CIVITAI / Workflows

Anima Base 1.0 Enhancement Node A/B Comparison Workflow

Watch the full video first if you want to understand the workflow logic quickly.This ComfyUI workflow is designed for Anima Base 1.0 enhancement node comparison, A/B testing, and practical model behavior analysis. Instead of only showing a finished generation result, this workflow is built like a controlled experiment. Each group contains an A baseline and a B experimental branch, making it easier to judge whether a specific node actually improves quality, stability, speed, or low-step performance. The workflow uses anima_baseV10.safetensors as the base model, qwen_3_06b_base.safetensors as the Qwen Image CLIP text encoder, and qwen_image_vae.safetensors as the VAE. The testing rule is strict: within each A/B group, the model file, CLIP, VAE, positive prompt, negative prompt, seed, resolution, sampler, scheduler, steps, and CFG are kept the same. The A branch is the clean baseline. The B branch only adds the target test node. This makes the comparison much more meaningful than casual prompt testing. The workflow is divided into two major categories. The D series is designed for distilled / Turbo low-step testing, using steps=8 and CFG=1. This section is useful for testing Turbo LoRA, NAGuidance, and CFGNorm under fast generation conditions. Turbo LoRA is tested to see whether it can compensate for low-step quality loss. NAGuidance is tested to see whether negative concepts can still affect generation when CFG is very low. CFGNorm is tested to observe whether it improves color, edge stability, and composition under Turbo-style sampling. The N series is designed for non-distilled standard CFG workflows, using steps=40 and CFG=4. This section tests CFGZeroStar, RescaleCFG, and SageAttention. CFGZeroStar is useful for observing changes in classifier-free guidance behavior. RescaleCFG is tested to see whether it can reduce overexposure, over-saturation, color damage, or CFG-related image collapse. SageAttention is treated mainly as a performance and attention acceleration test rather than a style enhancement node. The correct result should stay close to the baseline while improving speed or memory behavior. This workflow is especially useful for creators who want to understand which nodes are actually worth using in Anima Base production. It helps separate real improvement from psychological bias. By comparing A and B under controlled conditions, users can de