{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/iti-gen-inclusive-text-to-image-generation","title":"ITI-GEN: Inclusive Text-to-Image Generation","arxiv_id":"2309.05569","date":"2023-09-11","proceeding":"ICCV 2023 1","authors":["Cheng Zhang","Xuanbai Chen","Siqi Chai","Chen Henry Wu","Dmitry Lagun","Thabo Beeler","Fernando de la Torre"],"abstract":"Text-to-image generative models often reflect the biases of the training data, leading to unequal representations of underrepresented groups. This study investigates inclusive text-to-image generative models that generate images based on human-written prompts and ensure the resulting images are uniformly distributed across attributes of interest. Unfortunately, directly expressing the desired attributes in the prompt often leads to sub-optimal results due to linguistic ambiguity or model misrepresentation. Hence, this paper proposes a drastically different approach that adheres to the maxim that \"a picture is worth a thousand words\". We show that, for some attributes, images can represent concepts more expressively than text. For instance, categories of skin tones are typically hard to specify by text but can be easily represented by example images. Building upon these insights, we propose a novel approach, ITI-GEN, that leverages readily available reference images for Inclusive Text-to-Image GENeration. The key idea is learning a set of prompt embeddings to generate images that can effectively represent all desired attribute categories. More importantly, ITI-GEN requires no model fine-tuning, making it computationally efficient to augment existing text-to-image models. Extensive experiments demonstrate that ITI-GEN largely improves over state-of-the-art models to generate inclusive images from a prompt. Project page: https://czhang0528.github.io/iti-gen.","url_abs":"https://arxiv.org/abs/2309.05569v1","url_pdf":"https://arxiv.org/pdf/2309.05569v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"iti-gen-inclusive-text-to-image-generation","repo_url":"https://github.com/humansensinglab/ITI-GEN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"text-to-image-generation-1","task_name":"Text to Image Generation"},{"task_slug":"text-to-image-generation","task_name":"Text-to-Image Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2309.05569","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.05569"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/humansensinglab/ITI-GEN","reach":{"status":"ok","spdx":"NOASSERTION"}}],"summary":{"ran_draft_wrong":1,"ran":3,"ran_violates":1},"by_repo_kind":{"official":{"samples":5,"ran":5,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":5,"samples":[{"code_sha256_prefix":"8241c0562bc710fd","entry":"chunk","repo":"humansensinglab/ITI-GEN","repo_kind":"official","path":"generation.py","file_url":"https://github.com/humansensinglab/ITI-GEN/blob/HEAD/generation.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"8241c0562bc710fd"}},{"code_sha256_prefix":"ee52c52a0f03dbe6","entry":"get_category_for_attribute","repo":"humansensinglab/ITI-GEN","repo_kind":"official","path":"utils.py","file_url":"https://github.com/humansensinglab/ITI-GEN/blob/HEAD/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"ee52c52a0f03dbe6"}},{"code_sha256_prefix":"fec3eb5327e1bae5","entry":"get_dataset_for_attribute","repo":"humansensinglab/ITI-GEN","repo_kind":"official","path":"utils.py","file_url":"https://github.com/humansensinglab/ITI-GEN/blob/HEAD/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"fec3eb5327e1bae5"}},{"code_sha256_prefix":"1e63d588563eb90a","entry":"numpy_to_pil","repo":"humansensinglab/ITI-GEN","repo_kind":"official","path":"generation.py","file_url":"https://github.com/humansensinglab/ITI-GEN/blob/HEAD/generation.py","link_basis":"harvester_set","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"1e63d588563eb90a"}},{"code_sha256_prefix":"1cc1ed8e47791e6d","entry":"split_attr_list","repo":"humansensinglab/ITI-GEN","repo_kind":"official","path":"utils.py","file_url":"https://github.com/humansensinglab/ITI-GEN/blob/HEAD/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"1cc1ed8e47791e6d"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}