controlnet

diffsynth/models/wan_video_controlnet.py

@@ -0,0 +1,204 @@
+import torch
+import torch.nn as nn
+from typing import Tuple, Optional
+from einops import rearrange
+from .wan_video_dit import DiTBlock, precompute_freqs_cis_3d, MLP, sinusoidal_embedding_1d
+from .utils import hash_state_dict_keys
+
+
+
+class WanControlNetModel(torch.nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        in_dim: int,
+        ffn_dim: int,
+        out_dim: int,
+        text_dim: int,
+        freq_dim: int,
+        eps: float,
+        patch_size: Tuple[int, int, int],
+        num_heads: int,
+        num_layers: int,
+        has_image_input: bool,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.freq_dim = freq_dim
+        self.has_image_input = has_image_input
+        self.patch_size = patch_size
+
+        self.patch_embedding = nn.Conv3d(
+            in_dim, dim, kernel_size=patch_size, stride=patch_size)
+        self.text_embedding = nn.Sequential(
+            nn.Linear(text_dim, dim),
+            nn.GELU(approximate='tanh'),
+            nn.Linear(dim, dim)
+        )
+        self.time_embedding = nn.Sequential(
+            nn.Linear(freq_dim, dim),
+            nn.SiLU(),
+            nn.Linear(dim, dim)
+        )
+        self.time_projection = nn.Sequential(
+            nn.SiLU(), nn.Linear(dim, dim * 6))
+        self.blocks = nn.ModuleList([
+            DiTBlock(has_image_input, dim, num_heads, ffn_dim, eps)
+            for _ in range(num_layers)
+        ])
+        head_dim = dim // num_heads
+        self.freqs = precompute_freqs_cis_3d(head_dim)
+
+        if has_image_input:
+            self.img_emb = MLP(1280, dim)  # clip_feature_dim = 1280
+            
+        self.controlnet_conv_in = torch.nn.Conv3d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
+        self.controlnet_blocks = torch.nn.ModuleList([
+            torch.nn.Linear(dim, dim, bias=False)
+            for _ in range(num_layers)
+        ])
+
+    def patchify(self, x: torch.Tensor):
+        x = self.patch_embedding(x)
+        grid_size = x.shape[2:]
+        x = rearrange(x, 'b c f h w -> b (f h w) c').contiguous()
+        return x, grid_size  # x, grid_size: (f, h, w)
+
+    def unpatchify(self, x: torch.Tensor, grid_size: torch.Tensor):
+        return rearrange(
+            x, 'b (f h w) (x y z c) -> b c (f x) (h y) (w z)',
+            f=grid_size[0], h=grid_size[1], w=grid_size[2], 
+            x=self.patch_size[0], y=self.patch_size[1], z=self.patch_size[2]
+        )
+
+    def forward(self,
+                x: torch.Tensor,
+                timestep: torch.Tensor,
+                context: torch.Tensor,
+                clip_feature: Optional[torch.Tensor] = None,
+                y: Optional[torch.Tensor] = None,
+                controlnet_conditioning: Optional[torch.Tensor] = None,
+                use_gradient_checkpointing: bool = False,
+                use_gradient_checkpointing_offload: bool = False,
+                **kwargs,
+                ):
+        t = self.time_embedding(
+            sinusoidal_embedding_1d(self.freq_dim, timestep))
+        t_mod = self.time_projection(t).unflatten(1, (6, self.dim))
+        context = self.text_embedding(context)
+        
+        if self.has_image_input:
+            x = torch.cat([x, y], dim=1)  # (b, c_x + c_y, f, h, w)
+            clip_embdding = self.img_emb(clip_feature)
+            context = torch.cat([clip_embdding, context], dim=1)
+            
+        x = x + self.controlnet_conv_in(controlnet_conditioning)
+        x, (f, h, w) = self.patchify(x)
+        
+        freqs = torch.cat([
+            self.freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
+            self.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
+            self.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
+        ], dim=-1).reshape(f * h * w, 1, -1).to(x.device)
+        
+        def create_custom_forward(module):
+            def custom_forward(*inputs):
+                return module(*inputs)
+            return custom_forward
+
+        res_stack = []
+        for block in self.blocks:
+            if self.training and use_gradient_checkpointing:
+                if use_gradient_checkpointing_offload:
+                    with torch.autograd.graph.save_on_cpu():
+                        x = torch.utils.checkpoint.checkpoint(
+                            create_custom_forward(block),
+                            x, context, t_mod, freqs,
+                            use_reentrant=False,
+                        )
+                else:
+                    x = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(block),
+                        x, context, t_mod, freqs,
+                        use_reentrant=False,
+                    )
+            else:
+                x = block(x, context, t_mod, freqs)
+            res_stack.append(x)
+        
+        controlnet_res_stack = [block(res) for block, res in zip(self.controlnet_blocks, res_stack)]
+        return controlnet_res_stack
+
+    @staticmethod
+    def state_dict_converter():
+        return WanControlNetModelStateDictConverter()
+    
+    
+class WanControlNetModelStateDictConverter:
+    def __init__(self):
+        pass
+    
+    def from_diffusers(self, state_dict):
+        return state_dict
+    
+    def from_civitai(self, state_dict):
+        return state_dict
+    
+    def from_base_model(self, state_dict):
+        if hash_state_dict_keys(state_dict) == "9269f8db9040a9d860eaca435be61814":
+            config = {
+                "has_image_input": False,
+                "patch_size": [1, 2, 2],
+                "in_dim": 16,
+                "dim": 1536,
+                "ffn_dim": 8960,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 16,
+                "num_heads": 12,
+                "num_layers": 30,
+                "eps": 1e-6
+            }
+        elif hash_state_dict_keys(state_dict) == "aafcfd9672c3a2456dc46e1cb6e52c70":
+            config = {
+                "has_image_input": False,
+                "patch_size": [1, 2, 2],
+                "in_dim": 16,
+                "dim": 5120,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 16,
+                "num_heads": 40,
+                "num_layers": 40,
+                "eps": 1e-6
+            }
+        elif hash_state_dict_keys(state_dict) == "6bfcfb3b342cb286ce886889d519a77e":
+            config = {
+                "has_image_input": True,
+                "patch_size": [1, 2, 2],
+                "in_dim": 36,
+                "dim": 5120,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 16,
+                "num_heads": 40,
+                "num_layers": 40,
+                "eps": 1e-6
+            }
+        else:
+            config = {}
+        state_dict_ = {}
+        dtype, device = None, None
+        for name, param in state_dict.items():
+            if name.startswith("head."):
+                continue
+            state_dict_[name] = param
+            dtype, device = param.dtype, param.device
+        for block_id in range(config["num_layers"]):
+            zeros = torch.zeros((config["dim"], config["dim"]), dtype=dtype, device=device)
+            state_dict_[f"controlnet_blocks.{block_id}.weight"] = zeros.clone()
+        state_dict_["controlnet_conv_in.weight"] = torch.zeros((config["in_dim"], config["in_dim"], 1, 1, 1), dtype=dtype, device=device)
+        state_dict_["controlnet_conv_in.bias"] = torch.zeros((config["in_dim"],), dtype=dtype, device=device)
+        return state_dict_, config

diffsynth/pipelines/wan_video.py

@@ -17,6 +17,7 @@ from ..vram_management import enable_vram_management, AutoWrappedModule, AutoWra
 from ..models.wan_video_text_encoder import T5RelativeEmbedding, T5LayerNorm
 from ..models.wan_video_dit import RMSNorm, sinusoidal_embedding_1d
 from ..models.wan_video_vae import RMS_norm, CausalConv3d, Upsample
+from ..models.wan_video_controlnet import WanControlNetModel
 
 
 
@@ -30,7 +31,8 @@ class WanVideoPipeline(BasePipeline):
         self.image_encoder: WanImageEncoder = None
         self.dit: WanModel = None
         self.vae: WanVideoVAE = None
-        self.model_names = ['text_encoder', 'dit', 'vae']
+        self.controlnet: WanControlNetModel = None
+        self.model_names = ['text_encoder', 'dit', 'vae', 'image_encoder', 'controlnet']
         self.height_division_factor = 16
         self.width_division_factor = 16
 
@@ -189,6 +191,11 @@ class WanVideoPipeline(BasePipeline):
     def decode_video(self, latents, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)):
         frames = self.vae.decode(latents, device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
         return frames
+    
+    
+    def prepare_controlnet(self, controlnet_frames, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)):
+        controlnet_conditioning = self.encode_video(controlnet_frames, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=self.torch_dtype, device=self.device)
+        return {"controlnet_conditioning": controlnet_conditioning}
 
 
     @torch.no_grad()
@@ -212,6 +219,7 @@ class WanVideoPipeline(BasePipeline):
         tile_stride=(15, 26),
         tea_cache_l1_thresh=None,
         tea_cache_model_id="",
+        controlnet_frames=None,
         progress_bar_cmd=tqdm,
         progress_bar_st=None,
     ):
@@ -252,6 +260,15 @@ class WanVideoPipeline(BasePipeline):
         else:
             image_emb = {}
             
+        # ControlNet
+        if self.controlnet is not None and controlnet_frames is not None:
+            self.load_models_to_device(['vae', 'controlnet'])
+            controlnet_frames = self.preprocess_images(controlnet_frames)
+            controlnet_frames = torch.stack(controlnet_frames, dim=2).to(dtype=self.torch_dtype, device=self.device)
+            controlnet_kwargs = self.prepare_controlnet(controlnet_frames)
+        else:
+            controlnet_kwargs = {}
+            
         # Extra input
         extra_input = self.prepare_extra_input(latents)
         
@@ -260,14 +277,24 @@ class WanVideoPipeline(BasePipeline):
         tea_cache_nega = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh, model_id=tea_cache_model_id) if tea_cache_l1_thresh is not None else None}
 
         # Denoise
-        self.load_models_to_device(["dit"])
+        self.load_models_to_device(["dit", "controlnet"])
         for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
             timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)
 
             # Inference
-            noise_pred_posi = model_fn_wan_video(self.dit, latents, timestep=timestep, **prompt_emb_posi, **image_emb, **extra_input, **tea_cache_posi)
+            noise_pred_posi = model_fn_wan_video(
+                self.dit, controlnet=self.controlnet,
+                x=latents, timestep=timestep,
+                **prompt_emb_posi, **image_emb, **extra_input,
+                **tea_cache_posi, **controlnet_kwargs
+            )
             if cfg_scale != 1.0:
-                noise_pred_nega = model_fn_wan_video(self.dit, latents, timestep=timestep, **prompt_emb_nega, **image_emb, **extra_input, **tea_cache_nega)
+                noise_pred_nega = model_fn_wan_video(
+                    self.dit, controlnet=self.controlnet,
+                    x=latents, timestep=timestep,
+                    **prompt_emb_nega, **image_emb, **extra_input,
+                    **tea_cache_nega, **controlnet_kwargs
+                )
                 noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
             else:
                 noise_pred = noise_pred_posi
@@ -340,14 +367,29 @@ class TeaCache:
 
 def model_fn_wan_video(
     dit: WanModel,
-    x: torch.Tensor,
-    timestep: torch.Tensor,
-    context: torch.Tensor,
+    controlnet: WanControlNetModel = None,
+    x: torch.Tensor = None,
+    timestep: torch.Tensor = None,
+    context: torch.Tensor = None,
     clip_feature: Optional[torch.Tensor] = None,
     y: Optional[torch.Tensor] = None,
     tea_cache: TeaCache = None,
+    controlnet_conditioning: Optional[torch.Tensor] = None,
+    use_gradient_checkpointing: bool = False,
+    use_gradient_checkpointing_offload: bool = False,
     **kwargs,
 ):
+    # ControlNet
+    if controlnet is not None and controlnet_conditioning is not None:
+        controlnet_res_stack = controlnet(
+            x, timestep=timestep, context=context, clip_feature=clip_feature, y=y,
+            controlnet_conditioning=controlnet_conditioning,
+            use_gradient_checkpointing=use_gradient_checkpointing,
+            use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
+        )
+    else:
+        controlnet_res_stack = None
+    
     t = dit.time_embedding(sinusoidal_embedding_1d(dit.freq_dim, timestep))
     t_mod = dit.time_projection(t).unflatten(1, (6, dit.dim))
     context = dit.text_embedding(context)
@@ -370,13 +412,35 @@ def model_fn_wan_video(
         tea_cache_update = tea_cache.check(dit, x, t_mod)
     else:
         tea_cache_update = False
+        
+    def create_custom_forward(module):
+        def custom_forward(*inputs):
+            return module(*inputs)
+        return custom_forward
     
     if tea_cache_update:
         x = tea_cache.update(x)
     else:
         # blocks
-        for block in dit.blocks:
-            x = block(x, context, t_mod, freqs)
+        for block_id, block in enumerate(dit.blocks):
+            if dit.training and use_gradient_checkpointing:
+                if use_gradient_checkpointing_offload:
+                    with torch.autograd.graph.save_on_cpu():
+                        x = torch.utils.checkpoint.checkpoint(
+                            create_custom_forward(block),
+                            x, context, t_mod, freqs,
+                            use_reentrant=False,
+                        )
+                else:
+                    x = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(block),
+                        x, context, t_mod, freqs,
+                        use_reentrant=False,
+                    )
+            else:
+                x = block(x, context, t_mod, freqs)
+            if controlnet_res_stack is not None:
+                x = x + controlnet_res_stack[block_id]
         if tea_cache is not None:
             tea_cache.store(x)