| --- |
| base_model: CompVis/stable-diffusion-v1-4 |
| library_name: diffusers |
| license: creativeml-openrail-m |
| inference: true |
| tags: |
| - stable-diffusion |
| - stable-diffusion-diffusers |
| - text-to-image |
| - diffusers |
| - diffusers-training |
| - stable-diffusion |
| - stable-diffusion-diffusers |
| - text-to-image |
| - diffusers |
| - diffusers-training |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the training script had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
|
|
| # Text-to-image finetuning - Trkkk/stable_diffusion |
| |
| This pipeline was finetuned from **CompVis/stable-diffusion-v1-4** on the **Trkkk/text_to_img_street_scene** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A busy urban street filled with cars stuck in traffic in Hannover kroepcke.']: |
| |
|  |
| |
| |
| ## Pipeline usage |
| |
| You can use the pipeline like so: |
| |
| ```python |
| from diffusers import DiffusionPipeline |
| import torch |
| |
| pipeline = DiffusionPipeline.from_pretrained("Trkkk/stable_diffusion", torch_dtype=torch.float16) |
| prompt = "A busy urban street filled with cars stuck in traffic in Hannover kroepcke." |
| image = pipeline(prompt).images[0] |
| image.save("my_image.png") |
| ``` |
| |
| ## Training info |
| |
| These are the key hyperparameters used during training: |
| |
| * Epochs: 24 |
| * Learning rate: 1e-05 |
| * Batch size: 1 |
| * Gradient accumulation steps: 4 |
| * Image resolution: 256 |
| * Mixed-precision: fp16 |
| |
| |
| |
| ## 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] |
