| |
|
| | --- |
| | license: creativeml-openrail-m |
| | base_model: lambdalabs/miniSD-diffusers |
| | datasets: |
| | - kopyl/833-icons-dataset-1024-blip-large |
| | tags: |
| | - stable-diffusion |
| | - stable-diffusion-diffusers |
| | - text-to-image |
| | - diffusers |
| | inference: true |
| | --- |
| | |
| | # Text-to-image finetuning - kopyl/nano-sd-tuned-sample |
| | |
| | This pipeline was finetuned from **lambdalabs/miniSD-diffusers** on the **kopyl/833-icons-dataset-1024-blip-large** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['photo of a frog']: |
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| |  |
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|
| | ## Pipeline usage |
| |
|
| | You can use the pipeline like so: |
| |
|
| | ```python |
| | from diffusers import DiffusionPipeline |
| | import torch |
| | |
| | pipeline = DiffusionPipeline.from_pretrained("kopyl/nano-sd-tuned-sample", torch_dtype=torch.float16) |
| | prompt = "photo of a frog" |
| | 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: 1 |
| | * Image resolution: 256 |
| | * Mixed-precision: fp16 |
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| | More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/spammmmm1997/text2image-fine-tune/runs/hs9vbf2e). |
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