arielreplicate / scalable_diffusion_with_transformers
Latent diffusion models, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches
Prediction
arielreplicate/scalable_diffusion_with_transformers:089c3506e8fb6cdc7f7b0165cac893f1ae03e044fbff88ef7164d9f0b0dead85IDg7a6k4brknhrpmb2evkdfknw2mStatusSucceededSourceWebHardware–Total durationCreatedInput
- cfg_scale
- 4
- class_name
- centipede
- VAE_Decoder
- sd-vae-ft-mse
- num_outputs
- 4
- DiT_resolution
- 256x256
- num_sampling_steps
- 250
{ "cfg_scale": 4, "class_name": "centipede", "VAE_Decoder": "sd-vae-ft-mse", "num_outputs": 4, "DiT_resolution": "256x256", "num_sampling_steps": 250 }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 arielreplicate/scalable_diffusion_with_transformers using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "arielreplicate/scalable_diffusion_with_transformers:089c3506e8fb6cdc7f7b0165cac893f1ae03e044fbff88ef7164d9f0b0dead85", { input: { cfg_scale: 4, class_name: "centipede", VAE_Decoder: "sd-vae-ft-mse", num_outputs: 4, DiT_resolution: "256x256", num_sampling_steps: 250 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 arielreplicate/scalable_diffusion_with_transformers using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "arielreplicate/scalable_diffusion_with_transformers:089c3506e8fb6cdc7f7b0165cac893f1ae03e044fbff88ef7164d9f0b0dead85", input={ "cfg_scale": 4, "class_name": "centipede", "VAE_Decoder": "sd-vae-ft-mse", "num_outputs": 4, "DiT_resolution": "256x256", "num_sampling_steps": 250 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run arielreplicate/scalable_diffusion_with_transformers 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": "arielreplicate/scalable_diffusion_with_transformers:089c3506e8fb6cdc7f7b0165cac893f1ae03e044fbff88ef7164d9f0b0dead85", "input": { "cfg_scale": 4, "class_name": "centipede", "VAE_Decoder": "sd-vae-ft-mse", "num_outputs": 4, "DiT_resolution": "256x256", "num_sampling_steps": 250 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-12-23T10:47:58.291190Z", "created_at": "2022-12-23T10:40:01.144556Z", "data_removed": false, "error": null, "id": "g7a6k4brknhrpmb2evkdfknw2m", "input": { "cfg_scale": 4, "class_name": "centipede", "VAE_Decoder": "sd-vae-ft-mse", "num_outputs": 4, "DiT_resolution": "256x256", "num_sampling_steps": 250 }, "logs": "0%| | 0/250 [00:00<?, ?it/s]\n 0%| | 1/250 [00:04<17:24, 4.20s/it]\n 1%| | 2/250 [00:04<08:22, 2.02s/it]\n 1%| | 3/250 [00:05<05:32, 1.35s/it]\n 2%|▏ | 4/250 [00:05<04:11, 1.02s/it]\n 2%|▏ | 5/250 [00:06<03:26, 1.19it/s]\n 2%|▏ | 6/250 [00:06<02:59, 1.36it/s]\n 3%|▎ | 7/250 [00:07<02:42, 1.49it/s]\n 3%|▎ | 8/250 [00:07<02:31, 1.60it/s]\n 4%|▎ | 9/250 [00:08<02:23, 1.68it/s]\n 4%|▍ | 10/250 [00:08<02:18, 1.74it/s]\n 4%|▍ | 11/250 [00:09<02:14, 1.78it/s]\n 5%|▍ | 12/250 [00:10<02:11, 1.81it/s]\n 5%|▌ | 13/250 [00:10<02:09, 1.83it/s]\n 6%|▌ | 14/250 [00:11<02:08, 1.84it/s]\n 6%|▌ | 15/250 [00:11<02:07, 1.84it/s]\n 6%|▋ | 16/250 [00:12<02:06, 1.86it/s]\n 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94%|█████████▎| 234/250 [02:14<00:09, 1.70it/s]\n 94%|█████████▍| 235/250 [02:15<00:08, 1.71it/s]\n 94%|█████████▍| 236/250 [02:15<00:08, 1.72it/s]\n 95%|█████████▍| 237/250 [02:16<00:07, 1.72it/s]\n 95%|█████████▌| 238/250 [02:16<00:06, 1.72it/s]\n 96%|█████████▌| 239/250 [02:17<00:06, 1.72it/s]\n 96%|█████████▌| 240/250 [02:18<00:05, 1.72it/s]\n 96%|█████████▋| 241/250 [02:18<00:05, 1.72it/s]\n 97%|█████████▋| 242/250 [02:19<00:04, 1.71it/s]\n 97%|█████████▋| 243/250 [02:19<00:04, 1.72it/s]\n 98%|█████████▊| 244/250 [02:20<00:03, 1.72it/s]\n 98%|█████████▊| 245/250 [02:20<00:02, 1.72it/s]\n 98%|█████████▊| 246/250 [02:21<00:02, 1.72it/s]\n 99%|█████████▉| 247/250 [02:22<00:01, 1.71it/s]\n 99%|█████████▉| 248/250 [02:22<00:01, 1.71it/s]\n100%|█████████▉| 249/250 [02:23<00:00, 1.71it/s]\n100%|██████████| 250/250 [02:23<00:00, 1.71it/s]\n100%|██████████| 250/250 [02:23<00:00, 1.74it/s]", "metrics": { "predict_time": 145.536039, "total_time": 477.146634 }, "output": [ "https://replicate.delivery/pbxt/KspexOUj2z0FXqUUDX5GEqWoePSMHMTgv3CbbG46OJ9dlzMQA/output-0.png", "https://replicate.delivery/pbxt/JRXZmyMj415cHpyBlg2ljmuKr9Da53Z4N3Co9nUGe3fdlzMQA/output-1.png", "https://replicate.delivery/pbxt/Od6f1nFZYXwlICDFG9BUwfOmhm7mEynlwBV4VnAX6KcdlzMQA/output-2.png", "https://replicate.delivery/pbxt/ciN6jPOgJTbKPVoq2iuepVYxoWqure7mwckylCOebuQ9KnZgA/output-3.png" ], "started_at": "2022-12-23T10:45:32.755151Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/g7a6k4brknhrpmb2evkdfknw2m", "cancel": "https://api.replicate.com/v1/predictions/g7a6k4brknhrpmb2evkdfknw2m/cancel" }, "version": "089c3506e8fb6cdc7f7b0165cac893f1ae03e044fbff88ef7164d9f0b0dead85" }Generated in0%| | 0/250 [00:00<?, ?it/s] 0%| | 1/250 [00:04<17:24, 4.20s/it] 1%| | 2/250 [00:04<08:22, 2.02s/it] 1%| | 3/250 [00:05<05:32, 1.35s/it] 2%|▏ | 4/250 [00:05<04:11, 1.02s/it] 2%|▏ | 5/250 [00:06<03:26, 1.19it/s] 2%|▏ | 6/250 [00:06<02:59, 1.36it/s] 3%|▎ | 7/250 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arielreplicate/scalable_diffusion_with_transformers:089c3506e8fb6cdc7f7b0165cac893f1ae03e044fbff88ef7164d9f0b0dead85IDa2zgmpyjwzawnayk63qxhu5ztyStatusSucceededSourceWebHardware–Total durationCreatedInput
- cfg_scale
- 4
- class_name
- water ouzel, dipper
- VAE_Decoder
- sd-vae-ft-mse
- num_outputs
- 2
- DiT_resolution
- 512x512
- num_sampling_steps
- 250
{ "cfg_scale": 4, "class_name": "water ouzel, dipper", "VAE_Decoder": "sd-vae-ft-mse", "num_outputs": 2, "DiT_resolution": "512x512", "num_sampling_steps": 250 }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 arielreplicate/scalable_diffusion_with_transformers using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "arielreplicate/scalable_diffusion_with_transformers:089c3506e8fb6cdc7f7b0165cac893f1ae03e044fbff88ef7164d9f0b0dead85", { input: { cfg_scale: 4, class_name: "water ouzel, dipper", VAE_Decoder: "sd-vae-ft-mse", num_outputs: 2, DiT_resolution: "512x512", num_sampling_steps: 250 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 arielreplicate/scalable_diffusion_with_transformers using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "arielreplicate/scalable_diffusion_with_transformers:089c3506e8fb6cdc7f7b0165cac893f1ae03e044fbff88ef7164d9f0b0dead85", input={ "cfg_scale": 4, "class_name": "water ouzel, dipper", "VAE_Decoder": "sd-vae-ft-mse", "num_outputs": 2, "DiT_resolution": "512x512", "num_sampling_steps": 250 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run arielreplicate/scalable_diffusion_with_transformers 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": "arielreplicate/scalable_diffusion_with_transformers:089c3506e8fb6cdc7f7b0165cac893f1ae03e044fbff88ef7164d9f0b0dead85", "input": { "cfg_scale": 4, "class_name": "water ouzel, dipper", "VAE_Decoder": "sd-vae-ft-mse", "num_outputs": 2, "DiT_resolution": "512x512", "num_sampling_steps": 250 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-12-23T10:54:40.373355Z", "created_at": "2022-12-23T10:48:33.654545Z", "data_removed": false, "error": null, "id": "a2zgmpyjwzawnayk63qxhu5zty", "input": { "cfg_scale": 4, "class_name": "water ouzel, dipper", "VAE_Decoder": "sd-vae-ft-mse", "num_outputs": 2, "DiT_resolution": "512x512", "num_sampling_steps": 250 }, "logs": "0%| | 0/250 [00:00<?, ?it/s]\n 0%| | 1/250 [00:01<05:51, 1.41s/it]\n 1%| | 2/250 [00:02<05:40, 1.37s/it]\n 1%| | 3/250 [00:04<05:37, 1.37s/it]\n 2%|▏ | 4/250 [00:05<05:36, 1.37s/it]\n 2%|▏ | 5/250 [00:06<05:34, 1.37s/it]\n 2%|▏ | 6/250 [00:08<05:33, 1.37s/it]\n 3%|▎ | 7/250 [00:09<05:32, 1.37s/it]\n 3%|▎ | 8/250 [00:10<05:32, 1.37s/it]\n 4%|▎ | 9/250 [00:12<05:31, 1.38s/it]\n 4%|▍ | 10/250 [00:13<05:30, 1.38s/it]\n 4%|▍ | 11/250 [00:15<05:30, 1.38s/it]\n 5%|▍ | 12/250 [00:16<05:29, 1.38s/it]\n 5%|▌ | 13/250 [00:17<05:29, 1.39s/it]\n 6%|▌ | 14/250 [00:19<05:28, 1.39s/it]\n 6%|▌ | 15/250 [00:20<05:28, 1.40s/it]\n 6%|▋ | 16/250 [00:22<05:28, 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94%|█████████▎| 234/250 [05:41<00:23, 1.48s/it]\n 94%|█████████▍| 235/250 [05:42<00:22, 1.48s/it]\n 94%|█████████▍| 236/250 [05:43<00:20, 1.48s/it]\n 95%|█████████▍| 237/250 [05:45<00:19, 1.48s/it]\n 95%|█████████▌| 238/250 [05:46<00:17, 1.48s/it]\n 96%|█████████▌| 239/250 [05:48<00:16, 1.48s/it]\n 96%|█████████▌| 240/250 [05:49<00:14, 1.48s/it]\n 96%|█████████▋| 241/250 [05:51<00:13, 1.48s/it]\n 97%|█████████▋| 242/250 [05:52<00:11, 1.48s/it]\n 97%|█████████▋| 243/250 [05:54<00:10, 1.48s/it]\n 98%|█████████▊| 244/250 [05:55<00:08, 1.48s/it]\n 98%|█████████▊| 245/250 [05:57<00:07, 1.48s/it]\n 98%|█████████▊| 246/250 [05:58<00:05, 1.48s/it]\n 99%|█████████▉| 247/250 [06:00<00:04, 1.48s/it]\n 99%|█████████▉| 248/250 [06:01<00:02, 1.47s/it]\n100%|█████████▉| 249/250 [06:03<00:01, 1.48s/it]\n100%|██████████| 250/250 [06:04<00:00, 1.47s/it]\n100%|██████████| 250/250 [06:04<00:00, 1.46s/it]", "metrics": { "predict_time": 366.673709, "total_time": 366.71881 }, "output": [ "https://replicate.delivery/pbxt/0EfGizc7TN02PibGiCU2Kn0VRXRN1ogti0Itp2lNxjevrzMQA/output-0.png", "https://replicate.delivery/pbxt/ti23zeDFILWmICblo5msDvXdLXmxwVgmpMlmnhRrLKC41ZGIA/output-1.png" ], "started_at": "2022-12-23T10:48:33.699646Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/a2zgmpyjwzawnayk63qxhu5zty", "cancel": "https://api.replicate.com/v1/predictions/a2zgmpyjwzawnayk63qxhu5zty/cancel" }, "version": "089c3506e8fb6cdc7f7b0165cac893f1ae03e044fbff88ef7164d9f0b0dead85" }Generated in0%| | 0/250 [00:00<?, ?it/s] 0%| | 1/250 [00:01<05:51, 1.41s/it] 1%| | 2/250 [00:02<05:40, 1.37s/it] 1%| | 3/250 [00:04<05:37, 1.37s/it] 2%|▏ | 4/250 [00:05<05:36, 1.37s/it] 2%|▏ | 5/250 [00:06<05:34, 1.37s/it] 2%|▏ | 6/250 [00:08<05:33, 1.37s/it] 3%|▎ | 7/250 [00:09<05:32, 1.37s/it] 3%|▎ | 8/250 [00:10<05:32, 1.37s/it] 4%|▎ | 9/250 [00:12<05:31, 1.38s/it] 4%|▍ | 10/250 [00:13<05:30, 1.38s/it] 4%|▍ | 11/250 [00:15<05:30, 1.38s/it] 5%|▍ | 12/250 [00:16<05:29, 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arielreplicate/scalable_diffusion_with_transformers:089c3506e8fb6cdc7f7b0165cac893f1ae03e044fbff88ef7164d9f0b0dead85IDq735uuvsv5bepbupx4nmo554keStatusSucceededSourceWebHardware–Total durationCreatedInput
- cfg_scale
- "4"
- class_name
- tench, Tinca tinca
- VAE_Decoder
- sd-vae-ft-ema
- num_outputs
- 9
- DiT_resolution
- 256x256
- num_sampling_steps
- 250
{ "cfg_scale": "4", "class_name": "tench, Tinca tinca", "VAE_Decoder": "sd-vae-ft-ema", "num_outputs": 9, "DiT_resolution": "256x256", "num_sampling_steps": 250 }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 arielreplicate/scalable_diffusion_with_transformers using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "arielreplicate/scalable_diffusion_with_transformers:089c3506e8fb6cdc7f7b0165cac893f1ae03e044fbff88ef7164d9f0b0dead85", { input: { cfg_scale: "4", class_name: "tench, Tinca tinca", VAE_Decoder: "sd-vae-ft-ema", num_outputs: 9, DiT_resolution: "256x256", num_sampling_steps: 250 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 arielreplicate/scalable_diffusion_with_transformers using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "arielreplicate/scalable_diffusion_with_transformers:089c3506e8fb6cdc7f7b0165cac893f1ae03e044fbff88ef7164d9f0b0dead85", input={ "cfg_scale": "4", "class_name": "tench, Tinca tinca", "VAE_Decoder": "sd-vae-ft-ema", "num_outputs": 9, "DiT_resolution": "256x256", "num_sampling_steps": 250 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run arielreplicate/scalable_diffusion_with_transformers 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": "arielreplicate/scalable_diffusion_with_transformers:089c3506e8fb6cdc7f7b0165cac893f1ae03e044fbff88ef7164d9f0b0dead85", "input": { "cfg_scale": "4", "class_name": "tench, Tinca tinca", "VAE_Decoder": "sd-vae-ft-ema", "num_outputs": 9, "DiT_resolution": "256x256", "num_sampling_steps": 250 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-12-23T11:02:24.357466Z", "created_at": "2022-12-23T10:56:26.415990Z", "data_removed": false, "error": null, "id": "q735uuvsv5bepbupx4nmo554ke", "input": { "cfg_scale": "4", "class_name": "tench, Tinca tinca", "VAE_Decoder": "sd-vae-ft-ema", "num_outputs": 9, "DiT_resolution": "256x256", "num_sampling_steps": 250 }, "logs": "0%| | 0/250 [00:00<?, ?it/s]\n 0%| | 1/250 [00:01<05:39, 1.36s/it]\n 1%| | 2/250 [00:02<05:27, 1.32s/it]\n 1%| | 3/250 [00:03<05:23, 1.31s/it]\n 2%|▏ | 4/250 [00:05<05:22, 1.31s/it]\n 2%|▏ | 5/250 [00:06<05:21, 1.31s/it]\n 2%|▏ | 6/250 [00:07<05:19, 1.31s/it]\n 3%|▎ | 7/250 [00:09<05:19, 1.31s/it]\n 3%|▎ | 8/250 [00:10<05:18, 1.32s/it]\n 4%|▎ | 9/250 [00:11<05:17, 1.32s/it]\n 4%|▍ | 10/250 [00:13<05:17, 1.32s/it]\n 4%|▍ | 11/250 [00:14<05:16, 1.32s/it]\n 5%|▍ | 12/250 [00:15<05:16, 1.33s/it]\n 5%|▌ | 13/250 [00:17<05:15, 1.33s/it]\n 6%|▌ | 14/250 [00:18<05:14, 1.33s/it]\n 6%|▌ | 15/250 [00:19<05:14, 1.34s/it]\n 6%|▋ | 16/250 [00:21<05:14, 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94%|█████████▎| 234/250 [05:31<00:23, 1.44s/it]\n 94%|█████████▍| 235/250 [05:32<00:21, 1.44s/it]\n 94%|█████████▍| 236/250 [05:34<00:20, 1.44s/it]\n 95%|█████████▍| 237/250 [05:35<00:18, 1.44s/it]\n 95%|█████████▌| 238/250 [05:36<00:17, 1.44s/it]\n 96%|█████████▌| 239/250 [05:38<00:15, 1.44s/it]\n 96%|█████████▌| 240/250 [05:39<00:14, 1.44s/it]\n 96%|█████████▋| 241/250 [05:41<00:12, 1.44s/it]\n 97%|█████████▋| 242/250 [05:42<00:11, 1.44s/it]\n 97%|█████████▋| 243/250 [05:44<00:10, 1.44s/it]\n 98%|█████████▊| 244/250 [05:45<00:08, 1.44s/it]\n 98%|█████████▊| 245/250 [05:47<00:07, 1.44s/it]\n 98%|█████████▊| 246/250 [05:48<00:05, 1.44s/it]\n 99%|█████████▉| 247/250 [05:49<00:04, 1.44s/it]\n 99%|█████████▉| 248/250 [05:51<00:02, 1.44s/it]\n100%|█████████▉| 249/250 [05:52<00:01, 1.44s/it]\n100%|██████████| 250/250 [05:54<00:00, 1.44s/it]\n100%|██████████| 250/250 [05:54<00:00, 1.42s/it]", "metrics": { "predict_time": 357.892662, "total_time": 357.941476 }, "output": [ "https://replicate.delivery/pbxt/FFkIeREWx6ThMahcJioP1iwQeUCZwd8JXuOiWb3V3xjelnZgA/output-0.png", "https://replicate.delivery/pbxt/imVEJ0TzUwpmHFxq2pbrM9EFgEMTUCGfO2JfGTFRKG2elnZgA/output-1.png", "https://replicate.delivery/pbxt/IN1IEZl6mt6iPVqsfmSi2KZEE8GVV5PRPRVOFxBDIfpelnZgA/output-2.png", "https://replicate.delivery/pbxt/rhCtfcn0eFt410skPNGLtb5NasNLlBYOefmJfjnykFH6XeMDE/output-3.png", "https://replicate.delivery/pbxt/XV1QPNADGLquHxjcfM9eeUX5We67fWm2G8QuWDGCRwt4XeMDE/output-4.png", "https://replicate.delivery/pbxt/7KKZNGVVE9qyGpaKXZxX7F0ev35rMb4WUIk68TchenTflnZgA/output-5.png", "https://replicate.delivery/pbxt/Wju5jBcUHrqOP9vveRP26yvp9Luf0Dw1lAVllbxfbUGeLPzAB/output-6.png", "https://replicate.delivery/pbxt/GRQbJNJxPIISJle4JqrdNBCgNVRHpEwubTOBkKqFq6eflnZgA/output-7.png", "https://replicate.delivery/pbxt/gO6LToeKSHWlWSdOtNRqiS6rRdL0BheGnXfKSw6mOTiAmnZgA/output-8.png" ], "started_at": "2022-12-23T10:56:26.464804Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/q735uuvsv5bepbupx4nmo554ke", "cancel": "https://api.replicate.com/v1/predictions/q735uuvsv5bepbupx4nmo554ke/cancel" }, "version": "089c3506e8fb6cdc7f7b0165cac893f1ae03e044fbff88ef7164d9f0b0dead85" }Generated in0%| | 0/250 [00:00<?, ?it/s] 0%| | 1/250 [00:01<05:39, 1.36s/it] 1%| | 2/250 [00:02<05:27, 1.32s/it] 1%| | 3/250 [00:03<05:23, 1.31s/it] 2%|▏ | 4/250 [00:05<05:22, 1.31s/it] 2%|▏ | 5/250 [00:06<05:21, 1.31s/it] 2%|▏ | 6/250 [00:07<05:19, 1.31s/it] 3%|▎ | 7/250 [00:09<05:19, 1.31s/it] 3%|▎ | 8/250 [00:10<05:18, 1.32s/it] 4%|▎ | 9/250 [00:11<05:17, 1.32s/it] 4%|▍ | 10/250 [00:13<05:17, 1.32s/it] 4%|▍ | 11/250 [00:14<05:16, 1.32s/it] 5%|▍ | 12/250 [00:15<05:16, 1.33s/it] 5%|▌ | 13/250 [00:17<05:15, 1.33s/it] 6%|▌ | 14/250 [00:18<05:14, 1.33s/it] 6%|▌ | 15/250 [00:19<05:14, 1.34s/it] 6%|▋ | 16/250 [00:21<05:14, 1.34s/it] 7%|▋ | 17/250 [00:22<05:13, 1.35s/it] 7%|▋ | 18/250 [00:23<05:13, 1.35s/it] 8%|▊ | 19/250 [00:25<05:12, 1.35s/it] 8%|▊ | 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arielreplicate/scalable_diffusion_with_transformers:a11f7289b60685a4e48a0b09e41d091e27c2beb447877bc2117bfd6f9781c193IDstaemf3zg5cmbprtlycrkjpoymStatusSucceededSourceWebHardware–Total durationCreatedInput
- cfg_scale
- 4
- class_name
- boxer
- VAE_Decoder
- sd-vae-ft-ema
- num_outputs
- 1
- DiT_resolution
- 512x512
- num_sampling_steps
- 250
{ "cfg_scale": 4, "class_name": "boxer", "VAE_Decoder": "sd-vae-ft-ema", "num_outputs": 1, "DiT_resolution": "512x512", "num_sampling_steps": 250 }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 arielreplicate/scalable_diffusion_with_transformers using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "arielreplicate/scalable_diffusion_with_transformers:a11f7289b60685a4e48a0b09e41d091e27c2beb447877bc2117bfd6f9781c193", { input: { cfg_scale: 4, class_name: "boxer", VAE_Decoder: "sd-vae-ft-ema", num_outputs: 1, DiT_resolution: "512x512", num_sampling_steps: 250 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 arielreplicate/scalable_diffusion_with_transformers using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "arielreplicate/scalable_diffusion_with_transformers:a11f7289b60685a4e48a0b09e41d091e27c2beb447877bc2117bfd6f9781c193", input={ "cfg_scale": 4, "class_name": "boxer", "VAE_Decoder": "sd-vae-ft-ema", "num_outputs": 1, "DiT_resolution": "512x512", "num_sampling_steps": 250 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run arielreplicate/scalable_diffusion_with_transformers 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": "arielreplicate/scalable_diffusion_with_transformers:a11f7289b60685a4e48a0b09e41d091e27c2beb447877bc2117bfd6f9781c193", "input": { "cfg_scale": 4, "class_name": "boxer", "VAE_Decoder": "sd-vae-ft-ema", "num_outputs": 1, "DiT_resolution": "512x512", "num_sampling_steps": 250 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-12-23T11:32:59.321204Z", "created_at": "2022-12-23T11:29:45.216262Z", "data_removed": false, "error": null, "id": "staemf3zg5cmbprtlycrkjpoym", "input": { "cfg_scale": 4, "class_name": "boxer", "VAE_Decoder": "sd-vae-ft-ema", "num_outputs": 1, "DiT_resolution": "512x512", "num_sampling_steps": 250 }, "logs": "0%| | 0/250 [00:00<?, ?it/s]\n 0%| | 1/250 [00:04<18:15, 4.40s/it]\n 1%| | 2/250 [00:05<09:08, 2.21s/it]\n 1%| | 3/250 [00:05<06:14, 1.52s/it]\n 2%|▏ | 4/250 [00:06<04:52, 1.19s/it]\n 2%|▏ | 5/250 [00:07<04:07, 1.01s/it]\n 2%|▏ | 6/250 [00:07<03:40, 1.11it/s]\n 3%|▎ | 7/250 [00:08<03:22, 1.20it/s]\n 3%|▎ | 8/250 [00:09<03:10, 1.27it/s]\n 4%|▎ | 9/250 [00:09<03:01, 1.33it/s]\n 4%|▍ | 10/250 [00:10<02:55, 1.36it/s]\n 4%|▍ | 11/250 [00:11<02:51, 1.39it/s]\n 5%|▍ | 12/250 [00:11<02:48, 1.41it/s]\n 5%|▌ | 13/250 [00:12<02:46, 1.42it/s]\n 6%|▌ | 14/250 [00:13<02:45, 1.43it/s]\n 6%|▌ | 15/250 [00:14<02:44, 1.43it/s]\n 6%|▋ | 16/250 [00:14<02:42, 1.44it/s]\n 7%|▋ | 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"https://replicate.delivery/pbxt/f5fhW6xoE2rhIU6PbSXM0KlzGfeS6EqBmAYCTQ6KKdxrehmBC/output-0.png" ], "started_at": "2022-12-23T11:29:45.258062Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/staemf3zg5cmbprtlycrkjpoym", "cancel": "https://api.replicate.com/v1/predictions/staemf3zg5cmbprtlycrkjpoym/cancel" }, "version": "a11f7289b60685a4e48a0b09e41d091e27c2beb447877bc2117bfd6f9781c193" }Generated in0%| | 0/250 [00:00<?, ?it/s] 0%| | 1/250 [00:04<18:15, 4.40s/it] 1%| | 2/250 [00:05<09:08, 2.21s/it] 1%| | 3/250 [00:05<06:14, 1.52s/it] 2%|▏ | 4/250 [00:06<04:52, 1.19s/it] 2%|▏ | 5/250 [00:07<04:07, 1.01s/it] 2%|▏ | 6/250 [00:07<03:40, 1.11it/s] 3%|▎ | 7/250 [00:08<03:22, 1.20it/s] 3%|▎ | 8/250 [00:09<03:10, 1.27it/s] 4%|▎ | 9/250 [00:09<03:01, 1.33it/s] 4%|▍ | 10/250 [00:10<02:55, 1.36it/s] 4%|▍ | 11/250 [00:11<02:51, 1.39it/s] 5%|▍ | 12/250 [00:11<02:48, 1.41it/s] 5%|▌ | 13/250 [00:12<02:46, 1.42it/s] 6%|▌ | 14/250 [00:13<02:45, 1.43it/s] 6%|▌ | 15/250 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