Tested on M1 Max 64GB ComfyUI: one character LoRA across 6 Anima derivatives, same prompt/seed. Trigger-only never stabilizes, RDBT bolts to beast-ears, structure tags fix it.
Anima is a Cosmos-based DiT, not SDXL, so one Anima LoRA loads on every derivative checkpoint. 20+ CivitAI Anima models sorted by type, aesthetic, and prompt adherence.
Tested on M1 Max ComfyUI: two WAI-Anima character LoRAs in one image. Side-by-side works, but only non-overlapping inpaint keeps the design pixel-exact; Qwen-Image-Edit elongates the side ponytail even with training tags, and overlapping interaction poses jam at skeleton extraction.
WAI-Anima LoRA trained only on images the model itself made. Distribution shift drops to ~zero, so the sweet spot hits epoch 20 not 150 (7.5x faster). What the trigger bakes in vs what still needs tags, plus pose/angle control.
Tested on ComfyUI with Anima Turbo LoRA: hair intakes don't fire from the single tag. The 27-condition recipe — character NL reference + negative identity strip + (hair intakes:1.5) + 22.5° upper-front camera — also revealed the generic prompt was accidentally a Blue Archive character spec, and that Kanon's whole female cast has intakes as a cluster prior.
On M1 Max with Anima-Base v1.0 and WAI-Anima v1: the official negative is short. Trim the long Illustrious bad-hands list, move structure to positive tags, keep `safe` upfront.
Ran WAI-Anima v1.0 with a custom character LoRA on an M1 Mac to see if 2- and 3-character compositions actually hold up. Notes on what breaks and what holds at different LoRA weights, with practical settings that stay stable.
On M1 Max 64GB, SetLatentNoiseMask returns unchanged images with no error. LanPaint alone takes 32 min; adding Crop-and-Stitch drops text inpaint to 2:31.
Tested on M1 Max 64GB ComfyUI: Anima-Base v1.0 matches preview3-base in speed; WAI-Anima kana LoRA hits 22% on light prompts but 67% with hood+robe+embroidery added.
A verification log for converting color anime-style AI illustrations to manga-style monochrome. AI re-generation approaches lean to either color leakage or face drift, and pure deterministic local processing looks mechanical. Frames the next directions to try: putting a grayscale-only LoRA on Anima, and using See-through for part decomposition before mechanical composition.
With v3 captions kept as-is and only the training amount pushed up to Anima's official 12,000+ step recommendation, the direction hit rate went 100% at ep150-180, crashed to 0% at ep200, then partially recovered to 67% at ep227 — a non-monotonic curve. 600-720 exposures per training image is the sweet spot; over 800 triggers catastrophic forgetting. Learning rate 2e-5, ~11 hours / $10 of RunPod training plus a sweet-spot epoch scan.
