{"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/noisediffusion-correcting-noise-for-image","title":"NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation","arxiv_id":"2403.08840","date":"2024-03-13","proceeding":null,"authors":["Pengfei Zheng","Yonggang Zhang","Zhen Fang","Tongliang Liu","Defu Lian","Bo Han"],"abstract":"Image interpolation based on diffusion models is promising in creating fresh and interesting images. Advanced interpolation methods mainly focus on spherical linear interpolation, where images are encoded into the noise space and then interpolated for denoising to images. However, existing methods face challenges in effectively interpolating natural images (not generated by diffusion models), thereby restricting their practical applicability. Our experimental investigations reveal that these challenges stem from the invalidity of the encoding noise, which may no longer obey the expected noise distribution, e.g., a normal distribution. To address these challenges, we propose a novel approach to correct noise for image interpolation, NoiseDiffusion. Specifically, NoiseDiffusion approaches the invalid noise to the expected distribution by introducing subtle Gaussian noise and introduces a constraint to suppress noise with extreme values. In this context, promoting noise validity contributes to mitigating image artifacts, but the constraint and introduced exogenous noise typically lead to a reduction in signal-to-noise ratio, i.e., loss of original image information. Hence, NoiseDiffusion performs interpolation within the noisy image space and injects raw images into these noisy counterparts to address the challenge of information loss. Consequently, NoiseDiffusion enables us to interpolate natural images without causing artifacts or information loss, thus achieving the best interpolation results.","url_abs":"https://arxiv.org/abs/2403.08840v1","url_pdf":"https://arxiv.org/pdf/2403.08840v1.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":"noisediffusion-correcting-noise-for-image","repo_url":"https://github.com/tmlr-group/noisediffusion","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"},{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2403.08840","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.08840"}},"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/tmlr-group/NoiseDiffusion","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/tmlr-group/noisediffusion","reach":{"status":"ok"}}],"summary":{"ran":3,"ran_fixture":1,"ran_draft_wrong":2,"unverified":1},"by_repo_kind":{"official":{"samples":7,"ran":6,"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":7,"samples":[{"code_sha256_prefix":"6a0596df2c0e0675","entry":"DDIMSampler","repo":"tmlr-group/noisediffusion","repo_kind":"official","path":"controlnet/cldm/ddim_hacked.py","file_url":"https://github.com/tmlr-group/noisediffusion/blob/HEAD/controlnet/cldm/ddim_hacked.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"6a0596df2c0e0675"}},{"code_sha256_prefix":"f9ec7d70add02b6f","entry":"HWC3","repo":"tmlr-group/NoiseDiffusion","repo_kind":"official","path":"controlnet/annotator/util.py","file_url":"https://github.com/tmlr-group/NoiseDiffusion/blob/HEAD/controlnet/annotator/util.py","link_basis":"harvester_set","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"f9ec7d70add02b6f"}},{"code_sha256_prefix":"a15cfd7932844ef9","entry":"extract_into_tensor","repo":"tmlr-group/NoiseDiffusion","repo_kind":"official","path":"controlnet/cm.py","file_url":"https://github.com/tmlr-group/NoiseDiffusion/blob/HEAD/controlnet/cm.py","link_basis":"harvester_set","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"a15cfd7932844ef9"}},{"code_sha256_prefix":"0dbf855dab1ab575","entry":"get_node_name","repo":"tmlr-group/NoiseDiffusion","repo_kind":"official","path":"controlnet/tool_add_control.py","file_url":"https://github.com/tmlr-group/NoiseDiffusion/blob/HEAD/controlnet/tool_add_control.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":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"0dbf855dab1ab575"}},{"code_sha256_prefix":"b7c67bc0b9a272df","entry":"interpolate_linear","repo":"tmlr-group/NoiseDiffusion","repo_kind":"official","path":"controlnet/cm.py","file_url":"https://github.com/tmlr-group/NoiseDiffusion/blob/HEAD/controlnet/cm.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b7c67bc0b9a272df"}},{"code_sha256_prefix":"0a85a29b5459564e","entry":"slerp","repo":"tmlr-group/NoiseDiffusion","repo_kind":"official","path":"controlnet/cm.py","file_url":"https://github.com/tmlr-group/NoiseDiffusion/blob/HEAD/controlnet/cm.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"0a85a29b5459564e"}},{"code_sha256_prefix":"50fd68f6989503c6","entry":"resize_image","repo":"tmlr-group/NoiseDiffusion","repo_kind":"official","path":"controlnet/annotator/util.py","file_url":"https://github.com/tmlr-group/NoiseDiffusion/blob/HEAD/controlnet/annotator/util.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"50fd68f6989503c6"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}