Raehoshi Anima is an enhanced iteration built upon the Anima Base v1.0 architecture. This release focuses on elevating visual style, integrating extensive new concepts, and expanding character knowledge. The ultimate goal is to deliver a more polished, balanced, and visually stunning output while remaining faithful to the core strengths of the base model.
Prompt style: Booru-style tags and Natural Language
Installation & Requirements
Important Note: This model does not include a built-in Text Encoder or VAE. You must download these components separately to achieve the intended results.
File Placement Guide
raehoshi-anima-v1.0.safetensors goes in ComfyUI/models/diffusion_models
qwen_3_06b_base.safetensors goes in ComfyUI/models/text_encoders
qwen_image_vae.safetensors goes in ComfyUI/models/vae
Recommended settings
Sampler: Euler a or ER SDE
Scheduled type: Beta or Normal
Sampling steps: 32
CFG: 4-5
Resolution: Any resolution up to 1536 (ensure dimensions are divisible by 32)
Positive prompts:
masterpiece, best quality, score_7, absurdres
Negative prompts:
worst quality, low quality, score_1, score_2, score_3, artist name, blurry, jpeg artifacts, bad anatomy, bad hands, bad proportions, mutation, deformed, extra digits, fewer digits, missing arms, missing legs
Prompting Tips
Tag Ordering: For the most consistent results, follow this structured prompt order:
[Quality / Meta / Year / Safety tags] ➔ [1girl / 1boy / Character Count] ➔ [Character Name] ➔ [Series / Copyright] ➔ [Artist] ➔ [General Tags]
Character Accuracy: Always include the official series/copyright tags alongside the character name to significantly improve details and accuracy.
Hybrid Prompting: The model handles hybrid prompting seamlessly. Feel free to mix dan match danbooru-style tags with natural language descriptions (e.g., use tags for characters and natural language for background/action).
Training Details
Raehoshi Anima was trained using a custom personal fork of Diffusion-pipe across a comprehensive two-stage fine-tuning process. The dataset utilizes multi-level captioning with random selection and tag dropout to ensure flexibility.
Stage 1: Concept & Character Expansion
Dataset Size: ~25k images
Trained Resolution: 1024x1024
Hardware: NVIDIA RTX PRO 6000 (96GB VRAM)
Batch Size: 32
Gradient accumulation steps: 1
Learning Rate: 1.5e-6 (LLM Adapter LR: 2e-7)
Focus: Introducing new franchises, series, and character knowledge.
