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deepfates / hunyuan-inception
Hunyuan-Video model finetuned on Inception (2010). Trigger word is "NCPTN". Use "A video in the style of NCPTN, NCPTN" at the beginning of your prompt for best results.
- Public
- 84 runs
-
H100
- Fine-tune
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=deepfates/hunyuan-inception
or set up a project from scratch
Install Replicate’s Node.js client library:
npm install replicateSet 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-inception using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"deepfates/hunyuan-inception:a471cf828b9f03ea639c745bbd27dc931e6a575dfbbffb2fa7cad6d71a0dab9e",
{
input: {
crf: 19,
seed: 12345,
steps: 50,
width: 640,
height: 360,
prompt: "A video in the style of NCPTN, NCPTN The video clip features a close-up of a person's face, focusing on their eyes and part of their hair. The individual has a serious or contemplative expression, with their eyes looking directly at the camera. The background is blurred, with warm, orange hues that suggest a setting sun or a fiery environment. The person is wearing large, geometric earrings that add a distinctive touch to their appearance. The lighting highlights the person's facial features, particularly their eyes, which are the central focus of the shot. The overall mood of the clip is intense and focused, with the person's gaze conveying a sense of determination or resolve.",
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 replicateSet 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 replicateRun deepfates/hunyuan-inception using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"deepfates/hunyuan-inception:a471cf828b9f03ea639c745bbd27dc931e6a575dfbbffb2fa7cad6d71a0dab9e",
input={
"crf": 19,
"seed": 12345,
"steps": 50,
"width": 640,
"height": 360,
"prompt": "A video in the style of NCPTN, NCPTN The video clip features a close-up of a person's face, focusing on their eyes and part of their hair. The individual has a serious or contemplative expression, with their eyes looking directly at the camera. The background is blurred, with warm, orange hues that suggest a setting sun or a fiery environment. The person is wearing large, geometric earrings that add a distinctive touch to their appearance. The lighting highlights the person's facial features, particularly their eyes, which are the central focus of the shot. The overall mood of the clip is intense and focused, with the person's gaze conveying a sense of determination or resolve.",
"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-inception 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-inception:a471cf828b9f03ea639c745bbd27dc931e6a575dfbbffb2fa7cad6d71a0dab9e",
"input": {
"crf": 19,
"seed": 12345,
"steps": 50,
"width": 640,
"height": 360,
"prompt": "A video in the style of NCPTN, NCPTN The video clip features a close-up of a person\'s face, focusing on their eyes and part of their hair. The individual has a serious or contemplative expression, with their eyes looking directly at the camera. The background is blurred, with warm, orange hues that suggest a setting sun or a fiery environment. The person is wearing large, geometric earrings that add a distinctive touch to their appearance. The lighting highlights the person\'s facial features, particularly their eyes, which are the central focus of the shot. The overall mood of the clip is intense and focused, with the person\'s gaze conveying a sense of determination or resolve.",
"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/predictionsTo 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 cogIf 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-inception@sha256:a471cf828b9f03ea639c745bbd27dc931e6a575dfbbffb2fa7cad6d71a0dab9e \
-i 'crf=19' \
-i 'seed=12345' \
-i 'steps=50' \
-i 'width=640' \
-i 'height=360' \
-i $'prompt="A video in the style of NCPTN, NCPTN The video clip features a close-up of a person\'s face, focusing on their eyes and part of their hair. The individual has a serious or contemplative expression, with their eyes looking directly at the camera. The background is blurred, with warm, orange hues that suggest a setting sun or a fiery environment. The person is wearing large, geometric earrings that add a distinctive touch to their appearance. The lighting highlights the person\'s facial features, particularly their eyes, which are the central focus of the shot. The overall mood of the clip is intense and focused, with the person\'s gaze conveying a sense of determination or resolve."' \
-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-inception@sha256:a471cf828b9f03ea639c745bbd27dc931e6a575dfbbffb2fa7cad6d71a0dab9ecurl -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 NCPTN, NCPTN The video clip features a close-up of a person\'s face, focusing on their eyes and part of their hair. The individual has a serious or contemplative expression, with their eyes looking directly at the camera. The background is blurred, with warm, orange hues that suggest a setting sun or a fiery environment. The person is wearing large, geometric earrings that add a distinctive touch to their appearance. The lighting highlights the person\'s facial features, particularly their eyes, which are the central focus of the shot. The overall mood of the clip is intense and focused, with the person\'s gaze conveying a sense of determination or resolve.", "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.
Add a payment method to run this model.
Sign in with GitHub
By signing in, you agree to our
terms of service and privacy policy
Output
{
"completed_at": "2025-01-24T18:12:14.572867Z",
"created_at": "2025-01-24T18:02:14.422000Z",
"data_removed": false,
"error": null,
"id": "w0w0hvmj2srme0cmk5xam3pb2r",
"input": {
"seed": 12345,
"steps": 50,
"width": 640,
"height": 360,
"prompt": "A video in the style of NCPTN, NCPTN The video clip features a close-up of a person's face, focusing on their eyes and part of their hair. The individual has a serious or contemplative expression, with their eyes looking directly at the camera. The background is blurred, with warm, orange hues that suggest a setting sun or a fiery environment. The person is wearing large, geometric earrings that add a distinctive touch to their appearance. The lighting highlights the person's facial features, particularly their eyes, which are the central focus of the shot. The overall mood of the clip is intense and focused, with the person's gaze conveying a sense of determination or resolve.",
"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\n�� USING REPLICATE WEIGHTS (preferred method)\n🎯 USING REPLICATE WEIGHTS TAR FILE 🎯\n----------------------------------------\n📦 Processing replicate weights tar file...\n🔄 Will rename LoRA to: replicate_1f80f0f5-cc18-4ee1-b325-05df5412a17b.safetensors\n📂 Extracting tar contents...\n✅ Found lora_comfyui.safetensors in tar\n✨ Successfully copied LoRA to: ComfyUI/models/loras/replicate_1f80f0f5-cc18-4ee1-b325-05df5412a17b.safetensors\n----------------------------------------\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: 139\n[ComfyUI] clipL prompt attention_mask shape: torch.Size([1, 77]), masked tokens: 77\nExecuting node 41, title: HunyuanVideo Lora Select, class type: HyVideoLoraSelect\nExecuting node 1, title: HunyuanVideo Model Loader, class type: HyVideoModelLoader\n[ComfyUI] model_type FLOW\n[ComfyUI] The config attributes {'use_flow_sigmas': True, 'prediction_type': 'flow_prediction'} were passed to FlowMatchDiscreteScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file.\n[ComfyUI] Using accelerate to load and assign model weights to device...\n[ComfyUI] Loading LoRA: replicate_1f80f0f5-cc18-4ee1-b325-05df5412a17b with strength: 1.0\n[ComfyUI] Requested to load HyVideoModel\n[ComfyUI] loaded completely 9.5367431640625e+25 12555.953247070312 True\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] Scheduler config: FrozenDict([('num_train_timesteps', 1000), ('flow_shift', 9.0), ('reverse', True), ('solver', 'euler'), ('n_tokens', None), ('_use_default_values', ['num_train_timesteps', 'n_tokens'])])\n[ComfyUI]\n[ComfyUI] 0%| | 0/50 [00:00<?, ?it/s]\n[ComfyUI] 2%|▏ | 1/50 [00:02<01:52, 2.29s/it]\n[ComfyUI] 4%|▍ | 2/50 [00:04<01:36, 2.01s/it]\n[ComfyUI] 6%|▌ | 3/50 [00:06<01:40, 2.13s/it]\n[ComfyUI] 8%|▊ | 4/50 [00:08<01:40, 2.19s/it]\n[ComfyUI] 10%|█ | 5/50 [00:10<01:40, 2.23s/it]\n[ComfyUI] 12%|█▏ | 6/50 [00:13<01:38, 2.25s/it]\n[ComfyUI] 14%|█▍ | 7/50 [00:15<01:37, 2.26s/it]\n[ComfyUI] 16%|█▌ | 8/50 [00:17<01:35, 2.27s/it]\n[ComfyUI] 18%|█▊ | 9/50 [00:20<01:33, 2.28s/it]\n[ComfyUI] 20%|██ | 10/50 [00:22<01:31, 2.28s/it]\n[ComfyUI] 22%|██▏ | 11/50 [00:24<01:28, 2.28s/it]\n[ComfyUI] 24%|██▍ | 12/50 [00:26<01:26, 2.28s/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, 2.29s/it]\n[ComfyUI] 32%|███▏ | 16/50 [00:36<01:17, 2.29s/it]\n[ComfyUI] 34%|███▍ | 17/50 [00:38<01:15, 2.29s/it]\n[ComfyUI] 36%|███▌ | 18/50 [00:40<01:13, 2.29s/it]\n[ComfyUI] 38%|███▊ | 19/50 [00:42<01:10, 2.29s/it]\n[ComfyUI] 40%|████ | 20/50 [00:45<01:08, 2.29s/it]\n[ComfyUI] 42%|████▏ | 21/50 [00:47<01:06, 2.29s/it]\n[ComfyUI] 44%|████▍ | 22/50 [00:49<01:04, 2.29s/it]\n[ComfyUI] 46%|████▌ | 23/50 [00:52<01:01, 2.29s/it]\n[ComfyUI] 48%|████▊ | 24/50 [00:54<00:59, 2.29s/it]\n[ComfyUI] 50%|█████ | 25/50 [00:56<00:57, 2.29s/it]\n[ComfyUI] 52%|█████▏ | 26/50 [00:59<00:54, 2.29s/it]\n[ComfyUI] 54%|█████▍ | 27/50 [01:01<00:52, 2.29s/it]\n[ComfyUI] 56%|█████▌ | 28/50 [01:03<00:50, 2.29s/it]\n[ComfyUI] 58%|█████▊ | 29/50 [01:05<00:48, 2.29s/it]\n[ComfyUI] 60%|██████ | 30/50 [01:08<00:45, 2.29s/it]\n[ComfyUI] 62%|██████▏ | 31/50 [01:10<00:43, 2.29s/it]\n[ComfyUI] 64%|██████▍ | 32/50 [01:12<00:41, 2.29s/it]\n[ComfyUI] 66%|██████▌ | 33/50 [01:15<00:38, 2.29s/it]\n[ComfyUI] 68%|██████▊ | 34/50 [01:17<00:36, 2.29s/it]\n[ComfyUI] 70%|███████ | 35/50 [01:19<00:34, 2.29s/it]\n[ComfyUI] 72%|███████▏ | 36/50 [01:21<00:32, 2.29s/it]\n[ComfyUI] 74%|███████▍ | 37/50 [01:24<00:29, 2.29s/it]\n[ComfyUI] 76%|███████▌ | 38/50 [01:26<00:27, 2.29s/it]\n[ComfyUI] 78%|███████▊ | 39/50 [01:28<00:25, 2.29s/it]\n[ComfyUI] 80%|████████ | 40/50 [01:31<00:22, 2.29s/it]\n[ComfyUI] 82%|████████▏ | 41/50 [01:33<00:20, 2.29s/it]\n[ComfyUI] 84%|████████▍ | 42/50 [01:35<00:18, 2.29s/it]\n[ComfyUI] 86%|████████▌ | 43/50 [01:37<00:16, 2.29s/it]\n[ComfyUI] 88%|████████▊ | 44/50 [01:40<00:13, 2.29s/it]\n[ComfyUI] 90%|█████████ | 45/50 [01:42<00:11, 2.29s/it]\n[ComfyUI] 92%|█████████▏| 46/50 [01:44<00:09, 2.29s/it]\n[ComfyUI] 94%|█████████▍| 47/50 [01:47<00:06, 2.29s/it]\n[ComfyUI] 96%|█████████▌| 48/50 [01:49<00:04, 2.29s/it]\n[ComfyUI] 98%|█████████▊| 49/50 [01:51<00:02, 2.29s/it]\n[ComfyUI] 100%|██████████| 50/50 [01:53<00:00, 2.29s/it]\n[ComfyUI] 100%|██████████| 50/50 [01:53<00:00, 2.28s/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=16.344 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, 25.90it/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, 3.04it/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, 63.98it/s]\n[ComfyUI] Prompt executed in 142.75 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": 149.996172505,
"total_time": 600.150867
},
"output": "https://replicate.delivery/xezq/KVl3LEOuteyJcycUBQJ4s2b0wsVTkZTzwLjcvPSNgCNfnYIUA/HunyuanVideo_00001.mp4",
"started_at": "2025-01-24T18:09:44.576694Z",
"status": "succeeded",
"urls": {
"stream": "https://stream.replicate.com/v1/files/bsvm-torf6scsc3sswzbni6vjvyvlrgpk2cthp4nsz6jvm346gvxx2jyq",
"get": "https://api.replicate.com/v1/predictions/w0w0hvmj2srme0cmk5xam3pb2r",
"cancel": "https://api.replicate.com/v1/predictions/w0w0hvmj2srme0cmk5xam3pb2r/cancel"
},
"version": "a471cf828b9f03ea639c745bbd27dc931e6a575dfbbffb2fa7cad6d71a0dab9e"
}Generated in
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
�� USING REPLICATE WEIGHTS (preferred method)
🎯 USING REPLICATE WEIGHTS TAR FILE 🎯
----------------------------------------
📦 Processing replicate weights tar file...
🔄 Will rename LoRA to: replicate_1f80f0f5-cc18-4ee1-b325-05df5412a17b.safetensors
📂 Extracting tar contents...
✅ Found lora_comfyui.safetensors in tar
✨ Successfully copied LoRA to: ComfyUI/models/loras/replicate_1f80f0f5-cc18-4ee1-b325-05df5412a17b.safetensors
----------------------------------------
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: 139
[ComfyUI] clipL prompt attention_mask shape: torch.Size([1, 77]), masked tokens: 77
Executing node 41, title: HunyuanVideo Lora Select, class type: HyVideoLoraSelect
Executing node 1, title: HunyuanVideo Model Loader, class type: HyVideoModelLoader
[ComfyUI] model_type FLOW
[ComfyUI] The config attributes {'use_flow_sigmas': True, 'prediction_type': 'flow_prediction'} were passed to FlowMatchDiscreteScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file.
[ComfyUI] Using accelerate to load and assign model weights to device...
[ComfyUI] Loading LoRA: replicate_1f80f0f5-cc18-4ee1-b325-05df5412a17b with strength: 1.0
[ComfyUI] Requested to load HyVideoModel
[ComfyUI] loaded completely 9.5367431640625e+25 12555.953247070312 True
[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] Scheduler config: FrozenDict([('num_train_timesteps', 1000), ('flow_shift', 9.0), ('reverse', True), ('solver', 'euler'), ('n_tokens', None), ('_use_default_values', ['num_train_timesteps', 'n_tokens'])])
[ComfyUI]
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[ComfyUI] 30%|███ | 15/50 [00:33<01:20, 2.29s/it]
[ComfyUI] 32%|███▏ | 16/50 [00:36<01:17, 2.29s/it]
[ComfyUI] 34%|███▍ | 17/50 [00:38<01:15, 2.29s/it]
[ComfyUI] 36%|███▌ | 18/50 [00:40<01:13, 2.29s/it]
[ComfyUI] 38%|███▊ | 19/50 [00:42<01:10, 2.29s/it]
[ComfyUI] 40%|████ | 20/50 [00:45<01:08, 2.29s/it]
[ComfyUI] 42%|████▏ | 21/50 [00:47<01:06, 2.29s/it]
[ComfyUI] 44%|████▍ | 22/50 [00:49<01:04, 2.29s/it]
[ComfyUI] 46%|████▌ | 23/50 [00:52<01:01, 2.29s/it]
[ComfyUI] 48%|████▊ | 24/50 [00:54<00:59, 2.29s/it]
[ComfyUI] 50%|█████ | 25/50 [00:56<00:57, 2.29s/it]
[ComfyUI] 52%|█████▏ | 26/50 [00:59<00:54, 2.29s/it]
[ComfyUI] 54%|█████▍ | 27/50 [01:01<00:52, 2.29s/it]
[ComfyUI] 56%|█████▌ | 28/50 [01:03<00:50, 2.29s/it]
[ComfyUI] 58%|█████▊ | 29/50 [01:05<00:48, 2.29s/it]
[ComfyUI] 60%|██████ | 30/50 [01:08<00:45, 2.29s/it]
[ComfyUI] 62%|██████▏ | 31/50 [01:10<00:43, 2.29s/it]
[ComfyUI] 64%|██████▍ | 32/50 [01:12<00:41, 2.29s/it]
[ComfyUI] 66%|██████▌ | 33/50 [01:15<00:38, 2.29s/it]
[ComfyUI] 68%|██████▊ | 34/50 [01:17<00:36, 2.29s/it]
[ComfyUI] 70%|███████ | 35/50 [01:19<00:34, 2.29s/it]
[ComfyUI] 72%|███████▏ | 36/50 [01:21<00:32, 2.29s/it]
[ComfyUI] 74%|███████▍ | 37/50 [01:24<00:29, 2.29s/it]
[ComfyUI] 76%|███████▌ | 38/50 [01:26<00:27, 2.29s/it]
[ComfyUI] 78%|███████▊ | 39/50 [01:28<00:25, 2.29s/it]
[ComfyUI] 80%|████████ | 40/50 [01:31<00:22, 2.29s/it]
[ComfyUI] 82%|████████▏ | 41/50 [01:33<00:20, 2.29s/it]
[ComfyUI] 84%|████████▍ | 42/50 [01:35<00:18, 2.29s/it]
[ComfyUI] 86%|████████▌ | 43/50 [01:37<00:16, 2.29s/it]
[ComfyUI] 88%|████████▊ | 44/50 [01:40<00:13, 2.29s/it]
[ComfyUI] 90%|█████████ | 45/50 [01:42<00:11, 2.29s/it]
[ComfyUI] 92%|█████████▏| 46/50 [01:44<00:09, 2.29s/it]
[ComfyUI] 94%|█████████▍| 47/50 [01:47<00:06, 2.29s/it]
[ComfyUI] 96%|█████████▌| 48/50 [01:49<00:04, 2.29s/it]
[ComfyUI] 98%|█████████▊| 49/50 [01:51<00:02, 2.29s/it]
[ComfyUI] 100%|██████████| 50/50 [01:53<00:00, 2.29s/it]
[ComfyUI] 100%|██████████| 50/50 [01:53<00:00, 2.28s/it]
[ComfyUI] Allocated memory: memory=12.300 GB
[ComfyUI] Max allocated memory: max_memory=15.099 GB
[ComfyUI] Max reserved memory: max_reserved=16.344 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, 25.90it/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, 3.04it/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, 63.98it/s]
[ComfyUI] Prompt executed in 142.75 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.mp4Examples
Run time and cost
This model runs on Nvidia H100 GPU hardware. We don't yet have enough runs of this model to provide performance information.
