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Open-weight model · Text to image

geolip-beatrix-anima

by AbstractPhila AbstractPhil/geolip-beatrix-anima

geolip-beatrix-anima is an open-weight model for text to image from AbstractPhila, released under other. Its published files total 7.3 GB.

Experiments on steering Anima, a 2B illustration model built on NVIDIA Cosmos-Predict2, with a second conditioning source: Beatrix, a byte-level language model from the geolip line.

Parameters—
Context—
Weights7.1 GB
Licenseother
AccessOpen weights
Monthly Downloads—

Model Card

Experiments on steering Anima, a 2B illustration model built on NVIDIA Cosmos-Predict2, with a second conditioning source: Beatrix, a byte-level language model from the geolip line. Anima reads its prompt through a small language model (Qwen3 0.6B) and a light adapter, so an added signal is not drowned by a very large text encoder; that makes it a bed for testing how far a second source can steer the image without a full diffusion training run. The first experiments measure the bed itself: how its conditioning responds to mood words and to a mood direction added to it, and what LoRAs trained on the model's own mood images do. Each experiment has its own folder under experiments/ with a…

Excerpt from the card by AbstractPhila, licensed other.

Identity and Version

Repository
AbstractPhil/geolip-beatrix-anima
Publisher
AbstractPhila
Task
Text to image
Modality
Image
Library
Not stated by the source
Parameters
Not stated by the source
Languages
Not stated by the source
Revision
e925c2ddfe9d4cb5ee90d0c9947ef0b4dee0c163
First published
2026-10-04
Last updated
2026-10-08

Files and Weights

731 files, 7.3 GB in total. The weights are 110 files totalling 7.1 GB in safetensors.

Weights110 files · 7.1 GB
Configuration138 files · 6.8 MB
Documentation34 files · 297.7 KB
Other448 files · 204.0 MB
Repository1 file · 39.9 KB
Every file
FileTypeSizeSHA-256
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experiments/e002_lora_mood_up/lora/epoch2/adapter_model.safetensorsWeights138.7 MB 81b2e52e5758
experiments/e002_lora_mood_up/lora/epoch4/adapter_model.safetensorsWeights138.7 MB b81dfbf8be75
experiments/e002_lora_mood_up/lora/epoch6/adapter_model.safetensorsWeights138.7 MB 34b6e941d8a3
experiments/e002_lora_mood_up/lora/epoch8/adapter_model.safetensorsWeights138.7 MB e4f5e4679235
experiments/e003_lora_neutral_control/lora/epoch10/adapter_model.safetensorsWeights138.7 MB 17ca146609cd
experiments/e003_lora_neutral_control/lora/epoch2/adapter_model.safetensorsWeights138.7 MB b61ea9d301a6
experiments/e003_lora_neutral_control/lora/epoch4/adapter_model.safetensorsWeights138.7 MB 8cd06b592dc4
experiments/e003_lora_neutral_control/lora/epoch6/adapter_model.safetensorsWeights138.7 MB 8e253965d2e3
experiments/e003_lora_neutral_control/lora/epoch8/adapter_model.safetensorsWeights138.7 MB 33cdb37aa551
experiments/e004_lora_mood_down/lora/epoch10/adapter_model.safetensorsWeights138.7 MB f01f92dcbb0a
experiments/e004_lora_mood_down/lora/epoch2/adapter_model.safetensorsWeights138.7 MB b228a8d3b92f
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experiments/e004_lora_mood_down/lora/epoch8/adapter_model.safetensorsWeights138.7 MB 07322b4fbae8
experiments/e005_lora_mood_up_reseed/lora/epoch10/adapter_model.safetensorsWeights138.7 MB ad29bdab9d23
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experiments/e005_lora_mood_up_reseed/lora/epoch4/adapter_model.safetensorsWeights138.7 MB cd1d71daf511
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experiments/e005_lora_mood_up_reseed/lora/epoch8/adapter_model.safetensorsWeights138.7 MB 87024eede5df
experiments/e006_lora_mood_up_lr_1e-5/lora/epoch10/adapter_model.safetensorsWeights138.7 MB 22acb9da323e
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experiments/e006_lora_mood_up_lr_1e-5/lora/epoch4/adapter_model.safetensorsWeights138.7 MB 687fb19e9109
experiments/e006_lora_mood_up_lr_1e-5/lora/epoch6/adapter_model.safetensorsWeights138.7 MB 858f2b5b0534
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experiments/e007_lora_mood_up_lr_4e-5/lora/epoch2/adapter_model.safetensorsWeights138.7 MB 46eb76f52f51
experiments/e007_lora_mood_up_lr_4e-5/lora/epoch4/adapter_model.safetensorsWeights138.7 MB 3b546124ce6c
experiments/e007_lora_mood_up_lr_4e-5/lora/epoch6/adapter_model.safetensorsWeights138.7 MB 48f9e5e86d3e
experiments/e007_lora_mood_up_lr_4e-5/lora/epoch8/adapter_model.safetensorsWeights138.7 MB faa1b11be5e5
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.gitattributesRepository39.9 KB —

License and Download

License
other
Access
Open weights, no gate
Download size
7.1 GB
Download from AbstractPhila

Released by AbstractPhila through its official repository on Hugging Face. Read the license.

Built From

Memory Requirements

PrecisionWeights in memory
As published7.1 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About geolip-beatrix-anima

What license is geolip-beatrix-anima released under?

other, as its publisher declares it. Read the license text before commercial use.

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GGUF quantizations of Qwen/Qwen-Image-2.1 for local image generation using the original upstream base weights. Q4KM is recommended for the best balance of size and quality. Companion model files packaged for ComfyUI: Use the model with ComfyUI and ComfyUI-GGUF. All required companion files (GGUF transformer, text encoder, and VAE) are hosted directly in this repository. Download the files and place them in their respective ComfyUI directories: 1. Install ComfyUI-GGUF: Use the maintained fork with native Qwen-Image 2.1 support by cloning leejet/ComfyUI-GGUF into your custom nodes: (Note: If you have the older city96/ComfyUI-GGUF installed and encounter an Unknown model architecture! error…

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Model · Text to image

Juggernaut-XL-v9

RunDiffusion

The SDXL ecosystem is the single most mature corner of open image generation, and v9 is its most refined photorealism checkpoint. Choose Juggernaut XL v9 when you want: - Photorealism that holds up under scrutiny — skin texture, micro-contrast, and natural lighting that translates from concept to print. - Reasonable hardware — runs comfortably on 8 GB of VRAM, unlike newer DiT-based models that demand 16+ GB. - The full SDXL toolbox — drop-in compatibility with the thousands of SDXL ControlNets, IP-Adapter variants, AnimateDiff, regional prompting tools, and LoRAs already in your workflow. - Battle-tested reliability — 26+ months in production, used in agencies, studios, and shipping…

Open weights creativeml-openrail-m diffusers

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Qwen-Image-Lightning

Lightx2v

Please refer to Qwen-Image-Lightning github to learn how to use the models. make sure to install diffusers from main (pip install git+https://github.com/huggingface/diffusers.git)

Open weights apache-2.0 diffusers

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Z-Image-Lora

nphSi

+ Always use full LoRa name with "vrtlxxxx" trigger in prompt like "Alba Baptista (vrtlalbabaptista) in a swimming pool". "Woman" or "1girl" will NOT work due to my way i do captions. + Add the gender to the prompt for confusing names like "Alex Jones". + Remove the name when internal model knowledge is bad or censored or is confusing to model like "Sandy Cheeks" or "Kate Middleton". + When using a Lora with multiple triggers (vrtlxx,vrtlyy) do not use the real character name but only trigger or a combination of it. "vrtlMain" always combines all trigger-words. Angourie Rice, January Jones, Julianna Guill, Ursula Corbero, Judith Rakers, Alina Merkau, Kiernan Shipka, Leslie Bibb, Marie…

Open weights apache-2.0 diffusers

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Flux2-Klein-9B-True-V2

Wikee Yang

Recommend smthemex/ComfyUIUniBlockSwap plugin for LOW VRAM users, it only requires 4-6GB of VRAM to run the full bf16 model. 您可以尝试一下全新的 ComfyUI GGUF 模型加载插件 smthemex/ComfyUIDifGGUF,它能适配更多的 GGUF 文件格式,并且将很快集成低显存(4-8GB)显卡的 GGUF 模型块卸载管理能力。 Recommend to try the new GGUF ComfyUI loader plugin, smthemex/ComfyUIDifGGUF, it compatible with more GGUF format, and will add low VRAM (4-8GB) management for GGUF soon. 1. 图像的真实感和质感进一步改善,基本接近香蕉(Nano Banana)的水平。 2. 提示词遵循和还原能力,参数适配性和LoRA兼容性进一步改善,。 The V2 version of this model has undergone a full fine-tuning based on FLUX.2-Klein-9B-True-V1. Compared to the V1 version, it has some improvements and enhancements as below: 1. The realism and texture of images…

Open weights other diffusers

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Z-Image-Turbo-GGUF

Unsloth AI

Welcome to the official repository for the Z-Image(造相)project! Z-Image is a powerful and highly efficient image generation model with 6B parameters. Currently there are three variants: - Z-Image-Turbo – A distilled version of Z-Image that matches or exceeds leading competitors with only 8 NFEs (Number of Function Evaluations). It offers sub-second inference latency on enterprise-grade H800 GPUs and fits comfortably within 16G VRAM consumer devices. It excels in photorealistic image generation, bilingual text rendering (English & Chinese), and robust instruction adherence. - Z-Image-Base – The non-distilled foundation model. By releasing this checkpoint, we aim to unlock the full potential…

Open weights apache-2.0 ggml