiCrafterModel": "DownloadAndLoadDynamiCrafterModel",
-    "DownloadAndLoadCLIPModel": "DownloadAndLoadCLIPModel",
-    "DownloadAndLoadCLIPVisionModel": "DownloadAndLoadCLIPVisionModel",
-    "DynamiCrafterLoadInitNoise": "DynamiCrafterLoadInitNoise",
-    "DynamiCrafterFreeTrajSampler": "DynamiCrafterFreeTrajSampler"
+    "DynamiCrafterModelLoader": "DynamiCrafter ModelLoader",
+    "DynamiCrafterBatchInterpolation": "DynamiCrafter BatchInterpolation",
+    "ToonCrafterInterpolation": "ToonCrafter Interpolation",
+    "ToonCrafterDecode": "ToonCrafter Decode",
+    "DownloadAndLoadDynamiCrafterModel": "(Down)Load DynamiCrafterModel",
+    "DownloadAndLoadCLIPModel": "(Down)Load CLIPModel",
+    "DownloadAndLoadCLIPVisionModel": "(Down)Load CLIPVisionModel",
+    "DynamiCrafterLoadInitNoise": "DynamiCrafter LoadInitNoise",
+    "DownloadAndLoadDynamiCrafterCNModel": "(Down)Load DynamiCrafter CNModel",
+    "DynamiCrafterControlnetApply": "DynamiCrafter ControlnetApply",
+    "DynamiCrafterCNLoader": "DynamiCrafter CNLoader"
 }

utils/utils_freetraj.py

@@ -1,310 +0,0 @@
-import torch
-import torch.fft as fft
-import math
-
-
-def get_longpath(BOX_SIZE_H=0.3, BOX_SIZE_W=0.3, input_mode=4):
-
-    if input_mode == 1:
-        # mode 1
-        inputs = [[0, 0, 0 + BOX_SIZE_H, 0, 0 + BOX_SIZE_W], 
-                [7, 1-BOX_SIZE_H, 1, (1-BOX_SIZE_W) / 15 * 7, (1-BOX_SIZE_W) / 15 * 7 + BOX_SIZE_W], 
-                [8, 1-BOX_SIZE_H, 1, (1-BOX_SIZE_W) / 15 * 8, (1-BOX_SIZE_W) / 15 * 8 + BOX_SIZE_W], 
-                [15, 0, 0 + BOX_SIZE_H, 1-BOX_SIZE_W, 1],
-                [16, 0.1, 0.1 + BOX_SIZE_H, 0.9-BOX_SIZE_W, 0.9],
-                [25, 0.1, 0.1 + BOX_SIZE_H, 0.1, 0.1 + BOX_SIZE_W],
-                [31, 0.9-BOX_SIZE_H, 0.9, 0.1, 0.1 + BOX_SIZE_W],
-                [32, 1-BOX_SIZE_H, 1, 0, 0 + BOX_SIZE_W],
-                [39, 0, 0 + BOX_SIZE_H, (1-BOX_SIZE_W) / 15 * 7, (1-BOX_SIZE_W) / 15 * 7 + BOX_SIZE_W],
-                [40, 0, 0 + BOX_SIZE_H, (1-BOX_SIZE_W) / 15 * 8, (1-BOX_SIZE_W) / 15 * 8 + BOX_SIZE_W],
-                [47, 1-BOX_SIZE_H, 1, 1-BOX_SIZE_W, 1],
-                [48, 0.9-BOX_SIZE_H, 0.9, 0.9-BOX_SIZE_W, 0.9],
-                [57, 0.9-BOX_SIZE_H, 0.9, 0.1, 0.1 + BOX_SIZE_W],
-                [63, 0.1, 0.1 + BOX_SIZE_H, 0.1, 0.1 + BOX_SIZE_W]]
-    elif input_mode == 2:
-        # mode 2
-        inputs = [[0, 0.1, 0.1 + BOX_SIZE_H, 0.1, 0.1 + BOX_SIZE_W], 
-                  [6, 0.9-BOX_SIZE_H, 0.9, 0.1, 0.1 + BOX_SIZE_W], 
-                  [15, 0.9-BOX_SIZE_H, 0.9, 0.9-BOX_SIZE_W, 0.9],
-                  [16, 0.9-BOX_SIZE_H, 0.9, 0.9-BOX_SIZE_W, 0.9], 
-                  [22, 0.1, 0.1 + BOX_SIZE_H, 0.9-BOX_SIZE_W, 0.9], 
-                  [31, 0.1, 0.1 + BOX_SIZE_H, 0.1, 0.1 + BOX_SIZE_W],
-                  [32, 0.1, 0.1 + BOX_SIZE_H, 0.1, 0.1 + BOX_SIZE_W],
-                  [41, 0.1, 0.1 + BOX_SIZE_H, 0.9-BOX_SIZE_W, 0.9],
-                  [47, 0.9-BOX_SIZE_H, 0.9, 0.9-BOX_SIZE_W, 0.9],
-                  [48, 0.9-BOX_SIZE_H, 0.9, 0.9-BOX_SIZE_W, 0.9],
-                  [57, 0.9-BOX_SIZE_H, 0.9, 0.1, 0.1 + BOX_SIZE_W],
-                  [63, 0.1, 0.1 + BOX_SIZE_H, 0.1, 0.1 + BOX_SIZE_W]]
-    elif input_mode == 3:
-        # mode 3 ||||
-        inputs = [[0, 0, 0 + BOX_SIZE_H, 0, 0 + BOX_SIZE_W],
-            [9, 1-BOX_SIZE_H, 1, (1-BOX_SIZE_W) / 7 * 1, (1-BOX_SIZE_W) / 7 * 1 + BOX_SIZE_W],
-            [18, 0, 0 + BOX_SIZE_H, (1-BOX_SIZE_W) / 7 * 2, (1-BOX_SIZE_W) / 7 * 2 + BOX_SIZE_W],
-            [27, 1-BOX_SIZE_H, 1, (1-BOX_SIZE_W) / 7 * 3, (1-BOX_SIZE_W) / 7 * 3 + BOX_SIZE_W],
-            [36, 0, 0 + BOX_SIZE_H, (1-BOX_SIZE_W) / 7 * 4, (1-BOX_SIZE_W) / 7 * 4 + BOX_SIZE_W],
-            [45, 1-BOX_SIZE_H, 1, (1-BOX_SIZE_W) / 7 * 5, (1-BOX_SIZE_W) / 7 * 5 + BOX_SIZE_W],
-            [54, 0, 0 + BOX_SIZE_H, (1-BOX_SIZE_W) / 7 * 6, (1-BOX_SIZE_W) / 7 * 6 + BOX_SIZE_W],
-            [63, 1-BOX_SIZE_H, 1, 1-BOX_SIZE_W, 1]]
-    elif input_mode == 4:
-        # mode 4 ----
-        inputs = [[0, 0, 0 + BOX_SIZE_H, 0, 0 + BOX_SIZE_W],
-            [9, (1-BOX_SIZE_H) / 7 * 1, (1-BOX_SIZE_H) / 7 * 1 + BOX_SIZE_H, 1-BOX_SIZE_W, 1],
-            [18, (1-BOX_SIZE_H) / 7 * 2, (1-BOX_SIZE_H) / 7 * 2 + BOX_SIZE_H, 0, 0 + BOX_SIZE_W],
-            [27, (1-BOX_SIZE_H) / 7 * 3, (1-BOX_SIZE_H) / 7 * 3 + BOX_SIZE_H, 1-BOX_SIZE_W, 1],
-            [36, (1-BOX_SIZE_H) / 7 * 4, (1-BOX_SIZE_H) / 7 * 4 + BOX_SIZE_H, 0, 0 + BOX_SIZE_W],
-            [45, (1-BOX_SIZE_H) / 7 * 5, (1-BOX_SIZE_H) / 7 * 5 + BOX_SIZE_H, 1-BOX_SIZE_W, 1],
-            [54, (1-BOX_SIZE_H) / 7 * 6, (1-BOX_SIZE_H) / 7 * 6 + BOX_SIZE_H, 0, 0 + BOX_SIZE_W],
-            [63, 1-BOX_SIZE_H, 1, 1-BOX_SIZE_W, 1]]
-    else:
-        print('error')
-        exit()
-
-    outputs = plan_path(inputs)
-    # print(outputs)
-    return outputs
-
-def get_path(BOX_SIZE_H=0.3, BOX_SIZE_W=0.3, input_mode=0):
-
-    if input_mode == 0:
-        # \ d
-        inputs = [[0, 0, 0 + BOX_SIZE_H, 0, 0 + BOX_SIZE_W], [15, 1-BOX_SIZE_H, 1, 1-BOX_SIZE_W, 1]] 
-    elif input_mode == 1:
-        # / re d
-        inputs = [[0, 0, 0 + BOX_SIZE_H, 1-BOX_SIZE_W, 1], [15, 1-BOX_SIZE_H, 1, 0, 0 + BOX_SIZE_W]] 
-    elif input_mode == 2:        
-        # L
-        inputs = [[0, 0.1, 0.1 + BOX_SIZE_H, 0.1, 0.1 + BOX_SIZE_W], [6, 0.9-BOX_SIZE_H, 0.9, 0.1, 0.1 + BOX_SIZE_W], [15, 0.9-BOX_SIZE_H, 0.9, 0.9-BOX_SIZE_W, 0.9]] 
-    elif input_mode == 3:     
-        # re L
-        inputs = [[0, 0.9-BOX_SIZE_H, 0.9, 0.9-BOX_SIZE_W, 0.9], [6, 0.1, 0.1 + BOX_SIZE_H, 0.9-BOX_SIZE_W, 0.9], [15, 0.1, 0.1 + BOX_SIZE_H, 0.1, 0.1 + BOX_SIZE_W]] 
-    elif input_mode == 4:     
-        # V
-        inputs = [[0, 0, 0 + BOX_SIZE_H, 0, 0 + BOX_SIZE_W], [7, 1-BOX_SIZE_H, 1, (1-BOX_SIZE_W) / 15 * 7, (1-BOX_SIZE_W) / 15 * 7 + BOX_SIZE_W], [8, 1-BOX_SIZE_H, 1, (1-BOX_SIZE_W) / 15 * 8, (1-BOX_SIZE_W) / 15 * 8 + BOX_SIZE_W], [15, 0, 0 + BOX_SIZE_H, 1-BOX_SIZE_W, 1]]
-    elif input_mode == 5:     
-        # re V
-        inputs = [[0, 1-BOX_SIZE_H, 1, 1-BOX_SIZE_W, 1], [7, 0, 0 + BOX_SIZE_H, (1-BOX_SIZE_W) / 15 * 8, (1-BOX_SIZE_W) / 15 * 8 + BOX_SIZE_W], [8, 0, 0 + BOX_SIZE_H, (1-BOX_SIZE_W) / 15 * 7, (1-BOX_SIZE_W) / 15 * 7 + BOX_SIZE_W], [15, 1-BOX_SIZE_H, 1, 0, 0 + BOX_SIZE_W]]
-    elif input_mode == 6:    
-        # -- goback
-        inputs = [[0, 0.35, 0.35 + BOX_SIZE_H, 0.1, 0.1 + BOX_SIZE_W], [7, 0.35, 0.35 + BOX_SIZE_H, 0.9-BOX_SIZE_W, 0.9], [8, 0.35, 0.35 + BOX_SIZE_H, 0.9-BOX_SIZE_W, 0.9], [15, 0.35, 0.35 + BOX_SIZE_H, 0.1, 0.1 + BOX_SIZE_W]] 
-    elif input_mode == 7:    
-        # tri
-        inputs = [[0, 0.1, 0.1 + BOX_SIZE_H, 0.35, 0.35 + BOX_SIZE_W], [5, 0.9-BOX_SIZE_H, 0.9, 0.9-BOX_SIZE_W, 0.9], [10, 0.9-BOX_SIZE_H, 0.9, 0.1, 0.1 + BOX_SIZE_W], [15, 0.1, 0.1 + BOX_SIZE_H, 0.35, 0.35 + BOX_SIZE_W]]
-
-    outputs = plan_path(inputs)
-    return outputs
-
-# input: List([frame, h_start, h_end, w_start, w_end], ...)
-# return: List([h_start, h_end, w_start, w_end], ...)
-def plan_path(input):
-    len_input = len(input)
-    path = [input[0][1:]]
-    for i in range(1, len_input):
-        start = input[i-1]
-        end = input[i]
-        start_frame = start[0]
-        end_frame = end[0]
-        h_start_change = (end[1] - start[1]) / (end_frame - start_frame)
-        h_end_change = (end[2] - start[2]) / (end_frame - start_frame)
-        w_start_change = (end[3] - start[3]) / (end_frame - start_frame)
-        w_end_change = (end[4] - start[4]) / (end_frame - start_frame)
-        for j in range(start_frame+1, end_frame + 1):
-            increase_frame = j - start_frame
-            path += [[increase_frame * h_start_change + start[1], increase_frame * h_end_change + start[2], increase_frame * w_start_change + start[3], increase_frame * w_end_change + start[4]]]
- 
-    return path
-
-
-def gaussian_2d(x=0, y=0, mx=0, my=0, sx=1, sy=1):
-    """ 2d Gaussian weight function
-    """
-    gaussian_map = (
-        1
-        / (2 * math.pi * sx * sy)
-        * torch.exp(-((x - mx) ** 2 / (2 * sx**2) + (y - my) ** 2 / (2 * sy**2)))
-    )
-    gaussian_map.div_(gaussian_map.max())
-    return gaussian_map
-
-def gaussian_weight(height=32, width=32, KERNEL_DIVISION=3.0):
-
-    x = torch.linspace(0, height, height)
-    y = torch.linspace(0, width, width)
-    x, y = torch.meshgrid(x, y, indexing="ij")
-    noise_patch = (
-                    gaussian_2d(
-                        x,
-                        y,
-                        mx=int(height / 2),
-                        my=int(width / 2),
-                        sx=float(height / KERNEL_DIVISION),
-                        sy=float(width / KERNEL_DIVISION),
-                    )
-                ).half()
-    return noise_patch
-
-def freq_mix_3d(x, noise, LPF):
-    """
-    Noise reinitialization.
-
-    Args:
-        x: diffused latent
-        noise: randomly sampled noise
-        LPF: low pass filter
-    """
-    # FFT
-    x_freq = fft.fftn(x, dim=(-3, -2, -1))
-    x_freq = fft.fftshift(x_freq, dim=(-3, -2, -1))
-    noise_freq = fft.fftn(noise, dim=(-3, -2, -1))
-    noise_freq = fft.fftshift(noise_freq, dim=(-3, -2, -1))
-
-    # frequency mix
-    HPF = 1 - LPF
-    x_freq_low = x_freq * LPF
-    noise_freq_high = noise_freq * HPF
-    x_freq_mixed = x_freq_low + noise_freq_high # mix in freq domain
-
-    # IFFT
-    x_freq_mixed = fft.ifftshift(x_freq_mixed, dim=(-3, -2, -1))
-    x_mixed = fft.ifftn(x_freq_mixed, dim=(-3, -2, -1)).real
-
-    return x_mixed
-
-
-def get_freq_filter(shape, device, filter_type, n, d_s, d_t):
-    """
-    Form the frequency filter for noise reinitialization.
-
-    Args:
-        shape: shape of latent (B, C, T, H, W)
-        filter_type: type of the freq filter
-        n: (only for butterworth) order of the filter, larger n ~ ideal, smaller n ~ gaussian
-        d_s: normalized stop frequency for spatial dimensions (0.0-1.0)
-        d_t: normalized stop frequency for temporal dimension (0.0-1.0)
-    """
-    if filter_type == "gaussian":
-        return gaussian_low_pass_filter(shape=shape, d_s=d_s, d_t=d_t).to(device)
-    elif filter_type == "ideal":
-        return ideal_low_pass_filter(shape=shape, d_s=d_s, d_t=d_t).to(device)
-    elif filter_type == "box":
-        return box_low_pass_filter(shape=shape, d_s=d_s, d_t=d_t).to(device)
-    elif filter_type == "butterworth":
-        return butterworth_low_pass_filter(shape=shape, n=n, d_s=d_s, d_t=d_t).to(device)
-    else:
-        raise NotImplementedError
-
-def gaussian_low_pass_filter(shape, d_s=0.25, d_t=0.25):
-    """
-    Compute the gaussian low pass filter mask.
-
-    Args:
-        shape: shape of the filter (volume)
-        d_s: normalized stop frequency for spatial dimensions (0.0-1.0)
-        d_t: normalized stop frequency for temporal dimension (0.0-1.0)
-    """
-    T, H, W = shape[-3], shape[-2], shape[-1]
-    mask = torch.zeros(shape)
-    if d_s==0 or d_t==0:
-        return mask
-    for t in range(T):
-        for h in range(H):
-            for w in range(W):
-                d_square = (((d_s/d_t)*(2*t/T-1))**2 + (2*h/H-1)**2 + (2*w/W-1)**2)
-                mask[..., t,h,w] = math.exp(-1/(2*d_s**2) * d_square)
-    return mask
-
-
-def butterworth_low_pass_filter(shape, n=4, d_s=0.25, d_t=0.25):
-    """
-    Compute the butterworth low pass filter mask.
-
-    Args:
-        shape: shape of the filter (volume)
-        n: order of the filter, larger n ~ ideal, smaller n ~ gaussian
-        d_s: normalized stop frequency for spatial dimensions (0.0-1.0)
-        d_t: normalized stop frequency for temporal dimension (0.0-1.0)
-    """
-    T, H, W = shape[-3], shape[-2], shape[-1]
-    mask = torch.zeros(shape)
-    if d_s==0 or d_t==0:
-        return mask
-    for t in range(T):
-        for h in range(H):
-            for w in range(W):
-                d_square = (((d_s/d_t)*(2*t/T-1))**2 + (2*h/H-1)**2 + (2*w/W-1)**2)
-                mask[..., t,h,w] = 1 / (1 + (d_square / d_s**2)**n)
-    return mask
-
-
-def ideal_low_pass_filter(shape, d_s=0.25, d_t=0.25):
-    """
-    Compute the ideal low pass filter mask.
-
-    Args:
-        shape: shape of the filter (volume)
-        d_s: normalized stop frequency for spatial dimensions (0.0-1.0)
-        d_t: normalized stop frequency for temporal dimension (0.0-1.0)
-    """
-    T, H, W = shape[-3], shape[-2], shape[-1]
-    mask = torch.zeros(shape)
-    if d_s==0 or d_t==0:
-        return mask
-    for t in range(T):
-        for h in range(H):
-            for w in range(W):
-                d_square = (((d_s/d_t)*(2*t/T-1))**2 + (2*h/H-1)**2 + (2*w/W-1)**2)
-                mask[..., t,h,w] =  1 if d_square <= d_s*2 else 0
-    return mask
-
-
-def box_low_pass_filter(shape, d_s=0.25, d_t=0.25):
-    """
-    Compute the ideal low pass filter mask (approximated version).
-
-    Args:
-        shape: shape of the filter (volume)
-        d_s: normalized stop frequency for spatial dimensions (0.0-1.0)
-        d_t: normalized stop frequency for temporal dimension (0.0-1.0)
-    """
-    T, H, W = shape[-3], shape[-2], shape[-1]
-    mask = torch.zeros(shape)
-    if d_s==0 or d_t==0:
-        return mask
-
-    threshold_s = round(int(H // 2) * d_s)
-    threshold_t = round(T // 2 * d_t)
-
-    cframe, crow, ccol = T // 2, H // 2, W //2
-    mask[..., cframe - threshold_t:cframe + threshold_t, crow - threshold_s:crow + threshold_s, ccol - threshold_s:ccol + threshold_s] = 1.0
-
-    return mask
-
-def load_idx(prompt_file):
-    f = open(prompt_file, 'r')
-    idx_list = []
-    for idx, line in enumerate(f.readlines()):
-        l = line.strip()
-        if len(l) != 0:
-            indices = l.split(',')
-            indices_list = []
-            for index in indices:
-                indices_list.append(int(index))
-            idx_list.append(indices_list)
-        f.close()
-    return idx_list
-
-def load_traj(prompt_file):
-    f = open(prompt_file, 'r')
-    traj_list = []
-    for idx, line in enumerate(f.readlines()):
-        l = line.strip()
-        if len(l) != 0:
-            numbers = l.split(',')
-            numbers_list = []
-            for number_index in range(len(numbers)):
-                if number_index == 0:
-                    numbers_list.append(int(numbers[number_index]))
-                else:
-                    numbers_list.append(float(numbers[number_index]))
-            traj_list.append(numbers_list)
-        f.close()
-    return traj_list