Instructions to use maxpmx/output_multi_sd1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use maxpmx/output_multi_sd1 with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("maxpmx/output_multi_sd1") pipe = StableDiffusionControlNetPipeline.from_pretrained( "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
controlnet-maxpmx/output_multi_sd1
These are controlnet weights trained on stable-diffusion-v1-5/stable-diffusion-v1-5 with new type of conditioning. You can find some example images below.
prompt: denoised image
prompt: denoised image
prompt: super-resolution ER image
prompt: super-resolution F-actin image
prompt: super-resolution Microtubules image
prompt: Generate the corresponding DAPI protein image
prompt: Generate the corresponding CD11B protein image
prompt: Generate the corresponding DAPI protein image
prompt: Generate the corresponding CD11B protein image
prompt: Generate the corresponding DAPI protein image
prompt: Generate the corresponding CD11B protein image
prompt: CD68 RNA expression
prompt: CXCR4 RNA expression

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]
- Downloads last month
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Model tree for maxpmx/output_multi_sd1
Base model
stable-diffusion-v1-5/stable-diffusion-v1-5