1st results from the embedding I trained on my glitch art are interesting, though I'm not sure it's entirely what I'm looking for. It's possible trained on too much varied output (glitch art is very varied as a 'style'). Or might be more suited for a full on model 🤔

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I've released my Chemicalbending embedding for Stable Diffusion. This embedding was created using original physical works by me, created by pouring harsh chemicals on magazines and painting them onto each other as the inks melted.
https://t.co/T13fAeR1rX

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Some embeddings and hypernets on prev image 🔥 (same settings, same seed) on for DAY3:

⬆️⬅️: Emb, Style Psycho by
⬆️➡️: Emb, Style Hamunaptra by
⬇️⬅️: Hyp, wlop_a3 by
⬇️➡️: Hyp, incase_a3 by

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今日はembedding学習中です。 :)
8000step.. 長くかかりますね:(
これは3500stepでのサンプル画像です。

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Combining my chemicalbending SD embedding with a model honed in on papercraft/layered paper art. It loses a lot of the distinct chemicalbending look but it definitely gives the paper art a nice deranged sort of flavor

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another comparison, this time using ProtoGen_X5.3

left is with the embedding, right without https://t.co/PdZw5SeH60

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その固定部分をembeddingとかにして配布する意味はあるんだろうか?

※画像は本編と関係ありません

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released this textual inversion embedding:

✨ evreka_1-5 ✨

available on https://t.co/nvfGhUw1ET
or ko-fi → https://t.co/zDjkie3i0u

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step1200、ベクトル数50、学習速度(Embedding Learning Rate)0.005、学習枚数16枚でかなりうまくいった!!

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AUTOMATIC1111のembeddingでファインチューニングをした。1枚目と2枚目は自分の16枚の絵で学習し、step10万学習速度0.0005ベクトル数1、二枚目は"miku"のプロンプトを足した。3枚目は学習データをmimicの出した絵で71枚まで水増しし、学習速度を0.005ベクトル数5にしたもの

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Images of our new SD2 fidelity+coherence research project
made with
It uses raw SD2 + fine-tunings (TI embeddings)

All made possible thanks to , and the whole SD community.

More soon.

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Really digging these...done with dreamlike and Christmas Ornament Embedding.

https://t.co/qLEqsP9QTw

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複数視点でのキャラクターを生成するためのStable Diffusion v1.5用Embedding「CharTurner」が公開
https://t.co/rKmMD71oic

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I'm far from an expert 🤓, but been training chekpoints, using embeddings, hypernets, and making safe prompts for a while for many styles (here you can see same character in many styles).

Will be glad to help anyone on what I can 😊 Just DM

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同一シード、同一トークン数やつ
左がdreamartistのembedding外したやつ
右があるやつ

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I love Textual inversion in combination with 2 🙃. Negative embeddings have become my main focus when designing my text prompts. These images were created by combining Magic Facelift and bad_prompt, among others (Links in the comment).

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DaFID-512動かせた!
早速キラキラ系tokenであるglitterと、CLIP Embedding的に近いcos類似度を持つshimmerをチェック
下記画像の1枚目のpromptにそれぞれglitter、shimmerを追加して一万枚生成、DaFID-512で元の画像集合からの距離を測定、比較した

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"(token) as indiana jones, pixar (...)"

1) SD2.1-512 with Textual Embedding
2) SD2.1-768 - dreambooth (shivam)
3) SD1.5-512 - Dreambooth (joepenna)
4) SD2.1-512 Depth model

For each, best of 8

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This is the rabbit Stable Diffusion produced after using the "VikingPunk" embedding, with the same prompt:

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“I don't want photorealism, I just want to rock.” 🔥

Fragments embedding for Stable Diffusion 2.1
https://t.co/hdm1duWsSJ

(Source: https://t.co/z7IiOYizFk)

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