deepfates/hunyuan-the-matrix-trilogy | Run with an API on Replicate

deepfates / hunyuan-the-matrix-trilogy

Hunyuan-Video model finetuned on The Matrix Trilogy (1999). Trigger word is "THMTR". Use "A video in the style of THMTR, THMTR" at the beginning of your prompt for best results.

Cold

Public
111 runs
H100
Fine-tune

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

A video in the style of THMTR, THMTR The video clip depicts a horse-drawn carriage traveling along a foggy, winding road. The road is lined with trees on both sides, creating a dense and mysterious atmosphere. The carriage is a traditional style with four large wheels and a roof, pulled by a single horse. The horse is dark in color, possibly black or dark brown, and seems to be moving at a leisurely pace.
In the foreground, the road is clearly visible, with rocks and vegetation along the sides. The fog is thick, limiting visibility and adding to the sense of isolation and mystery. The trees are covered in leaves, suggesting a late spring or summer setting.
In the distanceA video in the style of THMTR, THMTR The video clip depicts a horse-drawn carriage traveling along a foggy, winding road. The road is lined with trees on both sides, creating a dense and mysterious atmosphere. The carriage is a traditional style with four large wheels and a roof, pulled by a single horse. The horse is dark in color, possibly black or dark brown, and seems to be moving at a leisurely pace.
In the foreground, the road is clearly visible, with rocks and vegetation along the sides. The fog is thick, limiting visibility and adding to the sense of isolation and mystery. The trees are covered in leaves, suggesting a late spring or summer setting.
In the distance

The text prompt describing your video scene.

Default: ""

lora_url

string

Shift + Return to add a new line

A URL pointing to your LoRA .safetensors file or a Hugging Face repo (e.g. 'user/repo' - uses the first .safetensors file).

Default: ""

lora_strength

number

(minimum: -10, maximum: 10)

Scale/strength for your LoRA.

Default: 1

scheduler

string

Algorithm used to generate the video frames.

Default: "DPMSolverMultistepScheduler"

steps

integer

(minimum: 1, maximum: 150)

Number of diffusion steps.

Default: 50

guidance_scale

number

(minimum: 0, maximum: 30)

Overall influence of text vs. model.

Default: 6

flow_shift

integer

(minimum: 0, maximum: 20)

Video continuity factor (flow).

Default: 9

num_frames

integer

(minimum: 1, maximum: 1440)

How many frames (duration) in the resulting video.

Default: 33

width

integer

(minimum: 64, maximum: 1536)

Width for the generated video.

Default: 640

height

integer

(minimum: 64, maximum: 1024)

Height for the generated video.

Default: 360

denoise_strength

number

(minimum: 0, maximum: 2)

Controls how strongly noise is applied each step.

Default: 1

force_offload

boolean

Whether to force model layers offloaded to CPU.

Default: true

frame_rate

integer

(minimum: 1, maximum: 60)

Video frame rate.

Default: 16

crf

integer

(minimum: 0, maximum: 51)

CRF (quality) for H264 encoding. Lower values = higher quality.

Default: 19

enhance_weight

number

(minimum: 0, maximum: 2)

Strength of the video enhancement effect.

Default: 0.3

enhance_single

boolean

Apply enhancement to individual frames.

Default: true

enhance_double

boolean

Apply enhancement across frame pairs.

Default: true

enhance_start

number

(minimum: 0, maximum: 1)

When to start enhancement in the video. Must be less than enhance_end.

Default: 0

enhance_end

number

(minimum: 0, maximum: 1)

When to end enhancement in the video. Must be greater than enhance_start.

Default: 1

seed

integer

Set a seed for reproducibility. Random by default.

Run this model in Node.js with one line of code:

npx create-replicate --model=deepfates/hunyuan-the-matrix-trilogy

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run deepfates/hunyuan-the-matrix-trilogy using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "deepfates/hunyuan-the-matrix-trilogy:e84c3dd23de21d8696fc4961b3960862a7efaf868393382119dab5a11acb0ad9",
  {
    input: {
      crf: 19,
      seed: 12345,
      steps: 50,
      width: 640,
      height: 360,
      prompt: "A video in the style of THMTR, THMTR The video clip depicts a horse-drawn carriage traveling along a foggy, winding road. The road is lined with trees on both sides, creating a dense and mysterious atmosphere. The carriage is a traditional style with four large wheels and a roof, pulled by a single horse. The horse is dark in color, possibly black or dark brown, and seems to be moving at a leisurely pace.\nIn the foreground, the road is clearly visible, with rocks and vegetation along the sides. The fog is thick, limiting visibility and adding to the sense of isolation and mystery. The trees are covered in leaves, suggesting a late spring or summer setting.\nIn the distance",
      lora_url: "",
      scheduler: "DPMSolverMultistepScheduler",
      flow_shift: 9,
      frame_rate: 16,
      num_frames: 66,
      enhance_end: 1,
      enhance_start: 0,
      force_offload: true,
      lora_strength: 1,
      enhance_double: true,
      enhance_single: true,
      enhance_weight: 0.3,
      guidance_scale: 6,
      denoise_strength: 1
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run deepfates/hunyuan-the-matrix-trilogy using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "deepfates/hunyuan-the-matrix-trilogy:e84c3dd23de21d8696fc4961b3960862a7efaf868393382119dab5a11acb0ad9",
    input={
        "crf": 19,
        "seed": 12345,
        "steps": 50,
        "width": 640,
        "height": 360,
        "prompt": "A video in the style of THMTR, THMTR The video clip depicts a horse-drawn carriage traveling along a foggy, winding road. The road is lined with trees on both sides, creating a dense and mysterious atmosphere. The carriage is a traditional style with four large wheels and a roof, pulled by a single horse. The horse is dark in color, possibly black or dark brown, and seems to be moving at a leisurely pace.\nIn the foreground, the road is clearly visible, with rocks and vegetation along the sides. The fog is thick, limiting visibility and adding to the sense of isolation and mystery. The trees are covered in leaves, suggesting a late spring or summer setting.\nIn the distance",
        "lora_url": "",
        "scheduler": "DPMSolverMultistepScheduler",
        "flow_shift": 9,
        "frame_rate": 16,
        "num_frames": 66,
        "enhance_end": 1,
        "enhance_start": 0,
        "force_offload": True,
        "lora_strength": 1,
        "enhance_double": True,
        "enhance_single": True,
        "enhance_weight": 0.3,
        "guidance_scale": 6,
        "denoise_strength": 1
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run deepfates/hunyuan-the-matrix-trilogy using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "deepfates/hunyuan-the-matrix-trilogy:e84c3dd23de21d8696fc4961b3960862a7efaf868393382119dab5a11acb0ad9",
    "input": {
      "crf": 19,
      "seed": 12345,
      "steps": 50,
      "width": 640,
      "height": 360,
      "prompt": "A video in the style of THMTR, THMTR The video clip depicts a horse-drawn carriage traveling along a foggy, winding road. The road is lined with trees on both sides, creating a dense and mysterious atmosphere. The carriage is a traditional style with four large wheels and a roof, pulled by a single horse. The horse is dark in color, possibly black or dark brown, and seems to be moving at a leisurely pace.\\nIn the foreground, the road is clearly visible, with rocks and vegetation along the sides. The fog is thick, limiting visibility and adding to the sense of isolation and mystery. The trees are covered in leaves, suggesting a late spring or summer setting.\\nIn the distance",
      "lora_url": "",
      "scheduler": "DPMSolverMultistepScheduler",
      "flow_shift": 9,
      "frame_rate": 16,
      "num_frames": 66,
      "enhance_end": 1,
      "enhance_start": 0,
      "force_offload": true,
      "lora_strength": 1,
      "enhance_double": true,
      "enhance_single": true,
      "enhance_weight": 0.3,
      "guidance_scale": 6,
      "denoise_strength": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2025-01-24T03:20:26.509237Z",
  "created_at": "2025-01-24T03:07:27.702000Z",
  "data_removed": false,
  "error": null,
  "id": "h01kexk0asrm80cmjs3v3cc0e0",
  "input": {
    "seed": 12345,
    "steps": 50,
    "width": 640,
    "height": 360,
    "prompt": "A video in the style of THMTR, THMTR The video clip depicts a horse-drawn carriage traveling along a foggy, winding road. The road is lined with trees on both sides, creating a dense and mysterious atmosphere. The carriage is a traditional style with four large wheels and a roof, pulled by a single horse. The horse is dark in color, possibly black or dark brown, and seems to be moving at a leisurely pace.\nIn the foreground, the road is clearly visible, with rocks and vegetation along the sides. The fog is thick, limiting visibility and adding to the sense of isolation and mystery. The trees are covered in leaves, suggesting a late spring or summer setting.\nIn the distance",
    "frame_rate": 16,
    "num_frames": 66,
    "lora_strength": 1,
    "guidance_scale": 6
  },
  "logs": "Seed set to: 12345\n⚠️  Adjusted dimensions from 640x360 to 640x368 to satisfy model requirements\n⚠️  Adjusted frame count from 66 to 65 to satisfy model requirements\nChecking inputs\n====================================\nChecking weights\n✅ hunyuan_video_720_fp8_e4m3fn.safetensors exists in ComfyUI/models/diffusion_models\n✅ hunyuan_video_vae_bf16.safetensors exists in ComfyUI/models/vae\n====================================\nRunning workflow\n[ComfyUI] got prompt\nExecuting node 30, title: HunyuanVideo TextEncode, class type: HyVideoTextEncode\n[ComfyUI] llm prompt attention_mask shape: torch.Size([1, 161]), masked tokens: 146\n[ComfyUI] clipL prompt attention_mask shape: torch.Size([1, 77]), masked tokens: 77\n[ComfyUI] Input (height, width, video_length) = (368, 640, 65)\nExecuting node 3, title: HunyuanVideo Sampler, class type: HyVideoSampler\n[ComfyUI] The config attributes {'reverse': True, 'solver': 'euler'} were passed to DPMSolverMultistepScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file.\n[ComfyUI] Sampling 65 frames in 17 latents at 640x368 with 50 inference steps\n[ComfyUI]\n[ComfyUI] 0%|          | 0/50 [00:00<?, ?it/s]\n[ComfyUI] 2%|▏         | 1/50 [00:02<01:53,  2.31s/it]\n[ComfyUI] 4%|▍         | 2/50 [00:04<01:37,  2.02s/it]\n[ComfyUI] 6%|▌         | 3/50 [00:06<01:41,  2.15s/it]\n[ComfyUI] 8%|▊         | 4/50 [00:08<01:41,  2.21s/it]\n[ComfyUI] 10%|█         | 5/50 [00:11<01:41,  2.25s/it]\n[ComfyUI] 12%|█▏        | 6/50 [00:13<01:39,  2.27s/it]\n[ComfyUI] 14%|█▍        | 7/50 [00:15<01:38,  2.28s/it]\n[ComfyUI] 16%|█▌        | 8/50 [00:17<01:36,  2.29s/it]\n[ComfyUI] 18%|█▊        | 9/50 [00:20<01:34,  2.29s/it]\n[ComfyUI] 20%|██        | 10/50 [00:22<01:31,  2.30s/it]\n[ComfyUI] 22%|██▏       | 11/50 [00:24<01:29,  2.30s/it]\n[ComfyUI] 24%|██▍       | 12/50 [00:27<01:27,  2.30s/it]\n[ComfyUI] 26%|██▌       | 13/50 [00:29<01:25,  2.30s/it]\n[ComfyUI] 28%|██▊       | 14/50 [00:31<01:22,  2.30s/it]\n[ComfyUI] 30%|███       | 15/50 [00:34<01:20,  2.30s/it]\n[ComfyUI] 32%|███▏      | 16/50 [00:36<01:18,  2.30s/it]\n[ComfyUI] 34%|███▍      | 17/50 [00:38<01:16,  2.30s/it]\n[ComfyUI] 36%|███▌      | 18/50 [00:41<01:13,  2.30s/it]\n[ComfyUI] 38%|███▊      | 19/50 [00:43<01:11,  2.31s/it]\n[ComfyUI] 40%|████      | 20/50 [00:45<01:09,  2.31s/it]\n[ComfyUI] 42%|████▏     | 21/50 [00:47<01:06,  2.31s/it]\n[ComfyUI] 44%|████▍     | 22/50 [00:50<01:04,  2.31s/it]\n[ComfyUI] 46%|████▌     | 23/50 [00:52<01:02,  2.31s/it]\n[ComfyUI] 48%|████▊     | 24/50 [00:54<00:59,  2.31s/it]\n[ComfyUI] 50%|█████     | 25/50 [00:57<00:57,  2.31s/it]\n[ComfyUI] 52%|█████▏    | 26/50 [00:59<00:55,  2.31s/it]\n[ComfyUI] 54%|█████▍    | 27/50 [01:01<00:53,  2.31s/it]\n[ComfyUI] 56%|█████▌    | 28/50 [01:04<00:50,  2.31s/it]\n[ComfyUI] 58%|█████▊    | 29/50 [01:06<00:48,  2.31s/it]\n[ComfyUI] 60%|██████    | 30/50 [01:08<00:46,  2.31s/it]\n[ComfyUI] 62%|██████▏   | 31/50 [01:10<00:43,  2.31s/it]\n[ComfyUI] 64%|██████▍   | 32/50 [01:13<00:41,  2.31s/it]\n[ComfyUI] 66%|██████▌   | 33/50 [01:15<00:39,  2.31s/it]\n[ComfyUI] 68%|██████▊   | 34/50 [01:17<00:36,  2.31s/it]\n[ComfyUI] 70%|███████   | 35/50 [01:20<00:34,  2.31s/it]\n[ComfyUI] 72%|███████▏  | 36/50 [01:22<00:32,  2.31s/it]\n[ComfyUI] 74%|███████▍  | 37/50 [01:24<00:29,  2.31s/it]\n[ComfyUI] 76%|███████▌  | 38/50 [01:27<00:27,  2.31s/it]\n[ComfyUI] 78%|███████▊  | 39/50 [01:29<00:25,  2.31s/it]\n[ComfyUI] 80%|████████  | 40/50 [01:31<00:23,  2.31s/it]\n[ComfyUI] 82%|████████▏ | 41/50 [01:34<00:20,  2.31s/it]\n[ComfyUI] 84%|████████▍ | 42/50 [01:36<00:18,  2.31s/it]\n[ComfyUI] 86%|████████▌ | 43/50 [01:38<00:16,  2.31s/it]\n[ComfyUI] 88%|████████▊ | 44/50 [01:40<00:13,  2.31s/it]\n[ComfyUI] 90%|█████████ | 45/50 [01:43<00:11,  2.31s/it]\n[ComfyUI] 92%|█████████▏| 46/50 [01:45<00:09,  2.31s/it]\n[ComfyUI] 94%|█████████▍| 47/50 [01:47<00:06,  2.31s/it]\n[ComfyUI] 96%|█████████▌| 48/50 [01:50<00:04,  2.31s/it]\n[ComfyUI] 98%|█████████▊| 49/50 [01:52<00:02,  2.31s/it]\n[ComfyUI] 100%|██████████| 50/50 [01:54<00:00,  2.31s/it]\n[ComfyUI] 100%|██████████| 50/50 [01:54<00:00,  2.30s/it]\n[ComfyUI] Allocated memory: memory=12.300 GB\n[ComfyUI] Max allocated memory: max_memory=15.099 GB\n[ComfyUI] Max reserved memory: max_reserved=16.281 GB\nExecuting node 5, title: HunyuanVideo Decode, class type: HyVideoDecode\n[ComfyUI]\n[ComfyUI] Decoding rows:   0%|          | 0/2 [00:00<?, ?it/s]\n[ComfyUI] Decoding rows:  50%|█████     | 1/2 [00:01<00:01,  1.74s/it]\n[ComfyUI] Decoding rows: 100%|██████████| 2/2 [00:02<00:00,  1.36s/it]\n[ComfyUI] Decoding rows: 100%|██████████| 2/2 [00:02<00:00,  1.42s/it]\n[ComfyUI]\n[ComfyUI] Blending tiles:   0%|          | 0/2 [00:00<?, ?it/s]\n[ComfyUI] Blending tiles: 100%|██████████| 2/2 [00:00<00:00, 25.54it/s]\n[ComfyUI] \n[ComfyUI] Decoding rows:   0%|          | 0/2 [00:00<?, ?it/s]\n[ComfyUI] Decoding rows:  50%|█████     | 1/2 [00:00<00:00,  2.51it/s]\n[ComfyUI] Decoding rows: 100%|██████████| 2/2 [00:00<00:00,  2.99it/s]\n[ComfyUI] Decoding rows: 100%|██████████| 2/2 [00:00<00:00,  2.91it/s]\n[ComfyUI]\n[ComfyUI] Blending tiles:   0%|          | 0/2 [00:00<?, ?it/s]\nExecuting node 34, title: Video Combine 🎥🅥🅗🅢, class type: VHS_VideoCombine\n[ComfyUI] Blending tiles: 100%|██████████| 2/2 [00:00<00:00, 64.15it/s]\n[ComfyUI] Prompt executed in 135.73 seconds\noutputs:  {'34': {'gifs': [{'filename': 'HunyuanVideo_00001.mp4', 'subfolder': '', 'type': 'output', 'format': 'video/h264-mp4', 'frame_rate': 16.0, 'workflow': 'HunyuanVideo_00001.png', 'fullpath': '/tmp/outputs/HunyuanVideo_00001.mp4'}]}}\n====================================\nHunyuanVideo_00001.png\nHunyuanVideo_00001.mp4",
  "metrics": {
    "predict_time": 137.416584363,
    "total_time": 778.807237
  },
  "output": "https://replicate.delivery/xezq/qDLd6D6zyeXLCCAtK9hNeblN3ZF8C2og7NiBLmpeVemqPugQB/HunyuanVideo_00001.mp4",
  "started_at": "2025-01-24T03:18:09.092652Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream.replicate.com/v1/files/bsvm-jnrsh3yvtuwstvhxj3leseuu3mf64k753kfdjgiuar4hkb73hpna",
    "get": "https://api.replicate.com/v1/predictions/h01kexk0asrm80cmjs3v3cc0e0",
    "cancel": "https://api.replicate.com/v1/predictions/h01kexk0asrm80cmjs3v3cc0e0/cancel"
  },
  "version": "e84c3dd23de21d8696fc4961b3960862a7efaf868393382119dab5a11acb0ad9"
}

Generated in

2 minutes 17 seconds

Tweak it ShareReport View full prediction

Seed set to: 12345
⚠️  Adjusted dimensions from 640x360 to 640x368 to satisfy model requirements
⚠️  Adjusted frame count from 66 to 65 to satisfy model requirements
Checking inputs
====================================
Checking weights
✅ hunyuan_video_720_fp8_e4m3fn.safetensors exists in ComfyUI/models/diffusion_models
✅ hunyuan_video_vae_bf16.safetensors exists in ComfyUI/models/vae
====================================
Running workflow
[ComfyUI] got prompt
Executing node 30, title: HunyuanVideo TextEncode, class type: HyVideoTextEncode
[ComfyUI] llm prompt attention_mask shape: torch.Size([1, 161]), masked tokens: 146
[ComfyUI] clipL prompt attention_mask shape: torch.Size([1, 77]), masked tokens: 77
[ComfyUI] Input (height, width, video_length) = (368, 640, 65)
Executing node 3, title: HunyuanVideo Sampler, class type: HyVideoSampler
[ComfyUI] The config attributes {'reverse': True, 'solver': 'euler'} were passed to DPMSolverMultistepScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file.
[ComfyUI] Sampling 65 frames in 17 latents at 640x368 with 50 inference steps
[ComfyUI]
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[ComfyUI] 64%|██████▍   | 32/50 [01:13<00:41,  2.31s/it]
[ComfyUI] 66%|██████▌   | 33/50 [01:15<00:39,  2.31s/it]
[ComfyUI] 68%|██████▊   | 34/50 [01:17<00:36,  2.31s/it]
[ComfyUI] 70%|███████   | 35/50 [01:20<00:34,  2.31s/it]
[ComfyUI] 72%|███████▏  | 36/50 [01:22<00:32,  2.31s/it]
[ComfyUI] 74%|███████▍  | 37/50 [01:24<00:29,  2.31s/it]
[ComfyUI] 76%|███████▌  | 38/50 [01:27<00:27,  2.31s/it]
[ComfyUI] 78%|███████▊  | 39/50 [01:29<00:25,  2.31s/it]
[ComfyUI] 80%|████████  | 40/50 [01:31<00:23,  2.31s/it]
[ComfyUI] 82%|████████▏ | 41/50 [01:34<00:20,  2.31s/it]
[ComfyUI] 84%|████████▍ | 42/50 [01:36<00:18,  2.31s/it]
[ComfyUI] 86%|████████▌ | 43/50 [01:38<00:16,  2.31s/it]
[ComfyUI] 88%|████████▊ | 44/50 [01:40<00:13,  2.31s/it]
[ComfyUI] 90%|█████████ | 45/50 [01:43<00:11,  2.31s/it]
[ComfyUI] 92%|█████████▏| 46/50 [01:45<00:09,  2.31s/it]
[ComfyUI] 94%|█████████▍| 47/50 [01:47<00:06,  2.31s/it]
[ComfyUI] 96%|█████████▌| 48/50 [01:50<00:04,  2.31s/it]
[ComfyUI] 98%|█████████▊| 49/50 [01:52<00:02,  2.31s/it]
[ComfyUI] 100%|██████████| 50/50 [01:54<00:00,  2.31s/it]
[ComfyUI] 100%|██████████| 50/50 [01:54<00:00,  2.30s/it]
[ComfyUI] Allocated memory: memory=12.300 GB
[ComfyUI] Max allocated memory: max_memory=15.099 GB
[ComfyUI] Max reserved memory: max_reserved=16.281 GB
Executing node 5, title: HunyuanVideo Decode, class type: HyVideoDecode
[ComfyUI]
[ComfyUI] Decoding rows:   0%|          | 0/2 [00:00<?, ?it/s]
[ComfyUI] Decoding rows:  50%|█████     | 1/2 [00:01<00:01,  1.74s/it]
[ComfyUI] Decoding rows: 100%|██████████| 2/2 [00:02<00:00,  1.36s/it]
[ComfyUI] Decoding rows: 100%|██████████| 2/2 [00:02<00:00,  1.42s/it]
[ComfyUI]
[ComfyUI] Blending tiles:   0%|          | 0/2 [00:00<?, ?it/s]
[ComfyUI] Blending tiles: 100%|██████████| 2/2 [00:00<00:00, 25.54it/s]
[ComfyUI] 
[ComfyUI] Decoding rows:   0%|          | 0/2 [00:00<?, ?it/s]
[ComfyUI] Decoding rows:  50%|█████     | 1/2 [00:00<00:00,  2.51it/s]
[ComfyUI] Decoding rows: 100%|██████████| 2/2 [00:00<00:00,  2.99it/s]
[ComfyUI] Decoding rows: 100%|██████████| 2/2 [00:00<00:00,  2.91it/s]
[ComfyUI]
[ComfyUI] Blending tiles:   0%|          | 0/2 [00:00<?, ?it/s]
Executing node 34, title: Video Combine 🎥🅥🅗🅢, class type: VHS_VideoCombine
[ComfyUI] Blending tiles: 100%|██████████| 2/2 [00:00<00:00, 64.15it/s]
[ComfyUI] Prompt executed in 135.73 seconds
outputs:  {'34': {'gifs': [{'filename': 'HunyuanVideo_00001.mp4', 'subfolder': '', 'type': 'output', 'format': 'video/h264-mp4', 'frame_rate': 16.0, 'workflow': 'HunyuanVideo_00001.png', 'fullpath': '/tmp/outputs/HunyuanVideo_00001.mp4'}]}}
====================================
HunyuanVideo_00001.png
HunyuanVideo_00001.mp4

Examples

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Run time and cost

This model costs approximately $0.64 to run on Replicate, or 1 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia H100 GPU hardware. Predictions typically complete within 7 minutes. The predict time for this model varies significantly based on the inputs.

Readme

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