fix bugs

"control camera poses", "control object trajectory", "control both camera and object motion"

.gitignore

@@ -3,4 +3,5 @@
 
 *.pyc
 gradio_temp
-*.pth
+*.pth
+/turbo/dist/index.html

README.md

@@ -12,7 +12,7 @@
 
 ## Nodes
 
-Four nodes `Motionctrl Sample` & `Load Motion Camera Preset` & `Load Motion Traj Preset` & `Select Image Indices`
+Four nodes `Load Motionctrl Checkpoint` & `Motionctrl Cond` & `Motionctrl Sample Simple` & `Load Motion Camera Preset` & `Load Motion Traj Preset` & `Select Image Indices` &`Motionctrl Sample`
 
 ## Tools
 
@@ -30,7 +30,7 @@ base workflow
 
 <img src="assets/base_wf.png" raw=true>
 
-https://github.com/chaojie/ComfyUI-MotionCtrl/blob/main/workflow_threenodes.json
+https://github.com/chaojie/ComfyUI-MotionCtrl/blob/main/workflow_motionctrl_base.json
 
 <video controls autoplay="true">
     <source 

nodes.py

@@ -70,7 +70,7 @@ def process_traj(points_str,frame_length):
 
     return optical_flow
     
-def save_results(video, fps=10,traj="[]",draw_traj_dot=False,cameras=[],draw_camera_dot=False):
+def save_results(video, fps=10,traj="[]",draw_traj_dot=False,cameras=[],draw_camera_dot=False,context_overlap=0):
     
     # b,c,t,h,w
     video = video.detach().cpu()
@@ -117,7 +117,7 @@ def save_results(video, fps=10,traj="[]",draw_traj_dot=False,cameras=[],draw_cam
         #writer.append_data(img)
 
     #writer.close()
-    return torch.cat(tuple(outframes), dim=0).unsqueeze(0)
+    return torch.cat(tuple(outframes[context_overlap:]), dim=0).unsqueeze(0)
 
 MOTION_CAMERA_OPTIONS = ["U", "D", "L", "R", "O", "O_0.2x", "O_0.4x", "O_1.0x", "O_2.0x", "O_0.2x", "O_0.2x", "Round-RI", "Round-RI_90", "Round-RI-120", "Round-ZoomIn", "SPIN-ACW-60", "SPIN-CW-60", "I", "I_0.2x", "I_0.4x", "I_1.0x", "I_2.0x", "1424acd0007d40b5", "d971457c81bca597", "018f7907401f2fef", "088b93f15ca8745d", "b133a504fc90a2d1"]
 
@@ -184,14 +184,311 @@ class LoadMotionTrajPreset:
         points = read_points(f'{comfy_path}/custom_nodes/ComfyUI-MotionCtrl/examples/trajectories/{motion_traj}.txt',frame_length)
         return (json.dumps(points),)
 
+MODE = ["control camera poses", "control object trajectory", "control both camera and object motion"]
+class MotionctrlLoader:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "ckpt_name": (folder_paths.get_filename_list("checkpoints"), {"default": "motionctrl.pth"}),
+                "frame_length": ("INT", {"default": 16}),
+            }
+        }
+        
+    RETURN_TYPES = ("MOTIONCTRL", "EMBEDDER", "VAE", "SAMPLER",)
+    RETURN_NAMES = ("model","clip","vae","ddim_sampler",)
+    FUNCTION = "load_checkpoint"
+    CATEGORY = "motionctrl"
+
+    def load_checkpoint(self, ckpt_name, frame_length):
+        gpu_num=1
+        gpu_no=0
+        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
+        comfy_path = os.path.dirname(folder_paths.__file__)
+        config_path = os.path.join(comfy_path, 'custom_nodes/ComfyUI-MotionCtrl/configs/inference/config_both.yaml')
+        args={"ckpt_path":f"{ckpt_path}","adapter_ckpt":None,"base":f"{config_path}","condtype":"both","prompt_dir":None,"n_samples":1,"ddim_steps":50,"ddim_eta":1.0,"bs":1,"height":256,"width":256,"unconditional_guidance_scale":1.0,"unconditional_guidance_scale_temporal":None,"seed":1234,"cond_T":800}
+        
+        config = OmegaConf.load(args["base"])
+        OmegaConf.update(config, "model.params.unet_config.params.temporal_length", frame_length)
+        model_config = config.pop("model", OmegaConf.create())
+        model = instantiate_from_config(model_config)
+        model = model.cuda(gpu_no)
+        assert os.path.exists(args["ckpt_path"]), f'Error: checkpoint {args["ckpt_path"]} Not Found!'
+        print(f'Loading checkpoint from {args["ckpt_path"]}')
+        model = load_model_checkpoint(model, args["ckpt_path"], args["adapter_ckpt"])
+        model.eval()
+
+        ddim_sampler = DDIMSampler(model)
+
+        return (model,model.cond_stage_model,model.first_stage_model,ddim_sampler,)
+
+
+class MotionctrlCond:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "model": ("MOTIONCTRL",),
+                "prompt": ("STRING", {"multiline": True, "default":"a rose swaying in the wind"}),
+                "camera": ("STRING", {"multiline": True, "default":"[[1,0,0,0,0,1,0,0,0,0,1,0.2]]"}),
+                "traj": ("STRING", {"multiline": True, "default":"[[117, 102]]"}),
+                "infer_mode": (MODE, {"default":"control both camera and object motion"}),
+                "context_overlap": ("INT", {"default": 0, "min": 0, "max": 32}),
+            }
+        }
+        
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING","TRAJ_LIST","RT_LIST","TRAJ_FEATURES","RT","NOISE_SHAPE","INT")
+    RETURN_NAMES = ("positive", "negative","traj_list","rt_list","traj","rt","noise_shape","context_overlap")
+    FUNCTION = "load_cond"
+    CATEGORY = "motionctrl"
+
+    def load_cond(self, model, prompt, camera, traj,infer_mode,context_overlap):
+        comfy_path = os.path.dirname(folder_paths.__file__)
+        camera_align_file = os.path.join(comfy_path, 'custom_nodes/ComfyUI-MotionCtrl/camera.json')
+        traj_align_file = os.path.join(comfy_path, 'custom_nodes/ComfyUI-MotionCtrl/traj.json')
+        frame_length=model.temporal_length
+
+        camera_align=json.loads(camera)
+        for i in range(frame_length):
+            if len(camera_align)<=i:
+                camera_align.append(camera_align[len(camera_align)-1])
+        camera=json.dumps(camera_align)
+        traj_align=json.loads(traj)
+        for i in range(frame_length):
+            if len(traj_align)<=i:
+                traj_align.append(traj_align[len(traj_align)-1])
+        traj=json.dumps(traj_align)
+
+        if context_overlap>0:
+            if os.path.exists(camera_align_file):
+                with open(camera_align_file, 'r') as file:
+                    pre_camera_align=json.load(file)
+                    camera_align=pre_camera_align[:context_overlap]+camera_align[:-context_overlap]
+
+            if os.path.exists(traj_align_file):
+                with open(traj_align_file, 'r') as file:
+                    pre_traj_align=json.load(file)
+                    traj_align=pre_traj_align[:context_overlap]+traj_align[:-context_overlap]
+
+            with open(camera_align_file, 'w') as file:
+                json.dump(camera_align, file)
+
+            with open(traj_align_file, 'w') as file:
+                json.dump(traj_align, file)
+        
+        prompts = prompt
+        RT = process_camera(camera,frame_length).reshape(-1,12)
+        RT_list = process_camera_list(camera,frame_length)
+        traj_flow = process_traj(traj,frame_length).transpose(3,0,1,2)
+        print(prompts)
+        print(RT.shape)
+        print(traj_flow.shape)
+
+        height=256
+        width=256
+
+        ## run over data
+        assert (height % 16 == 0) and (width % 16 == 0), "Error: image size [h,w] should be multiples of 16!"
+        
+        ## latent noise shape
+        h, w = height // 8, width // 8
+        channels = model.channels
+        frames = model.temporal_length
+        #frames = frame_length
+        noise_shape = [1, channels, frames, h, w]
+
+        if infer_mode == MODE[0]:
+            camera_poses = RT
+            camera_poses = torch.tensor(camera_poses).float()
+            camera_poses = camera_poses.unsqueeze(0)
+            trajs = None
+            if torch.cuda.is_available():
+                camera_poses = camera_poses.cuda()
+        elif infer_mode == MODE[1]:
+            trajs = traj_flow
+            trajs = torch.tensor(trajs).float()
+            trajs = trajs.unsqueeze(0)
+            camera_poses = None
+            if torch.cuda.is_available():
+                trajs = trajs.cuda()
+        else:
+            camera_poses = RT
+            trajs = traj_flow
+            camera_poses = torch.tensor(camera_poses).float()
+            trajs = torch.tensor(trajs).float()
+            camera_poses = camera_poses.unsqueeze(0)
+            trajs = trajs.unsqueeze(0)
+            if torch.cuda.is_available():
+                camera_poses = camera_poses.cuda()
+                trajs = trajs.cuda()
+        
+        batch_size = noise_shape[0]
+        prompts=prompt
+        ## get condition embeddings (support single prompt only)
+        if isinstance(prompts, str):
+            prompts = [prompts]
+
+        for i in range(len(prompts)):
+            prompts[i] = f'{prompts[i]}, {post_prompt}'
+
+        cond = model.get_learned_conditioning(prompts)
+        if camera_poses is not None:
+            RT = camera_poses[..., None]
+        else:
+            RT = None
+
+        traj_features = None
+        if trajs is not None:
+            traj_features = model.get_traj_features(trajs)
+        else:
+            traj_features = None
+        
+        uc = None
+        prompts = batch_size * [DEFAULT_NEGATIVE_PROMPT]
+        uc = model.get_learned_conditioning(prompts)
+        if traj_features is not None:
+            un_motion = model.get_traj_features(torch.zeros_like(trajs))
+        else:
+            un_motion = None
+        uc = {"features_adapter": un_motion, "uc": uc}
+
+        return (cond,uc,traj,RT_list,traj_features,RT,noise_shape,context_overlap)
+
+
+
+class MotionctrlSampleSimple:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "model": ("MOTIONCTRL",),
+                "clip": ("EMBEDDER",),
+                "vae": ("VAE",),
+                "ddim_sampler": ("SAMPLER",),
+                "positive": ("CONDITIONING",),
+                "negative": ("CONDITIONING",),
+                "traj_list": ("TRAJ_LIST",),
+                "rt_list": ("RT_LIST",),
+                "traj": ("TRAJ_FEATURES",),
+                "rt": ("RT",),
+                "steps": ("INT", {"default": 50}),
+                "seed": ("INT", {"default": 1234}),
+                "noise_shape":("NOISE_SHAPE",),
+                "context_overlap": ("INT", {"default": 0, "min": 0, "max": 32}),
+            },
+            "optional": {
+                "traj_tool": ("STRING",{"multiline": False, "default": "https://chaojie.github.io/ComfyUI-MotionCtrl/tools/draw.html"}),
+                "draw_traj_dot": ("BOOLEAN", {"default": False}),#, "label_on": "draw", "label_off": "not draw"
+                "draw_camera_dot": ("BOOLEAN", {"default": False}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "run_inference"
+    CATEGORY = "motionctrl"
+
+    def run_inference(self,model,clip,vae,ddim_sampler,positive, negative,traj_list,rt_list,traj,rt,steps,seed,noise_shape,context_overlap,traj_tool="https://chaojie.github.io/ComfyUI-MotionCtrl/tools/draw.html",draw_traj_dot=False,draw_camera_dot=False):
+        frame_length=model.temporal_length
+        device = model.betas.device
+        print(f'frame_length{frame_length}')
+        #noise_shape = [1, 4, 16, 32, 32]
+        unconditional_guidance_scale = 7.5
+        unconditional_guidance_scale_temporal = None
+        n_samples = 1
+        ddim_steps= steps
+        ddim_eta=1.0
+        cond_T=800
+        #seed = args["seed"]
+
+        if n_samples < 1:
+            n_samples = 1
+        if n_samples > 4:
+            n_samples = 4
+
+        seed_everything(seed)
+
+        batch_images=[]
+        batch_variants = []
+        intermediates = {}
+
+        x0=None
+        x_T=None
+        pre_x0=None
+        pre_x_T=None
+
+        comfy_path = os.path.dirname(folder_paths.__file__)
+        pred_x0_path = os.path.join(comfy_path, 'custom_nodes/ComfyUI-MotionCtrl/pred_x0.pt')
+        x_inter_path = os.path.join(comfy_path, 'custom_nodes/ComfyUI-MotionCtrl/x_inter.pt')
+        randt=torch.randn([noise_shape[0],noise_shape[1],frame_length-context_overlap,noise_shape[3],noise_shape[4]], device=device)
+        randt_np=randt.detach().cpu().numpy()
+
+        if context_overlap>0:
+            if os.path.exists(pred_x0_path):
+                pre_x0=torch.load(pred_x0_path)
+                pre_x0_np=pre_x0[-1].detach().cpu().numpy()
+                pre_x0_np_overlap = np.concatenate((pre_x0_np[:,:,-context_overlap:], randt_np), axis=2)
+                x0=torch.tensor(pre_x0_np_overlap, device=device)
+            if os.path.exists(x_inter_path):
+                pre_x_T=torch.load(x_inter_path)
+                pre_x_T_np=pre_x_T[-1].detach().cpu().numpy()
+                pre_x_T_np_overlap = np.concatenate((pre_x_T_np[:,:,-context_overlap:], randt_np), axis=2)
+                x_T=torch.tensor(pre_x_T_np_overlap, device=device)
+        
+        for _ in range(n_samples):
+            if ddim_sampler is not None:
+                samples, intermediates = ddim_sampler.sample(S=ddim_steps,
+                                                conditioning=positive,
+                                                batch_size=noise_shape[0],
+                                                shape=noise_shape[1:],
+                                                verbose=False,
+                                                unconditional_guidance_scale=unconditional_guidance_scale,
+                                                unconditional_conditioning=negative,
+                                                eta=ddim_eta,
+                                                temporal_length=noise_shape[2],
+                                                conditional_guidance_scale_temporal=unconditional_guidance_scale_temporal,
+                                                features_adapter=traj,
+                                                pose_emb=rt,
+                                                cond_T=cond_T,
+                                                x0=x0,
+                                                x_T=x_T
+                                                )        
+            #print(f'{samples}')
+            ## reconstruct from latent to pixel space
+            batch_images = model.decode_first_stage(samples)
+            batch_variants.append(batch_images)
+            '''
+            batch_images = model.decode_first_stage(intermediates['pred_x0'][0])
+            batch_variants.append(batch_images)
+            batch_images = model.decode_first_stage(intermediates['pred_x0'][1])
+            batch_variants.append(batch_images)
+            batch_images = model.decode_first_stage(intermediates['pred_x0'][2])
+            batch_variants.append(batch_images)
+            batch_images = model.decode_first_stage(intermediates['x_inter'][0])
+            batch_variants.append(batch_images)
+            batch_images = model.decode_first_stage(intermediates['x_inter'][1])
+            batch_variants.append(batch_images)
+            batch_images = model.decode_first_stage(intermediates['x_inter'][2])
+            batch_variants.append(batch_images)
+            '''
+        ## variants, batch, c, t, h, w
+        batch_variants = torch.stack(batch_variants, dim=1)
+        batch_variants = batch_variants[0]
+        
+        torch.save(intermediates['x_inter'], x_inter_path)
+        torch.save(intermediates['pred_x0'], pred_x0_path)
+        ret = save_results(batch_variants, fps=10,traj=traj_list,draw_traj_dot=draw_traj_dot,cameras=rt_list,draw_camera_dot=draw_camera_dot,context_overlap=context_overlap)
+        #print(ret)
+        return ret
+        
+
 class MotionctrlSample:
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
                 "prompt": ("STRING", {"multiline": True, "default":"a rose swaying in the wind"}),
-                "camera": ("STRING", {"multiline": True, "default":"[[1,0,0,0,0,1,0,0,0,0,1,0.2],[1,0,0,0,0,1,0,0,0,0,1,0.28750000000000003],[1,0,0,0,0,1,0,0,0,0,1,0.37500000000000006],[1,0,0,0,0,1,0,0,0,0,1,0.4625000000000001],[1,0,0,0,0,1,0,0,0,0,1,0.55],[1,0,0,0,0,1,0,0,0,0,1,0.6375000000000002],[1,0,0,0,0,1,0,0,0,0,1,0.7250000000000001],[1,0,0,0,0,1,0,0,0,0,1,0.8125000000000002],[1,0,0,0,0,1,0,0,0,0,1,0.9000000000000001],[1,0,0,0,0,1,0,0,0,0,1,0.9875000000000003],[1,0,0,0,0,1,0,0,0,0,1,1.0750000000000002],[1,0,0,0,0,1,0,0,0,0,1,1.1625000000000003],[1,0,0,0,0,1,0,0,0,0,1,1.2500000000000002],[1,0,0,0,0,1,0,0,0,0,1,1.3375000000000001],[1,0,0,0,0,1,0,0,0,0,1,1.4250000000000003],[1,0,0,0,0,1,0,0,0,0,1,1.5125000000000004]]"}),
+                "camera": ("STRING", {"multiline": True, "default":"[[1,0,0,0,0,1,0,0,0,0,1,0.2]]"}),
-                "traj": ("STRING", {"multiline": True, "default":"[[117, 102],[114, 102],[109, 102],[106, 102],[105, 102],[102, 102],[99, 102],[97, 102],[96, 102],[95, 102],[93, 102],[89, 102],[85, 103],[82, 103],[81, 103],[80, 103],[79, 103],[78, 103],[76, 103],[74, 104],[73, 104],[72, 104],[71, 104],[70, 105],[69, 105],[68, 105],[67, 105],[66, 106],[64, 107],[63, 108],[62, 108],[61, 108],[61, 109],[60, 109],[59, 109],[58, 109],[57, 110],[56, 110],[55, 111],[54, 111],[53, 111],[52, 111],[52, 112],[51, 112],[50, 112],[50, 113],[49, 113],[48, 113],[46, 114],[46, 115],[45, 115],[45, 116],[44, 116],[43, 117],[42, 117],[41, 117],[41, 118],[40, 118],[41, 118],[41, 119],[42, 119],[43, 119],[44, 119],[46, 119],[47, 119],[48, 119],[49, 119],[50, 119],[51, 119],[52, 119],[53, 119],[54, 119],[55, 119],[56, 118],[58, 118],[59, 118],[61, 118],[63, 118],[64, 117],[67, 117],[70, 117],[71, 117],[73, 117],[75, 116],[76, 116],[77, 116],[80, 116],[82, 116],[83, 116],[84, 116],[85, 116],[88, 116],[91, 116],[94, 116],[97, 116],[98, 116],[100, 116],[101, 117],[102, 117],[104, 117],[105, 117],[106, 117],[107, 117],[108, 117],[109, 117],[110, 117],[111, 117],[115, 117],[119, 117],[123, 117],[124, 117],[128, 117],[129, 117],[132, 117],[134, 117],[135, 117],[136, 117],[138, 117],[139, 117],[140, 117],[141, 117],[142, 116],[145, 116],[146, 116],[148, 116],[149, 116],[151, 115],[152, 115],[153, 115],[154, 115],[155, 114],[156, 114],[157, 114],[158, 114],[159, 114],[162, 114],[163, 113],[164, 113],[165, 113],[166, 113],[167, 113],[168, 113],[169, 113],[170, 113],[171, 113],[172, 113],[173, 113],[174, 113],[175, 113],[178, 113],[181, 113],[182, 113],[183, 113],[184, 113],[185, 113],[187, 113],[188, 113],[189, 113],[191, 113],[192, 113],[193, 113],[194, 113],[195, 113],[196, 113],[197, 113],[198, 113],[199, 113],[200, 113],[201, 113],[202, 113],[203, 113],[202, 113],[201, 113],[200, 113],[198, 113],[197, 113],[196, 113],[195, 112],[194, 112],[193, 112],[192, 112],[191, 111],[190, 111],[189, 111],[188, 110],[187, 110],[186, 110],[185, 110],[184, 110],[183, 110],[182, 110],[181, 110],[180, 110],[179, 110],[178, 110],[177, 110],[175, 110],[173, 110],[172, 110],[171, 110],[170, 110],[168, 110],[167, 110],[165, 110],[164, 110],[163, 110],[161, 111],[159, 111],[155, 111],[153, 111],[151, 111],[151, 112],[150, 112],[149, 112],[148, 112],[147, 112],[145, 112],[143, 113],[142, 113],[140, 113],[139, 113],[138, 113],[136, 113],[135, 113],[134, 113],[133, 114],[131, 114],[130, 114],[128, 115],[127, 115],[126, 115],[125, 115],[124, 115],[122, 115],[121, 115],[120, 115],[118, 116],[115, 116],[113, 116],[111, 116],[109, 117],[106, 117],[103, 117],[102, 117],[100, 117],[98, 117],[97, 117],[95, 117],[94, 117],[93, 117],[92, 117],[91, 117],[90, 117],[89, 117],[88, 117],[87, 117],[86, 117],[85, 117],[84, 117],[83, 117],[84, 117],[85, 117],[87, 117],[88, 117],[89, 117],[90, 117],[92, 117],[93, 117],[95, 117],[97, 117],[99, 117],[101, 117],[103, 117],[104, 117],[105, 117],[106, 117],[107, 117],[108, 117],[109, 117],[110, 117],[112, 117],[113, 117],[114, 117],[116, 117],[117, 117],[118, 117],[119, 117],[120, 117],[121, 117],[123, 117],[124, 117],[125, 117],[126, 117],[127, 117],[129, 117],[130, 117],[131, 117],[133, 117],[134, 117],[135, 117],[136, 117],[137, 117],[138, 117],[139, 117],[140, 117],[141, 117],[142, 117],[143, 117],[145, 117],[146, 117],[147, 117],[148, 117],[149, 117],[150, 117],[149, 117],[148, 117],[147, 117],[146, 117],[144, 117],[143, 118],[142, 118],[141, 118],[140, 118],[139, 118],[138, 118],[136, 118],[135, 118],[132, 119],[131, 119],[130, 119],[129, 119],[127, 119],[126, 119],[124, 119],[123, 119],[122, 119],[121, 119],[119, 119],[118, 119],[117, 119],[115, 119],[114, 119],[113, 119],[112, 119],[111, 119],[110, 119],[109, 119],[108, 119],[107, 119],[106, 119],[107, 119],[108, 119],[109, 119],[110, 119],[112, 119],[113, 119],[114, 119],[115, 119],[116, 119],[117, 119],[118, 119],[119, 119],[120, 119],[121, 119],[122, 119],[123, 119],[124, 119],[125, 119],[126, 119],[127, 119],[127, 119],[127, 119],[127, 119]]"}),
+                "traj": ("STRING", {"multiline": True, "default":"[[117, 102]]"}),
                 "frame_length": ("INT", {"default": 16}),
                 "steps": ("INT", {"default": 50}),
                 "seed": ("INT", {"default": 1234}),
@@ -406,7 +703,10 @@ class ImageSelector:
 
 NODE_CLASS_MAPPINGS = {
     "Motionctrl Sample":MotionctrlSample,
+    "Motionctrl Sample Simple":MotionctrlSampleSimple,
     "Load Motion Camera Preset":LoadMotionCameraPreset,
     "Load Motion Traj Preset":LoadMotionTrajPreset,
-    "Select Image Indices": ImageSelector
+    "Select Image Indices": ImageSelector,
+    "Load Motionctrl Checkpoint": MotionctrlLoader,
+    "Motionctrl Cond": MotionctrlCond,
 }

turbo/api.py

@@ -0,0 +1,172 @@
+#This is an example that uses the websockets api to know when a prompt execution is done
+#Once the prompt execution is done it downloads the images using the /history endpoint
+import os
+import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
+import uuid
+import json
+import urllib.request
+import urllib.parse
+from flask import Flask, request, jsonify, render_template, session, abort
+from flask_socketio import SocketIO, join_room, leave_room,send, emit
+import secrets
+
+from PIL import Image
+import io
+import base64
+
+import time
+from watchdog.observers import Observer
+from watchdog.events import FileSystemEventHandler
+
+class FileCreatedHandler(FileSystemEventHandler):
+    def on_created(self, event):
+        # 处理文件创建完成的逻辑
+        file_path = event.src_path
+        file_name = os.path.basename(file_path)
+        print(f"New file created: {file_name}")
+        
+        time.sleep(.1)
+        with open(file_path, 'rb') as fr:
+            image_data=fr.read()
+            b64img=base64.b64encode(image_data).decode('utf-8')
+            socketio.emit('server_response',{'b64img':b64img})
+
+folder_to_watch = "/home/admin/ComfyUI/output/motionctrl"  # 要监控的文件夹路径
+
+event_handler = FileCreatedHandler()  # 创建我们刚才定义的自定义处理类的实例
+observer = Observer()
+observer.schedule(event_handler, folder_to_watch, recursive=False)
+observer.start()
+
+server_address = "127.0.0.1:8188"
+client_id = str(uuid.uuid4())
+
+def queue_prompt(prompt):
+    p = {"prompt": prompt, "client_id": client_id}
+    data = json.dumps(p).encode('utf-8')
+    req =  urllib.request.Request("http://{}/prompt".format(server_address), data=data)
+    return json.loads(urllib.request.urlopen(req).read())
+
+def get_image(filename, subfolder, folder_type):
+    data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
+    url_values = urllib.parse.urlencode(data)
+    with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response:
+        return response.read()
+
+def get_history(prompt_id):
+    with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response:
+        return json.loads(response.read())
+
+def get_images(ws, prompt):
+    prompt_id = queue_prompt(prompt)['prompt_id']
+    output_images = {}
+    '''
+    while True:
+        out = ws.recv()
+        if isinstance(out, str):
+            message = json.loads(out)
+            if message['type'] == 'executing':
+                data = message['data']
+                if data['node'] is None and data['prompt_id'] == prompt_id:
+                    break #Execution is done
+        else:
+            continue #previews are binary data
+
+    history = get_history(prompt_id)[prompt_id]
+    for o in history['outputs']:
+        for node_id in history['outputs']:
+            node_output = history['outputs'][node_id]
+            if 'images' in node_output:
+                images_output = []
+                for image in node_output['images']:
+                    image_data = get_image(image['filename'], image['subfolder'], image['type'])
+                    images_output.append(image_data)
+            output_images[node_id] = images_output
+
+    return output_images
+    '''
+
+prompt={}
+with open('./workflow_api_motionctrl_turbo.json') as fr:
+    prompt = json.load(fr)
+
+ws = websocket.WebSocket()
+ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
+
+#Commented out code to display the output images:
+
+# for node_id in images:
+#     for image_data in images[node_id]:
+#         from PIL import Image
+#         import io
+#         image = Image.open(io.BytesIO(image_data))
+#         image.show()
+
+app = Flask(__name__, template_folder=os.path.abspath('.'), static_folder='assets')
+app.secret_key = secrets.token_hex(16)
+
+socketio = SocketIO(app, cors_allowed_origins='*')
+connected_sids = set()  # 存放已连接的客户端
+
+#后端程序
+lockroom='None'
+@socketio.on('connect')
+def on_connect():
+    connected_sids.add(request.sid)
+    print(f'{request.sid} 已连接')
+    socketio.start_background_task(background_thread_heartbeat)
+
+@socketio.on('disconnect')
+def on_disconnect():
+    connected_sids.remove(request.sid)
+    print(f'{request.sid} 已断开')
+
+@socketio.on('message')
+def handle_message(message):
+    """收消息"""
+    print(f'message:{request.sid} {message}')
+    json.loads(message)
+
+@socketio.on('camera_poses')
+def handle_message(camera_poses):
+    print(f'camera_poses:{request.sid} {camera_poses}')
+    cams=json.loads(camera_poses["camera_poses"])
+    trajs=json.loads(camera_poses["trajs"])
+    if len(cams)>1 and len(trajs)>1:
+        prompt["60"]["inputs"]["infer_mode"] = "control both camera and object motion"
+    elif len(trajs)>1:
+        prompt["60"]["inputs"]["infer_mode"] = "control object trajectory"
+    else:
+        prompt["60"]["inputs"]["infer_mode"] = "control camera poses"
+
+    prompt["60"]["inputs"]["prompt"] = camera_poses["prompt"]
+    prompt["60"]["inputs"]["camera"] = camera_poses["camera_poses"]
+    prompt["60"]["inputs"]["traj"] = camera_poses["trajs"]
+    images = get_images(ws, prompt)
+    '''
+    for node_id in images:
+        for image_data in images[node_id]:
+            b64img=base64.b64encode(image_data).decode('utf-8')
+            socketio.emit('server_response',{'b64img':b64img}, to=camera_poses["roomid"])
+    '''
+
+@socketio.on('server_reconnect')
+def server_reconnect(message):
+    print(f'server_reconnect:{request.sid} {message}')
+    join_room(message['roomid'])
+
+def background_thread_heartbeat():
+    global lockroom
+    while True:
+        socketio.emit('server_response',{'lockroom':lockroom})
+        socketio.sleep(5)
+
+
+@app.route('/')
+def index():
+    session['user'] = None
+    return render_template('index.html')
+
+if __name__ == '__main__':
+    socketio.run(app, host='0.0.0.0', port=5017, debug=True, allow_unsafe_werkzeug=True)
+    #app.run(host='0.0.0.0', port=5017)

turbo/index.html

@@ -0,0 +1,26 @@
+<!DOCTYPE html>
+<html lang="en">
+	<head>
+		<meta charset="utf-8">
+		<meta name="viewport" content="width=device-width, user-scalable=no, minimum-scale=1.0, maximum-scale=1.0">
+		<link type="text/css" rel="stylesheet" href="main.css">
+		<title>CAMERA MOTION DESIGNER</title>
+		<style>
+			body { margin: 0; }
+		</style>
+	</head>
+	<body>
+        <div id="info">
+			<button id="btn_translate">Translate</button>
+			<button id="btn_rotate">Rotate</button>
+			<input id="txt_prompt" value="a rose swaying in the wind" />
+			<button id="btn_addpoint" style="display:none;">Add Point</button>
+			<!--"W" translate | "E" rotate--><br />
+            <!--textarea id="tb_result" style="width:256px; height:128px;">[]</textarea><br/-->
+            <button id="btn_startrt">Start Real Time MotionCtrl</button><br/>
+			<div style="width:256px;height:256px;display:none;" class="imageContainer"></div>
+			<canvas id="canvas" height="256" width="256" style="border:1px dotted gray;"></canvas>
+		</div>
+		<script type="module" src="main.js"></script>
+	</body>
+</html>

turbo/main.css

@@ -0,0 +1,91 @@
+body {
+	margin: 0;
+	background-color: #000;
+	color: #fff;
+	font-family: Monospace;
+	font-size: 13px;
+	line-height: 24px;
+	overscroll-behavior: none;
+}
+
+a {
+	color: #ff0;
+	text-decoration: none;
+}
+
+a:hover {
+	text-decoration: underline;
+}
+
+button {
+	cursor: pointer;
+	text-transform: uppercase;
+}
+
+#info {
+	position: absolute;
+	top: 0px;
+    left: 0px;
+	padding: 10px;
+	box-sizing: border-box;
+	text-align: center;
+	/*-moz-user-select: none;
+	-webkit-user-select: none;
+	-ms-user-select: none;
+	user-select: none;
+	pointer-events: none;*/
+	z-index: 1; /* TODO Solve this in HTML */
+}
+
+a, button, input, select {
+	pointer-events: auto;
+}
+
+.lil-gui {
+	z-index: 2 !important; /* TODO Solve this in HTML */
+}
+
+@media all and ( max-width: 640px ) {
+	.lil-gui.root { 
+		right: auto;
+		top: auto;
+		max-height: 50%;
+		max-width: 80%;
+		bottom: 0;
+		left: 0;
+	}
+}
+
+#overlay {
+	position: absolute;
+	font-size: 16px;
+	z-index: 2;
+	top: 0;
+	left: 0;
+	width: 100%;
+	height: 100%;
+	display: flex;
+	align-items: center;
+	justify-content: center;
+	flex-direction: column;
+	background: rgba(0,0,0,0.7);
+}
+
+	#overlay button {
+		background: transparent;
+		border: 0;
+		border: 1px solid rgb(255, 255, 255);
+		border-radius: 4px;
+		color: #ffffff;
+		padding: 12px 18px;
+		text-transform: uppercase;
+		cursor: pointer;
+	}
+
+#notSupported {
+	width: 50%;
+	margin: auto;
+	background-color: #f00;
+	margin-top: 20px;
+	padding: 10px;
+}

turbo/main.js

@@ -0,0 +1,392 @@
+import * as THREE from 'three';
+
+import { OrbitControls } from 'three/examples/jsm/controls/OrbitControls.js';
+import { TransformControls } from 'three/examples/jsm/controls/TransformControls.js';
+import { io } from "https://cdn.socket.io/4.7.2/socket.io.esm.min.js";
+
+let cameraPersp, cameraPerspTransform, currentCamera, cameraPerspTransformHelper;
+let scene, renderer, control, orbit;
+
+let roomid=new Date().getTime();
+
+let cposes=[];
+let cmatrix=[];
+let userDrawnPixels1024=[];
+
+var canvas,ctx;
+var mouseX,mouseY,moving, mouseDown=0;
+function getTouchPos(e) {
+    if (!e)
+        var e = event;
+
+    if(e.touches) {
+        if (e.touches.length == 1) {
+            var touch = e.touches[0];
+            mouseX=touch.pageX-touch.target.offsetLeft;
+            mouseY=touch.pageY-touch.target.offsetTop;
+        }
+    }
+}
+function sketchpad_mouseDown(e) {
+    getMousePos(e);
+    userDrawnPixelsPush([mouseX, mouseY]);
+    mouseDown=1;
+}
+function mouseOrTouchUp(e) {
+    mouseDown=0;
+    moving=0;
+
+    if(!mouseX){
+        getMousePos(e);
+        userDrawnPixelsPush([mouseX, mouseY]);
+        moving=1;
+    }else{
+        getMousePos(e);
+        userDrawnPixelsPush([mouseX, mouseY]);
+        moving=1;
+    }
+}
+
+function sketchpad_mouseMove(e) {
+    getMousePos(e);
+
+    if (mouseDown==1) {
+        userDrawnPixelsPush([mouseX, mouseY]);
+        moving=1;
+    }
+}
+
+function getMousePos(e) {
+    if (!e)
+        var e = event;
+
+    if (e.offsetX) {
+        mouseX = e.offsetX;
+        mouseY = e.offsetY;
+    }
+    else if (e.layerX) {
+        mouseX = e.layerX;
+        mouseY = e.layerY;
+    }
+}
+
+function sketchpad_touchStart(e) {
+    getTouchPos(e);
+    userDrawnPixelsPush([mouseX, mouseY]);
+    event.preventDefault();
+
+    moving=1;
+    mouseDown=1;
+}
+
+function sketchpad_touchMove(e) {
+    getTouchPos(e);
+    userDrawnPixelsPush([mouseX, mouseY]);
+
+    event.preventDefault();
+}
+
+function userDrawnPixelsPush(point){
+    if(userDrawnPixels1024.length==0&&point[0]!=null){
+        userDrawnPixels1024.push([point[0]*4,point[1]*4]);
+    }else{
+        if(point[0]!=null&&(userDrawnPixels1024[userDrawnPixels1024.length-1][0]!=point[0]*4||userDrawnPixels1024[userDrawnPixels1024.length-1][1]!=point[1]*4)){
+            userDrawnPixels1024.push([point[0]*4,point[1]*4]);
+        }
+    }
+}
+
+var socket = io();
+socket.on('connect', function() {
+  socket.emit('server_reconnect', {roomid: roomid});
+});
+
+
+socket.on("server_response", function (msg,ack) {
+    console.log(msg);
+    //接收到后端发送过来的消息
+    var b64img = msg.b64img;
+    if(!b64img)return;
+
+    var image = new Image();
+    image.src = 'data:image/jpeg;base64,' + b64img;
+
+    image.onload=function(){
+        ctx.drawImage(image,0,0,256,256);
+        drawDot(ctx,userDrawnPixels1024[userDrawnPixels1024.length-1][0]/4,userDrawnPixels1024[userDrawnPixels1024.length-1][1]/4,6);
+    };
+ });
+
+init();
+render();
+
+
+
+function detect_change(){
+    var matrix=cameraPerspTransform.matrix.elements;
+    matrix[3]=cameraPerspTransform.position.x;
+    matrix[7]=cameraPerspTransform.position.y;
+    matrix[11]=cameraPerspTransform.position.z;
+    matrix=matrix.slice(0, 12);
+
+    if(!userDrawnPixels1024.length){
+        userDrawnPixels1024=[[128*4,128*4]];
+    }
+
+    if(JSON.stringify(cmatrix)!=JSON.stringify(matrix)){
+        if(userDrawnPixels1024.length>=16){
+            socket.emit('camera_poses', {roomid:roomid,prompt:document.getElementById('txt_prompt').value,camera_poses:JSON.stringify(cposes),trajs:JSON.stringify(userDrawnPixels1024)});
+            userDrawnPixels1024=[userDrawnPixels1024[userDrawnPixels1024.length-1]];
+            cposes=[matrix];
+        }else{
+            cposes.push(matrix);
+        }
+        cmatrix=matrix;
+    }else{
+        if(cposes.length>1||userDrawnPixels1024.length>=16){
+            socket.emit('camera_poses', {roomid:roomid,prompt:document.getElementById('txt_prompt').value,camera_poses:JSON.stringify(cposes),trajs:JSON.stringify(userDrawnPixels1024)});
+            userDrawnPixels1024=[userDrawnPixels1024[userDrawnPixels1024.length-1]];
+        }
+
+        cposes=[matrix];
+    }
+
+    setTimeout(function(){
+        detect_change();
+    },100);
+}
+
+function drawDot(ctx, x, y, size) {
+    ctx.fillStyle = "lightgrey";
+
+    ctx.beginPath();
+    // Draw a filled circle
+    ctx.arc(x, y, size, 0, Math.PI*2, true);
+    ctx.closePath();
+    ctx.fill();
+}
+
+function init() {
+    canvas = document.getElementById('canvas');
+
+    canvas.width  = 256;
+    canvas.height = 256;
+
+    if (canvas.getContext)
+    ctx = canvas.getContext('2d');
+    drawDot(ctx,128,128,6);
+
+    if (ctx) {
+        // React to mouse events on the canvas, and mouseup on the entire document
+        canvas.addEventListener('mousedown', sketchpad_mouseDown, false);
+        canvas.addEventListener('mousemove', sketchpad_mouseMove, false);
+        canvas.addEventListener('mouseup', mouseOrTouchUp, false);
+
+        // React to touch events on the canvas
+        canvas.addEventListener('touchstart', sketchpad_touchStart, false);
+        canvas.addEventListener('touchmove', sketchpad_touchMove, false);
+        canvas.addEventListener('touchend', mouseOrTouchUp, false);
+    }
+
+    renderer = new THREE.WebGLRenderer( { antialias: true } );
+    renderer.setPixelRatio( window.devicePixelRatio );
+    renderer.setSize( window.innerWidth, window.innerHeight );
+    document.body.appendChild( renderer.domElement );
+
+    const aspect = window.innerWidth / window.innerHeight;
+
+    cameraPersp = new THREE.PerspectiveCamera( 50, aspect, 0.01, 30000 );
+    cameraPerspTransform = new THREE.PerspectiveCamera( 50, aspect, 0.01, 30000 );
+    
+    cameraPerspTransformHelper = new THREE.CameraHelper( cameraPerspTransform );
+                
+    currentCamera = cameraPersp;
+
+    currentCamera.position.set( 5, 2.5, 5 );
+
+    scene = new THREE.Scene();
+    scene.add( new THREE.GridHelper( 5, 10, 0x888888, 0x444444 ) );
+
+    const ambientLight = new THREE.AmbientLight( 0xffffff );
+    scene.add( ambientLight );
+
+    const light = new THREE.DirectionalLight( 0xffffff, 4 );
+    light.position.set( 1, 1, 1 );
+    scene.add( light );
+
+    const texture = new THREE.TextureLoader().load( 'textures/crate.gif', render );
+    texture.colorSpace = THREE.SRGBColorSpace;
+    texture.anisotropy = renderer.capabilities.getMaxAnisotropy();
+
+    const geometry = new THREE.BoxGeometry();
+    const material = new THREE.MeshLambertMaterial( { map: texture } );
+
+    orbit = new OrbitControls( currentCamera, renderer.domElement );
+    orbit.update();
+    orbit.addEventListener( 'change', render );
+
+    control = new TransformControls( currentCamera, renderer.domElement );
+    control.addEventListener( 'change', render );
+
+    control.addEventListener( 'dragging-changed', function ( event ) {
+
+        orbit.enabled = ! event.value;
+
+    } );
+
+    const mesh = new THREE.Mesh( geometry, material );
+    scene.add( cameraPerspTransform );
+
+    control.attach( cameraPerspTransform );
+    scene.add( control );
+    scene.add( cameraPerspTransformHelper );
+    
+    document.getElementById('btn_translate').addEventListener('click',function(){
+        control.setMode( 'translate' );
+    });
+    
+    document.getElementById('btn_rotate').addEventListener('click',function(){
+        control.setMode( 'rotate' );
+    });
+    
+    document.getElementById('btn_addpoint').addEventListener('click',function(){
+        var ret=JSON.parse(document.getElementById('tb_result').value);
+        var matrix=cameraPerspTransform.matrix.elements;
+        matrix[3]=cameraPerspTransform.position.x;
+        matrix[7]=cameraPerspTransform.position.y;
+        matrix[11]=cameraPerspTransform.position.z;
+        matrix=matrix.slice(0, 12);
+        ret.push(matrix);
+        document.getElementById('tb_result').value=JSON.stringify(ret);
+    });
+
+    document.getElementById('btn_startrt').addEventListener('click',function(){
+        detect_change();
+    });
+
+    window.addEventListener( 'resize', onWindowResize );
+
+    window.addEventListener( 'keydown', function ( event ) {
+
+        switch ( event.keyCode ) {
+
+            case 81: // Q
+                control.setSpace( control.space === 'local' ? 'world' : 'local' );
+                break;
+
+            case 16: // Shift
+                control.setTranslationSnap( 100 );
+                control.setRotationSnap( THREE.MathUtils.degToRad( 15 ) );
+                control.setScaleSnap( 0.25 );
+                break;
+
+            case 87: // W
+                control.setMode( 'translate' );
+                break;
+
+            case 69: // E
+                control.setMode( 'rotate' );
+                break;
+
+            case 82: // R
+                control.setMode( 'scale' );
+                break;
+
+            case 67: // C
+                const position = currentCamera.position.clone();
+
+                currentCamera = currentCamera.isPerspectiveCamera ? cameraPerspTransform : cameraPersp;
+                currentCamera.position.copy( position );
+
+                orbit.object = currentCamera;
+                control.camera = currentCamera;
+
+                currentCamera.lookAt( orbit.target.x, orbit.target.y, orbit.target.z );
+                onWindowResize();
+                break;
+
+            case 86: // V
+                const randomFoV = Math.random() + 0.1;
+                const randomZoom = Math.random() + 0.1;
+
+                cameraPersp.fov = randomFoV * 160;
+                cameraPerspTransform.bottom = - randomFoV * 500;
+                cameraPerspTransform.top = randomFoV * 500;
+
+                cameraPersp.zoom = randomZoom * 5;
+                cameraPerspTransform.zoom = randomZoom * 5;
+                onWindowResize();
+                break;
+
+            case 187:
+            case 107: // +, =, num+
+                control.setSize( control.size + 0.1 );
+                break;
+
+            case 189:
+            case 109: // -, _, num-
+                control.setSize( Math.max( control.size - 0.1, 0.1 ) );
+                break;
+
+            case 88: // X
+                control.showX = ! control.showX;
+                break;
+
+            case 89: // Y
+                control.showY = ! control.showY;
+                break;
+
+            case 90: // Z
+                control.showZ = ! control.showZ;
+                break;
+
+            case 32: // Spacebar
+                control.enabled = ! control.enabled;
+                break;
+
+            case 27: // Esc
+                control.reset();
+                break;
+
+        }
+
+    } );
+
+    window.addEventListener( 'keyup', function ( event ) {
+
+        switch ( event.keyCode ) {
+
+            case 16: // Shift
+                control.setTranslationSnap( null );
+                control.setRotationSnap( null );
+                control.setScaleSnap( null );
+                break;
+
+        }
+
+    } );
+
+}
+
+function onWindowResize() {
+
+    const aspect = window.innerWidth / window.innerHeight;
+
+    cameraPersp.aspect = aspect;
+    cameraPersp.updateProjectionMatrix();
+
+    cameraPerspTransform.left = cameraPerspTransform.bottom * aspect;
+    cameraPerspTransform.right = cameraPerspTransform.top * aspect;
+    cameraPerspTransform.updateProjectionMatrix();
+
+    renderer.setSize( window.innerWidth, window.innerHeight );
+
+    render();
+
+}
+
+function render() {
+
+    renderer.render( scene, currentCamera );
+
+}

turbo/package.json

@@ -0,0 +1,8 @@
+{
+  "dependencies": {
+    "three": "^0.160.0"
+  },
+  "devDependencies": {
+    "vite": "^5.0.11"
+  }
+}

turbo/workflow_api_motionctrl_turbo.json

@@ -0,0 +1,91 @@
+{
+  "56": {
+    "inputs": {
+      "ckpt_name": "motionctrl.pth",
+      "frame_length": 16
+    },
+    "class_type": "Load Motionctrl Checkpoint"
+  },
+  "60": {
+    "inputs": {
+      "prompt": "a rose swaying in the wind",
+      "camera": "[[1,0,0,0,0,1,0,0,0,0,1,0.2]]",
+      "traj": "[[117, 102]]",
+      "infer_mode": "control camera poses",
+      "context_overlap": 4,
+      "model": [
+        "56",
+        0
+      ]
+    },
+    "class_type": "Motionctrl Cond"
+  },
+  "61": {
+    "inputs": {
+      "steps": 20,
+      "seed": 1647,
+      "context_overlap": [
+        "60",
+        7
+      ],
+      "traj_tool": "https://chaojie.github.io/ComfyUI-MotionCtrl/tools/draw.html",
+      "draw_traj_dot": false,
+      "draw_camera_dot": false,
+      "model": [
+        "56",
+        0
+      ],
+      "clip": [
+        "56",
+        1
+      ],
+      "vae": [
+        "56",
+        2
+      ],
+      "ddim_sampler": [
+        "56",
+        3
+      ],
+      "positive": [
+        "60",
+        0
+      ],
+      "negative": [
+        "60",
+        1
+      ],
+      "traj_list": [
+        "60",
+        2
+      ],
+      "rt_list": [
+        "60",
+        3
+      ],
+      "traj": [
+        "60",
+        4
+      ],
+      "rt": [
+        "60",
+        5
+      ],
+      "noise_shape": [
+        "60",
+        6
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workflow_motionctrl.json

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workflow_motionctrl_base.json

@@ -0,0 +1,489 @@
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+          "link": 142,
+          "widget": {
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+          "slot_index": 0
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+          "links": [
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+          ],
+          "shape": 3,
+          "slot_index": 6
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "Motionctrl Cond"
+      },
+      "widgets_values": [
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+        "[[1,0,0,0,0,1,0,0,0,0,1,0.2]]",
+        "[[117, 102]]",
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+  ],
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+  "config": {},
+  "extra": {},
+  "version": 0.4
+}

workflow_threenodes.json

@@ -1,250 +0,0 @@
-{
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-  "last_link_id": 18,
-  "nodes": [
-    {
-      "id": 7,
-      "type": "VHS_VideoCombine",
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-        513
-      ],
-      "size": [
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-        539
-      ],
-      "flags": {},
-      "order": 4,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 11
-        }
-      ],
-      "outputs": [],
-      "properties": {
-        "Node name for S&R": "VHS_VideoCombine"
-      },
-      "widgets_values": {
-        "frame_rate": 8,
-        "loop_count": 0,
-        "filename_prefix": "AnimateDiff",
-        "format": "video/h264-mp4",
-        "pingpong": false,
-        "save_image": true,
-        "crf": 20,
-        "save_metadata": true,
-        "audio_file": "",
-        "videopreview": {
-          "hidden": false,
-          "paused": false,
-          "params": {
-            "filename": "AnimateDiff_00449.mp4",
-            "subfolder": "",
-            "type": "output",
-            "format": "video/h264-mp4"
-          }
-        }
-      }
-    },
-    {
-      "id": 4,
-      "type": "PreviewImage",
-      "pos": [
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-        200
-      ],
-      "size": {
-        "0": 210,
-        "1": 246
-      },
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "inputs": [
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-          "name": "images",
-          "type": "IMAGE",
-          "link": 10
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "PreviewImage"
-      }
-    },
-    {
-      "id": 2,
-      "type": "Load Motion Traj Preset",
-      "pos": [
-        213,
-        368
-      ],
-      "size": {
-        "0": 315,
-        "1": 82
-      },
-      "flags": {},
-      "order": 0,
-      "mode": 0,
-      "outputs": [
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-          "type": "STRING",
-          "links": [
-            17
-          ],
-          "shape": 3,
-          "slot_index": 0
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-      ],
-      "properties": {
-        "Node name for S&R": "Load Motion Traj Preset"
-      },
-      "widgets_values": [
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-        32
-      ]
-    },
-    {
-      "id": 1,
-      "type": "Load Motion Camera Preset",
-      "pos": [
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-        126
-      ],
-      "size": {
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-        "1": 58
-      },
-      "flags": {},
-      "order": 1,
-      "mode": 0,
-      "outputs": [
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-          "type": "STRING",
-          "links": [
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-            18
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-          "slot_index": 0
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-      "properties": {
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-      "type": "Motionctrl Sample",
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-        199
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-      "size": [
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-        344
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-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "inputs": [
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-          "name": "camera",
-          "type": "STRING",
-          "link": 18,
-          "widget": {
-            "name": "camera"
-          }
-        },
-        {
-          "name": "traj",
-          "type": "STRING",
-          "link": 17,
-          "widget": {
-            "name": "traj"
-          }
-        }
-      ],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            10,
-            11
-          ],
-          "shape": 3,
-          "slot_index": 0
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-      ],
-      "properties": {
-        "Node name for S&R": "Motionctrl Sample"
-      },
-      "widgets_values": [
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-        true
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