This pipeline was finetuned from **CompVis/stable-diffusion-v1-4** on the **None** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: High-performance car wheel rim, detailed 3D rendering: 

        ![val_imgs_grid](./val_imgs_grid.png)

        
        ## Pipeline usage
        
        You can use the pipeline like so:
        
        ```python
        from diffusers import DiffusionPipeline
        import torch
        
        pipeline = DiffusionPipeline.from_pretrained("soyng/photorealistic-wheel-v1-0", torch_dtype=torch.float16)
        prompt = "H"
        image = pipeline(prompt).images[0]
        image.save("my_image.png")
        ```

        ## Training info
        
        These are the key hyperparameters used during training:
        
        * Epochs: 60
        * Learning rate: 1e-05
        * Batch size: 32
        * Gradient accumulation steps: 1
        * Image resolution: 512
        * Mixed-precision: None
        
        
                  More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/soyoung9306-slack/CompVis_stable-diffusion-v1-4-fine-tune/runs/27944xzb).