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AlexZheng
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README.md
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- stable-diffusion
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- text-to-image
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This is the fine-tuned Stable Diffusion model trained on images from the
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[](https://patreon.com/user?u=79196446)
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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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from diffusers import StableDiffusionPipeline
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import torch
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model_id = "
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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 = "
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image = pipe(prompt).images[0]
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image.save("./
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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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### Sample images from the model:
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### Sample images used for training:
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**Version 3** (arcane-diffusion-v3): This version uses the new _train-text-encoder_ setting and improves the quality and edibility of the model immensely. Trained on 95 images from the show in 8000 steps.
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**Version 2** (arcane-diffusion-v2): This uses the diffusers based dreambooth training and prior-preservation loss is way more effective. The diffusers where then converted with a script to a ckpt file in order to work with automatics repo.
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Training was done with 5k steps for a direct comparison to v1 and results show that it needs more steps for a more prominent result. Version 3 will be tested with 11k steps.
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**Version 1** (arcane-diffusion-5k): This model was trained using _Unfrozen Model Textual Inversion_ utilizing the _Training with prior-preservation loss_ methods. There is still a slight shift towards the style, while not using the arcane token.
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- stable-diffusion
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- text-to-image
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---
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# Galactic Diffusion
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This is the fine-tuned Stable Diffusion model trained on images from the <b>entergalactic</b> on Netflix..
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No tokens is needed.
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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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from diffusers import StableDiffusionPipeline
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import torch
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model_id = "alexzheng/"
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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 beautiful young female with long dark hair, clothed in full dress"
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image = pipe(prompt).images[0]
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image.save("./samples/0_0.png")
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```
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### Sample images
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"a beautiful young female with long dark hair, clothed in full dress"
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"a strong handsome young male clothed in metal armors"
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"a British shorthair cat sitting on the floor"
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"a golden retriever running in the park"
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"a blue shining Porsche sports car"
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"a modern concept house, two stories, no people"
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"a warm and sweet living room, a TV, no people"
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"a beautiful city night scene, no people"
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