zsxkib / draggan
🐲 DragGAN 🐉 - "Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold"
Prediction
zsxkib/draggan:7e2c9c3440593761e924ce2a87ba52ae986238c760b069e48eb66ac08695eccfInput
- learning_rate
- 0.004
- stylegan2_model
- self_distill/parrots_512_pytorch.pkl
- source_x_percentage
- 49
- source_y_percentage
- 63
- target_x_percentage
- 55
- target_y_percentage
- 45
- maximum_n_iterations
- 50
- show_points_and_arrows
- only_render_first_frame
{ "learning_rate": 0.004, "stylegan2_model": "self_distill/parrots_512_pytorch.pkl", "source_x_percentage": 49, "source_y_percentage": 63, "target_x_percentage": 55, "target_y_percentage": 45, "maximum_n_iterations": 50, "show_points_and_arrows": false, "only_render_first_frame": false }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run zsxkib/draggan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "zsxkib/draggan:7e2c9c3440593761e924ce2a87ba52ae986238c760b069e48eb66ac08695eccf", { input: { learning_rate: 0.004, stylegan2_model: "self_distill/parrots_512_pytorch.pkl", source_x_percentage: 49, source_y_percentage: 63, target_x_percentage: 55, target_y_percentage: 45, maximum_n_iterations: 50, show_points_and_arrows: false, only_render_first_frame: false } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run zsxkib/draggan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "zsxkib/draggan:7e2c9c3440593761e924ce2a87ba52ae986238c760b069e48eb66ac08695eccf", input={ "learning_rate": 0.004, "stylegan2_model": "self_distill/parrots_512_pytorch.pkl", "source_x_percentage": 49, "source_y_percentage": 63, "target_x_percentage": 55, "target_y_percentage": 45, "maximum_n_iterations": 50, "show_points_and_arrows": False, "only_render_first_frame": False } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run zsxkib/draggan 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": "zsxkib/draggan:7e2c9c3440593761e924ce2a87ba52ae986238c760b069e48eb66ac08695eccf", "input": { "learning_rate": 0.004, "stylegan2_model": "self_distill/parrots_512_pytorch.pkl", "source_x_percentage": 49, "source_y_percentage": 63, "target_x_percentage": 55, "target_y_percentage": 45, "maximum_n_iterations": 50, "show_points_and_arrows": false, "only_render_first_frame": false } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-07-01T12:25:47.472495Z", "created_at": "2023-07-01T12:24:15.280985Z", "data_removed": false, "error": null, "id": "x3fyckjbnpqnigfcdtm5d7rwsi", "input": { "learning_rate": 0.004, "stylegan2_model": "self_distill/parrots_512_pytorch.pkl", "source_x_percentage": 49, "source_y_percentage": 63, "target_x_percentage": 55, "target_y_percentage": 45, "maximum_n_iterations": 50, "show_points_and_arrows": false, "only_render_first_frame": false }, "logs": "self_distill/parrots_512_pytorch.pkl not found\nTry to download from huggingface: https://huggingface.co/aaronb/StyleGAN2-pkl/resolve/main/self_distill/parrots_512_pytorch.pkl\nparrots_512_pytorch.pkl: 0.00B [00:00, ?B/s]\nparrots_512_pytorch.pkl: 0%| | 8.19k/364M [00:00<1:56:55, 51.9kB/s]\nparrots_512_pytorch.pkl: 2%|▏ | 7.00M/364M [00:00<00:10, 33.1MB/s] \nparrots_512_pytorch.pkl: 4%|▍ | 15.7M/364M [00:00<00:06, 54.9MB/s]\nparrots_512_pytorch.pkl: 7%|▋ | 25.6M/364M [00:00<00:04, 70.6MB/s]\nparrots_512_pytorch.pkl: 10%|▉ | 35.3M/364M [00:00<00:04, 79.7MB/s]\nparrots_512_pytorch.pkl: 12%|█▏ | 42.7M/364M [00:00<00:04, 77.9MB/s]\nparrots_512_pytorch.pkl: 14%|█▍ | 51.9M/364M [00:00<00:03, 82.5MB/s]\nparrots_512_pytorch.pkl: 16%|█▋ | 59.9M/364M [00:00<00:03, 81.4MB/s]\nparrots_512_pytorch.pkl: 19%|█▉ | 69.4M/364M [00:00<00:03, 85.8MB/s]\nparrots_512_pytorch.pkl: 22%|██▏ | 78.7M/364M [00:01<00:03, 87.8MB/s]\nparrots_512_pytorch.pkl: 24%|██▍ | 87.4M/364M [00:01<00:03, 87.6MB/s]\nparrots_512_pytorch.pkl: 26%|██▌ | 95.2M/364M [00:01<00:03, 84.6MB/s]\nparrots_512_pytorch.pkl: 29%|██▊ | 105M/364M [00:01<00:02, 87.2MB/s] \nparrots_512_pytorch.pkl: 31%|███▏ | 114M/364M [00:01<00:02, 88.9MB/s]\nparrots_512_pytorch.pkl: 34%|███▎ | 122M/364M [00:01<00:02, 86.9MB/s]\nparrots_512_pytorch.pkl: 35%|███▌ | 129M/364M [00:01<00:02, 81.9MB/s]\nparrots_512_pytorch.pkl: 38%|███▊ | 139M/364M [00:01<00:02, 85.3MB/s]\nparrots_512_pytorch.pkl: 41%|████ | 148M/364M [00:01<00:02, 88.4MB/s]\nparrots_512_pytorch.pkl: 43%|████▎ | 158M/364M [00:01<00:02, 90.5MB/s]\nparrots_512_pytorch.pkl: 46%|████▌ | 166M/364M [00:02<00:02, 89.7MB/s]\nparrots_512_pytorch.pkl: 48%|████▊ | 176M/364M [00:02<00:02, 91.1MB/s]\nparrots_512_pytorch.pkl: 51%|█████ | 185M/364M [00:02<00:01, 91.0MB/s]\nparrots_512_pytorch.pkl: 53%|█████▎ | 194M/364M [00:02<00:01, 91.5MB/s]\nparrots_512_pytorch.pkl: 56%|█████▌ | 203M/364M [00:02<00:01, 91.1MB/s]\nparrots_512_pytorch.pkl: 58%|█████▊ | 213M/364M [00:02<00:01, 91.7MB/s]\nparrots_512_pytorch.pkl: 61%|██████ | 221M/364M [00:02<00:01, 89.3MB/s]\nparrots_512_pytorch.pkl: 63%|██████▎ | 230M/364M [00:02<00:01, 90.1MB/s]\nparrots_512_pytorch.pkl: 66%|██████▌ | 240M/364M [00:02<00:01, 92.4MB/s]\nparrots_512_pytorch.pkl: 69%|██████▊ | 249M/364M [00:02<00:01, 92.3MB/s]\nparrots_512_pytorch.pkl: 71%|███████ | 259M/364M [00:03<00:01, 93.1MB/s]\nparrots_512_pytorch.pkl: 74%|███████▎ | 268M/364M [00:03<00:01, 92.0MB/s]\nparrots_512_pytorch.pkl: 76%|███████▌ | 276M/364M [00:03<00:00, 89.9MB/s]\nparrots_512_pytorch.pkl: 79%|███████▊ | 286M/364M [00:03<00:00, 92.0MB/s]\nparrots_512_pytorch.pkl: 81%|████████▏ | 296M/364M [00:03<00:00, 93.2MB/s]\nparrots_512_pytorch.pkl: 84%|████████▍ | 305M/364M [00:03<00:00, 92.6MB/s]\nparrots_512_pytorch.pkl: 86%|████████▋ | 314M/364M [00:03<00:00, 93.3MB/s]\nparrots_512_pytorch.pkl: 89%|████████▉ | 323M/364M [00:03<00:00, 91.9MB/s]\nparrots_512_pytorch.pkl: 91%|█████████ | 332M/364M [00:03<00:00, 89.2MB/s]\nparrots_512_pytorch.pkl: 94%|█████████▎| 341M/364M [00:03<00:00, 89.8MB/s]\nparrots_512_pytorch.pkl: 96%|█████████▌| 350M/364M [00:04<00:00, 90.6MB/s]\nparrots_512_pytorch.pkl: 99%|█████████▊| 359M/364M [00:04<00:00, 89.6MB/s]\nparrots_512_pytorch.pkl: 364MB [00:04, 86.2MB/s]\nDownloaded to /root/draggan/checkpoints-pkl/self_distill/parrots_512_pytorch.pkl\nLoading networks from \"/root/draggan/checkpoints-pkl/self_distill/parrots_512_pytorch.pkl\"...\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:34, 1.42it/s]\n 4%|▍ | 2/50 [00:00<00:21, 2.20it/s]\n 6%|▌ | 3/50 [00:01<00:17, 2.72it/s]\n 8%|▊ | 4/50 [00:01<00:15, 3.05it/s]\n 10%|█ | 5/50 [00:01<00:13, 3.29it/s]\n 12%|█▏ | 6/50 [00:02<00:12, 3.45it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.56it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s]\n 20%|██ | 10/50 [00:03<00:10, 3.68it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.71it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.74it/s]\n 26%|██▌ | 13/50 [00:03<00:09, 3.73it/s]\n 28%|██▊ | 14/50 [00:04<00:09, 3.72it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.72it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.74it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.73it/s]\n 36%|███▌ | 18/50 [00:05<00:08, 3.67it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.66it/s]\n 42%|████▏ | 21/50 [00:06<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.69it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.72it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s]\n 50%|█████ | 25/50 [00:07<00:06, 3.64it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.65it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.69it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.65it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s]\n 64%|██████▍ | 32/50 [00:09<00:04, 3.64it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.62it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.62it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.65it/s]\n70%|███████ | 35/50 [00:09<00:04, 3.53it/s]", "metrics": { "predict_time": 17.211187, "total_time": 92.19151 }, "output": "https://replicate.delivery/pbxt/QLeCzdYhPjUyditeekWKR3RqfkSlu43FfXZiBMKihzoYpmbJC/video.mp4", "started_at": "2023-07-01T12:25:30.261308Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/x3fyckjbnpqnigfcdtm5d7rwsi", "cancel": "https://api.replicate.com/v1/predictions/x3fyckjbnpqnigfcdtm5d7rwsi/cancel" }, "version": "7e2c9c3440593761e924ce2a87ba52ae986238c760b069e48eb66ac08695eccf" }
Generated inself_distill/parrots_512_pytorch.pkl not found Try to download from huggingface: https://huggingface.co/aaronb/StyleGAN2-pkl/resolve/main/self_distill/parrots_512_pytorch.pkl parrots_512_pytorch.pkl: 0.00B [00:00, ?B/s] parrots_512_pytorch.pkl: 0%| | 8.19k/364M [00:00<1:56:55, 51.9kB/s] parrots_512_pytorch.pkl: 2%|▏ | 7.00M/364M [00:00<00:10, 33.1MB/s] parrots_512_pytorch.pkl: 4%|▍ | 15.7M/364M [00:00<00:06, 54.9MB/s] parrots_512_pytorch.pkl: 7%|▋ | 25.6M/364M [00:00<00:04, 70.6MB/s] parrots_512_pytorch.pkl: 10%|▉ | 35.3M/364M [00:00<00:04, 79.7MB/s] parrots_512_pytorch.pkl: 12%|█▏ | 42.7M/364M [00:00<00:04, 77.9MB/s] parrots_512_pytorch.pkl: 14%|█▍ | 51.9M/364M [00:00<00:03, 82.5MB/s] parrots_512_pytorch.pkl: 16%|█▋ | 59.9M/364M [00:00<00:03, 81.4MB/s] parrots_512_pytorch.pkl: 19%|█▉ | 69.4M/364M [00:00<00:03, 85.8MB/s] parrots_512_pytorch.pkl: 22%|██▏ | 78.7M/364M [00:01<00:03, 87.8MB/s] parrots_512_pytorch.pkl: 24%|██▍ | 87.4M/364M [00:01<00:03, 87.6MB/s] parrots_512_pytorch.pkl: 26%|██▌ | 95.2M/364M [00:01<00:03, 84.6MB/s] parrots_512_pytorch.pkl: 29%|██▊ | 105M/364M [00:01<00:02, 87.2MB/s] parrots_512_pytorch.pkl: 31%|███▏ | 114M/364M [00:01<00:02, 88.9MB/s] parrots_512_pytorch.pkl: 34%|███▎ | 122M/364M [00:01<00:02, 86.9MB/s] parrots_512_pytorch.pkl: 35%|███▌ | 129M/364M [00:01<00:02, 81.9MB/s] parrots_512_pytorch.pkl: 38%|███▊ | 139M/364M [00:01<00:02, 85.3MB/s] parrots_512_pytorch.pkl: 41%|████ | 148M/364M [00:01<00:02, 88.4MB/s] parrots_512_pytorch.pkl: 43%|████▎ | 158M/364M [00:01<00:02, 90.5MB/s] parrots_512_pytorch.pkl: 46%|████▌ | 166M/364M [00:02<00:02, 89.7MB/s] parrots_512_pytorch.pkl: 48%|████▊ | 176M/364M [00:02<00:02, 91.1MB/s] parrots_512_pytorch.pkl: 51%|█████ | 185M/364M [00:02<00:01, 91.0MB/s] parrots_512_pytorch.pkl: 53%|█████▎ | 194M/364M [00:02<00:01, 91.5MB/s] parrots_512_pytorch.pkl: 56%|█████▌ | 203M/364M [00:02<00:01, 91.1MB/s] parrots_512_pytorch.pkl: 58%|█████▊ | 213M/364M [00:02<00:01, 91.7MB/s] parrots_512_pytorch.pkl: 61%|██████ | 221M/364M [00:02<00:01, 89.3MB/s] parrots_512_pytorch.pkl: 63%|██████▎ | 230M/364M [00:02<00:01, 90.1MB/s] parrots_512_pytorch.pkl: 66%|██████▌ | 240M/364M [00:02<00:01, 92.4MB/s] parrots_512_pytorch.pkl: 69%|██████▊ | 249M/364M [00:02<00:01, 92.3MB/s] parrots_512_pytorch.pkl: 71%|███████ | 259M/364M [00:03<00:01, 93.1MB/s] parrots_512_pytorch.pkl: 74%|███████▎ | 268M/364M [00:03<00:01, 92.0MB/s] parrots_512_pytorch.pkl: 76%|███████▌ | 276M/364M [00:03<00:00, 89.9MB/s] parrots_512_pytorch.pkl: 79%|███████▊ | 286M/364M [00:03<00:00, 92.0MB/s] parrots_512_pytorch.pkl: 81%|████████▏ | 296M/364M [00:03<00:00, 93.2MB/s] parrots_512_pytorch.pkl: 84%|████████▍ | 305M/364M [00:03<00:00, 92.6MB/s] parrots_512_pytorch.pkl: 86%|████████▋ | 314M/364M [00:03<00:00, 93.3MB/s] parrots_512_pytorch.pkl: 89%|████████▉ | 323M/364M [00:03<00:00, 91.9MB/s] parrots_512_pytorch.pkl: 91%|█████████ | 332M/364M [00:03<00:00, 89.2MB/s] parrots_512_pytorch.pkl: 94%|█████████▎| 341M/364M [00:03<00:00, 89.8MB/s] parrots_512_pytorch.pkl: 96%|█████████▌| 350M/364M [00:04<00:00, 90.6MB/s] parrots_512_pytorch.pkl: 99%|█████████▊| 359M/364M [00:04<00:00, 89.6MB/s] parrots_512_pytorch.pkl: 364MB [00:04, 86.2MB/s] Downloaded to /root/draggan/checkpoints-pkl/self_distill/parrots_512_pytorch.pkl Loading networks from "/root/draggan/checkpoints-pkl/self_distill/parrots_512_pytorch.pkl"... 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:34, 1.42it/s] 4%|▍ | 2/50 [00:00<00:21, 2.20it/s] 6%|▌ | 3/50 [00:01<00:17, 2.72it/s] 8%|▊ | 4/50 [00:01<00:15, 3.05it/s] 10%|█ | 5/50 [00:01<00:13, 3.29it/s] 12%|█▏ | 6/50 [00:02<00:12, 3.45it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.56it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s] 20%|██ | 10/50 [00:03<00:10, 3.68it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.71it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.74it/s] 26%|██▌ | 13/50 [00:03<00:09, 3.73it/s] 28%|██▊ | 14/50 [00:04<00:09, 3.72it/s] 30%|███ | 15/50 [00:04<00:09, 3.72it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.74it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.73it/s] 36%|███▌ | 18/50 [00:05<00:08, 3.67it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s] 40%|████ | 20/50 [00:05<00:08, 3.66it/s] 42%|████▏ | 21/50 [00:06<00:07, 3.67it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.69it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.72it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s] 50%|█████ | 25/50 [00:07<00:06, 3.64it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.65it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.69it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.67it/s] 60%|██████ | 30/50 [00:08<00:05, 3.65it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s] 64%|██████▍ | 32/50 [00:09<00:04, 3.64it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.62it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.62it/s] 70%|███████ | 35/50 [00:09<00:04, 3.65it/s] 70%|███████ | 35/50 [00:09<00:04, 3.53it/s]
Prediction
zsxkib/draggan:7e2c9c3440593761e924ce2a87ba52ae986238c760b069e48eb66ac08695eccfID2nujs3rb3r2xwrdl65t23bjmheStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- learning_rate
- 0.002
- stylegan2_model
- ada/ffhq.pkl
- source_x_percentage
- 50
- source_y_percentage
- 21
- target_x_percentage
- 50
- target_y_percentage
- 16
- maximum_n_iterations
- 60
- show_points_and_arrows
- only_render_first_frame
{ "learning_rate": 0.002, "stylegan2_model": "ada/ffhq.pkl", "source_x_percentage": 50, "source_y_percentage": 21, "target_x_percentage": 50, "target_y_percentage": 16, "maximum_n_iterations": 60, "show_points_and_arrows": true, "only_render_first_frame": false }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run zsxkib/draggan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "zsxkib/draggan:7e2c9c3440593761e924ce2a87ba52ae986238c760b069e48eb66ac08695eccf", { input: { learning_rate: 0.002, stylegan2_model: "ada/ffhq.pkl", source_x_percentage: 50, source_y_percentage: 21, target_x_percentage: 50, target_y_percentage: 16, maximum_n_iterations: 60, show_points_and_arrows: true, only_render_first_frame: false } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run zsxkib/draggan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "zsxkib/draggan:7e2c9c3440593761e924ce2a87ba52ae986238c760b069e48eb66ac08695eccf", input={ "learning_rate": 0.002, "stylegan2_model": "ada/ffhq.pkl", "source_x_percentage": 50, "source_y_percentage": 21, "target_x_percentage": 50, "target_y_percentage": 16, "maximum_n_iterations": 60, "show_points_and_arrows": True, "only_render_first_frame": False } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run zsxkib/draggan 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": "zsxkib/draggan:7e2c9c3440593761e924ce2a87ba52ae986238c760b069e48eb66ac08695eccf", "input": { "learning_rate": 0.002, "stylegan2_model": "ada/ffhq.pkl", "source_x_percentage": 50, "source_y_percentage": 21, "target_x_percentage": 50, "target_y_percentage": 16, "maximum_n_iterations": 60, "show_points_and_arrows": true, "only_render_first_frame": false } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-07-01T12:48:14.729219Z", "created_at": "2023-07-01T12:47:29.081305Z", "data_removed": false, "error": null, "id": "2nujs3rb3r2xwrdl65t23bjmhe", "input": { "learning_rate": 0.002, "stylegan2_model": "ada/ffhq.pkl", "source_x_percentage": 50, "source_y_percentage": 21, "target_x_percentage": 50, "target_y_percentage": 16, "maximum_n_iterations": 60, "show_points_and_arrows": true, "only_render_first_frame": false }, "logs": "0%| | 0/60 [00:00<?, ?it/s]\n 2%|▏ | 1/60 [00:00<00:58, 1.01it/s]\n 3%|▎ | 2/60 [00:01<00:52, 1.11it/s]\n 5%|▌ | 3/60 [00:02<00:50, 1.14it/s]\n 7%|▋ | 4/60 [00:03<00:48, 1.15it/s]\n 8%|▊ | 5/60 [00:04<00:48, 1.14it/s]\n 10%|█ | 6/60 [00:05<00:46, 1.15it/s]\n 12%|█▏ | 7/60 [00:06<00:45, 1.15it/s]\n 13%|█▎ | 8/60 [00:06<00:44, 1.16it/s]\n 15%|█▌ | 9/60 [00:07<00:43, 1.18it/s]\n 17%|█▋ | 10/60 [00:08<00:42, 1.18it/s]\n 18%|█▊ | 11/60 [00:09<00:41, 1.18it/s]\n 20%|██ | 12/60 [00:10<00:42, 1.13it/s]\n 22%|██▏ | 13/60 [00:11<00:40, 1.15it/s]\n 23%|██▎ | 14/60 [00:12<00:39, 1.17it/s]\n 25%|██▌ | 15/60 [00:12<00:38, 1.17it/s]\n 27%|██▋ | 16/60 [00:13<00:37, 1.18it/s]\n 28%|██▊ | 17/60 [00:14<00:36, 1.17it/s]\n 30%|███ | 18/60 [00:15<00:37, 1.13it/s]\n 32%|███▏ | 19/60 [00:16<00:34, 1.17it/s]\n 33%|███▎ | 20/60 [00:17<00:32, 1.22it/s]\n 35%|███▌ | 21/60 [00:17<00:31, 1.24it/s]\n 37%|███▋ | 22/60 [00:18<00:30, 1.24it/s]\n 38%|███▊ | 23/60 [00:19<00:29, 1.25it/s]\n 40%|████ | 24/60 [00:20<00:28, 1.27it/s]\n 42%|████▏ | 25/60 [00:21<00:27, 1.28it/s]\n 43%|████▎ | 26/60 [00:21<00:26, 1.26it/s]\n 45%|████▌ | 27/60 [00:22<00:26, 1.23it/s]\n 47%|████▋ | 28/60 [00:23<00:26, 1.22it/s]\n 48%|████▊ | 29/60 [00:24<00:25, 1.21it/s]\n 50%|█████ | 30/60 [00:25<00:25, 1.19it/s]\n 52%|█████▏ | 31/60 [00:26<00:24, 1.19it/s]\n 53%|█████▎ | 32/60 [00:26<00:23, 1.20it/s]\n 55%|█████▌ | 33/60 [00:27<00:22, 1.20it/s]\n 57%|█████▋ | 34/60 [00:28<00:21, 1.20it/s]\n 58%|█████▊ | 35/60 [00:29<00:20, 1.20it/s]\n 60%|██████ | 36/60 [00:30<00:20, 1.19it/s]\n 62%|██████▏ | 37/60 [00:31<00:19, 1.18it/s]\n 63%|██████▎ | 38/60 [00:31<00:18, 1.19it/s]\n 65%|██████▌ | 39/60 [00:32<00:17, 1.19it/s]\n 67%|██████▋ | 40/60 [00:33<00:17, 1.18it/s]\n 68%|██████▊ | 41/60 [00:34<00:16, 1.17it/s]\n 70%|███████ | 42/60 [00:35<00:15, 1.17it/s]\n 72%|███████▏ | 43/60 [00:36<00:14, 1.16it/s]\n 73%|███████▎ | 44/60 [00:37<00:13, 1.18it/s]\n 75%|███████▌ | 45/60 [00:37<00:12, 1.23it/s]\n 77%|███████▋ | 46/60 [00:38<00:11, 1.22it/s]\n 78%|███████▊ | 47/60 [00:39<00:10, 1.25it/s]\n 80%|████████ | 48/60 [00:40<00:09, 1.26it/s]\n 82%|████████▏ | 49/60 [00:41<00:08, 1.25it/s]\n 83%|████████▎ | 50/60 [00:41<00:07, 1.28it/s]\n 85%|████████▌ | 51/60 [00:42<00:06, 1.31it/s]\n 87%|████████▋ | 52/60 [00:43<00:06, 1.32it/s]\n87%|████████▋ | 52/60 [00:43<00:06, 1.20it/s]", "metrics": { "predict_time": 45.731607, "total_time": 45.647914 }, "output": "https://replicate.delivery/pbxt/NTRlkEPS0iItMpnlJH3pJxCmXZLPc1O8uTIfPJDhCuRHlulIA/video.mp4", "started_at": "2023-07-01T12:47:28.997612Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/2nujs3rb3r2xwrdl65t23bjmhe", "cancel": "https://api.replicate.com/v1/predictions/2nujs3rb3r2xwrdl65t23bjmhe/cancel" }, "version": "7e2c9c3440593761e924ce2a87ba52ae986238c760b069e48eb66ac08695eccf" }
Generated in0%| | 0/60 [00:00<?, ?it/s] 2%|▏ | 1/60 [00:00<00:58, 1.01it/s] 3%|▎ | 2/60 [00:01<00:52, 1.11it/s] 5%|▌ | 3/60 [00:02<00:50, 1.14it/s] 7%|▋ | 4/60 [00:03<00:48, 1.15it/s] 8%|▊ | 5/60 [00:04<00:48, 1.14it/s] 10%|█ | 6/60 [00:05<00:46, 1.15it/s] 12%|█▏ | 7/60 [00:06<00:45, 1.15it/s] 13%|█▎ | 8/60 [00:06<00:44, 1.16it/s] 15%|█▌ | 9/60 [00:07<00:43, 1.18it/s] 17%|█▋ | 10/60 [00:08<00:42, 1.18it/s] 18%|█▊ | 11/60 [00:09<00:41, 1.18it/s] 20%|██ | 12/60 [00:10<00:42, 1.13it/s] 22%|██▏ | 13/60 [00:11<00:40, 1.15it/s] 23%|██▎ | 14/60 [00:12<00:39, 1.17it/s] 25%|██▌ | 15/60 [00:12<00:38, 1.17it/s] 27%|██▋ | 16/60 [00:13<00:37, 1.18it/s] 28%|██▊ | 17/60 [00:14<00:36, 1.17it/s] 30%|███ | 18/60 [00:15<00:37, 1.13it/s] 32%|███▏ | 19/60 [00:16<00:34, 1.17it/s] 33%|███▎ | 20/60 [00:17<00:32, 1.22it/s] 35%|███▌ | 21/60 [00:17<00:31, 1.24it/s] 37%|███▋ | 22/60 [00:18<00:30, 1.24it/s] 38%|███▊ | 23/60 [00:19<00:29, 1.25it/s] 40%|████ | 24/60 [00:20<00:28, 1.27it/s] 42%|████▏ | 25/60 [00:21<00:27, 1.28it/s] 43%|████▎ | 26/60 [00:21<00:26, 1.26it/s] 45%|████▌ | 27/60 [00:22<00:26, 1.23it/s] 47%|████▋ | 28/60 [00:23<00:26, 1.22it/s] 48%|████▊ | 29/60 [00:24<00:25, 1.21it/s] 50%|█████ | 30/60 [00:25<00:25, 1.19it/s] 52%|█████▏ | 31/60 [00:26<00:24, 1.19it/s] 53%|█████▎ | 32/60 [00:26<00:23, 1.20it/s] 55%|█████▌ | 33/60 [00:27<00:22, 1.20it/s] 57%|█████▋ | 34/60 [00:28<00:21, 1.20it/s] 58%|█████▊ | 35/60 [00:29<00:20, 1.20it/s] 60%|██████ | 36/60 [00:30<00:20, 1.19it/s] 62%|██████▏ | 37/60 [00:31<00:19, 1.18it/s] 63%|██████▎ | 38/60 [00:31<00:18, 1.19it/s] 65%|██████▌ | 39/60 [00:32<00:17, 1.19it/s] 67%|██████▋ | 40/60 [00:33<00:17, 1.18it/s] 68%|██████▊ | 41/60 [00:34<00:16, 1.17it/s] 70%|███████ | 42/60 [00:35<00:15, 1.17it/s] 72%|███████▏ | 43/60 [00:36<00:14, 1.16it/s] 73%|███████▎ | 44/60 [00:37<00:13, 1.18it/s] 75%|███████▌ | 45/60 [00:37<00:12, 1.23it/s] 77%|███████▋ | 46/60 [00:38<00:11, 1.22it/s] 78%|███████▊ | 47/60 [00:39<00:10, 1.25it/s] 80%|████████ | 48/60 [00:40<00:09, 1.26it/s] 82%|████████▏ | 49/60 [00:41<00:08, 1.25it/s] 83%|████████▎ | 50/60 [00:41<00:07, 1.28it/s] 85%|████████▌ | 51/60 [00:42<00:06, 1.31it/s] 87%|████████▋ | 52/60 [00:43<00:06, 1.32it/s] 87%|████████▋ | 52/60 [00:43<00:06, 1.20it/s]
Prediction
zsxkib/draggan:7e2c9c3440593761e924ce2a87ba52ae986238c760b069e48eb66ac08695eccfID2zfcpkzbxrb23xwiwiln2tnmyiStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- learning_rate
- 0.0019
- stylegan2_model
- self_distill/horses_256_pytorch.pkl
- source_x_percentage
- 89
- source_y_percentage
- 13
- target_x_percentage
- 60
- target_y_percentage
- 20
- maximum_n_iterations
- 20
- show_points_and_arrows
- only_render_first_frame
{ "learning_rate": 0.0019, "stylegan2_model": "self_distill/horses_256_pytorch.pkl", "source_x_percentage": 89, "source_y_percentage": 13, "target_x_percentage": 60, "target_y_percentage": 20, "maximum_n_iterations": 20, "show_points_and_arrows": true, "only_render_first_frame": false }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run zsxkib/draggan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "zsxkib/draggan:7e2c9c3440593761e924ce2a87ba52ae986238c760b069e48eb66ac08695eccf", { input: { learning_rate: 0.0019, stylegan2_model: "self_distill/horses_256_pytorch.pkl", source_x_percentage: 89, source_y_percentage: 13, target_x_percentage: 60, target_y_percentage: 20, maximum_n_iterations: 20, show_points_and_arrows: true, only_render_first_frame: false } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run zsxkib/draggan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "zsxkib/draggan:7e2c9c3440593761e924ce2a87ba52ae986238c760b069e48eb66ac08695eccf", input={ "learning_rate": 0.0019, "stylegan2_model": "self_distill/horses_256_pytorch.pkl", "source_x_percentage": 89, "source_y_percentage": 13, "target_x_percentage": 60, "target_y_percentage": 20, "maximum_n_iterations": 20, "show_points_and_arrows": True, "only_render_first_frame": False } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run zsxkib/draggan 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": "zsxkib/draggan:7e2c9c3440593761e924ce2a87ba52ae986238c760b069e48eb66ac08695eccf", "input": { "learning_rate": 0.0019, "stylegan2_model": "self_distill/horses_256_pytorch.pkl", "source_x_percentage": 89, "source_y_percentage": 13, "target_x_percentage": 60, "target_y_percentage": 20, "maximum_n_iterations": 20, "show_points_and_arrows": true, "only_render_first_frame": false } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-07-01T12:55:11.962714Z", "created_at": "2023-07-01T12:55:08.531871Z", "data_removed": false, "error": null, "id": "2zfcpkzbxrb23xwiwiln2tnmyi", "input": { "learning_rate": 0.0019, "stylegan2_model": "self_distill/horses_256_pytorch.pkl", "source_x_percentage": 89, "source_y_percentage": 13, "target_x_percentage": 60, "target_y_percentage": 20, "maximum_n_iterations": 20, "show_points_and_arrows": true, "only_render_first_frame": false }, "logs": "0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:04, 4.23it/s]\n 10%|█ | 2/20 [00:00<00:03, 5.28it/s]\n 15%|█▌ | 3/20 [00:00<00:02, 6.03it/s]\n 20%|██ | 4/20 [00:00<00:02, 6.46it/s]\n 25%|██▌ | 5/20 [00:00<00:02, 6.63it/s]\n 30%|███ | 6/20 [00:00<00:02, 6.80it/s]\n 35%|███▌ | 7/20 [00:01<00:01, 6.88it/s]\n 40%|████ | 8/20 [00:01<00:01, 6.88it/s]\n 45%|████▌ | 9/20 [00:01<00:01, 6.90it/s]\n 50%|█████ | 10/20 [00:01<00:01, 6.87it/s]\n 55%|█████▌ | 11/20 [00:01<00:01, 6.92it/s]\n 60%|██████ | 12/20 [00:01<00:01, 6.93it/s]\n 65%|██████▌ | 13/20 [00:01<00:01, 6.47it/s]\n 70%|███████ | 14/20 [00:02<00:00, 6.64it/s]\n 75%|███████▌ | 15/20 [00:02<00:00, 6.75it/s]\n 80%|████████ | 16/20 [00:02<00:00, 6.81it/s]\n 85%|████████▌ | 17/20 [00:02<00:00, 6.85it/s]\n 90%|█████████ | 18/20 [00:02<00:00, 6.91it/s]\n 95%|█████████▌| 19/20 [00:02<00:00, 6.90it/s]\n100%|██████████| 20/20 [00:03<00:00, 6.81it/s]\n100%|██████████| 20/20 [00:03<00:00, 6.65it/s]", "metrics": { "predict_time": 3.467728, "total_time": 3.430843 }, "output": "https://replicate.delivery/pbxt/Q2Gilq8hfv34GKl6a3CF8Q21f0PClNzJq7Phr8fneJDfFqbJC/video.mp4", "started_at": "2023-07-01T12:55:08.494986Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/2zfcpkzbxrb23xwiwiln2tnmyi", "cancel": "https://api.replicate.com/v1/predictions/2zfcpkzbxrb23xwiwiln2tnmyi/cancel" }, "version": "7e2c9c3440593761e924ce2a87ba52ae986238c760b069e48eb66ac08695eccf" }
Generated in0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:04, 4.23it/s] 10%|█ | 2/20 [00:00<00:03, 5.28it/s] 15%|█▌ | 3/20 [00:00<00:02, 6.03it/s] 20%|██ | 4/20 [00:00<00:02, 6.46it/s] 25%|██▌ | 5/20 [00:00<00:02, 6.63it/s] 30%|███ | 6/20 [00:00<00:02, 6.80it/s] 35%|███▌ | 7/20 [00:01<00:01, 6.88it/s] 40%|████ | 8/20 [00:01<00:01, 6.88it/s] 45%|████▌ | 9/20 [00:01<00:01, 6.90it/s] 50%|█████ | 10/20 [00:01<00:01, 6.87it/s] 55%|█████▌ | 11/20 [00:01<00:01, 6.92it/s] 60%|██████ | 12/20 [00:01<00:01, 6.93it/s] 65%|██████▌ | 13/20 [00:01<00:01, 6.47it/s] 70%|███████ | 14/20 [00:02<00:00, 6.64it/s] 75%|███████▌ | 15/20 [00:02<00:00, 6.75it/s] 80%|████████ | 16/20 [00:02<00:00, 6.81it/s] 85%|████████▌ | 17/20 [00:02<00:00, 6.85it/s] 90%|█████████ | 18/20 [00:02<00:00, 6.91it/s] 95%|█████████▌| 19/20 [00:02<00:00, 6.90it/s] 100%|██████████| 20/20 [00:03<00:00, 6.81it/s] 100%|██████████| 20/20 [00:03<00:00, 6.65it/s]
Prediction
zsxkib/draggan:6da45a995bebe0d2427a61f867a067f50fadba8bc1e66d4c50b087898aaf3f6eIDzfyhq2zb23alyg3o34g3cpppmyStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- learning_rate
- 0.005
- stylegan2_model
- ada/afhqcat.pkl
- source_x_percentage
- 50
- source_y_percentage
- 25
- target_x_percentage
- 90
- target_y_percentage
- 25
- maximum_n_iterations
- 50
- show_points_and_arrows
- only_render_first_frame
{ "learning_rate": 0.005, "stylegan2_model": "ada/afhqcat.pkl", "source_x_percentage": 50, "source_y_percentage": 25, "target_x_percentage": 90, "target_y_percentage": 25, "maximum_n_iterations": 50, "show_points_and_arrows": true, "only_render_first_frame": false }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run zsxkib/draggan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "zsxkib/draggan:6da45a995bebe0d2427a61f867a067f50fadba8bc1e66d4c50b087898aaf3f6e", { input: { learning_rate: 0.005, stylegan2_model: "ada/afhqcat.pkl", source_x_percentage: 50, source_y_percentage: 25, target_x_percentage: 90, target_y_percentage: 25, maximum_n_iterations: 50, show_points_and_arrows: true, only_render_first_frame: false } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run zsxkib/draggan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "zsxkib/draggan:6da45a995bebe0d2427a61f867a067f50fadba8bc1e66d4c50b087898aaf3f6e", input={ "learning_rate": 0.005, "stylegan2_model": "ada/afhqcat.pkl", "source_x_percentage": 50, "source_y_percentage": 25, "target_x_percentage": 90, "target_y_percentage": 25, "maximum_n_iterations": 50, "show_points_and_arrows": True, "only_render_first_frame": False } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run zsxkib/draggan 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": "zsxkib/draggan:6da45a995bebe0d2427a61f867a067f50fadba8bc1e66d4c50b087898aaf3f6e", "input": { "learning_rate": 0.005, "stylegan2_model": "ada/afhqcat.pkl", "source_x_percentage": 50, "source_y_percentage": 25, "target_x_percentage": 90, "target_y_percentage": 25, "maximum_n_iterations": 50, "show_points_and_arrows": true, "only_render_first_frame": false } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-07-01T09:43:32.470862Z", "created_at": "2023-07-01T09:43:16.913289Z", "data_removed": false, "error": null, "id": "zfyhq2zb23alyg3o34g3cpppmy", "input": { "learning_rate": 0.005, "stylegan2_model": "ada/afhqcat.pkl", "source_x_percentage": 50, "source_y_percentage": 25, "target_x_percentage": 90, "target_y_percentage": 25, "maximum_n_iterations": 50, "show_points_and_arrows": true, "only_render_first_frame": false }, "logs": "0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:20, 2.45it/s]\n 4%|▍ | 2/50 [00:00<00:16, 2.93it/s]\n 6%|▌ | 3/50 [00:00<00:14, 3.25it/s]\n 8%|▊ | 4/50 [00:01<00:13, 3.43it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.56it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.55it/s]\n 14%|█▍ | 7/50 [00:02<00:11, 3.60it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s]\n 18%|█▊ | 9/50 [00:02<00:10, 3.75it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.71it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.71it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.60it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.59it/s]\n 28%|██▊ | 14/50 [00:03<00:10, 3.57it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.55it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.47it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.47it/s]\n 36%|███▌ | 18/50 [00:05<00:09, 3.47it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.50it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.49it/s]\n 42%|████▏ | 21/50 [00:05<00:08, 3.49it/s]\n 44%|████▍ | 22/50 [00:06<00:08, 3.49it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.49it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.49it/s]\n 50%|█████ | 25/50 [00:07<00:07, 3.47it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.47it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.44it/s]\n 56%|█████▌ | 28/50 [00:08<00:06, 3.42it/s]\n 58%|█████▊ | 29/50 [00:08<00:06, 3.44it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.49it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.48it/s]\n 64%|██████▍ | 32/50 [00:09<00:05, 3.49it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.51it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.55it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.61it/s]\n 72%|███████▏ | 36/50 [00:10<00:03, 3.59it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.53it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.51it/s]\n 78%|███████▊ | 39/50 [00:11<00:03, 3.49it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.47it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.47it/s]\n 84%|████████▍ | 42/50 [00:12<00:02, 3.47it/s]\n 86%|████████▌ | 43/50 [00:12<00:01, 3.54it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.53it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.52it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.50it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.46it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.51it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.58it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.51it/s]", "metrics": { "predict_time": 15.591909, "total_time": 15.557573 }, "output": "https://replicate.delivery/pbxt/ntS0uBBFPWqwBBUutMZi3BZcHRHukwwrdTj4exbraImhOtlIA/video_322cf849-4e85-4582-8e90-cc80e72c89dd.mp4", "started_at": "2023-07-01T09:43:16.878953Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zfyhq2zb23alyg3o34g3cpppmy", "cancel": "https://api.replicate.com/v1/predictions/zfyhq2zb23alyg3o34g3cpppmy/cancel" }, "version": "6da45a995bebe0d2427a61f867a067f50fadba8bc1e66d4c50b087898aaf3f6e" }
Generated in0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:20, 2.45it/s] 4%|▍ | 2/50 [00:00<00:16, 2.93it/s] 6%|▌ | 3/50 [00:00<00:14, 3.25it/s] 8%|▊ | 4/50 [00:01<00:13, 3.43it/s] 10%|█ | 5/50 [00:01<00:12, 3.56it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.55it/s] 14%|█▍ | 7/50 [00:02<00:11, 3.60it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s] 18%|█▊ | 9/50 [00:02<00:10, 3.75it/s] 20%|██ | 10/50 [00:02<00:10, 3.71it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.71it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.60it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.59it/s] 28%|██▊ | 14/50 [00:03<00:10, 3.57it/s] 30%|███ | 15/50 [00:04<00:09, 3.55it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.47it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.47it/s] 36%|███▌ | 18/50 [00:05<00:09, 3.47it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.50it/s] 40%|████ | 20/50 [00:05<00:08, 3.49it/s] 42%|████▏ | 21/50 [00:05<00:08, 3.49it/s] 44%|████▍ | 22/50 [00:06<00:08, 3.49it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.49it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.49it/s] 50%|█████ | 25/50 [00:07<00:07, 3.47it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.47it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.44it/s] 56%|█████▌ | 28/50 [00:08<00:06, 3.42it/s] 58%|█████▊ | 29/50 [00:08<00:06, 3.44it/s] 60%|██████ | 30/50 [00:08<00:05, 3.49it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.48it/s] 64%|██████▍ | 32/50 [00:09<00:05, 3.49it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.51it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.55it/s] 70%|███████ | 35/50 [00:09<00:04, 3.61it/s] 72%|███████▏ | 36/50 [00:10<00:03, 3.59it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.53it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.51it/s] 78%|███████▊ | 39/50 [00:11<00:03, 3.49it/s] 80%|████████ | 40/50 [00:11<00:02, 3.47it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.47it/s] 84%|████████▍ | 42/50 [00:12<00:02, 3.47it/s] 86%|████████▌ | 43/50 [00:12<00:01, 3.54it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.53it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.52it/s] 92%|█████████▏| 46/50 [00:13<00:01, 3.50it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.46it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.51it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.58it/s] 100%|██████████| 50/50 [00:14<00:00, 3.65it/s] 100%|██████████| 50/50 [00:14<00:00, 3.51it/s]
Prediction
zsxkib/draggan:6da45a995bebe0d2427a61f867a067f50fadba8bc1e66d4c50b087898aaf3f6eIDle4wpszbtc2j5e5n3vrwqlwj5qStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- learning_rate
- 0.0005
- stylegan2_model
- self_distill/dogs_1024_pytorch.pkl
- source_x_percentage
- 55
- source_y_percentage
- 35
- target_x_percentage
- 20
- target_y_percentage
- 90
- maximum_n_iterations
- 50
- show_points_and_arrows
- only_render_first_frame
{ "learning_rate": 0.0005, "stylegan2_model": "self_distill/dogs_1024_pytorch.pkl", "source_x_percentage": 55, "source_y_percentage": 35, "target_x_percentage": 20, "target_y_percentage": 90, "maximum_n_iterations": 50, "show_points_and_arrows": true, "only_render_first_frame": false }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run zsxkib/draggan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "zsxkib/draggan:6da45a995bebe0d2427a61f867a067f50fadba8bc1e66d4c50b087898aaf3f6e", { input: { learning_rate: 0.0005, stylegan2_model: "self_distill/dogs_1024_pytorch.pkl", source_x_percentage: 55, source_y_percentage: 35, target_x_percentage: 20, target_y_percentage: 90, maximum_n_iterations: 50, show_points_and_arrows: true, only_render_first_frame: false } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run zsxkib/draggan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "zsxkib/draggan:6da45a995bebe0d2427a61f867a067f50fadba8bc1e66d4c50b087898aaf3f6e", input={ "learning_rate": 0.0005, "stylegan2_model": "self_distill/dogs_1024_pytorch.pkl", "source_x_percentage": 55, "source_y_percentage": 35, "target_x_percentage": 20, "target_y_percentage": 90, "maximum_n_iterations": 50, "show_points_and_arrows": True, "only_render_first_frame": False } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run zsxkib/draggan 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": "zsxkib/draggan:6da45a995bebe0d2427a61f867a067f50fadba8bc1e66d4c50b087898aaf3f6e", "input": { "learning_rate": 0.0005, "stylegan2_model": "self_distill/dogs_1024_pytorch.pkl", "source_x_percentage": 55, "source_y_percentage": 35, "target_x_percentage": 20, "target_y_percentage": 90, "maximum_n_iterations": 50, "show_points_and_arrows": true, "only_render_first_frame": false } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-07-01T11:53:34.330832Z", "created_at": "2023-07-01T11:52:46.585288Z", "data_removed": false, "error": null, "id": "le4wpszbtc2j5e5n3vrwqlwj5q", "input": { "learning_rate": 0.0005, "stylegan2_model": "self_distill/dogs_1024_pytorch.pkl", "source_x_percentage": 55, "source_y_percentage": 35, "target_x_percentage": 20, "target_y_percentage": 90, "maximum_n_iterations": 50, "show_points_and_arrows": true, "only_render_first_frame": false }, "logs": "0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<00:52, 1.06s/it]\n 4%|▍ | 2/50 [00:01<00:45, 1.06it/s]\n 6%|▌ | 3/50 [00:02<00:42, 1.11it/s]\n 8%|▊ | 4/50 [00:03<00:40, 1.13it/s]\n 10%|█ | 5/50 [00:04<00:40, 1.11it/s]\n 12%|█▏ | 6/50 [00:05<00:39, 1.12it/s]\n 14%|█▍ | 7/50 [00:06<00:38, 1.12it/s]\n 16%|█▌ | 8/50 [00:07<00:40, 1.03it/s]\n 18%|█▊ | 9/50 [00:08<00:38, 1.07it/s]\n 20%|██ | 10/50 [00:09<00:36, 1.11it/s]\n 22%|██▏ | 11/50 [00:09<00:34, 1.14it/s]\n 24%|██▍ | 12/50 [00:10<00:33, 1.15it/s]\n 26%|██▌ | 13/50 [00:11<00:31, 1.16it/s]\n 28%|██▊ | 14/50 [00:12<00:30, 1.17it/s]\n 30%|███ | 15/50 [00:13<00:29, 1.19it/s]\n 32%|███▏ | 16/50 [00:14<00:28, 1.20it/s]\n 34%|███▍ | 17/50 [00:14<00:27, 1.20it/s]\n 36%|███▌ | 18/50 [00:15<00:26, 1.20it/s]\n 38%|███▊ | 19/50 [00:16<00:25, 1.20it/s]\n 40%|████ | 20/50 [00:17<00:25, 1.19it/s]\n 42%|████▏ | 21/50 [00:18<00:24, 1.20it/s]\n 44%|████▍ | 22/50 [00:19<00:23, 1.21it/s]\n 46%|████▌ | 23/50 [00:19<00:22, 1.21it/s]\n 48%|████▊ | 24/50 [00:20<00:21, 1.19it/s]\n 50%|█████ | 25/50 [00:21<00:21, 1.16it/s]\n 52%|█████▏ | 26/50 [00:22<00:21, 1.14it/s]\n 54%|█████▍ | 27/50 [00:23<00:20, 1.12it/s]\n 56%|█████▌ | 28/50 [00:24<00:19, 1.11it/s]\n 58%|█████▊ | 29/50 [00:25<00:19, 1.11it/s]\n 60%|██████ | 30/50 [00:26<00:18, 1.11it/s]\n 62%|██████▏ | 31/50 [00:27<00:17, 1.10it/s]\n 64%|██████▍ | 32/50 [00:28<00:16, 1.10it/s]\n 66%|██████▌ | 33/50 [00:29<00:15, 1.10it/s]\n 68%|██████▊ | 34/50 [00:29<00:14, 1.10it/s]\n 70%|███████ | 35/50 [00:30<00:13, 1.09it/s]\n 72%|███████▏ | 36/50 [00:31<00:12, 1.09it/s]\n 74%|███████▍ | 37/50 [00:32<00:11, 1.09it/s]\n 76%|███████▌ | 38/50 [00:33<00:11, 1.09it/s]\n 78%|███████▊ | 39/50 [00:34<00:10, 1.08it/s]\n 80%|████████ | 40/50 [00:35<00:09, 1.08it/s]\n 82%|████████▏ | 41/50 [00:36<00:08, 1.08it/s]\n 84%|████████▍ | 42/50 [00:37<00:07, 1.09it/s]\n 86%|████████▌ | 43/50 [00:38<00:06, 1.11it/s]\n 88%|████████▊ | 44/50 [00:39<00:05, 1.13it/s]\n 90%|█████████ | 45/50 [00:39<00:04, 1.12it/s]\n 92%|█████████▏| 46/50 [00:40<00:03, 1.12it/s]\n 94%|█████████▍| 47/50 [00:41<00:02, 1.12it/s]\n 96%|█████████▌| 48/50 [00:42<00:01, 1.13it/s]\n 98%|█████████▊| 49/50 [00:43<00:00, 1.12it/s]\n100%|██████████| 50/50 [00:44<00:00, 1.09it/s]\n100%|██████████| 50/50 [00:44<00:00, 1.12it/s]", "metrics": { "predict_time": 47.773258, "total_time": 47.745544 }, "output": "https://replicate.delivery/pbxt/IVBQ03ROw9ZzPFpFP7kstDYgngL5ICt3Qlew8i8Gufm9WcLRA/video_db6cebe0-a688-4cd9-b934-7523dfc1e4ee.mp4", "started_at": "2023-07-01T11:52:46.557574Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/le4wpszbtc2j5e5n3vrwqlwj5q", "cancel": "https://api.replicate.com/v1/predictions/le4wpszbtc2j5e5n3vrwqlwj5q/cancel" }, "version": "6da45a995bebe0d2427a61f867a067f50fadba8bc1e66d4c50b087898aaf3f6e" }
Generated in0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<00:52, 1.06s/it] 4%|▍ | 2/50 [00:01<00:45, 1.06it/s] 6%|▌ | 3/50 [00:02<00:42, 1.11it/s] 8%|▊ | 4/50 [00:03<00:40, 1.13it/s] 10%|█ | 5/50 [00:04<00:40, 1.11it/s] 12%|█▏ | 6/50 [00:05<00:39, 1.12it/s] 14%|█▍ | 7/50 [00:06<00:38, 1.12it/s] 16%|█▌ | 8/50 [00:07<00:40, 1.03it/s] 18%|█▊ | 9/50 [00:08<00:38, 1.07it/s] 20%|██ | 10/50 [00:09<00:36, 1.11it/s] 22%|██▏ | 11/50 [00:09<00:34, 1.14it/s] 24%|██▍ | 12/50 [00:10<00:33, 1.15it/s] 26%|██▌ | 13/50 [00:11<00:31, 1.16it/s] 28%|██▊ | 14/50 [00:12<00:30, 1.17it/s] 30%|███ | 15/50 [00:13<00:29, 1.19it/s] 32%|███▏ | 16/50 [00:14<00:28, 1.20it/s] 34%|███▍ | 17/50 [00:14<00:27, 1.20it/s] 36%|███▌ | 18/50 [00:15<00:26, 1.20it/s] 38%|███▊ | 19/50 [00:16<00:25, 1.20it/s] 40%|████ | 20/50 [00:17<00:25, 1.19it/s] 42%|████▏ | 21/50 [00:18<00:24, 1.20it/s] 44%|████▍ | 22/50 [00:19<00:23, 1.21it/s] 46%|████▌ | 23/50 [00:19<00:22, 1.21it/s] 48%|████▊ | 24/50 [00:20<00:21, 1.19it/s] 50%|█████ | 25/50 [00:21<00:21, 1.16it/s] 52%|█████▏ | 26/50 [00:22<00:21, 1.14it/s] 54%|█████▍ | 27/50 [00:23<00:20, 1.12it/s] 56%|█████▌ | 28/50 [00:24<00:19, 1.11it/s] 58%|█████▊ | 29/50 [00:25<00:19, 1.11it/s] 60%|██████ | 30/50 [00:26<00:18, 1.11it/s] 62%|██████▏ | 31/50 [00:27<00:17, 1.10it/s] 64%|██████▍ | 32/50 [00:28<00:16, 1.10it/s] 66%|██████▌ | 33/50 [00:29<00:15, 1.10it/s] 68%|██████▊ | 34/50 [00:29<00:14, 1.10it/s] 70%|███████ | 35/50 [00:30<00:13, 1.09it/s] 72%|███████▏ | 36/50 [00:31<00:12, 1.09it/s] 74%|███████▍ | 37/50 [00:32<00:11, 1.09it/s] 76%|███████▌ | 38/50 [00:33<00:11, 1.09it/s] 78%|███████▊ | 39/50 [00:34<00:10, 1.08it/s] 80%|████████ | 40/50 [00:35<00:09, 1.08it/s] 82%|████████▏ | 41/50 [00:36<00:08, 1.08it/s] 84%|████████▍ | 42/50 [00:37<00:07, 1.09it/s] 86%|████████▌ | 43/50 [00:38<00:06, 1.11it/s] 88%|████████▊ | 44/50 [00:39<00:05, 1.13it/s] 90%|█████████ | 45/50 [00:39<00:04, 1.12it/s] 92%|█████████▏| 46/50 [00:40<00:03, 1.12it/s] 94%|█████████▍| 47/50 [00:41<00:02, 1.12it/s] 96%|█████████▌| 48/50 [00:42<00:01, 1.13it/s] 98%|█████████▊| 49/50 [00:43<00:00, 1.12it/s] 100%|██████████| 50/50 [00:44<00:00, 1.09it/s] 100%|██████████| 50/50 [00:44<00:00, 1.12it/s]
Prediction
zsxkib/draggan:6da45a995bebe0d2427a61f867a067f50fadba8bc1e66d4c50b087898aaf3f6eIDvclxzlzbr3l4i3srafgwjipbniStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- learning_rate
- 0.002
- stylegan2_model
- self_distill/giraffes_512_pytorch.pkl
- source_x_percentage
- 47
- source_y_percentage
- 95
- target_x_percentage
- 39
- target_y_percentage
- 30
- maximum_n_iterations
- 50
- show_points_and_arrows
- only_render_first_frame
{ "learning_rate": 0.002, "stylegan2_model": "self_distill/giraffes_512_pytorch.pkl", "source_x_percentage": 47, "source_y_percentage": 95, "target_x_percentage": 39, "target_y_percentage": 30, "maximum_n_iterations": 50, "show_points_and_arrows": true, "only_render_first_frame": false }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run zsxkib/draggan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "zsxkib/draggan:6da45a995bebe0d2427a61f867a067f50fadba8bc1e66d4c50b087898aaf3f6e", { input: { learning_rate: 0.002, stylegan2_model: "self_distill/giraffes_512_pytorch.pkl", source_x_percentage: 47, source_y_percentage: 95, target_x_percentage: 39, target_y_percentage: 30, maximum_n_iterations: 50, show_points_and_arrows: true, only_render_first_frame: false } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run zsxkib/draggan using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "zsxkib/draggan:6da45a995bebe0d2427a61f867a067f50fadba8bc1e66d4c50b087898aaf3f6e", input={ "learning_rate": 0.002, "stylegan2_model": "self_distill/giraffes_512_pytorch.pkl", "source_x_percentage": 47, "source_y_percentage": 95, "target_x_percentage": 39, "target_y_percentage": 30, "maximum_n_iterations": 50, "show_points_and_arrows": True, "only_render_first_frame": False } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run zsxkib/draggan 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": "zsxkib/draggan:6da45a995bebe0d2427a61f867a067f50fadba8bc1e66d4c50b087898aaf3f6e", "input": { "learning_rate": 0.002, "stylegan2_model": "self_distill/giraffes_512_pytorch.pkl", "source_x_percentage": 47, "source_y_percentage": 95, "target_x_percentage": 39, "target_y_percentage": 30, "maximum_n_iterations": 50, "show_points_and_arrows": true, "only_render_first_frame": false } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-07-01T11:58:46.176456Z", "created_at": "2023-07-01T11:58:33.960094Z", "data_removed": false, "error": null, "id": "vclxzlzbr3l4i3srafgwjipbni", "input": { "learning_rate": 0.002, "stylegan2_model": "self_distill/giraffes_512_pytorch.pkl", "source_x_percentage": 47, "source_y_percentage": 95, "target_x_percentage": 39, "target_y_percentage": 30, "maximum_n_iterations": 50, "show_points_and_arrows": true, "only_render_first_frame": false }, "logs": "0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:16, 2.95it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.75it/s]\n 6%|▌ | 3/50 [00:00<00:11, 4.10it/s]\n 8%|▊ | 4/50 [00:00<00:10, 4.28it/s]\n 10%|█ | 5/50 [00:01<00:10, 4.43it/s]\n 12%|█▏ | 6/50 [00:01<00:09, 4.49it/s]\n 14%|█▍ | 7/50 [00:01<00:09, 4.55it/s]\n 16%|█▌ | 8/50 [00:01<00:09, 4.49it/s]\n 18%|█▊ | 9/50 [00:02<00:09, 4.51it/s]\n 20%|██ | 10/50 [00:02<00:08, 4.53it/s]\n 22%|██▏ | 11/50 [00:02<00:08, 4.58it/s]\n 24%|██▍ | 12/50 [00:02<00:08, 4.63it/s]\n 26%|██▌ | 13/50 [00:02<00:07, 4.64it/s]\n 28%|██▊ | 14/50 [00:03<00:07, 4.67it/s]\n 30%|███ | 15/50 [00:03<00:07, 4.66it/s]\n 32%|███▏ | 16/50 [00:03<00:07, 4.68it/s]\n 34%|███▍ | 17/50 [00:03<00:07, 4.69it/s]\n 36%|███▌ | 18/50 [00:03<00:06, 4.70it/s]\n 38%|███▊ | 19/50 [00:04<00:06, 4.70it/s]\n 40%|████ | 20/50 [00:04<00:06, 4.68it/s]\n 42%|████▏ | 21/50 [00:04<00:06, 4.63it/s]\n 44%|████▍ | 22/50 [00:04<00:06, 4.62it/s]\n 46%|████▌ | 23/50 [00:05<00:05, 4.65it/s]\n 48%|████▊ | 24/50 [00:05<00:05, 4.67it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.70it/s]\n 52%|█████▏ | 26/50 [00:05<00:05, 4.69it/s]\n 54%|█████▍ | 27/50 [00:05<00:04, 4.70it/s]\n 56%|█████▌ | 28/50 [00:06<00:04, 4.62it/s]\n 58%|█████▊ | 29/50 [00:06<00:04, 4.53it/s]\n 60%|██████ | 30/50 [00:06<00:04, 4.48it/s]\n 62%|██████▏ | 31/50 [00:06<00:04, 4.40it/s]\n 64%|██████▍ | 32/50 [00:07<00:04, 4.47it/s]\n 66%|██████▌ | 33/50 [00:07<00:03, 4.54it/s]\n 68%|██████▊ | 34/50 [00:07<00:03, 4.57it/s]\n 70%|███████ | 35/50 [00:07<00:03, 4.56it/s]\n 72%|███████▏ | 36/50 [00:07<00:03, 4.48it/s]\n 74%|███████▍ | 37/50 [00:08<00:02, 4.48it/s]\n 76%|███████▌ | 38/50 [00:08<00:02, 4.58it/s]\n 78%|███████▊ | 39/50 [00:08<00:02, 4.63it/s]\n 80%|████████ | 40/50 [00:08<00:02, 4.66it/s]\n 82%|████████▏ | 41/50 [00:09<00:01, 4.67it/s]\n 84%|████████▍ | 42/50 [00:09<00:01, 4.70it/s]\n 86%|████████▌ | 43/50 [00:09<00:01, 4.73it/s]\n 88%|████████▊ | 44/50 [00:09<00:01, 4.74it/s]\n 90%|█████████ | 45/50 [00:09<00:01, 4.69it/s]\n 92%|█████████▏| 46/50 [00:10<00:00, 4.74it/s]\n 94%|█████████▍| 47/50 [00:10<00:00, 4.74it/s]\n 96%|█████████▌| 48/50 [00:10<00:00, 4.74it/s]\n 98%|█████████▊| 49/50 [00:10<00:00, 4.75it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.72it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.58it/s]", "metrics": { "predict_time": 12.241124, "total_time": 12.216362 }, "output": "https://replicate.delivery/pbxt/1WpI295qSFqdHpRpIo8c5ddlcYq9OLdHFqNq7MCkXYc9G3SE/video_d08497f3-b015-46c6-b656-459003b4a739.mp4", "started_at": "2023-07-01T11:58:33.935332Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vclxzlzbr3l4i3srafgwjipbni", "cancel": "https://api.replicate.com/v1/predictions/vclxzlzbr3l4i3srafgwjipbni/cancel" }, "version": "6da45a995bebe0d2427a61f867a067f50fadba8bc1e66d4c50b087898aaf3f6e" }
Generated in0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:16, 2.95it/s] 4%|▍ | 2/50 [00:00<00:12, 3.75it/s] 6%|▌ | 3/50 [00:00<00:11, 4.10it/s] 8%|▊ | 4/50 [00:00<00:10, 4.28it/s] 10%|█ | 5/50 [00:01<00:10, 4.43it/s] 12%|█▏ | 6/50 [00:01<00:09, 4.49it/s] 14%|█▍ | 7/50 [00:01<00:09, 4.55it/s] 16%|█▌ | 8/50 [00:01<00:09, 4.49it/s] 18%|█▊ | 9/50 [00:02<00:09, 4.51it/s] 20%|██ | 10/50 [00:02<00:08, 4.53it/s] 22%|██▏ | 11/50 [00:02<00:08, 4.58it/s] 24%|██▍ | 12/50 [00:02<00:08, 4.63it/s] 26%|██▌ | 13/50 [00:02<00:07, 4.64it/s] 28%|██▊ | 14/50 [00:03<00:07, 4.67it/s] 30%|███ | 15/50 [00:03<00:07, 4.66it/s] 32%|███▏ | 16/50 [00:03<00:07, 4.68it/s] 34%|███▍ | 17/50 [00:03<00:07, 4.69it/s] 36%|███▌ | 18/50 [00:03<00:06, 4.70it/s] 38%|███▊ | 19/50 [00:04<00:06, 4.70it/s] 40%|████ | 20/50 [00:04<00:06, 4.68it/s] 42%|████▏ | 21/50 [00:04<00:06, 4.63it/s] 44%|████▍ | 22/50 [00:04<00:06, 4.62it/s] 46%|████▌ | 23/50 [00:05<00:05, 4.65it/s] 48%|████▊ | 24/50 [00:05<00:05, 4.67it/s] 50%|█████ | 25/50 [00:05<00:05, 4.70it/s] 52%|█████▏ | 26/50 [00:05<00:05, 4.69it/s] 54%|█████▍ | 27/50 [00:05<00:04, 4.70it/s] 56%|█████▌ | 28/50 [00:06<00:04, 4.62it/s] 58%|█████▊ | 29/50 [00:06<00:04, 4.53it/s] 60%|██████ | 30/50 [00:06<00:04, 4.48it/s] 62%|██████▏ | 31/50 [00:06<00:04, 4.40it/s] 64%|██████▍ | 32/50 [00:07<00:04, 4.47it/s] 66%|██████▌ | 33/50 [00:07<00:03, 4.54it/s] 68%|██████▊ | 34/50 [00:07<00:03, 4.57it/s] 70%|███████ | 35/50 [00:07<00:03, 4.56it/s] 72%|███████▏ | 36/50 [00:07<00:03, 4.48it/s] 74%|███████▍ | 37/50 [00:08<00:02, 4.48it/s] 76%|███████▌ | 38/50 [00:08<00:02, 4.58it/s] 78%|███████▊ | 39/50 [00:08<00:02, 4.63it/s] 80%|████████ | 40/50 [00:08<00:02, 4.66it/s] 82%|████████▏ | 41/50 [00:09<00:01, 4.67it/s] 84%|████████▍ | 42/50 [00:09<00:01, 4.70it/s] 86%|████████▌ | 43/50 [00:09<00:01, 4.73it/s] 88%|████████▊ | 44/50 [00:09<00:01, 4.74it/s] 90%|█████████ | 45/50 [00:09<00:01, 4.69it/s] 92%|█████████▏| 46/50 [00:10<00:00, 4.74it/s] 94%|█████████▍| 47/50 [00:10<00:00, 4.74it/s] 96%|█████████▌| 48/50 [00:10<00:00, 4.74it/s] 98%|█████████▊| 49/50 [00:10<00:00, 4.75it/s] 100%|██████████| 50/50 [00:10<00:00, 4.72it/s] 100%|██████████| 50/50 [00:10<00:00, 4.58it/s]
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