iwasrobbed / sdxl-suspense
SDXL fine-tuned on the suspenseful style of old school comics (Updated 1 year, 7 months ago)
- Public
- 53K runs
- SDXL fine-tune
Prediction
iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242IDxmmwhclb5euszytvliksjwunmeStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A close up portrait of a woman in shock, 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
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A close up portrait of a woman in shock, 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": "", "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run iwasrobbed/sdxl-suspense using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", { input: { width: 1024, height: 1024, prompt: "A close up portrait of a woman in shock, 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: "", 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 iwasrobbed/sdxl-suspense using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", input={ "width": 1024, "height": 1024, "prompt": "A close up portrait of a woman in shock, 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": "", "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 iwasrobbed/sdxl-suspense 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": "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", "input": { "width": 1024, "height": 1024, "prompt": "A close up portrait of a woman in shock, 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": "", "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": "2023-11-02T05:38:40.114635Z", "created_at": "2023-11-02T05:38:21.310510Z", "data_removed": false, "error": null, "id": "xmmwhclb5euszytvliksjwunme", "input": { "width": 1024, "height": 1024, "prompt": "A close up portrait of a woman in shock, 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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 16288\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A close up portrait of a woman in shock, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.66it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.65it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.65it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.65it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.65it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.65it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.64it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.64it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.64it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.64it/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, 3.63it/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, 3.62it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.62it/s]\n 60%|██████ | 30/50 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[00:13<00:00, 3.63it/s]", "metrics": { "predict_time": 16.075613, "total_time": 18.804125 }, "output": [ "https://replicate.delivery/pbxt/OmNbex2sANWja6efLcyl0pYvWsAPbWAuEQe3xeyYFaP77zhOC/out-0.png" ], "started_at": "2023-11-02T05:38:24.039022Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xmmwhclb5euszytvliksjwunme", "cancel": "https://api.replicate.com/v1/predictions/xmmwhclb5euszytvliksjwunme/cancel" }, "version": "2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242" }Generated inUsing seed: 16288 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A close up portrait of a woman in shock, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.66it/s] 4%|▍ | 2/50 [00:00<00:13, 3.65it/s] 6%|▌ | 3/50 [00:00<00:12, 3.65it/s] 8%|▊ | 4/50 [00:01<00:12, 3.65it/s] 10%|█ | 5/50 [00:01<00:12, 3.65it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s] 14%|█▍ | 7/50 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iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242IDajod5ylbv4jrrfjcau72l4gpauStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A man walking down a dark alley with a street lamp overhead, 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
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A man walking down a dark alley with a street lamp overhead, 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": "", "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run iwasrobbed/sdxl-suspense using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", { input: { width: 1024, height: 1024, prompt: "A man walking down a dark alley with a street lamp overhead, 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: "", 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 iwasrobbed/sdxl-suspense using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", input={ "width": 1024, "height": 1024, "prompt": "A man walking down a dark alley with a street lamp overhead, 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": "", "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 iwasrobbed/sdxl-suspense 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": "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", "input": { "width": 1024, "height": 1024, "prompt": "A man walking down a dark alley with a street lamp overhead, 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": "", "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": "2023-11-02T05:39:25.486584Z", "created_at": "2023-11-02T05:39:03.924201Z", "data_removed": false, "error": null, "id": "ajod5ylbv4jrrfjcau72l4gpau", "input": { "width": 1024, "height": 1024, "prompt": "A man walking down a dark alley with a street lamp overhead, 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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 17403\nEnsuring enough disk space...\nFree disk space: 1840240803840\nDownloading weights: https://replicate.delivery/pbxt/n1YGjVCfK2S9YKS80ESDwOBW4wcR7llwdXVm5EdmT3VRLH6IA/trained_model.tar\nb'Downloaded 186 MB bytes in 2.529s (74 MB/s)\\nExtracted 186 MB in 0.050s (3.7 GB/s)\\n'\nDownloaded weights in 2.7397398948669434 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A man walking down a dark alley with a street lamp overhead, 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, 3.66it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.65it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.65it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.65it/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, 3.64it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.64it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.63it/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, 3.63it/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, 3.62it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.62it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.62it/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, 3.62it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.62it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.62it/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, 3.62it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.62it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.62it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.62it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.62it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.62it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.62it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.62it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.61it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.63it/s]", "metrics": { "predict_time": 18.34168, "total_time": 21.562383 }, "output": [ "https://replicate.delivery/pbxt/ffeefBkCCZQEFisCttV2bXH6ROAr2gYLz5aTv6vjjtPvB0hOC/out-0.png" ], "started_at": "2023-11-02T05:39:07.144904Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ajod5ylbv4jrrfjcau72l4gpau", "cancel": "https://api.replicate.com/v1/predictions/ajod5ylbv4jrrfjcau72l4gpau/cancel" }, "version": "2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242" }Generated inUsing seed: 17403 Ensuring enough disk space... Free disk space: 1840240803840 Downloading weights: https://replicate.delivery/pbxt/n1YGjVCfK2S9YKS80ESDwOBW4wcR7llwdXVm5EdmT3VRLH6IA/trained_model.tar b'Downloaded 186 MB bytes in 2.529s (74 MB/s)\nExtracted 186 MB in 0.050s (3.7 GB/s)\n' Downloaded weights in 2.7397398948669434 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A man walking down a dark alley with a street lamp overhead, 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, 3.66it/s] 8%|▊ | 4/50 [00:01<00:12, 3.65it/s] 10%|█ | 5/50 [00:01<00:12, 3.65it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.65it/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, 3.64it/s] 20%|██ | 10/50 [00:02<00:10, 3.64it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.63it/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, 3.63it/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, 3.62it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.62it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.62it/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, 3.62it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.62it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.62it/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, 3.62it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.62it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.62it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.62it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.62it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.62it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.62it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.62it/s] 100%|██████████| 50/50 [00:13<00:00, 3.61it/s] 100%|██████████| 50/50 [00:13<00:00, 3.63it/s]Prediction
iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242IDiii5tj3bdzrp7oecmkqo6v4bkqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A man reading the morning newspaper at the coffee table, 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
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A man reading the morning newspaper at the coffee table, 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": "", "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run iwasrobbed/sdxl-suspense using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", { input: { width: 1024, height: 1024, prompt: "A man reading the morning newspaper at the coffee table, 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: "", 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 iwasrobbed/sdxl-suspense using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", input={ "width": 1024, "height": 1024, "prompt": "A man reading the morning newspaper at the coffee table, 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": "", "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 iwasrobbed/sdxl-suspense 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": "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", "input": { "width": 1024, "height": 1024, "prompt": "A man reading the morning newspaper at the coffee table, 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": "", "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": "2023-11-02T05:40:19.965154Z", "created_at": "2023-11-02T05:40:02.472496Z", "data_removed": false, "error": null, "id": "iii5tj3bdzrp7oecmkqo6v4bkq", "input": { "width": 1024, "height": 1024, "prompt": "A man reading the morning newspaper at the coffee table, 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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 27820\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A man reading the morning newspaper at the coffee table, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.68it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.67it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.66it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.66it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.66it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.65it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.64it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.64it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.64it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.63it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.63it/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, 3.63it/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, 3.63it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.62it/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, 3.62it/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, 3.62it/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": 15.700778, "total_time": 17.492658 }, "output": [ "https://replicate.delivery/pbxt/YidKYvNJ4ZZfGSai3FlOe2rTrGUgRp9q8ik0olPwGgrDhO0RA/out-0.png" ], "started_at": "2023-11-02T05:40:04.264376Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/iii5tj3bdzrp7oecmkqo6v4bkq", "cancel": "https://api.replicate.com/v1/predictions/iii5tj3bdzrp7oecmkqo6v4bkq/cancel" }, "version": "2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242" }Generated inUsing seed: 27820 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A man reading the morning newspaper at the coffee table, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.68it/s] 4%|▍ | 2/50 [00:00<00:13, 3.67it/s] 6%|▌ | 3/50 [00:00<00:12, 3.66it/s] 8%|▊ | 4/50 [00:01<00:12, 3.66it/s] 10%|█ | 5/50 [00:01<00:12, 3.66it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.65it/s] 20%|██ | 10/50 [00:02<00:10, 3.64it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.64it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s] 30%|███ | 15/50 [00:04<00:09, 3.64it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.63it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.63it/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, 3.63it/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, 3.63it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s] 80%|████████ | 40/50 [00:11<00:02, 3.62it/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, 3.62it/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, 3.62it/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]Prediction
iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242IDb72nsflb5jufyodrzfwuysb4iuStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A superhero saving a child, 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
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A superhero saving a child, 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": "", "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run iwasrobbed/sdxl-suspense using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", { input: { width: 1024, height: 1024, prompt: "A superhero saving a child, 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: "", 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 iwasrobbed/sdxl-suspense using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", input={ "width": 1024, "height": 1024, "prompt": "A superhero saving a child, 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": "", "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 iwasrobbed/sdxl-suspense 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": "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", "input": { "width": 1024, "height": 1024, "prompt": "A superhero saving a child, 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": "", "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": "2023-11-02T05:41:04.751616Z", "created_at": "2023-11-02T05:40:47.113382Z", "data_removed": false, "error": null, "id": "b72nsflb5jufyodrzfwuysb4iu", "input": { "width": 1024, "height": 1024, "prompt": "A superhero saving a child, 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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 30768\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A superhero saving a child, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.68it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.67it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.67it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.66it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.66it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.65it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.65it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.65it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.65it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.65it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.65it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.65it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.64it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.64it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.64it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.64it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.64it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.64it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.64it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.63it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.64it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.64it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.64it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.63it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.64it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.64it/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, 3.63it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.63it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.64it/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, 3.64it/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, 3.64it/s]", "metrics": { "predict_time": 16.366695, "total_time": 17.638234 }, "output": [ "https://replicate.delivery/pbxt/mL73ILsBE8quC9e49SpOGyJr0gfC2dLMzWz1hITdHfe8G6QHB/out-0.png" ], "started_at": "2023-11-02T05:40:48.384921Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/b72nsflb5jufyodrzfwuysb4iu", "cancel": "https://api.replicate.com/v1/predictions/b72nsflb5jufyodrzfwuysb4iu/cancel" }, "version": "2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242" }Generated inUsing seed: 30768 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A superhero saving a child, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.68it/s] 4%|▍ | 2/50 [00:00<00:13, 3.67it/s] 6%|▌ | 3/50 [00:00<00:12, 3.67it/s] 8%|▊ | 4/50 [00:01<00:12, 3.66it/s] 10%|█ | 5/50 [00:01<00:12, 3.66it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.65it/s] 20%|██ | 10/50 [00:02<00:10, 3.65it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.65it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.65it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.65it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.65it/s] 30%|███ | 15/50 [00:04<00:09, 3.65it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s] 40%|████ | 20/50 [00:05<00:08, 3.64it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.64it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.64it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.64it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.64it/s] 50%|█████ | 25/50 [00:06<00:06, 3.64it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.64it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.63it/s] 60%|██████ | 30/50 [00:08<00:05, 3.64it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.64it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.64it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.63it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.64it/s] 70%|███████ | 35/50 [00:09<00:04, 3.64it/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, 3.63it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s] 80%|████████ | 40/50 [00:10<00:02, 3.63it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.64it/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, 3.64it/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, 3.64it/s]Prediction
iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242IDbdvlr7tbxuyxwcjwkrag3ak34qStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of TOK, a fluffy bird in a cold winter storm
- 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
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "In the style of TOK, a fluffy bird in a cold winter storm", "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": "", "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run iwasrobbed/sdxl-suspense using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", { input: { width: 1024, height: 1024, prompt: "In the style of TOK, a fluffy bird in a cold winter storm", 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: "", 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 iwasrobbed/sdxl-suspense using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", input={ "width": 1024, "height": 1024, "prompt": "In the style of TOK, a fluffy bird in a cold winter storm", "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": "", "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 iwasrobbed/sdxl-suspense 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": "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, a fluffy bird in a cold winter storm", "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": "", "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": "2023-11-02T14:11:49.983324Z", "created_at": "2023-11-02T14:11:33.804892Z", "data_removed": false, "error": null, "id": "bdvlr7tbxuyxwcjwkrag3ak34q", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, a fluffy bird in a cold winter storm", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 63147\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of <s0><s1>, a fluffy bird in a cold winter storm\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, 3.67it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.67it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.66it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.65it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.65it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.65it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.65it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.65it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.65it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.65it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.65it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.65it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.64it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.64it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.64it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.65it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.65it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.64it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.65it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.66it/s]\n 60%|██████ | 30/50 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[00:13<00:00, 3.66it/s]", "metrics": { "predict_time": 15.808819, "total_time": 16.178432 }, "output": [ "https://replicate.delivery/pbxt/YR4jWqrkpr5rCNTfjnW7PPO418PYeUsAIMImn4mBPyClAW0RA/out-0.png" ], "started_at": "2023-11-02T14:11:34.174505Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/bdvlr7tbxuyxwcjwkrag3ak34q", "cancel": "https://api.replicate.com/v1/predictions/bdvlr7tbxuyxwcjwkrag3ak34q/cancel" }, "version": "2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242" }Generated inUsing seed: 63147 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: In the style of <s0><s1>, a fluffy bird in a cold winter storm 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, 3.67it/s] 8%|▊ | 4/50 [00:01<00:12, 3.67it/s] 10%|█ | 5/50 [00:01<00:12, 3.66it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.65it/s] 20%|██ | 10/50 [00:02<00:10, 3.65it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.65it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.65it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.65it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.65it/s] 30%|███ | 15/50 [00:04<00:09, 3.65it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.65it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.65it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s] 40%|████ | 20/50 [00:05<00:08, 3.64it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.64it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.64it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.65it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.65it/s] 50%|█████ | 25/50 [00:06<00:06, 3.64it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.65it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.66it/s] 60%|██████ | 30/50 [00:08<00:05, 3.66it/s] 62%|██████▏ | 31/50 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iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242ID4aaol5tbeberdqhfeyfxvacaeuStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of TOK, a zombie apocalypse hellscape
- 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
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "In the style of TOK, a zombie apocalypse hellscape", "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": "", "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run iwasrobbed/sdxl-suspense using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", { input: { width: 1024, height: 1024, prompt: "In the style of TOK, a zombie apocalypse hellscape", 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: "", 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 iwasrobbed/sdxl-suspense using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", input={ "width": 1024, "height": 1024, "prompt": "In the style of TOK, a zombie apocalypse hellscape", "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": "", "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 iwasrobbed/sdxl-suspense 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": "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, a zombie apocalypse hellscape", "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": "", "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": "2023-11-02T14:13:42.791671Z", "created_at": "2023-11-02T14:13:24.380396Z", "data_removed": false, "error": null, "id": "4aaol5tbeberdqhfeyfxvacaeu", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, a zombie apocalypse hellscape", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 32293\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of <s0><s1>, a zombie apocalypse hellscape\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, 3.68it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.68it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.67it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.67it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.66it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.66it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.66it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.65it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.65it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.66it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.65it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.65it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.65it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.65it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.65it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.65it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.65it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.65it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.65it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.65it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.65it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.65it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.65it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.65it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.65it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.65it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.65it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]", "metrics": { "predict_time": 15.614186, "total_time": 18.411275 }, "output": [ "https://replicate.delivery/pbxt/8uP5017fc81mcK5j6piIlMzBAp5toVcKadOfFAAgbN0WCW0RA/out-0.png" ], "started_at": "2023-11-02T14:13:27.177485Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4aaol5tbeberdqhfeyfxvacaeu", "cancel": "https://api.replicate.com/v1/predictions/4aaol5tbeberdqhfeyfxvacaeu/cancel" }, "version": "2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242" }Generated inUsing seed: 32293 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: In the style of <s0><s1>, a zombie apocalypse hellscape 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, 3.68it/s] 6%|▌ | 3/50 [00:00<00:12, 3.68it/s] 8%|▊ | 4/50 [00:01<00:12, 3.67it/s] 10%|█ | 5/50 [00:01<00:12, 3.67it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s] 20%|██ | 10/50 [00:02<00:10, 3.66it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.66it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.66it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.65it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.65it/s] 30%|███ | 15/50 [00:04<00:09, 3.66it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.65it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.65it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.65it/s] 40%|████ | 20/50 [00:05<00:08, 3.65it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.65it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.65it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.65it/s] 50%|█████ | 25/50 [00:06<00:06, 3.65it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.65it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.65it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.65it/s] 60%|██████ | 30/50 [00:08<00:05, 3.65it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s] 64%|██████▍ | 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iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242IDzdrxpy3b4tkxjstfvgygdelm34StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of TOK, a little girl playing with dolls next to a warm fireplace
- 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
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "In the style of TOK, a little girl playing with dolls next to a warm fireplace", "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": "", "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run iwasrobbed/sdxl-suspense using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", { input: { width: 1024, height: 1024, prompt: "In the style of TOK, a little girl playing with dolls next to a warm fireplace", 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: "", 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 iwasrobbed/sdxl-suspense using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", input={ "width": 1024, "height": 1024, "prompt": "In the style of TOK, a little girl playing with dolls next to a warm fireplace", "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": "", "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 iwasrobbed/sdxl-suspense 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": "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, a little girl playing with dolls next to a warm fireplace", "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": "", "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": "2023-11-02T14:15:23.625582Z", "created_at": "2023-11-02T14:15:00.787687Z", "data_removed": false, "error": null, "id": "zdrxpy3b4tkxjstfvgygdelm34", "input": { "width": 1024, "height": 1024, "prompt": "In the style of TOK, a little girl playing with dolls next to a warm fireplace", "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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 26453\nEnsuring enough disk space...\nFree disk space: 1530782523392\nDownloading weights: https://replicate.delivery/pbxt/n1YGjVCfK2S9YKS80ESDwOBW4wcR7llwdXVm5EdmT3VRLH6IA/trained_model.tar\nb'Downloaded 186 MB bytes in 3.489s (53 MB/s)\\nExtracted 186 MB in 0.053s (3.5 GB/s)\\n'\nDownloaded weights in 3.876551389694214 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of <s0><s1>, a little girl playing with dolls next to a warm fireplace\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:14, 3.49it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.59it/s]\n 6%|▌ | 3/50 [00:00<00:13, 3.61it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.62it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.63it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.63it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.59it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.60it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.61it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.61it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.61it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.62it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.62it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.62it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.63it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.62it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.62it/s]\n 42%|████▏ | 21/50 [00:05<00:08, 3.62it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.62it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.62it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.62it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.62it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.62it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.62it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.62it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.62it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.62it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.62it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.62it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.61it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.62it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.62it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.61it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.61it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.61it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.61it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.61it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.62it/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, 3.62it/s]", "metrics": { "predict_time": 20.419795, "total_time": 22.837895 }, "output": [ "https://replicate.delivery/pbxt/x9ZS4ocIR0qZGZbl2rMngXcxYeniDnIl78329nmc9fw6DW0RA/out-0.png" ], "started_at": "2023-11-02T14:15:03.205787Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zdrxpy3b4tkxjstfvgygdelm34", "cancel": "https://api.replicate.com/v1/predictions/zdrxpy3b4tkxjstfvgygdelm34/cancel" }, "version": "2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242" }Generated inUsing seed: 26453 Ensuring enough disk space... Free disk space: 1530782523392 Downloading weights: https://replicate.delivery/pbxt/n1YGjVCfK2S9YKS80ESDwOBW4wcR7llwdXVm5EdmT3VRLH6IA/trained_model.tar b'Downloaded 186 MB bytes in 3.489s (53 MB/s)\nExtracted 186 MB in 0.053s (3.5 GB/s)\n' Downloaded weights in 3.876551389694214 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: In the style of <s0><s1>, a little girl playing with dolls next to a warm fireplace txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:14, 3.49it/s] 4%|▍ | 2/50 [00:00<00:13, 3.59it/s] 6%|▌ | 3/50 [00:00<00:13, 3.61it/s] 8%|▊ | 4/50 [00:01<00:12, 3.62it/s] 10%|█ | 5/50 [00:01<00:12, 3.63it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.63it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s] 20%|██ | 10/50 [00:02<00:11, 3.59it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.60it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.61it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.61it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.61it/s] 30%|███ | 15/50 [00:04<00:09, 3.62it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.62it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.62it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.63it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.62it/s] 40%|████ | 20/50 [00:05<00:08, 3.62it/s] 42%|████▏ | 21/50 [00:05<00:08, 3.62it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.62it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.62it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.62it/s] 50%|█████ | 25/50 [00:06<00:06, 3.62it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.62it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.62it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.62it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.62it/s] 60%|██████ | 30/50 [00:08<00:05, 3.62it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.62it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.62it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.61it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.62it/s] 70%|███████ | 35/50 [00:09<00:04, 3.62it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.61it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.61it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.61it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.61it/s] 80%|████████ | 40/50 [00:11<00:02, 3.61it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.62it/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, 3.62it/s]Prediction
iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242IDsxon7wdbsp672m7v3ikgs6whwqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- An airplane exploding just after takeoff, 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
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "An airplane exploding just after takeoff, 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": "", "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run iwasrobbed/sdxl-suspense using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", { input: { width: 1024, height: 1024, prompt: "An airplane exploding just after takeoff, 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: "", 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 iwasrobbed/sdxl-suspense using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", input={ "width": 1024, "height": 1024, "prompt": "An airplane exploding just after takeoff, 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": "", "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 iwasrobbed/sdxl-suspense 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": "iwasrobbed/sdxl-suspense:2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242", "input": { "width": 1024, "height": 1024, "prompt": "An airplane exploding just after takeoff, 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": "", "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": "2023-11-02T14:19:00.448026Z", "created_at": "2023-11-02T14:18:41.279012Z", "data_removed": false, "error": null, "id": "sxon7wdbsp672m7v3ikgs6whwq", "input": { "width": 1024, "height": 1024, "prompt": "An airplane exploding just after takeoff, 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": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 45241\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: An airplane exploding just after takeoff, in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.64it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.64it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.64it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.63it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.63it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.63it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.62it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.63it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.62it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.62it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.62it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.62it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.62it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.62it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.62it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.61it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.61it/s]\n 42%|████▏ | 21/50 [00:05<00:08, 3.61it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.61it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.61it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.61it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.61it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.61it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.61it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.61it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.61it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.61it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.60it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.60it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.61it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.61it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.60it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.60it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.61it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.61it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.60it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.61it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.61it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.61it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.61it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.61it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.61it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.60it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.60it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.60it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.60it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.60it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.61it/s]", "metrics": { "predict_time": 16.98872, "total_time": 19.169014 }, "output": [ "https://replicate.delivery/pbxt/9G8lRTPfADxTFi9J9v8LXgVdYsObBJu1Wv0ujLFC4ZjpDL6IA/out-0.png" ], "started_at": "2023-11-02T14:18:43.459306Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/sxon7wdbsp672m7v3ikgs6whwq", "cancel": "https://api.replicate.com/v1/predictions/sxon7wdbsp672m7v3ikgs6whwq/cancel" }, "version": "2717cb6a3d2505d13e1e05ba16cbfe188f86609b9060b785220d3c30cefe6242" }Generated inUsing seed: 45241 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: An airplane exploding just after takeoff, in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.64it/s] 4%|▍ | 2/50 [00:00<00:13, 3.64it/s] 6%|▌ | 3/50 [00:00<00:12, 3.64it/s] 8%|▊ | 4/50 [00:01<00:12, 3.63it/s] 10%|█ | 5/50 [00:01<00:12, 3.63it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.63it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.62it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s] 20%|██ | 10/50 [00:02<00:11, 3.63it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.62it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.62it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.62it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.62it/s] 30%|███ | 15/50 [00:04<00:09, 3.62it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.62it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.62it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.61it/s] 40%|████ | 20/50 [00:05<00:08, 3.61it/s] 42%|████▏ | 21/50 [00:05<00:08, 3.61it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.61it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.61it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.61it/s] 50%|█████ | 25/50 [00:06<00:06, 3.61it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.61it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.61it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.61it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.61it/s] 60%|██████ | 30/50 [00:08<00:05, 3.61it/s] 62%|██████▏ | 31/50 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