{"schema_version":"onlylabs.public_signal.v1","title":"InclusionAI (Ant Group) Model: inclusionAI/Ming-Image-0.1-Design-Layer","description":"InclusionAI (Ant Group) model signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/02928aca-c762-4813-a6dc-51fb88746fae","json_url":"https://onlylabs.fyi/signals/02928aca-c762-4813-a6dc-51fb88746fae/signal.json","generated_at":"2026-10-12T17:42:07.842Z","evidence_latest_fetched_at":"2026-09-23T07:20:24.880292+00:00","signal_first_seen_at":"2026-09-23T07:20:09.914983+00:00","org":{"slug":"inclusionai","name":"InclusionAI (Ant Group)","category":"neolab","category_label":"Neolab","dossier_url":"https://onlylabs.fyi/labs/inclusionai","dossier_json_url":"https://onlylabs.fyi/labs/inclusionai/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/02928aca-c762-4813-a6dc-51fb88746fae","signal_json":"https://onlylabs.fyi/signals/02928aca-c762-4813-a6dc-51fb88746fae/signal.json","source":"https://huggingface.co/inclusionAI/Ming-Image-0.1-Design-Layer","lab_dossier":"https://onlylabs.fyi/labs/inclusionai","lab_dossier_json":"https://onlylabs.fyi/labs/inclusionai/dossier.json","analysis":"https://onlylabs.fyi/analysis/inclusionai","analysis_json":"https://onlylabs.fyi/analysis/inclusionai/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/inclusionai/evidence.json","category":"https://onlylabs.fyi/neolabs","category_json":"https://onlylabs.fyi/neolabs.json","category_feed":"https://onlylabs.fyi/neolabs/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json?category=neolab","topic":"https://onlylabs.fyi/topics/releases","topic_signals_json":"https://onlylabs.fyi/topics/releases/signals.json?category=neolab","topic_feed":"https://onlylabs.fyi/topics/releases/feed.xml?category=neolab","data_business":null},"answer_pack":{"answer":"InclusionAI (Ant Group) published inclusionAI/Ming-Image-0.1-Design-Layer. 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Quick Start Use the companion [Ming-Image repository](https://github.com/inclusionAI/Ming-Image) for installation and inference: ```bash git clone https://github.com/inclusionAI/Ming-Image cd Ming-Image pip install -r requirements.txt python infer.py \\ --model inclusionAI/Ming-Image-0.1-Design-Layer \\ --task layer-decompose \\ --input-image assets/layer_samples/card_making_input.png \\ --prompt assets/layer_samples/card_making_prompt.txt \\ --attn-implementation flash_attention_2 \\ --resolution 1024 \\ --output-dir outputs/layers ``` This runs the released six-layer card-making example. See the full [layer-decomposition demo](https://github.com/inclusionAI/Ming-Image#layer-decomposition-demo) for the output structure and layer-count behavior. Prompt enhancement (PE) can use `Ling-3.0-flash-VL` or `qwen3.8-27B`; see [layer-decomposition prompt rewriting](https://github.com/inclusionAI/Ming-Image#layer-decomposition-prompt-rewriting). 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