zeke / dogbooth
A DreamBooth model trained using GitHub Actions. Prompt identifier is `dgg`
- Public
- 427 runs
-
A100 (80GB)
- GitHub
Prediction
zeke/dogbooth:953c494579f3431d54400dcba7ca590f20962e29eba9700fb6a2f0ece3d45b20Input
- width
- 512
- height
- 512
- prompt
- dgg as a pixar character
- num_outputs
- 1
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "dgg as a pixar character", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 }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 zeke/dogbooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "zeke/dogbooth:953c494579f3431d54400dcba7ca590f20962e29eba9700fb6a2f0ece3d45b20", { input: { width: 512, height: 512, prompt: "dgg as a pixar character", num_outputs: 1, guidance_scale: 7.5, num_inference_steps: 50 } } ); // 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 zeke/dogbooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "zeke/dogbooth:953c494579f3431d54400dcba7ca590f20962e29eba9700fb6a2f0ece3d45b20", input={ "width": 512, "height": 512, "prompt": "dgg as a pixar character", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run zeke/dogbooth 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": "zeke/dogbooth:953c494579f3431d54400dcba7ca590f20962e29eba9700fb6a2f0ece3d45b20", "input": { "width": 512, "height": 512, "prompt": "dgg as a pixar character", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/zeke/dogbooth@sha256:953c494579f3431d54400dcba7ca590f20962e29eba9700fb6a2f0ece3d45b20 \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="dgg as a pixar character"' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/zeke/dogbooth@sha256:953c494579f3431d54400dcba7ca590f20962e29eba9700fb6a2f0ece3d45b20curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 512, "height": 512, "prompt": "dgg as a pixar character", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-12-01T21:15:27.869371Z", "created_at": "2022-12-01T21:15:07.831014Z", "data_removed": false, "error": null, "id": "xj2oauwehfeubezfcdqo5q43ge", "input": { "width": 512, "height": 512, "prompt": "dgg as a pixar character", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Loading pipeline...\nUsing seed: 9689\nGlobal seed set to 9689\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:04<03:39, 4.49s/it]\n 4%|▍ | 2/50 [00:04<01:35, 2.00s/it]\n 6%|▌ | 3/50 [00:05<00:56, 1.20s/it]\n 8%|▊ | 4/50 [00:05<00:38, 1.21it/s]\n 10%|█ | 5/50 [00:05<00:28, 1.60it/s]\n 12%|█▏ | 6/50 [00:05<00:22, 2.00it/s]\n 14%|█▍ | 7/50 [00:06<00:18, 2.37it/s]\n 16%|█▌ | 8/50 [00:06<00:15, 2.71it/s]\n 18%|█▊ | 9/50 [00:06<00:13, 2.98it/s]\n 20%|██ | 10/50 [00:06<00:12, 3.21it/s]\n 22%|██▏ | 11/50 [00:07<00:11, 3.38it/s]\n 24%|██▍ | 12/50 [00:07<00:10, 3.52it/s]\n 26%|██▌ | 13/50 [00:07<00:10, 3.60it/s]\n 28%|██▊ | 14/50 [00:07<00:09, 3.67it/s]\n 30%|███ | 15/50 [00:08<00:09, 3.72it/s]\n 32%|███▏ | 16/50 [00:08<00:09, 3.77it/s]\n 34%|███▍ | 17/50 [00:08<00:08, 3.78it/s]\n 36%|███▌ | 18/50 [00:08<00:08, 3.80it/s]\n 38%|███▊ | 19/50 [00:09<00:08, 3.81it/s]\n 40%|████ | 20/50 [00:09<00:07, 3.83it/s]\n 42%|████▏ | 21/50 [00:09<00:07, 3.85it/s]\n 44%|████▍ | 22/50 [00:09<00:07, 3.84it/s]\n 46%|████▌ | 23/50 [00:10<00:07, 3.83it/s]\n 48%|████▊ | 24/50 [00:10<00:06, 3.84it/s]\n 50%|█████ | 25/50 [00:10<00:06, 3.84it/s]\n 52%|█████▏ | 26/50 [00:10<00:06, 3.85it/s]\n 54%|█████▍ | 27/50 [00:11<00:05, 3.85it/s]\n 56%|█████▌ | 28/50 [00:11<00:05, 3.85it/s]\n 58%|█████▊ | 29/50 [00:11<00:05, 3.84it/s]\n 60%|██████ | 30/50 [00:12<00:05, 3.83it/s]\n 62%|██████▏ | 31/50 [00:12<00:04, 3.84it/s]\n 64%|██████▍ | 32/50 [00:12<00:04, 3.84it/s]\n 66%|██████▌ | 33/50 [00:12<00:04, 3.85it/s]\n 68%|██████▊ | 34/50 [00:13<00:04, 3.84it/s]\n 70%|███████ | 35/50 [00:13<00:03, 3.84it/s]\n 72%|███████▏ | 36/50 [00:13<00:03, 3.84it/s]\n 74%|███████▍ | 37/50 [00:13<00:03, 3.84it/s]\n 76%|███████▌ | 38/50 [00:14<00:03, 3.84it/s]\n 78%|███████▊ | 39/50 [00:14<00:02, 3.84it/s]\n 80%|████████ | 40/50 [00:14<00:02, 3.85it/s]\n 82%|████████▏ | 41/50 [00:14<00:02, 3.83it/s]\n 84%|████████▍ | 42/50 [00:15<00:02, 3.83it/s]\n 86%|████████▌ | 43/50 [00:15<00:01, 3.83it/s]\n 88%|████████▊ | 44/50 [00:15<00:01, 3.84it/s]\n 90%|█████████ | 45/50 [00:15<00:01, 3.83it/s]\n 92%|█████████▏| 46/50 [00:16<00:01, 3.84it/s]\n 94%|█████████▍| 47/50 [00:16<00:00, 3.83it/s]\n 96%|█████████▌| 48/50 [00:16<00:00, 3.82it/s]\n 98%|█████████▊| 49/50 [00:16<00:00, 3.81it/s]\n100%|██████████| 50/50 [00:17<00:00, 3.82it/s]\n100%|██████████| 50/50 [00:17<00:00, 2.90it/s]", "metrics": { "predict_time": 19.999775, "total_time": 20.038357 }, "output": [ "https://replicate.delivery/pbxt/r2naKsI29b5MC5sDBdkOeL3NurfYa6vMtcqsbVW9yCxvtsFQA/out-0.png" ], "started_at": "2022-12-01T21:15:07.869596Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xj2oauwehfeubezfcdqo5q43ge", "cancel": "https://api.replicate.com/v1/predictions/xj2oauwehfeubezfcdqo5q43ge/cancel" }, "version": "953c494579f3431d54400dcba7ca590f20962e29eba9700fb6a2f0ece3d45b20" }Generated inLoading pipeline... Using seed: 9689 Global seed set to 9689 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:04<03:39, 4.49s/it] 4%|▍ | 2/50 [00:04<01:35, 2.00s/it] 6%|▌ | 3/50 [00:05<00:56, 1.20s/it] 8%|▊ | 4/50 [00:05<00:38, 1.21it/s] 10%|█ | 5/50 [00:05<00:28, 1.60it/s] 12%|█▏ | 6/50 [00:05<00:22, 2.00it/s] 14%|█▍ | 7/50 [00:06<00:18, 2.37it/s] 16%|█▌ | 8/50 [00:06<00:15, 2.71it/s] 18%|█▊ | 9/50 [00:06<00:13, 2.98it/s] 20%|██ | 10/50 [00:06<00:12, 3.21it/s] 22%|██▏ | 11/50 [00:07<00:11, 3.38it/s] 24%|██▍ | 12/50 [00:07<00:10, 3.52it/s] 26%|██▌ | 13/50 [00:07<00:10, 3.60it/s] 28%|██▊ | 14/50 [00:07<00:09, 3.67it/s] 30%|███ | 15/50 [00:08<00:09, 3.72it/s] 32%|███▏ | 16/50 [00:08<00:09, 3.77it/s] 34%|███▍ | 17/50 [00:08<00:08, 3.78it/s] 36%|███▌ | 18/50 [00:08<00:08, 3.80it/s] 38%|███▊ | 19/50 [00:09<00:08, 3.81it/s] 40%|████ | 20/50 [00:09<00:07, 3.83it/s] 42%|████▏ | 21/50 [00:09<00:07, 3.85it/s] 44%|████▍ | 22/50 [00:09<00:07, 3.84it/s] 46%|████▌ | 23/50 [00:10<00:07, 3.83it/s] 48%|████▊ | 24/50 [00:10<00:06, 3.84it/s] 50%|█████ | 25/50 [00:10<00:06, 3.84it/s] 52%|█████▏ | 26/50 [00:10<00:06, 3.85it/s] 54%|█████▍ | 27/50 [00:11<00:05, 3.85it/s] 56%|█████▌ | 28/50 [00:11<00:05, 3.85it/s] 58%|█████▊ | 29/50 [00:11<00:05, 3.84it/s] 60%|██████ | 30/50 [00:12<00:05, 3.83it/s] 62%|██████▏ | 31/50 [00:12<00:04, 3.84it/s] 64%|██████▍ | 32/50 [00:12<00:04, 3.84it/s] 66%|██████▌ | 33/50 [00:12<00:04, 3.85it/s] 68%|██████▊ | 34/50 [00:13<00:04, 3.84it/s] 70%|███████ | 35/50 [00:13<00:03, 3.84it/s] 72%|███████▏ | 36/50 [00:13<00:03, 3.84it/s] 74%|███████▍ | 37/50 [00:13<00:03, 3.84it/s] 76%|███████▌ | 38/50 [00:14<00:03, 3.84it/s] 78%|███████▊ | 39/50 [00:14<00:02, 3.84it/s] 80%|████████ | 40/50 [00:14<00:02, 3.85it/s] 82%|████████▏ | 41/50 [00:14<00:02, 3.83it/s] 84%|████████▍ | 42/50 [00:15<00:02, 3.83it/s] 86%|████████▌ | 43/50 [00:15<00:01, 3.83it/s] 88%|████████▊ | 44/50 [00:15<00:01, 3.84it/s] 90%|█████████ | 45/50 [00:15<00:01, 3.83it/s] 92%|█████████▏| 46/50 [00:16<00:01, 3.84it/s] 94%|█████████▍| 47/50 [00:16<00:00, 3.83it/s] 96%|█████████▌| 48/50 [00:16<00:00, 3.82it/s] 98%|█████████▊| 49/50 [00:16<00:00, 3.81it/s] 100%|██████████| 50/50 [00:17<00:00, 3.82it/s] 100%|██████████| 50/50 [00:17<00:00, 2.90it/s]Prediction
zeke/dogbooth:953c494579f3431d54400dcba7ca590f20962e29eba9700fb6a2f0ece3d45b20IDth6lebztunhgnki5imiaq7vdnmStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a photo of dgg wearing sunglasses
- num_outputs
- 1
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "a photo of dgg wearing sunglasses", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 }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 zeke/dogbooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "zeke/dogbooth:953c494579f3431d54400dcba7ca590f20962e29eba9700fb6a2f0ece3d45b20", { input: { width: 512, height: 512, prompt: "a photo of dgg wearing sunglasses", num_outputs: 1, guidance_scale: 7.5, num_inference_steps: 50 } } ); // 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 zeke/dogbooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "zeke/dogbooth:953c494579f3431d54400dcba7ca590f20962e29eba9700fb6a2f0ece3d45b20", input={ "width": 512, "height": 512, "prompt": "a photo of dgg wearing sunglasses", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run zeke/dogbooth 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": "zeke/dogbooth:953c494579f3431d54400dcba7ca590f20962e29eba9700fb6a2f0ece3d45b20", "input": { "width": 512, "height": 512, "prompt": "a photo of dgg wearing sunglasses", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/zeke/dogbooth@sha256:953c494579f3431d54400dcba7ca590f20962e29eba9700fb6a2f0ece3d45b20 \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="a photo of dgg wearing sunglasses"' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/zeke/dogbooth@sha256:953c494579f3431d54400dcba7ca590f20962e29eba9700fb6a2f0ece3d45b20curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 512, "height": 512, "prompt": "a photo of dgg wearing sunglasses", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-12-01T21:17:48.350672Z", "created_at": "2022-12-01T21:17:34.444672Z", "data_removed": false, "error": null, "id": "th6lebztunhgnki5imiaq7vdnm", "input": { "width": 512, "height": 512, "prompt": "a photo of dgg wearing sunglasses", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Loading pipeline...\nUsing seed: 19718\nGlobal seed set to 19718\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:15, 3.17it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.54it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.69it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.76it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.74it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.77it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.80it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.82it/s]\n 18%|█▊ | 9/50 [00:02<00:10, 3.80it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.81it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.81it/s]\n 24%|██▍ | 12/50 [00:03<00:09, 3.81it/s]\n 26%|██▌ | 13/50 [00:03<00:09, 3.80it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.80it/s]\n 30%|███ | 15/50 [00:03<00:09, 3.80it/s]\n 32%|███▏ | 16/50 [00:04<00:08, 3.80it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.80it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.79it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.80it/s]\n 40%|████ | 20/50 [00:05<00:07, 3.79it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.79it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.79it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.80it/s]\n 48%|████▊ | 24/50 [00:06<00:06, 3.79it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.79it/s]\n 52%|█████▏ | 26/50 [00:06<00:06, 3.78it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.79it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.79it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.79it/s]\n 60%|██████ | 30/50 [00:07<00:05, 3.79it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.80it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.80it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.79it/s]\n 68%|██████▊ | 34/50 [00:08<00:04, 3.80it/s]\n 70%|███████ | 35/50 [00:09<00:03, 3.79it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.79it/s]\n 74%|███████▍ | 37/50 [00:09<00:03, 3.79it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.80it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.80it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.79it/s]\n 82%|████████▏ | 41/50 [00:10<00:02, 3.79it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.79it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.80it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.79it/s]\n 90%|█████████ | 45/50 [00:11<00:01, 3.80it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.80it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.80it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.79it/s]\n 98%|█████████▊| 49/50 [00:12<00:00, 3.80it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.80it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.79it/s]", "metrics": { "predict_time": 13.869148, "total_time": 13.906 }, "output": [ "https://replicate.delivery/pbxt/neppoAKxWFwlDyoKktjyA0B6YsqImlolSZlRat9Vjei8vsFQA/out-0.png" ], "started_at": "2022-12-01T21:17:34.481524Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/th6lebztunhgnki5imiaq7vdnm", "cancel": "https://api.replicate.com/v1/predictions/th6lebztunhgnki5imiaq7vdnm/cancel" }, "version": "953c494579f3431d54400dcba7ca590f20962e29eba9700fb6a2f0ece3d45b20" }Generated inLoading pipeline... Using seed: 19718 Global seed set to 19718 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:15, 3.17it/s] 4%|▍ | 2/50 [00:00<00:13, 3.54it/s] 6%|▌ | 3/50 [00:00<00:12, 3.69it/s] 8%|▊ | 4/50 [00:01<00:12, 3.76it/s] 10%|█ | 5/50 [00:01<00:12, 3.74it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.77it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.80it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.82it/s] 18%|█▊ | 9/50 [00:02<00:10, 3.80it/s] 20%|██ | 10/50 [00:02<00:10, 3.81it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.81it/s] 24%|██▍ | 12/50 [00:03<00:09, 3.81it/s] 26%|██▌ | 13/50 [00:03<00:09, 3.80it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.80it/s] 30%|███ | 15/50 [00:03<00:09, 3.80it/s] 32%|███▏ | 16/50 [00:04<00:08, 3.80it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.80it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.79it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.80it/s] 40%|████ | 20/50 [00:05<00:07, 3.79it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.79it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.79it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.80it/s] 48%|████▊ | 24/50 [00:06<00:06, 3.79it/s] 50%|█████ | 25/50 [00:06<00:06, 3.79it/s] 52%|█████▏ | 26/50 [00:06<00:06, 3.78it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.79it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.79it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.79it/s] 60%|██████ | 30/50 [00:07<00:05, 3.79it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.80it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.80it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.79it/s] 68%|██████▊ | 34/50 [00:08<00:04, 3.80it/s] 70%|███████ | 35/50 [00:09<00:03, 3.79it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.79it/s] 74%|███████▍ | 37/50 [00:09<00:03, 3.79it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.80it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.80it/s] 80%|████████ | 40/50 [00:10<00:02, 3.79it/s] 82%|████████▏ | 41/50 [00:10<00:02, 3.79it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.79it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.80it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.79it/s] 90%|█████████ | 45/50 [00:11<00:01, 3.80it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.80it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.80it/s] 96%|█████████▌| 48/50 [00:12<00:00, 3.79it/s] 98%|█████████▊| 49/50 [00:12<00:00, 3.80it/s] 100%|██████████| 50/50 [00:13<00:00, 3.80it/s] 100%|██████████| 50/50 [00:13<00:00, 3.79it/s]Prediction
zeke/dogbooth:953c494579f3431d54400dcba7ca590f20962e29eba9700fb6a2f0ece3d45b20IDsnqvash3nnhh7m6oj3py5f5bpqStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a velvet painting of dgg playing poker
- num_outputs
- 1
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "a velvet painting of dgg playing poker", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 }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 zeke/dogbooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "zeke/dogbooth:953c494579f3431d54400dcba7ca590f20962e29eba9700fb6a2f0ece3d45b20", { input: { width: 512, height: 512, prompt: "a velvet painting of dgg playing poker", num_outputs: 1, guidance_scale: 7.5, num_inference_steps: 50 } } ); // 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 zeke/dogbooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "zeke/dogbooth:953c494579f3431d54400dcba7ca590f20962e29eba9700fb6a2f0ece3d45b20", input={ "width": 512, "height": 512, "prompt": "a velvet painting of dgg playing poker", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run zeke/dogbooth 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": "zeke/dogbooth:953c494579f3431d54400dcba7ca590f20962e29eba9700fb6a2f0ece3d45b20", "input": { "width": 512, "height": 512, "prompt": "a velvet painting of dgg playing poker", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/zeke/dogbooth@sha256:953c494579f3431d54400dcba7ca590f20962e29eba9700fb6a2f0ece3d45b20 \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="a velvet painting of dgg playing poker"' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/zeke/dogbooth@sha256:953c494579f3431d54400dcba7ca590f20962e29eba9700fb6a2f0ece3d45b20curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 512, "height": 512, "prompt": "a velvet painting of dgg playing poker", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-12-01T21:16:20.527599Z", "created_at": "2022-12-01T21:16:06.509393Z", "data_removed": false, "error": null, "id": "snqvash3nnhh7m6oj3py5f5bpq", "input": { "width": 512, "height": 512, "prompt": "a velvet painting of dgg playing poker", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Loading pipeline...\nUsing seed: 64596\nGlobal seed set to 64596\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:17, 2.87it/s]\n 4%|▍ | 2/50 [00:00<00:14, 3.38it/s]\n 6%|▌ | 3/50 [00:00<00:13, 3.58it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.68it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.71it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.75it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.77it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.78it/s]\n 18%|█▊ | 9/50 [00:02<00:10, 3.77it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.78it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.79it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.80it/s]\n 26%|██▌ | 13/50 [00:03<00:09, 3.79it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.79it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.79it/s]\n 32%|███▏ | 16/50 [00:04<00:08, 3.80it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.79it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.80it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.80it/s]\n 40%|████ | 20/50 [00:05<00:07, 3.80it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.79it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.80it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.80it/s]\n 48%|████▊ | 24/50 [00:06<00:06, 3.79it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.79it/s]\n 52%|█████▏ | 26/50 [00:06<00:06, 3.79it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.79it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.79it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.79it/s]\n 60%|██████ | 30/50 [00:07<00:05, 3.78it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.79it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.79it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.80it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.79it/s]\n 70%|███████ | 35/50 [00:09<00:03, 3.80it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.80it/s]\n 74%|███████▍ | 37/50 [00:09<00:03, 3.80it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.79it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.79it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.79it/s]\n 82%|████████▏ | 41/50 [00:10<00:02, 3.79it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.79it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.78it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.78it/s]\n 90%|█████████ | 45/50 [00:11<00:01, 3.78it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.77it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.77it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.77it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.76it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.76it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.77it/s]", "metrics": { "predict_time": 13.97899, "total_time": 14.018206 }, "output": [ "https://replicate.delivery/pbxt/IKUPYpMp1j6sCZSUwfYi5PKxczfAyfdjTf9KOCMTnKvQ6yWAB/out-0.png" ], "started_at": "2022-12-01T21:16:06.548609Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/snqvash3nnhh7m6oj3py5f5bpq", "cancel": "https://api.replicate.com/v1/predictions/snqvash3nnhh7m6oj3py5f5bpq/cancel" }, "version": "953c494579f3431d54400dcba7ca590f20962e29eba9700fb6a2f0ece3d45b20" }Generated inLoading pipeline... Using seed: 64596 Global seed set to 64596 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:17, 2.87it/s] 4%|▍ | 2/50 [00:00<00:14, 3.38it/s] 6%|▌ | 3/50 [00:00<00:13, 3.58it/s] 8%|▊ | 4/50 [00:01<00:12, 3.68it/s] 10%|█ | 5/50 [00:01<00:12, 3.71it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.75it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.77it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.78it/s] 18%|█▊ | 9/50 [00:02<00:10, 3.77it/s] 20%|██ | 10/50 [00:02<00:10, 3.78it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.79it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.80it/s] 26%|██▌ | 13/50 [00:03<00:09, 3.79it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.79it/s] 30%|███ | 15/50 [00:04<00:09, 3.79it/s] 32%|███▏ | 16/50 [00:04<00:08, 3.80it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.79it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.80it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.80it/s] 40%|████ | 20/50 [00:05<00:07, 3.80it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.79it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.80it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.80it/s] 48%|████▊ | 24/50 [00:06<00:06, 3.79it/s] 50%|█████ | 25/50 [00:06<00:06, 3.79it/s] 52%|█████▏ | 26/50 [00:06<00:06, 3.79it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.79it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.79it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.79it/s] 60%|██████ | 30/50 [00:07<00:05, 3.78it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.79it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.79it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.80it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.79it/s] 70%|███████ | 35/50 [00:09<00:03, 3.80it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.80it/s] 74%|███████▍ | 37/50 [00:09<00:03, 3.80it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.79it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.79it/s] 80%|████████ | 40/50 [00:10<00:02, 3.79it/s] 82%|████████▏ | 41/50 [00:10<00:02, 3.79it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.79it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.78it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.78it/s] 90%|█████████ | 45/50 [00:11<00:01, 3.78it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.77it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.77it/s] 96%|█████████▌| 48/50 [00:12<00:00, 3.77it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.76it/s] 100%|██████████| 50/50 [00:13<00:00, 3.76it/s] 100%|██████████| 50/50 [00:13<00:00, 3.77it/s]
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