tstramer / archer-diffusion
- Public
- 34.8K runs
-
A100 (80GB)
Prediction
tstramer/archer-diffusion:5eb8c570de53a4325cb8e05ea591bd32befde542edb84991da4e416c1adeef52IDn4qpx4yr2varzgk2zs5q4c4i6aStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- seed
- 5
- width
- 512
- height
- 512
- prompt
- a magical princess with golden hair, archer style
- scheduler
- K-LMS
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "seed": 5, "width": 512, "height": 512, "prompt": "a magical princess with golden hair, archer style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport 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 tstramer/archer-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "tstramer/archer-diffusion:5eb8c570de53a4325cb8e05ea591bd32befde542edb84991da4e416c1adeef52", { input: { seed: 5, width: 512, height: 512, prompt: "a magical princess with golden hair, archer style", scheduler: "K-LMS", num_outputs: 1, guidance_scale: 7.5, prompt_strength: 0.8, 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 tstramer/archer-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "tstramer/archer-diffusion:5eb8c570de53a4325cb8e05ea591bd32befde542edb84991da4e416c1adeef52", input={ "seed": 5, "width": 512, "height": 512, "prompt": "a magical princess with golden hair, archer style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run tstramer/archer-diffusion 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": "tstramer/archer-diffusion:5eb8c570de53a4325cb8e05ea591bd32befde542edb84991da4e416c1adeef52", "input": { "seed": 5, "width": 512, "height": 512, "prompt": "a magical princess with golden hair, archer style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-11-07T22:43:17.282488Z", "created_at": "2022-11-07T22:40:42.936801Z", "data_removed": false, "error": null, "id": "n4qpx4yr2varzgk2zs5q4c4i6a", "input": { "seed": 5, "width": 512, "height": 512, "prompt": "a magical princess with golden hair, archer style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 5\n\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:02<01:45, 2.16s/it]\n 6%|▌ | 3/50 [00:02<00:28, 1.63it/s]\n 10%|█ | 5/50 [00:02<00:15, 2.99it/s]\n 14%|█▍ | 7/50 [00:02<00:09, 4.45it/s]\n 18%|█▊ | 9/50 [00:02<00:06, 5.94it/s]\n 22%|██▏ | 11/50 [00:02<00:05, 7.41it/s]\n 26%|██▌ | 13/50 [00:03<00:04, 8.75it/s]\n 30%|███ | 15/50 [00:03<00:03, 9.91it/s]\n 34%|███▍ | 17/50 [00:03<00:03, 10.88it/s]\n 38%|███▊ | 19/50 [00:03<00:02, 11.60it/s]\n 42%|████▏ | 21/50 [00:03<00:02, 12.20it/s]\n 46%|████▌ | 23/50 [00:03<00:02, 12.67it/s]\n 50%|█████ | 25/50 [00:03<00:01, 13.01it/s]\n 54%|█████▍ | 27/50 [00:04<00:01, 13.26it/s]\n 58%|█████▊ | 29/50 [00:04<00:01, 13.43it/s]\n 62%|██████▏ | 31/50 [00:04<00:01, 13.56it/s]\n 66%|██████▌ | 33/50 [00:04<00:01, 13.51it/s]\n 70%|███████ | 35/50 [00:04<00:01, 13.36it/s]\n 74%|███████▍ | 37/50 [00:04<00:00, 13.48it/s]\n 78%|███████▊ | 39/50 [00:04<00:00, 13.38it/s]\n 82%|████████▏ | 41/50 [00:05<00:00, 13.53it/s]\n 86%|████████▌ | 43/50 [00:05<00:00, 13.62it/s]\n 90%|█████████ | 45/50 [00:05<00:00, 13.71it/s]\n 94%|█████████▍| 47/50 [00:05<00:00, 13.54it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 13.63it/s]\n100%|██████████| 50/50 [00:05<00:00, 8.70it/s]", "metrics": { "predict_time": 8.734473, "total_time": 154.345687 }, "output": [ "https://replicate.delivery/pbxt/EsehVzK7wizCHizcOZQaf3IeWEoVJt1pAiQ7SfUfJO9oAecfHA/out-0.png" ], "started_at": "2022-11-07T22:43:08.548015Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/n4qpx4yr2varzgk2zs5q4c4i6a", "cancel": "https://api.replicate.com/v1/predictions/n4qpx4yr2varzgk2zs5q4c4i6a/cancel" }, "version": "5eb8c570de53a4325cb8e05ea591bd32befde542edb84991da4e416c1adeef52" }Generated inUsing seed: 5 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:02<01:45, 2.16s/it] 6%|▌ | 3/50 [00:02<00:28, 1.63it/s] 10%|█ | 5/50 [00:02<00:15, 2.99it/s] 14%|█▍ | 7/50 [00:02<00:09, 4.45it/s] 18%|█▊ | 9/50 [00:02<00:06, 5.94it/s] 22%|██▏ | 11/50 [00:02<00:05, 7.41it/s] 26%|██▌ | 13/50 [00:03<00:04, 8.75it/s] 30%|███ | 15/50 [00:03<00:03, 9.91it/s] 34%|███▍ | 17/50 [00:03<00:03, 10.88it/s] 38%|███▊ | 19/50 [00:03<00:02, 11.60it/s] 42%|████▏ | 21/50 [00:03<00:02, 12.20it/s] 46%|████▌ | 23/50 [00:03<00:02, 12.67it/s] 50%|█████ | 25/50 [00:03<00:01, 13.01it/s] 54%|█████▍ | 27/50 [00:04<00:01, 13.26it/s] 58%|█████▊ | 29/50 [00:04<00:01, 13.43it/s] 62%|██████▏ | 31/50 [00:04<00:01, 13.56it/s] 66%|██████▌ | 33/50 [00:04<00:01, 13.51it/s] 70%|███████ | 35/50 [00:04<00:01, 13.36it/s] 74%|███████▍ | 37/50 [00:04<00:00, 13.48it/s] 78%|███████▊ | 39/50 [00:04<00:00, 13.38it/s] 82%|████████▏ | 41/50 [00:05<00:00, 13.53it/s] 86%|████████▌ | 43/50 [00:05<00:00, 13.62it/s] 90%|█████████ | 45/50 [00:05<00:00, 13.71it/s] 94%|█████████▍| 47/50 [00:05<00:00, 13.54it/s] 98%|█████████▊| 49/50 [00:05<00:00, 13.63it/s] 100%|██████████| 50/50 [00:05<00:00, 8.70it/s]Prediction
tstramer/archer-diffusion:5eb8c570de53a4325cb8e05ea591bd32befde542edb84991da4e416c1adeef52IDnrz2elsauff5vcn2mhjhg54nv4StatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- seed
- 5
- width
- 512
- height
- 512
- prompt
- archer style, a beautiful cat, highly detailed, 8K
- scheduler
- K-LMS
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- "151"
{ "seed": 5, "width": 512, "height": 512, "prompt": "archer style, a beautiful cat, highly detailed, 8K", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "151" }Install Replicate’s Node.js client library:npm install replicateImport 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 tstramer/archer-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "tstramer/archer-diffusion:5eb8c570de53a4325cb8e05ea591bd32befde542edb84991da4e416c1adeef52", { input: { seed: 5, width: 512, height: 512, prompt: "archer style, a beautiful cat, highly detailed, 8K", scheduler: "K-LMS", num_outputs: 1, guidance_scale: 7.5, prompt_strength: 0.8, num_inference_steps: "151" } } ); // 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 tstramer/archer-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "tstramer/archer-diffusion:5eb8c570de53a4325cb8e05ea591bd32befde542edb84991da4e416c1adeef52", input={ "seed": 5, "width": 512, "height": 512, "prompt": "archer style, a beautiful cat, highly detailed, 8K", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "151" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run tstramer/archer-diffusion 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": "tstramer/archer-diffusion:5eb8c570de53a4325cb8e05ea591bd32befde542edb84991da4e416c1adeef52", "input": { "seed": 5, "width": 512, "height": 512, "prompt": "archer style, a beautiful cat, highly detailed, 8K", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "151" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-11-08T02:01:22.564683Z", "created_at": "2022-11-08T02:01:11.302438Z", "data_removed": false, "error": null, "id": "nrz2elsauff5vcn2mhjhg54nv4", "input": { "seed": 5, "width": 512, "height": 512, "prompt": "archer style, a beautiful cat, highly detailed, 8K", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "151" }, "logs": "Using seed: 5\n\n 0%| | 0/151 [00:00<?, ?it/s]\n 1%|▏ | 2/151 [00:00<00:11, 12.58it/s]\n 3%|▎ | 4/151 [00:00<00:10, 13.54it/s]\n 4%|▍ | 6/151 [00:00<00:10, 13.63it/s]\n 5%|▌ | 8/151 [00:00<00:10, 13.83it/s]\n 7%|▋ | 10/151 [00:00<00:10, 13.94it/s]\n 8%|▊ | 12/151 [00:00<00:10, 13.85it/s]\n 9%|▉ | 14/151 [00:01<00:10, 13.67it/s]\n 11%|█ | 16/151 [00:01<00:09, 13.81it/s]\n 12%|█▏ | 18/151 [00:01<00:09, 13.91it/s]\n 13%|█▎ | 20/151 [00:01<00:09, 13.98it/s]\n 15%|█▍ | 22/151 [00:01<00:09, 14.03it/s]\n 16%|█▌ | 24/151 [00:01<00:09, 14.05it/s]\n 17%|█▋ | 26/151 [00:01<00:09, 13.88it/s]\n 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"metrics": { "predict_time": 11.22036, "total_time": 11.262245 }, "output": [ "https://replicate.delivery/pbxt/ojFIuieiPc35A6hW8NbdnP7qmY4y5nozpEC8OamLIeAyp29PA/out-0.png" ], "started_at": "2022-11-08T02:01:11.344323Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/nrz2elsauff5vcn2mhjhg54nv4", "cancel": "https://api.replicate.com/v1/predictions/nrz2elsauff5vcn2mhjhg54nv4/cancel" }, "version": "5eb8c570de53a4325cb8e05ea591bd32befde542edb84991da4e416c1adeef52" }Generated inUsing seed: 5 0%| | 0/151 [00:00<?, ?it/s] 1%|▏ | 2/151 [00:00<00:11, 12.58it/s] 3%|▎ | 4/151 [00:00<00:10, 13.54it/s] 4%|▍ | 6/151 [00:00<00:10, 13.63it/s] 5%|▌ | 8/151 [00:00<00:10, 13.83it/s] 7%|▋ | 10/151 [00:00<00:10, 13.94it/s] 8%|▊ | 12/151 [00:00<00:10, 13.85it/s] 9%|▉ | 14/151 [00:01<00:10, 13.67it/s] 11%|█ | 16/151 [00:01<00:09, 13.81it/s] 12%|█▏ | 18/151 [00:01<00:09, 13.91it/s] 13%|█▎ | 20/151 [00:01<00:09, 13.98it/s] 15%|█▍ | 22/151 [00:01<00:09, 14.03it/s] 16%|█▌ | 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[00:08<00:02, 13.90it/s] 77%|███████▋ | 116/151 [00:08<00:02, 13.75it/s] 78%|███████▊ | 118/151 [00:08<00:02, 13.87it/s] 79%|███████▉ | 120/151 [00:08<00:02, 13.94it/s] 81%|████████ | 122/151 [00:08<00:02, 13.91it/s] 82%|████████▏ | 124/151 [00:08<00:01, 13.85it/s] 83%|████████▎ | 126/151 [00:09<00:01, 13.92it/s] 85%|████████▍ | 128/151 [00:09<00:01, 13.99it/s] 86%|████████▌ | 130/151 [00:09<00:01, 14.00it/s] 87%|████████▋ | 132/151 [00:09<00:01, 14.04it/s] 89%|████████▊ | 134/151 [00:09<00:01, 14.08it/s] 90%|█████████ | 136/151 [00:09<00:01, 14.07it/s] 91%|█████████▏| 138/151 [00:09<00:00, 13.95it/s] 93%|█████████▎| 140/151 [00:10<00:00, 13.95it/s] 94%|█████████▍| 142/151 [00:10<00:00, 13.98it/s] 95%|█████████▌| 144/151 [00:10<00:00, 14.01it/s] 97%|█████████▋| 146/151 [00:10<00:00, 14.00it/s] 98%|█████████▊| 148/151 [00:10<00:00, 14.02it/s] 99%|█████████▉| 150/151 [00:10<00:00, 13.94it/s] 100%|██████████| 151/151 [00:10<00:00, 13.90it/s]
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