rileyhacks007 / sdkl-pointillism-test
(Updated 1 year, 9 months ago)
- Public
- 33 runs
- SDXL fine-tune
Prediction
rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132ID4x5ahftb3wune4x7tlzghbfnwmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- 1960s US national park poster for yellowstone In the style of Pointillism
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "1960s US national park poster for yellowstone In the style of Pointillism", "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, "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 rileyhacks007/sdkl-pointillism-test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", { input: { width: 1024, height: 1024, prompt: "1960s US national park poster for yellowstone In the style of Pointillism", 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, 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 rileyhacks007/sdkl-pointillism-test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", input={ "width": 1024, "height": 1024, "prompt": "1960s US national park poster for yellowstone In the style of Pointillism", "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, "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 rileyhacks007/sdkl-pointillism-test 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": "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", "input": { "width": 1024, "height": 1024, "prompt": "1960s US national park poster for yellowstone In the style of Pointillism", "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, "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": "2023-09-04T17:37:54.614027Z", "created_at": "2023-09-04T17:37:39.223733Z", "data_removed": false, "error": null, "id": "4x5ahftb3wune4x7tlzghbfnwm", "input": { "width": 1024, "height": 1024, "prompt": "1960s US national park poster for yellowstone In the style of Pointillism", "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, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 58528\nPrompt: 1960s US national park poster for yellowstone In the style of Pointillism\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.75it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.74it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.74it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.73it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.72it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.73it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.72it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.72it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.72it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.72it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.72it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.68it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.70it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.70it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.70it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.71it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.71it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.71it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.71it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.71it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.71it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.71it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.71it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.72it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.72it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.72it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.73it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.73it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.73it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.72it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.72it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.73it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.72it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.73it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.73it/s]\n 74%|███████▍ | 37/50 [00:09<00:03, 3.73it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.72it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.72it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.72it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.72it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.72it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.72it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.72it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.72it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.72it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.72it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.72it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.72it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.72it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.72it/s]", "metrics": { "predict_time": 15.40996, "total_time": 15.390294 }, "output": [ "https://replicate.delivery/pbxt/Kl7bcu7F3H4DMxvP6Yskden1s9eEnp5OrIg8SZO82jwxf4BjA/out-0.png" ], "started_at": "2023-09-04T17:37:39.204067Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4x5ahftb3wune4x7tlzghbfnwm", "cancel": "https://api.replicate.com/v1/predictions/4x5ahftb3wune4x7tlzghbfnwm/cancel" }, "version": "8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132" }Generated inUsing seed: 58528 Prompt: 1960s US national park poster for yellowstone In the style of Pointillism txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.75it/s] 4%|▍ | 2/50 [00:00<00:12, 3.74it/s] 6%|▌ | 3/50 [00:00<00:12, 3.74it/s] 8%|▊ | 4/50 [00:01<00:12, 3.73it/s] 10%|█ | 5/50 [00:01<00:12, 3.72it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.73it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.72it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.72it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.72it/s] 20%|██ | 10/50 [00:02<00:10, 3.72it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.72it/s] 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3.73it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.73it/s] 74%|███████▍ | 37/50 [00:09<00:03, 3.73it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.72it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.72it/s] 80%|████████ | 40/50 [00:10<00:02, 3.72it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.72it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.72it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.72it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.72it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.72it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.72it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.72it/s] 96%|█████████▌| 48/50 [00:12<00:00, 3.72it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.72it/s] 100%|██████████| 50/50 [00:13<00:00, 3.72it/s] 100%|██████████| 50/50 [00:13<00:00, 3.72it/s]Prediction
rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132IDumgjmedbpx3qz5ptrimtdlb2aqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- portrait of Jeremy Clarkson In the style of Pointillism
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "portrait of Jeremy Clarkson In the style of Pointillism", "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, "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 rileyhacks007/sdkl-pointillism-test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", { input: { width: 1024, height: 1024, prompt: "portrait of Jeremy Clarkson In the style of Pointillism", 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, 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 rileyhacks007/sdkl-pointillism-test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", input={ "width": 1024, "height": 1024, "prompt": "portrait of Jeremy Clarkson In the style of Pointillism", "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, "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 rileyhacks007/sdkl-pointillism-test 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": "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", "input": { "width": 1024, "height": 1024, "prompt": "portrait of Jeremy Clarkson In the style of Pointillism", "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, "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": "2023-09-04T17:36:38.011490Z", "created_at": "2023-09-04T17:36:22.593104Z", "data_removed": false, "error": null, "id": "umgjmedbpx3qz5ptrimtdlb2aq", "input": { "width": 1024, "height": 1024, "prompt": "portrait of Jeremy Clarkson In the style of Pointillism", "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, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 31259\nPrompt: portrait of Jeremy Clarkson In the style of Pointillism\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.76it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.75it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.73it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.73it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.73it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.73it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.72it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.72it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.72it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.72it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.71it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.71it/s]\n 26%|██▌ | 13/50 [00:03<00:09, 3.71it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.71it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.71it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.71it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.71it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.71it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.71it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.71it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.71it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.71it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.71it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.71it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.71it/s]\n 52%|█████▏ | 26/50 [00:06<00:06, 3.71it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.71it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.72it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.72it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.72it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.72it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.73it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.73it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.73it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.73it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.73it/s]\n 74%|███████▍ | 37/50 [00:09<00:03, 3.73it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.72it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.72it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.72it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.72it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.72it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.72it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.72it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.72it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.72it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.72it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.72it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.72it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.72it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.72it/s]", "metrics": { "predict_time": 15.411509, "total_time": 15.418386 }, "output": [ "https://replicate.delivery/pbxt/ePDMseeHMfxWgSlsvKjcp0hQfeQdJk9UvT3VVj6ViE8CpHPYE/out-0.png" ], "started_at": "2023-09-04T17:36:22.599981Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/umgjmedbpx3qz5ptrimtdlb2aq", "cancel": "https://api.replicate.com/v1/predictions/umgjmedbpx3qz5ptrimtdlb2aq/cancel" }, "version": "8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132" }Generated inUsing seed: 31259 Prompt: portrait of Jeremy Clarkson In the style of Pointillism txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.76it/s] 4%|▍ | 2/50 [00:00<00:12, 3.75it/s] 6%|▌ | 3/50 [00:00<00:12, 3.73it/s] 8%|▊ | 4/50 [00:01<00:12, 3.73it/s] 10%|█ | 5/50 [00:01<00:12, 3.73it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.73it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.72it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.72it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.72it/s] 20%|██ | 10/50 [00:02<00:10, 3.72it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.71it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.71it/s] 26%|██▌ | 13/50 [00:03<00:09, 3.71it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.71it/s] 30%|███ | 15/50 [00:04<00:09, 3.71it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.71it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.71it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.71it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.71it/s] 40%|████ | 20/50 [00:05<00:08, 3.71it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.71it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.71it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.71it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.71it/s] 50%|█████ | 25/50 [00:06<00:06, 3.71it/s] 52%|█████▏ | 26/50 [00:06<00:06, 3.71it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.71it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.72it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.72it/s] 60%|██████ | 30/50 [00:08<00:05, 3.72it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.72it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.73it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.73it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.73it/s] 70%|███████ | 35/50 [00:09<00:04, 3.73it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.73it/s] 74%|███████▍ | 37/50 [00:09<00:03, 3.73it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.72it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.72it/s] 80%|████████ | 40/50 [00:10<00:02, 3.72it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.72it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.72it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.72it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.72it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.72it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.72it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.72it/s] 96%|█████████▌| 48/50 [00:12<00:00, 3.72it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.72it/s] 100%|██████████| 50/50 [00:13<00:00, 3.72it/s] 100%|██████████| 50/50 [00:13<00:00, 3.72it/s]Prediction
rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132IDglz6o4lb2k2lbkb4gbizn47nd4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A golden retriever dog catching a frisbee mid-air In the style of Pointillism
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A golden retriever dog catching a frisbee mid-air In the style of Pointillism", "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, "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 rileyhacks007/sdkl-pointillism-test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", { input: { width: 1024, height: 1024, prompt: "A golden retriever dog catching a frisbee mid-air In the style of Pointillism", 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, 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 rileyhacks007/sdkl-pointillism-test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", input={ "width": 1024, "height": 1024, "prompt": "A golden retriever dog catching a frisbee mid-air In the style of Pointillism", "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, "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 rileyhacks007/sdkl-pointillism-test 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": "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", "input": { "width": 1024, "height": 1024, "prompt": "A golden retriever dog catching a frisbee mid-air In the style of Pointillism", "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, "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": "2023-09-04T17:33:57.737361Z", "created_at": "2023-09-04T17:33:42.399705Z", "data_removed": false, "error": null, "id": "glz6o4lb2k2lbkb4gbizn47nd4", "input": { "width": 1024, "height": 1024, "prompt": "A golden retriever dog catching a frisbee mid-air In the style of Pointillism", "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, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 63325\nPrompt: A golden retriever dog catching a frisbee mid-air In the style of Pointillism\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.77it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.76it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.75it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.74it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.73it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.73it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.73it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.73it/s]\n 18%|█▊ | 9/50 [00:02<00:10, 3.73it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.72it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.72it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.72it/s]\n 26%|██▌ | 13/50 [00:03<00:09, 3.72it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.72it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.72it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.71it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.72it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.71it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.71it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.71it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.71it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.71it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.71it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.71it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.71it/s]\n 52%|█████▏ | 26/50 [00:06<00:06, 3.71it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.71it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.71it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.71it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.71it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.71it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.72it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.72it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.73it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.73it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.73it/s]\n 74%|███████▍ | 37/50 [00:09<00:03, 3.73it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.73it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.73it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.73it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.73it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.73it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.73it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.73it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.73it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.73it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.73it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.72it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.73it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.73it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.72it/s]", "metrics": { "predict_time": 15.366134, "total_time": 15.337656 }, "output": [ "https://replicate.delivery/pbxt/tGXLHKOkY9LBIRUIFZsqfjP5VNI0zXpPVTyohe5Bx9AEc8gRA/out-0.png" ], "started_at": "2023-09-04T17:33:42.371227Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/glz6o4lb2k2lbkb4gbizn47nd4", "cancel": "https://api.replicate.com/v1/predictions/glz6o4lb2k2lbkb4gbizn47nd4/cancel" }, "version": "8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132" }Generated inUsing seed: 63325 Prompt: A golden retriever dog catching a frisbee mid-air In the style of Pointillism txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.77it/s] 4%|▍ | 2/50 [00:00<00:12, 3.76it/s] 6%|▌ | 3/50 [00:00<00:12, 3.75it/s] 8%|▊ | 4/50 [00:01<00:12, 3.74it/s] 10%|█ | 5/50 [00:01<00:12, 3.73it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.73it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.73it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.73it/s] 18%|█▊ | 9/50 [00:02<00:10, 3.73it/s] 20%|██ | 10/50 [00:02<00:10, 3.72it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.72it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.72it/s] 26%|██▌ | 13/50 [00:03<00:09, 3.72it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.72it/s] 30%|███ | 15/50 [00:04<00:09, 3.72it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.71it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.72it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.71it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.71it/s] 40%|████ | 20/50 [00:05<00:08, 3.71it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.71it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.71it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.71it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.71it/s] 50%|█████ | 25/50 [00:06<00:06, 3.71it/s] 52%|█████▏ | 26/50 [00:06<00:06, 3.71it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.71it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.71it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.71it/s] 60%|██████ | 30/50 [00:08<00:05, 3.71it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.71it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.72it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.72it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.73it/s] 70%|███████ | 35/50 [00:09<00:04, 3.73it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.73it/s] 74%|███████▍ | 37/50 [00:09<00:03, 3.73it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.73it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.73it/s] 80%|████████ | 40/50 [00:10<00:02, 3.73it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.73it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.73it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.73it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.73it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.73it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.73it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.73it/s] 96%|█████████▌| 48/50 [00:12<00:00, 3.72it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.73it/s] 100%|██████████| 50/50 [00:13<00:00, 3.73it/s] 100%|██████████| 50/50 [00:13<00:00, 3.72it/s]Prediction
rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132IDk4taewdbnzfomse4pdqtuijq3eStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- The stonehenge In the style of Pointillism
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "The stonehenge In the style of Pointillism", "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, "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 rileyhacks007/sdkl-pointillism-test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", { input: { width: 1024, height: 1024, prompt: "The stonehenge In the style of Pointillism", 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, 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 rileyhacks007/sdkl-pointillism-test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", input={ "width": 1024, "height": 1024, "prompt": "The stonehenge In the style of Pointillism", "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, "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 rileyhacks007/sdkl-pointillism-test 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": "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", "input": { "width": 1024, "height": 1024, "prompt": "The stonehenge In the style of Pointillism", "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, "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": "2023-09-04T17:28:26.241657Z", "created_at": "2023-09-04T17:28:10.923878Z", "data_removed": false, "error": null, "id": "k4taewdbnzfomse4pdqtuijq3e", "input": { "width": 1024, "height": 1024, "prompt": "The stonehenge In the style of Pointillism", "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, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 9784\nPrompt: The stonehenge In the style of Pointillism\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.76it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.75it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.74it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.73it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.72it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.72it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.73it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.72it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.72it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.72it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.72it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.72it/s]\n 26%|██▌ | 13/50 [00:03<00:09, 3.72it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.72it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.71it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.72it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.71it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.71it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.71it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.72it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.72it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.72it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.73it/s]\n 48%|████▊ | 24/50 [00:06<00:06, 3.73it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.73it/s]\n 52%|█████▏ | 26/50 [00:06<00:06, 3.73it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.73it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.73it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.73it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.73it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.73it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.73it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.73it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.73it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.73it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.73it/s]\n 74%|███████▍ | 37/50 [00:09<00:03, 3.73it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.73it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.73it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.72it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.73it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.73it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.72it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.73it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.73it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.72it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.72it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.72it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.73it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.72it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.73it/s]", "metrics": { "predict_time": 15.355573, "total_time": 15.317779 }, "output": [ "https://replicate.delivery/pbxt/iWxeBOnQKFQlbqtpeqBhu2iEJfCPJ6NX0C9XmeC3VoFmbxDGB/out-0.png" ], "started_at": "2023-09-04T17:28:10.886084Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/k4taewdbnzfomse4pdqtuijq3e", "cancel": "https://api.replicate.com/v1/predictions/k4taewdbnzfomse4pdqtuijq3e/cancel" }, "version": "8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132" }Generated inUsing seed: 9784 Prompt: The stonehenge In the style of Pointillism txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.76it/s] 4%|▍ | 2/50 [00:00<00:12, 3.75it/s] 6%|▌ | 3/50 [00:00<00:12, 3.74it/s] 8%|▊ | 4/50 [00:01<00:12, 3.73it/s] 10%|█ | 5/50 [00:01<00:12, 3.72it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.72it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.73it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.72it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.72it/s] 20%|██ | 10/50 [00:02<00:10, 3.72it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.72it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.72it/s] 26%|██▌ | 13/50 [00:03<00:09, 3.72it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.72it/s] 30%|███ | 15/50 [00:04<00:09, 3.71it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.72it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.71it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.71it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.71it/s] 40%|████ | 20/50 [00:05<00:08, 3.72it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.72it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.72it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.73it/s] 48%|████▊ | 24/50 [00:06<00:06, 3.73it/s] 50%|█████ | 25/50 [00:06<00:06, 3.73it/s] 52%|█████▏ | 26/50 [00:06<00:06, 3.73it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.73it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.73it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.73it/s] 60%|██████ | 30/50 [00:08<00:05, 3.73it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.73it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.73it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.73it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.73it/s] 70%|███████ | 35/50 [00:09<00:04, 3.73it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.73it/s] 74%|███████▍ | 37/50 [00:09<00:03, 3.73it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.73it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.73it/s] 80%|████████ | 40/50 [00:10<00:02, 3.72it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.73it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.73it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.72it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.73it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.73it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.72it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.72it/s] 96%|█████████▌| 48/50 [00:12<00:00, 3.72it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.73it/s] 100%|██████████| 50/50 [00:13<00:00, 3.72it/s] 100%|██████████| 50/50 [00:13<00:00, 3.73it/s]Prediction
rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132IDs4lksotbdxjgdb2rbf7kix4ptqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Man taking a call on his iPhone on the sidewalk in san Francisco, In the style of Pointillism
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "Man taking a call on his iPhone on the sidewalk in san Francisco, In the style of Pointillism", "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, "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 rileyhacks007/sdkl-pointillism-test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", { input: { width: 1024, height: 1024, prompt: "Man taking a call on his iPhone on the sidewalk in san Francisco, In the style of Pointillism", 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, 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 rileyhacks007/sdkl-pointillism-test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", input={ "width": 1024, "height": 1024, "prompt": "Man taking a call on his iPhone on the sidewalk in san Francisco, In the style of Pointillism", "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, "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 rileyhacks007/sdkl-pointillism-test 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": "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", "input": { "width": 1024, "height": 1024, "prompt": "Man taking a call on his iPhone on the sidewalk in san Francisco, In the style of Pointillism", "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, "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": "2023-09-04T17:25:21.551292Z", "created_at": "2023-09-04T17:25:06.171397Z", "data_removed": false, "error": null, "id": "s4lksotbdxjgdb2rbf7kix4ptq", "input": { "width": 1024, "height": 1024, "prompt": "Man taking a call on his iPhone on the sidewalk in san Francisco, In the style of Pointillism", "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, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 28786\nPrompt: Man taking a call on his iPhone on the sidewalk in san Francisco, In the style of Pointillism\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.75it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.74it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.74it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.73it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.72it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.73it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.72it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.72it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.72it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.72it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.72it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.71it/s]\n 26%|██▌ | 13/50 [00:03<00:09, 3.72it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.72it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.73it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.73it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.73it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.73it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.73it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.73it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.73it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.73it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.73it/s]\n 48%|████▊ | 24/50 [00:06<00:06, 3.73it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.73it/s]\n 52%|█████▏ | 26/50 [00:06<00:06, 3.73it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.74it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.73it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.73it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.73it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.73it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.73it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.73it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.73it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.73it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.73it/s]\n 74%|███████▍ | 37/50 [00:09<00:03, 3.73it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.73it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.73it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.73it/s]\n 82%|████████▏ | 41/50 [00:10<00:02, 3.73it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.72it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.72it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.72it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.72it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.72it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.72it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.72it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.72it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.72it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.73it/s]", "metrics": { "predict_time": 15.375221, "total_time": 15.379895 }, "output": [ "https://replicate.delivery/pbxt/qVNSvXOcShakNhA65B6nNQ25dfiVowaQGCdAwyArJdaAKegRA/out-0.png" ], "started_at": "2023-09-04T17:25:06.176071Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/s4lksotbdxjgdb2rbf7kix4ptq", "cancel": "https://api.replicate.com/v1/predictions/s4lksotbdxjgdb2rbf7kix4ptq/cancel" }, "version": "8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132" }Generated inUsing seed: 28786 Prompt: Man taking a call on his iPhone on the sidewalk in san Francisco, In the style of Pointillism txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.75it/s] 4%|▍ | 2/50 [00:00<00:12, 3.74it/s] 6%|▌ | 3/50 [00:00<00:12, 3.74it/s] 8%|▊ | 4/50 [00:01<00:12, 3.73it/s] 10%|█ | 5/50 [00:01<00:12, 3.72it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.73it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.72it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.72it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.72it/s] 20%|██ | 10/50 [00:02<00:10, 3.72it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.72it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.71it/s] 26%|██▌ | 13/50 [00:03<00:09, 3.72it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.72it/s] 30%|███ | 15/50 [00:04<00:09, 3.73it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.73it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.73it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.73it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.73it/s] 40%|████ | 20/50 [00:05<00:08, 3.73it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.73it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.73it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.73it/s] 48%|████▊ | 24/50 [00:06<00:06, 3.73it/s] 50%|█████ | 25/50 [00:06<00:06, 3.73it/s] 52%|█████▏ | 26/50 [00:06<00:06, 3.73it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.74it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.73it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.73it/s] 60%|██████ | 30/50 [00:08<00:05, 3.73it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.73it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.73it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.73it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.73it/s] 70%|███████ | 35/50 [00:09<00:04, 3.73it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.73it/s] 74%|███████▍ | 37/50 [00:09<00:03, 3.73it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.73it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.73it/s] 80%|████████ | 40/50 [00:10<00:02, 3.73it/s] 82%|████████▏ | 41/50 [00:10<00:02, 3.73it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.72it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.72it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.72it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.72it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.72it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.72it/s] 96%|█████████▌| 48/50 [00:12<00:00, 3.72it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.72it/s] 100%|██████████| 50/50 [00:13<00:00, 3.72it/s] 100%|██████████| 50/50 [00:13<00:00, 3.73it/s]Prediction
rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132IDrer6vulb54ybwucm6kjngdrdhqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Girl with the pearl earing In the style of Pointillism
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "Girl with the pearl earing In the style of Pointillism", "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, "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 rileyhacks007/sdkl-pointillism-test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", { input: { width: 1024, height: 1024, prompt: "Girl with the pearl earing In the style of Pointillism", 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, 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 rileyhacks007/sdkl-pointillism-test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", input={ "width": 1024, "height": 1024, "prompt": "Girl with the pearl earing In the style of Pointillism", "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, "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 rileyhacks007/sdkl-pointillism-test 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": "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", "input": { "width": 1024, "height": 1024, "prompt": "Girl with the pearl earing In the style of Pointillism", "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, "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": "2023-09-04T17:23:52.589483Z", "created_at": "2023-09-04T17:23:37.158964Z", "data_removed": false, "error": null, "id": "rer6vulb54ybwucm6kjngdrdhq", "input": { "width": 1024, "height": 1024, "prompt": "Girl with the pearl earing In the style of Pointillism", "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, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 56228\nPrompt: Girl with the pearl earing In the style of Pointillism\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.75it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.74it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.74it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.72it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.72it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.72it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.72it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.72it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.72it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.72it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.72it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.72it/s]\n 26%|██▌ | 13/50 [00:03<00:09, 3.73it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.73it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.73it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.73it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.74it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.74it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.73it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.73it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.74it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.73it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.73it/s]\n 48%|████▊ | 24/50 [00:06<00:06, 3.73it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.73it/s]\n 52%|█████▏ | 26/50 [00:06<00:06, 3.73it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.73it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.73it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.73it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.73it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.73it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.73it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.73it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.73it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.73it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.73it/s]\n 74%|███████▍ | 37/50 [00:09<00:03, 3.73it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.73it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.73it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.73it/s]\n 82%|████████▏ | 41/50 [00:10<00:02, 3.73it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.72it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.72it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.72it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.72it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.72it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.72it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.72it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.72it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.72it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.73it/s]", "metrics": { "predict_time": 15.448896, "total_time": 15.430519 }, "output": [ "https://replicate.delivery/pbxt/hAs1gdmvx5ZKL5rvEUerw99GgOa2KLr1jmgXHJrpt1kTJegRA/out-0.png" ], "started_at": "2023-09-04T17:23:37.140587Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rer6vulb54ybwucm6kjngdrdhq", "cancel": "https://api.replicate.com/v1/predictions/rer6vulb54ybwucm6kjngdrdhq/cancel" }, "version": "8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132" }Generated inUsing seed: 56228 Prompt: Girl with the pearl earing In the style of Pointillism txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.75it/s] 4%|▍ | 2/50 [00:00<00:12, 3.74it/s] 6%|▌ | 3/50 [00:00<00:12, 3.74it/s] 8%|▊ | 4/50 [00:01<00:12, 3.72it/s] 10%|█ | 5/50 [00:01<00:12, 3.72it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.72it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.72it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.72it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.72it/s] 20%|██ | 10/50 [00:02<00:10, 3.72it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.72it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.72it/s] 26%|██▌ | 13/50 [00:03<00:09, 3.73it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.73it/s] 30%|███ | 15/50 [00:04<00:09, 3.73it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.73it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.74it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.74it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.73it/s] 40%|████ | 20/50 [00:05<00:08, 3.73it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.74it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.73it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.73it/s] 48%|████▊ | 24/50 [00:06<00:06, 3.73it/s] 50%|█████ | 25/50 [00:06<00:06, 3.73it/s] 52%|█████▏ | 26/50 [00:06<00:06, 3.73it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.73it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.73it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.73it/s] 60%|██████ | 30/50 [00:08<00:05, 3.73it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.73it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.73it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.73it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.73it/s] 70%|███████ | 35/50 [00:09<00:04, 3.73it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.73it/s] 74%|███████▍ | 37/50 [00:09<00:03, 3.73it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.73it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.73it/s] 80%|████████ | 40/50 [00:10<00:02, 3.73it/s] 82%|████████▏ | 41/50 [00:10<00:02, 3.73it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.72it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.72it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.72it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.72it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.72it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.72it/s] 96%|█████████▌| 48/50 [00:12<00:00, 3.72it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.72it/s] 100%|██████████| 50/50 [00:13<00:00, 3.72it/s] 100%|██████████| 50/50 [00:13<00:00, 3.73it/s]Prediction
rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132IDujmtdl3b74d23azboeogcu2eyiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- The new york city skyline In the style of Pointillism
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "The new york city skyline In the style of Pointillism", "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, "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 rileyhacks007/sdkl-pointillism-test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", { input: { width: 1024, height: 1024, prompt: "The new york city skyline In the style of Pointillism", 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, 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 rileyhacks007/sdkl-pointillism-test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", input={ "width": 1024, "height": 1024, "prompt": "The new york city skyline In the style of Pointillism", "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, "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 rileyhacks007/sdkl-pointillism-test 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": "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", "input": { "width": 1024, "height": 1024, "prompt": "The new york city skyline In the style of Pointillism", "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, "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": "2023-09-04T17:21:58.766239Z", "created_at": "2023-09-04T17:21:43.450612Z", "data_removed": false, "error": null, "id": "ujmtdl3b74d23azboeogcu2eyi", "input": { "width": 1024, "height": 1024, "prompt": "The new york city skyline In the style of Pointillism", "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, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 35465\nPrompt: The new york city skyline In the style of Pointillism\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.75it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.74it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.73it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.72it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.73it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.73it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.73it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.73it/s]\n 18%|█▊ | 9/50 [00:02<00:10, 3.73it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.73it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.74it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.73it/s]\n 26%|██▌ | 13/50 [00:03<00:09, 3.73it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.73it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.73it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.73it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.73it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.73it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.73it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.73it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.73it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.73it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.73it/s]\n 48%|████▊ | 24/50 [00:06<00:06, 3.73it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.73it/s]\n 52%|█████▏ | 26/50 [00:06<00:06, 3.73it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.73it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.73it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.73it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.72it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.73it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.72it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.73it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.72it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.72it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.72it/s]\n 74%|███████▍ | 37/50 [00:09<00:03, 3.72it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.72it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.72it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.72it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.72it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.71it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.71it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.71it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.71it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.71it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.71it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.71it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.71it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.71it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.72it/s]", "metrics": { "predict_time": 15.361709, "total_time": 15.315627 }, "output": [ "https://replicate.delivery/pbxt/5L2ro6fBikxZXa2CLTMAHd65GuUZH6fV8uQ9lzKISHH1Q8gRA/out-0.png" ], "started_at": "2023-09-04T17:21:43.404530Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ujmtdl3b74d23azboeogcu2eyi", "cancel": "https://api.replicate.com/v1/predictions/ujmtdl3b74d23azboeogcu2eyi/cancel" }, "version": "8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132" }Generated inUsing seed: 35465 Prompt: The new york city skyline In the style of Pointillism txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.75it/s] 4%|▍ | 2/50 [00:00<00:12, 3.74it/s] 6%|▌ | 3/50 [00:00<00:12, 3.73it/s] 8%|▊ | 4/50 [00:01<00:12, 3.72it/s] 10%|█ | 5/50 [00:01<00:12, 3.73it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.73it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.73it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.73it/s] 18%|█▊ | 9/50 [00:02<00:10, 3.73it/s] 20%|██ | 10/50 [00:02<00:10, 3.73it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.74it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.73it/s] 26%|██▌ | 13/50 [00:03<00:09, 3.73it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.73it/s] 30%|███ | 15/50 [00:04<00:09, 3.73it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.73it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.73it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.73it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.73it/s] 40%|████ | 20/50 [00:05<00:08, 3.73it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.73it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.73it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.73it/s] 48%|████▊ | 24/50 [00:06<00:06, 3.73it/s] 50%|█████ | 25/50 [00:06<00:06, 3.73it/s] 52%|█████▏ | 26/50 [00:06<00:06, 3.73it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.73it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.73it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.73it/s] 60%|██████ | 30/50 [00:08<00:05, 3.72it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.73it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.72it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.73it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.72it/s] 70%|███████ | 35/50 [00:09<00:04, 3.72it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.72it/s] 74%|███████▍ | 37/50 [00:09<00:03, 3.72it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.72it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.72it/s] 80%|████████ | 40/50 [00:10<00:02, 3.72it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.72it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.71it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.71it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.71it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.71it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.71it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.71it/s] 96%|█████████▌| 48/50 [00:12<00:00, 3.71it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.71it/s] 100%|██████████| 50/50 [00:13<00:00, 3.71it/s] 100%|██████████| 50/50 [00:13<00:00, 3.72it/s]Prediction
rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132ID6xy2qktby6vgsg4boduhfetoheStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- The mona Lisa In the style of Pointillism
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "The mona Lisa In the style of Pointillism", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "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 rileyhacks007/sdkl-pointillism-test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", { input: { width: 1024, height: 1024, prompt: "The mona Lisa In the style of Pointillism", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, 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 rileyhacks007/sdkl-pointillism-test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", input={ "width": 1024, "height": 1024, "prompt": "The mona Lisa In the style of Pointillism", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "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 rileyhacks007/sdkl-pointillism-test 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": "rileyhacks007/sdkl-pointillism-test:8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132", "input": { "width": 1024, "height": 1024, "prompt": "The mona Lisa In the style of Pointillism", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "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": "2023-09-04T17:14:47.252755Z", "created_at": "2023-09-04T17:13:51.366942Z", "data_removed": false, "error": null, "id": "6xy2qktby6vgsg4boduhfetohe", "input": { "width": 1024, "height": 1024, "prompt": "The mona Lisa In the style of Pointillism", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 60129\nPrompt: The mona Lisa In the style of Pointillism\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:48, 1.01it/s]\n 4%|▍ | 2/50 [00:01<00:47, 1.01it/s]\n 6%|▌ | 3/50 [00:02<00:46, 1.01it/s]\n 8%|▊ | 4/50 [00:03<00:45, 1.01it/s]\n 10%|█ | 5/50 [00:04<00:44, 1.01it/s]\n 12%|█▏ | 6/50 [00:05<00:43, 1.01it/s]\n 14%|█▍ | 7/50 [00:06<00:42, 1.01it/s]\n 16%|█▌ | 8/50 [00:07<00:41, 1.01it/s]\n 18%|█▊ | 9/50 [00:08<00:40, 1.01it/s]\n 20%|██ | 10/50 [00:09<00:39, 1.01it/s]\n 22%|██▏ | 11/50 [00:10<00:38, 1.01it/s]\n 24%|██▍ | 12/50 [00:11<00:37, 1.01it/s]\n 26%|██▌ | 13/50 [00:12<00:36, 1.01it/s]\n 28%|██▊ | 14/50 [00:13<00:35, 1.01it/s]\n 30%|███ | 15/50 [00:14<00:34, 1.01it/s]\n 32%|███▏ | 16/50 [00:15<00:33, 1.01it/s]\n 34%|███▍ | 17/50 [00:16<00:32, 1.01it/s]\n 36%|███▌ | 18/50 [00:17<00:31, 1.01it/s]\n 38%|███▊ | 19/50 [00:18<00:30, 1.01it/s]\n 40%|████ | 20/50 [00:19<00:29, 1.01it/s]\n 42%|████▏ | 21/50 [00:20<00:28, 1.01it/s]\n 44%|████▍ | 22/50 [00:21<00:27, 1.01it/s]\n 46%|████▌ | 23/50 [00:22<00:26, 1.01it/s]\n 48%|████▊ | 24/50 [00:23<00:25, 1.01it/s]\n 50%|█████ | 25/50 [00:24<00:24, 1.01it/s]\n 52%|█████▏ | 26/50 [00:25<00:23, 1.01it/s]\n 54%|█████▍ | 27/50 [00:26<00:22, 1.01it/s]\n 56%|█████▌ | 28/50 [00:27<00:21, 1.01it/s]\n 58%|█████▊ | 29/50 [00:28<00:20, 1.01it/s]\n 60%|██████ | 30/50 [00:29<00:19, 1.01it/s]\n 62%|██████▏ | 31/50 [00:30<00:18, 1.00it/s]\n 64%|██████▍ | 32/50 [00:31<00:17, 1.00it/s]\n 66%|██████▌ | 33/50 [00:32<00:16, 1.00it/s]\n 68%|██████▊ | 34/50 [00:33<00:15, 1.00it/s]\n 70%|███████ | 35/50 [00:34<00:14, 1.00it/s]\n 72%|███████▏ | 36/50 [00:35<00:13, 1.00it/s]\n 74%|███████▍ | 37/50 [00:36<00:12, 1.00it/s]\n 76%|███████▌ | 38/50 [00:37<00:11, 1.00it/s]\n 78%|███████▊ | 39/50 [00:38<00:10, 1.00it/s]\n 80%|████████ | 40/50 [00:39<00:09, 1.00it/s]\n 82%|████████▏ | 41/50 [00:40<00:08, 1.00it/s]\n 84%|████████▍ | 42/50 [00:41<00:07, 1.00it/s]\n 86%|████████▌ | 43/50 [00:42<00:06, 1.00it/s]\n 88%|████████▊ | 44/50 [00:43<00:05, 1.00it/s]\n 90%|█████████ | 45/50 [00:44<00:04, 1.00it/s]\n 92%|█████████▏| 46/50 [00:45<00:03, 1.00it/s]\n 94%|█████████▍| 47/50 [00:46<00:02, 1.00it/s]\n 96%|█████████▌| 48/50 [00:47<00:01, 1.00it/s]\n 98%|█████████▊| 49/50 [00:48<00:00, 1.00it/s]\n100%|██████████| 50/50 [00:49<00:00, 1.00it/s]\n100%|██████████| 50/50 [00:49<00:00, 1.01it/s]", "metrics": { "predict_time": 55.880488, "total_time": 55.885813 }, "output": [ "https://replicate.delivery/pbxt/tfKBCEvU0vwHHy3fIx8ndZPM7h7mjguwuJHeW92tKHAJU4BjA/out-0.png", "https://replicate.delivery/pbxt/zRYWLdejLLxf7EEgRaNpx6Bnftt68C50I436XDakT2hLU4BjA/out-1.png", "https://replicate.delivery/pbxt/ZeW7mIGq5LzUTqgkxE1C7n9uQm5TmOgRMfeb425P4ANMU4BjA/out-2.png", "https://replicate.delivery/pbxt/asXlZSg6DfwNMi30W89yVc3N66sPgqebvfGhon653NfaowDGB/out-3.png" ], "started_at": "2023-09-04T17:13:51.372267Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6xy2qktby6vgsg4boduhfetohe", "cancel": "https://api.replicate.com/v1/predictions/6xy2qktby6vgsg4boduhfetohe/cancel" }, "version": "8ff6e17fb6a0dbb1870fa8d0095404985ee641bd9add5aa8952b06c0ad4c3132" }Generated inUsing seed: 60129 Prompt: The mona Lisa In the style of Pointillism txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:48, 1.01it/s] 4%|▍ | 2/50 [00:01<00:47, 1.01it/s] 6%|▌ | 3/50 [00:02<00:46, 1.01it/s] 8%|▊ | 4/50 [00:03<00:45, 1.01it/s] 10%|█ | 5/50 [00:04<00:44, 1.01it/s] 12%|█▏ | 6/50 [00:05<00:43, 1.01it/s] 14%|█▍ | 7/50 [00:06<00:42, 1.01it/s] 16%|█▌ | 8/50 [00:07<00:41, 1.01it/s] 18%|█▊ | 9/50 [00:08<00:40, 1.01it/s] 20%|██ | 10/50 [00:09<00:39, 1.01it/s] 22%|██▏ | 11/50 [00:10<00:38, 1.01it/s] 24%|██▍ | 12/50 [00:11<00:37, 1.01it/s] 26%|██▌ | 13/50 [00:12<00:36, 1.01it/s] 28%|██▊ | 14/50 [00:13<00:35, 1.01it/s] 30%|███ | 15/50 [00:14<00:34, 1.01it/s] 32%|███▏ | 16/50 [00:15<00:33, 1.01it/s] 34%|███▍ | 17/50 [00:16<00:32, 1.01it/s] 36%|███▌ | 18/50 [00:17<00:31, 1.01it/s] 38%|███▊ | 19/50 [00:18<00:30, 1.01it/s] 40%|████ | 20/50 [00:19<00:29, 1.01it/s] 42%|████▏ | 21/50 [00:20<00:28, 1.01it/s] 44%|████▍ | 22/50 [00:21<00:27, 1.01it/s] 46%|████▌ | 23/50 [00:22<00:26, 1.01it/s] 48%|████▊ | 24/50 [00:23<00:25, 1.01it/s] 50%|█████ | 25/50 [00:24<00:24, 1.01it/s] 52%|█████▏ | 26/50 [00:25<00:23, 1.01it/s] 54%|█████▍ | 27/50 [00:26<00:22, 1.01it/s] 56%|█████▌ | 28/50 [00:27<00:21, 1.01it/s] 58%|█████▊ | 29/50 [00:28<00:20, 1.01it/s] 60%|██████ | 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