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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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inference: true |
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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 |
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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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--- |
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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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# GameNgen fine-tuning - arnaudstiegler/gameNgen-baseline-45ksteps |
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Full finetune of CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the P-H-B-D-a16z/ViZDoom-Deathmatch-PPO-Lrg dataset. You can find some example images in the following. |
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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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45k steps on P-H-B-D-a16z/ViZDoom-Deathmatch-PPO-XLrg |
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