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license: creativeml-openrail-m |
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**Mo Di Diffusion** |
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This is the fine-tuned Stable Diffusion model trained on screenshots from the modern age Disney movies. |
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Use the tokens **_modern disney style_** in your prompts for the effect. |
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If you enjoy this model, please check out my other models on [Huggingface](https://huggingface.co/nitrosocke) |
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**Videogame Characters rendered with the model:** |
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**Animal Characters rendered with the model:** |
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**Cars and Landscapes rendered with the model:** |
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### 🧨 Diffusers |
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This model can be used just like any other Stable Diffusion model. For more information, |
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please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). |
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You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX](). |
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```python |
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from diffusers import StableDiffusionPipeline |
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import torch |
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model_id = "nitrosocke/mo-di-diffusion" |
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pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) |
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pipe = pipe.to("cuda") |
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prompt = "a magical princess with golden hair, modern disney style" |
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image = pipe(prompt).images[0] |
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image.save("./magical_princess.png") |
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``` |
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# Gradio & Colab |
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We also support a [Gradio](https://github.com/gradio-app/gradio) Web UI and Colab with Diffusers to run fine-tuned Stable Diffusion models: |
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[](https://huggingface.co/spaces/anzorq/finetuned_diffusion) |
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[](https://colab.research.google.com/drive/1j5YvfMZoGdDGdj3O3xRU1m4ujKYsElZO?usp=sharing) |
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#### Prompt and settings for Lara Croft: |
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**modern disney lara croft** |
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_Steps: 50, Sampler: Euler a, CFG scale: 7, Seed: 3940025417, Size: 512x768_ |
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#### Prompt and settings for Simba: |
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**modern disney (baby simba) Negative prompt: person human** |
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_Steps: 50, Sampler: Euler a, CFG scale: 7, Seed: 1355059992, Size: 512x512_ |
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This model was trained using the diffusers based dreambooth training and prior-preservation loss in 9.000 steps and using the _train-text-encoder_ feature. |
