This workflow is designed for accelerated Anima text-to-image generation. Its main purpose is to give creators a faster and cleaner way to generate high-quality anime-style images with Anima Preview, while keeping the workflow simple enough for direct online use, prompt testing, and Civitai showcase creation.
The workflow uses anima-preview.safetensors as the base Anima model, qwen_3_06b_base.safetensors as the text encoder, and qwen_image_vae.safetensors as the VAE. It also applies the animaPreviewRdbt.4bB9.safetensors acceleration / enhancement LoRA through a model-only LoRA loader. This means the workflow is not a heavy multi-branch comparison graph. It is a focused accelerated text-to-image pipeline: load model, apply acceleration LoRA, encode prompt, generate from an empty latent, decode, slightly enhance contrast, and save the final image.
The canvas is built with EmptyLatentImagePresets at 1152 x 896, a wide illustration format that works well for anime posters, Civitai preview images, social media covers, thumbnail concepts, and character showcase outputs. The workflow starts from a pure latent image, so no input image is required. Everything is driven by the text prompt.
The positive prompt in the workflow is structured for premium anime illustration output. It describes a polished adult anime character scene with strong lighting, sunset atmosphere, ocean background, water reflection, dynamic body pose, detailed rendering, vibrant colors, and commercial poster quality. This makes the workflow suitable for fast character illustration, anime cover testing, beauty-style character art, concept previewing, and prompt iteration.
The negative prompt suppresses common issues such as worst quality, low quality, low score outputs, blur, JPEG artifacts, signatures, and artist names. This is useful for keeping the accelerated route cleaner, especially when generating many test images quickly. The goal is not only speed, but stable output that is still presentable.
The main generation stage uses KSamplerAdvanced with Euler ancestral sampling, normal scheduler, full denoise, and a controlled step setup. The CFG is kept relatively low, around 1.4, which fits acceleration-style generation better than overly high guidance. After decoding through the Qwen image VAE, the result is passed through AdjustContrast with a mild contrast boost, then saved as a final image. The workflow also includes an animated WEBP output route, useful for presentation, preview sharing, or quick comparison clips.
This workflow is especially useful for creators who want a practical Anima text-to-image setup that runs faster than a heavier full-quality graph while still producing visually strong anime artwork. It is suitable for RunningHub online publishing, Civitai examples, YouTube tutorial assets, Bilibili cover testing, prompt research, and fast batch-style creative exploration.
In short, this is a compact Anima accelerated text-to-image workflow. It removes unnecessary complexity and focuses on speed, prompt responsiveness, and clean final output. If you want to see how the acceleration LoRA is connected, how the prompt is structured, and how the final image is produced, watch the full tutorial from the YouTube link above.
⚙️ Try the Workflow Online
👉 Workflow: https://www.runninghub.ai/post/2029162522611290113?inviteCode=rh-v1111
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.
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📺 Bilibili Updates (Mainland China & 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: https://www.bilibili.com/video/BV15dPezmEaz/
I will continue updating model resources on Quark Drive:
👉 https://pan.quark.cn/s/20c6f6f8d87b
These resources are mainly prepared for local users, making creation and learning more convenient.
⚙️ 在线体验工作流
👉 工作流： https://www.runninghub.ai/post/2029162522611290113?inviteCode=rh-v1111
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。
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📺 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： https://www.bilibili.com/video/BV15dPezmEaz/
我会在 夸克网盘 持续更新模型资源：
👉 https://pan.quark.cn/s/20c6f6f8d87b
这些资源主要面向本地用户，方便进行创作与学习。