0xtuba / archillect-lora
Generates images in the style of Archillect
- Public
- 5.8K runs
-
H100
Prediction
0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42IDsxjbkqcpf5rm00chr6vvmmbh0wStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- data center in the style of archillect, grayscale
- 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": "data center in the style of archillect, grayscale", "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 replicate
Import 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 0xtuba/archillect-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42", { input: { model: "dev", prompt: "data center in the style of archillect, grayscale", 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 replicate
Import the client:import replicate
Run 0xtuba/archillect-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42", input={ "model": "dev", "prompt": "data center in the style of archillect, grayscale", "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 0xtuba/archillect-lora 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": "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42", "input": { "model": "dev", "prompt": "data center in the style of archillect, grayscale", "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/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-05T10:25:41.074682Z", "created_at": "2024-09-05T10:25:19.993000Z", "data_removed": false, "error": null, "id": "sxjbkqcpf5rm00chr6vvmmbh0w", "input": { "model": "dev", "prompt": "data center in the style of archillect, grayscale", "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: 55802\nPrompt: data center in the style of archillect, grayscale\ntxt2img mode\nUsing dev model\nfree=8604409741312\nDownloading weights\n2024-09-05T10:25:20Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpuqehlle4/weights url=https://replicate.delivery/yhqm/mj2J5t3eRv0KRKGQ4C0C6XArbfz0v6Eh9psYsecljJReebObC/trained_model.tar\n2024-09-05T10:25:23Z | INFO | [ Complete ] dest=/tmp/tmpuqehlle4/weights size=\"172 MB\" total_elapsed=3.299s url=https://replicate.delivery/yhqm/mj2J5t3eRv0KRKGQ4C0C6XArbfz0v6Eh9psYsecljJReebObC/trained_model.tar\nDownloaded weights in 3.33s\nLoaded LoRAs in 12.51s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.47it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.92it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.70it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.60it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.55it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.52it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.50it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.49it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.48it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.48it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.48it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.47it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.47it/s]\n 50%|█████ | 14/28 [00:03<00:04, 3.47it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.47it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.47it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.47it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.47it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.47it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.47it/s]\n 75%|███████▌ | 21/28 [00:06<00:02, 3.46it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.47it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.46it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.47it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.47it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.47it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.47it/s]\n100%|██████████| 28/28 [00:08<00:00, 3.47it/s]\n100%|██████████| 28/28 [00:08<00:00, 3.49it/s]", "metrics": { "predict_time": 21.071949953, "total_time": 21.081682 }, "output": [ "https://replicate.delivery/yhqm/E7F5mn6751rGNBhRuXLgiQgaEcl03jqNhhQpVuQ5S7KJ5c2E/out-0.webp" ], "started_at": "2024-09-05T10:25:20.002732Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/sxjbkqcpf5rm00chr6vvmmbh0w", "cancel": "https://api.replicate.com/v1/predictions/sxjbkqcpf5rm00chr6vvmmbh0w/cancel" }, "version": "599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42" }
Generated inUsing seed: 55802 Prompt: data center in the style of archillect, grayscale txt2img mode Using dev model free=8604409741312 Downloading weights 2024-09-05T10:25:20Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpuqehlle4/weights url=https://replicate.delivery/yhqm/mj2J5t3eRv0KRKGQ4C0C6XArbfz0v6Eh9psYsecljJReebObC/trained_model.tar 2024-09-05T10:25:23Z | INFO | [ Complete ] dest=/tmp/tmpuqehlle4/weights size="172 MB" total_elapsed=3.299s url=https://replicate.delivery/yhqm/mj2J5t3eRv0KRKGQ4C0C6XArbfz0v6Eh9psYsecljJReebObC/trained_model.tar Downloaded weights in 3.33s Loaded LoRAs in 12.51s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.47it/s] 7%|▋ | 2/28 [00:00<00:06, 3.92it/s] 11%|█ | 3/28 [00:00<00:06, 3.70it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.60it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.55it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.52it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.50it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.49it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.48it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.48it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.48it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.47it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.47it/s] 50%|█████ | 14/28 [00:03<00:04, 3.47it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.47it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.47it/s] 61%|██████ | 17/28 [00:04<00:03, 3.47it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.47it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.47it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.47it/s] 75%|███████▌ | 21/28 [00:06<00:02, 3.46it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.47it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.46it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.47it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.47it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.47it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.47it/s] 100%|██████████| 28/28 [00:08<00:00, 3.47it/s] 100%|██████████| 28/28 [00:08<00:00, 3.49it/s]
Prediction
0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42Input
- model
- dev
- prompt
- small empty room with many computer screens in the style of ARCHLLCT, grayscale
- 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": "small empty room with many computer screens in the style of ARCHLLCT, grayscale", "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 replicate
Import 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 0xtuba/archillect-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42", { input: { model: "dev", prompt: "small empty room with many computer screens in the style of ARCHLLCT, grayscale", 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 replicate
Import the client:import replicate
Run 0xtuba/archillect-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42", input={ "model": "dev", "prompt": "small empty room with many computer screens in the style of ARCHLLCT, grayscale", "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 0xtuba/archillect-lora 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": "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42", "input": { "model": "dev", "prompt": "small empty room with many computer screens in the style of ARCHLLCT, grayscale", "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/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-05T10:36:20.467623Z", "created_at": "2024-09-05T10:36:02.760000Z", "data_removed": false, "error": null, "id": "p9sprtk591rm40chr70srw8fwr", "input": { "model": "dev", "prompt": "small empty room with many computer screens in the style of ARCHLLCT, grayscale", "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: 49085\nPrompt: small empty room with many computer screens in the style of ARCHLLCT, grayscale\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 9.08s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.46it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.91it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.69it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.60it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.55it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.52it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.51it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.49it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.49it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.48it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.48it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.47it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.47it/s]\n 50%|█████ | 14/28 [00:03<00:04, 3.47it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.47it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.47it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.47it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.46it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.47it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.46it/s]\n 75%|███████▌ | 21/28 [00:06<00:02, 3.46it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.46it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.46it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.46it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.46it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.47it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.45it/s]\n100%|██████████| 28/28 [00:08<00:00, 3.46it/s]\n100%|██████████| 28/28 [00:08<00:00, 3.49it/s]", "metrics": { "predict_time": 17.69888967, "total_time": 17.707623 }, "output": [ "https://replicate.delivery/yhqm/O0eNA4366iUPHqW5DLdnLjZdgoXwtEzZLe7huLppYafJdnzmA/out-0.webp" ], "started_at": "2024-09-05T10:36:02.768734Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/p9sprtk591rm40chr70srw8fwr", "cancel": "https://api.replicate.com/v1/predictions/p9sprtk591rm40chr70srw8fwr/cancel" }, "version": "599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42" }
Generated inUsing seed: 49085 Prompt: small empty room with many computer screens in the style of ARCHLLCT, grayscale txt2img mode Using dev model Loaded LoRAs in 9.08s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.46it/s] 7%|▋ | 2/28 [00:00<00:06, 3.91it/s] 11%|█ | 3/28 [00:00<00:06, 3.69it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.60it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.55it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.52it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.51it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.49it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.49it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.48it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.48it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.47it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.47it/s] 50%|█████ | 14/28 [00:03<00:04, 3.47it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.47it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.47it/s] 61%|██████ | 17/28 [00:04<00:03, 3.47it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.46it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.47it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.46it/s] 75%|███████▌ | 21/28 [00:06<00:02, 3.46it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.46it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.46it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.46it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.46it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.47it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.45it/s] 100%|██████████| 28/28 [00:08<00:00, 3.46it/s] 100%|██████████| 28/28 [00:08<00:00, 3.49it/s]
Prediction
0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42ID0wekf80stnrm60chr7196zt7q8StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- hospital ward in the style of ARCHLLCT
- 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": "hospital ward in the style of ARCHLLCT", "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 replicate
Import 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 0xtuba/archillect-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42", { input: { model: "dev", prompt: "hospital ward in the style of ARCHLLCT", 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 replicate
Import the client:import replicate
Run 0xtuba/archillect-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42", input={ "model": "dev", "prompt": "hospital ward in the style of ARCHLLCT", "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 0xtuba/archillect-lora 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": "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42", "input": { "model": "dev", "prompt": "hospital ward in the style of ARCHLLCT", "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/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-05T10:37:14.774239Z", "created_at": "2024-09-05T10:36:48.981000Z", "data_removed": false, "error": null, "id": "0wekf80stnrm60chr7196zt7q8", "input": { "model": "dev", "prompt": "hospital ward in the style of ARCHLLCT", "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: 9290\nPrompt: hospital ward in the style of ARCHLLCT\ntxt2img mode\nUsing dev model\nfree=8317040365568\nDownloading weights\n2024-09-05T10:36:49Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpuz9b2g26/weights url=https://replicate.delivery/yhqm/mj2J5t3eRv0KRKGQ4C0C6XArbfz0v6Eh9psYsecljJReebObC/trained_model.tar\n2024-09-05T10:36:50Z | INFO | [ Complete ] dest=/tmp/tmpuz9b2g26/weights size=\"172 MB\" total_elapsed=1.088s url=https://replicate.delivery/yhqm/mj2J5t3eRv0KRKGQ4C0C6XArbfz0v6Eh9psYsecljJReebObC/trained_model.tar\nDownloaded weights in 1.12s\nLoaded LoRAs in 17.18s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.48it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.94it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.70it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.61it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.56it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.53it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.51it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.50it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.49it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.48it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.47it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.47it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.47it/s]\n 50%|█████ | 14/28 [00:03<00:04, 3.47it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.47it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.46it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.47it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.47it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.47it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.47it/s]\n 75%|███████▌ | 21/28 [00:05<00:02, 3.47it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.47it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.47it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.47it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.47it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.47it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.47it/s]\n100%|██████████| 28/28 [00:08<00:00, 3.47it/s]\n100%|██████████| 28/28 [00:08<00:00, 3.49it/s]", "metrics": { "predict_time": 25.782560963, "total_time": 25.793239 }, "output": [ "https://replicate.delivery/yhqm/fe8EkEMSRFrTC0fFzw1hTPHCfW5oq1wUwdHIeuFjh46T7dObC/out-0.webp" ], "started_at": "2024-09-05T10:36:48.991678Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/0wekf80stnrm60chr7196zt7q8", "cancel": "https://api.replicate.com/v1/predictions/0wekf80stnrm60chr7196zt7q8/cancel" }, "version": "599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42" }
Generated inUsing seed: 9290 Prompt: hospital ward in the style of ARCHLLCT txt2img mode Using dev model free=8317040365568 Downloading weights 2024-09-05T10:36:49Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpuz9b2g26/weights url=https://replicate.delivery/yhqm/mj2J5t3eRv0KRKGQ4C0C6XArbfz0v6Eh9psYsecljJReebObC/trained_model.tar 2024-09-05T10:36:50Z | INFO | [ Complete ] dest=/tmp/tmpuz9b2g26/weights size="172 MB" total_elapsed=1.088s url=https://replicate.delivery/yhqm/mj2J5t3eRv0KRKGQ4C0C6XArbfz0v6Eh9psYsecljJReebObC/trained_model.tar Downloaded weights in 1.12s Loaded LoRAs in 17.18s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.48it/s] 7%|▋ | 2/28 [00:00<00:06, 3.94it/s] 11%|█ | 3/28 [00:00<00:06, 3.70it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.61it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.56it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.53it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.51it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.50it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.49it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.48it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.47it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.47it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.47it/s] 50%|█████ | 14/28 [00:03<00:04, 3.47it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.47it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.46it/s] 61%|██████ | 17/28 [00:04<00:03, 3.47it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.47it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.47it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.47it/s] 75%|███████▌ | 21/28 [00:05<00:02, 3.47it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.47it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.47it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.47it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.47it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.47it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.47it/s] 100%|██████████| 28/28 [00:08<00:00, 3.47it/s] 100%|██████████| 28/28 [00:08<00:00, 3.49it/s]
Prediction
0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42IDfa48j6vnqhrm20chr71vzktv6cStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- serious looking anime female portrait with long dark hair in the style of ARCHLLCT, grayscale, manga
- 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": "serious looking anime female portrait with long dark hair in the style of ARCHLLCT, grayscale, manga", "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 replicate
Import 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 0xtuba/archillect-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42", { input: { model: "dev", prompt: "serious looking anime female portrait with long dark hair in the style of ARCHLLCT, grayscale, manga", 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 replicate
Import the client:import replicate
Run 0xtuba/archillect-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42", input={ "model": "dev", "prompt": "serious looking anime female portrait with long dark hair in the style of ARCHLLCT, grayscale, manga", "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 0xtuba/archillect-lora 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": "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42", "input": { "model": "dev", "prompt": "serious looking anime female portrait with long dark hair in the style of ARCHLLCT, grayscale, manga", "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/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-05T10:38:35.809300Z", "created_at": "2024-09-05T10:38:18.044000Z", "data_removed": false, "error": null, "id": "fa48j6vnqhrm20chr71vzktv6c", "input": { "model": "dev", "prompt": "serious looking anime female portrait with long dark hair in the style of ARCHLLCT, grayscale, manga", "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: 25146\nPrompt: serious looking anime female portrait with long dark hair in the style of ARCHLLCT, grayscale, manga\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 9.26s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.49it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.95it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.72it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.63it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.58it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.55it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.53it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.52it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.51it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.50it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.50it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.50it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.50it/s]\n 50%|█████ | 14/28 [00:03<00:04, 3.50it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.49it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.49it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.49it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.49it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.49it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.49it/s]\n 75%|███████▌ | 21/28 [00:05<00:02, 3.49it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.49it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.49it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.49it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.49it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.49it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.49it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.49it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.52it/s]", "metrics": { "predict_time": 17.755344913, "total_time": 17.7653 }, "output": [ "https://replicate.delivery/yhqm/cvrXppDLCeQTQyNVyMA2siQRf54mutqzB9gY5vlToLwrwzZTA/out-0.webp" ], "started_at": "2024-09-05T10:38:18.053955Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fa48j6vnqhrm20chr71vzktv6c", "cancel": "https://api.replicate.com/v1/predictions/fa48j6vnqhrm20chr71vzktv6c/cancel" }, "version": "599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42" }
Generated inUsing seed: 25146 Prompt: serious looking anime female portrait with long dark hair in the style of ARCHLLCT, grayscale, manga txt2img mode Using dev model Loaded LoRAs in 9.26s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.49it/s] 7%|▋ | 2/28 [00:00<00:06, 3.95it/s] 11%|█ | 3/28 [00:00<00:06, 3.72it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.63it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.58it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.55it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.53it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.52it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.51it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.50it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.50it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.50it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.50it/s] 50%|█████ | 14/28 [00:03<00:04, 3.50it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.49it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.49it/s] 61%|██████ | 17/28 [00:04<00:03, 3.49it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.49it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.49it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.49it/s] 75%|███████▌ | 21/28 [00:05<00:02, 3.49it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.49it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.49it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.49it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.49it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.49it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.49it/s] 100%|██████████| 28/28 [00:07<00:00, 3.49it/s] 100%|██████████| 28/28 [00:07<00:00, 3.52it/s]
Prediction
0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42ID5r5sbxn8x5rm40chr75vagwg5rStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- robot smoking cigarette in the style of ARCHLLCT, grayscale
- lora_scale
- 0.78
- 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": "robot smoking cigarette in the style of ARCHLLCT, grayscale", "lora_scale": 0.78, "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 replicate
Import 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 0xtuba/archillect-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42", { input: { model: "dev", prompt: "robot smoking cigarette in the style of ARCHLLCT, grayscale", lora_scale: 0.78, 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 replicate
Import the client:import replicate
Run 0xtuba/archillect-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42", input={ "model": "dev", "prompt": "robot smoking cigarette in the style of ARCHLLCT, grayscale", "lora_scale": 0.78, "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 0xtuba/archillect-lora 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": "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42", "input": { "model": "dev", "prompt": "robot smoking cigarette in the style of ARCHLLCT, grayscale", "lora_scale": 0.78, "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/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-05T10:47:41.303434Z", "created_at": "2024-09-05T10:47:15.433000Z", "data_removed": false, "error": null, "id": "5r5sbxn8x5rm40chr75vagwg5r", "input": { "model": "dev", "prompt": "robot smoking cigarette in the style of ARCHLLCT, grayscale", "lora_scale": 0.78, "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: 11090\nPrompt: robot smoking cigarette in the style of ARCHLLCT, grayscale\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 8.37s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:08, 3.35it/s]\n 7%|▋ | 2/28 [00:00<00:07, 3.69it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.59it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.55it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.52it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.50it/s]\n 25%|██▌ | 7/28 [00:01<00:06, 3.48it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.48it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.47it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.47it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.46it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.47it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.47it/s]\n 50%|█████ | 14/28 [00:04<00:04, 3.47it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.46it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.47it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.47it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.47it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.46it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.46it/s]\n 75%|███████▌ | 21/28 [00:06<00:02, 3.46it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.47it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.47it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.47it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.46it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.46it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.47it/s]\n100%|██████████| 28/28 [00:08<00:00, 3.46it/s]\n100%|██████████| 28/28 [00:08<00:00, 3.48it/s]", "metrics": { "predict_time": 17.002299305, "total_time": 25.870434 }, "output": [ "https://replicate.delivery/yhqm/P41QSqUe5OR8e0uEvAIZtveI4MpmyO8cf0c1gSJuxY40kPnNB/out-0.webp" ], "started_at": "2024-09-05T10:47:24.301135Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5r5sbxn8x5rm40chr75vagwg5r", "cancel": "https://api.replicate.com/v1/predictions/5r5sbxn8x5rm40chr75vagwg5r/cancel" }, "version": "599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42" }
Generated inUsing seed: 11090 Prompt: robot smoking cigarette in the style of ARCHLLCT, grayscale txt2img mode Using dev model Loaded LoRAs in 8.37s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:08, 3.35it/s] 7%|▋ | 2/28 [00:00<00:07, 3.69it/s] 11%|█ | 3/28 [00:00<00:06, 3.59it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.55it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.52it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.50it/s] 25%|██▌ | 7/28 [00:01<00:06, 3.48it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.48it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.47it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.47it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.46it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.47it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.47it/s] 50%|█████ | 14/28 [00:04<00:04, 3.47it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.46it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.47it/s] 61%|██████ | 17/28 [00:04<00:03, 3.47it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.47it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.46it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.46it/s] 75%|███████▌ | 21/28 [00:06<00:02, 3.46it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.47it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.47it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.47it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.46it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.46it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.47it/s] 100%|██████████| 28/28 [00:08<00:00, 3.46it/s] 100%|██████████| 28/28 [00:08<00:00, 3.48it/s]
Prediction
0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42IDc2jv46d7q5rm40chr778e650x8StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- waves crashing on a cliff in the style of ARCHLLCT, grayscale
- 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": "waves crashing on a cliff in the style of ARCHLLCT, grayscale", "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 replicate
Import 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 0xtuba/archillect-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42", { input: { model: "dev", prompt: "waves crashing on a cliff in the style of ARCHLLCT, grayscale", 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 replicate
Import the client:import replicate
Run 0xtuba/archillect-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42", input={ "model": "dev", "prompt": "waves crashing on a cliff in the style of ARCHLLCT, grayscale", "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 0xtuba/archillect-lora 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": "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42", "input": { "model": "dev", "prompt": "waves crashing on a cliff in the style of ARCHLLCT, grayscale", "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/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-05T10:50:49.536861Z", "created_at": "2024-09-05T10:50:31.737000Z", "data_removed": false, "error": null, "id": "c2jv46d7q5rm40chr778e650x8", "input": { "model": "dev", "prompt": "waves crashing on a cliff in the style of ARCHLLCT, grayscale", "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: 11890\nPrompt: waves crashing on a cliff in the style of ARCHLLCT, grayscale\ntxt2img mode\nUsing dev model\nfree=8392381210624\nDownloading weights\n2024-09-05T10:50:31Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpfipqocpf/weights url=https://replicate.delivery/yhqm/mj2J5t3eRv0KRKGQ4C0C6XArbfz0v6Eh9psYsecljJReebObC/trained_model.tar\n2024-09-05T10:50:32Z | INFO | [ Complete ] dest=/tmp/tmpfipqocpf/weights size=\"172 MB\" total_elapsed=1.065s url=https://replicate.delivery/yhqm/mj2J5t3eRv0KRKGQ4C0C6XArbfz0v6Eh9psYsecljJReebObC/trained_model.tar\nDownloaded weights in 1.09s\nLoaded LoRAs in 9.24s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.49it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.95it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.73it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.63it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.58it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.55it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.53it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.52it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.51it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.51it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.50it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.50it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.50it/s]\n 50%|█████ | 14/28 [00:03<00:04, 3.50it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.50it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.50it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.50it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.50it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.50it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.50it/s]\n 75%|███████▌ | 21/28 [00:05<00:02, 3.50it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.50it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.49it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.50it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.49it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.50it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.50it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.49it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.52it/s]", "metrics": { "predict_time": 17.790276337999998, "total_time": 17.799861 }, "output": [ "https://replicate.delivery/yhqm/ofPug2uOlyzzYKDo4meEelHlXjUpiuMSXVONIJjC2sgT4nzmA/out-0.webp" ], "started_at": "2024-09-05T10:50:31.746584Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/c2jv46d7q5rm40chr778e650x8", "cancel": "https://api.replicate.com/v1/predictions/c2jv46d7q5rm40chr778e650x8/cancel" }, "version": "599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42" }
Generated inUsing seed: 11890 Prompt: waves crashing on a cliff in the style of ARCHLLCT, grayscale txt2img mode Using dev model free=8392381210624 Downloading weights 2024-09-05T10:50:31Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpfipqocpf/weights url=https://replicate.delivery/yhqm/mj2J5t3eRv0KRKGQ4C0C6XArbfz0v6Eh9psYsecljJReebObC/trained_model.tar 2024-09-05T10:50:32Z | INFO | [ Complete ] dest=/tmp/tmpfipqocpf/weights size="172 MB" total_elapsed=1.065s url=https://replicate.delivery/yhqm/mj2J5t3eRv0KRKGQ4C0C6XArbfz0v6Eh9psYsecljJReebObC/trained_model.tar Downloaded weights in 1.09s Loaded LoRAs in 9.24s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.49it/s] 7%|▋ | 2/28 [00:00<00:06, 3.95it/s] 11%|█ | 3/28 [00:00<00:06, 3.73it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.63it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.58it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.55it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.53it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.52it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.51it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.51it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.50it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.50it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.50it/s] 50%|█████ | 14/28 [00:03<00:04, 3.50it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.50it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.50it/s] 61%|██████ | 17/28 [00:04<00:03, 3.50it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.50it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.50it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.50it/s] 75%|███████▌ | 21/28 [00:05<00:02, 3.50it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.50it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.49it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.50it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.49it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.50it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.50it/s] 100%|██████████| 28/28 [00:07<00:00, 3.49it/s] 100%|██████████| 28/28 [00:07<00:00, 3.52it/s]
Prediction
0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42IDc2jv46d7q5rm40chr778e650x8StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- waves crashing on a cliff in the style of ARCHLLCT, grayscale
- 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": "waves crashing on a cliff in the style of ARCHLLCT, grayscale", "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 replicate
Import 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 0xtuba/archillect-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42", { input: { model: "dev", prompt: "waves crashing on a cliff in the style of ARCHLLCT, grayscale", 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 replicate
Import the client:import replicate
Run 0xtuba/archillect-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42", input={ "model": "dev", "prompt": "waves crashing on a cliff in the style of ARCHLLCT, grayscale", "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 0xtuba/archillect-lora 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": "0xtuba/archillect-lora:599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42", "input": { "model": "dev", "prompt": "waves crashing on a cliff in the style of ARCHLLCT, grayscale", "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/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-05T10:50:49.536861Z", "created_at": "2024-09-05T10:50:31.737000Z", "data_removed": false, "error": null, "id": "c2jv46d7q5rm40chr778e650x8", "input": { "model": "dev", "prompt": "waves crashing on a cliff in the style of ARCHLLCT, grayscale", "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: 11890\nPrompt: waves crashing on a cliff in the style of ARCHLLCT, grayscale\ntxt2img mode\nUsing dev model\nfree=8392381210624\nDownloading weights\n2024-09-05T10:50:31Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpfipqocpf/weights url=https://replicate.delivery/yhqm/mj2J5t3eRv0KRKGQ4C0C6XArbfz0v6Eh9psYsecljJReebObC/trained_model.tar\n2024-09-05T10:50:32Z | INFO | [ Complete ] dest=/tmp/tmpfipqocpf/weights size=\"172 MB\" total_elapsed=1.065s url=https://replicate.delivery/yhqm/mj2J5t3eRv0KRKGQ4C0C6XArbfz0v6Eh9psYsecljJReebObC/trained_model.tar\nDownloaded weights in 1.09s\nLoaded LoRAs in 9.24s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.49it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.95it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.73it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.63it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.58it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.55it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.53it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.52it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.51it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.51it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.50it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.50it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.50it/s]\n 50%|█████ | 14/28 [00:03<00:04, 3.50it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.50it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.50it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.50it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.50it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.50it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.50it/s]\n 75%|███████▌ | 21/28 [00:05<00:02, 3.50it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.50it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.49it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.50it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.49it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.50it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.50it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.49it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.52it/s]", "metrics": { "predict_time": 17.790276337999998, "total_time": 17.799861 }, "output": [ "https://replicate.delivery/yhqm/ofPug2uOlyzzYKDo4meEelHlXjUpiuMSXVONIJjC2sgT4nzmA/out-0.webp" ], "started_at": "2024-09-05T10:50:31.746584Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/c2jv46d7q5rm40chr778e650x8", "cancel": "https://api.replicate.com/v1/predictions/c2jv46d7q5rm40chr778e650x8/cancel" }, "version": "599ad8df6667caa3ab865345dcb0f98b673a4e4505a8ffc2eea52bd8ba152b42" }
Generated inUsing seed: 11890 Prompt: waves crashing on a cliff in the style of ARCHLLCT, grayscale txt2img mode Using dev model free=8392381210624 Downloading weights 2024-09-05T10:50:31Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpfipqocpf/weights url=https://replicate.delivery/yhqm/mj2J5t3eRv0KRKGQ4C0C6XArbfz0v6Eh9psYsecljJReebObC/trained_model.tar 2024-09-05T10:50:32Z | INFO | [ Complete ] dest=/tmp/tmpfipqocpf/weights size="172 MB" total_elapsed=1.065s url=https://replicate.delivery/yhqm/mj2J5t3eRv0KRKGQ4C0C6XArbfz0v6Eh9psYsecljJReebObC/trained_model.tar Downloaded weights in 1.09s Loaded LoRAs in 9.24s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.49it/s] 7%|▋ | 2/28 [00:00<00:06, 3.95it/s] 11%|█ | 3/28 [00:00<00:06, 3.73it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.63it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.58it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.55it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.53it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.52it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.51it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.51it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.50it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.50it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.50it/s] 50%|█████ | 14/28 [00:03<00:04, 3.50it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.50it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.50it/s] 61%|██████ | 17/28 [00:04<00:03, 3.50it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.50it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.50it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.50it/s] 75%|███████▌ | 21/28 [00:05<00:02, 3.50it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.50it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.49it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.50it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.49it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.50it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.50it/s] 100%|██████████| 28/28 [00:07<00:00, 3.49it/s] 100%|██████████| 28/28 [00:07<00:00, 3.52it/s]
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