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README.md

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 ---
+language:
+- en
 license: creativeml-openrail-m
+thumbnail: "https://huggingface.co/Guizmus/SDArt_synesthesia/resolve/main/showcase.jpg"
+tags:
+- stable-diffusion
+- text-to-image
+- image-to-image
 ---
+# SDArt : Synesthesia (version based on 1.5)
+
+![Showcase](https://huggingface.co/Guizmus/SDArt_synesthesia/resolve/main/showcase.jpg)
+
+## Theme
+
+Dear @Challengers ,
+
+> In a world where colors have flavor, and sounds have texture, 
+> The senses intertwine to create a sensory rapture.
+> Welcome to “Synesthesia”- where the ordinary is extraordinary,
+> And art takes on a meaning that’s truly visionary! :Rainbowpink: 
+
+Synesthesia /ˌsɪn.əsˈθiː.ʒə/: an anomalous blending of the senses in which the stimulation of one modality simultaneously produces sensation in a different modality. Synesthetes hear colors, feel sounds, and taste shapes.
+
+* Create an image that captures the essence of synesthesia sensory! Explore the intersections of sound, color, and texture. What’s it like tasting a melody, feeling a scent, or seeing colored words?
+
+* What would it look it look like to taste the sound of a thunderstorm? How would you visualize the sensation of feeling the warmth of the sun on your skin? Or how about tasting a particular color–such as a bright red apple or a cool blueberry?
+
+* Explore the possibilities of synesthesia and its many interpretations!
+
+
+## Model description 
+
+This is a model related to the "Picture of the Week" contest on Stable Diffusion discord..
+
+I try to make a model out of all the submission for people to continue enjoy the theme after the even, and see a little of their designs in other people's creations. The token stays "SDArt" and I balance the learning on the low side, so that it doesn't just replicate creations.
+
+The total dataset is made of 39 pictures. It was trained on [Stable diffusion 1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5). I used [EveryDream](https://github.com/victorchall/EveryDream2trainer) to do the training, 100 total repeat per picture. The pictures were tagged using the token "SDArt", and an arbitrary token I choose. The dataset is provided below, as well as a list of usernames and their corresponding token.
+
+The recommended sampling is k_Euler_a or DPM++ 2M Karras on 20 steps, CFGS 7.5 .
+
+## Trained tokens
+
+* SDArt
+* dyce
+* bnp
+* keel
+* aten
+* fcu
+* lpg
+* mth
+* elio
+* gani
+* pfa
+* kprc
+* cpec
+* kuro
+* asot
+* psst
+* sqm
+* irgc
+* cq
+* utm
+* guin
+* crit
+* mlas
+* isch
+* vedi
+* dds
+* acu
+* oxi
+* kohl
+* maar
+* mako
+* mds
+* mert
+* mgt
+* miki
+* minh
+* mohd
+* mss
+* muc
+* mwf
+
+## Download links
+
+[SafeTensors](https://huggingface.co/Guizmus/SDArt_synesthesia/resolve/main/SDArt_synesthesia.safetensors)
+
+[CKPT](https://huggingface.co/Guizmus/SDArt_synesthesia/resolve/main/SDArt_synesthesia.ckpt)
+
+[Dataset](https://huggingface.co/Guizmus/SDArt_synesthesia/resolve/main/dataset.zip)
+
+## 🧨 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/SDArt_synesthesia"
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+prompt = "SDArt minh"
+image = pipe(prompt).images[0]
+
+image.save("./SDArt.png")
+```