Instructions to use vanes430/Anima-2.9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusion Single File
How to use vanes430/Anima-2.9B-GGUF with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Diffusers
How to use vanes430/Anima-2.9B-GGUF with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("vanes430/Anima-2.9B-GGUF", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Anima-2.9B (GGUF Quantized Format)
Quantized GGUF weights for Anima-2.9B by Gazingstars123 for efficient inference in ComfyUI using ComfyUI-GGUF.
π Disclaimer & Attribution
- Original Model: Developed by Gazingstars123 based on circlestone-labs/Anima.
- Conversion: Converted to GGUF format by vanes430.
- License: CircleStone Labs Non-Commercial License.
Support original author: Ko-fi | PayPal
Available Quantizations
| Filename | Quant Type | Size | Recommended VRAM | Quality |
|---|---|---|---|---|
Anima-2.9B-preview-v1-Q8_0.gguf |
Q8_0 | ~3.15 GB | 6GB+ | βοΈ Best Quality (Near Lossless) |
Anima-2.9B-preview-v1-Q5_0.gguf |
Q5_0 | ~2.10 GB | 4GB - 6GB | High Quality / Balanced |
Anima-2.9B-preview-v1-Q4_0.gguf |
Q4_0 | ~1.75 GB | 4GB | Fast / Low VRAM |
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Hardware compatibility
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Model tree for vanes430/Anima-2.9B-GGUF
Base model
nvidia/Cosmos-Predict2-2B-Text2Image Finetuned
circlestone-labs/Anima Finetuned
Gazingstars123/Anima-2.9B