cyber42
/
remedios_varo
SDXL fine tuned on a set of paintings of Remedios Varo. Activate the fine tuning with the prefix: "In the style of RemediosVaro, ..."! Helps to include the negative prompt: "broken, disfigured, dismembered people".
- Public
- 358 runs
-
L40S
- SDXL fine-tune
Prediction
cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fdIDk6t2fmtbufvv2cy2jtzcwkubuiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of RemediosVaro, a person looking at her image in a lake
- 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 RemediosVaro, a person looking at her image in a lake", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cyber42/remedios_varo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", { input: { width: 1024, height: 1024, prompt: "In the style of RemediosVaro, a person looking at her image in a lake", 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 cyber42/remedios_varo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", input={ "width": 1024, "height": 1024, "prompt": "In the style of RemediosVaro, a person looking at her image in a lake", "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 cyber42/remedios_varo 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": "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", "input": { "width": 1024, "height": 1024, "prompt": "In the style of RemediosVaro, a person looking at her image in a lake", "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": "2023-10-25T22:08:23.473634Z", "created_at": "2023-10-25T22:07:55.696525Z", "data_removed": false, "error": null, "id": "k6t2fmtbufvv2cy2jtzcwkubui", "input": { "width": 1024, "height": 1024, "prompt": "In the style of RemediosVaro, a person looking at her image in a lake", "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: 45600\nEnsuring enough disk space...\nFree disk space: 2447670820864\nDownloading weights: https://pbxt.replicate.delivery/zWSDjZjiBeVSGautk3UsukdhKG1Fg7xn6lJuvt5cnNntF64IA/trained_model.tar\nb'Downloaded 186 MB bytes in 4.804s (39 MB/s)\\nExtracted 186 MB in 0.067s (2.8 GB/s)\\n'\nDownloaded weights in 5.175899982452393 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of RemediosVaro, a person looking at her image in a lake\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)\nreturn F.conv2d(input, weight, bias, self.stride,\n 2%|▏ | 1/50 [00:00<00:46, 1.06it/s]\n 4%|▍ | 2/50 [00:01<00:26, 1.82it/s]\n 6%|▌ | 3/50 [00:01<00:19, 2.36it/s]\n 8%|▊ | 4/50 [00:01<00:16, 2.74it/s]\n 10%|█ | 5/50 [00:02<00:14, 3.01it/s]\n 12%|█▏ | 6/50 [00:02<00:13, 3.20it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.33it/s]\n 16%|█▌ | 8/50 [00:02<00:12, 3.43it/s]\n 18%|█▊ | 9/50 [00:03<00:11, 3.50it/s]\n 20%|██ | 10/50 [00:03<00:11, 3.54it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.57it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.59it/s]\n 26%|██▌ | 13/50 [00:04<00:10, 3.61it/s]\n 28%|██▊ | 14/50 [00:04<00:09, 3.62it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.63it/s]\n 32%|███▏ | 16/50 [00:05<00:09, 3.64it/s]\n 34%|███▍ | 17/50 [00:05<00:09, 3.65it/s]\n 36%|███▌ | 18/50 [00:05<00:08, 3.66it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s]\n 40%|████ | 20/50 [00:06<00:08, 3.66it/s]\n 42%|████▏ | 21/50 [00:06<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.67it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s]\n 48%|████▊ | 24/50 [00:07<00:07, 3.67it/s]\n 50%|█████ | 25/50 [00:07<00:06, 3.67it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s]\n 54%|█████▍ | 27/50 [00:08<00:06, 3.67it/s]\n 56%|█████▌ | 28/50 [00:08<00:06, 3.66it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.66it/s]\n 62%|██████▏ | 31/50 [00:09<00:05, 3.66it/s]\n 64%|██████▍ | 32/50 [00:09<00:04, 3.66it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.66it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s]\n 70%|███████ | 35/50 [00:10<00:04, 3.66it/s]\n 72%|███████▏ | 36/50 [00:10<00:03, 3.66it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]\n 76%|███████▌ | 38/50 [00:11<00:03, 3.66it/s]\n 78%|███████▊ | 39/50 [00:11<00:03, 3.66it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.66it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s]\n 84%|████████▍ | 42/50 [00:12<00:02, 3.66it/s]\n 86%|████████▌ | 43/50 [00:12<00:01, 3.66it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.66it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.66it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.65it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s]\n 98%|█████████▊| 49/50 [00:14<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.49it/s]", "metrics": { "predict_time": 22.889806, "total_time": 27.777109 }, "output": [ "https://replicate.delivery/pbxt/fUcnTL4bOKVSAad6UvrfC8EJeVSHMLedrNMxXfssQ1K06hOOC/out-0.png" ], "started_at": "2023-10-25T22:08:00.583828Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/k6t2fmtbufvv2cy2jtzcwkubui", "cancel": "https://api.replicate.com/v1/predictions/k6t2fmtbufvv2cy2jtzcwkubui/cancel" }, "version": "c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd" }Generated inUsing seed: 45600 Ensuring enough disk space... Free disk space: 2447670820864 Downloading weights: https://pbxt.replicate.delivery/zWSDjZjiBeVSGautk3UsukdhKG1Fg7xn6lJuvt5cnNntF64IA/trained_model.tar b'Downloaded 186 MB bytes in 4.804s (39 MB/s)\nExtracted 186 MB in 0.067s (2.8 GB/s)\n' Downloaded weights in 5.175899982452393 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: In the style of RemediosVaro, a person looking at her image in a lake txt2img mode 0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.) return F.conv2d(input, weight, bias, self.stride, 2%|▏ | 1/50 [00:00<00:46, 1.06it/s] 4%|▍ | 2/50 [00:01<00:26, 1.82it/s] 6%|▌ | 3/50 [00:01<00:19, 2.36it/s] 8%|▊ | 4/50 [00:01<00:16, 2.74it/s] 10%|█ | 5/50 [00:02<00:14, 3.01it/s] 12%|█▏ | 6/50 [00:02<00:13, 3.20it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.33it/s] 16%|█▌ | 8/50 [00:02<00:12, 3.43it/s] 18%|█▊ | 9/50 [00:03<00:11, 3.50it/s] 20%|██ | 10/50 [00:03<00:11, 3.54it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.57it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.59it/s] 26%|██▌ | 13/50 [00:04<00:10, 3.61it/s] 28%|██▊ | 14/50 [00:04<00:09, 3.62it/s] 30%|███ | 15/50 [00:04<00:09, 3.63it/s] 32%|███▏ | 16/50 [00:05<00:09, 3.64it/s] 34%|███▍ | 17/50 [00:05<00:09, 3.65it/s] 36%|███▌ | 18/50 [00:05<00:08, 3.66it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s] 40%|████ | 20/50 [00:06<00:08, 3.66it/s] 42%|████▏ | 21/50 [00:06<00:07, 3.67it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.67it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s] 48%|████▊ | 24/50 [00:07<00:07, 3.67it/s] 50%|█████ | 25/50 [00:07<00:06, 3.67it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s] 54%|█████▍ | 27/50 [00:08<00:06, 3.67it/s] 56%|█████▌ | 28/50 [00:08<00:06, 3.66it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.67it/s] 60%|██████ | 30/50 [00:08<00:05, 3.66it/s] 62%|██████▏ | 31/50 [00:09<00:05, 3.66it/s] 64%|██████▍ | 32/50 [00:09<00:04, 3.66it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.66it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s] 70%|███████ | 35/50 [00:10<00:04, 3.66it/s] 72%|███████▏ | 36/50 [00:10<00:03, 3.66it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s] 76%|███████▌ | 38/50 [00:11<00:03, 3.66it/s] 78%|███████▊ | 39/50 [00:11<00:03, 3.66it/s] 80%|████████ | 40/50 [00:11<00:02, 3.66it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s] 84%|████████▍ | 42/50 [00:12<00:02, 3.66it/s] 86%|████████▌ | 43/50 [00:12<00:01, 3.66it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.66it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s] 92%|█████████▏| 46/50 [00:13<00:01, 3.66it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.65it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s] 98%|█████████▊| 49/50 [00:14<00:00, 3.65it/s] 100%|██████████| 50/50 [00:14<00:00, 3.65it/s] 100%|██████████| 50/50 [00:14<00:00, 3.49it/s]Prediction
cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fdID52bt2ydbutzowgh3ji5ufukzoyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of RemediosVaro, an angel looming the thread of time leading to the center or the galaxy
- 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 RemediosVaro, an angel looming the thread of time leading to the center or the galaxy", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cyber42/remedios_varo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", { input: { width: 1024, height: 1024, prompt: "In the style of RemediosVaro, an angel looming the thread of time leading to the center or the galaxy", 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 cyber42/remedios_varo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", input={ "width": 1024, "height": 1024, "prompt": "In the style of RemediosVaro, an angel looming the thread of time leading to the center or the galaxy", "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 cyber42/remedios_varo 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": "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", "input": { "width": 1024, "height": 1024, "prompt": "In the style of RemediosVaro, an angel looming the thread of time leading to the center or the galaxy", "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": "2023-10-25T22:10:15.951904Z", "created_at": "2023-10-25T22:09:58.210861Z", "data_removed": false, "error": null, "id": "52bt2ydbutzowgh3ji5ufukzoy", "input": { "width": 1024, "height": 1024, "prompt": "In the style of RemediosVaro, an angel looming the thread of time leading to the center or the galaxy", "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: 32208\nEnsuring enough disk space...\nFree disk space: 1591970009088\nDownloading weights: https://pbxt.replicate.delivery/zWSDjZjiBeVSGautk3UsukdhKG1Fg7xn6lJuvt5cnNntF64IA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.292s (636 MB/s)\\nExtracted 186 MB in 0.066s (2.8 GB/s)\\n'\nDownloaded weights in 0.5222804546356201 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of RemediosVaro, an angel looming the thread of time leading to the center or the galaxy\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.70it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.69it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.69it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.68it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.68it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.67it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.67it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.67it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.66it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.66it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.66it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.66it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.66it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.66it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.68it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.68it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.68it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.68it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.68it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]", "metrics": { "predict_time": 16.193877, "total_time": 17.741043 }, "output": [ "https://replicate.delivery/pbxt/DoFWN2cKNF5PP1nLx14dPgHoIJvg6w787EniMGS2z52REdcE/out-0.png" ], "started_at": "2023-10-25T22:09:59.758027Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/52bt2ydbutzowgh3ji5ufukzoy", "cancel": "https://api.replicate.com/v1/predictions/52bt2ydbutzowgh3ji5ufukzoy/cancel" }, "version": "c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd" }Generated inUsing seed: 32208 Ensuring enough disk space... Free disk space: 1591970009088 Downloading weights: https://pbxt.replicate.delivery/zWSDjZjiBeVSGautk3UsukdhKG1Fg7xn6lJuvt5cnNntF64IA/trained_model.tar b'Downloaded 186 MB bytes in 0.292s (636 MB/s)\nExtracted 186 MB in 0.066s (2.8 GB/s)\n' Downloaded weights in 0.5222804546356201 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: In the style of RemediosVaro, an angel looming the thread of time leading to the center or the galaxy txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.70it/s] 4%|▍ | 2/50 [00:00<00:13, 3.69it/s] 6%|▌ | 3/50 [00:00<00:12, 3.69it/s] 8%|▊ | 4/50 [00:01<00:12, 3.68it/s] 10%|█ | 5/50 [00:01<00:12, 3.68it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s] 20%|██ | 10/50 [00:02<00:10, 3.67it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s] 30%|███ | 15/50 [00:04<00:09, 3.67it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s] 40%|████ | 20/50 [00:05<00:08, 3.67it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.66it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.66it/s] 50%|█████ | 25/50 [00:06<00:06, 3.66it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.66it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.66it/s] 60%|██████ | 30/50 [00:08<00:05, 3.66it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.68it/s] 70%|███████ | 35/50 [00:09<00:04, 3.68it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.68it/s] 80%|████████ | 40/50 [00:10<00:02, 3.68it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.68it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.68it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.68it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s]Prediction
cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fdID62y55o3bv5cqa3bk4ow7tkq5aeStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of RemediosVaro, a witch looking at a mirror in a flying machine
- 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 RemediosVaro, a witch looking at a mirror in a flying machine", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cyber42/remedios_varo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", { input: { width: 1024, height: 1024, prompt: "In the style of RemediosVaro, a witch looking at a mirror in a flying machine", 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 cyber42/remedios_varo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", input={ "width": 1024, "height": 1024, "prompt": "In the style of RemediosVaro, a witch looking at a mirror in a flying machine", "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 cyber42/remedios_varo 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": "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", "input": { "width": 1024, "height": 1024, "prompt": "In the style of RemediosVaro, a witch looking at a mirror in a flying machine", "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": "2023-10-25T22:12:54.159525Z", "created_at": "2023-10-25T22:12:26.227854Z", "data_removed": false, "error": null, "id": "62y55o3bv5cqa3bk4ow7tkq5ae", "input": { "width": 1024, "height": 1024, "prompt": "In the style of RemediosVaro, a witch looking at a mirror in a flying machine", "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: 45534\nEnsuring enough disk space...\nFree disk space: 1573957431296\nDownloading weights: https://pbxt.replicate.delivery/zWSDjZjiBeVSGautk3UsukdhKG1Fg7xn6lJuvt5cnNntF64IA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.182s (1.0 GB/s)\\nExtracted 186 MB in 0.052s (3.6 GB/s)\\n'\nDownloaded weights in 0.3179905414581299 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of RemediosVaro, a witch looking at a mirror in a flying machine\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:14, 3.40it/s]\n 4%|▍ | 2/50 [00:00<00:14, 3.39it/s]\n 6%|▌ | 3/50 [00:00<00:13, 3.37it/s]\n 8%|▊ | 4/50 [00:01<00:13, 3.37it/s]\n 10%|█ | 5/50 [00:01<00:13, 3.37it/s]\n 12%|█▏ | 6/50 [00:01<00:13, 3.37it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.37it/s]\n 16%|█▌ | 8/50 [00:02<00:12, 3.36it/s]\n 18%|█▊ | 9/50 [00:02<00:12, 3.36it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.36it/s]\n 22%|██▏ | 11/50 [00:03<00:11, 3.36it/s]\n 24%|██▍ | 12/50 [00:03<00:11, 3.36it/s]\n 26%|██▌ | 13/50 [00:03<00:11, 3.36it/s]\n 28%|██▊ | 14/50 [00:04<00:10, 3.36it/s]\n 30%|███ | 15/50 [00:04<00:10, 3.36it/s]\n 32%|███▏ | 16/50 [00:04<00:10, 3.36it/s]\n 34%|███▍ | 17/50 [00:05<00:09, 3.36it/s]\n 36%|███▌ | 18/50 [00:05<00:09, 3.36it/s]\n 38%|███▊ | 19/50 [00:05<00:09, 3.36it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.36it/s]\n 42%|████▏ | 21/50 [00:06<00:08, 3.36it/s]\n 44%|████▍ | 22/50 [00:06<00:08, 3.36it/s]\n 46%|████▌ | 23/50 [00:06<00:08, 3.36it/s]\n 48%|████▊ | 24/50 [00:07<00:07, 3.35it/s]\n 50%|█████ | 25/50 [00:07<00:07, 3.35it/s]\n 52%|█████▏ | 26/50 [00:07<00:07, 3.35it/s]\n 54%|█████▍ | 27/50 [00:08<00:06, 3.35it/s]\n 56%|█████▌ | 28/50 [00:08<00:06, 3.35it/s]\n 58%|█████▊ | 29/50 [00:08<00:06, 3.36it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.35it/s]\n 62%|██████▏ | 31/50 [00:09<00:05, 3.35it/s]\n 64%|██████▍ | 32/50 [00:09<00:05, 3.35it/s]\n 66%|██████▌ | 33/50 [00:09<00:05, 3.35it/s]\n 68%|██████▊ | 34/50 [00:10<00:04, 3.35it/s]\n 70%|███████ | 35/50 [00:10<00:04, 3.35it/s]\n 72%|███████▏ | 36/50 [00:10<00:04, 3.35it/s]\n 74%|███████▍ | 37/50 [00:11<00:03, 3.35it/s]\n 76%|███████▌ | 38/50 [00:11<00:03, 3.34it/s]\n 78%|███████▊ | 39/50 [00:11<00:03, 3.34it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.35it/s]\n 82%|████████▏ | 41/50 [00:12<00:02, 3.34it/s]\n 84%|████████▍ | 42/50 [00:12<00:02, 3.34it/s]\n 86%|████████▌ | 43/50 [00:12<00:02, 3.34it/s]\n 88%|████████▊ | 44/50 [00:13<00:01, 3.34it/s]\n 90%|█████████ | 45/50 [00:13<00:01, 3.34it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.34it/s]\n 94%|█████████▍| 47/50 [00:14<00:00, 3.34it/s]\n 96%|█████████▌| 48/50 [00:14<00:00, 3.35it/s]\n 98%|█████████▊| 49/50 [00:14<00:00, 3.35it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.35it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.35it/s]", "metrics": { "predict_time": 18.005959, "total_time": 27.931671 }, "output": [ "https://replicate.delivery/pbxt/IfejfuZAOyd7PpEyd0pTc8cp0kwqszfzWCmqvmIpOUWVORHHB/out-0.png" ], "started_at": "2023-10-25T22:12:36.153566Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/62y55o3bv5cqa3bk4ow7tkq5ae", "cancel": "https://api.replicate.com/v1/predictions/62y55o3bv5cqa3bk4ow7tkq5ae/cancel" }, "version": "c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd" }Generated inUsing seed: 45534 Ensuring enough disk space... Free disk space: 1573957431296 Downloading weights: https://pbxt.replicate.delivery/zWSDjZjiBeVSGautk3UsukdhKG1Fg7xn6lJuvt5cnNntF64IA/trained_model.tar b'Downloaded 186 MB bytes in 0.182s (1.0 GB/s)\nExtracted 186 MB in 0.052s (3.6 GB/s)\n' Downloaded weights in 0.3179905414581299 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: In the style of RemediosVaro, a witch looking at a mirror in a flying machine txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:14, 3.40it/s] 4%|▍ | 2/50 [00:00<00:14, 3.39it/s] 6%|▌ | 3/50 [00:00<00:13, 3.37it/s] 8%|▊ | 4/50 [00:01<00:13, 3.37it/s] 10%|█ | 5/50 [00:01<00:13, 3.37it/s] 12%|█▏ | 6/50 [00:01<00:13, 3.37it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.37it/s] 16%|█▌ | 8/50 [00:02<00:12, 3.36it/s] 18%|█▊ | 9/50 [00:02<00:12, 3.36it/s] 20%|██ | 10/50 [00:02<00:11, 3.36it/s] 22%|██▏ | 11/50 [00:03<00:11, 3.36it/s] 24%|██▍ | 12/50 [00:03<00:11, 3.36it/s] 26%|██▌ | 13/50 [00:03<00:11, 3.36it/s] 28%|██▊ | 14/50 [00:04<00:10, 3.36it/s] 30%|███ | 15/50 [00:04<00:10, 3.36it/s] 32%|███▏ | 16/50 [00:04<00:10, 3.36it/s] 34%|███▍ | 17/50 [00:05<00:09, 3.36it/s] 36%|███▌ | 18/50 [00:05<00:09, 3.36it/s] 38%|███▊ | 19/50 [00:05<00:09, 3.36it/s] 40%|████ | 20/50 [00:05<00:08, 3.36it/s] 42%|████▏ | 21/50 [00:06<00:08, 3.36it/s] 44%|████▍ | 22/50 [00:06<00:08, 3.36it/s] 46%|████▌ | 23/50 [00:06<00:08, 3.36it/s] 48%|████▊ | 24/50 [00:07<00:07, 3.35it/s] 50%|█████ | 25/50 [00:07<00:07, 3.35it/s] 52%|█████▏ | 26/50 [00:07<00:07, 3.35it/s] 54%|█████▍ | 27/50 [00:08<00:06, 3.35it/s] 56%|█████▌ | 28/50 [00:08<00:06, 3.35it/s] 58%|█████▊ | 29/50 [00:08<00:06, 3.36it/s] 60%|██████ | 30/50 [00:08<00:05, 3.35it/s] 62%|██████▏ | 31/50 [00:09<00:05, 3.35it/s] 64%|██████▍ | 32/50 [00:09<00:05, 3.35it/s] 66%|██████▌ | 33/50 [00:09<00:05, 3.35it/s] 68%|██████▊ | 34/50 [00:10<00:04, 3.35it/s] 70%|███████ | 35/50 [00:10<00:04, 3.35it/s] 72%|███████▏ | 36/50 [00:10<00:04, 3.35it/s] 74%|███████▍ | 37/50 [00:11<00:03, 3.35it/s] 76%|███████▌ | 38/50 [00:11<00:03, 3.34it/s] 78%|███████▊ | 39/50 [00:11<00:03, 3.34it/s] 80%|████████ | 40/50 [00:11<00:02, 3.35it/s] 82%|████████▏ | 41/50 [00:12<00:02, 3.34it/s] 84%|████████▍ | 42/50 [00:12<00:02, 3.34it/s] 86%|████████▌ | 43/50 [00:12<00:02, 3.34it/s] 88%|████████▊ | 44/50 [00:13<00:01, 3.34it/s] 90%|█████████ | 45/50 [00:13<00:01, 3.34it/s] 92%|█████████▏| 46/50 [00:13<00:01, 3.34it/s] 94%|█████████▍| 47/50 [00:14<00:00, 3.34it/s] 96%|█████████▌| 48/50 [00:14<00:00, 3.35it/s] 98%|█████████▊| 49/50 [00:14<00:00, 3.35it/s] 100%|██████████| 50/50 [00:14<00:00, 3.35it/s] 100%|██████████| 50/50 [00:14<00:00, 3.35it/s]Prediction
cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fdIDxdo3se3bu63yobch2jmyyok2fyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 42
- width
- 1024
- height
- 1024
- prompt
- In the style of RemediosVaro, a man sailing a flying boat over a river
- 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
{ "seed": 42, "width": 1024, "height": 1024, "prompt": "In the style of RemediosVaro, a man sailing a flying boat over a river", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cyber42/remedios_varo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", { input: { seed: 42, width: 1024, height: 1024, prompt: "In the style of RemediosVaro, a man sailing a flying boat over a river", 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 cyber42/remedios_varo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", input={ "seed": 42, "width": 1024, "height": 1024, "prompt": "In the style of RemediosVaro, a man sailing a flying boat over a river", "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 cyber42/remedios_varo 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": "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", "input": { "seed": 42, "width": 1024, "height": 1024, "prompt": "In the style of RemediosVaro, a man sailing a flying boat over a river", "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": "2023-10-25T22:16:28.754185Z", "created_at": "2023-10-25T22:16:08.082490Z", "data_removed": false, "error": null, "id": "xdo3se3bu63yobch2jmyyok2fy", "input": { "seed": 42, "width": 1024, "height": 1024, "prompt": "In the style of RemediosVaro, a man sailing a flying boat over a river", "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: 42\nEnsuring enough disk space...\nFree disk space: 1730162360320\nDownloading weights: https://pbxt.replicate.delivery/zWSDjZjiBeVSGautk3UsukdhKG1Fg7xn6lJuvt5cnNntF64IA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.825s (225 MB/s)\\nExtracted 186 MB in 0.062s (3.0 GB/s)\\n'\nDownloaded weights in 0.9775912761688232 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of RemediosVaro, a man sailing a flying boat over a river\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, 3.66it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.66it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.66it/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, 3.64it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.64it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.61it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.61it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.62it/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, 3.62it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.62it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.62it/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, 3.62it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.63it/s]", "metrics": { "predict_time": 17.988198, "total_time": 20.671695 }, "output": [ "https://replicate.delivery/pbxt/erGy1CRBAP1oVqG6TygutllQoqjbw5Wf1AOfqEwzGvo3tojjA/out-0.png" ], "started_at": "2023-10-25T22:16:10.765987Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xdo3se3bu63yobch2jmyyok2fy", "cancel": "https://api.replicate.com/v1/predictions/xdo3se3bu63yobch2jmyyok2fy/cancel" }, "version": "c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd" }Generated inUsing seed: 42 Ensuring enough disk space... Free disk space: 1730162360320 Downloading weights: https://pbxt.replicate.delivery/zWSDjZjiBeVSGautk3UsukdhKG1Fg7xn6lJuvt5cnNntF64IA/trained_model.tar b'Downloaded 186 MB bytes in 0.825s (225 MB/s)\nExtracted 186 MB in 0.062s (3.0 GB/s)\n' Downloaded weights in 0.9775912761688232 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: In the style of RemediosVaro, a man sailing a flying boat over a river 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, 3.66it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.66it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.66it/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, 3.64it/s] 30%|███ | 15/50 [00:04<00:09, 3.64it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.61it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.61it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.62it/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, 3.62it/s] 80%|████████ | 40/50 [00:11<00:02, 3.62it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.62it/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, 3.62it/s] 100%|██████████| 50/50 [00:13<00:00, 3.63it/s]Prediction
cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fdIDly3k4ftbk2zlk7cg6xulxlsrlqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 42
- width
- 1024
- height
- 1024
- prompt
- In the style of a RemediosVaro, the beauty and the beast
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "seed": 42, "width": 1024, "height": 1024, "prompt": "In the style of a RemediosVaro, the beauty and the beast", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cyber42/remedios_varo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", { input: { seed: 42, width: 1024, height: 1024, prompt: "In the style of a RemediosVaro, the beauty and the beast", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, 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 cyber42/remedios_varo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", input={ "seed": 42, "width": 1024, "height": 1024, "prompt": "In the style of a RemediosVaro, the beauty and the beast", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "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 cyber42/remedios_varo 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": "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", "input": { "seed": 42, "width": 1024, "height": 1024, "prompt": "In the style of a RemediosVaro, the beauty and the beast", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "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": "2023-10-25T22:27:33.537853Z", "created_at": "2023-10-25T22:27:17.277364Z", "data_removed": false, "error": null, "id": "ly3k4ftbk2zlk7cg6xulxlsrlq", "input": { "seed": 42, "width": 1024, "height": 1024, "prompt": "In the style of a RemediosVaro, the beauty and the beast", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "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: 42\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of a RemediosVaro, the beauty and the beast\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, 3.65it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.66it/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<00:12, 3.66it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.66it/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, 3.66it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.66it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.66it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.66it/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, 3.65it/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, 3.64it/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, 3.64it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.64it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.64it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.64it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.64it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.64it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.64it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.64it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]", "metrics": { "predict_time": 15.542844, "total_time": 16.260489 }, "output": [ "https://replicate.delivery/pbxt/UpZQBOZhryLRFltyOxBNOMM0k2oSvQe7ds1JeU4BQh6Vh0xRA/out-0.png" ], "started_at": "2023-10-25T22:27:17.995009Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ly3k4ftbk2zlk7cg6xulxlsrlq", "cancel": "https://api.replicate.com/v1/predictions/ly3k4ftbk2zlk7cg6xulxlsrlq/cancel" }, "version": "c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd" }Generated inUsing seed: 42 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: In the style of a RemediosVaro, the beauty and the beast 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, 3.65it/s] 6%|▌ | 3/50 [00:00<00:12, 3.66it/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<00:12, 3.66it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.66it/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, 3.66it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s] 30%|███ | 15/50 [00:04<00:09, 3.66it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.66it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s] 40%|████ | 20/50 [00:05<00:08, 3.66it/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, 3.65it/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, 3.64it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.65it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.65it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s] 70%|███████ | 35/50 [00:09<00:04, 3.65it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.65it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.64it/s] 80%|████████ | 40/50 [00:10<00:02, 3.64it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.64it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.64it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.64it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.64it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.64it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.64it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.64it/s] 100%|██████████| 50/50 [00:13<00:00, 3.64it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s]Prediction
cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fdID36giqqlbbk745gbcxlsmw5rq6eStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 42
- width
- 1024
- height
- 1024
- prompt
- In the style of a RemediosVaro, a telescope looking at a gravitational wave
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "seed": 42, "width": 1024, "height": 1024, "prompt": "In the style of a RemediosVaro, a telescope looking at a gravitational wave", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cyber42/remedios_varo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", { input: { seed: 42, width: 1024, height: 1024, prompt: "In the style of a RemediosVaro, a telescope looking at a gravitational wave", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, 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 cyber42/remedios_varo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", input={ "seed": 42, "width": 1024, "height": 1024, "prompt": "In the style of a RemediosVaro, a telescope looking at a gravitational wave", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "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 cyber42/remedios_varo 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": "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", "input": { "seed": 42, "width": 1024, "height": 1024, "prompt": "In the style of a RemediosVaro, a telescope looking at a gravitational wave", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "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": "2023-10-25T22:31:43.457414Z", "created_at": "2023-10-25T22:31:19.181562Z", "data_removed": false, "error": null, "id": "36giqqlbbk745gbcxlsmw5rq6e", "input": { "seed": 42, "width": 1024, "height": 1024, "prompt": "In the style of a RemediosVaro, a telescope looking at a gravitational wave", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "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: 42\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of a RemediosVaro, a telescope looking at a gravitational wave\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, 3.67it/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, 3.66it/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, 3.64it/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, 3.64it/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, 3.64it/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, 3.63it/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, 3.64it/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, 3.64it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.63it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.62it/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 [00:10<00:02, 3.63it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.62it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/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, 3.63it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.64it/s]", "metrics": { "predict_time": 16.80851, "total_time": 24.275852 }, "output": [ "https://replicate.delivery/pbxt/BHfgfndd36vWD0onYvIhhuv6hLlFQefNlmaovffFzZmmTJdcE/out-0.png" ], "started_at": "2023-10-25T22:31:26.648904Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/36giqqlbbk745gbcxlsmw5rq6e", "cancel": "https://api.replicate.com/v1/predictions/36giqqlbbk745gbcxlsmw5rq6e/cancel" }, "version": "c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd" }Generated inUsing seed: 42 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: In the style of a RemediosVaro, a telescope looking at a gravitational wave 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, 3.67it/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, 3.66it/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, 3.64it/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, 3.64it/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, 3.64it/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, 3.63it/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, 3.64it/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, 3.64it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.63it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.62it/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 [00:10<00:02, 3.63it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.62it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/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, 3.63it/s] 100%|██████████| 50/50 [00:13<00:00, 3.64it/s]Prediction
cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fdIDgwtq7idbahkqhmvnftuwk2va7uStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 42
- width
- 1024
- height
- 1024
- prompt
- In the style of a RemediosVaro, a spaceship escaping a gravitational wave from the center of the galaxy
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "seed": 42, "width": 1024, "height": 1024, "prompt": "In the style of a RemediosVaro, a spaceship escaping a gravitational wave from the center of the galaxy", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cyber42/remedios_varo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", { input: { seed: 42, width: 1024, height: 1024, prompt: "In the style of a RemediosVaro, a spaceship escaping a gravitational wave from the center of the galaxy", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, 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 cyber42/remedios_varo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", input={ "seed": 42, "width": 1024, "height": 1024, "prompt": "In the style of a RemediosVaro, a spaceship escaping a gravitational wave from the center of the galaxy", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "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 cyber42/remedios_varo 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": "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", "input": { "seed": 42, "width": 1024, "height": 1024, "prompt": "In the style of a RemediosVaro, a spaceship escaping a gravitational wave from the center of the galaxy", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "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": "2023-10-25T22:32:46.217756Z", "created_at": "2023-10-25T22:32:28.898626Z", "data_removed": false, "error": null, "id": "gwtq7idbahkqhmvnftuwk2va7u", "input": { "seed": 42, "width": 1024, "height": 1024, "prompt": "In the style of a RemediosVaro, a spaceship escaping a gravitational wave from the center of the galaxy", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "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: 42\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of a RemediosVaro, a spaceship escaping a gravitational wave from the center of the galaxy\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, 3.63it/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, 3.62it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.62it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.62it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.62it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.62it/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, 3.61it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.61it/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, 3.61it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.61it/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, 3.61it/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, 3.60it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.60it/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, 3.60it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.60it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.60it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.61it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.61it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.61it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.61it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.61it/s]", "metrics": { "predict_time": 16.699113, "total_time": 17.31913 }, "output": [ "https://replicate.delivery/pbxt/BJPjQTtO0UqJLB0NNQ18cQ1eFEG6axTTqZe00K6cp55Nm0xRA/out-0.png" ], "started_at": "2023-10-25T22:32:29.518643Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/gwtq7idbahkqhmvnftuwk2va7u", "cancel": "https://api.replicate.com/v1/predictions/gwtq7idbahkqhmvnftuwk2va7u/cancel" }, "version": "c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd" }Generated inUsing seed: 42 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: In the style of a RemediosVaro, a spaceship escaping a gravitational wave from the center of the galaxy 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, 3.63it/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, 3.62it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.62it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.62it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.62it/s] 20%|██ | 10/50 [00:02<00:11, 3.62it/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, 3.61it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.61it/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 [00:08<00:05, 3.61it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.61it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.61it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.61it/s] 70%|███████ | 35/50 [00:09<00:04, 3.61it/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, 3.60it/s] 80%|████████ | 40/50 [00:11<00:02, 3.60it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.61it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.61it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.61it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.60it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.60it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.60it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.61it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.61it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.61it/s] 100%|██████████| 50/50 [00:13<00:00, 3.61it/s] 100%|██████████| 50/50 [00:13<00:00, 3.61it/s]Prediction
cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fdIDqol3oj3biv5fuhxib5b6nfmtxyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- In the style of RemediosVaro, a girl sleeping in her bed
- 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 RemediosVaro, a girl sleeping in her bed", "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"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cyber42/remedios_varo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", { input: { width: 1024, height: 1024, prompt: "In the style of RemediosVaro, a girl sleeping in her bed", 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 cyber42/remedios_varo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", input={ "width": 1024, "height": 1024, "prompt": "In the style of RemediosVaro, a girl sleeping in her bed", "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 cyber42/remedios_varo 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": "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", "input": { "width": 1024, "height": 1024, "prompt": "In the style of RemediosVaro, a girl sleeping in her bed", "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": "2023-10-25T22:34:43.760247Z", "created_at": "2023-10-25T22:34:26.802814Z", "data_removed": false, "error": null, "id": "qol3oj3biv5fuhxib5b6nfmtxy", "input": { "width": 1024, "height": 1024, "prompt": "In the style of RemediosVaro, a girl sleeping in her bed", "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: 49035\nEnsuring enough disk space...\nFree disk space: 1958671024128\nDownloading weights: https://pbxt.replicate.delivery/zWSDjZjiBeVSGautk3UsukdhKG1Fg7xn6lJuvt5cnNntF64IA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.125s (1.5 GB/s)\\nExtracted 186 MB in 0.063s (3.0 GB/s)\\n'\nDownloaded weights in 0.32384538650512695 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of RemediosVaro, a girl sleeping in her bed\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, 3.66it/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, 3.66it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.66it/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, 3.66it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.66it/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, 3.65it/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, 3.64it/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, 3.63it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/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, 3.64it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/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, 3.64it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.64it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.64it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.63it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/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, 3.64it/s]", "metrics": { "predict_time": 16.346478, "total_time": 16.957433 }, "output": [ "https://replicate.delivery/pbxt/SGXWUqSYkn7MCxNvClCgeb4RPQ7oYeYbIBaZG1a3dv3Do0xRA/out-0.png" ], "started_at": "2023-10-25T22:34:27.413769Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/qol3oj3biv5fuhxib5b6nfmtxy", "cancel": "https://api.replicate.com/v1/predictions/qol3oj3biv5fuhxib5b6nfmtxy/cancel" }, "version": "c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd" }Generated inUsing seed: 49035 Ensuring enough disk space... Free disk space: 1958671024128 Downloading weights: https://pbxt.replicate.delivery/zWSDjZjiBeVSGautk3UsukdhKG1Fg7xn6lJuvt5cnNntF64IA/trained_model.tar b'Downloaded 186 MB bytes in 0.125s (1.5 GB/s)\nExtracted 186 MB in 0.063s (3.0 GB/s)\n' Downloaded weights in 0.32384538650512695 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: In the style of RemediosVaro, a girl sleeping in her bed 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, 3.66it/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, 3.66it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.66it/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, 3.66it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.66it/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, 3.65it/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, 3.64it/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, 3.63it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/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, 3.64it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/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, 3.64it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.64it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.64it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s] 80%|████████ | 40/50 [00:10<00:02, 3.63it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.63it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.63it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.63it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.63it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.63it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.63it/s] 100%|██████████| 50/50 [00:13<00:00, 3.63it/s] 100%|██████████| 50/50 [00:13<00:00, 3.64it/s]Prediction
cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fdIDbfd6qxlbifnt6i45k4nhmoyrteStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 42
- width
- 1024
- height
- 1024
- prompt
- In the style of RemediosVaro, the beauty and the beast
- 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
- broken, disfigured, dismembered people
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "seed": 42, "width": 1024, "height": 1024, "prompt": "In the style of RemediosVaro, the beauty and the beast", "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": "broken, disfigured, dismembered people", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cyber42/remedios_varo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", { input: { seed: 42, width: 1024, height: 1024, prompt: "In the style of RemediosVaro, the beauty and the beast", 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: "broken, disfigured, dismembered people", 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 cyber42/remedios_varo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", input={ "seed": 42, "width": 1024, "height": 1024, "prompt": "In the style of RemediosVaro, the beauty and the beast", "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": "broken, disfigured, dismembered people", "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 cyber42/remedios_varo 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": "cyber42/remedios_varo:c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd", "input": { "seed": 42, "width": 1024, "height": 1024, "prompt": "In the style of RemediosVaro, the beauty and the beast", "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": "broken, disfigured, dismembered people", "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": "2023-10-25T22:45:06.626017Z", "created_at": "2023-10-25T22:44:48.490034Z", "data_removed": false, "error": null, "id": "bfd6qxlbifnt6i45k4nhmoyrte", "input": { "seed": 42, "width": 1024, "height": 1024, "prompt": "In the style of RemediosVaro, the beauty and the beast", "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": "broken, disfigured, dismembered people", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 42\nEnsuring enough disk space...\nFree disk space: 1863025176576\nDownloading weights: https://pbxt.replicate.delivery/zWSDjZjiBeVSGautk3UsukdhKG1Fg7xn6lJuvt5cnNntF64IA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.209s (891 MB/s)\\nExtracted 186 MB in 0.073s (2.5 GB/s)\\n'\nDownloaded weights in 0.48294830322265625 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: In the style of RemediosVaro, the beauty and the beast\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.63it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.63it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.63it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.62it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.62it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.62it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.62it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.62it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.62it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.62it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.61it/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, 3.61it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.61it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.61it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.61it/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, 3.61it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.61it/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, 3.61it/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, 3.61it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.60it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.59it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.56it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.57it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.58it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.58it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.59it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.59it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.60it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.61it/s]", "metrics": { "predict_time": 17.092804, "total_time": 18.135983 }, "output": [ "https://replicate.delivery/pbxt/4ENGPSu9Qz49EdRnL1X6cJodO8IljRMH6SrLakVXavZcMdcE/out-0.png" ], "started_at": "2023-10-25T22:44:49.533213Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/bfd6qxlbifnt6i45k4nhmoyrte", "cancel": "https://api.replicate.com/v1/predictions/bfd6qxlbifnt6i45k4nhmoyrte/cancel" }, "version": "c8acb09ba51d3c1f0d87c7a69266ca4d1550761ee01a9b102ccd22df723ef5fd" }Generated inUsing seed: 42 Ensuring enough disk space... Free disk space: 1863025176576 Downloading weights: https://pbxt.replicate.delivery/zWSDjZjiBeVSGautk3UsukdhKG1Fg7xn6lJuvt5cnNntF64IA/trained_model.tar b'Downloaded 186 MB bytes in 0.209s (891 MB/s)\nExtracted 186 MB in 0.073s (2.5 GB/s)\n' Downloaded weights in 0.48294830322265625 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: In the style of RemediosVaro, the beauty and the beast txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.63it/s] 4%|▍ | 2/50 [00:00<00:13, 3.63it/s] 6%|▌ | 3/50 [00:00<00:12, 3.63it/s] 8%|▊ | 4/50 [00:01<00:12, 3.62it/s] 10%|█ | 5/50 [00:01<00:12, 3.62it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.62it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.62it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.62it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.62it/s] 20%|██ | 10/50 [00:02<00:11, 3.62it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.61it/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, 3.61it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.61it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.61it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.61it/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 [00:08<00:05, 3.61it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.61it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.61it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.61it/s] 70%|███████ | 35/50 [00:09<00:04, 3.61it/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, 3.61it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.60it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.59it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.56it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.57it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.58it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.58it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.59it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.59it/s] 100%|██████████| 50/50 [00:13<00:00, 3.60it/s] 100%|██████████| 50/50 [00:13<00:00, 3.61it/s]
Want to make some of these yourself?
Run this model