Merge pull request #1034 from modelscope/video_as_prompt

Video as prompt

diffsynth/pipelines/wan_video_new.py

@@ -22,6 +22,7 @@ from ..models.wan_video_image_encoder import WanImageEncoder
 from ..models.wan_video_vace import VaceWanModel
 from ..models.wan_video_motion_controller import WanMotionControllerModel
 from ..models.wan_video_animate_adapter import WanAnimateAdapter
+from ..models.wan_video_mot import MotWanModel
 from ..models.longcat_video_dit import LongCatVideoTransformer3DModel
 from ..schedulers.flow_match import FlowMatchScheduler
 from ..prompters import WanPrompter
@@ -47,9 +48,10 @@ class WanVideoPipeline(BasePipeline):
         self.motion_controller: WanMotionControllerModel = None
         self.vace: VaceWanModel = None
         self.vace2: VaceWanModel = None
+        self.vap: MotWanModel = None
         self.animate_adapter: WanAnimateAdapter = None
-        self.in_iteration_models = ("dit", "motion_controller", "vace", "animate_adapter")
-        self.in_iteration_models_2 = ("dit2", "motion_controller", "vace2", "animate_adapter")
+        self.in_iteration_models = ("dit", "motion_controller", "vace", "animate_adapter", "vap")
+        self.in_iteration_models_2 = ("dit2", "motion_controller", "vace2", "animate_adapter", "vap")
         self.unit_runner = PipelineUnitRunner()
         self.units = [
             WanVideoUnit_ShapeChecker(),
@@ -69,6 +71,7 @@ class WanVideoPipeline(BasePipeline):
             WanVideoPostUnit_AnimatePoseLatents(),
             WanVideoPostUnit_AnimateFacePixelValues(),
             WanVideoPostUnit_AnimateInpaint(),
+            WanVideoUnit_VAP(),
             WanVideoUnit_UnifiedSequenceParallel(),
             WanVideoUnit_TeaCache(),
             WanVideoUnit_CfgMerger(),
@@ -392,6 +395,7 @@ class WanVideoPipeline(BasePipeline):
         pipe.image_encoder = model_manager.fetch_model("wan_video_image_encoder")
         pipe.motion_controller = model_manager.fetch_model("wan_video_motion_controller")
         vace = model_manager.fetch_model("wan_video_vace", index=2)
+        pipe.vap = model_manager.fetch_model("wan_video_vap")
         if isinstance(vace, list):
             pipe.vace, pipe.vace2 = vace
         else:
@@ -455,6 +459,10 @@ class WanVideoPipeline(BasePipeline):
         animate_face_video: Optional[list[Image.Image]] = None,
         animate_inpaint_video: Optional[list[Image.Image]] = None,
         animate_mask_video: Optional[list[Image.Image]] = None,
+        # VAP
+        vap_video: Optional[list[Image.Image]] = None,
+        vap_prompt: Optional[str] = " ",
+        negative_vap_prompt: Optional[str] = " ",
         # Randomness
         seed: Optional[int] = None,
         rand_device: Optional[str] = "cpu",
@@ -493,10 +501,12 @@ class WanVideoPipeline(BasePipeline):
         # Inputs
         inputs_posi = {
             "prompt": prompt,
+            "vap_prompt": vap_prompt,
             "tea_cache_l1_thresh": tea_cache_l1_thresh, "tea_cache_model_id": tea_cache_model_id, "num_inference_steps": num_inference_steps,
         }
         inputs_nega = {
             "negative_prompt": negative_prompt,
+            "negative_vap_prompt": negative_vap_prompt,
             "tea_cache_l1_thresh": tea_cache_l1_thresh, "tea_cache_model_id": tea_cache_model_id, "num_inference_steps": num_inference_steps,
         }
         inputs_shared = {
@@ -516,6 +526,7 @@ class WanVideoPipeline(BasePipeline):
             "sliding_window_size": sliding_window_size, "sliding_window_stride": sliding_window_stride,
             "input_audio": input_audio, "audio_sample_rate": audio_sample_rate, "s2v_pose_video": s2v_pose_video, "audio_embeds": audio_embeds, "s2v_pose_latents": s2v_pose_latents, "motion_video": motion_video,
             "animate_pose_video": animate_pose_video, "animate_face_video": animate_face_video, "animate_inpaint_video": animate_inpaint_video, "animate_mask_video": animate_mask_video,
+            "vap_video": vap_video, 
         }
         for unit in self.units:
             inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
@@ -927,6 +938,71 @@ class WanVideoUnit_VACE(PipelineUnit):
         else:
             return {"vace_context": None, "vace_scale": vace_scale}
 
+class WanVideoUnit_VAP(PipelineUnit):
+    def __init__(self):
+        super().__init__(
+            take_over=True,
+            onload_model_names=("text_encoder", "vae", "image_encoder")
+        )
+
+    def process(self, pipe: WanVideoPipeline, inputs_shared, inputs_posi, inputs_nega):
+        if inputs_shared.get("vap_video") is None:
+            return inputs_shared, inputs_posi, inputs_nega
+        else:
+            # 1. encode vap prompt
+            pipe.load_models_to_device(["text_encoder"])
+            vap_prompt, negative_vap_prompt = inputs_posi.get("vap_prompt", ""), inputs_nega.get("negative_vap_prompt", "")
+            vap_prompt_emb = pipe.prompter.encode_prompt(vap_prompt, positive=inputs_posi.get('positive',None), device=pipe.device)
+            negative_vap_prompt_emb = pipe.prompter.encode_prompt(negative_vap_prompt, positive=inputs_nega.get('positive',None), device=pipe.device)
+            inputs_posi.update({"context_vap":vap_prompt_emb})
+            inputs_nega.update({"context_vap":negative_vap_prompt_emb})
+            # 2. prepare vap image clip embedding
+            pipe.load_models_to_device(["vae", "image_encoder"])
+            vap_video, end_image = inputs_shared.get("vap_video"), inputs_shared.get("end_image")
+
+            num_frames, height, width, mot_num = inputs_shared.get("num_frames"),inputs_shared.get("height"), inputs_shared.get("width"), inputs_shared.get("mot_num",1)
+            
+            image_vap = pipe.preprocess_image(vap_video[0].resize((width, height))).to(pipe.device)
+
+            vap_clip_context = pipe.image_encoder.encode_image([image_vap])
+            if end_image is not None:
+                vap_end_image = pipe.preprocess_image(vap_video[-1].resize((width, height))).to(pipe.device)
+                if pipe.dit.has_image_pos_emb:
+                    vap_clip_context = torch.concat([vap_clip_context, pipe.image_encoder.encode_image([vap_end_image])], dim=1)
+            vap_clip_context = vap_clip_context.to(dtype=pipe.torch_dtype, device=pipe.device)
+            inputs_shared.update({"vap_clip_feature":vap_clip_context})
+
+            # 3. prepare vap latents            
+            msk = torch.ones(1, num_frames, height//8, width//8, device=pipe.device)
+            msk[:, 1:] = 0
+            if end_image is not None:
+                msk[:, -1:] = 1
+                last_image_vap = pipe.preprocess_image(vap_video[-1].resize((width, height))).to(pipe.device)
+                vae_input = torch.concat([image_vap.transpose(0,1), torch.zeros(3, num_frames-2, height, width).to(image_vap.device), last_image_vap.transpose(0,1)],dim=1)
+            else:
+                vae_input = torch.concat([image_vap.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image_vap.device)], dim=1)
+            
+            msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1)
+            msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8)
+            msk = msk.transpose(1, 2)[0]
+
+            tiled,tile_size,tile_stride = inputs_shared.get("tiled"), inputs_shared.get("tile_size"), inputs_shared.get("tile_stride")
+
+            y = pipe.vae.encode([vae_input.to(dtype=pipe.torch_dtype, device=pipe.device)], device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
+            y = y.to(dtype=pipe.torch_dtype, device=pipe.device)
+            y = torch.concat([msk, y])
+            y = y.unsqueeze(0)
+            y = y.to(dtype=pipe.torch_dtype, device=pipe.device)
+
+            vap_video = pipe.preprocess_video(vap_video)
+            vap_latent = pipe.vae.encode(vap_video, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device)
+
+            vap_latent = torch.concat([vap_latent,y], dim=1).to(dtype=pipe.torch_dtype, device=pipe.device)
+            inputs_shared.update({"vap_hidden_state":vap_latent})
+            pipe.load_models_to_device([])
+
+            return inputs_shared, inputs_posi, inputs_nega
+
 
 
 class WanVideoUnit_UnifiedSequenceParallel(PipelineUnit):
@@ -1285,6 +1361,7 @@ def model_fn_wan_video(
     dit: WanModel,
     motion_controller: WanMotionControllerModel = None,
     vace: VaceWanModel = None,
+    vap: MotWanModel = None,
     animate_adapter: WanAnimateAdapter = None,
     latents: torch.Tensor = None,
     timestep: torch.Tensor = None,
@@ -1297,6 +1374,9 @@ def model_fn_wan_video(
     audio_embeds: Optional[torch.Tensor] = None,
     motion_latents: Optional[torch.Tensor] = None,
     s2v_pose_latents: Optional[torch.Tensor] = None,
+    vap_hidden_state = None,
+    vap_clip_feature = None,
+    context_vap = None,
     drop_motion_frames: bool = True,
     tea_cache: TeaCache = None,
     use_unified_sequence_parallel: bool = False,
@@ -1406,7 +1486,7 @@ def model_fn_wan_video(
     if clip_feature is not None and dit.require_clip_embedding:
         clip_embdding = dit.img_emb(clip_feature)
         context = torch.cat([clip_embdding, context], dim=1)
-    
+        
     # Camera control
     x = dit.patchify(x, control_camera_latents_input)
     
@@ -1431,6 +1511,25 @@ def model_fn_wan_video(
         dit.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
         dit.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
     ], dim=-1).reshape(f * h * w, 1, -1).to(x.device)
+
+    # VAP 
+    if vap is not None:
+        # hidden state
+        x_vap = vap_hidden_state
+        x_vap = vap.patchify(x_vap)
+        x_vap = rearrange(x_vap, 'b c f h w -> b (f h w) c').contiguous()
+        # Timestep
+        clean_timestep = torch.ones(timestep.shape, device=timestep.device).to(timestep.dtype)
+        t = vap.time_embedding(sinusoidal_embedding_1d(vap.freq_dim, clean_timestep))
+        t_mod_vap = vap.time_projection(t).unflatten(1, (6, vap.dim))
+
+        # rope
+        freqs_vap = vap.compute_freqs_mot(f,h,w).to(x.device)
+
+        # context
+        vap_clip_embedding = vap.img_emb(vap_clip_feature)
+        context_vap = vap.text_embedding(context_vap)
+        context_vap = torch.cat([vap_clip_embedding, context_vap], dim=1)
     
     # TeaCache
     if tea_cache is not None:
@@ -1460,23 +1559,45 @@ def model_fn_wan_video(
                 return module(*inputs)
             return custom_forward
         
+        def create_custom_forward_vap(block, vap):
+            def custom_forward(*inputs):
+                return vap(block, *inputs)
+            return custom_forward
+        
         for block_id, block in enumerate(dit.blocks):
             # Block
-            if use_gradient_checkpointing_offload:
-                with torch.autograd.graph.save_on_cpu():
+            if vap is not None and block_id in vap.mot_layers_mapping:
+                if use_gradient_checkpointing_offload:
+                    with torch.autograd.graph.save_on_cpu():
+                        x, x_vap = torch.utils.checkpoint.checkpoint(
+                            create_custom_forward_vap(block, vap),
+                            x, context, t_mod, freqs, x_vap, context_vap, t_mod_vap, freqs_vap, block_id,
+                            use_reentrant=False,
+                        )
+                elif use_gradient_checkpointing:
+                    x, x_vap = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward_vap(block, vap),
+                        x, context, t_mod, freqs, x_vap, context_vap, t_mod_vap, freqs_vap, block_id,
+                        use_reentrant=False,
+                    )
+                else:
+                    x, x_vap = vap(block, x, context, t_mod, freqs, x_vap, context_vap, t_mod_vap, freqs_vap, block_id)
+            else:
+                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,
+                        )
+                elif use_gradient_checkpointing:
                     x = torch.utils.checkpoint.checkpoint(
                         create_custom_forward(block),
                         x, context, t_mod, freqs,
                         use_reentrant=False,
                     )
-            elif use_gradient_checkpointing:
-                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)
+                else:
+                    x = block(x, context, t_mod, freqs)
             
             # VACE
             if vace_context is not None and block_id in vace.vace_layers_mapping: