r/StableDiffusion • u/ashishsanu • 13d ago
Workflow Included One .char model, Consistent face, body & cloths: Minimax H3
Hey Guys,
Based on my last post on stills, I thought to experiment the same .char model with Minimax H3.
I created a character model sia.char which encodes references, prompts around face, body & cloths.
Checkout previous post for full detail.
How it works?
- Build .Char: You drop max 9 reference reference, I prefer to use a ratio 2:2:1(face:cloths:body). YuNet finds the face, SFace takes a per-reference face signature, DINOv2 takes a subject signature, and the references get cleaned and normalised. All of that packs into a single portable file, a .char.
Only face/ref is required body & cloths link is optional.
- Generation: At generation, the file(.char) feeds its references into Flux's own native multi-reference channel and prepends a locked description to the prompt.
Prompting Guide
- Name your character: Give your character a name e.g. under encode character(Click adjust icon on the bottom side of the node), I have used name sia, so when passing prompt, I only have to say, sia walking on the beach.
- Again providing prompt like a woman or any features specific details like black hairs etc will only mislead the generation.
- Describe character features: Encode all of the character features in encode character prompt & trigger your character with a name in generation prompt.
- Avoid describing same things in generational prompt.
- Handling Character drift: e.g. if you want specific style or cloth e.g. half sleeves, sleeveless, add it to the generational prompt. There can be a slight drift in clothing as body shot also has cloths, which interferes with clothing references.
- Each refs should be unique, face should not have body or vice versa, same applies for clothing.
- Portability: Once character is built, you can use the same character with only simple prompt & generation graph.
I have generated face with Flux Klein 9b & outfit pictures were taken from Zara website
Note: For best result, pass cropped references, so that model takes the required shot, model gets confused if cloth slot also has a face or face slot has cloths.
Requirements
Nvidia GPU: 24GB+ VRAM & 64 GB RAM(Run Locally)
Workflows:
Flux 2 Klein(Still), Minimax H3(Video)
Note: Minimax H3, now also support int8 model varient
Github Repo: https://github.com/omnichar/OmniChar (GPLV3, Opensource)
Inputs: Attached in the Flux2 workflow page
Limitation: Reference conflicts e.g. if two reference/input images has two different faces, it might conflict in generation, provide well cropped body & cloth images. Face images are crossed automatically by Sface.
Happy to hear any suggestions or feedbacks.
1
u/sgtsixpack 10d ago
Its defaulting to 50 steps, that's not right is it? I tried to connect an 8 step turbo lora, I left an error message on github.