Instructions to use Gitanjali1801/ctrl_b_and_b_3_july_2025_2_condition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Gitanjali1801/ctrl_b_and_b_3_july_2025_2_condition with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("Gitanjali1801/ctrl_b_and_b_3_july_2025_2_condition") pipe = StableDiffusionControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-2-1-base", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
controlnet-Gitanjali1801/ctrl_b_and_b_3_july_2025_2_condition
These are controlnet weights trained on stabilityai/stable-diffusion-2-1-base with new type of conditioning. You can find some example images below.
prompt: John, a reserved man, panics after hearing footsteps in an alley. He shields his face, fearing harm. A stranger stops, apologizes, and returns Johnโs lost wallet. Grateful, John relaxes, realizing the importance of kindness and not leaping to conclusions. He walks home, moved by the encounter.
prompt: People gathered together with a flag in the background, capturing the sense of unity and community from John's story while ensuring a neutral, inclusive tone.

Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]
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Model tree for Gitanjali1801/ctrl_b_and_b_3_july_2025_2_condition
Base model
stabilityai/stable-diffusion-2-1-base