hzwer / iccv2019-learningtopaint
Teach Machines to Paint
Prediction
hzwer/iccv2019-learningtopaint:690cd9bfaecc6f227a4ee3cc5b5516f3859d6915125101144a7c241be4cbfc9cIDbjkmjyhddjd6la3sxpzlyuctleStatusSucceededSourceWebHardware–Total duration–CreatedInput
- image
- renderer
- triangle
{ "image": "https://replicate.delivery/mgxm/175a4668-2151-40e5-8499-e6e84af10542/lisa.png", "renderer": "triangle" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run hzwer/iccv2019-learningtopaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hzwer/iccv2019-learningtopaint:690cd9bfaecc6f227a4ee3cc5b5516f3859d6915125101144a7c241be4cbfc9c", { input: { image: "https://replicate.delivery/mgxm/175a4668-2151-40e5-8499-e6e84af10542/lisa.png", renderer: "triangle" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun hzwer/iccv2019-learningtopaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hzwer/iccv2019-learningtopaint:690cd9bfaecc6f227a4ee3cc5b5516f3859d6915125101144a7c241be4cbfc9c", input={ "image": "https://replicate.delivery/mgxm/175a4668-2151-40e5-8499-e6e84af10542/lisa.png", "renderer": "triangle" } ) # The hzwer/iccv2019-learningtopaint model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/hzwer/iccv2019-learningtopaint/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run hzwer/iccv2019-learningtopaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "hzwer/iccv2019-learningtopaint:690cd9bfaecc6f227a4ee3cc5b5516f3859d6915125101144a7c241be4cbfc9c", "input": { "image": "https://replicate.delivery/mgxm/175a4668-2151-40e5-8499-e6e84af10542/lisa.png", "renderer": "triangle" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2021-08-27T22:56:23.984032Z", "created_at": "2021-08-27T22:52:53.259047Z", "data_removed": false, "error": null, "id": "bjkmjyhddjd6la3sxpzlyuctle", "input": { "image": "https://replicate.delivery/mgxm/175a4668-2151-40e5-8499-e6e84af10542/lisa.png", "renderer": "triangle" }, "logs": "canvas step 0, L2Loss = 0.03997287154197693\ncanvas step 1, L2Loss = 0.022229744121432304\ncanvas step 2, L2Loss = 0.01675523817539215\ncanvas step 3, L2Loss = 0.013573813252151012\ncanvas step 4, L2Loss = 0.012320900335907936\ncanvas step 5, L2Loss = 0.011542919091880322\ncanvas step 6, L2Loss = 0.010394248180091381\ncanvas step 7, L2Loss = 0.00984362605959177\ncanvas step 8, L2Loss = 0.009365100413560867\ncanvas step 9, L2Loss = 0.008832967840135098\ncanvas step 10, L2Loss = 0.008636057376861572\ncanvas step 11, L2Loss = 0.008324887603521347\ncanvas step 12, L2Loss = 0.007920647971332073\ncanvas step 13, L2Loss = 0.007482276763767004\ncanvas step 14, L2Loss = 0.00717482715845108\ncanvas step 15, L2Loss = 0.007089617662131786\ncanvas step 16, L2Loss = 0.006491135340183973\ncanvas step 17, L2Loss = 0.006249904166907072\ncanvas step 18, L2Loss = 0.006046295166015625\ncanvas step 19, L2Loss = 0.005860033445060253\ncanvas step 20, L2Loss = 0.005872267764061689\ncanvas step 21, L2Loss = 0.00566840311512351\ncanvas step 22, L2Loss = 0.005612283945083618\ncanvas step 23, L2Loss = 0.005540042649954557\ncanvas step 24, L2Loss = 0.005471577402204275\ncanvas step 25, L2Loss = 0.005446691066026688\ncanvas step 26, L2Loss = 0.005451116245239973\ncanvas step 27, L2Loss = 0.00531425978988409\ncanvas step 28, L2Loss = 0.005194230005145073\ncanvas step 29, L2Loss = 0.005153748206794262\ncanvas step 30, L2Loss = 0.0051300255581736565\ncanvas step 31, L2Loss = 0.005130386911332607\ncanvas step 32, L2Loss = 0.005068782716989517\ncanvas step 33, L2Loss = 0.004991540685296059\ncanvas step 34, L2Loss = 0.0049132308922708035\ncanvas step 35, L2Loss = 0.004845760762691498\ncanvas step 36, L2Loss = 0.004773181863129139\ncanvas step 37, L2Loss = 0.004727147053927183\ncanvas step 38, L2Loss = 0.004669420421123505\ncanvas step 39, L2Loss = 0.004620404914021492\ndivided canvas step 0, L2Loss = 0.003307740669697523\ndivided canvas step 1, L2Loss = 0.003022675635293126\ndivided canvas step 2, L2Loss = 0.002739946125075221\ndivided canvas step 3, L2Loss = 0.0025829109363257885\ndivided canvas step 4, L2Loss = 0.0024592261761426926\ndivided canvas step 5, L2Loss = 0.0022820893209427595\ndivided canvas step 6, L2Loss = 0.0021267924457788467\ndivided canvas step 7, L2Loss = 0.002044535940513015\ndivided canvas step 8, L2Loss = 0.0019409225787967443\ndivided canvas step 9, L2Loss = 0.0018689618445932865\ndivided canvas step 10, L2Loss = 0.0018029434140771627\ndivided canvas step 11, L2Loss = 0.0017460198141634464\ndivided canvas step 12, L2Loss = 0.001700163003988564\ndivided canvas step 13, L2Loss = 0.001670832629315555\ndivided canvas step 14, L2Loss = 0.001637538312934339\ndivided canvas step 15, L2Loss = 0.0016061669448390603\ndivided canvas step 16, L2Loss = 0.0015731289749965072\ndivided canvas step 17, L2Loss = 0.0015442294534295797\ndivided canvas step 18, L2Loss = 0.0015228502452373505\ndivided canvas step 19, L2Loss = 0.001496538519859314\ndivided canvas step 20, L2Loss = 0.0014746275264769793\ndivided canvas step 21, L2Loss = 0.001459478517062962\ndivided canvas step 22, L2Loss = 0.001444359077140689\ndivided canvas step 23, L2Loss = 0.0014375821920111775\ndivided canvas step 24, L2Loss = 0.0014250167878344655\ndivided canvas step 25, L2Loss = 0.0014068989548832178\ndivided canvas step 26, L2Loss = 0.0013894503936171532\ndivided canvas step 27, L2Loss = 0.0013786253985017538\ndivided canvas step 28, L2Loss = 0.001360491500236094\ndivided canvas step 29, L2Loss = 0.001350260223262012\ndivided canvas step 30, L2Loss = 0.0013426643563434482\ndivided canvas step 31, L2Loss = 0.0013279904378578067\ndivided canvas step 32, L2Loss = 0.0013150947634130716\ndivided canvas step 33, L2Loss = 0.0013005108339712024\ndivided canvas step 34, L2Loss = 0.0012901554582640529\ndivided canvas step 35, L2Loss = 0.0012693328317254782\ndivided canvas step 36, L2Loss = 0.0012676238548010588\ndivided canvas step 37, L2Loss = 0.0012575527653098106\ndivided canvas step 38, L2Loss = 0.0012474938994273543\ndivided canvas step 39, L2Loss = 0.0012422115541994572\ngenerating gif", "metrics": { "total_time": 210.724985 }, "output": [ { "file": "https://replicate.delivery/mgxm/3107c8fd-00ee-42d1-9334-ed130e0d63a5/out.gif" } ], "started_at": "2021-11-30T21:53:44.881798Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/bjkmjyhddjd6la3sxpzlyuctle", "cancel": "https://api.replicate.com/v1/predictions/bjkmjyhddjd6la3sxpzlyuctle/cancel" }, "version": "690cd9bfaecc6f227a4ee3cc5b5516f3859d6915125101144a7c241be4cbfc9c" }canvas step 0, L2Loss = 0.03997287154197693 canvas step 1, L2Loss = 0.022229744121432304 canvas step 2, L2Loss = 0.01675523817539215 canvas step 3, L2Loss = 0.013573813252151012 canvas step 4, L2Loss = 0.012320900335907936 canvas step 5, L2Loss = 0.011542919091880322 canvas step 6, L2Loss = 0.010394248180091381 canvas step 7, L2Loss = 0.00984362605959177 canvas step 8, L2Loss = 0.009365100413560867 canvas step 9, L2Loss = 0.008832967840135098 canvas step 10, L2Loss = 0.008636057376861572 canvas step 11, L2Loss = 0.008324887603521347 canvas step 12, L2Loss = 0.007920647971332073 canvas step 13, L2Loss = 0.007482276763767004 canvas step 14, L2Loss = 0.00717482715845108 canvas step 15, L2Loss = 0.007089617662131786 canvas step 16, L2Loss = 0.006491135340183973 canvas step 17, L2Loss = 0.006249904166907072 canvas step 18, L2Loss = 0.006046295166015625 canvas step 19, L2Loss = 0.005860033445060253 canvas step 20, L2Loss = 0.005872267764061689 canvas step 21, L2Loss = 0.00566840311512351 canvas step 22, L2Loss = 0.005612283945083618 canvas step 23, L2Loss = 0.005540042649954557 canvas step 24, L2Loss = 0.005471577402204275 canvas step 25, L2Loss = 0.005446691066026688 canvas step 26, L2Loss = 0.005451116245239973 canvas step 27, L2Loss = 0.00531425978988409 canvas step 28, L2Loss = 0.005194230005145073 canvas step 29, L2Loss = 0.005153748206794262 canvas step 30, L2Loss = 0.0051300255581736565 canvas step 31, L2Loss = 0.005130386911332607 canvas step 32, L2Loss = 0.005068782716989517 canvas step 33, L2Loss = 0.004991540685296059 canvas step 34, L2Loss = 0.0049132308922708035 canvas step 35, L2Loss = 0.004845760762691498 canvas step 36, L2Loss = 0.004773181863129139 canvas step 37, L2Loss = 0.004727147053927183 canvas step 38, L2Loss = 0.004669420421123505 canvas step 39, L2Loss = 0.004620404914021492 divided canvas step 0, L2Loss = 0.003307740669697523 divided canvas step 1, L2Loss = 0.003022675635293126 divided canvas step 2, L2Loss = 0.002739946125075221 divided canvas step 3, L2Loss = 0.0025829109363257885 divided canvas step 4, L2Loss = 0.0024592261761426926 divided canvas step 5, L2Loss = 0.0022820893209427595 divided canvas step 6, L2Loss = 0.0021267924457788467 divided canvas step 7, L2Loss = 0.002044535940513015 divided canvas step 8, L2Loss = 0.0019409225787967443 divided canvas step 9, L2Loss = 0.0018689618445932865 divided canvas step 10, L2Loss = 0.0018029434140771627 divided canvas step 11, L2Loss = 0.0017460198141634464 divided canvas step 12, L2Loss = 0.001700163003988564 divided canvas step 13, L2Loss = 0.001670832629315555 divided canvas step 14, L2Loss = 0.001637538312934339 divided canvas step 15, L2Loss = 0.0016061669448390603 divided canvas step 16, L2Loss = 0.0015731289749965072 divided canvas step 17, L2Loss = 0.0015442294534295797 divided canvas step 18, L2Loss = 0.0015228502452373505 divided canvas step 19, L2Loss = 0.001496538519859314 divided canvas step 20, L2Loss = 0.0014746275264769793 divided canvas step 21, L2Loss = 0.001459478517062962 divided canvas step 22, L2Loss = 0.001444359077140689 divided canvas step 23, L2Loss = 0.0014375821920111775 divided canvas step 24, L2Loss = 0.0014250167878344655 divided canvas step 25, L2Loss = 0.0014068989548832178 divided canvas step 26, L2Loss = 0.0013894503936171532 divided canvas step 27, L2Loss = 0.0013786253985017538 divided canvas step 28, L2Loss = 0.001360491500236094 divided canvas step 29, L2Loss = 0.001350260223262012 divided canvas step 30, L2Loss = 0.0013426643563434482 divided canvas step 31, L2Loss = 0.0013279904378578067 divided canvas step 32, L2Loss = 0.0013150947634130716 divided canvas step 33, L2Loss = 0.0013005108339712024 divided canvas step 34, L2Loss = 0.0012901554582640529 divided canvas step 35, L2Loss = 0.0012693328317254782 divided canvas step 36, L2Loss = 0.0012676238548010588 divided canvas step 37, L2Loss = 0.0012575527653098106 divided canvas step 38, L2Loss = 0.0012474938994273543 divided canvas step 39, L2Loss = 0.0012422115541994572 generating gifPrediction
hzwer/iccv2019-learningtopaint:690cd9bfaecc6f227a4ee3cc5b5516f3859d6915125101144a7c241be4cbfc9cIDfbbn7fkuavdr3lpdi6p3l7s6paStatusSucceededSourceWebHardware–Total duration–CreatedInput
- image
- renderer
- round
{ "image": "https://replicate.delivery/mgxm/100d3786-1a9b-4b31-bf8a-9bf9a8afa5d5/lisa.png", "renderer": "round" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run hzwer/iccv2019-learningtopaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hzwer/iccv2019-learningtopaint:690cd9bfaecc6f227a4ee3cc5b5516f3859d6915125101144a7c241be4cbfc9c", { input: { image: "https://replicate.delivery/mgxm/100d3786-1a9b-4b31-bf8a-9bf9a8afa5d5/lisa.png", renderer: "round" } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun hzwer/iccv2019-learningtopaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hzwer/iccv2019-learningtopaint:690cd9bfaecc6f227a4ee3cc5b5516f3859d6915125101144a7c241be4cbfc9c", input={ "image": "https://replicate.delivery/mgxm/100d3786-1a9b-4b31-bf8a-9bf9a8afa5d5/lisa.png", "renderer": "round" } ) # The hzwer/iccv2019-learningtopaint model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/hzwer/iccv2019-learningtopaint/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run hzwer/iccv2019-learningtopaint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "hzwer/iccv2019-learningtopaint:690cd9bfaecc6f227a4ee3cc5b5516f3859d6915125101144a7c241be4cbfc9c", "input": { "image": "https://replicate.delivery/mgxm/100d3786-1a9b-4b31-bf8a-9bf9a8afa5d5/lisa.png", "renderer": "round" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2021-08-27T22:56:37.136961Z", "created_at": "2021-08-27T22:53:02.336235Z", "data_removed": false, "error": null, "id": "fbbn7fkuavdr3lpdi6p3l7s6pa", "input": { "image": "https://replicate.delivery/mgxm/100d3786-1a9b-4b31-bf8a-9bf9a8afa5d5/lisa.png", "renderer": "round" }, "logs": "canvas step 0, L2Loss = 0.039006251841783524\ncanvas step 1, L2Loss = 0.022239740937948227\ncanvas step 2, L2Loss = 0.0170460008084774\ncanvas step 3, L2Loss = 0.014569155871868134\ncanvas step 4, L2Loss = 0.01293716486543417\ncanvas step 5, L2Loss = 0.011259777471423149\ncanvas step 6, L2Loss = 0.010296232998371124\ncanvas step 7, L2Loss = 0.009566431865096092\ncanvas step 8, L2Loss = 0.008865758776664734\ncanvas step 9, L2Loss = 0.00825444608926773\ncanvas step 10, L2Loss = 0.00770191615447402\ncanvas step 11, L2Loss = 0.007237820886075497\ncanvas step 12, L2Loss = 0.006821040064096451\ncanvas step 13, L2Loss = 0.0066191209480166435\ncanvas step 14, L2Loss = 0.006359129212796688\ncanvas step 15, L2Loss = 0.006161543540656567\ncanvas step 16, L2Loss = 0.006057530641555786\ncanvas step 17, L2Loss = 0.005871483590453863\ncanvas step 18, L2Loss = 0.00567550677806139\ncanvas step 19, L2Loss = 0.005536259617656469\ncanvas step 20, L2Loss = 0.0052982657216489315\ncanvas step 21, L2Loss = 0.005161481909453869\ncanvas step 22, L2Loss = 0.004977410193532705\ncanvas step 23, L2Loss = 0.004831238649785519\ncanvas step 24, L2Loss = 0.00470705283805728\ncanvas step 25, L2Loss = 0.004578921012580395\ncanvas step 26, L2Loss = 0.004466079641133547\ncanvas step 27, L2Loss = 0.004370721988379955\ncanvas step 28, L2Loss = 0.004283050075173378\ncanvas step 29, L2Loss = 0.004222136456519365\ncanvas step 30, L2Loss = 0.004123236984014511\ncanvas step 31, L2Loss = 0.004063745494931936\ncanvas step 32, L2Loss = 0.004000510089099407\ncanvas step 33, L2Loss = 0.00395746948197484\ncanvas step 34, L2Loss = 0.0038879455532878637\ncanvas step 35, L2Loss = 0.0038289721123874187\ncanvas step 36, L2Loss = 0.003769268048927188\ncanvas step 37, L2Loss = 0.0037203095853328705\ncanvas step 38, L2Loss = 0.003681522561237216\ncanvas step 39, L2Loss = 0.0036464135628193617\ndivided canvas step 0, L2Loss = 0.0026806246023625135\ndivided canvas step 1, L2Loss = 0.002533277263864875\ndivided canvas step 2, L2Loss = 0.0023449461441487074\ndivided canvas step 3, L2Loss = 0.0021970183588564396\ndivided canvas step 4, L2Loss = 0.0021001093555241823\ndivided canvas step 5, L2Loss = 0.0020208447240293026\ndivided canvas step 6, L2Loss = 0.0019504756201058626\ndivided canvas step 7, L2Loss = 0.0018901836592704058\ndivided canvas step 8, L2Loss = 0.0018446579342707992\ndivided canvas step 9, L2Loss = 0.0017991707427427173\ndivided canvas step 10, L2Loss = 0.0017473113257437944\ndivided canvas step 11, L2Loss = 0.0016920140478760004\ndivided canvas step 12, L2Loss = 0.0016357189742848277\ndivided canvas step 13, L2Loss = 0.00158073497004807\ndivided canvas step 14, L2Loss = 0.0015281356172636151\ndivided canvas step 15, L2Loss = 0.001490775728598237\ndivided canvas step 16, L2Loss = 0.0014448269503191113\ndivided canvas step 17, L2Loss = 0.0014031952014192939\ndivided canvas step 18, L2Loss = 0.0013759138528257608\ndivided canvas step 19, L2Loss = 0.001343616982921958\ndivided canvas step 20, L2Loss = 0.0013085637474432588\ndivided canvas step 21, L2Loss = 0.0012788322055712342\ndivided canvas step 22, L2Loss = 0.0012566358782351017\ndivided canvas step 23, L2Loss = 0.0012317112414166331\ndivided canvas step 24, L2Loss = 0.0012085968628525734\ndivided canvas step 25, L2Loss = 0.001186839654110372\ndivided canvas step 26, L2Loss = 0.0011676127323880792\ndivided canvas step 27, L2Loss = 0.0011542171705514193\ndivided canvas step 28, L2Loss = 0.0011350155109539628\ndivided canvas step 29, L2Loss = 0.0011226956266909838\ndivided canvas step 30, L2Loss = 0.0011087976163253188\ndivided canvas step 31, L2Loss = 0.0010976565536111593\ndivided canvas step 32, L2Loss = 0.0010864132782444358\ndivided canvas step 33, L2Loss = 0.001074790838174522\ndivided canvas step 34, L2Loss = 0.0010628047166392207\ndivided canvas step 35, L2Loss = 0.0010551243321970105\ndivided canvas step 36, L2Loss = 0.0010432746494188905\ndivided canvas step 37, L2Loss = 0.001033053733408451\ndivided canvas step 38, L2Loss = 0.001022878335788846\ndivided canvas step 39, L2Loss = 0.0010109224822372198\ngenerating gif", "metrics": {}, "output": [ { "file": "https://replicate.delivery/mgxm/df2d637d-e1ca-42db-a169-6b811f6c2c01/out.gif" } ], "started_at": null, "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fbbn7fkuavdr3lpdi6p3l7s6pa", "cancel": "https://api.replicate.com/v1/predictions/fbbn7fkuavdr3lpdi6p3l7s6pa/cancel" }, "version": "690cd9bfaecc6f227a4ee3cc5b5516f3859d6915125101144a7c241be4cbfc9c" }canvas step 0, L2Loss = 0.039006251841783524 canvas step 1, L2Loss = 0.022239740937948227 canvas step 2, L2Loss = 0.0170460008084774 canvas step 3, L2Loss = 0.014569155871868134 canvas step 4, L2Loss = 0.01293716486543417 canvas step 5, L2Loss = 0.011259777471423149 canvas step 6, L2Loss = 0.010296232998371124 canvas step 7, L2Loss = 0.009566431865096092 canvas step 8, L2Loss = 0.008865758776664734 canvas step 9, L2Loss = 0.00825444608926773 canvas step 10, L2Loss = 0.00770191615447402 canvas step 11, L2Loss = 0.007237820886075497 canvas step 12, L2Loss = 0.006821040064096451 canvas step 13, L2Loss = 0.0066191209480166435 canvas step 14, L2Loss = 0.006359129212796688 canvas step 15, L2Loss = 0.006161543540656567 canvas step 16, L2Loss = 0.006057530641555786 canvas step 17, L2Loss = 0.005871483590453863 canvas step 18, L2Loss = 0.00567550677806139 canvas step 19, L2Loss = 0.005536259617656469 canvas step 20, L2Loss = 0.0052982657216489315 canvas step 21, L2Loss = 0.005161481909453869 canvas step 22, L2Loss = 0.004977410193532705 canvas step 23, L2Loss = 0.004831238649785519 canvas step 24, L2Loss = 0.00470705283805728 canvas step 25, L2Loss = 0.004578921012580395 canvas step 26, L2Loss = 0.004466079641133547 canvas step 27, L2Loss = 0.004370721988379955 canvas step 28, L2Loss = 0.004283050075173378 canvas step 29, L2Loss = 0.004222136456519365 canvas step 30, L2Loss = 0.004123236984014511 canvas step 31, L2Loss = 0.004063745494931936 canvas step 32, L2Loss = 0.004000510089099407 canvas step 33, L2Loss = 0.00395746948197484 canvas step 34, L2Loss = 0.0038879455532878637 canvas step 35, L2Loss = 0.0038289721123874187 canvas step 36, L2Loss = 0.003769268048927188 canvas step 37, L2Loss = 0.0037203095853328705 canvas step 38, L2Loss = 0.003681522561237216 canvas step 39, L2Loss = 0.0036464135628193617 divided canvas step 0, L2Loss = 0.0026806246023625135 divided canvas step 1, L2Loss = 0.002533277263864875 divided canvas step 2, L2Loss = 0.0023449461441487074 divided canvas step 3, L2Loss = 0.0021970183588564396 divided canvas step 4, L2Loss = 0.0021001093555241823 divided canvas step 5, L2Loss = 0.0020208447240293026 divided canvas step 6, L2Loss = 0.0019504756201058626 divided canvas step 7, L2Loss = 0.0018901836592704058 divided canvas step 8, L2Loss = 0.0018446579342707992 divided canvas step 9, L2Loss = 0.0017991707427427173 divided canvas step 10, L2Loss = 0.0017473113257437944 divided canvas step 11, L2Loss = 0.0016920140478760004 divided canvas step 12, L2Loss = 0.0016357189742848277 divided canvas step 13, L2Loss = 0.00158073497004807 divided canvas step 14, L2Loss = 0.0015281356172636151 divided canvas step 15, L2Loss = 0.001490775728598237 divided canvas step 16, L2Loss = 0.0014448269503191113 divided canvas step 17, L2Loss = 0.0014031952014192939 divided canvas step 18, L2Loss = 0.0013759138528257608 divided canvas step 19, L2Loss = 0.001343616982921958 divided canvas step 20, L2Loss = 0.0013085637474432588 divided canvas step 21, L2Loss = 0.0012788322055712342 divided canvas step 22, L2Loss = 0.0012566358782351017 divided canvas step 23, L2Loss = 0.0012317112414166331 divided canvas step 24, L2Loss = 0.0012085968628525734 divided canvas step 25, L2Loss = 0.001186839654110372 divided canvas step 26, L2Loss = 0.0011676127323880792 divided canvas step 27, L2Loss = 0.0011542171705514193 divided canvas step 28, L2Loss = 0.0011350155109539628 divided canvas step 29, L2Loss = 0.0011226956266909838 divided canvas step 30, L2Loss = 0.0011087976163253188 divided canvas step 31, L2Loss = 0.0010976565536111593 divided canvas step 32, L2Loss = 0.0010864132782444358 divided canvas step 33, L2Loss = 0.001074790838174522 divided canvas step 34, L2Loss = 0.0010628047166392207 divided canvas step 35, L2Loss = 0.0010551243321970105 divided canvas step 36, L2Loss = 0.0010432746494188905 divided canvas step 37, L2Loss = 0.001033053733408451 divided canvas step 38, L2Loss = 0.001022878335788846 divided canvas step 39, L2Loss = 0.0010109224822372198 generating gif
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