--- base_model: stable-diffusion-v1-5/stable-diffusion-v1-5 library_name: diffusers license: creativeml-openrail-m inference: true tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet - diffusers-training --- # 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 ![images_0)](./images_0.png) prompt: denoised image ![images_1)](./images_1.png) prompt: super-resolution ER image ![images_2)](./images_2.png) prompt: super-resolution F-actin image ![images_3)](./images_3.png) prompt: super-resolution Microtubules image ![images_4)](./images_4.png) prompt: Generate the corresponding DAPI protein image ![images_5)](./images_5.png) prompt: Generate the corresponding CD11B protein image ![images_6)](./images_6.png) prompt: Generate the corresponding DAPI protein image ![images_7)](./images_7.png) prompt: Generate the corresponding CD11B protein image ![images_8)](./images_8.png) prompt: Generate the corresponding DAPI protein image ![images_9)](./images_9.png) prompt: Generate the corresponding CD11B protein image ![images_10)](./images_10.png) prompt: CD68 RNA expression ![images_11)](./images_11.png) prompt: CXCR4 RNA expression ![images_12)](./images_12.png) ## Intended uses & limitations #### How to use ```python # 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]