--- title: MiniMax-H3-Image-EntroPack canonical_url: "https://www.modelscope.cn/models/DiffSynth-Studio/MiniMax-H3-Image-EntroPack" md_url: "https://www.modelscope.cn/models/DiffSynth-Studio/MiniMax-H3-Image-EntroPack.md" repository: DiffSynth-Studio/MiniMax-H3-Image-EntroPack last_updated: 2026-11-11 pipeline_tag: text-to-image-synthesis tasks: - text-to-image-synthesis parameters: 183.8B tensor_type: - U8 - BF16 - F32 library_name: - safetensors downloads: 44 stars: 1 --- # MiniMax-H3-Image-EntroPack > MiniMax-H3-Image-EntroPack - DiffSynth-Studio 在 ModelScope 开源的模型。MiniMax-H3-Image (AdaLN-pruned) — EntroPack Pre-Quantized Models - **Repository**: DiffSynth-Studio/MiniMax-H3-Image-EntroPack - **Tasks**: text-to-image-synthesis - **Parameters**: 183.8B - **Downloads**: 44 - **Stars**: 1 - **Last updated**: 2026-11-11 Source: https://www.modelscope.cn/models/DiffSynth-Studio/MiniMax-H3-Image-EntroPack --- # MiniMax-H3-Image (AdaLN-pruned) — EntroPack Pre-Quantized Models We provide pre-quantized weight packages produced by [**EntroPack**](https://github.com/modelscope/entropack) for the AdaLN-pruned **MiniMax-H3-Image** transformer ([MiniMax/MiniMax-H3-Image](https://www.modelscope.cn/models/MiniMax/MiniMax-H3-Image)), which can be used with the quantization features of [**DiffSynth-Studio**](https://github.com/modelscope/DiffSynth-Studio). Use them with ComfyUI through [DiffSynth-ComfyUI](https://github.com/modelscope/DiffSynth-ComfyUI). A ready-to-run ComfyUI workflow is included in [`comfyui/featured_minimax_h3_image_entropack.json`](comfyui/featured_minimax_h3_image_entropack.json). The pruned transformer replaces every AdaLN projection and the timestep embedder with lookup tables for the six distilled sampling steps: 20.1B parameters instead of 33.1B, and 40.2 GB instead of 66.3 GB in bfloat16. **This replacement is completely lossless: every lookup-table row is precomputed from the original weights, so the pruned transformer produces exactly the same output as the unpruned checkpoint at the same prompt and seed. It is inference-only and applies only to the distilled six-step schedule.** The packages run with as little as 24 GB of VRAM. > **On a 24 GB GPU, use `dit_7bit` or `dit_7bit_int8`; on a 12 GB or smaller GPU, use `dit_4p5bit` or `dit_4p5bit_int8`.** The DiT is provided at five bitrate levels: * **`8bit` / `7bit` / `6bit`** — uniform entropy coding, with the quantization-sensitive layers held at 8.5 bpp; * **`4.5bit`** — the same mixed allocation, with the main blocks at the bitrate derived from the 4.5 bpp weighted target; * **`extreme_3bpp`** — extreme quantization: the same mixed allocation with the main blocks at 2.91 bpp, the two input projections at 8.5 bpp and the token-refiner blocks at 5.0 bpp; * **`int8_*`** — weights are first **INT8 W8A8-quantized** with convrot, then stored by EntroPack; the `int8_8bit` package stores the INT8 codes verbatim, the others entropy-code them to the level's bitrate. The forward pass uses **INT8 GEMM**, which delivers a faster inference speed than the BF16 GEMM used by the other levels. The INT8 GEMM path requires Triton and a CUDA GPU with compute capability 8.0 or newer. The text encoder is not re-quantized for this repository: reuse `models/MiniMax-H3/text_encoder_3bpp.safetensors` from [DiffSynth-Studio/EntroPackPreQuants](https://www.modelscope.cn/models/DiffSynth-Studio/EntroPackPreQuants), the package shared by every MiniMax-H3 model (video, audio and image). The image VAE is provided as an INT8 package (`models/vae_int8.safetensors`): the ViT decoder's linear layers use INT8 GEMM with convrot, while the convolutional encoder and the normalization layers stay in bfloat16. Every `*.safetensors` ships with a same-named `*.json` that records the quantization config. `actual bpp = 8 × Σ storage bytes / Σ numel` is measured on the package artifacts. ## Preview All previews are generated with the same prompt, seed 42 and size 4096×4096; `bf16` is the released transformer and `bf16_pruned` the AdaLN-pruned transformer, both in full precision.
bf16 (reference) bf16 pruned
dit_8bit dit_8bit_int8
dit_7bit dit_7bit_int8
dit_6bit dit_6bit_int8
dit_4p5bit dit_4p5bit_int8
dit_extreme_3bpp (extreme quantization)
The two full-precision rows are identical. ## DiT | name | file | actual bpp | size | |---|---|---|---| | bf16_pruned (full precision) | [`models/bf16_table_pruned.safetensors`](https://www.modelscope.cn/models/DiffSynth-Studio/MiniMax-H3-Image-EntroPack/file/view/master/models%2Fbf16_table_pruned.safetensors) | 16.028 | 40.20 GB | | dit_8bit | [`models/dit_8bit.safetensors`](https://www.modelscope.cn/models/DiffSynth-Studio/MiniMax-H3-Image-EntroPack/file/view/master/models%2Fdit_8bit.safetensors) | 8.107 | 20.34 GB | | dit_7bit | [`models/dit_7bit.safetensors`](https://www.modelscope.cn/models/DiffSynth-Studio/MiniMax-H3-Image-EntroPack/file/view/master/models%2Fdit_7bit.safetensors) | 7.134 | 17.89 GB | | dit_6bit | [`models/dit_6bit.safetensors`](https://www.modelscope.cn/models/DiffSynth-Studio/MiniMax-H3-Image-EntroPack/file/view/master/models%2Fdit_6bit.safetensors) | 6.119 | 15.35 GB | | dit_4p5bit | [`models/dit_4p5bit.safetensors`](https://www.modelscope.cn/models/DiffSynth-Studio/MiniMax-H3-Image-EntroPack/file/view/master/models%2Fdit_4p5bit.safetensors) | 4.587 | 11.51 GB | | dit_8bit_int8 (INT8 GEMM) | [`models/dit_8bit_int8.safetensors`](https://www.modelscope.cn/models/DiffSynth-Studio/MiniMax-H3-Image-EntroPack/file/view/master/models%2Fdit_8bit_int8.safetensors) | 8.054 | 20.20 GB | | dit_7bit_int8 (INT8 GEMM) | [`models/dit_7bit_int8.safetensors`](https://www.modelscope.cn/models/DiffSynth-Studio/MiniMax-H3-Image-EntroPack/file/view/master/models%2Fdit_7bit_int8.safetensors) | 7.076 | 17.75 GB | | dit_6bit_int8 (INT8 GEMM) | [`models/dit_6bit_int8.safetensors`](https://www.modelscope.cn/models/DiffSynth-Studio/MiniMax-H3-Image-EntroPack/file/view/master/models%2Fdit_6bit_int8.safetensors) | 6.072 | 15.23 GB | | dit_4p5bit_int8 (INT8 GEMM) | [`models/dit_4p5bit_int8.safetensors`](https://www.modelscope.cn/models/DiffSynth-Studio/MiniMax-H3-Image-EntroPack/file/view/master/models%2Fdit_4p5bit_int8.safetensors) | 4.568 | 11.46 GB | | dit_extreme_3bpp (extreme quantization) | [`models/dit_extreme_3bpp.safetensors`](https://www.modelscope.cn/models/DiffSynth-Studio/MiniMax-H3-Image-EntroPack/file/view/master/models%2Fdit_extreme_3bpp.safetensors) | 3.057 | 7.67 GB | ## Text Encoder | name | file | actual bpp | size | |---|---|---|---| | text_encoder_3bpp | [`DiffSynth-Studio/EntroPackPreQuants:models/MiniMax-H3/text_encoder_3bpp.safetensors`](https://www.modelscope.cn/models/DiffSynth-Studio/EntroPackPreQuants/file/view/master/models%2FMiniMax-H3%2Ftext_encoder_3bpp.safetensors) | 3.531 | 11.02 GB | | text_encoder_4bpp | [`DiffSynth-Studio/EntroPackPreQuants:models/MiniMax-H3/text_encoder_4bpp.safetensors`](https://www.modelscope.cn/models/DiffSynth-Studio/EntroPackPreQuants/file/view/master/models%2FMiniMax-H3%2Ftext_encoder_4bpp.safetensors) | 4.541 | 14.17 GB | | text_encoder_5bpp | [`DiffSynth-Studio/EntroPackPreQuants:models/MiniMax-H3/text_encoder_5bpp.safetensors`](https://www.modelscope.cn/models/DiffSynth-Studio/EntroPackPreQuants/file/view/master/models%2FMiniMax-H3%2Ftext_encoder_5bpp.safetensors) | 5.575 | 17.40 GB | | text_encoder_6bpp | [`DiffSynth-Studio/EntroPackPreQuants:models/MiniMax-H3/text_encoder_6bpp.safetensors`](https://www.modelscope.cn/models/DiffSynth-Studio/EntroPackPreQuants/file/view/master/models%2FMiniMax-H3%2Ftext_encoder_6bpp.safetensors) | 6.604 | 20.61 GB | | text_encoder_7bpp | [`DiffSynth-Studio/EntroPackPreQuants:models/MiniMax-H3/text_encoder_7bpp.safetensors`](https://www.modelscope.cn/models/DiffSynth-Studio/EntroPackPreQuants/file/view/master/models%2FMiniMax-H3%2Ftext_encoder_7bpp.safetensors) | 7.629 | 23.81 GB | | text_encoder_8bpp | [`DiffSynth-Studio/EntroPackPreQuants:models/MiniMax-H3/text_encoder_8bpp.safetensors`](https://www.modelscope.cn/models/DiffSynth-Studio/EntroPackPreQuants/file/view/master/models%2FMiniMax-H3%2Ftext_encoder_8bpp.safetensors) | 8.597 | 26.83 GB | | text_encoder_int8_convrot (INT8 GEMM) | [`DiffSynth-Studio/EntroPackPreQuants:models/MiniMax-H3/text_encoder_int8_convrot.safetensors`](https://www.modelscope.cn/models/DiffSynth-Studio/EntroPackPreQuants/file/view/master/models%2FMiniMax-H3%2Ftext_encoder_int8_convrot.safetensors) | 8.506 | 26.55 GB | ## VAE | name | file | actual bpp | size | |---|---|---|---| | vae_int8 (INT8 GEMM) | [`models/vae_int8.safetensors`](https://www.modelscope.cn/models/DiffSynth-Studio/MiniMax-H3-Image-EntroPack/file/view/master/models%2Fvae_int8.safetensors) | 8.035 | 0.77 GB | The 98 linear layers of the ViT decoder are INT8 with convrot; the convolutional encoder and the normalization layers stay in bfloat16. ## Usage ### Install ```bash # DiffSynth-Studio git clone https://github.com/modelscope/DiffSynth-Studio.git cd DiffSynth-Studio && pip install -e . # EntroPack (pick the extra matching your CUDA version: cuda13 / cuda12) git clone https://github.com/modelscope/entropack.git cd entropack && pip install -e '.[cuda13]' ``` If EntroPack is already installed but its CUDA backend cannot start, the error names the missing piece, for example: ``` entropack.registry.DispatchError: No backend can handle 'lattice_rans': cuda: cupy is not installed (No module named 'cupy'); install a matching build, e.g. `pip install cupy-cuda13x` for CUDA 13 or `pip install cupy-cuda12x` for CUDA 12 ``` Install the CuPy build that matches the CUDA major version of your PyTorch (`pip install cupy-cuda13x` for CUDA 13, `pip install cupy-cuda12x` for CUDA 12), or reinstall EntroPack with the matching extra (`pip install "entropack[cuda13]"` / `pip install "entropack[cuda12]"`). ### ComfyUI The packages are also available as ComfyUI nodes through [DiffSynth-ComfyUI](https://github.com/modelscope/DiffSynth-ComfyUI). A ready-to-run workflow is shipped as [`comfyui/featured_minimax_h3_image_entropack.json`](comfyui/featured_minimax_h3_image_entropack.json): it loads the 7-bit INT8 DiT, the 7bpp text encoder and the INT8 VAE, and generates a 4096×4096 image in six distilled steps. Load the file in ComfyUI and queue it; the models are downloaded and registered from their json sidecars automatically. ### Example scripts * Inference: [standard](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/minimax_h3_image/model_inference/MiniMax-H3-Image-Pruned-EntroPack.py) / [low VRAM](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/minimax_h3_image/model_inference_low_vram/MiniMax-H3-Image-Pruned-EntroPack.py) * LoRA training: [train](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/minimax_h3_image/model_training/lora/MiniMax-H3-Image-EntroPack.sh) / [validation](https://github.com/modelscope/DiffSynth-Studio/blob/main/examples/minimax_h3_image/model_training/validate_lora/MiniMax-H3-Image-EntroPack.py) `dit_package` and `text_encoder_package` are chosen independently: any combination works (e.g. `dit_int8_8bit` with `text_encoder_4bpp`). The 7-bit DiT and the 4-bit text encoder are pre-registered in DiffSynth-Studio, so they load with a plain `ModelConfig`; the helper below registers any package's json sidecar before loading: ```python import json import torch from PIL import Image from diffsynth.configs import MODEL_CONFIGS from diffsynth.pipelines.minimax_h3_image import MiniMaxH3ImagePipeline, ModelConfig dit_model_id = "DiffSynth-Studio/MiniMax-H3-Image-EntroPack" text_encoder_model_id = "DiffSynth-Studio/EntroPackPreQuants" dit_package = "models/dit_7bit.safetensors" text_encoder_package = "models/MiniMax-H3/text_encoder_3bpp.safetensors" def register_prequantized(model_id, package_pattern): package = ModelConfig(model_id=model_id, origin_file_pattern=package_pattern) package.download_if_necessary() sidecar = ModelConfig(model_id=model_id, origin_file_pattern=package_pattern.replace(".safetensors", ".json")) sidecar.download_if_necessary() with open(sidecar.path) as handle: entry = json.load(handle) if not any(config.get("model_hash") == entry["model_hash"] for config in MODEL_CONFIGS): MODEL_CONFIGS.append(entry) return package pipe = MiniMaxH3ImagePipeline.from_pretrained( torch_dtype=torch.bfloat16, device="cuda", model_configs=[ register_prequantized(dit_model_id, dit_package), register_prequantized(text_encoder_model_id, text_encoder_package), ModelConfig(model_id="MiniMax/MiniMax-H3-Image", origin_file_pattern="pdd_fused_heads.safetensors"), ModelConfig(model_id="DiffSynth-Studio/MiniMax-H3-Image-EntroPack", origin_file_pattern="models/vae_int8.safetensors"), ], processor_config=ModelConfig(model_id="MiniMax/MiniMax-H3-Image", origin_file_pattern="processor/"), ) prompt = ( "Full-color cinematic outdoor night photograph of a stunning young Asian woman in her early twenties with a" "natural warm ivory complexion and a healthy glow, wearing a soft blush pink off-shoulder dress with a fitted" "bodice, framed from the waist up against softly illuminated historic architecture. She stands near the" "centre of the frame, facing the camera with a gentle closed-lip smile, her hands resting loosely clasped in" "front of her waist with relaxed elbows; she has a slim oval face with softly defined cheekbones and a" "delicate narrow jaw, dark expressive eyes, natural-shaped brows, and subtle natural makeup with soft red" "lips. Her long black hair falls in loose natural waves over her shoulders, with fine strands framing her" "face. The fabric of the dress falls in soft folds across her shoulders and chest. In the frame-left" "background, the word \"EntroPack\" glows as a hand-drawn bubbly yellow neon sign mounted on a softly floodlit" "dark stone building above the street; rows of warm-lit windows, stone cornices and shopfronts remain clearly" "readable around the sign in gentle amber light, while the thick rounded irregular letters ride a gently" "bouncing baseline with a bright core and a soft outer glow, kept sharp and unblurred against the night. The" "background recedes into deep blue tones: the Eiffel Tower rises above distant buildings on the frame-right" "as a softly silhouetted tapering lattice outline in the shallow depth of field, while small amber, muted" "turquoise and rose-coloured lights dissolve into rounded bokeh across the upper background, leaving a darker" "area behind her head. Soft neutral frontal illumination with gentle falloff reveals her face with natural" "warm skin tone and subtle texture, a faint cooler rim separates her hair from the night, and the skirt of" "her dress fills the lower edge of the frame." ) image = pipe(prompt=prompt, height=4096, width=4096, seed=42) image.save("image.jpg") ```