{
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        "Loads the main Wan video model.\nThis is the model that actually generates the video frames.\n\nInput: The Wan video model file you select.\nOutput: The loaded video model, ready to be used by the sampler."
      ],
      "widgets_values_named": {
        "text": "Loads the main Wan video model.\nThis is the model that actually generates the video frames.\n\nInput: The Wan video model file you select.\nOutput: The loaded video model, ready to be used by the sampler."
      },
      "color": "#432",
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      "properties": {
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            "url": "https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_ti2v_5B_fp16.safetensors",
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      "color": "#432",
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    {
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      "type": "CLIPLoader",
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          "links": [
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        }
      ],
      "properties": {
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        "models": [
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            "url": "https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors",
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        "Loads the converter used to turn the AI’s hidden video data into normal visible frames.\n\nThe model does not generate normal images directly. It first creates the video in a hidden format called a latent. The VAE helps convert that hidden result into frames we can actually see.\n\nInput: The VAE file we choose.\n\nOutput: The loaded VAE, which will later be used by VAE Decode."
      ],
      "widgets_values_named": {
        "text": "Loads the converter used to turn the AI’s hidden video data into normal visible frames.\n\nThe model does not generate normal images directly. It first creates the video in a hidden format called a latent. The VAE helps convert that hidden result into frames we can actually see.\n\nInput: The VAE file we choose.\n\nOutput: The loaded VAE, which will later be used by VAE Decode."
      },
      "color": "#323",
      "bgcolor": "#535"
    },
    {
      "id": 55,
      "type": "Wan22ImageToVideoLatent",
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      "mode": 0,
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          "link": 105
        },
        {
          "name": "start_image",
          "shape": 7,
          "type": "IMAGE",
          "link": null
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      ],
      "outputs": [
        {
          "name": "LATENT",
          "type": "LATENT",
          "links": [
            104
          ]
        }
      ],
      "properties": {
        "Node name for S&R": "Wan22ImageToVideoLatent",
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    {
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      "type": "VAELoader",
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      "flags": {},
      "order": 4,
      "mode": 0,
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          "name": "VAE",
          "type": "VAE",
          "slot_index": 0,
          "links": [
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        }
      ],
      "properties": {
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        "models": [
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            "url": "https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan2.2_vae.safetensors",
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          "type": "CONDITIONING",
          "slot_index": 0,
          "links": [
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      "title": "CLIP Text Encode (Positive Prompt)",
      "properties": {
        "Node name for S&R": "CLIPTextEncode",
        "cnr_id": "comfy-core",
        "ver": "0.3.45"
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        "A cinematic luxury perfume commercial. A beautiful faceted glass perfume bottle filled with deep amber liquid stands on a glossy black pedestal in a dark elegant studio. Warm golden light reflects through the glass and crystal cap, creating rich highlights and realistic refractions. Fine golden dust particles drift slowly through the air while soft reflections move across the bottle. The camera makes a very slow smooth sideways slide, keeping the perfume bottle centered and sharp throughout the shot. Premium product cinematography, realistic glass, sophisticated black and gold color palette, dramatic soft lighting, shallow depth of field, highly realistic, polished commercial quality, elegant and expensive atmosphere."
      ],
      "widgets_values_named": {
        "text": "A cinematic luxury perfume commercial. A beautiful faceted glass perfume bottle filled with deep amber liquid stands on a glossy black pedestal in a dark elegant studio. Warm golden light reflects through the glass and crystal cap, creating rich highlights and realistic refractions. Fine golden dust particles drift slowly through the air while soft reflections move across the bottle. The camera makes a very slow smooth sideways slide, keeping the perfume bottle centered and sharp throughout the shot. Premium product cinematography, realistic glass, sophisticated black and gold color palette, dramatic soft lighting, shallow depth of field, highly realistic, polished commercial quality, elegant and expensive atmosphere."
      },
      "color": "#232",
      "bgcolor": "#353"
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          "type": "CONDITIONING",
          "slot_index": 0,
          "links": [
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        }
      ],
      "title": "CLIP Text Encode (Negative Prompt)",
      "properties": {
        "Node name for S&R": "CLIPTextEncode",
        "cnr_id": "comfy-core",
        "ver": "0.3.45"
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        "blurry, low quality, distorted bottle, warped glass, duplicate bottle, changing product shape, flickering, unstable lighting, camera shake, fast camera movement, excessive motion, messy background, cartoon, CGI look, oversaturated colors, text artifacts, deformed objects"
      ],
      "widgets_values_named": {
        "text": "blurry, low quality, distorted bottle, warped glass, duplicate bottle, changing product shape, flickering, unstable lighting, camera shake, fast camera movement, excessive motion, messy background, cartoon, CGI look, oversaturated colors, text artifacts, deformed objects"
      },
      "color": "#322",
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    {
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      "order": 5,
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      "outputs": [],
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      "widgets_values": [
        "Creates the hidden video area where Wan will generate the frames.\n\nThink of this as creating the blank video space before generation starts. It also decides the video resolution and how many frames the model should create.\n\nEven though the node says ImageToVideo, it can still be used without a starting image for text-to-video.\n\nWidth: Controls how wide the video will be.\n\nHeight: Controls how tall the video will be.\n\nLength: Controls how many frames Wan will generate. More frames usually means a longer video and more generation time.\n\nBatch Size: Controls how many videos are generated in one run.\n\nOptional Start Image: If we connect an image here, Wan uses that image as the starting frame for image-to-video.\n\nOutput: The hidden video space that is sent to KSampler."
      ],
      "widgets_values_named": {
        "text": "Creates the hidden video area where Wan will generate the frames.\n\nThink of this as creating the blank video space before generation starts. It also decides the video resolution and how many frames the model should create.\n\nEven though the node says ImageToVideo, it can still be used without a starting image for text-to-video.\n\nWidth: Controls how wide the video will be.\n\nHeight: Controls how tall the video will be.\n\nLength: Controls how many frames Wan will generate. More frames usually means a longer video and more generation time.\n\nBatch Size: Controls how many videos are generated in one run.\n\nOptional Start Image: If we connect an image here, Wan uses that image as the starting frame for image-to-video.\n\nOutput: The hidden video space that is sent to KSampler."
      },
      "color": "#233",
      "bgcolor": "#355"
    },
    {
      "id": 61,
      "type": "Note",
      "pos": [
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      "size": [
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      "flags": {},
      "order": 6,
      "mode": 0,
      "inputs": [],
      "outputs": [],
      "properties": {},
      "widgets_values": [
        "Loads the text-understanding model.\nThe video model cannot understand a written prompt directly, so this helps translate our prompt into instructions it can use.\n\nInput: The text encoder file we select.\nOutput: A text reader that can process our prompt."
      ],
      "widgets_values_named": {
        "text": "Loads the text-understanding model.\nThe video model cannot understand a written prompt directly, so this helps translate our prompt into instructions it can use.\n\nInput: The text encoder file we select.\nOutput: A text reader that can process our prompt."
      },
      "color": "#432",
      "bgcolor": "#653"
    },
    {
      "id": 48,
      "type": "ModelSamplingSD3",
      "pos": [
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      "size": [
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      "flags": {
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      "order": 14,
      "mode": 0,
      "inputs": [
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          "type": "MODEL",
          "link": 94
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      ],
      "outputs": [
        {
          "name": "MODEL",
          "type": "MODEL",
          "slot_index": 0,
          "links": [
            95
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        }
      ],
      "properties": {
        "Node name for S&R": "ModelSamplingSD3",
        "cnr_id": "comfy-core",
        "ver": "0.3.45"
      },
      "widgets_values": [
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      "widgets_values_named": {
        "shift": 8
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      "color": "#332922",
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    {
      "id": 64,
      "type": "Note",
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      "mode": 0,
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      "widgets_values": [
        "Takes the prompt we write and turns it into instructions the video model can understand.\n\nThis is where we describe what we want in the video, such as the subject, environment, lighting, movement and camera behavior.\n\nInput: Our written positive prompt + the text reader from Load CLIP.\n\nOutput: Instructions telling Wan what we want the video to contain."
      ],
      "widgets_values_named": {
        "text": "Takes the prompt we write and turns it into instructions the video model can understand.\n\nThis is where we describe what we want in the video, such as the subject, environment, lighting, movement and camera behavior.\n\nInput: Our written positive prompt + the text reader from Load CLIP.\n\nOutput: Instructions telling Wan what we want the video to contain."
      },
      "color": "#232",
      "bgcolor": "#353"
    },
    {
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      "type": "Note",
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      "order": 8,
      "mode": 0,
      "inputs": [],
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      "widgets_values": [
        "Tells the model what we want it to avoid while generating the video.\n\nFor example, we can ask it to avoid blurry frames, distorted objects, bad anatomy, flickering or unwanted camera movement.\n\nInput: Our negative prompt + the text reader from Load CLIP.\n\nOutput: Instructions telling Wan what we do not want in the video."
      ],
      "widgets_values_named": {
        "text": "Tells the model what we want it to avoid while generating the video.\n\nFor example, we can ask it to avoid blurry frames, distorted objects, bad anatomy, flickering or unwanted camera movement.\n\nInput: Our negative prompt + the text reader from Load CLIP.\n\nOutput: Instructions telling Wan what we do not want in the video."
      },
      "color": "#322",
      "bgcolor": "#533"
    },
    {
      "id": 68,
      "type": "Note",
      "pos": [
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      "flags": {},
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      "mode": 0,
      "inputs": [],
      "outputs": [],
      "properties": {},
      "widgets_values": [
        "This is where the actual video generation happens.\n\nWan begins with random noise inside the hidden video space. KSampler then repeatedly removes that noise while following our positive and negative prompt instructions. Over those steps, the video frames gradually begin to form.\n\nInput: The Wan model, positive prompt instructions, negative prompt instructions and the hidden video space.\n\nSeed: Controls the starting random noise. Changing the seed usually produces a different video.\n\nSteps: Controls how many times the model works on and refines the result. More steps take longer, and after a certain point they may not improve the video much.\n\nCFG: Controls how strongly the prompt guides the generation. Higher values generally push the model harder toward the prompt, but the best value depends on the model.\n\nSampler: Chooses the method used to gradually remove the noise and form the video.\n\nScheduler: Controls how that noise-removal process is spread across the generation steps.\n\nDenoise: Controls how much generation is applied. A value of 1.0 means full generation.\n\nOutput: The generated video frames, still in the hidden latent format."
      ],
      "widgets_values_named": {
        "text": "This is where the actual video generation happens.\n\nWan begins with random noise inside the hidden video space. KSampler then repeatedly removes that noise while following our positive and negative prompt instructions. Over those steps, the video frames gradually begin to form.\n\nInput: The Wan model, positive prompt instructions, negative prompt instructions and the hidden video space.\n\nSeed: Controls the starting random noise. Changing the seed usually produces a different video.\n\nSteps: Controls how many times the model works on and refines the result. More steps take longer, and after a certain point they may not improve the video much.\n\nCFG: Controls how strongly the prompt guides the generation. Higher values generally push the model harder toward the prompt, but the best value depends on the model.\n\nSampler: Chooses the method used to gradually remove the noise and form the video.\n\nScheduler: Controls how that noise-removal process is spread across the generation steps.\n\nDenoise: Controls how much generation is applied. A value of 1.0 means full generation.\n\nOutput: The generated video frames, still in the hidden latent format."
      },
      "color": "#223",
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    {
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      "type": "KSampler",
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      "inputs": [
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          "type": "MODEL",
          "link": 95
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        {
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          "type": "CONDITIONING",
          "link": 46
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        {
          "name": "negative",
          "type": "CONDITIONING",
          "link": 52
        },
        {
          "name": "latent_image",
          "type": "LATENT",
          "link": 104
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      ],
      "outputs": [
        {
          "name": "LATENT",
          "type": "LATENT",
          "slot_index": 0,
          "links": [
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      ],
      "properties": {
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        "cnr_id": "comfy-core",
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        "seed": 916768591791521,
        "control_after_generate": "randomize",
        "steps": 20,
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        "scheduler": "simple",
        "denoise": 1
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    {
      "id": 8,
      "type": "VAEDecode",
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      "order": 19,
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      "inputs": [
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          "type": "LATENT",
          "link": 35
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        {
          "name": "vae",
          "type": "VAE",
          "link": 76
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      ],
      "outputs": [
        {
          "name": "IMAGE",
          "type": "IMAGE",
          "slot_index": 0,
          "links": [
            107
          ]
        }
      ],
      "properties": {
        "Node name for S&R": "VAEDecode",
        "cnr_id": "comfy-core",
        "ver": "0.3.45"
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      "color": "#432",
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      "id": 69,
      "type": "Note",
      "pos": [
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      "order": 10,
      "mode": 0,
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      "widgets_values": [
        "Turns the hidden generated frames into normal images we can actually see.\n\nKSampler has already generated the video, but at this stage it is still in latent form. VAE Decode converts that hidden information into visible image frames.\n\nInput: The latent frames from KSampler + the VAE we loaded earlier.\n\nOutput: Normal visible image frames.\n\nA simple way to remember it:\n\nKSampler creates the frames in hidden form → VAE Decode makes them visible."
      ],
      "widgets_values_named": {
        "text": "Turns the hidden generated frames into normal images we can actually see.\n\nKSampler has already generated the video, but at this stage it is still in latent form. VAE Decode converts that hidden information into visible image frames.\n\nInput: The latent frames from KSampler + the VAE we loaded earlier.\n\nOutput: Normal visible image frames.\n\nA simple way to remember it:\n\nKSampler creates the frames in hidden form → VAE Decode makes them visible."
      },
      "color": "#432",
      "bgcolor": "#653"
    },
    {
      "id": 57,
      "type": "CreateVideo",
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      "flags": {},
      "order": 20,
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      "inputs": [
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          "type": "IMAGE",
          "link": 107
        },
        {
          "name": "audio",
          "shape": 7,
          "type": "AUDIO",
          "link": null
        }
      ],
      "outputs": [
        {
          "name": "VIDEO",
          "type": "VIDEO",
          "links": [
            108
          ]
        }
      ],
      "properties": {
        "Node name for S&R": "CreateVideo",
        "cnr_id": "comfy-core",
        "ver": "0.3.45"
      },
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        "sRGB"
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        "fps": 24,
        "bit_depth": 8,
        "color_space": "sRGB"
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      "type": "Note",
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      "widgets_values": [
        "Prepares the Wan model for the way this workflow samples and generates the video.\nThe Shift setting changes how the denoising steps are arranged during generation. For this workflow, it is best to keep it at the recommended value.\n\nInput: The loaded Wan model.\nOutput: The prepared model sent to KSampler."
      ],
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