<div data-youtube-video><iframe width="640" height="480" allowfullscreen="true" autoplay="false" disablekbcontrols="false" enableiframeapi="false" endtime="0" ivloadpolicy="0" loop="false" modestbranding="false" origin playlist src="https://www.youtube.com/embed/wdpemAhTCyM?rel=1" start="0"></iframe></div>This workflow is designed for Anima acceleration comparison testing. Its main purpose is to compare different Anima generation routes, sampling stages, and acceleration settings under the same prompt, same latent size, and similar visual conditions. Instead of only showing one final image, this workflow is built to help creators understand how different sampling strategies affect image quality, speed, structure, detail, and final style.The workflow uses anima-preview.safetensors as the main Anima model, qwen_3_06b_base.safetensors as the text encoder, and qwen_image_vae.safetensors as the VAE. The latent canvas is created with EmptyLatentImagePresets at 1152 x 896, which gives a wide illustration format suitable for anime comparison tests, character previews, and visual benchmark examples. The prompt describes an anime-style fox girl in a snowy night scene holding a sign, which is a useful test prompt because it includes character identity, clothing, environment, lighting, atmosphere, and readable text.The negative prompt is also shared across the comparison routes, suppressing problems such as worst quality, low quality, blurry output, JPEG artifacts, signatures, and artist names. This keeps the comparison cleaner, because each route is tested against the same quality-control conditions.The key value of this workflow is that it does not test Anima as a single black-box output. It breaks generation into multiple sampling paths. Some routes use Anima Preview directly, while other routes apply an additional Anima RDBT-style LoRA path. The workflow includes multiple KSamplerAdvanced nodes with different start and end steps, making it possible to observe how early-stage, mid-stage, and later-stage sampling changes the final image. This is useful for understanding which part of the denoising process controls composition, which part affects details, and which route produces better speed-quality balance.The workflow also uses image concatenation nodes to place outputs side by side. This makes it easier to visually compare results instead of checking images one by one. You can compare whether a route gives better line quality, cleaner character design, stronger lighting, better color harmony, more stable text, or fewer artifacts. The workflow can also export comparison results as animated WEBP, making it useful for tutorial demonstrations, Civitai previews, RunningHub examples, and YouTube explanation videos.This setup is especially useful for creators who want to test acceleration methods before publishing a workflow. A faster route is only valuable if the result remains usable. This workflow helps identify whether accelerated Anima generation can preserve composition, character quality, and visual polish, or whether it loses too much detail compared with a fuller sampling path.In short, this is a practical Anima speed and sampling comparison workflow. It helps users test different Anima routes, compare output quality, and decide which generation setting is best for online publishing, fast iteration, or final-quality output. If you want to see how the acceleration routes are connected and how the different outputs compare visually, watch the full tutorial from the YouTube link above.⚙️ Try the Workflow Online👉 Workflow: <a target="_blank" rel="ugc" href="https://www.runninghub.ai/post/2029161577533939714?inviteCode=rh-v1111">https://www.runninghub.ai/post/2029161577533939714?inviteCode=rh-v1111</a>Open the link above to run the workflow directly online and view the generation results in real time.If the results meet your expectations, you can also deploy it locally for further customization.🎁 Fan Benefits: Register now to get 1000 points, plus 100 daily login points — enjoy 4090-level performance and 48 GB of powerful compute!📺 Bilibili Updates (Mainland China &amp; Asia-Pacific)If you are in Mainland China or the Asia-Pacific region, you can watch the video below for workflow demos and a detailed creative breakdown.📺 Bilibili Video: <a target="_blank" rel="ugc" href="https://www.bilibili.com/video/BV15dPezmEaz/">https://www.bilibili.com/video/BV15dPezmEaz/</a>I will continue updating model resources on Quark Drive:👉 <a target="_blank" rel="ugc" href="https://pan.quark.cn/s/20c6f6f8d87b">https://pan.quark.cn/s/20c6f6f8d87b</a>These resources are mainly prepared for local users, making creation and learning more convenient.⚙️ 在线体验工作流👉 工作流： <a target="_blank" rel="ugc" href="https://www.runninghub.ai/post/2029161577533939714?inviteCode=rh-v1111">https://www.runninghub.ai/post/2029161577533939714?inviteCode=rh-v1111</a>打开上方链接即可直接运行该工作流，实时查看生成效果。如果觉得效果理想，你也可以在本地进行自定义部署。🎁 粉丝福利： 注册即送 1000 积分，每日登录 100 积分，畅玩 4090 体验 48 G 超级性能！📺 Bilibili 更新（中国大陆及南亚太地区）如果你在中国大陆或南亚太地区，可以通过下方视频查看该工作流的实测效果与构思讲解。📺 B站视频： <a target="_blank" rel="ugc" href="https://www.bilibili.com/video/BV15dPezmEaz/">https://www.bilibili.com/video/BV15dPezmEaz/</a>我会在 夸克网盘 持续更新模型资源：👉 <a target="_blank" rel="ugc" href="https://pan.quark.cn/s/20c6f6f8d87b">https://pan.quark.cn/s/20c6f6f8d87b</a>这些资源主要面向本地用户，方便进行创作与学习。

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