End of training
Browse files- .gitattributes +4 -0
- README.md +25 -0
- checkpoint-500/optimizer.bin +3 -0
- checkpoint-500/pytorch_lora_weights.bin +3 -0
- checkpoint-500/random_states_0.pkl +3 -0
- checkpoint-500/scaler.pt +3 -0
- checkpoint-500/scheduler.bin +3 -0
- image_0.png +3 -0
- image_1.png +3 -0
- image_2.png +3 -0
- image_3.png +3 -0
- inference.py +13 -0
- pytorch_lora_weights.bin +3 -0
.gitattributes
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README.md
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---
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license: creativeml-openrail-m
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base_model: stabilityai/stable-diffusion-xl-base-0.9
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instance_prompt: a photo of sks dog
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tags:
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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
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- lora
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inference: true
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---
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# LoRA DreamBooth - nikad/lora-trained-xl
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These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-0.9. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
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![img_0](./image_0.png)
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![img_1](./image_1.png)
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![img_2](./image_2.png)
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![img_3](./image_3.png)
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LoRA for the text encoder was enabled: False.
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checkpoint-500/optimizer.bin
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version https://git-lfs.github.com/spec/v1
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size 47392445
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checkpoint-500/pytorch_lora_weights.bin
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version https://git-lfs.github.com/spec/v1
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size 23664755
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checkpoint-500/random_states_0.pkl
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version https://git-lfs.github.com/spec/v1
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size 14599
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checkpoint-500/scaler.pt
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version https://git-lfs.github.com/spec/v1
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size 557
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checkpoint-500/scheduler.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:62783ff6f0ae2c2ccc177b097e430c03940c5e70c38a191df273c5cf7ab1227c
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size 563
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image_0.png
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Git LFS Details
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image_1.png
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Git LFS Details
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image_2.png
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Git LFS Details
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image_3.png
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Git LFS Details
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inference.py
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from huggingface_hub.repocard import RepoCard
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from diffusers import DiffusionPipeline
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import torch
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lora_model_id = <"lora-sdxl-dreambooth-id">
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card = RepoCard.load(lora_model_id)
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base_model_id = card.data.to_dict()["base_model"]
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pipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16)
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pipe = pipe.to("cuda")
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pipe.load_lora_weights(lora_model_id)
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image = pipe("A picture of a sks dog in a bucket", num_inference_steps=25).images[0]
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image.save("sks_dog.png")
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pytorch_lora_weights.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:31bc0ee66a9b3e8c7c5c4c9db3d0d001222ea8d607cecd1fa073ae59dd690321
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size 23664755
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