{"id":"e3f2b845-8f2c-4b5a-9caf-eac1029d3e7e","revision":0,"last_node_id":164,"last_link_id":332,"nodes":[{"id":116,"type":"MarkdownNote","pos":[-2030,4850],"size":[450,880],"flags":{},"order":0,"mode":0,"inputs":[],"outputs":[],"title":"Note: MiniMax H3","properties":{"ue_properties":{"widget_ue_connectable":{},"version":"7.8","input_ue_unconnectable":{}}},"widgets_values":["## MiniMax H3\n\n[MiniMax H3](https://www.minimax.io/blog/minimax-h3) is MiniMax's general-purpose, omni-modal generation model. It jointly understands text, image, video, and audio, and generates video with **native stereo audio**: voice, sound effects, and music are modeled jointly in a single forward pass, not layered on afterward. Output is up to 2K resolution, 24fps, and up to about 15 seconds.\n\n## ComfyUI links\n- [ComfyUI#15224](https://github.com/Comfy-Org/ComfyUI/pull/15224)\n- [🤗 Comfy-Org/MiniMax-H3](https://huggingface.co/Comfy-Org/MiniMax-H3)\n\n## About this workflow\n\nThis template runs the **reference-to-video (ref2va)** task using the `MiniMaxH3ReferenceToVideo` node. It takes any mix of reference images, videos, and standalone audio, and weaves them into the generation to lock in a character's identity, a style, a motion, a camera move, or a voice.\n\n**Key inputs**\n\n- **ref_images / ref_videos / ref_video_audios / ref_audios**: up to 9 reference images, 3 reference videos (each may carry its own paired soundtrack), and 3 standalone reference audio clips\n- **prompt**: reference the inputs by tag, in the exact order they were connected, for example `<Picture 1>`, `<Video 1>`, `<Audio 1>`, then describe the target scene, motion, and audio\n- **ref_image_size**: `match` scales references down to the generation's resolution (faster); `max` keeps up to a 2048px short edge for stronger identity fidelity, at the cost of speed since reference tokens ride along every sampling step\n- **width / height**: set via Resolution Selector.\n- **duration (seconds)**: converted to a valid frame `length` by the Math Expression node\n\n**Sampling and decode**\n\n- Sampler: `res_multistep`. `beta` or `normal` scheduler tends to outperform `simple` for reference-heavy prompts like this one\n- The sampler's joint audio+video `LATENT` output feeds directly into both `VAEDecode` (video, `minimax_h3_video_vae_fp16`) and `VAEDecodeAudio` (audio, `minimax_h3_audio_vae_fp32`); each decode node automatically pulls its own half out of the packed latent. `CreateVideo` then muxes the two into a single MP4 with synced sound\n- The diffusion model here is `minimax_h3_ref2va_pruned_int8_convrot.safetensors`, a different set of weights from the `fl2va` model used by the t2v/i2v templates\n\nRef2va's output is very sensitive to prompt wording; matching the reference tags precisely and being explicit about which reference drives which part of the shot tends to work 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