deepfates/hunyuan-blade-runner – Run with an API on Replicate

deepfates / hunyuan-blade-runner

Hunyuan-Video model finetuned on Blade Runner (1982). Trigger word is "BLDRN". Use "A video in the style of BLDRN, BLDRN" at the beginning of your prompt for best results.

Cold

Public
98 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 BLDRN, BLDRN The video clip depicts a detailed portrait of a woman's face. She has fair skin and bright, intense blue eyes that gaze directly ahead. Her hair is dark and wavy, cascading down her shoulders in a curly pattern. She is dressed in a dark dress with a square neckline adorned with intricate metallic accents. The dress has a detailed, almost mosaic-like pattern, suggesting a ceremonial or formal attire.
The woman's expression is stoic and serious, conveying a sense of determination or resolve. The background is dark, contrasting with the intricate details of her dress and the brightness of her eyes. The lighting is soft and diffused, casting a warm glow onA video in the style of BLDRN, BLDRN The video clip depicts a detailed portrait of a woman's face. She has fair skin and bright, intense blue eyes that gaze directly ahead. Her hair is dark and wavy, cascading down her shoulders in a curly pattern. She is dressed in a dark dress with a square neckline adorned with intricate metallic accents. The dress has a detailed, almost mosaic-like pattern, suggesting a ceremonial or formal attire.
The woman's expression is stoic and serious, conveying a sense of determination or resolve. The background is dark, contrasting with the intricate details of her dress and the brightness of her eyes. The lighting is soft and diffused, casting a warm glow on

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-blade-runner

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";

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

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

const output = await replicate.run(
  "deepfates/hunyuan-blade-runner:43195cc4a870109a20805229ba9e5801a875005f019e0ee7ae3a69c4b654228c",
  {
    input: {
      crf: 19,
      seed: 12345,
      steps: 50,
      width: 640,
      height: 360,
      prompt: "A video in the style of BLDRN, BLDRN The video clip depicts a detailed portrait of a woman's face. She has fair skin and bright, intense blue eyes that gaze directly ahead. Her hair is dark and wavy, cascading down her shoulders in a curly pattern. She is dressed in a dark dress with a square neckline adorned with intricate metallic accents. The dress has a detailed, almost mosaic-like pattern, suggesting a ceremonial or formal attire.\nThe woman's expression is stoic and serious, conveying a sense of determination or resolve. The background is dark, contrasting with the intricate details of her dress and the brightness of her eyes. The lighting is soft and diffused, casting a warm glow on",
      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-blade-runner using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "deepfates/hunyuan-blade-runner:43195cc4a870109a20805229ba9e5801a875005f019e0ee7ae3a69c4b654228c",
    input={
        "crf": 19,
        "seed": 12345,
        "steps": 50,
        "width": 640,
        "height": 360,
        "prompt": "A video in the style of BLDRN, BLDRN The video clip depicts a detailed portrait of a woman's face. She has fair skin and bright, intense blue eyes that gaze directly ahead. Her hair is dark and wavy, cascading down her shoulders in a curly pattern. She is dressed in a dark dress with a square neckline adorned with intricate metallic accents. The dress has a detailed, almost mosaic-like pattern, suggesting a ceremonial or formal attire.\nThe woman's expression is stoic and serious, conveying a sense of determination or resolve. The background is dark, contrasting with the intricate details of her dress and the brightness of her eyes. The lighting is soft and diffused, casting a warm glow on",
        "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-blade-runner 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-blade-runner:43195cc4a870109a20805229ba9e5801a875005f019e0ee7ae3a69c4b654228c",
    "input": {
      "crf": 19,
      "seed": 12345,
      "steps": 50,
      "width": 640,
      "height": 360,
      "prompt": "A video in the style of BLDRN, BLDRN The video clip depicts a detailed portrait of a woman\'s face. She has fair skin and bright, intense blue eyes that gaze directly ahead. Her hair is dark and wavy, cascading down her shoulders in a curly pattern. She is dressed in a dark dress with a square neckline adorned with intricate metallic accents. The dress has a detailed, almost mosaic-like pattern, suggesting a ceremonial or formal attire.\\nThe woman\'s expression is stoic and serious, conveying a sense of determination or resolve. The background is dark, contrasting with the intricate details of her dress and the brightness of her eyes. The lighting is soft and diffused, casting a warm glow on",
      "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.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/deepfates/hunyuan-blade-runner@sha256:43195cc4a870109a20805229ba9e5801a875005f019e0ee7ae3a69c4b654228c \
  -i 'crf=19' \
  -i 'seed=12345' \
  -i 'steps=50' \
  -i 'width=640' \
  -i 'height=360' \
  -i $'prompt="A video in the style of BLDRN, BLDRN The video clip depicts a detailed portrait of a woman\'s face. She has fair skin and bright, intense blue eyes that gaze directly ahead. Her hair is dark and wavy, cascading down her shoulders in a curly pattern. She is dressed in a dark dress with a square neckline adorned with intricate metallic accents. The dress has a detailed, almost mosaic-like pattern, suggesting a ceremonial or formal attire.\\nThe woman\'s expression is stoic and serious, conveying a sense of determination or resolve. The background is dark, contrasting with the intricate details of her dress and the brightness of her eyes. The lighting is soft and diffused, casting a warm glow on"' \
  -i 'lora_url=""' \
  -i 'scheduler="DPMSolverMultistepScheduler"' \
  -i 'flow_shift=9' \
  -i 'frame_rate=16' \
  -i 'num_frames=66' \
  -i 'enhance_end=1' \
  -i 'enhance_start=0' \
  -i 'force_offload=true' \
  -i 'lora_strength=1' \
  -i 'enhance_double=true' \
  -i 'enhance_single=true' \
  -i 'enhance_weight=0.3' \
  -i 'guidance_scale=6' \
  -i 'denoise_strength=1'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/deepfates/hunyuan-blade-runner@sha256:43195cc4a870109a20805229ba9e5801a875005f019e0ee7ae3a69c4b654228c
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "crf": 19,
      "seed": 12345,
      "steps": 50,
      "width": 640,
      "height": 360,
      "prompt": "A video in the style of BLDRN, BLDRN The video clip depicts a detailed portrait of a woman\'s face. She has fair skin and bright, intense blue eyes that gaze directly ahead. Her hair is dark and wavy, cascading down her shoulders in a curly pattern. She is dressed in a dark dress with a square neckline adorned with intricate metallic accents. The dress has a detailed, almost mosaic-like pattern, suggesting a ceremonial or formal attire.\\nThe woman\'s expression is stoic and serious, conveying a sense of determination or resolve. The background is dark, contrasting with the intricate details of her dress and the brightness of her eyes. The lighting is soft and diffused, casting a warm glow on",
      "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
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

{
  "completed_at": "2025-01-24T00:28:34.872737Z",
  "created_at": "2025-01-24T00:25:09.496000Z",
  "data_removed": false,
  "error": null,
  "id": "6ndvjky8f1rma0cmjpsbepx9ew",
  "input": {
    "crf": 19,
    "seed": 12345,
    "steps": 50,
    "width": 640,
    "height": 360,
    "prompt": "A video in the style of BLDRN, BLDRN The video clip depicts a detailed portrait of a woman's face. She has fair skin and bright, intense blue eyes that gaze directly ahead. Her hair is dark and wavy, cascading down her shoulders in a curly pattern. She is dressed in a dark dress with a square neckline adorned with intricate metallic accents. The dress has a detailed, almost mosaic-like pattern, suggesting a ceremonial or formal attire.\nThe woman's expression is stoic and serious, conveying a sense of determination or resolve. The background is dark, contrasting with the intricate details of her dress and the brightness of her eyes. The lighting is soft and diffused, casting a warm glow on",
    "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
  },
  "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_vae_bf16.safetensors exists in ComfyUI/models/vae\n✅ hunyuan_video_720_fp8_e4m3fn.safetensors exists in ComfyUI/models/diffusion_models\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<01:52,  2.30s/it]\n[ComfyUI] 4%|▍         | 2/50 [00:04<01:36,  2.01s/it]\n[ComfyUI] 6%|▌         | 3/50 [00:06<01:40,  2.14s/it]\n[ComfyUI] 8%|▊         | 4/50 [00:08<01:41,  2.20s/it]\n[ComfyUI] 10%|█         | 5/50 [00:10<01:40,  2.24s/it]\n[ComfyUI] 12%|█▏        | 6/50 [00:13<01:39,  2.26s/it]\n[ComfyUI] 14%|█▍        | 7/50 [00:15<01:37,  2.27s/it]\n[ComfyUI] 16%|█▌        | 8/50 [00:17<01:35,  2.28s/it]\n[ComfyUI] 18%|█▊        | 9/50 [00:20<01:33,  2.28s/it]\n[ComfyUI] 20%|██        | 10/50 [00:22<01:31,  2.29s/it]\n[ComfyUI] 22%|██▏       | 11/50 [00:24<01:29,  2.29s/it]\n[ComfyUI] 24%|██▍       | 12/50 [00:27<01:27,  2.29s/it]\n[ComfyUI] 26%|██▌       | 13/50 [00:29<01:24,  2.29s/it]\n[ComfyUI] 28%|██▊       | 14/50 [00:31<01:22,  2.29s/it]\n[ComfyUI] 30%|███       | 15/50 [00:33<01:20,  2.30s/it]\n[ComfyUI] 32%|███▏      | 16/50 [00:36<01:18,  2.30s/it]\n[ComfyUI] 34%|███▍      | 17/50 [00:38<01:15,  2.30s/it]\n[ComfyUI] 36%|███▌      | 18/50 [00:40<01:13,  2.30s/it]\n[ComfyUI] 38%|███▊      | 19/50 [00:43<01:11,  2.30s/it]\n[ComfyUI] 40%|████      | 20/50 [00:45<01:08,  2.30s/it]\n[ComfyUI] 42%|████▏     | 21/50 [00:47<01:06,  2.30s/it]\n[ComfyUI] 44%|████▍     | 22/50 [00:50<01:04,  2.30s/it]\n[ComfyUI] 46%|████▌     | 23/50 [00:52<01:02,  2.30s/it]\n[ComfyUI] 48%|████▊     | 24/50 [00:54<00:59,  2.30s/it]\n[ComfyUI] 50%|█████     | 25/50 [00:56<00:57,  2.30s/it]\n[ComfyUI] 52%|█████▏    | 26/50 [00:59<00:55,  2.30s/it]\n[ComfyUI] 54%|█████▍    | 27/50 [01:01<00:52,  2.30s/it]\n[ComfyUI] 56%|█████▌    | 28/50 [01:03<00:50,  2.30s/it]\n[ComfyUI] 58%|█████▊    | 29/50 [01:06<00:48,  2.30s/it]\n[ComfyUI] 60%|██████    | 30/50 [01:08<00:45,  2.30s/it]\n[ComfyUI] 62%|██████▏   | 31/50 [01:10<00:43,  2.30s/it]\n[ComfyUI] 64%|██████▍   | 32/50 [01:12<00:41,  2.30s/it]\n[ComfyUI] 66%|██████▌   | 33/50 [01:15<00:39,  2.30s/it]\n[ComfyUI] 68%|██████▊   | 34/50 [01:17<00:36,  2.30s/it]\n[ComfyUI] 70%|███████   | 35/50 [01:19<00:34,  2.30s/it]\n[ComfyUI] 72%|███████▏  | 36/50 [01:22<00:32,  2.30s/it]\n[ComfyUI] 74%|███████▍  | 37/50 [01:24<00:29,  2.30s/it]\n[ComfyUI] 76%|███████▌  | 38/50 [01:26<00:27,  2.30s/it]\n[ComfyUI] 78%|███████▊  | 39/50 [01:29<00:25,  2.30s/it]\n[ComfyUI] 80%|████████  | 40/50 [01:31<00:22,  2.30s/it]\n[ComfyUI] 82%|████████▏ | 41/50 [01:33<00:20,  2.30s/it]\n[ComfyUI] 84%|████████▍ | 42/50 [01:35<00:18,  2.30s/it]\n[ComfyUI] 86%|████████▌ | 43/50 [01:38<00:16,  2.30s/it]\n[ComfyUI] 88%|████████▊ | 44/50 [01:40<00:13,  2.30s/it]\n[ComfyUI] 90%|█████████ | 45/50 [01:42<00:11,  2.29s/it]\n[ComfyUI] 92%|█████████▏| 46/50 [01:45<00:09,  2.30s/it]\n[ComfyUI] 94%|█████████▍| 47/50 [01:47<00:06,  2.30s/it]\n[ComfyUI] 96%|█████████▌| 48/50 [01:49<00:04,  2.29s/it]\n[ComfyUI] 98%|█████████▊| 49/50 [01:52<00:02,  2.30s/it]\n[ComfyUI] 100%|██████████| 50/50 [01:54<00:00,  2.29s/it]\n[ComfyUI] 100%|██████████| 50/50 [01:54<00:00,  2.29s/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=16.312 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,  1.46s/it]\n[ComfyUI] Decoding rows: 100%|██████████| 2/2 [00:02<00:00,  1.24s/it]\n[ComfyUI] Decoding rows: 100%|██████████| 2/2 [00:02<00:00,  1.27s/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, 25.98it/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,  2.56it/s]\n[ComfyUI] Decoding rows: 100%|██████████| 2/2 [00:00<00:00,  3.03it/s]\n[ComfyUI] Decoding rows: 100%|██████████| 2/2 [00:00<00:00,  2.95it/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, 64.77it/s]\n[ComfyUI] Prompt executed in 133.54 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": 151.776902657,
    "total_time": 205.376737
  },
  "output": "https://replicate.delivery/xezq/UQWeszmyT51IGCLnMx3ALzZX2pB1QSd2fU18cAK8Zo1yCJIUA/HunyuanVideo_00001.mp4",
  "started_at": "2025-01-24T00:26:03.095834Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream.replicate.com/v1/files/bsvm-mhd6obiexh64ujj4nvrtjsmi7yk74xroojyznypipgaany2b5vca",
    "get": "https://api.replicate.com/v1/predictions/6ndvjky8f1rma0cmjpsbepx9ew",
    "cancel": "https://api.replicate.com/v1/predictions/6ndvjky8f1rma0cmjpsbepx9ew/cancel"
  },
  "version": "43195cc4a870109a20805229ba9e5801a875005f019e0ee7ae3a69c4b654228c"
}

Generated in

2 minutes 32 seconds

Tweak it ShareReport

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_vae_bf16.safetensors exists in ComfyUI/models/vae
✅ hunyuan_video_720_fp8_e4m3fn.safetensors exists in ComfyUI/models/diffusion_models
====================================
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]
[ComfyUI] 0%|          | 0/50 [00:00<?, ?it/s]
[ComfyUI] 2%|▏         | 1/50 [00:02<01:52,  2.30s/it]
[ComfyUI] 4%|▍         | 2/50 [00:04<01:36,  2.01s/it]
[ComfyUI] 6%|▌         | 3/50 [00:06<01:40,  2.14s/it]
[ComfyUI] 8%|▊         | 4/50 [00:08<01:41,  2.20s/it]
[ComfyUI] 10%|█         | 5/50 [00:10<01:40,  2.24s/it]
[ComfyUI] 12%|█▏        | 6/50 [00:13<01:39,  2.26s/it]
[ComfyUI] 14%|█▍        | 7/50 [00:15<01:37,  2.27s/it]
[ComfyUI] 16%|█▌        | 8/50 [00:17<01:35,  2.28s/it]
[ComfyUI] 18%|█▊        | 9/50 [00:20<01:33,  2.28s/it]
[ComfyUI] 20%|██        | 10/50 [00:22<01:31,  2.29s/it]
[ComfyUI] 22%|██▏       | 11/50 [00:24<01:29,  2.29s/it]
[ComfyUI] 24%|██▍       | 12/50 [00:27<01:27,  2.29s/it]
[ComfyUI] 26%|██▌       | 13/50 [00:29<01:24,  2.29s/it]
[ComfyUI] 28%|██▊       | 14/50 [00:31<01:22,  2.29s/it]
[ComfyUI] 30%|███       | 15/50 [00:33<01:20,  2.30s/it]
[ComfyUI] 32%|███▏      | 16/50 [00:36<01:18,  2.30s/it]
[ComfyUI] 34%|███▍      | 17/50 [00:38<01:15,  2.30s/it]
[ComfyUI] 36%|███▌      | 18/50 [00:40<01:13,  2.30s/it]
[ComfyUI] 38%|███▊      | 19/50 [00:43<01:11,  2.30s/it]
[ComfyUI] 40%|████      | 20/50 [00:45<01:08,  2.30s/it]
[ComfyUI] 42%|████▏     | 21/50 [00:47<01:06,  2.30s/it]
[ComfyUI] 44%|████▍     | 22/50 [00:50<01:04,  2.30s/it]
[ComfyUI] 46%|████▌     | 23/50 [00:52<01:02,  2.30s/it]
[ComfyUI] 48%|████▊     | 24/50 [00:54<00:59,  2.30s/it]
[ComfyUI] 50%|█████     | 25/50 [00:56<00:57,  2.30s/it]
[ComfyUI] 52%|█████▏    | 26/50 [00:59<00:55,  2.30s/it]
[ComfyUI] 54%|█████▍    | 27/50 [01:01<00:52,  2.30s/it]
[ComfyUI] 56%|█████▌    | 28/50 [01:03<00:50,  2.30s/it]
[ComfyUI] 58%|█████▊    | 29/50 [01:06<00:48,  2.30s/it]
[ComfyUI] 60%|██████    | 30/50 [01:08<00:45,  2.30s/it]
[ComfyUI] 62%|██████▏   | 31/50 [01:10<00:43,  2.30s/it]
[ComfyUI] 64%|██████▍   | 32/50 [01:12<00:41,  2.30s/it]
[ComfyUI] 66%|██████▌   | 33/50 [01:15<00:39,  2.30s/it]
[ComfyUI] 68%|██████▊   | 34/50 [01:17<00:36,  2.30s/it]
[ComfyUI] 70%|███████   | 35/50 [01:19<00:34,  2.30s/it]
[ComfyUI] 72%|███████▏  | 36/50 [01:22<00:32,  2.30s/it]
[ComfyUI] 74%|███████▍  | 37/50 [01:24<00:29,  2.30s/it]
[ComfyUI] 76%|███████▌  | 38/50 [01:26<00:27,  2.30s/it]
[ComfyUI] 78%|███████▊  | 39/50 [01:29<00:25,  2.30s/it]
[ComfyUI] 80%|████████  | 40/50 [01:31<00:22,  2.30s/it]
[ComfyUI] 82%|████████▏ | 41/50 [01:33<00:20,  2.30s/it]
[ComfyUI] 84%|████████▍ | 42/50 [01:35<00:18,  2.30s/it]
[ComfyUI] 86%|████████▌ | 43/50 [01:38<00:16,  2.30s/it]
[ComfyUI] 88%|████████▊ | 44/50 [01:40<00:13,  2.30s/it]
[ComfyUI] 90%|█████████ | 45/50 [01:42<00:11,  2.29s/it]
[ComfyUI] 92%|█████████▏| 46/50 [01:45<00:09,  2.30s/it]
[ComfyUI] 94%|█████████▍| 47/50 [01:47<00:06,  2.30s/it]
[ComfyUI] 96%|█████████▌| 48/50 [01:49<00:04,  2.29s/it]
[ComfyUI] 98%|█████████▊| 49/50 [01:52<00:02,  2.30s/it]
[ComfyUI] 100%|██████████| 50/50 [01:54<00:00,  2.29s/it]
[ComfyUI] 100%|██████████| 50/50 [01:54<00:00,  2.29s/it]
[ComfyUI] Allocated memory: memory=12.300 GB
[ComfyUI] Max allocated memory: max_memory=15.099 GB
[ComfyUI] Max reserved memory: max_reserved=16.312 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,  1.46s/it]
[ComfyUI] Decoding rows: 100%|██████████| 2/2 [00:02<00:00,  1.24s/it]
[ComfyUI] Decoding rows: 100%|██████████| 2/2 [00:02<00:00,  1.27s/it]
[ComfyUI]
[ComfyUI] Blending tiles:   0%|          | 0/2 [00:00<?, ?it/s]
[ComfyUI] Blending tiles: 100%|██████████| 2/2 [00:00<00:00, 25.98it/s]
[ComfyUI] 
[ComfyUI] Decoding rows:   0%|          | 0/2 [00:00<?, ?it/s]
[ComfyUI] Decoding rows:  50%|█████     | 1/2 [00:00<00:00,  2.56it/s]
[ComfyUI] Decoding rows: 100%|██████████| 2/2 [00:00<00:00,  3.03it/s]
[ComfyUI] Decoding rows: 100%|██████████| 2/2 [00:00<00:00,  2.95it/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, 64.77it/s]
[ComfyUI] Prompt executed in 133.54 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