yosun
/
camcorgi-flux
Write a prompt mentioning CAM corgi to have @corgi.cam show up in your picture!
Prediction
yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951fIDftqcr9x391rm00chst0tvf7bxmStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- CAM corgi at the beach in a convertible
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "CAM corgi at the beach in a convertible", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", { input: { model: "dev", prompt: "CAM corgi at the beach in a convertible", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", input={ "model": "dev", "prompt": "CAM corgi at the beach in a convertible", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run yosun/camcorgi-flux 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": "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", "input": { "model": "dev", "prompt": "CAM corgi at the beach in a convertible", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-07T22:01:45.761923Z", "created_at": "2024-09-07T22:01:28.136000Z", "data_removed": false, "error": null, "id": "ftqcr9x391rm00chst0tvf7bxm", "input": { "model": "dev", "prompt": "CAM corgi at the beach in a convertible", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 26629\nPrompt: CAM corgi at the beach in a convertible\n[!] txt2img mode\nUsing dev model\nfree=8555718565888\nDownloading weights\n2024-09-07T22:01:28Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp04iy7mt5/weights url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar\n2024-09-07T22:01:29Z | INFO | [ Complete ] dest=/tmp/tmp04iy7mt5/weights size=\"172 MB\" total_elapsed=1.662s url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar\nDownloaded weights in 1.69s\nLoaded LoRAs in 9.71s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.78it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.27it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.04it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.94it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 3.88it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.85it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.83it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.82it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 3.81it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.80it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.80it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.80it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.79it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.79it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.79it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.79it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.79it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.79it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 3.79it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.79it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.79it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.79it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.79it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.79it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.79it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.79it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.79it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.79it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.82it/s]", "metrics": { "predict_time": 17.616382002, "total_time": 17.625923 }, "output": [ "https://replicate.delivery/yhqm/0fxzGIhLwBU9ZKNHbR31SmelbPOswjSyRKtMIgARPbpJ9naTA/out-0.webp" ], "started_at": "2024-09-07T22:01:28.145541Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ftqcr9x391rm00chst0tvf7bxm", "cancel": "https://api.replicate.com/v1/predictions/ftqcr9x391rm00chst0tvf7bxm/cancel" }, "version": "1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f" }Generated inUsing seed: 26629 Prompt: CAM corgi at the beach in a convertible [!] txt2img mode Using dev model free=8555718565888 Downloading weights 2024-09-07T22:01:28Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp04iy7mt5/weights url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar 2024-09-07T22:01:29Z | INFO | [ Complete ] dest=/tmp/tmp04iy7mt5/weights size="172 MB" total_elapsed=1.662s url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar Downloaded weights in 1.69s Loaded LoRAs in 9.71s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.78it/s] 7%|▋ | 2/28 [00:00<00:06, 4.27it/s] 11%|█ | 3/28 [00:00<00:06, 4.04it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.94it/s] 18%|█▊ | 5/28 [00:01<00:05, 3.88it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.85it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.83it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.82it/s] 32%|███▏ | 9/28 [00:02<00:04, 3.81it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.80it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.80it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.80it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.79it/s] 50%|█████ | 14/28 [00:03<00:03, 3.79it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.79it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.79it/s] 61%|██████ | 17/28 [00:04<00:02, 3.79it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.79it/s] 68%|██████▊ | 19/28 [00:04<00:02, 3.79it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.79it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.79it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.79it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.79it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.79it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.79it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.79it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.79it/s] 100%|██████████| 28/28 [00:07<00:00, 3.79it/s] 100%|██████████| 28/28 [00:07<00:00, 3.82it/s]Prediction
yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951fID2y0csw7sz1rm20chst1raggr30StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- CAM corgi on a rocketship on Mars with Elon Musk
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "CAM corgi on a rocketship on Mars with Elon Musk", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", { input: { model: "dev", prompt: "CAM corgi on a rocketship on Mars with Elon Musk", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", input={ "model": "dev", "prompt": "CAM corgi on a rocketship on Mars with Elon Musk", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run yosun/camcorgi-flux 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": "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", "input": { "model": "dev", "prompt": "CAM corgi on a rocketship on Mars with Elon Musk", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-07T22:04:21.297026Z", "created_at": "2024-09-07T22:04:01.400000Z", "data_removed": false, "error": null, "id": "2y0csw7sz1rm20chst1raggr30", "input": { "model": "dev", "prompt": "CAM corgi on a rocketship on Mars with Elon Musk", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 58040\nPrompt: CAM corgi on a rocketship on Mars with Elon Musk\n[!] txt2img mode\nUsing dev model\nfree=9337190944768\nDownloading weights\n2024-09-07T22:04:04Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpwqz2z02w/weights url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar\n2024-09-07T22:04:06Z | INFO | [ Complete ] dest=/tmp/tmpwqz2z02w/weights size=\"172 MB\" total_elapsed=1.517s url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar\nDownloaded weights in 1.56s\nLoaded LoRAs in 8.77s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.77it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.26it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.03it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.93it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 3.87it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.84it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.82it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.81it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 3.80it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.80it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.79it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.79it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.79it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.78it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.78it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.78it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.78it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.78it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 3.78it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.78it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.78it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.78it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.78it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.78it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.78it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.78it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.78it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.78it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.81it/s]", "metrics": { "predict_time": 16.6908327, "total_time": 19.897026 }, "output": [ "https://replicate.delivery/yhqm/f3lDaRHAhgTKGaxfmGrUFRlbXiv1mrzGW32qoApVQtVlfP1mA/out-0.webp" ], "started_at": "2024-09-07T22:04:04.606193Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/2y0csw7sz1rm20chst1raggr30", "cancel": "https://api.replicate.com/v1/predictions/2y0csw7sz1rm20chst1raggr30/cancel" }, "version": "1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f" }Generated inUsing seed: 58040 Prompt: CAM corgi on a rocketship on Mars with Elon Musk [!] txt2img mode Using dev model free=9337190944768 Downloading weights 2024-09-07T22:04:04Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpwqz2z02w/weights url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar 2024-09-07T22:04:06Z | INFO | [ Complete ] dest=/tmp/tmpwqz2z02w/weights size="172 MB" total_elapsed=1.517s url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar Downloaded weights in 1.56s Loaded LoRAs in 8.77s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.77it/s] 7%|▋ | 2/28 [00:00<00:06, 4.26it/s] 11%|█ | 3/28 [00:00<00:06, 4.03it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.93it/s] 18%|█▊ | 5/28 [00:01<00:05, 3.87it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.84it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.82it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.81it/s] 32%|███▏ | 9/28 [00:02<00:04, 3.80it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.80it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.79it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.79it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.79it/s] 50%|█████ | 14/28 [00:03<00:03, 3.78it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.78it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.78it/s] 61%|██████ | 17/28 [00:04<00:02, 3.78it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.78it/s] 68%|██████▊ | 19/28 [00:04<00:02, 3.78it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.78it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.78it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.78it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.78it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.78it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.78it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.78it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.78it/s] 100%|██████████| 28/28 [00:07<00:00, 3.78it/s] 100%|██████████| 28/28 [00:07<00:00, 3.81it/s]Prediction
yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951fIDepge5957vhrm00chst5r8j7wgrStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- CAM corgi as painted by rembrandt
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "CAM corgi as painted by rembrandt", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", { input: { model: "dev", prompt: "CAM corgi as painted by rembrandt", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", input={ "model": "dev", "prompt": "CAM corgi as painted by rembrandt", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run yosun/camcorgi-flux 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": "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", "input": { "model": "dev", "prompt": "CAM corgi as painted by rembrandt", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-07T22:12:43.765234Z", "created_at": "2024-09-07T22:12:24.668000Z", "data_removed": false, "error": null, "id": "epge5957vhrm00chst5r8j7wgr", "input": { "model": "dev", "prompt": "CAM corgi as painted by rembrandt", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 64320\nPrompt: CAM corgi as painted by rembrandt\n[!] txt2img mode\nUsing dev model\nfree=8107768631296\nDownloading weights\n2024-09-07T22:12:24Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpbo2_bs28/weights url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar\n2024-09-07T22:12:26Z | INFO | [ Complete ] dest=/tmp/tmpbo2_bs28/weights size=\"172 MB\" total_elapsed=1.878s url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar\nDownloaded weights in 1.91s\nLoaded LoRAs in 10.77s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.79it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.27it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.04it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.94it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 3.89it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.86it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.84it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.82it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 3.82it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.81it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.81it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.80it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.80it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.80it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.80it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.80it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.80it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.79it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 3.79it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.80it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.80it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.80it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.80it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.80it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.80it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.80it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.80it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.80it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.82it/s]", "metrics": { "predict_time": 19.090075993, "total_time": 19.097234 }, "output": [ "https://replicate.delivery/yhqm/8ST679IaeTw5bKSlhlvGfkVQBPvrEfjjRDCCzH9lEpL2OQ1mA/out-0.webp" ], "started_at": "2024-09-07T22:12:24.675158Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/epge5957vhrm00chst5r8j7wgr", "cancel": "https://api.replicate.com/v1/predictions/epge5957vhrm00chst5r8j7wgr/cancel" }, "version": "1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f" }Generated inUsing seed: 64320 Prompt: CAM corgi as painted by rembrandt [!] txt2img mode Using dev model free=8107768631296 Downloading weights 2024-09-07T22:12:24Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpbo2_bs28/weights url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar 2024-09-07T22:12:26Z | INFO | [ Complete ] dest=/tmp/tmpbo2_bs28/weights size="172 MB" total_elapsed=1.878s url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar Downloaded weights in 1.91s Loaded LoRAs in 10.77s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.79it/s] 7%|▋ | 2/28 [00:00<00:06, 4.27it/s] 11%|█ | 3/28 [00:00<00:06, 4.04it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.94it/s] 18%|█▊ | 5/28 [00:01<00:05, 3.89it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.86it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.84it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.82it/s] 32%|███▏ | 9/28 [00:02<00:04, 3.82it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.81it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.81it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.80it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.80it/s] 50%|█████ | 14/28 [00:03<00:03, 3.80it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.80it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.80it/s] 61%|██████ | 17/28 [00:04<00:02, 3.80it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.79it/s] 68%|██████▊ | 19/28 [00:04<00:02, 3.79it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.80it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.80it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.80it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.80it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.80it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.80it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.80it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.80it/s] 100%|██████████| 28/28 [00:07<00:00, 3.80it/s] 100%|██████████| 28/28 [00:07<00:00, 3.82it/s]Prediction
yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951fIDt3r97v192drm40chst6bjz7sawStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- painting of CAM corgi as painted by rembrandt
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "painting of CAM corgi as painted by rembrandt", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", { input: { model: "dev", prompt: "painting of CAM corgi as painted by rembrandt", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", input={ "model": "dev", "prompt": "painting of CAM corgi as painted by rembrandt", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run yosun/camcorgi-flux 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": "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", "input": { "model": "dev", "prompt": "painting of CAM corgi as painted by rembrandt", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-07T22:13:05.685163Z", "created_at": "2024-09-07T22:12:57.747000Z", "data_removed": false, "error": null, "id": "t3r97v192drm40chst6bjz7saw", "input": { "model": "dev", "prompt": "painting of CAM corgi as painted by rembrandt", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 60943\nPrompt: painting of CAM corgi as painted by rembrandt\n[!] txt2img mode\nUsing dev model\nWeights already loaded\nLoaded LoRAs in 0.03s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.80it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.29it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.05it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.95it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 3.89it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.86it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.84it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.83it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 3.82it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.81it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.81it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.81it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.80it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.80it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.80it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.80it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.80it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.80it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 3.80it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.80it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.80it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.80it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.80it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.80it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.80it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.80it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.80it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.80it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.83it/s]", "metrics": { "predict_time": 7.928642303, "total_time": 7.938163 }, "output": [ "https://replicate.delivery/yhqm/PQtd2qaqm2qZL9662r8Q6vfFGlhnfM5cbYHhP4FtlYOxHoaTA/out-0.webp" ], "started_at": "2024-09-07T22:12:57.756521Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/t3r97v192drm40chst6bjz7saw", "cancel": "https://api.replicate.com/v1/predictions/t3r97v192drm40chst6bjz7saw/cancel" }, "version": "1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f" }Generated inUsing seed: 60943 Prompt: painting of CAM corgi as painted by rembrandt [!] txt2img mode Using dev model Weights already loaded Loaded LoRAs in 0.03s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.80it/s] 7%|▋ | 2/28 [00:00<00:06, 4.29it/s] 11%|█ | 3/28 [00:00<00:06, 4.05it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.95it/s] 18%|█▊ | 5/28 [00:01<00:05, 3.89it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.86it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.84it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.83it/s] 32%|███▏ | 9/28 [00:02<00:04, 3.82it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.81it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.81it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.81it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.80it/s] 50%|█████ | 14/28 [00:03<00:03, 3.80it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.80it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.80it/s] 61%|██████ | 17/28 [00:04<00:02, 3.80it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.80it/s] 68%|██████▊ | 19/28 [00:04<00:02, 3.80it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.80it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.80it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.80it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.80it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.80it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.80it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.80it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.80it/s] 100%|██████████| 28/28 [00:07<00:00, 3.80it/s] 100%|██████████| 28/28 [00:07<00:00, 3.83it/s]Prediction
yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951fIDpdc8nkn8q1rm00chst6bd5eczcStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- painting of CAM corgi as painted by leonardo da vinci
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "painting of CAM corgi as painted by leonardo da vinci ", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", { input: { model: "dev", prompt: "painting of CAM corgi as painted by leonardo da vinci ", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", input={ "model": "dev", "prompt": "painting of CAM corgi as painted by leonardo da vinci ", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run yosun/camcorgi-flux 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": "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", "input": { "model": "dev", "prompt": "painting of CAM corgi as painted by leonardo da vinci ", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-07T22:13:52.544616Z", "created_at": "2024-09-07T22:13:30.424000Z", "data_removed": false, "error": null, "id": "pdc8nkn8q1rm00chst6bd5eczc", "input": { "model": "dev", "prompt": "painting of CAM corgi as painted by leonardo da vinci ", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 53074\nPrompt: painting of CAM corgi as painted by leonardo da vinci\n[!] txt2img mode\nUsing dev model\nfree=8428013973504\nDownloading weights\n2024-09-07T22:13:35Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmph2b_ar1b/weights url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar\n2024-09-07T22:13:36Z | INFO | [ Complete ] dest=/tmp/tmph2b_ar1b/weights size=\"172 MB\" total_elapsed=1.320s url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar\nDownloaded weights in 1.35s\nLoaded LoRAs in 9.42s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.79it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.28it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.04it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.94it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 3.89it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.85it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.83it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.82it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 3.81it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.80it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.80it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.79it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.79it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.79it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.79it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.79it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.79it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.79it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 3.79it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.79it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.79it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.79it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.79it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.79it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.79it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.79it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.79it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.79it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.81it/s]", "metrics": { "predict_time": 17.316314841, "total_time": 22.120616 }, "output": [ "https://replicate.delivery/yhqm/5kfh8BWMf6tGqUX16aMFL3WFWtQk6lYLUBwEtaDajG9gIoaTA/out-0.webp" ], "started_at": "2024-09-07T22:13:35.228302Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/pdc8nkn8q1rm00chst6bd5eczc", "cancel": "https://api.replicate.com/v1/predictions/pdc8nkn8q1rm00chst6bd5eczc/cancel" }, "version": "1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f" }Generated inUsing seed: 53074 Prompt: painting of CAM corgi as painted by leonardo da vinci [!] txt2img mode Using dev model free=8428013973504 Downloading weights 2024-09-07T22:13:35Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmph2b_ar1b/weights url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar 2024-09-07T22:13:36Z | INFO | [ Complete ] dest=/tmp/tmph2b_ar1b/weights size="172 MB" total_elapsed=1.320s url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar Downloaded weights in 1.35s Loaded LoRAs in 9.42s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.79it/s] 7%|▋ | 2/28 [00:00<00:06, 4.28it/s] 11%|█ | 3/28 [00:00<00:06, 4.04it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.94it/s] 18%|█▊ | 5/28 [00:01<00:05, 3.89it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.85it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.83it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.82it/s] 32%|███▏ | 9/28 [00:02<00:04, 3.81it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.80it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.80it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.79it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.79it/s] 50%|█████ | 14/28 [00:03<00:03, 3.79it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.79it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.79it/s] 61%|██████ | 17/28 [00:04<00:02, 3.79it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.79it/s] 68%|██████▊ | 19/28 [00:04<00:02, 3.79it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.79it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.79it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.79it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.79it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.79it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.79it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.79it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.79it/s] 100%|██████████| 28/28 [00:07<00:00, 3.79it/s] 100%|██████████| 28/28 [00:07<00:00, 3.81it/s]Prediction
yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951fIDp2yzk76r91rm60chst6va4s27cStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- painting of CAM corgi holding an iPhone as painted by rembrandt
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "painting of CAM corgi holding an iPhone as painted by rembrandt ", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", { input: { model: "dev", prompt: "painting of CAM corgi holding an iPhone as painted by rembrandt ", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", input={ "model": "dev", "prompt": "painting of CAM corgi holding an iPhone as painted by rembrandt ", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run yosun/camcorgi-flux 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": "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", "input": { "model": "dev", "prompt": "painting of CAM corgi holding an iPhone as painted by rembrandt ", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-07T22:15:04.843431Z", "created_at": "2024-09-07T22:14:48.136000Z", "data_removed": false, "error": null, "id": "p2yzk76r91rm60chst6va4s27c", "input": { "model": "dev", "prompt": "painting of CAM corgi holding an iPhone as painted by rembrandt ", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 14380\nPrompt: painting of CAM corgi holding an iPhone as painted by rembrandt\n[!] txt2img mode\nUsing dev model\nfree=9280066633728\nDownloading weights\n2024-09-07T22:14:48Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmps6dvrbox/weights url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar\n2024-09-07T22:14:49Z | INFO | [ Complete ] dest=/tmp/tmps6dvrbox/weights size=\"172 MB\" total_elapsed=1.078s url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar\nDownloaded weights in 1.18s\nLoaded LoRAs in 8.81s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.80it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.29it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.05it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.95it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 3.89it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.86it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.84it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.83it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 3.82it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.81it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.81it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.81it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.80it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.80it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.80it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.80it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.80it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.80it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 3.80it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.80it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.80it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.80it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.80it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.80it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.80it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.80it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.80it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.80it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.83it/s]", "metrics": { "predict_time": 16.698834716, "total_time": 16.707431 }, "output": [ "https://replicate.delivery/yhqm/1LzD3AizK9JoMhHpv2PxFp0eOeDbb52YqIDxvRtIYTEoJoaTA/out-0.webp" ], "started_at": "2024-09-07T22:14:48.144596Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/p2yzk76r91rm60chst6va4s27c", "cancel": "https://api.replicate.com/v1/predictions/p2yzk76r91rm60chst6va4s27c/cancel" }, "version": "1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f" }Generated inUsing seed: 14380 Prompt: painting of CAM corgi holding an iPhone as painted by rembrandt [!] txt2img mode Using dev model free=9280066633728 Downloading weights 2024-09-07T22:14:48Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmps6dvrbox/weights url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar 2024-09-07T22:14:49Z | INFO | [ Complete ] dest=/tmp/tmps6dvrbox/weights size="172 MB" total_elapsed=1.078s url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar Downloaded weights in 1.18s Loaded LoRAs in 8.81s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.80it/s] 7%|▋ | 2/28 [00:00<00:06, 4.29it/s] 11%|█ | 3/28 [00:00<00:06, 4.05it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.95it/s] 18%|█▊ | 5/28 [00:01<00:05, 3.89it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.86it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.84it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.83it/s] 32%|███▏ | 9/28 [00:02<00:04, 3.82it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.81it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.81it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.81it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.80it/s] 50%|█████ | 14/28 [00:03<00:03, 3.80it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.80it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.80it/s] 61%|██████ | 17/28 [00:04<00:02, 3.80it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.80it/s] 68%|██████▊ | 19/28 [00:04<00:02, 3.80it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.80it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.80it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.80it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.80it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.80it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.80it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.80it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.80it/s] 100%|██████████| 28/28 [00:07<00:00, 3.80it/s] 100%|██████████| 28/28 [00:07<00:00, 3.83it/s]Prediction
yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951fIDn4fhac2fznrm20chstavtn5c8gStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- CAM corgi as painted by rembrandt
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "CAM corgi as painted by rembrandt", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", { input: { model: "dev", prompt: "CAM corgi as painted by rembrandt", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", input={ "model": "dev", "prompt": "CAM corgi as painted by rembrandt", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run yosun/camcorgi-flux 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": "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", "input": { "model": "dev", "prompt": "CAM corgi as painted by rembrandt", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-07T22:23:17.531576Z", "created_at": "2024-09-07T22:22:57.533000Z", "data_removed": false, "error": null, "id": "n4fhac2fznrm20chstavtn5c8g", "input": { "model": "dev", "prompt": "CAM corgi as painted by rembrandt", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 64913\nPrompt: CAM corgi as painted by rembrandt\n[!] txt2img mode\nUsing dev model\nfree=8104339898368\nDownloading weights\n2024-09-07T22:22:57Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpmmf95ykq/weights url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar\n2024-09-07T22:22:58Z | INFO | [ Complete ] dest=/tmp/tmpmmf95ykq/weights size=\"172 MB\" total_elapsed=1.104s url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar\nDownloaded weights in 1.13s\nLoaded LoRAs in 11.80s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.77it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.26it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.02it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.92it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 3.86it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.83it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.81it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.80it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.79it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.78it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.78it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.78it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.78it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.77it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.77it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.77it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.77it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.77it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 3.77it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.77it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.77it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.77it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.77it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.77it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.77it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.77it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.77it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.77it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.80it/s]", "metrics": { "predict_time": 19.991615481, "total_time": 19.998576 }, "output": [ "https://replicate.delivery/yhqm/YzD9ZGJG9655PBgrY5t6zirx0uf2C09dCARXTPKveHbVRoaTA/out-0.webp" ], "started_at": "2024-09-07T22:22:57.539961Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/n4fhac2fznrm20chstavtn5c8g", "cancel": "https://api.replicate.com/v1/predictions/n4fhac2fznrm20chstavtn5c8g/cancel" }, "version": "1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f" }Generated inUsing seed: 64913 Prompt: CAM corgi as painted by rembrandt [!] txt2img mode Using dev model free=8104339898368 Downloading weights 2024-09-07T22:22:57Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpmmf95ykq/weights url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar 2024-09-07T22:22:58Z | INFO | [ Complete ] dest=/tmp/tmpmmf95ykq/weights size="172 MB" total_elapsed=1.104s url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar Downloaded weights in 1.13s Loaded LoRAs in 11.80s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.77it/s] 7%|▋ | 2/28 [00:00<00:06, 4.26it/s] 11%|█ | 3/28 [00:00<00:06, 4.02it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.92it/s] 18%|█▊ | 5/28 [00:01<00:05, 3.86it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.83it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.81it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.80it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.79it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.78it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.78it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.78it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.78it/s] 50%|█████ | 14/28 [00:03<00:03, 3.77it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.77it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.77it/s] 61%|██████ | 17/28 [00:04<00:02, 3.77it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.77it/s] 68%|██████▊ | 19/28 [00:04<00:02, 3.77it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.77it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.77it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.77it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.77it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.77it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.77it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.77it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.77it/s] 100%|██████████| 28/28 [00:07<00:00, 3.77it/s] 100%|██████████| 28/28 [00:07<00:00, 3.80it/s]Prediction
yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951fInput
- model
- dev
- prompt
- photo of CAM corgi accepting the nobel peace prize
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "photo of CAM corgi accepting the nobel peace prize", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", { input: { model: "dev", prompt: "photo of CAM corgi accepting the nobel peace prize", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", input={ "model": "dev", "prompt": "photo of CAM corgi accepting the nobel peace prize", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run yosun/camcorgi-flux 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": "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", "input": { "model": "dev", "prompt": "photo of CAM corgi accepting the nobel peace prize", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-07T22:29:11.219109Z", "created_at": "2024-09-07T22:28:51.300000Z", "data_removed": false, "error": null, "id": "pjzw7x5nwhrm40chstd9wq1mr4", "input": { "model": "dev", "prompt": "photo of CAM corgi accepting the nobel peace prize", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 53211\nPrompt: photo of CAM corgi accepting the nobel peace prize\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 8.04s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.79it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.28it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.04it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.94it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 3.89it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.86it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.84it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.82it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 3.81it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.81it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.80it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.80it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.80it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.80it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.80it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.80it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.80it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.80it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 3.80it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.80it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.80it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.80it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.80it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.80it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.80it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.80it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.80it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.80it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.82it/s]", "metrics": { "predict_time": 15.951570752, "total_time": 19.919109 }, "output": [ "https://replicate.delivery/yhqm/EvhHdgDyEn70GZbkQZGrFQwYuIETKaRehQYM17QlDD3bLUtJA/out-0.webp" ], "started_at": "2024-09-07T22:28:55.267538Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/pjzw7x5nwhrm40chstd9wq1mr4", "cancel": "https://api.replicate.com/v1/predictions/pjzw7x5nwhrm40chstd9wq1mr4/cancel" }, "version": "1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f" }Generated inUsing seed: 53211 Prompt: photo of CAM corgi accepting the nobel peace prize [!] txt2img mode Using dev model Loaded LoRAs in 8.04s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.79it/s] 7%|▋ | 2/28 [00:00<00:06, 4.28it/s] 11%|█ | 3/28 [00:00<00:06, 4.04it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.94it/s] 18%|█▊ | 5/28 [00:01<00:05, 3.89it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.86it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.84it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.82it/s] 32%|███▏ | 9/28 [00:02<00:04, 3.81it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.81it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.80it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.80it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.80it/s] 50%|█████ | 14/28 [00:03<00:03, 3.80it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.80it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.80it/s] 61%|██████ | 17/28 [00:04<00:02, 3.80it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.80it/s] 68%|██████▊ | 19/28 [00:04<00:02, 3.80it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.80it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.80it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.80it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.80it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.80it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.80it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.80it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.80it/s] 100%|██████████| 28/28 [00:07<00:00, 3.80it/s] 100%|██████████| 28/28 [00:07<00:00, 3.82it/s]Prediction
yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951fIDe6x1p3qc6hrm20chstda0qvw7mStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- photo of CAM corgi teaching quantum physics at caltech
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "photo of CAM corgi teaching quantum physics at caltech", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", { input: { model: "dev", prompt: "photo of CAM corgi teaching quantum physics at caltech", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", input={ "model": "dev", "prompt": "photo of CAM corgi teaching quantum physics at caltech", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run yosun/camcorgi-flux 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": "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", "input": { "model": "dev", "prompt": "photo of CAM corgi teaching quantum physics at caltech", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-07T22:29:27.095022Z", "created_at": "2024-09-07T22:29:05.204000Z", "data_removed": false, "error": null, "id": "e6x1p3qc6hrm20chstda0qvw7m", "input": { "model": "dev", "prompt": "photo of CAM corgi teaching quantum physics at caltech", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 4054\nPrompt: photo of CAM corgi teaching quantum physics at caltech\n[!] txt2img mode\nUsing dev model\nfree=8260291723264\nDownloading weights\n2024-09-07T22:29:05Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpm7_mldm2/weights url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar\n2024-09-07T22:29:08Z | INFO | [ Complete ] dest=/tmp/tmpm7_mldm2/weights size=\"172 MB\" total_elapsed=3.090s url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar\nDownloaded weights in 3.13s\nLoaded LoRAs in 11.99s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:01<00:31, 1.18s/it]\n 7%|▋ | 2/28 [00:01<00:15, 1.63it/s]\n 11%|█ | 3/28 [00:01<00:11, 2.21it/s]\n 14%|█▍ | 4/28 [00:01<00:09, 2.64it/s]\n 18%|█▊ | 5/28 [00:02<00:07, 2.96it/s]\n 21%|██▏ | 6/28 [00:02<00:06, 3.20it/s]\n 25%|██▌ | 7/28 [00:02<00:06, 3.37it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.49it/s]\n 32%|███▏ | 9/28 [00:03<00:05, 3.58it/s]\n 36%|███▌ | 10/28 [00:03<00:04, 3.64it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.68it/s]\n 43%|████▎ | 12/28 [00:04<00:04, 3.71it/s]\n 46%|████▋ | 13/28 [00:04<00:04, 3.73it/s]\n 50%|█████ | 14/28 [00:04<00:03, 3.75it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.76it/s]\n 57%|█████▋ | 16/28 [00:05<00:03, 3.76it/s]\n 61%|██████ | 17/28 [00:05<00:02, 3.77it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.77it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.78it/s]\n 71%|███████▏ | 20/28 [00:06<00:02, 3.78it/s]\n 75%|███████▌ | 21/28 [00:06<00:01, 3.78it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.78it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.78it/s]\n 86%|████████▌ | 24/28 [00:07<00:01, 3.78it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.78it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.78it/s]\n 96%|█████████▋| 27/28 [00:08<00:00, 3.78it/s]\n100%|██████████| 28/28 [00:08<00:00, 3.78it/s]\n100%|██████████| 28/28 [00:08<00:00, 3.39it/s]", "metrics": { "predict_time": 21.880623066, "total_time": 21.891022 }, "output": [ "https://replicate.delivery/yhqm/SKzB9Hj9nXZ8LpWqmPWdY78MOp4C0AJKg3wCGY0dEajxFq2E/out-0.webp" ], "started_at": "2024-09-07T22:29:05.214399Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/e6x1p3qc6hrm20chstda0qvw7m", "cancel": "https://api.replicate.com/v1/predictions/e6x1p3qc6hrm20chstda0qvw7m/cancel" }, "version": "1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f" }Generated inUsing seed: 4054 Prompt: photo of CAM corgi teaching quantum physics at caltech [!] txt2img mode Using dev model free=8260291723264 Downloading weights 2024-09-07T22:29:05Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpm7_mldm2/weights url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar 2024-09-07T22:29:08Z | INFO | [ Complete ] dest=/tmp/tmpm7_mldm2/weights size="172 MB" total_elapsed=3.090s url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar Downloaded weights in 3.13s Loaded LoRAs in 11.99s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:01<00:31, 1.18s/it] 7%|▋ | 2/28 [00:01<00:15, 1.63it/s] 11%|█ | 3/28 [00:01<00:11, 2.21it/s] 14%|█▍ | 4/28 [00:01<00:09, 2.64it/s] 18%|█▊ | 5/28 [00:02<00:07, 2.96it/s] 21%|██▏ | 6/28 [00:02<00:06, 3.20it/s] 25%|██▌ | 7/28 [00:02<00:06, 3.37it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.49it/s] 32%|███▏ | 9/28 [00:03<00:05, 3.58it/s] 36%|███▌ | 10/28 [00:03<00:04, 3.64it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.68it/s] 43%|████▎ | 12/28 [00:04<00:04, 3.71it/s] 46%|████▋ | 13/28 [00:04<00:04, 3.73it/s] 50%|█████ | 14/28 [00:04<00:03, 3.75it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.76it/s] 57%|█████▋ | 16/28 [00:05<00:03, 3.76it/s] 61%|██████ | 17/28 [00:05<00:02, 3.77it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.77it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.78it/s] 71%|███████▏ | 20/28 [00:06<00:02, 3.78it/s] 75%|███████▌ | 21/28 [00:06<00:01, 3.78it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.78it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.78it/s] 86%|████████▌ | 24/28 [00:07<00:01, 3.78it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.78it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.78it/s] 96%|█████████▋| 27/28 [00:08<00:00, 3.78it/s] 100%|██████████| 28/28 [00:08<00:00, 3.78it/s] 100%|██████████| 28/28 [00:08<00:00, 3.39it/s]Prediction
yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951fIDfzzyg0x575rm60chstdvtnhvhmStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- photo of CAM corgi teaching quantum physics on a chalkboard
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "photo of CAM corgi teaching quantum physics on a chalkboard", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", { input: { model: "dev", prompt: "photo of CAM corgi teaching quantum physics on a chalkboard", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", input={ "model": "dev", "prompt": "photo of CAM corgi teaching quantum physics on a chalkboard", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run yosun/camcorgi-flux 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": "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", "input": { "model": "dev", "prompt": "photo of CAM corgi teaching quantum physics on a chalkboard", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-07T22:30:08.476543Z", "created_at": "2024-09-07T22:29:52.569000Z", "data_removed": false, "error": null, "id": "fzzyg0x575rm60chstdvtnhvhm", "input": { "model": "dev", "prompt": "photo of CAM corgi teaching quantum physics on a chalkboard", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 22645\nPrompt: photo of CAM corgi teaching quantum physics on a chalkboard\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 8.03s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.79it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.28it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.04it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.93it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 3.88it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.85it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.83it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.81it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 3.81it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.80it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.80it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.80it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.80it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.79it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.79it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.79it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.79it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.79it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 3.79it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.79it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.79it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.79it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.79it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.79it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.79it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.79it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.79it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.79it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.82it/s]", "metrics": { "predict_time": 15.902170235, "total_time": 15.907543 }, "output": [ "https://replicate.delivery/yhqm/ONfqbwnyn02gVq4PZj9qKC7uRpzKefwGmpf04SmTpHQCfCVbC/out-0.webp" ], "started_at": "2024-09-07T22:29:52.574373Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fzzyg0x575rm60chstdvtnhvhm", "cancel": "https://api.replicate.com/v1/predictions/fzzyg0x575rm60chstdvtnhvhm/cancel" }, "version": "1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f" }Generated inUsing seed: 22645 Prompt: photo of CAM corgi teaching quantum physics on a chalkboard [!] txt2img mode Using dev model Loaded LoRAs in 8.03s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.79it/s] 7%|▋ | 2/28 [00:00<00:06, 4.28it/s] 11%|█ | 3/28 [00:00<00:06, 4.04it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.93it/s] 18%|█▊ | 5/28 [00:01<00:05, 3.88it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.85it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.83it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.81it/s] 32%|███▏ | 9/28 [00:02<00:04, 3.81it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.80it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.80it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.80it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.80it/s] 50%|█████ | 14/28 [00:03<00:03, 3.79it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.79it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.79it/s] 61%|██████ | 17/28 [00:04<00:02, 3.79it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.79it/s] 68%|██████▊ | 19/28 [00:04<00:02, 3.79it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.79it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.79it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.79it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.79it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.79it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.79it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.79it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.79it/s] 100%|██████████| 28/28 [00:07<00:00, 3.79it/s] 100%|██████████| 28/28 [00:07<00:00, 3.82it/s]Prediction
yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951fID29s2aw15k1rme0cn2y5ty5p5p4StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- photo of CAM corgi as a flying unicorn
- go_fast
- lora_scale
- 1
- megapixels
- 1
- num_outputs
- 4
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "photo of CAM corgi as a flying unicorn", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", { input: { model: "dev", prompt: "photo of CAM corgi as a flying unicorn", go_fast: false, lora_scale: 1, megapixels: "1", num_outputs: 4, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", input={ "model": "dev", "prompt": "photo of CAM corgi as a flying unicorn", "go_fast": False, "lora_scale": 1, "megapixels": "1", "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run yosun/camcorgi-flux 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": "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", "input": { "model": "dev", "prompt": "photo of CAM corgi as a flying unicorn", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2025-02-18T05:33:45.090416Z", "created_at": "2025-02-18T05:32:29.976000Z", "data_removed": false, "error": null, "id": "29s2aw15k1rme0cn2y5ty5p5p4", "input": { "model": "dev", "prompt": "photo of CAM corgi as a flying unicorn", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Weights already loaded\nLoaded LoRAs in 0.02s\nUsing seed: 40616\nPrompt: photo of CAM corgi as a flying unicorn\n[!] txt2img mode\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:24, 1.09it/s]\n 7%|▋ | 2/28 [00:01<00:21, 1.23it/s]\n 11%|█ | 3/28 [00:02<00:21, 1.16it/s]\n 14%|█▍ | 4/28 [00:03<00:21, 1.13it/s]\n 18%|█▊ | 5/28 [00:04<00:20, 1.12it/s]\n 21%|██▏ | 6/28 [00:05<00:19, 1.11it/s]\n 25%|██▌ | 7/28 [00:06<00:19, 1.10it/s]\n 29%|██▊ | 8/28 [00:07<00:18, 1.10it/s]\n 32%|███▏ | 9/28 [00:08<00:17, 1.10it/s]\n 36%|███▌ | 10/28 [00:08<00:16, 1.09it/s]\n 39%|███▉ | 11/28 [00:09<00:15, 1.09it/s]\n 43%|████▎ | 12/28 [00:10<00:14, 1.09it/s]\n 46%|████▋ | 13/28 [00:11<00:13, 1.09it/s]\n 50%|█████ | 14/28 [00:12<00:12, 1.09it/s]\n 54%|█████▎ | 15/28 [00:13<00:11, 1.09it/s]\n 57%|█████▋ | 16/28 [00:14<00:10, 1.09it/s]\n 61%|██████ | 17/28 [00:15<00:10, 1.09it/s]\n 64%|██████▍ | 18/28 [00:16<00:09, 1.09it/s]\n 68%|██████▊ | 19/28 [00:17<00:08, 1.09it/s]\n 71%|███████▏ | 20/28 [00:18<00:07, 1.09it/s]\n 75%|███████▌ | 21/28 [00:19<00:06, 1.09it/s]\n 79%|███████▊ | 22/28 [00:19<00:05, 1.09it/s]\n 82%|████████▏ | 23/28 [00:20<00:04, 1.09it/s]\n 86%|████████▌ | 24/28 [00:21<00:03, 1.09it/s]\n 89%|████████▉ | 25/28 [00:22<00:02, 1.09it/s]\n 93%|█████████▎| 26/28 [00:23<00:01, 1.09it/s]\n 96%|█████████▋| 27/28 [00:24<00:00, 1.09it/s]\n100%|██████████| 28/28 [00:25<00:00, 1.09it/s]\n100%|██████████| 28/28 [00:25<00:00, 1.10it/s]\nTotal safe images: 4 out of 4", "metrics": { "predict_time": 26.593121887, "total_time": 75.114416 }, "output": [ "https://replicate.delivery/xezq/P1GDijkjHG4SNlxrEZcaYeoCBHdrToEAftvelj6tCYjzt5goA/out-0.webp", "https://replicate.delivery/xezq/JxVraXkF68KxJ5sPMEf8ddErj1XXfFOy716MErA3rYY52cQUA/out-1.webp", "https://replicate.delivery/xezq/Pf0qAWG8SKxvFiVeJxbZuNKiM9kyrEHzNwO7hZ21uOu52cQUA/out-2.webp", "https://replicate.delivery/xezq/dXTmCI0vz1rVNBUu8yE6BKsGbFNPwwzsCY19soZeses52cQUA/out-3.webp" ], "started_at": "2025-02-18T05:33:18.497294Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-wrnguy75oe52mioyxeebdz7cm7mwnt7wrefw7ior6ohlsanretza", "get": "https://api.replicate.com/v1/predictions/29s2aw15k1rme0cn2y5ty5p5p4", "cancel": "https://api.replicate.com/v1/predictions/29s2aw15k1rme0cn2y5ty5p5p4/cancel" }, "version": "1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f" }Generated inWeights already loaded Loaded LoRAs in 0.02s Using seed: 40616 Prompt: photo of CAM corgi as a flying unicorn [!] txt2img mode 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:24, 1.09it/s] 7%|▋ | 2/28 [00:01<00:21, 1.23it/s] 11%|█ | 3/28 [00:02<00:21, 1.16it/s] 14%|█▍ | 4/28 [00:03<00:21, 1.13it/s] 18%|█▊ | 5/28 [00:04<00:20, 1.12it/s] 21%|██▏ | 6/28 [00:05<00:19, 1.11it/s] 25%|██▌ | 7/28 [00:06<00:19, 1.10it/s] 29%|██▊ | 8/28 [00:07<00:18, 1.10it/s] 32%|███▏ | 9/28 [00:08<00:17, 1.10it/s] 36%|███▌ | 10/28 [00:08<00:16, 1.09it/s] 39%|███▉ | 11/28 [00:09<00:15, 1.09it/s] 43%|████▎ | 12/28 [00:10<00:14, 1.09it/s] 46%|████▋ | 13/28 [00:11<00:13, 1.09it/s] 50%|█████ | 14/28 [00:12<00:12, 1.09it/s] 54%|█████▎ | 15/28 [00:13<00:11, 1.09it/s] 57%|█████▋ | 16/28 [00:14<00:10, 1.09it/s] 61%|██████ | 17/28 [00:15<00:10, 1.09it/s] 64%|██████▍ | 18/28 [00:16<00:09, 1.09it/s] 68%|██████▊ | 19/28 [00:17<00:08, 1.09it/s] 71%|███████▏ | 20/28 [00:18<00:07, 1.09it/s] 75%|███████▌ | 21/28 [00:19<00:06, 1.09it/s] 79%|███████▊ | 22/28 [00:19<00:05, 1.09it/s] 82%|████████▏ | 23/28 [00:20<00:04, 1.09it/s] 86%|████████▌ | 24/28 [00:21<00:03, 1.09it/s] 89%|████████▉ | 25/28 [00:22<00:02, 1.09it/s] 93%|█████████▎| 26/28 [00:23<00:01, 1.09it/s] 96%|█████████▋| 27/28 [00:24<00:00, 1.09it/s] 100%|██████████| 28/28 [00:25<00:00, 1.09it/s] 100%|██████████| 28/28 [00:25<00:00, 1.10it/s] Total safe images: 4 out of 4Prediction
yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951fID8re9shz9qhrme0cn2y58b2v838StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- CAM corgi as a flying unicorn
- go_fast
- lora_scale
- 1
- megapixels
- 1
- num_outputs
- 4
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "CAM corgi as a flying unicorn", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", { input: { model: "dev", prompt: "CAM corgi as a flying unicorn", go_fast: false, lora_scale: 1, megapixels: "1", num_outputs: 4, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 yosun/camcorgi-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", input={ "model": "dev", "prompt": "CAM corgi as a flying unicorn", "go_fast": False, "lora_scale": 1, "megapixels": "1", "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run yosun/camcorgi-flux 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": "yosun/camcorgi-flux:1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f", "input": { "model": "dev", "prompt": "CAM corgi as a flying unicorn", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2025-02-18T05:33:18.168004Z", "created_at": "2025-02-18T05:32:14.652000Z", "data_removed": false, "error": null, "id": "8re9shz9qhrme0cn2y58b2v838", "input": { "model": "dev", "prompt": "CAM corgi as a flying unicorn", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "free=27090759573504\nDownloading weights\n2025-02-18T05:32:44Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpzzfkza2_/weights url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar\n2025-02-18T05:32:51Z | INFO | [ Complete ] dest=/tmp/tmpzzfkza2_/weights size=\"172 MB\" total_elapsed=6.705s url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar\nDownloaded weights in 6.73s\nLoaded LoRAs in 7.29s\nUsing seed: 47434\nPrompt: CAM corgi as a flying unicorn\n[!] txt2img mode\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:24, 1.10it/s]\n 7%|▋ | 2/28 [00:01<00:21, 1.23it/s]\n 11%|█ | 3/28 [00:02<00:21, 1.16it/s]\n 14%|█▍ | 4/28 [00:03<00:21, 1.13it/s]\n 18%|█▊ | 5/28 [00:04<00:20, 1.12it/s]\n 21%|██▏ | 6/28 [00:05<00:19, 1.11it/s]\n 25%|██▌ | 7/28 [00:06<00:19, 1.10it/s]\n 29%|██▊ | 8/28 [00:07<00:18, 1.10it/s]\n 32%|███▏ | 9/28 [00:08<00:17, 1.10it/s]\n 36%|███▌ | 10/28 [00:08<00:16, 1.09it/s]\n 39%|███▉ | 11/28 [00:09<00:15, 1.09it/s]\n 43%|████▎ | 12/28 [00:10<00:14, 1.09it/s]\n 46%|████▋ | 13/28 [00:11<00:13, 1.09it/s]\n 50%|█████ | 14/28 [00:12<00:12, 1.09it/s]\n 54%|█████▎ | 15/28 [00:13<00:11, 1.09it/s]\n 57%|█████▋ | 16/28 [00:14<00:10, 1.09it/s]\n 61%|██████ | 17/28 [00:15<00:10, 1.09it/s]\n 64%|██████▍ | 18/28 [00:16<00:09, 1.09it/s]\n 68%|██████▊ | 19/28 [00:17<00:08, 1.09it/s]\n 71%|███████▏ | 20/28 [00:18<00:07, 1.09it/s]\n 75%|███████▌ | 21/28 [00:19<00:06, 1.09it/s]\n 79%|███████▊ | 22/28 [00:19<00:05, 1.09it/s]\n 82%|████████▏ | 23/28 [00:20<00:04, 1.09it/s]\n 86%|████████▌ | 24/28 [00:21<00:03, 1.09it/s]\n 89%|████████▉ | 25/28 [00:22<00:02, 1.09it/s]\n 93%|█████████▎| 26/28 [00:23<00:01, 1.09it/s]\n 96%|█████████▋| 27/28 [00:24<00:00, 1.09it/s]\n100%|██████████| 28/28 [00:25<00:00, 1.09it/s]\n100%|██████████| 28/28 [00:25<00:00, 1.10it/s]\nTotal safe images: 4 out of 4", "metrics": { "predict_time": 33.735385767, "total_time": 63.516004 }, "output": [ "https://replicate.delivery/xezq/rTtJvPl2gkY0Pxtf8oNw22W5fTrTe9F8MuBGaEDrVdN8s5goA/out-0.webp", "https://replicate.delivery/xezq/5rbsPhTeMt1bQiTn703vcvBJJGCwPtsXIkcR8tQxyQDPbOIKA/out-1.webp", "https://replicate.delivery/xezq/seDKinmFIuQ7ASQsjSGD5ufG8UpfbnNnmsTDqzv2OUg8s5goA/out-2.webp", "https://replicate.delivery/xezq/6UFAedeDCWgCn0v8Ud0e2uLey1eebfd6phacTlGDMMcCPbOIKA/out-3.webp" ], "started_at": "2025-02-18T05:32:44.432618Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-ath4m4wk25avp5hw2cekagesazci2usktm7yfoji76jkkzekzcwa", "get": "https://api.replicate.com/v1/predictions/8re9shz9qhrme0cn2y58b2v838", "cancel": "https://api.replicate.com/v1/predictions/8re9shz9qhrme0cn2y58b2v838/cancel" }, "version": "1773b4ce1e43deb754dca9da8119fa64cb2ab3650cbe1e99e69f555b3b1d951f" }Generated infree=27090759573504 Downloading weights 2025-02-18T05:32:44Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpzzfkza2_/weights url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar 2025-02-18T05:32:51Z | INFO | [ Complete ] dest=/tmp/tmpzzfkza2_/weights size="172 MB" total_elapsed=6.705s url=https://replicate.delivery/yhqm/ZvGsoqmhtwq6Mt0idB01hYmezBEspnlMaTsSoDlETgwnpStJA/trained_model.tar Downloaded weights in 6.73s Loaded LoRAs in 7.29s Using seed: 47434 Prompt: CAM corgi as a flying unicorn [!] txt2img mode 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:24, 1.10it/s] 7%|▋ | 2/28 [00:01<00:21, 1.23it/s] 11%|█ | 3/28 [00:02<00:21, 1.16it/s] 14%|█▍ | 4/28 [00:03<00:21, 1.13it/s] 18%|█▊ | 5/28 [00:04<00:20, 1.12it/s] 21%|██▏ | 6/28 [00:05<00:19, 1.11it/s] 25%|██▌ | 7/28 [00:06<00:19, 1.10it/s] 29%|██▊ | 8/28 [00:07<00:18, 1.10it/s] 32%|███▏ | 9/28 [00:08<00:17, 1.10it/s] 36%|███▌ | 10/28 [00:08<00:16, 1.09it/s] 39%|███▉ | 11/28 [00:09<00:15, 1.09it/s] 43%|████▎ | 12/28 [00:10<00:14, 1.09it/s] 46%|████▋ | 13/28 [00:11<00:13, 1.09it/s] 50%|█████ | 14/28 [00:12<00:12, 1.09it/s] 54%|█████▎ | 15/28 [00:13<00:11, 1.09it/s] 57%|█████▋ | 16/28 [00:14<00:10, 1.09it/s] 61%|██████ | 17/28 [00:15<00:10, 1.09it/s] 64%|██████▍ | 18/28 [00:16<00:09, 1.09it/s] 68%|██████▊ | 19/28 [00:17<00:08, 1.09it/s] 71%|███████▏ | 20/28 [00:18<00:07, 1.09it/s] 75%|███████▌ | 21/28 [00:19<00:06, 1.09it/s] 79%|███████▊ | 22/28 [00:19<00:05, 1.09it/s] 82%|████████▏ | 23/28 [00:20<00:04, 1.09it/s] 86%|████████▌ | 24/28 [00:21<00:03, 1.09it/s] 89%|████████▉ | 25/28 [00:22<00:02, 1.09it/s] 93%|█████████▎| 26/28 [00:23<00:01, 1.09it/s] 96%|█████████▋| 27/28 [00:24<00:00, 1.09it/s] 100%|██████████| 28/28 [00:25<00:00, 1.09it/s] 100%|██████████| 28/28 [00:25<00:00, 1.10it/s] Total safe images: 4 out of 4
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