Image-Text-to-Video
Diffusers
Safetensors
MiniMaxH3ModularPipeline
text-to-video
image-to-video
video-to-video
text-to-audio-video
image-to-audio-video
image-text-to-audio-video
video-to-audio-video
audio-to-audio-video
audio-video-generation
multimodal
synchronized-audio-video
reference-to-audio-video
Instructions to use MiniMaxAI/MiniMax-H3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MiniMaxAI/MiniMax-H3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MiniMaxAI/MiniMax-H3", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
yuanhe commited on
Commit ·
48d93ed
1
Parent(s): 5d9b308
Add repository model index for HF downloads
Browse files- README.md +4 -2
- model_index.json +139 -0
README.md
CHANGED
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@@ -194,12 +194,14 @@ Each checkpoint is distributed as a self\-contained Hugging Face\-style reposito
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Download the model. The repository hosts the original checkpoint (`FL2VA/`, `Ref2VA/`) and the diffusers format side by side, so scope the download to what your framework needs:
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```bash
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# Original checkpoint, both task families (SGLang, vLLM):
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hf download MiniMaxAI/MiniMax-H3 --include "FL2VA/*" "Ref2VA/*" --local-dir MiniMax-H3
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# Or a single task family:
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hf download MiniMaxAI/MiniMax-H3 --include "FL2VA/*" --local-dir MiniMax-H3
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```
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diffusers users do not need a manual download: `ModularPipeline.from_pretrained("MiniMaxAI/MiniMax-H3")` fetches exactly the components it needs. See the [diffusers documentation](https://github.com/huggingface/diffusers/blob/minimax-h3/docs/source/en/api/pipelines/minimax_h3.md) for loading recipes.
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Download the model. The repository hosts the original checkpoint (`FL2VA/`, `Ref2VA/`) and the diffusers format side by side, so scope the download to what your framework needs:
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`model_index.json` is the repository-level modular index. The task-family-specific diffusers indexes remain under `FL2VA/model_index.json` and `Ref2VA/model_index.json`.
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```bash
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# Original checkpoint, both task families (SGLang, vLLM):
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hf download MiniMaxAI/MiniMax-H3 --include "model_index.json" "modular_model_index.json" "FL2VA/*" "Ref2VA/*" --local-dir MiniMax-H3
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# Or a single task family:
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hf download MiniMaxAI/MiniMax-H3 --include "model_index.json" "modular_model_index.json" "FL2VA/*" --local-dir MiniMax-H3
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```
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diffusers users do not need a manual download: `ModularPipeline.from_pretrained("MiniMaxAI/MiniMax-H3")` fetches exactly the components it needs. See the [diffusers documentation](https://github.com/huggingface/diffusers/blob/minimax-h3/docs/source/en/api/pipelines/minimax_h3.md) for loading recipes.
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model_index.json
ADDED
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@@ -0,0 +1,139 @@
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{
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"_class_name": "MiniMaxH3ModularPipeline",
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"_diffusers_version": "0.36.0.dev0",
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"_blocks_class_name": "MiniMaxH3Blocks",
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"_minimax_h3": {
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"schema_version": 1,
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"index_scope": "repository",
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"task_family_indexes": {
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"fl2va": "FL2VA/model_index.json",
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"ref2va": "Ref2VA/model_index.json"
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}
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},
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"text_encoder": [
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"transformers",
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"Qwen3VLForConditionalGeneration",
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{
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"type_hint": [
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"transformers",
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"Qwen3VLForConditionalGeneration"
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],
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"pretrained_model_name_or_path": "MiniMaxAI/MiniMax-H3",
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"subfolder": "text_encoder",
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"variant": null,
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"revision": null
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}
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],
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"tokenizer": [
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"transformers",
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"Qwen2TokenizerFast",
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{
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"type_hint": [
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"transformers",
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"Qwen2TokenizerFast"
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],
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"pretrained_model_name_or_path": "MiniMaxAI/MiniMax-H3",
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"subfolder": "tokenizer",
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"variant": null,
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"revision": null
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}
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],
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"processor": [
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"transformers",
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"Qwen3VLProcessor",
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{
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"type_hint": [
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"transformers",
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"Qwen3VLProcessor"
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],
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"pretrained_model_name_or_path": "MiniMaxAI/MiniMax-H3",
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"subfolder": "processor",
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"variant": null,
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"revision": null
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}
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],
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"vae": [
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"diffusers",
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"AutoencoderKLMiniMaxH3",
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{
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"type_hint": [
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"diffusers",
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"AutoencoderKLMiniMaxH3"
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],
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"pretrained_model_name_or_path": "MiniMaxAI/MiniMax-H3",
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"subfolder": "vae",
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"variant": null,
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"revision": null
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}
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],
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"audio_vae": [
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"diffusers",
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"AutoencoderKLMiniMaxH3Audio",
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{
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"type_hint": [
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"diffusers",
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"AutoencoderKLMiniMaxH3Audio"
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],
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"pretrained_model_name_or_path": "MiniMaxAI/MiniMax-H3",
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"subfolder": "audio_vae",
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"variant": null,
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"revision": null
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}
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],
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"transformer": [
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"diffusers",
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"MiniMaxH3Transformer3DModel",
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{
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"type_hint": [
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"diffusers",
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"MiniMaxH3Transformer3DModel"
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],
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"pretrained_model_name_or_path": "MiniMaxAI/MiniMax-H3",
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"subfolder": "transformer",
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"variant": null,
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"revision": null
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}
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],
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"transformer_ref": [
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"diffusers",
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"MiniMaxH3Transformer3DModel",
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{
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"type_hint": [
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"diffusers",
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"MiniMaxH3Transformer3DModel"
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],
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"pretrained_model_name_or_path": "MiniMaxAI/MiniMax-H3",
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"subfolder": "transformer_ref",
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"variant": null,
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"revision": null
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}
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],
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"scheduler": [
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"diffusers",
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"MiniMaxH3Scheduler",
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{
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"type_hint": [
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"diffusers",
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"MiniMaxH3Scheduler"
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],
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"pretrained_model_name_or_path": "MiniMaxAI/MiniMax-H3",
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"subfolder": "scheduler",
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"variant": null,
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"revision": null
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}
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],
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"audio_scheduler": [
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"diffusers",
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"MiniMaxH3Scheduler",
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{
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"type_hint": [
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"diffusers",
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"MiniMaxH3Scheduler"
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],
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"pretrained_model_name_or_path": "MiniMaxAI/MiniMax-H3",
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"subfolder": "audio_scheduler",
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"variant": null,
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"revision": null
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}
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]
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}
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