CIVITAI / Workflows

Anima HiresFix 1.5x High-Resolution Refinement Workflow

Watch the full video first if you want to understand how this Anima HiresFix workflow works in practice. The video shows how Anima Base can generate a 1024 image first, then use a 1.5x latent refinement stage to improve detail, structure, and final image quality without making the workflow overly complex. This ComfyUI workflow is designed for Anima Base 1.0 high-resolution refinement using a classic HiresFix-style two-stage generation route. Its main purpose is to create a stable 1024×1024 base image first, then upscale the latent to 1536×1536 and run a second controlled refinement pass. Compared with a simple one-pass Anima generation workflow, this structure gives the final image more detail, cleaner linework, better texture density, and stronger overall polish. The workflow is built around anima_baseV10.safetensors as the main model. After loading the model, ModelSamplingAuraFlow applies a shift value of 3, preparing the Anima model for the intended sampling behavior. The text side uses qwen_3_06b_base.safetensors as the text encoder with the stable_diffusion type setting. The output is decoded with qwen_image_vae.safetensors, which keeps the workflow aligned with the Anima / Qwen Image VAE ecosystem. The first generation stage uses an EmptyLatentImage at 1024×1024 with batch size 1. The positive prompt defines the image direction, while the negative prompt suppresses low-quality output, weak score tags, blurry artifacts, JPEG artifacts, and unwanted artist-name artifacts. The first KSampler is configured as the base sample stage, using 34 steps, CFG 4.5, er_sde sampler, simple scheduler, and denoise 1. This stage is responsible for building the main composition, character structure, pose, lighting, and global visual identity. After the base latent is generated, the workflow sends it into LatentUpscale. The upscale method is bicubic, and the target size is 1536×1536, which is exactly a 1.5x increase from the original 1024 base. This is important because it improves the working resolution without jumping too aggressively into an unstable size. A moderate 1.5x latent upscale is often easier to control than a larger upscale when the goal is refinement rather than complete regeneration. The second KSampler performs the HiresFix refinement stage. It uses 18 steps, CFG 4.5, er_sde sampler, simple scheduler, and denoise 0.35. The lower denoise value means t