안정적인 확산 3.5 미디엄: 기능 및 커뮤니티 채택

### 안정적인 확산 3.5 미디엄: 커뮤니티 채택 및 성과 인사이트
* 🤖 SD 3.5 미디엄 모델은 오픈 소스 커뮤니티에서 채택될 가능성을 보여주며, 기본적으로 **1440x1440 이미지를 생성**할 수 있고 **추론 속도가 더 빠르기** 때문에 Flux와 경쟁할 수 있는 잠재력을 가지고 있습니다.
* ⚙ 이 모델의 토큰 제한은 **약 500개**이며, **256개 토큰 제한을 초과**하면 준수에 문제가 발생하고 출력에 추가적인 아티팩트가 발생할 수 있으므로 주의해야 합니다.
* 📊 다운로드 가능한 모델은 **텍스트 인코더가 없는 5GB가 조금 넘는 모델**과 **텍스트 인코더가 포함된 약 11.5GB의 Comfy에서 제공하는 모델** 두 가지가 있습니다.
* 🎨 출력 품질 측면에서는 **색상과 대비에 대한 즉각적인 준수**가 강력하지만, **수작업 렌더링에는 여전히 개선**이 필요합니다.
* 🖼 플럭스에 비해 **SD 3.5 미디엄은 포토리얼리즘과 피부 질감이 뛰어나지만 텍스트 생성은 대형 모델에 비해 일관성이 떨어진다**고 하므로 만족스러운 결과를 얻으려면 여러 세대를 거쳐야 합니다.
* 🛠 레이어 건너뛰기 안내 노드가 있는 SLG 워크플로우를 도입하면 완벽한 해결책은 아니지만 **특히 다리와 같이 문제가 있는 부위의 해부학적 정확도**를 개선하는 데 도움이 될 수 있습니다.
* 💻 Ryzen 5800 X의 성능 테스트는 다양한 추론 시간을 보여주며, **1440x1440 이미지를 생성하는 데 약 1분 36초**가 소요되어 더 작은 크기에도 불구하고 모델의 효율성을 강조합니다.
* 🔗 Stability AI는 **2.5억 개의 파라미터로 더 작고, 처음 12개의 트랜스포머 레이어에 이중 주의 블록**을 제공하여 신속한 처리를 위한 컨텍스트 길이를 향상시킨 **SD 3.5 Medium 모델**을 성공적으로 출시했습니다.

더 빠른 생성 속도와 향상된 이미지 품질로 인해 Stable Diffusion 3.5 Medium은 커뮤니티의 강력한 채택을 앞두고 있습니다.

so Stability AI stuck to their word and released Stable Diffusion 3.5. Medium, as always, there'll be a link to everything you need in the description below. And before we look at the workflow in some examples, there are some things I want to point out. This model is 2 and 1/2 B, so smaller file size generation times should be a lot faster as well. But what's also different is the native resolution is 1440. So theoretically, you can generate larger images right off the bat. Under the model description, they talk about how there's dual attention blocks in the first 12 Transformer layers. Keep this in mind for later. This is actually going to be very important in terms of context length. You can go about 256 tokens. If you don't know what tokens are, it's just a way where prompts are broken down. If I take a look at this prompt here, we see the token count is at 62. So the word astral has two tokens. Each of the colors represents one token. So wave is a token, half is a token, even

the commas take up these tokens. One of the limiting factors of SDXL was that you were limited to 77 of these tokens, so long prompts didn't work with SDXL. I believe Flux is at 500 roughly, and I could be wrong, but I think SD 3.5 large is around that 500 token mark as well. Now, that's important to know because if you go over that 256 FA limit, things might start to break down, adherence could suffer, and you can also get some additional artifacts. The other thing to be aware of, they say here that it's a different training data set than the large model, so your prompts won't necessarily be similar. Now, there are two models you can download. The first one is just the model itself, not including the text encoders, and it's just over 5 GB. The other model that's available was provided by Comfy, and this one includes the text encoders at about 11 1/2 GB. So, it's really up to you which one you want.

to run, you can put both of these models in your checkpoints folder. The workflows are pretty similar to what I showed you with SD 3.5 large, but I do want to point out there's another one called SLG example. This is the default SLG workflow, and they provide a before and after. What's different about this workflow is this additional node called skip layer guidance sd3. Make sure you're on the latest version of kyui. This was just implemented, I guess, today. So earlier, I mentioned about there being 12 layers. What this does, it skips seven, eight, and nine. And this node may help with anatomy deformities. It's not a cure for it, but it may help. I left it on its default setting, and I left all the default configurations together. This was the prompt that was provided. So here's the original image, and we see the legs are kind of messed up, but with this new node, we see that the legs are much better, right? Now, I'll be honest, I don't know how this all works. I just discovered it.

myself but try the default settings at first, and it looks like it's connected to the model sampling node, and then the output goes to model under your K sampler. Now the recommended settings are 30 to 40 steps, starting at CFG 4.5 and up, sampler DPM Plus+ 2 msgm uniform. I've tried Oiler beta and simple, and it works fine in terms of inference speed on my Ryzen 5800 X, 32 GB of RAM, 3060 TI, 8 GB of VRAM. For these times, I was doing 40 steps, 1280 by 720, 32 seconds, 896 X 1152, 38 seconds, 1024x 1024, 38 seconds, and 1440 by 1440, a minute and 36. These examples are just random prompts that I put together, comparing it between large and medium. As mentioned before, they're trained on different datasets, so you are going to get different results. This one was quite interesting because in the prompt, I had colored pencil, whereas large leans towards photo realism.

generally speaking, the prompt adherence is really good in terms of color and contrast. I have no complaints here, and it's no surprise. Again, with the hands, they could definitely use some improvements, although I would say on some of the characters I've done, the hands look pretty decent. But considering it's a 2 and 1/12b model and much smaller in file size, I could definitely see the community adopting this model, doing fine tunes and lauras, not to mention the quicker inference times. And just like the large model, the photo realism, the skin textures look better, I would say, than flux, whereas with flux, it tends to have that hyper-realistic plastic look. In terms of text, it performed pretty well with just a few words. When I tried doing the full-word stable diffusion 3.5 medium, it took quite a few generations just to get one decent one. So I would say the text coherency is far back from the large model. It definitely needs some improvement, but if you keep it to like

two to four words, it should be fine. So, I'm curious what you think about SD 3.5 medium. Do you think the open-source community is going to adopt this model to use for training and luras? Personally, I think this is how SD is going to gain some ground on flux. The fact that you can generate 1440 by 1440 images natively and the quicker inference speeds are definitely a plus. Now, in case you haven't seen the SD 3.5 large video that I did last week, make sure to check it out right here. Until next video, my friends, I'll see you when I see you.

Stable Diffusion 3.5 Medium Smaller & Faster!

Monzon Media

더 작아진 파일 크기와 더 빨라진 생성 시간이 특징인 스테이블 디퓨전 3.

5가 Stability AI에 의해 출시되었습니다.

모델의 기본 해상도는 1440으로 더 큰 이미지를 생성할 수 있습니다.

또한 처음 12개의 트랜스포머 레이어에 이중 주의 블록을 통합하여 약 256토크의 컨텍스트 길이로 신속하게 분석할 수 있습니다.

5에서 시작하는 30~40단계의 권장 설정으로 출력에 연결됩니다.

Oiler 베타 및 간단한 샘플러는 32GB RAM 및 3060 TI가 장착된 Ryzen 5800 X에서 추론 속도에 적합하게 작동합니다.

대형 모델과 중형 모델의 데이터 세트가 다르면 출력 결과도 달라지며, 대형 모델은 사진 사실주의에 가까워집니다.

1/2B 모델은 더 큰 모델에 비해 손이 잘 표현되고 사진의 사실감이 더 뛰어나며 즉각적인 부착이 잘 이루어집니다.

텍스트 일관성이 부족하여 적절한 결과를 얻으려면 여러 세대를 거쳐야 합니다.

레이어 건너뛰기 안내를 위한 추가 노드가 포함된 새로운 SLG 워크플로는 해부학적인 기형을 개선하는 데 도움이 될 수 있습니다.

5 미디어의 오픈 소스 모델은 트레이닝과 루라에 대한 관심을 끌 것으로 예상되며, 잠재적으로 Flux와 경쟁할 수 있습니다.

이 모델은 네이티브 1440×1440 이미지 생성과 더 빠른 추론 속도를 제공합니다.

토큰 제한은 Flux와 비슷하며, 다운로드 가능한 모델은 5GB가 조금 넘는 모델과 텍스트 인코더가 포함된 11.

5GB의 Comfy에서 제공하는 모델 두 가지가 있습니다.