| | --- |
| | license: cc-by-nc-4.0 |
| | library_name: diffusers |
| | tags: |
| | - stable-diffusion |
| | - stable-diffusion-diffusers |
| | - text-to-image |
| | - diffusers |
| | - diffusers-training |
| | - stable-diffusion |
| | - stable-diffusion-diffusers |
| | - text-to-image |
| | - diffusers |
| | - diffusers-training |
| | base_model: runwayml/stable-diffusion-v1-5 |
| | inference: true |
| | --- |
| | |
| | <!-- 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 - tatsuMura/test-sd-model |
| |
|
| | This pipeline was finetuned from **runwayml/stable-diffusion-v1-5** on the **tatsuMura/sdhumantestdata** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['HPB girl, long hair']: |
| |
|
| |
|
| | ## Pipeline usage |
| |
|
| | You can use the pipeline like so: |
| |
|
| | ```python |
| | from diffusers import DiffusionPipeline |
| | import torch |
| | |
| | pipeline = DiffusionPipeline.from_pretrained("tatsuMura/test-sd-model", torch_dtype=torch.float16) |
| | prompt = "HPB girl, long hair" |
| | image = pipeline(prompt).images[0] |
| | image.save("my_image.png") |
| | ``` |
| |
|
| | ## Training info |
| |
|
| | These are the key hyperparameters used during training: |
| |
|
| | * Epochs: 1 |
| | * Learning rate: 1e-05 |
| | * Batch size: 1 |
| | * Gradient accumulation steps: 4 |
| | * Image resolution: 768 |
| | * Mixed-precision: fp16 |
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| |
|
| | ## 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] |
