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--- |
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license: creativeml-openrail-m |
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library_name: diffusers |
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tags: |
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- text-to-image |
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- dreambooth |
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- diffusers-training |
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- stable-diffusion |
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- stable-diffusion-diffusers |
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base_model: runwayml/stable-diffusion-v1-5 |
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inference: true |
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instance_prompt: disney style |
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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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# Cartoonify |
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This is a dreambooth model derived from `runwayml/stable-diffusion-v1-5` with additional fine-tuning of the text encoder. The weights were trained from a popular animation studio using [DreamBooth](https://dreambooth.github.io/). Use the tokens **_disney style_** in your prompts for the effect. |
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You can find some example images below: |
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<p float="left"> |
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<img width=256 height=256 src="./images/king.png"> |
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<img width=256 height=256 src="./images/legend_of_zelda.png"> |
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<img width=256 height=256 src="./images/pony.png"> |
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<img width=256 height=256 src="./images/princess.png"> |
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<img width=256 height=256 src="./images/red_ferrari.png"> |
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</p> |
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## Intended uses & limitations |
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#### How to use |
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```python |
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import torch |
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from diffusers import StableDiffusionPipeline |
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# basic usage |
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repo_id = "lavaman131/cartoonify" |
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device = torch.device("cuda") |
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torch_dtype = torch.float16 if device.type in ["mps", "cuda"] else torch.float32 |
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pipeline = StableDiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch_dtype).to(device) |
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image = pipeline("PROMPT GOES HERE").images[0] |
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image.save("output.png") |
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``` |
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#### Limitations and bias |
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As with any diffusion model, playing around with the prompt and classifier-free guidance parameter is required until you get the results you want. Zoomed-out subjects seem to loose clairity in the face. For additional safety in image generation, we use the Stable Diffusion safety checker. |
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## Training details |
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The model was fine-tuned for 3500 steps on around 200 images of modern Disney characters, backgrounds, and animals. The ratios for each were 70%, 20%, and 10% respectively on an RTX A5000 GPU (24GB VRAM). |
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The training code used can be found [here](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py). The regularization images used for training can be found [here](https://github.com/aitrepreneur/SD-Regularization-Images-Style-Dreambooth/tree/main/style_ddim). |
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