minke.yu/sglang
1
0
Fork 0
Code Issues Pull Requests Actions 12 Packages Projects Releases Wiki Activity

[doc] standardize diffusion cookbook model pages (#34247)

Browse Source 
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
This commit is contained in:
Mick
2026-08-21 10:25:40 +08:00
committed by GitHub
co-authored by Claude Opus 5
parent 7e80e889a2
commit e0cf75d9bd
32 changed files with 2712 additions and 602 deletions
Download Patch File Download Diff File Expand all files Collapse all files
docs/cookbook/diffusion/Krea/Krea-2.mdx
+7 -13
View File
@@ -6,24 +6,18 @@ metatags:
import { DiffusionModelTags } from '/src/snippets/diffusion/model-tags.jsx';
<DiffusionModelTags tags={["image", "text-to-image", "turbo", "raw"]} />
<DiffusionModelTags tags={["image", "text-to-image", "8-step Turbo", "high-fidelity Raw", "Qwen3-VL"]} />
## 1. Model Introduction
[Krea-2](https://huggingface.co/krea/Krea-2-Turbo) is a high-quality text-to-image diffusion model from [Krea](https://www.krea.ai/). It ships in two variants that share the same backbone and differ only in their sampling recipe:
[Krea-2](https://huggingface.co/krea/Krea-2-Turbo) is Krea's photorealistic text-to-image family, built as a single-stream MMDiT with a Qwen3-VL text encoder and Qwen-Image VAE. Both public variants use the same native SGLang pipeline and differ mainly in their sampling target.
- **[Krea-2-Turbo](https://huggingface.co/krea/Krea-2-Turbo)** - a distilled, few-step model that produces photorealistic images in only **8 inference steps** with no classifier-free guidance (`guidance_scale = 1.0`), ideal for fast and interactive generation.
- **[Krea-2-Raw](https://huggingface.co/krea/Krea-2-Raw)** - the base (non-distilled) model that trades speed for maximum fidelity, using a longer schedule (~52 steps) with classifier-free guidance (`guidance_scale ≈ 4.5`).
Choose Turbo for interactive generation: it is distilled to 8 steps with `guidance_scale=1.0`. Choose Raw when maximum fidelity matters more than latency: it uses roughly 52 steps with classifier-free guidance. Neither checkpoint is an image-editing model; use the Qwen-Image-Edit or FLUX.2 path when an input image must be preserved or transformed.
Both variants are built on a single-stream MMDiT with a Qwen3-VL text encoder and the Qwen-Image VAE, and are distributed in the standard diffusers layout (a `model_index.json` plus sharded `transformer/`, `text_encoder/`, `vae/`, `tokenizer/`, and `scheduler/` folders). SGLang loads them **natively** - just point `--model-path` at the repo, no conversion step required.
**Key Features:**
- **Two variants, one pipeline**: switch between fast (Turbo) and high-fidelity (Raw) by changing only the model path and the sampling settings.
- **Photorealistic generation** at 1024x1024 and other resolutions.
- **Native diffusers loading**: components (DiT, text encoder, VAE, scheduler) are read straight from the repo's `model_index.json`.
For more details, see the [Krea-2-Turbo](https://huggingface.co/krea/Krea-2-Turbo) and [Krea-2-Raw](https://huggingface.co/krea/Krea-2-Raw) HuggingFace pages.
| Variant | Model ID | Sampling profile |
| --- | --- | --- |
| Turbo | `krea/Krea-2-Turbo` | 8 steps, no CFG; fastest path |
| Raw | `krea/Krea-2-Raw` | About 52 steps with CFG; higher-fidelity path |
## 2. SGLang-diffusion Installation