Deploy HunyuanVideo
VideoHunyuanVideo 1.5 is Tencent's flagship video generation model with 8.3B parameters. It supports both text-to-video and image-to-video workflows, running efficiently on consumer GPUs with approximately 14GB VRAM.
Deploy HunyuanVideo in minutes
Starting at $0.72/hr on dedicated GPU
Available Variants (2)
| Model | GPU | VRAM | Price | Action |
|---|---|---|---|---|
HunyuanVideo 1.5 Text-to-Video Text-to-Video | RTX A6000 | 48 GB | $0.72/hr | Deploy |
HunyuanVideo 1.5 Image-to-Video Image-to-Video | RTX A6000 | 48 GB | $0.72/hr | Deploy |
Prices include the service fee. Charges follow actual running time.
Requirements
ModelPilot assigns a 48GB cloud GPU to this deployment. Actual local VRAM requirements vary with model variant, precision, quantization, resolution, and workflow settings.
On ModelPilot, deploy on a dedicated cloud GPU (up to 80GB VRAM) starting at $0.72/hr with no setup required.
Use Cases
- ✓Text-to-video generation
- ✓Image animation
- ✓Short-form video content
- ✓Visual effects prototyping
Related Models
Known Issues & Fixes
Real failures from running HunyuanVideo and similar pipelines, with the mechanism behind each one.
'Value not in list' on ONE encoder even though the others are present, or conditioning is silently broken
Cause: Encoder count is model-specific and modern models pair a small CLIP with a large, easy-to-forget LLM/VL encoder: HiDream's QuadrupleCLIPLoader needs FOUR (incl. llama-3.1-8b fp8_scaled ~8.5GB); HunyuanVideo needs DUAL (clip_l + llava_llama3, +vision for i2v); Qwen-Image needs Qwen-2.5-VL-7B (~7GB fp8 to ~16GB bf16); Z-Image needs Qwen-3-4B (8GB). A 'not in list' error usually means you shipped N-1.
Fix: Ship the full encoder set for the exact model; the missing piece is almost always the large LLM/VL encoder, not the CLIP.
'MetadataIncompleteBuffer' / 'HeaderTooLarge' at model LOAD, or (secondary) opaque CUDA errors / NaN downstream, with no obvious download failure
Cause: A truncated safetensors fails cleanly (reads an 8-byte header-length prefix then expects that many bytes). The shared validator (validate_download in download_utils.sh) only checks non-empty/not-HTML, so a truncated file passes and is cached forever; wget's exit code is unreliable through HF CDN redirects. Some scripts have too-low size floors (hunyuan MIN_SIZE_DIFFUSION=12GB for a ~25GB model would pass a ~50%-truncated file).
Fix: Validate by size against per-variant expected content-length (sha256 ideally). On MetadataIncompleteBuffer, delete the cached file and redownload. Lead with the safetensors error; opaque CUDA/NaN is the corrupt-but-not-truncated secondary case.
Motion-transfer output is garbage/noise even though the reference image, output mask and background look fine — the model seems to 'transfer onto smoke'
Cause: The driving clip wasn't real full-body human motion (e.g. abstract light-art/VFX), so DWPose extracts no valid skeleton. Motion-transfer models faithfully reproduce whatever motion is actually in the driver; a bad driver yields empty/nonsensical conditioning no subject tuning can fix.
Fix: QA the DRIVER frame-by-frame first — it must contain a clearly-tracked full-body human. The reference/mask/background can look fine while the pose track is empty.
The queued motion segment starts much later than the source-frame calculation predicts, contains fewer frames than the configured cap, or uses a boring tail pose instead of the selected choreography.
Cause: `skip_first_frames` is applied in the frame domain produced by `force_rate`. With `force_rate=16`, skip 145 means about 9.06 seconds even when the source file is 25 fps; treating it as source frame 145 incorrectly predicts a 5.8-second start.
Fix: Pre-trim and pre-resample the exact driver segment locally, upload that asset, and set `skip_first_frames=0`. If runtime skipping is necessary, calculate the skip against the forced output rate, not the source rate.
An 81-frame short takes nearly twice as long as expected and returns 153 frames even though the uploaded driver contains only 81.
Cause: The graph's context window is 77 frames with an overlap start at frame 76. Loading 81 frames creates windows `0-77` and `76-153`; the second window is padded to full length and the combined output is not truncated to the driver's original frame count.
Fix: Cap bounded controls at 77 frames. For intentional long-form runs, retain overlapping windows but truncate the final encoded output to the actual driver frame count. Treat raw padded duration as an implementation artifact, not valid long-motion evidence.
After transfer the composite keeps the SOURCE person's face/identity, preserves the entire driver as a second body over the reference, or propagates garbage across the clip.
Cause: (a) SAM2 seed points cover only torso/legs so out-of-mask source pixels (including the face) survive; (b) SAM2 is seeded on a title card or solid-color frame and propagates a meaningless mask; (c) positive points are reused from a differently framed driver and land on background. In the showcase orchestrator, normalized config coordinates are expanded into raw pixels against the driving video's actual width and height; they index driver frame zero, not the subject reference image.
Fix: Seed on a frame showing real content; overlay every positive point on driver frame zero; ensure every point hits the intended replace subject, including head and limbs; make PointsEditor canvas dimensions match the driver frame; inspect the blacked-out background preview before sampling.
Hitting a specific error message? Browse ComfyUI error fixes.
Frequently Asked Questions
How much GPU memory is allocated for HunyuanVideo?
The listed ModelPilot deployment uses a 48GB cloud GPU. Local memory needs can vary with precision, quantization, and workflow settings.
How much does it cost to run HunyuanVideo?
Starting at $0.72/hr on a dedicated GPU. Charges are calculated from actual running time, with auto-stop when credits run out.
How long does HunyuanVideo take to deploy?
Video models typically take 15–30 minutes for first deployment (model download + setup). Subsequent starts are faster.
Can I run HunyuanVideo on my local GPU?
It depends on the selected variant, precision, quantization, and workflow settings. Compare the variants below with your available VRAM; the table shows ModelPilot's cloud GPU allocation, not a universal local minimum.
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