Initial commit - v1

.gitattributes

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+showcase.png filter=lfs diff=lfs merge=lfs -text

MosaicArt_v1.ckpt

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+size 2132888287

MosaicArt_v1_ema.ckpt

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README.md

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 ---
+language:
+- en
 license: creativeml-openrail-m
+thumbnail: "https://huggingface.co/Guizmus/MosaicArt/resolve/main/showcase.jpg"
+tags:
+- stable-diffusion
+- text-to-image
+- image-to-image
+inference: false
 ---
+
+# Mosaic Art
+<p>
+	<img src="https://huggingface.co/Guizmus/MosaicArt/resolve/main/showcase.jpg"/><br/>
+	This is a Dreamboothed Stable Diffusion model trained on pictures of mosaic art.<br/>
+	The total dataset is made of 46 pictures, and the training has been done on [runawayml 1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) and the [new VAE](https://huggingface.co/stabilityai/sd-vae-ft-mse). I used [EveryDream](https://github.com/victorchall/EveryDream-trainer) to do the training, using full caption on the pictures with almost no recurring word outside the main concept, so that no additionnal regularisation was needed. Out of e0 to e11 epochs, e8 was selected as the best application of style while not overtraining. Prior preservation was constated as good. A total of 9 epochs of 40 repeats with a learning rate of 1e-6.<br/>
+	The token "Mosaic Art" will bring in the new concept, trained as a style.<br/>
+	The recommended sampling is k_Euler_a or DPM++ 2M Karras on 20 steps, CFGS 7.5 .
+	
+</p>
+
+[CKPT](https://huggingface.co/Guizmus/MosaicArt/resolve/main/MosaicArt_v1.ckpt)
+
+[CKPT with ema weights](https://huggingface.co/Guizmus/MosaicArt/resolve/main/MosaicArt_v1_ema.ckpt)
+
+[Dataset](https://huggingface.co/Guizmus/MosaicArt/resolve/main/dataset_v1.ckpt)
+
+## 🧨 Diffusers
+
+This model can be used just like any other Stable Diffusion model. For more information,
+please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
+
+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]().
+
+```python
+from diffusers import StableDiffusionPipeline
+import torch
+
+model_id = "Guizmus/MosaicArt"
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+prompt = "Mosaic Art dog on the moon"
+image = pipe(prompt).images[0]
+
+image.save("./MosaicArt.png")
+```

dataset_v1.zip

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