fofr / sdxl-barbie
A fine-tuned SDXL lora based on the Barbie movie
- Public
- 40.8K runs
-
L40S
Prediction
fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0eIDfomb3j3bp7v2kingbkffnue5gmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- underexposed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/sdxl-barbie using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e", { input: { width: 1024, height: 1024, prompt: "A photo in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "underexposed", prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun fofr/sdxl-barbie using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e", input={ "width": 1024, "height": 1024, "prompt": "A photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fofr/sdxl-barbie 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": "fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e", "input": { "width": 1024, "height": 1024, "prompt": "A photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-08T13:21:18.155708Z", "created_at": "2023-08-08T13:21:02.393527Z", "data_removed": false, "error": null, "id": "fomb3j3bp7v2kingbkffnue5gm", "input": { "width": 1024, "height": 1024, "prompt": "A photo in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 49280\nPrompt: A photo in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.68it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.68it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.68it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.67it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.67it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.68it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.68it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.68it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.69it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.68it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.68it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.68it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.68it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.68it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.68it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.67it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.67it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.67it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]", "metrics": { "predict_time": 15.772346, "total_time": 15.762181 }, "output": [ "https://replicate.delivery/pbxt/STPvBA4rC8I1Ah0cGSjkzhJlqZ8dmLhRvmep4EDCMh2mmfXRA/out-0.png" ], "started_at": "2023-08-08T13:21:02.383362Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fomb3j3bp7v2kingbkffnue5gm", "cancel": "https://api.replicate.com/v1/predictions/fomb3j3bp7v2kingbkffnue5gm/cancel" }, "version": "657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e" }Generated inUsing seed: 49280 Prompt: A photo in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.69it/s] 4%|▍ | 2/50 [00:00<00:13, 3.68it/s] 6%|▌ | 3/50 [00:00<00:12, 3.68it/s] 8%|▊ | 4/50 [00:01<00:12, 3.68it/s] 10%|█ | 5/50 [00:01<00:12, 3.67it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s] 20%|██ | 10/50 [00:02<00:10, 3.67it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.68it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.68it/s] 30%|███ | 15/50 [00:04<00:09, 3.68it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.69it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.68it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.68it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s] 40%|████ | 20/50 [00:05<00:08, 3.68it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s] 50%|█████ | 25/50 [00:06<00:06, 3.68it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.68it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s] 60%|██████ | 30/50 [00:08<00:05, 3.68it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.68it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.68it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s] 70%|███████ | 35/50 [00:09<00:04, 3.67it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.67it/s] 80%|████████ | 40/50 [00:10<00:02, 3.67it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s]Prediction
fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0eIDx3wl6vlbexjwkyy6ahqemcoggyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @fofrInput
- width
- 1360
- height
- 768
- prompt
- A landscape photo in the style of TOK, pink
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.95
- negative_prompt
- underexposed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1360, "height": 768, "prompt": "A landscape photo in the style of TOK, pink", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.95, "negative_prompt": "underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/sdxl-barbie using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e", { input: { width: 1360, height: 768, prompt: "A landscape photo in the style of TOK, pink", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.95, negative_prompt: "underexposed", prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun fofr/sdxl-barbie using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e", input={ "width": 1360, "height": 768, "prompt": "A landscape photo in the style of TOK, pink", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.95, "negative_prompt": "underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fofr/sdxl-barbie 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": "fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e", "input": { "width": 1360, "height": 768, "prompt": "A landscape photo in the style of TOK, pink", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.95, "negative_prompt": "underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-08T13:26:38.954565Z", "created_at": "2023-08-08T13:25:43.066341Z", "data_removed": false, "error": null, "id": "x3wl6vlbexjwkyy6ahqemcoggy", "input": { "width": 1360, "height": 768, "prompt": "A landscape photo in the style of TOK, pink", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.95, "negative_prompt": "underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 3446\nPrompt: A landscape photo in the style of <s0><s1>, pink\ntxt2img mode\n 0%| | 0/47 [00:00<?, ?it/s]\n 2%|▏ | 1/47 [00:01<00:46, 1.01s/it]\n 4%|▍ | 2/47 [00:02<00:45, 1.01s/it]\n 6%|▋ | 3/47 [00:03<00:44, 1.01s/it]\n 9%|▊ | 4/47 [00:04<00:43, 1.01s/it]\n 11%|█ | 5/47 [00:05<00:42, 1.01s/it]\n 13%|█▎ | 6/47 [00:06<00:41, 1.01s/it]\n 15%|█▍ | 7/47 [00:07<00:40, 1.01s/it]\n 17%|█▋ | 8/47 [00:08<00:39, 1.01s/it]\n 19%|█▉ | 9/47 [00:09<00:38, 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"metrics": { "predict_time": 55.910433, "total_time": 55.888224 }, "output": [ "https://replicate.delivery/pbxt/hDaH1vV0ArKMNNCeNoftfuEajDwiiHebwN8iBp1pFffOj0frIA/out-0.png", "https://replicate.delivery/pbxt/khggFU5lyRZTB1LvdUs5WF5QQdGE4DSAxUCTCCtEe05GpfXRA/out-1.png", "https://replicate.delivery/pbxt/bMA5mZfXlAxSKKWb3Y8lStdOvWAlWTqmeFihswXUGv2OSfviA/out-2.png", "https://replicate.delivery/pbxt/OWHx2L1ZqGIbHlmOWNUqIa8TZ2xyTMbkqDBY2UdCK7qj0frIA/out-3.png" ], "started_at": "2023-08-08T13:25:43.044132Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/x3wl6vlbexjwkyy6ahqemcoggy", "cancel": "https://api.replicate.com/v1/predictions/x3wl6vlbexjwkyy6ahqemcoggy/cancel" }, "version": "657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e" }Generated inUsing seed: 3446 Prompt: A landscape photo in the style of <s0><s1>, pink txt2img mode 0%| | 0/47 [00:00<?, ?it/s] 2%|▏ | 1/47 [00:01<00:46, 1.01s/it] 4%|▍ | 2/47 [00:02<00:45, 1.01s/it] 6%|▋ | 3/47 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[00:00<00:01, 1.22it/s] 67%|██████▋ | 2/3 [00:01<00:00, 1.21it/s] 100%|██████████| 3/3 [00:02<00:00, 1.21it/s] 100%|██████████| 3/3 [00:02<00:00, 1.21it/s]Prediction
fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0eIDtb74eulbdxqzv4hy3qv4phvclmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @fofrInput
- width
- 1360
- height
- 768
- prompt
- In the style of TOK, an IKEA catalogue photo of a living room
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.95
- negative_prompt
- underexposed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1360, "height": 768, "prompt": "In the style of TOK, an IKEA catalogue photo of a living room", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.95, "negative_prompt": "underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/sdxl-barbie using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e", { input: { width: 1360, height: 768, prompt: "In the style of TOK, an IKEA catalogue photo of a living room", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.95, negative_prompt: "underexposed", prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun fofr/sdxl-barbie using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e", input={ "width": 1360, "height": 768, "prompt": "In the style of TOK, an IKEA catalogue photo of a living room", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.95, "negative_prompt": "underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fofr/sdxl-barbie 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": "fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e", "input": { "width": 1360, "height": 768, "prompt": "In the style of TOK, an IKEA catalogue photo of a living room", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.95, "negative_prompt": "underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-08T13:30:24.198680Z", "created_at": "2023-08-08T13:29:29.344310Z", "data_removed": false, "error": null, "id": "tb74eulbdxqzv4hy3qv4phvclm", "input": { "width": 1360, "height": 768, "prompt": "In the style of TOK, an IKEA catalogue photo of a living room", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.95, "negative_prompt": "underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 49581\nPrompt: In the style of <s0><s1>, an IKEA catalogue photo of a living room\ntxt2img mode\n 0%| | 0/47 [00:00<?, ?it/s]\n 2%|▏ | 1/47 [00:01<00:46, 1.01s/it]\n 4%|▍ | 2/47 [00:02<00:45, 1.00s/it]\n 6%|▋ | 3/47 [00:03<00:44, 1.00s/it]\n 9%|▊ | 4/47 [00:04<00:43, 1.00s/it]\n 11%|█ | 5/47 [00:05<00:42, 1.00s/it]\n 13%|█▎ | 6/47 [00:06<00:41, 1.01s/it]\n 15%|█▍ | 7/47 [00:07<00:40, 1.01s/it]\n 17%|█▋ | 8/47 [00:08<00:39, 1.01s/it]\n 19%|█▉ | 9/47 [00:09<00:38, 1.01s/it]\n 21%|██▏ | 10/47 [00:10<00:37, 1.01s/it]\n 23%|██▎ | 11/47 [00:11<00:36, 1.01s/it]\n 26%|██▌ | 12/47 [00:12<00:35, 1.01s/it]\n 28%|██▊ | 13/47 [00:13<00:34, 1.01s/it]\n 30%|██▉ | 14/47 [00:14<00:33, 1.01s/it]\n 32%|███▏ | 15/47 [00:15<00:32, 1.01s/it]\n 34%|███▍ | 16/47 [00:16<00:31, 1.01s/it]\n 36%|███▌ | 17/47 [00:17<00:30, 1.01s/it]\n 38%|███▊ | 18/47 [00:18<00:29, 1.01s/it]\n 40%|████ | 19/47 [00:19<00:28, 1.01s/it]\n 43%|████▎ | 20/47 [00:20<00:27, 1.01s/it]\n 45%|████▍ | 21/47 [00:21<00:26, 1.01s/it]\n 47%|████▋ | 22/47 [00:22<00:25, 1.01s/it]\n 49%|████▉ | 23/47 [00:23<00:24, 1.01s/it]\n 51%|█████ | 24/47 [00:24<00:23, 1.01s/it]\n 53%|█████▎ | 25/47 [00:25<00:22, 1.01s/it]\n 55%|█████▌ | 26/47 [00:26<00:21, 1.01s/it]\n 57%|█████▋ | 27/47 [00:27<00:20, 1.01s/it]\n 60%|█████▉ | 28/47 [00:28<00:19, 1.01s/it]\n 62%|██████▏ | 29/47 [00:29<00:18, 1.01s/it]\n 64%|██████▍ | 30/47 [00:30<00:17, 1.01s/it]\n 66%|██████▌ | 31/47 [00:31<00:16, 1.01s/it]\n 68%|██████▊ | 32/47 [00:32<00:15, 1.01s/it]\n 70%|███████ | 33/47 [00:33<00:14, 1.01s/it]\n 72%|███████▏ | 34/47 [00:34<00:13, 1.01s/it]\n 74%|███████▍ | 35/47 [00:35<00:12, 1.01s/it]\n 77%|███████▋ | 36/47 [00:36<00:11, 1.01s/it]\n 79%|███████▊ | 37/47 [00:37<00:10, 1.01s/it]\n 81%|████████ | 38/47 [00:38<00:09, 1.01s/it]\n 83%|████████▎ | 39/47 [00:39<00:08, 1.01s/it]\n 85%|████████▌ | 40/47 [00:40<00:07, 1.01s/it]\n 87%|████████▋ | 41/47 [00:41<00:06, 1.01s/it]\n 89%|████████▉ | 42/47 [00:42<00:05, 1.01s/it]\n 91%|█████████▏| 43/47 [00:43<00:04, 1.01s/it]\n 94%|█████████▎| 44/47 [00:44<00:03, 1.01s/it]\n 96%|█████████▌| 45/47 [00:45<00:02, 1.01s/it]\n 98%|█████████▊| 46/47 [00:46<00:01, 1.01s/it]\n100%|██████████| 47/47 [00:47<00:00, 1.01s/it]\n100%|██████████| 47/47 [00:47<00:00, 1.01s/it]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:01, 1.22it/s]\n 67%|██████▋ | 2/3 [00:01<00:00, 1.22it/s]\n100%|██████████| 3/3 [00:02<00:00, 1.21it/s]\n100%|██████████| 3/3 [00:02<00:00, 1.22it/s]\n/root/.pyenv/versions/3.9.17/lib/python3.9/site-packages/diffusers/image_processor.py:65: RuntimeWarning: invalid value encountered in cast\nimages = (images * 255).round().astype(\"uint8\")\nPotential NSFW content was detected in one or more images. A black image will be returned instead. Try again with a different prompt and/or seed.\nNSFW content detected in image 0", "metrics": { "predict_time": 54.823339, "total_time": 54.85437 }, "output": [ "https://replicate.delivery/pbxt/YhojEAArpP6FF1OIcjrAjfUzapIwzfbKmBiMbQVhx0DuVfviA/out-1.png", "https://replicate.delivery/pbxt/R0Tn4w9jkZK1EFHVC8uk0dUEW4qenDS7kwenRGHMfvsfW9fKC/out-2.png", "https://replicate.delivery/pbxt/mVrvC94vvQY5LlnlHw6zMEXghcbTDJJ9R7uIyqMfllj3qfXRA/out-3.png" ], "started_at": "2023-08-08T13:29:29.375341Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/tb74eulbdxqzv4hy3qv4phvclm", "cancel": "https://api.replicate.com/v1/predictions/tb74eulbdxqzv4hy3qv4phvclm/cancel" }, "version": "657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e" }Generated inUsing seed: 49581 Prompt: In the style of <s0><s1>, an IKEA catalogue photo of a living room txt2img mode 0%| | 0/47 [00:00<?, ?it/s] 2%|▏ | 1/47 [00:01<00:46, 1.01s/it] 4%|▍ | 2/47 [00:02<00:45, 1.00s/it] 6%|▋ | 3/47 [00:03<00:44, 1.00s/it] 9%|▊ | 4/47 [00:04<00:43, 1.00s/it] 11%|█ | 5/47 [00:05<00:42, 1.00s/it] 13%|█▎ | 6/47 [00:06<00:41, 1.01s/it] 15%|█▍ | 7/47 [00:07<00:40, 1.01s/it] 17%|█▋ | 8/47 [00:08<00:39, 1.01s/it] 19%|█▉ | 9/47 [00:09<00:38, 1.01s/it] 21%|██▏ | 10/47 [00:10<00:37, 1.01s/it] 23%|██▎ | 11/47 [00:11<00:36, 1.01s/it] 26%|██▌ | 12/47 [00:12<00:35, 1.01s/it] 28%|██▊ | 13/47 [00:13<00:34, 1.01s/it] 30%|██▉ | 14/47 [00:14<00:33, 1.01s/it] 32%|███▏ | 15/47 [00:15<00:32, 1.01s/it] 34%|███▍ | 16/47 [00:16<00:31, 1.01s/it] 36%|███▌ | 17/47 [00:17<00:30, 1.01s/it] 38%|███▊ | 18/47 [00:18<00:29, 1.01s/it] 40%|████ | 19/47 [00:19<00:28, 1.01s/it] 43%|████▎ | 20/47 [00:20<00:27, 1.01s/it] 45%|████▍ | 21/47 [00:21<00:26, 1.01s/it] 47%|████▋ | 22/47 [00:22<00:25, 1.01s/it] 49%|████▉ | 23/47 [00:23<00:24, 1.01s/it] 51%|█████ | 24/47 [00:24<00:23, 1.01s/it] 53%|█████▎ | 25/47 [00:25<00:22, 1.01s/it] 55%|█████▌ | 26/47 [00:26<00:21, 1.01s/it] 57%|█████▋ | 27/47 [00:27<00:20, 1.01s/it] 60%|█████▉ | 28/47 [00:28<00:19, 1.01s/it] 62%|██████▏ | 29/47 [00:29<00:18, 1.01s/it] 64%|██████▍ | 30/47 [00:30<00:17, 1.01s/it] 66%|██████▌ | 31/47 [00:31<00:16, 1.01s/it] 68%|██████▊ | 32/47 [00:32<00:15, 1.01s/it] 70%|███████ | 33/47 [00:33<00:14, 1.01s/it] 72%|███████▏ | 34/47 [00:34<00:13, 1.01s/it] 74%|███████▍ | 35/47 [00:35<00:12, 1.01s/it] 77%|███████▋ | 36/47 [00:36<00:11, 1.01s/it] 79%|███████▊ | 37/47 [00:37<00:10, 1.01s/it] 81%|████████ | 38/47 [00:38<00:09, 1.01s/it] 83%|████████▎ | 39/47 [00:39<00:08, 1.01s/it] 85%|████████▌ | 40/47 [00:40<00:07, 1.01s/it] 87%|████████▋ | 41/47 [00:41<00:06, 1.01s/it] 89%|████████▉ | 42/47 [00:42<00:05, 1.01s/it] 91%|█████████▏| 43/47 [00:43<00:04, 1.01s/it] 94%|█████████▎| 44/47 [00:44<00:03, 1.01s/it] 96%|█████████▌| 45/47 [00:45<00:02, 1.01s/it] 98%|█████████▊| 46/47 [00:46<00:01, 1.01s/it] 100%|██████████| 47/47 [00:47<00:00, 1.01s/it] 100%|██████████| 47/47 [00:47<00:00, 1.01s/it] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:01, 1.22it/s] 67%|██████▋ | 2/3 [00:01<00:00, 1.22it/s] 100%|██████████| 3/3 [00:02<00:00, 1.21it/s] 100%|██████████| 3/3 [00:02<00:00, 1.22it/s] /root/.pyenv/versions/3.9.17/lib/python3.9/site-packages/diffusers/image_processor.py:65: RuntimeWarning: invalid value encountered in cast images = (images * 255).round().astype("uint8") Potential NSFW content was detected in one or more images. A black image will be returned instead. Try again with a different prompt and/or seed. NSFW content detected in image 0Prediction
fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0eID467cp33bj3jckfaf7rialitaeaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 50661
- width
- 1360
- height
- 768
- prompt
- In the style of TOK, a photo of a sports car
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.95
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "seed": 50661, "width": 1360, "height": 768, "prompt": "In the style of TOK, a photo of a sports car", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.95, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/sdxl-barbie using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e", { input: { seed: 50661, width: 1360, height: 768, prompt: "In the style of TOK, a photo of a sports car", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.95, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun fofr/sdxl-barbie using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e", input={ "seed": 50661, "width": 1360, "height": 768, "prompt": "In the style of TOK, a photo of a sports car", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.95, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fofr/sdxl-barbie 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": "fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e", "input": { "seed": 50661, "width": 1360, "height": 768, "prompt": "In the style of TOK, a photo of a sports car", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.95, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-08T13:37:51.615066Z", "created_at": "2023-08-08T13:36:55.552695Z", "data_removed": false, "error": null, "id": "467cp33bj3jckfaf7rialitaea", "input": { "seed": 50661, "width": 1360, "height": 768, "prompt": "In the style of TOK, a photo of a sports car", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.95, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 50661\nPrompt: In the style of <s0><s1>, a photo of a sports car\ntxt2img mode\n 0%| | 0/47 [00:00<?, ?it/s]\n 2%|▏ | 1/47 [00:01<00:46, 1.00s/it]\n 4%|▍ | 2/47 [00:02<00:45, 1.00s/it]\n 6%|▋ | 3/47 [00:03<00:44, 1.00s/it]\n 9%|▊ | 4/47 [00:04<00:43, 1.01s/it]\n 11%|█ | 5/47 [00:05<00:42, 1.01s/it]\n 13%|█▎ | 6/47 [00:06<00:41, 1.01s/it]\n 15%|█▍ | 7/47 [00:07<00:40, 1.01s/it]\n 17%|█▋ | 8/47 [00:08<00:39, 1.01s/it]\n 19%|█▉ | 9/47 [00:09<00:38, 1.01s/it]\n 21%|██▏ | 10/47 [00:10<00:37, 1.00s/it]\n 23%|██▎ | 11/47 [00:11<00:36, 1.00s/it]\n 26%|██▌ | 12/47 [00:12<00:35, 1.00s/it]\n 28%|██▊ | 13/47 [00:13<00:34, 1.00s/it]\n 30%|██▉ | 14/47 [00:14<00:33, 1.00s/it]\n 32%|███▏ | 15/47 [00:15<00:32, 1.01s/it]\n 34%|███▍ | 16/47 [00:16<00:31, 1.01s/it]\n 36%|███▌ | 17/47 [00:17<00:30, 1.01s/it]\n 38%|███▊ | 18/47 [00:18<00:29, 1.01s/it]\n 40%|████ | 19/47 [00:19<00:28, 1.01s/it]\n 43%|████▎ | 20/47 [00:20<00:27, 1.01s/it]\n 45%|████▍ | 21/47 [00:21<00:26, 1.01s/it]\n 47%|████▋ | 22/47 [00:22<00:25, 1.01s/it]\n 49%|████▉ | 23/47 [00:23<00:24, 1.01s/it]\n 51%|█████ | 24/47 [00:24<00:23, 1.01s/it]\n 53%|█████▎ | 25/47 [00:25<00:22, 1.01s/it]\n 55%|█████▌ | 26/47 [00:26<00:21, 1.01s/it]\n 57%|█████▋ | 27/47 [00:27<00:20, 1.01s/it]\n 60%|█████▉ | 28/47 [00:28<00:19, 1.01s/it]\n 62%|██████▏ | 29/47 [00:29<00:18, 1.01s/it]\n 64%|██████▍ | 30/47 [00:30<00:17, 1.01s/it]\n 66%|██████▌ | 31/47 [00:31<00:16, 1.01s/it]\n 68%|██████▊ | 32/47 [00:32<00:15, 1.01s/it]\n 70%|███████ | 33/47 [00:33<00:14, 1.01s/it]\n 72%|███████▏ | 34/47 [00:34<00:13, 1.01s/it]\n 74%|███████▍ | 35/47 [00:35<00:12, 1.01s/it]\n 77%|███████▋ | 36/47 [00:36<00:11, 1.01s/it]\n 79%|███████▊ | 37/47 [00:37<00:10, 1.01s/it]\n 81%|████████ | 38/47 [00:38<00:09, 1.01s/it]\n 83%|████████▎ | 39/47 [00:39<00:08, 1.01s/it]\n 85%|████████▌ | 40/47 [00:40<00:07, 1.02s/it]\n 87%|████████▋ | 41/47 [00:41<00:06, 1.02s/it]\n 89%|████████▉ | 42/47 [00:42<00:05, 1.01s/it]\n 91%|█████████▏| 43/47 [00:43<00:04, 1.01s/it]\n 94%|█████████▎| 44/47 [00:44<00:03, 1.01s/it]\n 96%|█████████▌| 45/47 [00:45<00:02, 1.01s/it]\n 98%|█████████▊| 46/47 [00:46<00:01, 1.01s/it]\n100%|██████████| 47/47 [00:47<00:00, 1.01s/it]\n100%|██████████| 47/47 [00:47<00:00, 1.01s/it]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:01, 1.22it/s]\n 67%|██████▋ | 2/3 [00:01<00:00, 1.22it/s]\n100%|██████████| 3/3 [00:02<00:00, 1.22it/s]\n100%|██████████| 3/3 [00:02<00:00, 1.22it/s]", "metrics": { "predict_time": 56.050377, "total_time": 56.062371 }, "output": [ "https://replicate.delivery/pbxt/cfMLLWWISA25Z6fKJ901eAfcxTv0wX7G1ZN8RNSdK10wy9fKC/out-0.png", "https://replicate.delivery/pbxt/ZKxfuDZr0ZUQYSHntXENKm9nuZiySSNf6eQfwv7DXE46y9fKC/out-1.png", "https://replicate.delivery/pbxt/lCAuVkuzJNofcqOy4QPoBRXq6tPiHdfm7qxJnHoLfcUc5efKC/out-2.png", "https://replicate.delivery/pbxt/ejuaKrQqxelOmEH4DqzsHZlBFgnt5sqNWul1ncAPnz0vcfviA/out-3.png" ], "started_at": "2023-08-08T13:36:55.564689Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/467cp33bj3jckfaf7rialitaea", "cancel": "https://api.replicate.com/v1/predictions/467cp33bj3jckfaf7rialitaea/cancel" }, "version": "657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e" }Generated inUsing seed: 50661 Prompt: In the style of <s0><s1>, a photo of a sports car txt2img mode 0%| | 0/47 [00:00<?, ?it/s] 2%|▏ | 1/47 [00:01<00:46, 1.00s/it] 4%|▍ | 2/47 [00:02<00:45, 1.00s/it] 6%|▋ | 3/47 [00:03<00:44, 1.00s/it] 9%|▊ | 4/47 [00:04<00:43, 1.01s/it] 11%|█ | 5/47 [00:05<00:42, 1.01s/it] 13%|█▎ | 6/47 [00:06<00:41, 1.01s/it] 15%|█▍ | 7/47 [00:07<00:40, 1.01s/it] 17%|█▋ | 8/47 [00:08<00:39, 1.01s/it] 19%|█▉ | 9/47 [00:09<00:38, 1.01s/it] 21%|██▏ | 10/47 [00:10<00:37, 1.00s/it] 23%|██▎ | 11/47 [00:11<00:36, 1.00s/it] 26%|██▌ | 12/47 [00:12<00:35, 1.00s/it] 28%|██▊ | 13/47 [00:13<00:34, 1.00s/it] 30%|██▉ | 14/47 [00:14<00:33, 1.00s/it] 32%|███▏ | 15/47 [00:15<00:32, 1.01s/it] 34%|███▍ | 16/47 [00:16<00:31, 1.01s/it] 36%|███▌ | 17/47 [00:17<00:30, 1.01s/it] 38%|███▊ | 18/47 [00:18<00:29, 1.01s/it] 40%|████ | 19/47 [00:19<00:28, 1.01s/it] 43%|████▎ | 20/47 [00:20<00:27, 1.01s/it] 45%|████▍ | 21/47 [00:21<00:26, 1.01s/it] 47%|████▋ | 22/47 [00:22<00:25, 1.01s/it] 49%|████▉ | 23/47 [00:23<00:24, 1.01s/it] 51%|█████ | 24/47 [00:24<00:23, 1.01s/it] 53%|█████▎ | 25/47 [00:25<00:22, 1.01s/it] 55%|█████▌ | 26/47 [00:26<00:21, 1.01s/it] 57%|█████▋ | 27/47 [00:27<00:20, 1.01s/it] 60%|█████▉ | 28/47 [00:28<00:19, 1.01s/it] 62%|██████▏ | 29/47 [00:29<00:18, 1.01s/it] 64%|██████▍ | 30/47 [00:30<00:17, 1.01s/it] 66%|██████▌ | 31/47 [00:31<00:16, 1.01s/it] 68%|██████▊ | 32/47 [00:32<00:15, 1.01s/it] 70%|███████ | 33/47 [00:33<00:14, 1.01s/it] 72%|███████▏ | 34/47 [00:34<00:13, 1.01s/it] 74%|███████▍ | 35/47 [00:35<00:12, 1.01s/it] 77%|███████▋ | 36/47 [00:36<00:11, 1.01s/it] 79%|███████▊ | 37/47 [00:37<00:10, 1.01s/it] 81%|████████ | 38/47 [00:38<00:09, 1.01s/it] 83%|████████▎ | 39/47 [00:39<00:08, 1.01s/it] 85%|████████▌ | 40/47 [00:40<00:07, 1.02s/it] 87%|████████▋ | 41/47 [00:41<00:06, 1.02s/it] 89%|████████▉ | 42/47 [00:42<00:05, 1.01s/it] 91%|█████████▏| 43/47 [00:43<00:04, 1.01s/it] 94%|█████████▎| 44/47 [00:44<00:03, 1.01s/it] 96%|█████████▌| 45/47 [00:45<00:02, 1.01s/it] 98%|█████████▊| 46/47 [00:46<00:01, 1.01s/it] 100%|██████████| 47/47 [00:47<00:00, 1.01s/it] 100%|██████████| 47/47 [00:47<00:00, 1.01s/it] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:01, 1.22it/s] 67%|██████▋ | 2/3 [00:01<00:00, 1.22it/s] 100%|██████████| 3/3 [00:02<00:00, 1.22it/s] 100%|██████████| 3/3 [00:02<00:00, 1.22it/s]Prediction
fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0eIDitsq6ntb6mbnzzy4nm3272f4ieStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @fofrInput
- width
- 1024
- height
- 1024
- prompt
- A photo of a pink cat in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.5
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- underexposed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo of a pink cat in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.5, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/sdxl-barbie using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e", { input: { width: 1024, height: 1024, prompt: "A photo of a pink cat in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.5, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "underexposed", prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun fofr/sdxl-barbie using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e", input={ "width": 1024, "height": 1024, "prompt": "A photo of a pink cat in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.5, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fofr/sdxl-barbie 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": "fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a pink cat in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.5, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-08T15:00:46.316367Z", "created_at": "2023-08-08T15:00:30.653902Z", "data_removed": false, "error": null, "id": "itsq6ntb6mbnzzy4nm3272f4ie", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a pink cat in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.5, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 33411\nPrompt: A photo of a pink cat in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.70it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.70it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.69it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.69it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.68it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.68it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.68it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.68it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.68it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.68it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.67it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.67it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.67it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.67it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.67it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.67it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.67it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.66it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.66it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]", "metrics": { "predict_time": 15.688393, "total_time": 15.662465 }, "output": [ "https://replicate.delivery/pbxt/YhFxKzvMcNJVFhANdtjKdGlKO4IYkOWQJZIuxK2aRGVnKAWE/out-0.png" ], "started_at": "2023-08-08T15:00:30.627974Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/itsq6ntb6mbnzzy4nm3272f4ie", "cancel": "https://api.replicate.com/v1/predictions/itsq6ntb6mbnzzy4nm3272f4ie/cancel" }, "version": "657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e" }Generated inUsing seed: 33411 Prompt: A photo of a pink cat in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.69it/s] 4%|▍ | 2/50 [00:00<00:12, 3.70it/s] 6%|▌ | 3/50 [00:00<00:12, 3.70it/s] 8%|▊ | 4/50 [00:01<00:12, 3.69it/s] 10%|█ | 5/50 [00:01<00:12, 3.69it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.68it/s] 20%|██ | 10/50 [00:02<00:10, 3.68it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.68it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.68it/s] 30%|███ | 15/50 [00:04<00:09, 3.68it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.68it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s] 40%|████ | 20/50 [00:05<00:08, 3.67it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.67it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.67it/s] 50%|█████ | 25/50 [00:06<00:06, 3.67it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s] 60%|██████ | 30/50 [00:08<00:05, 3.67it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s] 70%|███████ | 35/50 [00:09<00:04, 3.67it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.67it/s] 80%|████████ | 40/50 [00:10<00:02, 3.67it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.66it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.66it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s]Prediction
fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0eIDjmjrkmdbi2t64wm5wg6acyw2sqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @cbh123Input
- width
- 1024
- height
- 1024
- prompt
- An atomic bomb in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- underexposed
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "An atomic bomb in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/sdxl-barbie using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e", { input: { width: 1024, height: 1024, prompt: "An atomic bomb in the style of TOK", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "underexposed", prompt_strength: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun fofr/sdxl-barbie using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e", input={ "width": 1024, "height": 1024, "prompt": "An atomic bomb in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fofr/sdxl-barbie 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": "fofr/sdxl-barbie:657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e", "input": { "width": 1024, "height": 1024, "prompt": "An atomic bomb in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-08-08T16:08:03.867355Z", "created_at": "2023-08-08T16:07:48.776568Z", "data_removed": false, "error": null, "id": "jmjrkmdbi2t64wm5wg6acyw2sq", "input": { "width": 1024, "height": 1024, "prompt": "An atomic bomb in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "underexposed", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 55997\nPrompt: An atomic bomb in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.67it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.66it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.65it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.64it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.64it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.64it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.64it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.64it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.63it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.63it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.64it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.63it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.63it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.63it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.63it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.63it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.63it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.63it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.63it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.64it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.63it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.63it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.63it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.63it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.63it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.63it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.63it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.63it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.63it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.63it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.63it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.64it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.63it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.63it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.63it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.63it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.63it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.63it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.63it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.63it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.63it/s]", "metrics": { "predict_time": 15.165796, "total_time": 15.090787 }, "output": [ "https://replicate.delivery/pbxt/bEfhNDpiJdQHbaelWWf6qoS77ExAmJmB1j6yUGvn5oBGTDwiA/out-0.png" ], "started_at": "2023-08-08T16:07:48.701559Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jmjrkmdbi2t64wm5wg6acyw2sq", "cancel": "https://api.replicate.com/v1/predictions/jmjrkmdbi2t64wm5wg6acyw2sq/cancel" }, "version": "657c074cdd0e0098e39dae981194c4e852ad5bc88c7fbbeb0682afae714a6b0e" }Generated inUsing seed: 55997 Prompt: An atomic bomb in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.67it/s] 4%|▍ | 2/50 [00:00<00:13, 3.66it/s] 6%|▌ | 3/50 [00:00<00:12, 3.65it/s] 8%|▊ | 4/50 [00:01<00:12, 3.64it/s] 10%|█ | 5/50 [00:01<00:12, 3.64it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.64it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.64it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s] 20%|██ | 10/50 [00:02<00:11, 3.64it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.63it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.63it/s] 30%|███ | 15/50 [00:04<00:09, 3.64it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.63it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.63it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.63it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s] 40%|████ | 20/50 [00:05<00:08, 3.63it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.63it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.63it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.63it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.63it/s] 50%|█████ | 25/50 [00:06<00:06, 3.64it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.63it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.63it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.63it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.63it/s] 60%|██████ | 30/50 [00:08<00:05, 3.63it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.63it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.63it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.63it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s] 70%|███████ | 35/50 [00:09<00:04, 3.63it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.63it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.63it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.64it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s] 80%|████████ | 40/50 [00:11<00:02, 3.63it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.63it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.63it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.63it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.63it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.63it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.63it/s] 100%|██████████| 50/50 [00:13<00:00, 3.63it/s] 100%|██████████| 50/50 [00:13<00:00, 3.63it/s]
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