Instructions to use ethanfel/Krea-2-Base-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ethanfel/Krea-2-Base-Diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ethanfel/Krea-2-Base-Diffusers", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 1,287 Bytes
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license: other
license_name: krea-2-community
license_link: https://www.krea.ai/krea-2-licensing
pipeline_tag: text-to-image
library_name: diffusers
base_model: krea/Krea-2-Raw
base_model_relation: finetune
tags:
- text-to-image
- flow-matching
- krea
---
# Krea 2 Base — Diffusers (working conversion)
A **diffusers-loadable** conversion of the Krea 2 **Base/RAW** checkpoint that loads cleanly with
`diffusers.Krea2Transformer2DModel` / `Krea2Pipeline` (transformer keys renamed to the diffusers
layout, per-block modulation table reshaped, and the two final-layer `up`/`down` weights dropped to
match the diffusers Krea2 port). The `vae`, `text_encoder`, `tokenizer` and `scheduler` are the
standard diffusers components.
Intended for **fine-tuning / LoRA training** (train on Base/RAW, run inference on Krea 2 Turbo).
```python
import torch
from diffusers import Krea2Pipeline
pipe = Krea2Pipeline.from_pretrained("ethanfel/Krea-2-Base-Diffusers", torch_dtype=torch.bfloat16)
```
In ai-toolkit (krea2 branch), set `model.name_or_path: "ethanfel/Krea-2-Base-Diffusers"` and
`model.arch: "krea_2"`.
Converted with `scripts/convert_krea2_community_to_diffusers.py`. Weights are redistributed under the
[Krea 2 Community License](https://www.krea.ai/krea-2-licensing).
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