--- title: MiniMax-H3-Image-PE canonical_url: "https://www.modelscope.ai/models/MiniMax/MiniMax-H3-Image-PE" md_url: "https://www.modelscope.ai/models/MiniMax/MiniMax-H3-Image-PE.md" repository: MiniMax/MiniMax-H3-Image-PE last_updated: 2026-11-10 license: other pipeline_tag: text-generation tasks: - text-generation model_type: - qwen3_5 architectures: - Qwen3_5ForConditionalGeneration base_model: - Qwen/Qwen3.8-27B base_model_relation: quantized parameters: 16.2B tensor_type: - F32 - U8 - F8_E4M3 - BF16 library_name: - lora - safetensors - pytorch frameworks: - pytorch language: - en downloads: 6 stars: 0 tags: - prompt-expansion - text-to-image - minimax-h3 - fp4 - sglang --- # MiniMax-H3-Image-PE > MiniMax-H3-Image-PE - An open-source model by MiniMax on ModelScope. Use MiniMax-H3-Image directly via API. MiniMax/MiniMax-H3-Image-PE is a 16.2B-parameter text-generation model on ModelScope. licensed under other. derived from Qwen/Qwen3.8-27B. - **Repository**: MiniMax/MiniMax-H3-Image-PE - **License**: other - **Tasks**: text-generation - **Parameters**: 16.2B - **Base model**: Qwen/Qwen3.8-27B - **Tags**: prompt-expansion, text-to-image, minimax-h3, fp4, sglang - **Downloads**: 6 - **Stars**: 0 - **Last updated**: 2026-11-10 Source: https://www.modelscope.ai/models/MiniMax/MiniMax-H3-Image-PE ---

# MiniMax-H3-Image-PE ## Online API Use MiniMax-H3-Image directly via API. - Global: [platform.minimax.io](https://platform.minimax.io/docs/guides/image-generation) | CN: [platform.minimax.cn](https://platform.minimax.cn/docs/guides/image-generation) API docs: - Text-to-image: [EN-docs](https://platform.minimax.io/docs/api-reference/image-generation-h3-generations), [CN-docs](https://platform.minimax.cn/docs/api-reference/image-generation-h3-generations) - Image editing: [EN-docs](https://platform.minimax.io/docs/api-reference/image-generation-h3-edits), [CN-docs](https://platform.minimax.cn/docs/api-reference/image-generation-h3-edits) - Prompt expansion: [EN-docs](https://platform.minimax.io/docs/api-reference/image-generation-h3-context-ir), [CN-docs](https://platform.minimax.cn/docs/api-reference/image-generation-h3-context-ir) ## Introduction MiniMax-H3-Image-PE provides prompt expansion (PE) for [MiniMax-H3-Image](https://huggingface.co/MiniMaxAI/MiniMax-H3-Image). It turns a short request into a detailed description of the intended image, together with an aspect-ratio recommendation for image generation. This repository covers two workflows: | Workflow | Input | Released assets | | --- | --- | --- | | **Text-to-image (T2I) PE** | A text instruction | MiniMax-H3-Image-PE weights (fine-tuned from [Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B), FP4), task and system prompts, and a client example | | **Image editing PE** | An editing instruction and reference images | A task prompt and message-assembly example for a vision-language model of your choice. **Image editing PE is still under development; this release provides its prompt only.** | The examples below explain how to use MiniMax-H3-Image-PE and the image editing task prompt. ## How to use T2I PE Run the examples from a local copy of this repository so the relative prompt and script paths resolve correctly. ### Serve MiniMax-H3-Image-PE We recommend SGLang with `sglang>=0.5.23` and `xgrammar>=0.2.7`. ```bash sglang serve \ --model-path MiniMaxAI/MiniMax-H3-Image-PE \ --served-model-name MiniMax-H3-Image-PE \ --model-type llm \ --reasoning-parser qwen3 \ --grammar-backend xgrammar \ --constrained-json-disable-any-whitespace \ --host 0.0.0.0 \ --port 30000 ``` SGLang reads the FP4 configuration from the checkpoint. XGrammar constrains the answer to the JSON schema, and the reasoning parser separates reasoning from the final answer. ### Expand a prompt Load the task prompt and insert your instruction with `.format(user_query)`: ```python from pathlib import Path task_prompt = Path("prompt/t2i/task_prompt.txt").read_text(encoding="utf-8") user_query = "An orange tabby cat sleeping on a sunlit windowsill." user_prompt = task_prompt.format(user_query) ``` Send it with the system prompt and request parameters: ```python import json from urllib.request import Request, urlopen system_prompt = Path("prompt/t2i/system_prompt.txt").read_text(encoding="utf-8").strip() params = json.loads(Path("prompt/t2i/request_params.json").read_text(encoding="utf-8")) payload = { **params, "model": "MiniMax-H3-Image-PE", "messages": [ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt}, ], "stream": False, } request = Request( "http://0.0.0.0:30000/v1/chat/completions", data=json.dumps(payload).encode("utf-8"), headers={"Content-Type": "application/json"}, ) with urlopen(request, timeout=1200) as response: result = json.load(response) pe_result = json.loads(result["choices"][0]["message"]["content"]) print(pe_result) ``` The included client performs the same request and checks the response: ```bash python3 scripts/expand_prompt.py \ --url http://0.0.0.0:30000 \ --model MiniMax-H3-Image-PE \ "An orange tabby cat sleeping on a sunlit windowsill." ``` ### Request parameters The examples load the defaults from [`prompt/t2i/request_params.json`](prompt/t2i/request_params.json): | Parameter | Value | | --- | --- | | `temperature` / `top_p` / `top_k` | 1.0 / 0.95 / 20 | | `min_p` | 0.0 | | `presence_penalty` / `repetition_penalty` | 0.0 / 1.0 | | `max_tokens` | 4096, including thinking and the final answer | | `reasoning_effort` | `xhigh` | | `enable_thinking` / `preserve_thinking` | `true` / `true` | | `response_format` | JSON schema for `expanded_caption` and `aspect_ratio` | ### Output With `--reasoning-parser qwen3`, `message.content` contains the JSON answer. Reasoning is returned separately in `message.reasoning_content`. Example output: ```json { "expanded_caption": "A warm photograph shows an orange tabby cat sleeping on a sunlit windowsill, with soft afternoon light falling across its fur.", "aspect_ratio": "3:2" } ``` When your aspect-ratio selection is `auto`, use the `aspect_ratio` returned by the model for image generation. If you select an explicit ratio, use your selected ratio instead of the model's recommendation at the image-generation stage. ## How to use Image editing PE Use the task prompt with a vision-language model that supports interleaved text and images. The example below describes message assembly without selecting a model or making an API call. ### Inputs Provide the following inputs: | Input | Description | | --- | --- | | `user_query` | The editing instruction. Refer to images in their supplied order. | | `references` | At least one prepared image, in order, with its original display aspect ratio as a reduced `W:H` string. Use `None` when the source ratio is unknown. | | `aspect_ratio` | The target ratio: `adaptive` to let the model choose, or an explicit positive-integer `W:H` string. | ### Assemble the message Load [`prompt/edit/task_prompt.txt`](prompt/edit/task_prompt.txt) unchanged as the first content part. Follow it with the image count, each image's label and source ratio immediately before that image, and your editing instruction and target ratio in the final text part. This produces one user message. The fixed task prompt forms a shared prefix across requests, allowing reuse by services that support prefix caching. Cache behavior depends on the provider. In the pseudocode below, `source_image` and `reference_image` are already prepared image inputs. The text and image parts are conceptual; adapt their representation to your model's interface. ```python import json from pathlib import Path task_prompt = Path("prompt/edit/task_prompt.txt").read_text(encoding="utf-8") user_query = "Replace the bag in the first image with the bag in the second image." aspect_ratio = "adaptive" # Or an explicit ratio, such as "16:9". references = [ {"image": source_image, "aspect_ratio": "3:2"}, {"image": reference_image, "aspect_ratio": "1:1"}, ] count = len(references) content = [ {"type": "text", "text": task_prompt}, {"type": "text", "text": ( f"Reference Image Count:\n" f"{count} input image(s), labeled