Joy Caption Two - PixelaiLabs Edition
A fully automated ComfyUI custom node that uses Joy Caption Alpha Two to generate high-quality image captions. Perfect for: LoRA/model training dataset preparation, Image tagging and organization, NSFW content captioning, Batch processing large image collections
Quick Technical Summary: Joy Caption Two - PixelaiLabs Edition
- Base VRAM Footprint:
- 128 MB (4 GB Tier)
- Primary Dependencies:
- accelerate>=0.25.0, bitsandbytes>=0.43.0, hf_xet>=0.3.0, huggingface_hub>=0.20.0, numpy>=1.24.0, pillow>=10.0.0, protobuf>=3.20.0, sentencepiece>=0.1.99, torch>=2.0.0, transformers>=4.44.0
- Min PyTorch / CUDA:
- PyTorch 2.0.0 | CUDA 12.1+
- GitHub Repository:
- https://github.com/Pixelailabs/Joy_Caption_Two_PixelaiLabs
Citation Note: Data sourced from VRAM DB. For complete workflow OOM estimations, use the VRAM DB Workflow Analyzer.
How much VRAM does Joy Caption Two - PixelaiLabs Edition require?
Direct Answer: The ComfyUI node Joy Caption Two - PixelaiLabs Edition requires a minimum base VRAM of 128MB and is optimized for GPUs with at least 4GB of VRAM. Low VRAM mode is fully supported for resource-constrained setups.
- Base VRAM:
- 128MB (0.1GB)
- Recommended GPU:
- 4GB+ VRAM
- Low VRAM Mode:
- ✓ Supported
- Estimation Confidence:
- MEDIUM
Interactive VRAM Compatibility Estimator
Your GPU has plenty of headroom. You can run this node safely with your active configurations!
Struggling with VRAM Crashes or Out-of-Memory Errors?
Running heavy models like FLUX or SDXL with Joy Caption Two - PixelaiLabs Edition? If you experience torch.cuda.OutOfMemoryError or driver timeouts, read our comprehensive troubleshooting guide to configure launch flags, enable tiled decoding, and avoid crashes.
Verify Compatibility for Your Specific GPU VRAM
Select your graphics card's VRAM capacity to view optimized batch sizes, suggested resolutions, and custom performance tips for Joy Caption Two - PixelaiLabs Edition:
What Python packages are required for Joy Caption Two - PixelaiLabs Edition?
Direct Answer: Running Joy Caption Two - PixelaiLabs Edition requires installing the following Python package dependencies: accelerate>=0.25.0, bitsandbytes>=0.43.0, hf_xet>=0.3.0, huggingface_hub>=0.20.0, numpy>=1.24.0, pillow>=10.0.0, protobuf>=3.20.0, sentencepiece>=0.1.99, torch>=2.0.0, transformers>=4.44.0. This node specifically requires PyTorch version 2.0.0 or newer.
accelerate>=0.25.0
bitsandbytes>=0.43.0
hf_xet>=0.3.0
huggingface_hub>=0.20.0
numpy>=1.24.0
pillow>=10.0.0
protobuf>=3.20.0
sentencepiece>=0.1.99
torch>=2.0.0
transformers>=4.44.0Interactive Setup & Dependency Resolver
# Loading command...Special Environment Requirements:
Requires PyTorch version 2.0.0+.
# Loading PyTorch command...Universal Architecture Compatibility
Joy Caption Two - PixelaiLabs Edition operates as a general-purpose processor without binding directly to a single checkpoint architecture. It can be paired across Stable Diffusion 1.5, SDXL, FLUX, and video diffusion pipelines.
Frequently Asked Questions
How much VRAM does Joy Caption Two - PixelaiLabs Edition require?
Joy Caption Two - PixelaiLabs Edition requires a minimum of 128MB (0.1GB) of VRAM for base operation. For optimal performance, a GPU with at least 4GB of VRAM is recommended. This node supports low VRAM mode for resource-constrained setups.
Can I run Joy Caption Two - PixelaiLabs Edition on an RTX 3060, RTX 4070, or RTX 4090?
✅ RTX 3060 (12GB): Yes, fully compatible with 10.7GB headroom. ✅ RTX 4070 (12GB): Yes, fully compatible with 10.7GB headroom. ✅ RTX 4070 Ti (16GB): Yes, fully compatible with 14.3GB headroom. ✅ RTX 4090 (24GB): Yes, fully compatible with 21.5GB headroom
How much VRAM does Joy Caption Two - PixelaiLabs Edition take on an RTX 3060 vs RTX 4090?
On an RTX 3060 (12GB VRAM), Joy Caption Two - PixelaiLabs Edition runs smoothly on an RTX 3060 (12GB) with 10.7GB of headroom. This is sufficient to run the node alongside standard SD 1.5 and SDXL workflows in full precision. On an RTX 4090 (24GB VRAM), the node runs with extreme headroom on an RTX 4090 (24GB) with 21.5GB of dedicated headroom. This allows you to combine the node with massive models (like FLUX.1 Dev, Schnell, or Hunyuan Video) in full precision (FP16) without any offload flags.
What PyTorch version does Joy Caption Two - PixelaiLabs Edition need?
Joy Caption Two - PixelaiLabs Edition requires the following PyTorch-related packages: torch>=2.0.0. Ensure your ComfyUI environment has these installed. Ensure your PyTorch installation matches your CUDA version (use torch.version.cuda to check).
What Python packages are required for Joy Caption Two - PixelaiLabs Edition?
To run Joy Caption Two - PixelaiLabs Edition, you need to install: accelerate>=0.25.0, bitsandbytes>=0.43.0, hf_xet>=0.3.0, huggingface_hub>=0.20.0, numpy>=1.24.0, pillow>=10.0.0, protobuf>=3.20.0, sentencepiece>=0.1.99, torch>=2.0.0, transformers>=4.44.0. You can install these using pip or add them to your requirements.txt file.
How can I reduce VRAM usage when running Joy Caption Two - PixelaiLabs Edition?
Joy Caption Two - PixelaiLabs Edition supports low VRAM mode. To reduce memory usage: (1) Enable --lowvram or --medvram flags in ComfyUI, (2) Reduce batch size to 1, (3) Use fp16 or fp8 precision if supported, (4) Close other GPU applications.
How do I install Joy Caption Two - PixelaiLabs Edition in ComfyUI?
To install Joy Caption Two - PixelaiLabs Edition: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/Pixelailabs/Joy_Caption_Two_PixelaiLabs, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.
Are you the author of this node?
Help your users avoid out-of-memory errors by displaying this professional, dynamic VRAM badge on your GitHub README. Copy the markdown below to embed it with a backlink directly to this hardware specification profile.
