description: <p>This one is pretty simple again, its been trained on interior images from architecture made by Frank Lloyd Wright, because all images trained were real it will lean into realism on any model it is applied to, and because <u>the dataset is almost devoid of humans you will want to weight up prompts involving people or characters</u> unless they are well known by the model you are using, <u>weighting down the lora will also work</u>. The rooms are mostly from the late 40s through the 60s but it is flexible enough and the checkpoint will bleed in to change the stylization a bit.<br /><br />triggers:<br /><span style="color:#40c057">flwindoors</span>: this is the general trigger, because it isn't a known tag it seems to be weaker and can probably allow more function flexibility when used alone.</p><p><br /><span style="color:#40c057">room</span>: this is a known trigger and when paired with flwindoors makes the lora work more strongly in its effect on images.<br /></p><p>negatives to consider:<br /><span style="color:#fa5252">fireplace</span>: lots of fireplaces they must have been hip and cool back in the day</p><p><span style="color:#fa5252">painting</span>: seems some checkpoints love filling the walls with paintings, sometimes it is a nice accent but other times theres just way too many of them </p><p>when prompting be sure to include furniture for the best effect, chairs tables lamps plants, whatever you want to fill out the scene, window or windows is also a good term to set a mood. other than those most anything you want to prompt should work but may need weighting to come through.<br /></p><p>example:<br /><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/09ce0f1a-c011-4e82-a6cd-3a69010855d0/width=525/09ce0f1a-c011-4e82-a6cd-3a69010855d0.jpeg" />flwindoors, room, furniture, couch, chair, coffeetable, window, nachos and salsa,   score_9, score_8_up, score_7_up, score_6_up, score_5_up, score_4_up,source_anime, plants<br /></p>
modelVersions.availability: Public
modelVersions.baseModel: Pony
modelVersions.downloadUrl: https://civitai.com/api/download/models/589972
modelVersions.downloaded: true
modelVersions.downloadUrl: https://civitai.com/api/download/models/589972
modelVersions.AutoV1: 8832A9B8
modelVersions.AutoV2: 2CAAC0E1B3
modelVersions.AutoV3: B7E777AF0F53
modelVersions.BLAKE3: 8C4AB417B53404877DE00AE5BD6335B883B0518A8E41922F3A67BDB3148A29D1
modelVersions.CRC32: 0421E511
modelVersions.SHA256: 2CAAC0E1B357D8F2844B8EBD8A1507C34F72834F2A06B9301D509536636DD390
modelVersions.format: SafeTensor
modelVersions.name: EXPERIMENTBUILDINGS_-indoors.safetensors
modelVersions.pickleScanMessage: No Pickle imports
modelVersions.pickleScanResult: Success
modelVersions.scannedAt: 2024-06-23T14:06:14.387Z
modelVersions.type: Model
modelVersions.virusScanResult: Success
modelVersions.downloadUrl: https://civitai.com/api/download/models/589972?type=Training%20Data
modelVersions.AutoV1: 9AF78AD6
modelVersions.AutoV2: 55190AA32A
modelVersions.BLAKE3: B82A84360F561B87445BE65C3D41368DD5CAF8278E20DDC2155325F1408E4458
modelVersions.CRC32: 4C8270D3
modelVersions.SHA256: 55190AA32A224E0C878DD2312ADAC561C03CA7F588EB10C0B0980BFF2B676657
modelVersions.format: Other
modelVersions.name: 589972_training_data.zip
modelVersions.pickleScanMessage: No Pickle imports
modelVersions.pickleScanResult: Success
modelVersions.scannedAt: 2024-06-22T10:15:42.161Z
modelVersions.type: Training Data
modelVersions.virusScanResult: Success
modelVersions.hash: U8Hm[Y020f=W$v56~C=|0f?Z9GJC}tn79You
modelVersions.type: image
modelVersions.url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/026a7fd3-fc08-4e73-93ed-ed4680f2e220/width=450/16879742.jpeg
modelVersions.hash: UBCZOw}q4T?b?^Io4n%LRRNHNHog00D*k?E2
modelVersions.type: image
modelVersions.url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/368223fc-960c-4931-9181-1c4bd3b81ae3/width=450/16879894.jpeg
modelVersions.hash: UDCY,WGH~Wo}EM?Gogt79GrW9aaKMx9GIoRO
modelVersions.type: image
modelVersions.url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/cb79112a-0421-4ca2-a9a2-796746ab13d5/width=450/16879893.jpeg
modelVersions.hash: ULEof6~q-;-;R4Rj%LofNGM{9FIU-:-pRjWC
modelVersions.type: image
modelVersions.url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/8c9b50c6-0b66-43eb-aa89-606ac558aef1/width=450/16879848.jpeg
modelVersions.hash: U6Bq6j00y@DOnhDj_NIpY+_21j?v0~?vL}%2
modelVersions.type: image
modelVersions.url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/fa1572b2-9a30-4e08-bf1b-6cda30d5e423/width=450/16879831.jpeg
modelVersions.hash: UBDl[y};00PUYkJ.vz$M?a%L9b4=x[xu?F-n
modelVersions.type: image
modelVersions.url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/81029b30-1bb6-4689-b07a-0087ff93e496/width=450/16879817.jpeg
modelVersions.hash: UCHeI8Dj0L_2};%1_NIA.99YI9%gpcRPWAS3
modelVersions.type: image
modelVersions.url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/1f417b8f-8846-401b-80e5-afb33545d994/width=450/16879820.jpeg
modelVersions.hash: UDCY?qGb~V.8^09^snxu4:=xR:xF0z#7E2aJ
modelVersions.type: image
modelVersions.url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/ab8720f0-2bd6-45c8-ab29-cbef12387ade/width=450/16879874.jpeg
modelVersions.hash: UMGIM;Mc56x^%$00s,_2~qRORPogxaRjofxu
modelVersions.type: image
modelVersions.url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/6a612021-9e10-49c9-94e8-7ce5c48aa075/width=450/16879815.jpeg
modelVersions.hash: UEE.X[,.~qb^%fD%%Mt701XnIVwc8_%MD$IV
modelVersions.type: image
modelVersions.url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/e00501a2-f116-4d9c-8469-b5edebe0efe0/width=450/16879882.jpeg
modelVersions.hash: UGFFdBtRo#?b~q9FE1%M_3M{00V@-;M{D%ae
modelVersions.type: image
modelVersions.url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/88460a93-eb7e-4b3e-8192-e2a7edddee53/width=450/16879847.jpeg
modelVersions.hash: UEFE[mtlGG~U^MOtM|?G0M$zwJE2Osn3Rj9u
modelVersions.type: image
modelVersions.url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/bcb0d02e-d0e9-469a-9c19-6e4512afc571/width=450/16879872.jpeg
modelVersions.hash: UCELjJ*0-;-;Mw?a?aw{rD4:IAoz00DiIUD%
modelVersions.type: image
modelVersions.url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/96e2b5f4-80f1-43da-81a7-33daf08d6605/width=450/16879875.jpeg
modelVersions.hash: UFEBsq?^t.?INKSi^j%MEgofaeNa0KI99FD%
modelVersions.type: image
modelVersions.url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/0370a467-4a87-480c-b9d9-0f9acde26733/width=450/16879880.jpeg
modelVersions.hash: U6ASf7$j00.8%NNaIn%M0KI:}tRPH?xD-:Mx
modelVersions.type: image
modelVersions.url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/cafa725d-141d-4d63-a6a2-87ad4dcd6f6d/width=450/16879846.jpeg
modelVersions.hash: UBCFq.*0F|~B}qE14;-U01DO0LIUTd^*-pIq
modelVersions.type: image
modelVersions.url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/764f6560-9647-492f-8dfd-50d75ecc86ab/width=450/16879884.jpeg
modelVersions.hash: U6A,j_K700000M%1x[WA00$epI~pMvE2^+kY
modelVersions.type: image
modelVersions.url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/2fae3292-b657-4c7f-9711-ca7c824ecd44/width=450/16879885.jpeg
modelVersions.hash: UPFh@ik?.9R5_N-;?bR*tmxZ-pM|WBVtR+fk
modelVersions.type: image
modelVersions.url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/55287eb0-2f24-4d31-81cd-6125e63ac56c/width=450/16879895.jpeg
modelVersions.hash: UGEoug%K0L_N_MIpE1?bo~V@Z~%MOaM|VY%M
modelVersions.type: image
modelVersions.url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/c3733138-ea36-41a5-9482-e124efa5a608/width=450/16880179.jpeg
modelVersions.name: FLW_indoors
modelVersions.publishedAt: 2024-06-23T14:47:07.664Z
modelVersions.trainedWords: flwindoors room
name: EXPERIMENT:BUILDINGS -indoors
tags: concept interiors furniture scenery indoors room windows