Instructions to use RaiffsBits/deep_thought with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use RaiffsBits/deep_thought with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0,black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("RaiffsBits/deep_thought") prompt = "Make and ai story" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| tags: | |
| - text-to-image | |
| - lora | |
| - diffusers | |
| - template:diffusion-lora | |
| widget: | |
| - text: Make and ai story | |
| parameters: | |
| negative_prompt: dont use real faces | |
| output: | |
| url: images/an AI being reborn in a cyberpunk future setting (1).png | |
| base_model: | |
| - stabilityai/stable-diffusion-xl-base-1.0 | |
| - black-forest-labs/FLUX.1-dev | |
| instance_prompt: null | |
| license: ms-pl | |
| datasets: | |
| - O1-OPEN/OpenO1-SFT | |
| - NovaSky-AI/Sky-T1_data_17k | |
| - DAMO-NLP-SG/multimodal_textbook | |
| language: | |
| - en | |
| metrics: | |
| - character | |
| - accuracy | |
| - bertscore | |
| - bleu | |
| - bleurt | |
| - brier_score | |
| pipeline_tag: text-to-image | |
| # Triton | |
| <Gallery /> | |
| ## Model description | |
| A triton model | |
| ## Download model | |
| [Download](/RaiffsBits/deep_thought/tree/main) them in the Files & versions tab. |