{"schema_version":"onlylabs.public_signal.v1","title":"OpenAI Model: openai/shap-e-img2img","description":"OpenAI model signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/012e2891-77a0-4728-853c-eb038e6e0166","json_url":"https://onlylabs.fyi/signals/012e2891-77a0-4728-853c-eb038e6e0166/signal.json","generated_at":"2026-06-11T03:27:51.51915+00:00","org":{"slug":"openai","name":"OpenAI","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/openai","dossier_json_url":"https://onlylabs.fyi/labs/openai/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/012e2891-77a0-4728-853c-eb038e6e0166","signal_json":"https://onlylabs.fyi/signals/012e2891-77a0-4728-853c-eb038e6e0166/signal.json","source":"https://huggingface.co/openai/shap-e-img2img","lab_dossier":"https://onlylabs.fyi/labs/openai","lab_dossier_json":"https://onlylabs.fyi/labs/openai/dossier.json","analysis":"https://onlylabs.fyi/analysis/openai","analysis_json":"https://onlylabs.fyi/analysis/openai/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/openai/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":"https://onlylabs.fyi/topics/releases","topic_signals_json":"https://onlylabs.fyi/topics/releases/signals.json","topic_feed":"https://onlylabs.fyi/topics/releases/feed.xml","data_business":null},"answer_pack":{"answer":"OpenAI published openai/shap-e-img2img. 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This model signal is evidence of what shipped on model infrastructure and how the release is positioned. High-signal details: license mit · 931 HF downloads. onlylabs links this event to 1 captured evidence page and 6 related model signals.","semantic_triples":[{"subject":"OpenAI","predicate":"published model","object":"openai/shap-e-img2img","text":"OpenAI published model openai/shap-e-img2img."},{"subject":"openai/shap-e-img2img","predicate":"is classified as","object":"model signal","text":"openai/shap-e-img2img is classified as model signal."},{"subject":"openai/shap-e-img2img","predicate":"belongs to","object":"releases desk","text":"openai/shap-e-img2img belongs to releases desk."},{"subject":"openai/shap-e-img2img","predicate":"has evidence coverage","object":"1 captured evidence page","text":"openai/shap-e-img2img has evidence coverage 1 captured evidence page."}]},"signal":{"id":"012e2891-77a0-4728-853c-eb038e6e0166","url":"https://onlylabs.fyi/signals/012e2891-77a0-4728-853c-eb038e6e0166","json_url":"https://onlylabs.fyi/signals/012e2891-77a0-4728-853c-eb038e6e0166/signal.json","source_url":"https://huggingface.co/openai/shap-e-img2img","title":"openai/shap-e-img2img","summary":"OpenAI published a model signal. onlylabs watches model cards, launch context, and traction.","context":null,"kind":{"key":"model_released","label":"Model"},"org":{"slug":"openai","name":"OpenAI","category":"frontier-lab"},"occurred_at":"2023-07-04T13:25:57+00:00","first_seen_at":"2026-06-06T01:49:31.958342+00:00","date_source":"source","evidence_coverage":{"target_pages":1,"captured_pages":1,"readable_pages":1,"capture_methods":["plain"],"missing_page_urls":[],"failed_page_urls":[],"blocked_page_urls":[],"page_urls":["https://huggingface.co/openai/shap-e-img2img/raw/main/README.md"]},"facets":{"pipeline_tag":"image-to-image","license":"mit"},"traction":{"github_stars":null,"hn_points":null,"hn_comments":null,"hn_story_id":null,"hf_downloads":931,"hf_likes":58},"data_radar":null},"primary_evidence_page":{"url":"https://huggingface.co/openai/shap-e-img2img/raw/main/README.md","final_url":"https://huggingface.co/openai/shap-e-img2img/raw/main/README.md","title":"openai/shap-e-img2img model card","http_status":200,"content_type":"text/plain; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T03:27:51.51915+00:00","bytes":4298,"raw_path":"20a75c236e00d1f688c4beab3ef5911e2a8ab01027241f049be41dcd48f80ca0.md","content_hash":"7590801447f3de203a8860ed7be32a8ff751e7245d1ec7bfefc0b958933f90b3","excerpt_chars":1200,"truncated":true,"excerpt":"--- license: mit tags: - image-to-image - text-to-3d - diffusers - shap-e --- Shap-E Shap-E introduces a diffusion process that can generate a 3D image from a text prompt. It was introduced in [Shap-E: Generating Conditional 3D Implicit Functions](https://arxiv.org/abs/2305.02463) by Heewoo Jun and Alex Nichol from OpenAI. Original repository of Shap-E can be found here: https://github.com/openai/shap-e. _The authors of Shap-E didn't author this model card. They provide a separate model card [here](https://github.com/openai/shap-e/blob/main/model-card.md)._ Introduction The abstract of the Shap-E paper: *We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D generative models which produce a single output representation, Shap-E directly generates the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields. We train Shap-E in two stages: first, we train an encoder that deterministically maps 3D assets into the parameters of an implicit function; second, we train a conditional diffusion model on outputs of the encoder. When trained on a large dataset of paired 3D and text data, our resulting models..."},"evidence_pages":[{"url":"https://huggingface.co/openai/shap-e-img2img/raw/main/README.md","final_url":"https://huggingface.co/openai/shap-e-img2img/raw/main/README.md","title":"openai/shap-e-img2img model card","http_status":200,"content_type":"text/plain; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T03:27:51.51915+00:00","bytes":4298,"raw_path":"20a75c236e00d1f688c4beab3ef5911e2a8ab01027241f049be41dcd48f80ca0.md","content_hash":"7590801447f3de203a8860ed7be32a8ff751e7245d1ec7bfefc0b958933f90b3","excerpt_chars":1200,"truncated":true,"excerpt":"--- license: mit tags: - image-to-image - text-to-3d - diffusers - shap-e --- Shap-E Shap-E introduces a diffusion process that can generate a 3D image from a text prompt. It was introduced in [Shap-E: Generating Conditional 3D Implicit Functions](https://arxiv.org/abs/2305.02463) by Heewoo Jun and Alex Nichol from OpenAI. Original repository of Shap-E can be found here: https://github.com/openai/shap-e. _The authors of Shap-E didn't author this model card. They provide a separate model card [here](https://github.com/openai/shap-e/blob/main/model-card.md)._ Introduction The abstract of the Shap-E paper: *We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D generative models which produce a single output representation, Shap-E directly generates the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields. We train Shap-E in two stages: first, we train an encoder that deterministically maps 3D assets into the parameters of an implicit function; second, we train a conditional diffusion model on outputs of the encoder. 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