{"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/not-all-steps-are-created-equal-selective","title":"Not All Steps are Created Equal: Selective Diffusion Distillation for Image Manipulation","arxiv_id":"2307.08448","date":"2023-07-17","proceeding":"ICCV 2023 1","authors":["Luozhou Wang","Shuai Yang","Shu Liu","Ying-Cong Chen"],"abstract":"Conditional diffusion models have demonstrated impressive performance in image manipulation tasks. The general pipeline involves adding noise to the image and then denoising it. However, this method faces a trade-off problem: adding too much noise affects the fidelity of the image while adding too little affects its editability. This largely limits their practical applicability. In this paper, we propose a novel framework, Selective Diffusion Distillation (SDD), that ensures both the fidelity and editability of images. Instead of directly editing images with a diffusion model, we train a feedforward image manipulation network under the guidance of the diffusion model. Besides, we propose an effective indicator to select the semantic-related timestep to obtain the correct semantic guidance from the diffusion model. This approach successfully avoids the dilemma caused by the diffusion process. Our extensive experiments demonstrate the advantages of our framework. Code is released at https://github.com/AndysonYs/Selective-Diffusion-Distillation.","url_abs":"https://arxiv.org/abs/2307.08448v1","url_pdf":"https://arxiv.org/pdf/2307.08448v1.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":"not-all-steps-are-created-equal-selective","repo_url":"https://github.com/andysonys/selective-diffusion-distillation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"all","task_name":"All"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-manipulation","task_name":"Image Manipulation"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2307.08448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.08448"}},"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":"deterministic:regex_extraction","url":"https://github.com/AndysonYs/Selective-Diffusion-Distillation","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran_fixture":3,"ran":2,"ran_draft_wrong":2},"by_repo_kind":{"official":{"samples":7,"ran":7,"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":0,"samples":[{"code_sha256_prefix":"61bfd1f9c061215b","entry":"fused_leaky_relu","repo":"AndysonYs/Selective-Diffusion-Distillation","repo_kind":"official","path":"models/stylegan2/modules.py","file_url":"https://github.com/AndysonYs/Selective-Diffusion-Distillation/blob/HEAD/models/stylegan2/modules.py","link_basis":"plan_row","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"61bfd1f9c061215b"}},{"code_sha256_prefix":"b28543ed94f9d144","entry":"get_block","repo":"AndysonYs/Selective-Diffusion-Distillation","repo_kind":"official","path":"loss/models/face_model.py","file_url":"https://github.com/AndysonYs/Selective-Diffusion-Distillation/blob/HEAD/loss/models/face_model.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b28543ed94f9d144"}},{"code_sha256_prefix":"d764d1b681a351f3","entry":"get_blocks","repo":"AndysonYs/Selective-Diffusion-Distillation","repo_kind":"official","path":"loss/models/face_model.py","file_url":"https://github.com/AndysonYs/Selective-Diffusion-Distillation/blob/HEAD/loss/models/face_model.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"d764d1b681a351f3"}},{"code_sha256_prefix":"29b9a890f149a3a6","entry":"get_keys","repo":"AndysonYs/Selective-Diffusion-Distillation","repo_kind":"official","path":"models/mapper/style_mapper.py","file_url":"https://github.com/AndysonYs/Selective-Diffusion-Distillation/blob/HEAD/models/mapper/style_mapper.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"29b9a890f149a3a6"}},{"code_sha256_prefix":"c54fea429589425d","entry":"l2_norm","repo":"AndysonYs/Selective-Diffusion-Distillation","repo_kind":"official","path":"loss/models/face_model.py","file_url":"https://github.com/AndysonYs/Selective-Diffusion-Distillation/blob/HEAD/loss/models/face_model.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c54fea429589425d"}},{"code_sha256_prefix":"238fedc043c13f62","entry":"upfirdn2d","repo":"AndysonYs/Selective-Diffusion-Distillation","repo_kind":"official","path":"models/stylegan2/modules.py","file_url":"https://github.com/AndysonYs/Selective-Diffusion-Distillation/blob/HEAD/models/stylegan2/modules.py","link_basis":"plan_row","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"238fedc043c13f62"}},{"code_sha256_prefix":"fe99cfd294676edb","entry":"upfirdn2d_native","repo":"AndysonYs/Selective-Diffusion-Distillation","repo_kind":"official","path":"models/stylegan2/modules.py","file_url":"https://github.com/AndysonYs/Selective-Diffusion-Distillation/blob/HEAD/models/stylegan2/modules.py","link_basis":"plan_row","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"fe99cfd294676edb"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}