Prediction
ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036IDqyvp8dh8s1rm00chj1cszat6hcStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- sunny beach in the style of KUJI
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 42
{ "model": "dev", "prompt": "sunny beach in the style of KUJI", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 42 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run ludocomito/flux-kuji using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036", { input: { model: "dev", prompt: "sunny beach in the style of KUJI", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 0.8, num_inference_steps: 42 } } ); // 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 ludocomito/flux-kuji using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036", input={ "model": "dev", "prompt": "sunny beach in the style of KUJI", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 42 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run ludocomito/flux-kuji 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": "ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036", "input": { "model": "dev", "prompt": "sunny beach in the style of KUJI", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 42 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-26T20:21:15.085923Z", "created_at": "2024-08-26T20:20:47.944000Z", "data_removed": false, "error": null, "id": "qyvp8dh8s1rm00chj1cszat6hc", "input": { "model": "dev", "prompt": "sunny beach in the style of KUJI", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 42 }, "logs": "Using seed: 9545\nPrompt: sunny beach in the style of KUJI\ntxt2img mode\nUsing dev model\nfree=9530279374848\nDownloading weights\n2024-08-26T20:20:49Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpdjcmfqfx/weights url=https://replicate.delivery/yhqm/kT1XobqVJKblIl6YnYiTHMf6zti7TepRVf0KG1GbLk23pStmA/trained_model.tar\n2024-08-26T20:20:53Z | INFO | [ Complete ] dest=/tmp/tmpdjcmfqfx/weights size=\"172 MB\" total_elapsed=3.468s url=https://replicate.delivery/yhqm/kT1XobqVJKblIl6YnYiTHMf6zti7TepRVf0KG1GbLk23pStmA/trained_model.tar\nDownloaded weights in 3.50s\nLoaded LoRAs in 13.42s\n 0%| | 0/42 [00:00<?, ?it/s]\n 2%|▏ | 1/42 [00:00<00:11, 3.70it/s]\n 5%|▍ | 2/42 [00:00<00:09, 4.25it/s]\n 7%|▋ | 3/42 [00:00<00:09, 3.97it/s]\n 10%|▉ | 4/42 [00:01<00:09, 3.85it/s]\n 12%|█▏ | 5/42 [00:01<00:09, 3.79it/s]\n 14%|█▍ | 6/42 [00:01<00:09, 3.76it/s]\n 17%|█▋ | 7/42 [00:01<00:09, 3.73it/s]\n 19%|█▉ | 8/42 [00:02<00:09, 3.72it/s]\n 21%|██▏ | 9/42 [00:02<00:08, 3.71it/s]\n 24%|██▍ | 10/42 [00:02<00:08, 3.70it/s]\n 26%|██▌ | 11/42 [00:02<00:08, 3.69it/s]\n 29%|██▊ | 12/42 [00:03<00:08, 3.69it/s]\n 31%|███ | 13/42 [00:03<00:07, 3.69it/s]\n 33%|███▎ | 14/42 [00:03<00:07, 3.69it/s]\n 36%|███▌ | 15/42 [00:04<00:07, 3.69it/s]\n 38%|███▊ | 16/42 [00:04<00:07, 3.69it/s]\n 40%|████ | 17/42 [00:04<00:06, 3.69it/s]\n 43%|████▎ | 18/42 [00:04<00:06, 3.69it/s]\n 45%|████▌ | 19/42 [00:05<00:06, 3.68it/s]\n 48%|████▊ | 20/42 [00:05<00:05, 3.68it/s]\n 50%|█████ | 21/42 [00:05<00:05, 3.68it/s]\n 52%|█████▏ | 22/42 [00:05<00:05, 3.68it/s]\n 55%|█████▍ | 23/42 [00:06<00:05, 3.68it/s]\n 57%|█████▋ | 24/42 [00:06<00:04, 3.68it/s]\n 60%|█████▉ | 25/42 [00:06<00:04, 3.69it/s]\n 62%|██████▏ | 26/42 [00:06<00:04, 3.69it/s]\n 64%|██████▍ | 27/42 [00:07<00:04, 3.68it/s]\n 67%|██████▋ | 28/42 [00:07<00:03, 3.68it/s]\n 69%|██████▉ | 29/42 [00:07<00:03, 3.69it/s]\n 71%|███████▏ | 30/42 [00:08<00:03, 3.68it/s]\n 74%|███████▍ | 31/42 [00:08<00:02, 3.68it/s]\n 76%|███████▌ | 32/42 [00:08<00:02, 3.68it/s]\n 79%|███████▊ | 33/42 [00:08<00:02, 3.69it/s]\n 81%|████████ | 34/42 [00:09<00:02, 3.69it/s]\n 83%|████████▎ | 35/42 [00:09<00:01, 3.68it/s]\n 86%|████████▌ | 36/42 [00:09<00:01, 3.68it/s]\n 88%|████████▊ | 37/42 [00:09<00:01, 3.69it/s]\n 90%|█████████ | 38/42 [00:10<00:01, 3.69it/s]\n 93%|█████████▎| 39/42 [00:10<00:00, 3.68it/s]\n 95%|█████████▌| 40/42 [00:10<00:00, 3.68it/s]\n 98%|█████████▊| 41/42 [00:11<00:00, 3.68it/s]\n100%|██████████| 42/42 [00:11<00:00, 3.68it/s]\n100%|██████████| 42/42 [00:11<00:00, 3.70it/s]", "metrics": { "predict_time": 25.329081845, "total_time": 27.141923 }, "output": [ "https://replicate.delivery/yhqm/iMbpFxwoKXptIhqWGGVNmKQwuSmHMyu7YKXzly5GNYsuVq1E/out-0.webp" ], "started_at": "2024-08-26T20:20:49.756841Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/qyvp8dh8s1rm00chj1cszat6hc", "cancel": "https://api.replicate.com/v1/predictions/qyvp8dh8s1rm00chj1cszat6hc/cancel" }, "version": "5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036" }Generated inUsing seed: 9545 Prompt: sunny beach in the style of KUJI txt2img mode Using dev model free=9530279374848 Downloading weights 2024-08-26T20:20:49Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpdjcmfqfx/weights url=https://replicate.delivery/yhqm/kT1XobqVJKblIl6YnYiTHMf6zti7TepRVf0KG1GbLk23pStmA/trained_model.tar 2024-08-26T20:20:53Z | INFO | [ Complete ] dest=/tmp/tmpdjcmfqfx/weights size="172 MB" total_elapsed=3.468s url=https://replicate.delivery/yhqm/kT1XobqVJKblIl6YnYiTHMf6zti7TepRVf0KG1GbLk23pStmA/trained_model.tar Downloaded weights in 3.50s Loaded LoRAs in 13.42s 0%| | 0/42 [00:00<?, ?it/s] 2%|▏ | 1/42 [00:00<00:11, 3.70it/s] 5%|▍ | 2/42 [00:00<00:09, 4.25it/s] 7%|▋ | 3/42 [00:00<00:09, 3.97it/s] 10%|▉ | 4/42 [00:01<00:09, 3.85it/s] 12%|█▏ | 5/42 [00:01<00:09, 3.79it/s] 14%|█▍ | 6/42 [00:01<00:09, 3.76it/s] 17%|█▋ | 7/42 [00:01<00:09, 3.73it/s] 19%|█▉ | 8/42 [00:02<00:09, 3.72it/s] 21%|██▏ | 9/42 [00:02<00:08, 3.71it/s] 24%|██▍ | 10/42 [00:02<00:08, 3.70it/s] 26%|██▌ | 11/42 [00:02<00:08, 3.69it/s] 29%|██▊ | 12/42 [00:03<00:08, 3.69it/s] 31%|███ | 13/42 [00:03<00:07, 3.69it/s] 33%|███▎ | 14/42 [00:03<00:07, 3.69it/s] 36%|███▌ | 15/42 [00:04<00:07, 3.69it/s] 38%|███▊ | 16/42 [00:04<00:07, 3.69it/s] 40%|████ | 17/42 [00:04<00:06, 3.69it/s] 43%|████▎ | 18/42 [00:04<00:06, 3.69it/s] 45%|████▌ | 19/42 [00:05<00:06, 3.68it/s] 48%|████▊ | 20/42 [00:05<00:05, 3.68it/s] 50%|█████ | 21/42 [00:05<00:05, 3.68it/s] 52%|█████▏ | 22/42 [00:05<00:05, 3.68it/s] 55%|█████▍ | 23/42 [00:06<00:05, 3.68it/s] 57%|█████▋ | 24/42 [00:06<00:04, 3.68it/s] 60%|█████▉ | 25/42 [00:06<00:04, 3.69it/s] 62%|██████▏ | 26/42 [00:06<00:04, 3.69it/s] 64%|██████▍ | 27/42 [00:07<00:04, 3.68it/s] 67%|██████▋ | 28/42 [00:07<00:03, 3.68it/s] 69%|██████▉ | 29/42 [00:07<00:03, 3.69it/s] 71%|███████▏ | 30/42 [00:08<00:03, 3.68it/s] 74%|███████▍ | 31/42 [00:08<00:02, 3.68it/s] 76%|███████▌ | 32/42 [00:08<00:02, 3.68it/s] 79%|███████▊ | 33/42 [00:08<00:02, 3.69it/s] 81%|████████ | 34/42 [00:09<00:02, 3.69it/s] 83%|████████▎ | 35/42 [00:09<00:01, 3.68it/s] 86%|████████▌ | 36/42 [00:09<00:01, 3.68it/s] 88%|████████▊ | 37/42 [00:09<00:01, 3.69it/s] 90%|█████████ | 38/42 [00:10<00:01, 3.69it/s] 93%|█████████▎| 39/42 [00:10<00:00, 3.68it/s] 95%|█████████▌| 40/42 [00:10<00:00, 3.68it/s] 98%|█████████▊| 41/42 [00:11<00:00, 3.68it/s] 100%|██████████| 42/42 [00:11<00:00, 3.68it/s] 100%|██████████| 42/42 [00:11<00:00, 3.70it/s]Prediction
ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036IDaas42vs801rm20chj1dbt63g0rStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- new york landscape in the style of KUJI
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 42
{ "model": "dev", "prompt": "new york landscape in the style of KUJI", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 42 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run ludocomito/flux-kuji using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036", { input: { model: "dev", prompt: "new york landscape in the style of KUJI", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 0.8, num_inference_steps: 42 } } ); // 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 ludocomito/flux-kuji using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036", input={ "model": "dev", "prompt": "new york landscape in the style of KUJI", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 42 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run ludocomito/flux-kuji 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": "ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036", "input": { "model": "dev", "prompt": "new york landscape in the style of KUJI", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 42 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-26T20:22:42.395144Z", "created_at": "2024-08-26T20:21:53.280000Z", "data_removed": false, "error": null, "id": "aas42vs801rm20chj1dbt63g0r", "input": { "model": "dev", "prompt": "new york landscape in the style of KUJI", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 42 }, "logs": "Using seed: 24855\nPrompt: new york landscape in the style of KUJI\ntxt2img mode\nUsing dev model\nfree=9529328406528\nDownloading weights\n2024-08-26T20:22:17Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpb9tixi6f/weights url=https://replicate.delivery/yhqm/kT1XobqVJKblIl6YnYiTHMf6zti7TepRVf0KG1GbLk23pStmA/trained_model.tar\n2024-08-26T20:22:20Z | INFO | [ Complete ] dest=/tmp/tmpb9tixi6f/weights size=\"172 MB\" total_elapsed=3.592s url=https://replicate.delivery/yhqm/kT1XobqVJKblIl6YnYiTHMf6zti7TepRVf0KG1GbLk23pStmA/trained_model.tar\nDownloaded weights in 3.62s\nLoaded LoRAs in 13.09s\n 0%| | 0/42 [00:00<?, ?it/s]\n 2%|▏ | 1/42 [00:00<00:11, 3.67it/s]\n 5%|▍ | 2/42 [00:00<00:09, 4.22it/s]\n 7%|▋ | 3/42 [00:00<00:09, 3.96it/s]\n 10%|▉ | 4/42 [00:01<00:09, 3.84it/s]\n 12%|█▏ | 5/42 [00:01<00:09, 3.77it/s]\n 14%|█▍ | 6/42 [00:01<00:09, 3.73it/s]\n 17%|█▋ | 7/42 [00:01<00:09, 3.71it/s]\n 19%|█▉ | 8/42 [00:02<00:09, 3.69it/s]\n 21%|██▏ | 9/42 [00:02<00:08, 3.68it/s]\n 24%|██▍ | 10/42 [00:02<00:08, 3.67it/s]\n 26%|██▌ | 11/42 [00:02<00:08, 3.67it/s]\n 29%|██▊ | 12/42 [00:03<00:08, 3.67it/s]\n 31%|███ | 13/42 [00:03<00:07, 3.67it/s]\n 33%|███▎ | 14/42 [00:03<00:07, 3.66it/s]\n 36%|███▌ | 15/42 [00:04<00:07, 3.67it/s]\n 38%|███▊ | 16/42 [00:04<00:07, 3.66it/s]\n 40%|████ | 17/42 [00:04<00:06, 3.66it/s]\n 43%|████▎ | 18/42 [00:04<00:06, 3.66it/s]\n 45%|████▌ | 19/42 [00:05<00:06, 3.67it/s]\n 48%|████▊ | 20/42 [00:05<00:06, 3.66it/s]\n 50%|█████ | 21/42 [00:05<00:05, 3.66it/s]\n 52%|█████▏ | 22/42 [00:05<00:05, 3.66it/s]\n 55%|█████▍ | 23/42 [00:06<00:05, 3.66it/s]\n 57%|█████▋ | 24/42 [00:06<00:04, 3.66it/s]\n 60%|█████▉ | 25/42 [00:06<00:04, 3.66it/s]\n 62%|██████▏ | 26/42 [00:07<00:04, 3.66it/s]\n 64%|██████▍ | 27/42 [00:07<00:04, 3.66it/s]\n 67%|██████▋ | 28/42 [00:07<00:03, 3.66it/s]\n 69%|██████▉ | 29/42 [00:07<00:03, 3.66it/s]\n 71%|███████▏ | 30/42 [00:08<00:03, 3.66it/s]\n 74%|███████▍ | 31/42 [00:08<00:03, 3.66it/s]\n 76%|███████▌ | 32/42 [00:08<00:02, 3.66it/s]\n 79%|███████▊ | 33/42 [00:08<00:02, 3.66it/s]\n 81%|████████ | 34/42 [00:09<00:02, 3.66it/s]\n 83%|████████▎ | 35/42 [00:09<00:01, 3.66it/s]\n 86%|████████▌ | 36/42 [00:09<00:01, 3.66it/s]\n 88%|████████▊ | 37/42 [00:10<00:01, 3.66it/s]\n 90%|█████████ | 38/42 [00:10<00:01, 3.66it/s]\n 93%|█████████▎| 39/42 [00:10<00:00, 3.66it/s]\n 95%|█████████▌| 40/42 [00:10<00:00, 3.66it/s]\n 98%|█████████▊| 41/42 [00:11<00:00, 3.66it/s]\n100%|██████████| 42/42 [00:11<00:00, 3.66it/s]\n100%|██████████| 42/42 [00:11<00:00, 3.68it/s]", "metrics": { "predict_time": 25.140521913, "total_time": 49.115144 }, "output": [ "https://replicate.delivery/yhqm/POFQJL2eM03GYyaocoRxs09NEWNMlOAprsKZEZJbFrBJsUrJA/out-0.webp" ], "started_at": "2024-08-26T20:22:17.254623Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/aas42vs801rm20chj1dbt63g0r", "cancel": "https://api.replicate.com/v1/predictions/aas42vs801rm20chj1dbt63g0r/cancel" }, "version": "5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036" }Generated inUsing seed: 24855 Prompt: new york landscape in the style of KUJI txt2img mode Using dev model free=9529328406528 Downloading weights 2024-08-26T20:22:17Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpb9tixi6f/weights url=https://replicate.delivery/yhqm/kT1XobqVJKblIl6YnYiTHMf6zti7TepRVf0KG1GbLk23pStmA/trained_model.tar 2024-08-26T20:22:20Z | INFO | [ Complete ] dest=/tmp/tmpb9tixi6f/weights size="172 MB" total_elapsed=3.592s url=https://replicate.delivery/yhqm/kT1XobqVJKblIl6YnYiTHMf6zti7TepRVf0KG1GbLk23pStmA/trained_model.tar Downloaded weights in 3.62s Loaded LoRAs in 13.09s 0%| | 0/42 [00:00<?, ?it/s] 2%|▏ | 1/42 [00:00<00:11, 3.67it/s] 5%|▍ | 2/42 [00:00<00:09, 4.22it/s] 7%|▋ | 3/42 [00:00<00:09, 3.96it/s] 10%|▉ | 4/42 [00:01<00:09, 3.84it/s] 12%|█▏ | 5/42 [00:01<00:09, 3.77it/s] 14%|█▍ | 6/42 [00:01<00:09, 3.73it/s] 17%|█▋ | 7/42 [00:01<00:09, 3.71it/s] 19%|█▉ | 8/42 [00:02<00:09, 3.69it/s] 21%|██▏ | 9/42 [00:02<00:08, 3.68it/s] 24%|██▍ | 10/42 [00:02<00:08, 3.67it/s] 26%|██▌ | 11/42 [00:02<00:08, 3.67it/s] 29%|██▊ | 12/42 [00:03<00:08, 3.67it/s] 31%|███ | 13/42 [00:03<00:07, 3.67it/s] 33%|███▎ | 14/42 [00:03<00:07, 3.66it/s] 36%|███▌ | 15/42 [00:04<00:07, 3.67it/s] 38%|███▊ | 16/42 [00:04<00:07, 3.66it/s] 40%|████ | 17/42 [00:04<00:06, 3.66it/s] 43%|████▎ | 18/42 [00:04<00:06, 3.66it/s] 45%|████▌ | 19/42 [00:05<00:06, 3.67it/s] 48%|████▊ | 20/42 [00:05<00:06, 3.66it/s] 50%|█████ | 21/42 [00:05<00:05, 3.66it/s] 52%|█████▏ | 22/42 [00:05<00:05, 3.66it/s] 55%|█████▍ | 23/42 [00:06<00:05, 3.66it/s] 57%|█████▋ | 24/42 [00:06<00:04, 3.66it/s] 60%|█████▉ | 25/42 [00:06<00:04, 3.66it/s] 62%|██████▏ | 26/42 [00:07<00:04, 3.66it/s] 64%|██████▍ | 27/42 [00:07<00:04, 3.66it/s] 67%|██████▋ | 28/42 [00:07<00:03, 3.66it/s] 69%|██████▉ | 29/42 [00:07<00:03, 3.66it/s] 71%|███████▏ | 30/42 [00:08<00:03, 3.66it/s] 74%|███████▍ | 31/42 [00:08<00:03, 3.66it/s] 76%|███████▌ | 32/42 [00:08<00:02, 3.66it/s] 79%|███████▊ | 33/42 [00:08<00:02, 3.66it/s] 81%|████████ | 34/42 [00:09<00:02, 3.66it/s] 83%|████████▎ | 35/42 [00:09<00:01, 3.66it/s] 86%|████████▌ | 36/42 [00:09<00:01, 3.66it/s] 88%|████████▊ | 37/42 [00:10<00:01, 3.66it/s] 90%|█████████ | 38/42 [00:10<00:01, 3.66it/s] 93%|█████████▎| 39/42 [00:10<00:00, 3.66it/s] 95%|█████████▌| 40/42 [00:10<00:00, 3.66it/s] 98%|█████████▊| 41/42 [00:11<00:00, 3.66it/s] 100%|██████████| 42/42 [00:11<00:00, 3.66it/s] 100%|██████████| 42/42 [00:11<00:00, 3.68it/s]Prediction
ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036ID0h88fnny3xrm40chj1dt4qfkemStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- Photo of the colosseum in the style of KUJI, realistic 4k with flares
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "Photo of the colosseum in the style of KUJI, realistic 4k with flares", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run ludocomito/flux-kuji using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036", { input: { model: "dev", prompt: "Photo of the colosseum in the style of KUJI, realistic 4k with flares", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 0.8, 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 ludocomito/flux-kuji using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036", input={ "model": "dev", "prompt": "Photo of the colosseum in the style of KUJI, realistic 4k with flares", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run ludocomito/flux-kuji 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": "ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036", "input": { "model": "dev", "prompt": "Photo of the colosseum in the style of KUJI, realistic 4k with flares", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "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": "2024-08-26T20:24:01.966501Z", "created_at": "2024-08-26T20:23:37.247000Z", "data_removed": false, "error": null, "id": "0h88fnny3xrm40chj1dt4qfkem", "input": { "model": "dev", "prompt": "Photo of the colosseum in the style of KUJI, realistic 4k with flares", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 53470\nPrompt: Photo of the colosseum in the style of KUJI, realistic 4k with flares\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 8.01s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.66it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.22it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.96it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.83it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.76it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.73it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.71it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.69it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.68it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.68it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.67it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.66it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.65it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.65it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.66it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.65it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.65it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.66it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.66it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.66it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.65it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.66it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.66it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.66it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.66it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.66it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.67it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.66it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.69it/s]", "metrics": { "predict_time": 16.163080307, "total_time": 24.719501 }, "output": [ "https://replicate.delivery/yhqm/nQCxDpo98joRGFud1GgR4lHXp9xe1kvf3EIa8Ps6mCrhZpWTA/out-0.webp" ], "started_at": "2024-08-26T20:23:45.803421Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/0h88fnny3xrm40chj1dt4qfkem", "cancel": "https://api.replicate.com/v1/predictions/0h88fnny3xrm40chj1dt4qfkem/cancel" }, "version": "5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036" }Generated inUsing seed: 53470 Prompt: Photo of the colosseum in the style of KUJI, realistic 4k with flares txt2img mode Using dev model Loaded LoRAs in 8.01s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.66it/s] 7%|▋ | 2/28 [00:00<00:06, 4.22it/s] 11%|█ | 3/28 [00:00<00:06, 3.96it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.83it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.76it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.73it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.71it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.69it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.68it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.68it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.67it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.66it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.65it/s] 50%|█████ | 14/28 [00:03<00:03, 3.65it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.66it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.65it/s] 61%|██████ | 17/28 [00:04<00:03, 3.65it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.66it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.66it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.66it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.65it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.66it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.66it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.66it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.66it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.66it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.67it/s] 100%|██████████| 28/28 [00:07<00:00, 3.66it/s] 100%|██████████| 28/28 [00:07<00:00, 3.69it/s]Prediction
ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036ID1xqdepx4n1rm00chj24a4s665wStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A young woman with tousled hair and minimal makeup, wearing an oversized flannel shirt, looking directly at the camera with a slight smile. The background is slightly out of focus, suggesting a cozy bedroom, in the style of KUJI
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 42
{ "model": "dev", "prompt": "A young woman with tousled hair and minimal makeup, wearing an oversized flannel shirt, looking directly at the camera with a slight smile. The background is slightly out of focus, suggesting a cozy bedroom, in the style of KUJI", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 42 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run ludocomito/flux-kuji using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036", { input: { model: "dev", prompt: "A young woman with tousled hair and minimal makeup, wearing an oversized flannel shirt, looking directly at the camera with a slight smile. The background is slightly out of focus, suggesting a cozy bedroom, in the style of KUJI", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 0.8, num_inference_steps: 42 } } ); // 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 ludocomito/flux-kuji using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036", input={ "model": "dev", "prompt": "A young woman with tousled hair and minimal makeup, wearing an oversized flannel shirt, looking directly at the camera with a slight smile. The background is slightly out of focus, suggesting a cozy bedroom, in the style of KUJI", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 42 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run ludocomito/flux-kuji 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": "ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036", "input": { "model": "dev", "prompt": "A young woman with tousled hair and minimal makeup, wearing an oversized flannel shirt, looking directly at the camera with a slight smile. The background is slightly out of focus, suggesting a cozy bedroom, in the style of KUJI", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 42 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-26T21:13:06.065379Z", "created_at": "2024-08-26T21:12:39.848000Z", "data_removed": false, "error": null, "id": "1xqdepx4n1rm00chj24a4s665w", "input": { "model": "dev", "prompt": "A young woman with tousled hair and minimal makeup, wearing an oversized flannel shirt, looking directly at the camera with a slight smile. The background is slightly out of focus, suggesting a cozy bedroom, in the style of KUJI", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 42 }, "logs": "Using seed: 6367\nPrompt: A young woman with tousled hair and minimal makeup, wearing an oversized flannel shirt, looking directly at the camera with a slight smile. The background is slightly out of focus, suggesting a cozy bedroom, in the style of KUJI\ntxt2img mode\nUsing dev model\nfree=9362899152896\nDownloading weights\n2024-08-26T21:12:44Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmprvnm6glq/weights url=https://replicate.delivery/yhqm/kT1XobqVJKblIl6YnYiTHMf6zti7TepRVf0KG1GbLk23pStmA/trained_model.tar\n2024-08-26T21:12:46Z | INFO | [ Complete ] dest=/tmp/tmprvnm6glq/weights size=\"172 MB\" total_elapsed=1.751s url=https://replicate.delivery/yhqm/kT1XobqVJKblIl6YnYiTHMf6zti7TepRVf0KG1GbLk23pStmA/trained_model.tar\nDownloaded weights in 1.79s\nLoaded LoRAs in 9.59s\n 0%| | 0/42 [00:00<?, ?it/s]\n 2%|▏ | 1/42 [00:00<00:11, 3.66it/s]\n 5%|▍ | 2/42 [00:00<00:09, 4.23it/s]\n 7%|▋ | 3/42 [00:00<00:09, 3.96it/s]\n 10%|▉ | 4/42 [00:01<00:09, 3.84it/s]\n 12%|█▏ | 5/42 [00:01<00:09, 3.78it/s]\n 14%|█▍ | 6/42 [00:01<00:09, 3.74it/s]\n 17%|█▋ | 7/42 [00:01<00:09, 3.72it/s]\n 19%|█▉ | 8/42 [00:02<00:09, 3.70it/s]\n 21%|██▏ | 9/42 [00:02<00:08, 3.69it/s]\n 24%|██▍ | 10/42 [00:02<00:08, 3.69it/s]\n 26%|██▌ | 11/42 [00:02<00:08, 3.68it/s]\n 29%|██▊ | 12/42 [00:03<00:08, 3.68it/s]\n 31%|███ | 13/42 [00:03<00:07, 3.67it/s]\n 33%|███▎ | 14/42 [00:03<00:07, 3.67it/s]\n 36%|███▌ | 15/42 [00:04<00:07, 3.67it/s]\n 38%|███▊ | 16/42 [00:04<00:07, 3.67it/s]\n 40%|████ | 17/42 [00:04<00:06, 3.67it/s]\n 43%|████▎ | 18/42 [00:04<00:06, 3.67it/s]\n 45%|████▌ | 19/42 [00:05<00:06, 3.67it/s]\n 48%|████▊ | 20/42 [00:05<00:05, 3.67it/s]\n 50%|█████ | 21/42 [00:05<00:05, 3.66it/s]\n 52%|█████▏ | 22/42 [00:05<00:05, 3.67it/s]\n 55%|█████▍ | 23/42 [00:06<00:05, 3.67it/s]\n 57%|█████▋ | 24/42 [00:06<00:04, 3.67it/s]\n 60%|█████▉ | 25/42 [00:06<00:04, 3.66it/s]\n 62%|██████▏ | 26/42 [00:07<00:04, 3.67it/s]\n 64%|██████▍ | 27/42 [00:07<00:04, 3.67it/s]\n 67%|██████▋ | 28/42 [00:07<00:03, 3.67it/s]\n 69%|██████▉ | 29/42 [00:07<00:03, 3.67it/s]\n 71%|███████▏ | 30/42 [00:08<00:03, 3.67it/s]\n 74%|███████▍ | 31/42 [00:08<00:02, 3.67it/s]\n 76%|███████▌ | 32/42 [00:08<00:02, 3.67it/s]\n 79%|███████▊ | 33/42 [00:08<00:02, 3.66it/s]\n 81%|████████ | 34/42 [00:09<00:02, 3.67it/s]\n 83%|████████▎ | 35/42 [00:09<00:01, 3.67it/s]\n 86%|████████▌ | 36/42 [00:09<00:01, 3.67it/s]\n 88%|████████▊ | 37/42 [00:10<00:01, 3.67it/s]\n 90%|█████████ | 38/42 [00:10<00:01, 3.67it/s]\n 93%|█████████▎| 39/42 [00:10<00:00, 3.67it/s]\n 95%|█████████▌| 40/42 [00:10<00:00, 3.67it/s]\n 98%|█████████▊| 41/42 [00:11<00:00, 3.67it/s]\n100%|██████████| 42/42 [00:11<00:00, 3.67it/s]\n100%|██████████| 42/42 [00:11<00:00, 3.69it/s]", "metrics": { "predict_time": 21.498044869, "total_time": 26.217379 }, "output": [ "https://replicate.delivery/yhqm/MFNebpZdSUS1YiD9WbKUmkQ1HyEOeZrGhjowBIPiGsfDPUtmA/out-0.webp" ], "started_at": "2024-08-26T21:12:44.567334Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/1xqdepx4n1rm00chj24a4s665w", "cancel": "https://api.replicate.com/v1/predictions/1xqdepx4n1rm00chj24a4s665w/cancel" }, "version": "5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036" }Generated inUsing seed: 6367 Prompt: A young woman with tousled hair and minimal makeup, wearing an oversized flannel shirt, looking directly at the camera with a slight smile. The background is slightly out of focus, suggesting a cozy bedroom, in the style of KUJI txt2img mode Using dev model free=9362899152896 Downloading weights 2024-08-26T21:12:44Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmprvnm6glq/weights url=https://replicate.delivery/yhqm/kT1XobqVJKblIl6YnYiTHMf6zti7TepRVf0KG1GbLk23pStmA/trained_model.tar 2024-08-26T21:12:46Z | INFO | [ Complete ] dest=/tmp/tmprvnm6glq/weights size="172 MB" total_elapsed=1.751s url=https://replicate.delivery/yhqm/kT1XobqVJKblIl6YnYiTHMf6zti7TepRVf0KG1GbLk23pStmA/trained_model.tar Downloaded weights in 1.79s Loaded LoRAs in 9.59s 0%| | 0/42 [00:00<?, ?it/s] 2%|▏ | 1/42 [00:00<00:11, 3.66it/s] 5%|▍ | 2/42 [00:00<00:09, 4.23it/s] 7%|▋ | 3/42 [00:00<00:09, 3.96it/s] 10%|▉ | 4/42 [00:01<00:09, 3.84it/s] 12%|█▏ | 5/42 [00:01<00:09, 3.78it/s] 14%|█▍ | 6/42 [00:01<00:09, 3.74it/s] 17%|█▋ | 7/42 [00:01<00:09, 3.72it/s] 19%|█▉ | 8/42 [00:02<00:09, 3.70it/s] 21%|██▏ | 9/42 [00:02<00:08, 3.69it/s] 24%|██▍ | 10/42 [00:02<00:08, 3.69it/s] 26%|██▌ | 11/42 [00:02<00:08, 3.68it/s] 29%|██▊ | 12/42 [00:03<00:08, 3.68it/s] 31%|███ | 13/42 [00:03<00:07, 3.67it/s] 33%|███▎ | 14/42 [00:03<00:07, 3.67it/s] 36%|███▌ | 15/42 [00:04<00:07, 3.67it/s] 38%|███▊ | 16/42 [00:04<00:07, 3.67it/s] 40%|████ | 17/42 [00:04<00:06, 3.67it/s] 43%|████▎ | 18/42 [00:04<00:06, 3.67it/s] 45%|████▌ | 19/42 [00:05<00:06, 3.67it/s] 48%|████▊ | 20/42 [00:05<00:05, 3.67it/s] 50%|█████ | 21/42 [00:05<00:05, 3.66it/s] 52%|█████▏ | 22/42 [00:05<00:05, 3.67it/s] 55%|█████▍ | 23/42 [00:06<00:05, 3.67it/s] 57%|█████▋ | 24/42 [00:06<00:04, 3.67it/s] 60%|█████▉ | 25/42 [00:06<00:04, 3.66it/s] 62%|██████▏ | 26/42 [00:07<00:04, 3.67it/s] 64%|██████▍ | 27/42 [00:07<00:04, 3.67it/s] 67%|██████▋ | 28/42 [00:07<00:03, 3.67it/s] 69%|██████▉ | 29/42 [00:07<00:03, 3.67it/s] 71%|███████▏ | 30/42 [00:08<00:03, 3.67it/s] 74%|███████▍ | 31/42 [00:08<00:02, 3.67it/s] 76%|███████▌ | 32/42 [00:08<00:02, 3.67it/s] 79%|███████▊ | 33/42 [00:08<00:02, 3.66it/s] 81%|████████ | 34/42 [00:09<00:02, 3.67it/s] 83%|████████▎ | 35/42 [00:09<00:01, 3.67it/s] 86%|████████▌ | 36/42 [00:09<00:01, 3.67it/s] 88%|████████▊ | 37/42 [00:10<00:01, 3.67it/s] 90%|█████████ | 38/42 [00:10<00:01, 3.67it/s] 93%|█████████▎| 39/42 [00:10<00:00, 3.67it/s] 95%|█████████▌| 40/42 [00:10<00:00, 3.67it/s] 98%|█████████▊| 41/42 [00:11<00:00, 3.67it/s] 100%|██████████| 42/42 [00:11<00:00, 3.67it/s] 100%|██████████| 42/42 [00:11<00:00, 3.69it/s]Prediction
ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036IDwpzmb117thrm00chj26axgk86mStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A video game arcade filled with classic cabinets, neon lights, and kids playing Space Invaders include lens flare and effects, flmft in the style of KUJI
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 42
{ "model": "dev", "prompt": "A video game arcade filled with classic cabinets, neon lights, and kids playing Space Invaders include lens flare and effects, flmft in the style of KUJI", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 42 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run ludocomito/flux-kuji using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036", { input: { model: "dev", prompt: "A video game arcade filled with classic cabinets, neon lights, and kids playing Space Invaders include lens flare and effects, flmft in the style of KUJI", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 0.8, num_inference_steps: 42 } } ); // 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 ludocomito/flux-kuji using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036", input={ "model": "dev", "prompt": "A video game arcade filled with classic cabinets, neon lights, and kids playing Space Invaders include lens flare and effects, flmft in the style of KUJI", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 42 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run ludocomito/flux-kuji 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": "ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036", "input": { "model": "dev", "prompt": "A video game arcade filled with classic cabinets, neon lights, and kids playing Space Invaders include lens flare and effects, flmft in the style of KUJI", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 42 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-26T21:17:20.215915Z", "created_at": "2024-08-26T21:16:30.036000Z", "data_removed": false, "error": null, "id": "wpzmb117thrm00chj26axgk86m", "input": { "model": "dev", "prompt": "A video game arcade filled with classic cabinets, neon lights, and kids playing Space Invaders include lens flare and effects, flmft in the style of KUJI", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 42 }, "logs": "Using seed: 33404\nPrompt: A video game arcade filled with classic cabinets, neon lights, and kids playing Space Invaders include lens flare and effects, flmft in the style of KUJI\ntxt2img mode\nUsing dev model\nfree=10078450290688\nDownloading weights\n2024-08-26T21:16:38Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpoj_crkxb/weights url=https://replicate.delivery/yhqm/kT1XobqVJKblIl6YnYiTHMf6zti7TepRVf0KG1GbLk23pStmA/trained_model.tar\n2024-08-26T21:16:40Z | INFO | [ Complete ] dest=/tmp/tmpoj_crkxb/weights size=\"172 MB\" total_elapsed=1.467s url=https://replicate.delivery/yhqm/kT1XobqVJKblIl6YnYiTHMf6zti7TepRVf0KG1GbLk23pStmA/trained_model.tar\nDownloaded weights in 1.49s\nLoaded LoRAs in 29.63s\n 0%| | 0/42 [00:00<?, ?it/s]\n 2%|▏ | 1/42 [00:00<00:11, 3.69it/s]\n 5%|▍ | 2/42 [00:00<00:09, 4.25it/s]\n 7%|▋ | 3/42 [00:00<00:09, 3.96it/s]\n 10%|▉ | 4/42 [00:01<00:09, 3.84it/s]\n 12%|█▏ | 5/42 [00:01<00:09, 3.78it/s]\n 14%|█▍ | 6/42 [00:01<00:09, 3.75it/s]\n 17%|█▋ | 7/42 [00:01<00:09, 3.72it/s]\n 19%|█▉ | 8/42 [00:02<00:09, 3.70it/s]\n 21%|██▏ | 9/42 [00:02<00:08, 3.69it/s]\n 24%|██▍ | 10/42 [00:02<00:08, 3.69it/s]\n 26%|██▌ | 11/42 [00:02<00:08, 3.69it/s]\n 29%|██▊ | 12/42 [00:03<00:08, 3.67it/s]\n 31%|███ | 13/42 [00:03<00:07, 3.69it/s]\n 33%|███▎ | 14/42 [00:03<00:07, 3.69it/s]\n 36%|███▌ | 15/42 [00:04<00:07, 3.68it/s]\n 38%|███▊ | 16/42 [00:04<00:07, 3.67it/s]\n 40%|████ | 17/42 [00:04<00:06, 3.68it/s]\n 43%|████▎ | 18/42 [00:04<00:06, 3.68it/s]\n 45%|████▌ | 19/42 [00:05<00:06, 3.67it/s]\n 48%|████▊ | 20/42 [00:05<00:05, 3.67it/s]\n 50%|█████ | 21/42 [00:05<00:05, 3.67it/s]\n 52%|█████▏ | 22/42 [00:05<00:05, 3.68it/s]\n 55%|█████▍ | 23/42 [00:06<00:05, 3.68it/s]\n 57%|█████▋ | 24/42 [00:06<00:04, 3.67it/s]\n 60%|█████▉ | 25/42 [00:06<00:04, 3.68it/s]\n 62%|██████▏ | 26/42 [00:07<00:04, 3.68it/s]\n 64%|██████▍ | 27/42 [00:07<00:04, 3.68it/s]\n 67%|██████▋ | 28/42 [00:07<00:03, 3.67it/s]\n 69%|██████▉ | 29/42 [00:07<00:03, 3.68it/s]\n 71%|███████▏ | 30/42 [00:08<00:03, 3.68it/s]\n 74%|███████▍ | 31/42 [00:08<00:02, 3.68it/s]\n 76%|███████▌ | 32/42 [00:08<00:02, 3.68it/s]\n 79%|███████▊ | 33/42 [00:08<00:02, 3.68it/s]\n 81%|████████ | 34/42 [00:09<00:02, 3.68it/s]\n 83%|████████▎ | 35/42 [00:09<00:01, 3.67it/s]\n 86%|████████▌ | 36/42 [00:09<00:01, 3.67it/s]\n 88%|████████▊ | 37/42 [00:10<00:01, 3.67it/s]\n 90%|█████████ | 38/42 [00:10<00:01, 3.68it/s]\n 93%|█████████▎| 39/42 [00:10<00:00, 3.67it/s]\n 95%|█████████▌| 40/42 [00:10<00:00, 3.67it/s]\n 98%|█████████▊| 41/42 [00:11<00:00, 3.68it/s]\n100%|██████████| 42/42 [00:11<00:00, 3.68it/s]\n100%|██████████| 42/42 [00:11<00:00, 3.70it/s]", "metrics": { "predict_time": 41.531074791, "total_time": 50.179915 }, "output": [ "https://replicate.delivery/yhqm/nj7ACeyMMrwuaCLaSoLqf9NsQIVQT54RbqX9eex8zl9CuoaNB/out-0.webp" ], "started_at": "2024-08-26T21:16:38.684841Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wpzmb117thrm00chj26axgk86m", "cancel": "https://api.replicate.com/v1/predictions/wpzmb117thrm00chj26axgk86m/cancel" }, "version": "5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036" }Generated inUsing seed: 33404 Prompt: A video game arcade filled with classic cabinets, neon lights, and kids playing Space Invaders include lens flare and effects, flmft in the style of KUJI txt2img mode Using dev model free=10078450290688 Downloading weights 2024-08-26T21:16:38Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpoj_crkxb/weights url=https://replicate.delivery/yhqm/kT1XobqVJKblIl6YnYiTHMf6zti7TepRVf0KG1GbLk23pStmA/trained_model.tar 2024-08-26T21:16:40Z | INFO | [ Complete ] dest=/tmp/tmpoj_crkxb/weights size="172 MB" total_elapsed=1.467s url=https://replicate.delivery/yhqm/kT1XobqVJKblIl6YnYiTHMf6zti7TepRVf0KG1GbLk23pStmA/trained_model.tar Downloaded weights in 1.49s Loaded LoRAs in 29.63s 0%| | 0/42 [00:00<?, ?it/s] 2%|▏ | 1/42 [00:00<00:11, 3.69it/s] 5%|▍ | 2/42 [00:00<00:09, 4.25it/s] 7%|▋ | 3/42 [00:00<00:09, 3.96it/s] 10%|▉ | 4/42 [00:01<00:09, 3.84it/s] 12%|█▏ | 5/42 [00:01<00:09, 3.78it/s] 14%|█▍ | 6/42 [00:01<00:09, 3.75it/s] 17%|█▋ | 7/42 [00:01<00:09, 3.72it/s] 19%|█▉ | 8/42 [00:02<00:09, 3.70it/s] 21%|██▏ | 9/42 [00:02<00:08, 3.69it/s] 24%|██▍ | 10/42 [00:02<00:08, 3.69it/s] 26%|██▌ | 11/42 [00:02<00:08, 3.69it/s] 29%|██▊ | 12/42 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[00:09<00:01, 3.67it/s] 86%|████████▌ | 36/42 [00:09<00:01, 3.67it/s] 88%|████████▊ | 37/42 [00:10<00:01, 3.67it/s] 90%|█████████ | 38/42 [00:10<00:01, 3.68it/s] 93%|█████████▎| 39/42 [00:10<00:00, 3.67it/s] 95%|█████████▌| 40/42 [00:10<00:00, 3.67it/s] 98%|█████████▊| 41/42 [00:11<00:00, 3.68it/s] 100%|██████████| 42/42 [00:11<00:00, 3.68it/s] 100%|██████████| 42/42 [00:11<00:00, 3.70it/s]Prediction
ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036ID3nwc7kqpk5rm20chj2vtstw5m4StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A mountain peak piercing through a sea of clouds at sunrise, with alpenglow on the summits, lens flare streaking across, flmft in the style of KUJI
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 35
{ "model": "dev", "prompt": "A mountain peak piercing through a sea of clouds at sunrise, with alpenglow on the summits, lens flare streaking across, flmft in the style of KUJI", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 35 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run ludocomito/flux-kuji using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036", { input: { model: "dev", prompt: "A mountain peak piercing through a sea of clouds at sunrise, with alpenglow on the summits, lens flare streaking across, flmft in the style of KUJI", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 0.8, num_inference_steps: 35 } } ); // 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 ludocomito/flux-kuji using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036", input={ "model": "dev", "prompt": "A mountain peak piercing through a sea of clouds at sunrise, with alpenglow on the summits, lens flare streaking across, flmft in the style of KUJI", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 35 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run ludocomito/flux-kuji 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": "ludocomito/flux-kuji:5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036", "input": { "model": "dev", "prompt": "A mountain peak piercing through a sea of clouds at sunrise, with alpenglow on the summits, lens flare streaking across, flmft in the style of KUJI", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 35 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-26T22:04:43.467048Z", "created_at": "2024-08-26T22:04:21.017000Z", "data_removed": false, "error": null, "id": "3nwc7kqpk5rm20chj2vtstw5m4", "input": { "model": "dev", "prompt": "A mountain peak piercing through a sea of clouds at sunrise, with alpenglow on the summits, lens flare streaking across, flmft in the style of KUJI", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 35 }, "logs": "Using seed: 13542\nPrompt: A mountain peak piercing through a sea of clouds at sunrise, with alpenglow on the summits, lens flare streaking across, flmft in the style of KUJI\ntxt2img mode\nUsing dev model\nfree=9557950914560\nDownloading weights\n2024-08-26T22:04:22Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpb2a37pi8/weights url=https://replicate.delivery/yhqm/kT1XobqVJKblIl6YnYiTHMf6zti7TepRVf0KG1GbLk23pStmA/trained_model.tar\n2024-08-26T22:04:24Z | INFO | [ Complete ] dest=/tmp/tmpb2a37pi8/weights size=\"172 MB\" total_elapsed=1.830s url=https://replicate.delivery/yhqm/kT1XobqVJKblIl6YnYiTHMf6zti7TepRVf0KG1GbLk23pStmA/trained_model.tar\nDownloaded weights in 1.86s\nLoaded LoRAs in 10.98s\n 0%| | 0/35 [00:00<?, ?it/s]\n 3%|▎ | 1/35 [00:00<00:09, 3.62it/s]\n 6%|▌ | 2/35 [00:00<00:07, 4.19it/s]\n 9%|▊ | 3/35 [00:00<00:08, 3.92it/s]\n 11%|█▏ | 4/35 [00:01<00:08, 3.80it/s]\n 14%|█▍ | 5/35 [00:01<00:08, 3.74it/s]\n 17%|█▋ | 6/35 [00:01<00:07, 3.71it/s]\n 20%|██ | 7/35 [00:01<00:07, 3.68it/s]\n 23%|██▎ | 8/35 [00:02<00:07, 3.67it/s]\n 26%|██▌ | 9/35 [00:02<00:07, 3.66it/s]\n 29%|██▊ | 10/35 [00:02<00:06, 3.65it/s]\n 31%|███▏ | 11/35 [00:02<00:06, 3.64it/s]\n 34%|███▍ | 12/35 [00:03<00:06, 3.64it/s]\n 37%|███▋ | 13/35 [00:03<00:06, 3.64it/s]\n 40%|████ | 14/35 [00:03<00:05, 3.64it/s]\n 43%|████▎ | 15/35 [00:04<00:05, 3.64it/s]\n 46%|████▌ | 16/35 [00:04<00:05, 3.63it/s]\n 49%|████▊ | 17/35 [00:04<00:04, 3.63it/s]\n 51%|█████▏ | 18/35 [00:04<00:04, 3.63it/s]\n 54%|█████▍ | 19/35 [00:05<00:04, 3.63it/s]\n 57%|█████▋ | 20/35 [00:05<00:04, 3.63it/s]\n 60%|██████ | 21/35 [00:05<00:03, 3.63it/s]\n 63%|██████▎ | 22/35 [00:05<00:03, 3.63it/s]\n 66%|██████▌ | 23/35 [00:06<00:03, 3.63it/s]\n 69%|██████▊ | 24/35 [00:06<00:03, 3.63it/s]\n 71%|███████▏ | 25/35 [00:06<00:02, 3.63it/s]\n 74%|███████▍ | 26/35 [00:07<00:02, 3.63it/s]\n 77%|███████▋ | 27/35 [00:07<00:02, 3.63it/s]\n 80%|████████ | 28/35 [00:07<00:01, 3.62it/s]\n 83%|████████▎ | 29/35 [00:07<00:01, 3.62it/s]\n 86%|████████▌ | 30/35 [00:08<00:01, 3.62it/s]\n 89%|████████▊ | 31/35 [00:08<00:01, 3.63it/s]\n 91%|█████████▏| 32/35 [00:08<00:00, 3.62it/s]\n 94%|█████████▍| 33/35 [00:09<00:00, 3.63it/s]\n 97%|█████████▋| 34/35 [00:09<00:00, 3.63it/s]\n100%|██████████| 35/35 [00:09<00:00, 3.63it/s]\n100%|██████████| 35/35 [00:09<00:00, 3.65it/s]", "metrics": { "predict_time": 21.060747129, "total_time": 22.450048 }, "output": [ "https://replicate.delivery/yhqm/Xmk4LVTDfQSSESi32cCMtb2lSethFpPdHNiTnYFeJUj3vVtmA/out-0.webp" ], "started_at": "2024-08-26T22:04:22.406301Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/3nwc7kqpk5rm20chj2vtstw5m4", "cancel": "https://api.replicate.com/v1/predictions/3nwc7kqpk5rm20chj2vtstw5m4/cancel" }, "version": "5001e4db8ed2bce55e46f4323c8735bf571a66223a2be97d082c029852b2e036" }Generated inUsing seed: 13542 Prompt: A mountain peak piercing through a sea of clouds at sunrise, with alpenglow on the summits, lens flare streaking across, flmft in the style of KUJI txt2img mode Using dev model free=9557950914560 Downloading weights 2024-08-26T22:04:22Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpb2a37pi8/weights url=https://replicate.delivery/yhqm/kT1XobqVJKblIl6YnYiTHMf6zti7TepRVf0KG1GbLk23pStmA/trained_model.tar 2024-08-26T22:04:24Z | INFO | [ Complete ] dest=/tmp/tmpb2a37pi8/weights size="172 MB" total_elapsed=1.830s url=https://replicate.delivery/yhqm/kT1XobqVJKblIl6YnYiTHMf6zti7TepRVf0KG1GbLk23pStmA/trained_model.tar Downloaded weights in 1.86s Loaded LoRAs in 10.98s 0%| | 0/35 [00:00<?, ?it/s] 3%|▎ | 1/35 [00:00<00:09, 3.62it/s] 6%|▌ | 2/35 [00:00<00:07, 4.19it/s] 9%|▊ | 3/35 [00:00<00:08, 3.92it/s] 11%|█▏ | 4/35 [00:01<00:08, 3.80it/s] 14%|█▍ | 5/35 [00:01<00:08, 3.74it/s] 17%|█▋ | 6/35 [00:01<00:07, 3.71it/s] 20%|██ | 7/35 [00:01<00:07, 3.68it/s] 23%|██▎ | 8/35 [00:02<00:07, 3.67it/s] 26%|██▌ | 9/35 [00:02<00:07, 3.66it/s] 29%|██▊ | 10/35 [00:02<00:06, 3.65it/s] 31%|███▏ | 11/35 [00:02<00:06, 3.64it/s] 34%|███▍ | 12/35 [00:03<00:06, 3.64it/s] 37%|███▋ | 13/35 [00:03<00:06, 3.64it/s] 40%|████ | 14/35 [00:03<00:05, 3.64it/s] 43%|████▎ | 15/35 [00:04<00:05, 3.64it/s] 46%|████▌ | 16/35 [00:04<00:05, 3.63it/s] 49%|████▊ | 17/35 [00:04<00:04, 3.63it/s] 51%|█████▏ | 18/35 [00:04<00:04, 3.63it/s] 54%|█████▍ | 19/35 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