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--- |
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base_model: CompVis/stable-diffusion-v1-4 |
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library_name: diffusers |
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license: creativeml-openrail-m |
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tags: |
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- stable-diffusion |
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- stable-diffusion-diffusers |
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- text-to-image |
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- diffusers |
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- diffusers-training |
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- stable-diffusion-xl |
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- stable-diffusion-xl-diffusers |
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- text-to-image |
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- diffusers-training |
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- diffusers |
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inference: true |
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--- |
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<!-- This model card has been generated automatically according to the information the training script had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Text-to-image finetuning - haorandai/Orange_Vehicle_Bicycle_lr0.005_e0.1_1_with1constraints |
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This pipeline was finetuned from **CompVis/stable-diffusion-v1-4** on the **haorandai/Orange_Vehicle_Bicycle_lr0.005_e0.1_1_with1constraints** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: None: |
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## Pipeline usage |
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You can use the pipeline like so: |
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```python |
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from diffusers import DiffusionPipeline |
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import torch |
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pipeline = DiffusionPipeline.from_pretrained("haorandai/Orange_Vehicle_Bicycle_lr0.005_e0.1_1_with1constraints", torch_dtype=torch.float16) |
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prompt = "None" |
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image = pipeline(prompt).images[0] |
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image.save("my_image.png") |
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``` |
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## Training info |
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These are the key hyperparameters used during training: |
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* Epochs: 200 |
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* Learning rate: 1e-05 |
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* Batch size: 1 |
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* Gradient accumulation steps: 4 |
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* Image resolution: 512 |
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* Mixed-precision: fp16 |
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## Intended uses & limitations |
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#### How to use |
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```python |
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# TODO: add an example code snippet for running this diffusion pipeline |
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``` |
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#### Limitations and bias |
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[TODO: provide examples of latent issues and potential remediations] |
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## Training details |
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[TODO: describe the data used to train the model] |
