tstramer / robo-diffusion
- Public
- 575 runs
-
A100 (80GB)
Prediction
tstramer/robo-diffusion:2a3e101220c997b2566a6a9f43b42af911a0088a84be663bf3cb82ad76cbf166ID43szyj6hkfaxzl76zguh5uohdaStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- A photo of a nousr robot
- scheduler
- K-LMS
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "A photo of a nousr robot", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run tstramer/robo-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "tstramer/robo-diffusion:2a3e101220c997b2566a6a9f43b42af911a0088a84be663bf3cb82ad76cbf166", { input: { width: 512, height: 512, prompt: "A photo of a nousr robot", 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/robo-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "tstramer/robo-diffusion:2a3e101220c997b2566a6a9f43b42af911a0088a84be663bf3cb82ad76cbf166", input={ "width": 512, "height": 512, "prompt": "A photo of a nousr robot", "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/robo-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/robo-diffusion:2a3e101220c997b2566a6a9f43b42af911a0088a84be663bf3cb82ad76cbf166", "input": { "width": 512, "height": 512, "prompt": "A photo of a nousr robot", "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": "2022-11-08T01:32:36.552220Z", "created_at": "2022-11-08T01:29:49.268386Z", "data_removed": false, "error": null, "id": "43szyj6hkfaxzl76zguh5uohda", "input": { "width": 512, "height": 512, "prompt": "A photo of a nousr robot", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 1965\n\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:02<01:50, 2.25s/it]\n 6%|▌ | 3/50 [00:02<00:29, 1.57it/s]\n 10%|█ | 5/50 [00:02<00:15, 2.88it/s]\n 14%|█▍ | 7/50 [00:02<00:09, 4.33it/s]\n 18%|█▊ | 9/50 [00:02<00:07, 5.81it/s]\n 22%|██▏ | 11/50 [00:02<00:05, 7.29it/s]\n 26%|██▌ | 13/50 [00:03<00:04, 8.65it/s]\n 30%|███ | 15/50 [00:03<00:03, 9.84it/s]\n 34%|███▍ | 17/50 [00:03<00:03, 10.85it/s]\n 38%|███▊ | 19/50 [00:03<00:02, 11.64it/s]\n 42%|████▏ | 21/50 [00:03<00:02, 12.24it/s]\n 46%|████▌ | 23/50 [00:03<00:02, 12.70it/s]\n 50%|█████ | 25/50 [00:03<00:01, 13.03it/s]\n 54%|█████▍ | 27/50 [00:04<00:01, 13.24it/s]\n 58%|█████▊ | 29/50 [00:04<00:01, 13.33it/s]\n 62%|██████▏ | 31/50 [00:04<00:01, 13.52it/s]\n 66%|██████▌ | 33/50 [00:04<00:01, 13.59it/s]\n 70%|███████ | 35/50 [00:04<00:01, 13.66it/s]\n 74%|███████▍ | 37/50 [00:04<00:00, 13.65it/s]\n 78%|███████▊ | 39/50 [00:05<00:00, 13.66it/s]\n 82%|████████▏ | 41/50 [00:05<00:00, 13.66it/s]\n 86%|████████▌ | 43/50 [00:05<00:00, 13.69it/s]\n 90%|█████████ | 45/50 [00:05<00:00, 13.73it/s]\n 94%|█████████▍| 47/50 [00:05<00:00, 13.77it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 13.80it/s]\n100%|██████████| 50/50 [00:05<00:00, 8.60it/s]", "metrics": { "predict_time": 8.123318, "total_time": 167.283834 }, "output": [ "https://replicate.delivery/pbxt/cwkIM2nV3EIcPBx2JpC4nQ5CYMhE0f5WATU8PfLaCgV0O29PA/out-0.png" ], "started_at": "2022-11-08T01:32:28.428902Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/43szyj6hkfaxzl76zguh5uohda", "cancel": "https://api.replicate.com/v1/predictions/43szyj6hkfaxzl76zguh5uohda/cancel" }, "version": "2a3e101220c997b2566a6a9f43b42af911a0088a84be663bf3cb82ad76cbf166" }Generated inUsing seed: 1965 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:02<01:50, 2.25s/it] 6%|▌ | 3/50 [00:02<00:29, 1.57it/s] 10%|█ | 5/50 [00:02<00:15, 2.88it/s] 14%|█▍ | 7/50 [00:02<00:09, 4.33it/s] 18%|█▊ | 9/50 [00:02<00:07, 5.81it/s] 22%|██▏ | 11/50 [00:02<00:05, 7.29it/s] 26%|██▌ | 13/50 [00:03<00:04, 8.65it/s] 30%|███ | 15/50 [00:03<00:03, 9.84it/s] 34%|███▍ | 17/50 [00:03<00:03, 10.85it/s] 38%|███▊ | 19/50 [00:03<00:02, 11.64it/s] 42%|████▏ | 21/50 [00:03<00:02, 12.24it/s] 46%|████▌ | 23/50 [00:03<00:02, 12.70it/s] 50%|█████ | 25/50 [00:03<00:01, 13.03it/s] 54%|█████▍ | 27/50 [00:04<00:01, 13.24it/s] 58%|█████▊ | 29/50 [00:04<00:01, 13.33it/s] 62%|██████▏ | 31/50 [00:04<00:01, 13.52it/s] 66%|██████▌ | 33/50 [00:04<00:01, 13.59it/s] 70%|███████ | 35/50 [00:04<00:01, 13.66it/s] 74%|███████▍ | 37/50 [00:04<00:00, 13.65it/s] 78%|███████▊ | 39/50 [00:05<00:00, 13.66it/s] 82%|████████▏ | 41/50 [00:05<00:00, 13.66it/s] 86%|████████▌ | 43/50 [00:05<00:00, 13.69it/s] 90%|█████████ | 45/50 [00:05<00:00, 13.73it/s] 94%|█████████▍| 47/50 [00:05<00:00, 13.77it/s] 98%|█████████▊| 49/50 [00:05<00:00, 13.80it/s] 100%|██████████| 50/50 [00:05<00:00, 8.60it/s]Prediction
tstramer/robo-diffusion:2a3e101220c997b2566a6a9f43b42af911a0088a84be663bf3cb82ad76cbf166ID3jxp5ymop5gx5b5vkkpjozcatmStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a phto of a nousr robot, 8k, ultrarealistic
- scheduler
- K-LMS
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- "150"
{ "width": 512, "height": 512, "prompt": "a phto of a nousr robot, 8k, ultrarealistic", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "150" }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 tstramer/robo-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "tstramer/robo-diffusion:2a3e101220c997b2566a6a9f43b42af911a0088a84be663bf3cb82ad76cbf166", { input: { width: 512, height: 512, prompt: "a phto of a nousr robot, 8k, ultrarealistic", scheduler: "K-LMS", num_outputs: 1, guidance_scale: 7.5, prompt_strength: 0.8, num_inference_steps: "150" } } ); // 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/robo-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "tstramer/robo-diffusion:2a3e101220c997b2566a6a9f43b42af911a0088a84be663bf3cb82ad76cbf166", input={ "width": 512, "height": 512, "prompt": "a phto of a nousr robot, 8k, ultrarealistic", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "150" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run tstramer/robo-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/robo-diffusion:2a3e101220c997b2566a6a9f43b42af911a0088a84be663bf3cb82ad76cbf166", "input": { "width": 512, "height": 512, "prompt": "a phto of a nousr robot, 8k, ultrarealistic", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "150" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-11-08T01:44:58.749268Z", "created_at": "2022-11-08T01:44:47.370963Z", "data_removed": false, "error": null, "id": "3jxp5ymop5gx5b5vkkpjozcatm", "input": { "width": 512, "height": 512, "prompt": "a phto of a nousr robot, 8k, ultrarealistic", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "150" }, "logs": "Using seed: 24929\n\n 0%| | 0/150 [00:00<?, ?it/s]\n 1%|▏ | 2/150 [00:00<00:12, 12.10it/s]\n 3%|▎ | 4/150 [00:00<00:11, 13.09it/s]\n 4%|▍ | 6/150 [00:00<00:10, 13.44it/s]\n 5%|▌ | 8/150 [00:00<00:10, 13.61it/s]\n 7%|▋ | 10/150 [00:00<00:10, 13.68it/s]\n 8%|▊ | 12/150 [00:00<00:10, 13.74it/s]\n 9%|▉ | 14/150 [00:01<00:09, 13.75it/s]\n 11%|█ | 16/150 [00:01<00:09, 13.61it/s]\n 12%|█▏ | 18/150 [00:01<00:09, 13.74it/s]\n 13%|█▎ | 20/150 [00:01<00:09, 13.78it/s]\n 15%|█▍ | 22/150 [00:01<00:09, 13.85it/s]\n 16%|█▌ | 24/150 [00:01<00:09, 13.91it/s]\n 17%|█▋ | 26/150 [00:01<00:08, 13.84it/s]\n 19%|█▊ | 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"metrics": { "predict_time": 11.34477, "total_time": 11.378305 }, "output": [ "https://replicate.delivery/pbxt/XazouHZ7ePw8PCdiROGkIJtGEfIKXUy7IqNM5ev2JR310s7fA/out-0.png" ], "started_at": "2022-11-08T01:44:47.404498Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/3jxp5ymop5gx5b5vkkpjozcatm", "cancel": "https://api.replicate.com/v1/predictions/3jxp5ymop5gx5b5vkkpjozcatm/cancel" }, "version": "2a3e101220c997b2566a6a9f43b42af911a0088a84be663bf3cb82ad76cbf166" }Generated inUsing seed: 24929 0%| | 0/150 [00:00<?, ?it/s] 1%|▏ | 2/150 [00:00<00:12, 12.10it/s] 3%|▎ | 4/150 [00:00<00:11, 13.09it/s] 4%|▍ | 6/150 [00:00<00:10, 13.44it/s] 5%|▌ | 8/150 [00:00<00:10, 13.61it/s] 7%|▋ | 10/150 [00:00<00:10, 13.68it/s] 8%|▊ | 12/150 [00:00<00:10, 13.74it/s] 9%|▉ | 14/150 [00:01<00:09, 13.75it/s] 11%|█ | 16/150 [00:01<00:09, 13.61it/s] 12%|█▏ | 18/150 [00:01<00:09, 13.74it/s] 13%|█▎ | 20/150 [00:01<00:09, 13.78it/s] 15%|█▍ | 22/150 [00:01<00:09, 13.85it/s] 16%|█▌ | 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[00:08<00:02, 13.70it/s] 77%|███████▋ | 116/150 [00:08<00:02, 13.73it/s] 79%|███████▊ | 118/150 [00:08<00:02, 13.78it/s] 80%|████████ | 120/150 [00:08<00:02, 13.82it/s] 81%|████████▏ | 122/150 [00:08<00:02, 13.86it/s] 83%|████████▎ | 124/150 [00:09<00:01, 13.91it/s] 84%|████████▍ | 126/150 [00:09<00:01, 13.94it/s] 85%|████████▌ | 128/150 [00:09<00:01, 13.94it/s] 87%|████████▋ | 130/150 [00:09<00:01, 13.91it/s] 88%|████████▊ | 132/150 [00:09<00:01, 13.92it/s] 89%|████████▉ | 134/150 [00:09<00:01, 13.93it/s] 91%|█████████ | 136/150 [00:09<00:01, 13.93it/s] 92%|█████████▏| 138/150 [00:10<00:00, 13.93it/s] 93%|█████████▎| 140/150 [00:10<00:00, 13.75it/s] 95%|█████████▍| 142/150 [00:10<00:00, 13.70it/s] 96%|█████████▌| 144/150 [00:10<00:00, 13.68it/s] 97%|█████████▋| 146/150 [00:10<00:00, 13.71it/s] 99%|█████████▊| 148/150 [00:10<00:00, 13.76it/s] 100%|██████████| 150/150 [00:10<00:00, 13.76it/s] 100%|██████████| 150/150 [00:10<00:00, 13.70it/s]
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