cloneofsimo
/
lora
LoRA Inference model with Stable Diffusion
Prediction
cloneofsimo/lora:bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afcIDl72kfrtqpfhqzl2z567xp75eymStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a photo of an astronaut riding a horse in the style of <1>
- lora_urls
- https://replicate.delivery/pbxt/S8wVSt0vXr5mEFDjP5XkmMPjLPCaDmv1Rw6AzRMDEhoFqqGE/tmp_fs4evyhbob-ross.safetensors
- scheduler
- DPMSolverMultistep
- lora_scales
- 0.6
- num_outputs
- "1"
- guidance_scale
- 7.5
- negative_prompt
- frame
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "a photo of an astronaut riding a horse in the style of <1>", "lora_urls": "https://replicate.delivery/pbxt/S8wVSt0vXr5mEFDjP5XkmMPjLPCaDmv1Rw6AzRMDEhoFqqGE/tmp_fs4evyhbob-ross.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": "1", "guidance_scale": 7.5, "negative_prompt": "frame", "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 cloneofsimo/lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cloneofsimo/lora:bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc", { input: { width: 512, height: 512, prompt: "a photo of an astronaut riding a horse in the style of <1>", lora_urls: "https://replicate.delivery/pbxt/S8wVSt0vXr5mEFDjP5XkmMPjLPCaDmv1Rw6AzRMDEhoFqqGE/tmp_fs4evyhbob-ross.safetensors", scheduler: "DPMSolverMultistep", lora_scales: "0.6", num_outputs: "1", guidance_scale: 7.5, negative_prompt: "frame", 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 cloneofsimo/lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cloneofsimo/lora:bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc", input={ "width": 512, "height": 512, "prompt": "a photo of an astronaut riding a horse in the style of <1>", "lora_urls": "https://replicate.delivery/pbxt/S8wVSt0vXr5mEFDjP5XkmMPjLPCaDmv1Rw6AzRMDEhoFqqGE/tmp_fs4evyhbob-ross.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": "1", "guidance_scale": 7.5, "negative_prompt": "frame", "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cloneofsimo/lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc", "input": { "width": 512, "height": 512, "prompt": "a photo of an astronaut riding a horse in the style of <1>", "lora_urls": "https://replicate.delivery/pbxt/S8wVSt0vXr5mEFDjP5XkmMPjLPCaDmv1Rw6AzRMDEhoFqqGE/tmp_fs4evyhbob-ross.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": "1", "guidance_scale": 7.5, "negative_prompt": "frame", "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": "2023-02-03T12:38:35.022205Z", "created_at": "2023-02-03T12:38:28.194601Z", "data_removed": false, "error": null, "id": "l72kfrtqpfhqzl2z567xp75eym", "input": { "width": 512, "height": 512, "prompt": "a photo of an astronaut riding a horse in the style of <1>", "lora_urls": "https://replicate.delivery/pbxt/S8wVSt0vXr5mEFDjP5XkmMPjLPCaDmv1Rw6AzRMDEhoFqqGE/tmp_fs4evyhbob-ross.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": "1", "guidance_scale": 7.5, "negative_prompt": "frame", "num_inference_steps": 50 }, "logs": "Using seed: 54983\nThe requested LoRAs are loaded.\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 7.12it/s]\n 4%|▍ | 2/50 [00:00<00:06, 7.51it/s]\n 6%|▌ | 3/50 [00:00<00:05, 8.02it/s]\n 8%|▊ | 4/50 [00:00<00:05, 8.28it/s]\n 10%|█ | 5/50 [00:00<00:05, 8.43it/s]\n 12%|█▏ | 6/50 [00:00<00:05, 8.53it/s]\n 14%|█▍ | 7/50 [00:00<00:05, 8.56it/s]\n 16%|█▌ | 8/50 [00:00<00:04, 8.62it/s]\n 18%|█▊ | 9/50 [00:01<00:04, 8.66it/s]\n 20%|██ | 10/50 [00:01<00:04, 8.68it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 8.71it/s]\n 24%|██▍ | 12/50 [00:01<00:04, 8.72it/s]\n 26%|██▌ | 13/50 [00:01<00:04, 8.73it/s]\n 28%|██▊ | 14/50 [00:01<00:04, 8.74it/s]\n 30%|███ | 15/50 [00:01<00:04, 8.74it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 8.74it/s]\n 34%|███▍ | 17/50 [00:01<00:03, 8.74it/s]\n 36%|███▌ | 18/50 [00:02<00:03, 8.75it/s]\n 38%|███▊ | 19/50 [00:02<00:03, 8.75it/s]\n 40%|████ | 20/50 [00:02<00:03, 8.75it/s]\n 42%|████▏ | 21/50 [00:02<00:03, 8.75it/s]\n 44%|████▍ | 22/50 [00:02<00:03, 8.75it/s]\n 46%|████▌ | 23/50 [00:02<00:03, 8.75it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 8.74it/s]\n 50%|█████ | 25/50 [00:02<00:02, 8.74it/s]\n 52%|█████▏ | 26/50 [00:03<00:02, 8.74it/s]\n 54%|█████▍ | 27/50 [00:03<00:02, 8.74it/s]\n 56%|█████▌ | 28/50 [00:03<00:02, 8.74it/s]\n 58%|█████▊ | 29/50 [00:03<00:02, 8.75it/s]\n 60%|██████ | 30/50 [00:03<00:02, 8.75it/s]\n 62%|██████▏ | 31/50 [00:03<00:02, 8.75it/s]\n 64%|██████▍ | 32/50 [00:03<00:02, 8.75it/s]\n 66%|██████▌ | 33/50 [00:03<00:01, 8.73it/s]\n 68%|██████▊ | 34/50 [00:03<00:01, 8.73it/s]\n 70%|███████ | 35/50 [00:04<00:01, 8.74it/s]\n 72%|███████▏ | 36/50 [00:04<00:01, 8.74it/s]\n 74%|███████▍ | 37/50 [00:04<00:01, 8.75it/s]\n 76%|███████▌ | 38/50 [00:04<00:01, 8.75it/s]\n 78%|███████▊ | 39/50 [00:04<00:01, 8.75it/s]\n 80%|████████ | 40/50 [00:04<00:01, 8.75it/s]\n 82%|████████▏ | 41/50 [00:04<00:01, 8.61it/s]\n 84%|████████▍ | 42/50 [00:04<00:00, 8.65it/s]\n 86%|████████▌ | 43/50 [00:04<00:00, 8.68it/s]\n 88%|████████▊ | 44/50 [00:05<00:00, 8.71it/s]\n 90%|█████████ | 45/50 [00:05<00:00, 8.72it/s]\n 92%|█████████▏| 46/50 [00:05<00:00, 8.71it/s]\n 94%|█████████▍| 47/50 [00:05<00:00, 8.73it/s]\n 96%|█████████▌| 48/50 [00:05<00:00, 8.74it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 8.73it/s]\n100%|██████████| 50/50 [00:05<00:00, 8.71it/s]\n100%|██████████| 50/50 [00:05<00:00, 8.67it/s]", "metrics": { "predict_time": 6.745202, "total_time": 6.827604 }, "output": [ "https://replicate.delivery/pbxt/BDiWGFzmyfUNVqzDnfTKlrDKAm7E9rmApSdSvfVKfeYXJZVDC/out-0.png" ], "started_at": "2023-02-03T12:38:28.277003Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/l72kfrtqpfhqzl2z567xp75eym", "cancel": "https://api.replicate.com/v1/predictions/l72kfrtqpfhqzl2z567xp75eym/cancel" }, "version": "bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc" }Generated inUsing seed: 54983 The requested LoRAs are loaded. 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:06, 7.12it/s] 4%|▍ | 2/50 [00:00<00:06, 7.51it/s] 6%|▌ | 3/50 [00:00<00:05, 8.02it/s] 8%|▊ | 4/50 [00:00<00:05, 8.28it/s] 10%|█ | 5/50 [00:00<00:05, 8.43it/s] 12%|█▏ | 6/50 [00:00<00:05, 8.53it/s] 14%|█▍ | 7/50 [00:00<00:05, 8.56it/s] 16%|█▌ | 8/50 [00:00<00:04, 8.62it/s] 18%|█▊ | 9/50 [00:01<00:04, 8.66it/s] 20%|██ | 10/50 [00:01<00:04, 8.68it/s] 22%|██▏ | 11/50 [00:01<00:04, 8.71it/s] 24%|██▍ | 12/50 [00:01<00:04, 8.72it/s] 26%|██▌ | 13/50 [00:01<00:04, 8.73it/s] 28%|██▊ | 14/50 [00:01<00:04, 8.74it/s] 30%|███ | 15/50 [00:01<00:04, 8.74it/s] 32%|███▏ | 16/50 [00:01<00:03, 8.74it/s] 34%|███▍ | 17/50 [00:01<00:03, 8.74it/s] 36%|███▌ | 18/50 [00:02<00:03, 8.75it/s] 38%|███▊ | 19/50 [00:02<00:03, 8.75it/s] 40%|████ | 20/50 [00:02<00:03, 8.75it/s] 42%|████▏ | 21/50 [00:02<00:03, 8.75it/s] 44%|████▍ | 22/50 [00:02<00:03, 8.75it/s] 46%|████▌ | 23/50 [00:02<00:03, 8.75it/s] 48%|████▊ | 24/50 [00:02<00:02, 8.74it/s] 50%|█████ | 25/50 [00:02<00:02, 8.74it/s] 52%|█████▏ | 26/50 [00:03<00:02, 8.74it/s] 54%|█████▍ | 27/50 [00:03<00:02, 8.74it/s] 56%|█████▌ | 28/50 [00:03<00:02, 8.74it/s] 58%|█████▊ | 29/50 [00:03<00:02, 8.75it/s] 60%|██████ | 30/50 [00:03<00:02, 8.75it/s] 62%|██████▏ | 31/50 [00:03<00:02, 8.75it/s] 64%|██████▍ | 32/50 [00:03<00:02, 8.75it/s] 66%|██████▌ | 33/50 [00:03<00:01, 8.73it/s] 68%|██████▊ | 34/50 [00:03<00:01, 8.73it/s] 70%|███████ | 35/50 [00:04<00:01, 8.74it/s] 72%|███████▏ | 36/50 [00:04<00:01, 8.74it/s] 74%|███████▍ | 37/50 [00:04<00:01, 8.75it/s] 76%|███████▌ | 38/50 [00:04<00:01, 8.75it/s] 78%|███████▊ | 39/50 [00:04<00:01, 8.75it/s] 80%|████████ | 40/50 [00:04<00:01, 8.75it/s] 82%|████████▏ | 41/50 [00:04<00:01, 8.61it/s] 84%|████████▍ | 42/50 [00:04<00:00, 8.65it/s] 86%|████████▌ | 43/50 [00:04<00:00, 8.68it/s] 88%|████████▊ | 44/50 [00:05<00:00, 8.71it/s] 90%|█████████ | 45/50 [00:05<00:00, 8.72it/s] 92%|█████████▏| 46/50 [00:05<00:00, 8.71it/s] 94%|█████████▍| 47/50 [00:05<00:00, 8.73it/s] 96%|█████████▌| 48/50 [00:05<00:00, 8.74it/s] 98%|█████████▊| 49/50 [00:05<00:00, 8.73it/s] 100%|██████████| 50/50 [00:05<00:00, 8.71it/s] 100%|██████████| 50/50 [00:05<00:00, 8.67it/s]Prediction
cloneofsimo/lora:bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afcIDv7kbrzbsdjchxc3d37z5yheb6iStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a photo of an astronaut riding a horse in the style of <1>
- lora_urls
- https://replicate.delivery/pbxt/c32Ba8UOS6bFDBwybc16WDREfzWCqeCRUzL3YtTgNrTIrqaQA/tmpan_f4msxcaravaggio.safetensors
- scheduler
- DPMSolverMultistep
- lora_scales
- 0.6
- num_outputs
- "1"
- guidance_scale
- 7.5
- negative_prompt
- frame
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "a photo of an astronaut riding a horse in the style of <1>", "lora_urls": "https://replicate.delivery/pbxt/c32Ba8UOS6bFDBwybc16WDREfzWCqeCRUzL3YtTgNrTIrqaQA/tmpan_f4msxcaravaggio.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": "1", "guidance_scale": 7.5, "negative_prompt": "frame", "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 cloneofsimo/lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cloneofsimo/lora:bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc", { input: { width: 512, height: 512, prompt: "a photo of an astronaut riding a horse in the style of <1>", lora_urls: "https://replicate.delivery/pbxt/c32Ba8UOS6bFDBwybc16WDREfzWCqeCRUzL3YtTgNrTIrqaQA/tmpan_f4msxcaravaggio.safetensors", scheduler: "DPMSolverMultistep", lora_scales: "0.6", num_outputs: "1", guidance_scale: 7.5, negative_prompt: "frame", 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 cloneofsimo/lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cloneofsimo/lora:bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc", input={ "width": 512, "height": 512, "prompt": "a photo of an astronaut riding a horse in the style of <1>", "lora_urls": "https://replicate.delivery/pbxt/c32Ba8UOS6bFDBwybc16WDREfzWCqeCRUzL3YtTgNrTIrqaQA/tmpan_f4msxcaravaggio.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": "1", "guidance_scale": 7.5, "negative_prompt": "frame", "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cloneofsimo/lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc", "input": { "width": 512, "height": 512, "prompt": "a photo of an astronaut riding a horse in the style of <1>", "lora_urls": "https://replicate.delivery/pbxt/c32Ba8UOS6bFDBwybc16WDREfzWCqeCRUzL3YtTgNrTIrqaQA/tmpan_f4msxcaravaggio.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": "1", "guidance_scale": 7.5, "negative_prompt": "frame", "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": "2023-02-03T12:45:52.881913Z", "created_at": "2023-02-03T12:45:45.977984Z", "data_removed": false, "error": null, "id": "v7kbrzbsdjchxc3d37z5yheb6i", "input": { "width": 512, "height": 512, "prompt": "a photo of an astronaut riding a horse in the style of <1>", "lora_urls": "https://replicate.delivery/pbxt/c32Ba8UOS6bFDBwybc16WDREfzWCqeCRUzL3YtTgNrTIrqaQA/tmpan_f4msxcaravaggio.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": "1", "guidance_scale": 7.5, "negative_prompt": "frame", "num_inference_steps": 50 }, "logs": "Using seed: 12291\nThe requested LoRAs are loaded.\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 7.09it/s]\n 4%|▍ | 2/50 [00:00<00:06, 7.93it/s]\n 6%|▌ | 3/50 [00:00<00:05, 8.20it/s]\n 8%|▊ | 4/50 [00:00<00:05, 8.40it/s]\n 10%|█ | 5/50 [00:00<00:05, 8.50it/s]\n 12%|█▏ | 6/50 [00:00<00:05, 8.56it/s]\n 14%|█▍ | 7/50 [00:00<00:04, 8.61it/s]\n 16%|█▌ | 8/50 [00:00<00:04, 8.63it/s]\n 18%|█▊ | 9/50 [00:01<00:04, 8.64it/s]\n 20%|██ | 10/50 [00:01<00:04, 8.67it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 8.68it/s]\n 24%|██▍ | 12/50 [00:01<00:04, 8.68it/s]\n 26%|██▌ | 13/50 [00:01<00:04, 8.69it/s]\n 28%|██▊ | 14/50 [00:01<00:04, 8.69it/s]\n 30%|███ | 15/50 [00:01<00:04, 8.69it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 8.70it/s]\n 34%|███▍ | 17/50 [00:01<00:03, 8.70it/s]\n 36%|███▌ | 18/50 [00:02<00:03, 8.69it/s]\n 38%|███▊ | 19/50 [00:02<00:03, 8.71it/s]\n 40%|████ | 20/50 [00:02<00:03, 8.71it/s]\n 42%|████▏ | 21/50 [00:02<00:03, 8.62it/s]\n 44%|████▍ | 22/50 [00:02<00:03, 8.66it/s]\n 46%|████▌ | 23/50 [00:02<00:03, 8.69it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 8.70it/s]\n 50%|█████ | 25/50 [00:02<00:02, 8.70it/s]\n 52%|█████▏ | 26/50 [00:03<00:02, 8.70it/s]\n 54%|█████▍ | 27/50 [00:03<00:02, 8.70it/s]\n 56%|█████▌ | 28/50 [00:03<00:02, 8.71it/s]\n 58%|█████▊ | 29/50 [00:03<00:02, 8.72it/s]\n 60%|██████ | 30/50 [00:03<00:02, 8.72it/s]\n 62%|██████▏ | 31/50 [00:03<00:02, 8.73it/s]\n 64%|██████▍ | 32/50 [00:03<00:02, 8.73it/s]\n 66%|██████▌ | 33/50 [00:03<00:01, 8.72it/s]\n 68%|██████▊ | 34/50 [00:03<00:01, 8.72it/s]\n 70%|███████ | 35/50 [00:04<00:01, 8.65it/s]\n 72%|███████▏ | 36/50 [00:04<00:01, 8.67it/s]\n 74%|███████▍ | 37/50 [00:04<00:01, 8.68it/s]\n 76%|███████▌ | 38/50 [00:04<00:01, 8.69it/s]\n 78%|███████▊ | 39/50 [00:04<00:01, 8.70it/s]\n 80%|████████ | 40/50 [00:04<00:01, 8.69it/s]\n 82%|████████▏ | 41/50 [00:04<00:01, 8.69it/s]\n 84%|████████▍ | 42/50 [00:04<00:00, 8.71it/s]\n 86%|████████▌ | 43/50 [00:04<00:00, 8.71it/s]\n 88%|████████▊ | 44/50 [00:05<00:00, 8.67it/s]\n 90%|█████████ | 45/50 [00:05<00:00, 8.67it/s]\n 92%|█████████▏| 46/50 [00:05<00:00, 8.67it/s]\n 94%|█████████▍| 47/50 [00:05<00:00, 8.68it/s]\n 96%|█████████▌| 48/50 [00:05<00:00, 8.69it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 8.67it/s]\n100%|██████████| 50/50 [00:05<00:00, 8.44it/s]\n100%|██████████| 50/50 [00:05<00:00, 8.63it/s]", "metrics": { "predict_time": 6.825229, "total_time": 6.903929 }, "output": [ "https://replicate.delivery/pbxt/nySfVHmp27xpO6bGXVwn0LycmGVyveDBSC8OQeRTe7ABAtqBB/out-0.png" ], "started_at": "2023-02-03T12:45:46.056684Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/v7kbrzbsdjchxc3d37z5yheb6i", "cancel": "https://api.replicate.com/v1/predictions/v7kbrzbsdjchxc3d37z5yheb6i/cancel" }, "version": "bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc" }Generated inUsing seed: 12291 The requested LoRAs are loaded. 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:06, 7.09it/s] 4%|▍ | 2/50 [00:00<00:06, 7.93it/s] 6%|▌ | 3/50 [00:00<00:05, 8.20it/s] 8%|▊ | 4/50 [00:00<00:05, 8.40it/s] 10%|█ | 5/50 [00:00<00:05, 8.50it/s] 12%|█▏ | 6/50 [00:00<00:05, 8.56it/s] 14%|█▍ | 7/50 [00:00<00:04, 8.61it/s] 16%|█▌ | 8/50 [00:00<00:04, 8.63it/s] 18%|█▊ | 9/50 [00:01<00:04, 8.64it/s] 20%|██ | 10/50 [00:01<00:04, 8.67it/s] 22%|██▏ | 11/50 [00:01<00:04, 8.68it/s] 24%|██▍ | 12/50 [00:01<00:04, 8.68it/s] 26%|██▌ | 13/50 [00:01<00:04, 8.69it/s] 28%|██▊ | 14/50 [00:01<00:04, 8.69it/s] 30%|███ | 15/50 [00:01<00:04, 8.69it/s] 32%|███▏ | 16/50 [00:01<00:03, 8.70it/s] 34%|███▍ | 17/50 [00:01<00:03, 8.70it/s] 36%|███▌ | 18/50 [00:02<00:03, 8.69it/s] 38%|███▊ | 19/50 [00:02<00:03, 8.71it/s] 40%|████ | 20/50 [00:02<00:03, 8.71it/s] 42%|████▏ | 21/50 [00:02<00:03, 8.62it/s] 44%|████▍ | 22/50 [00:02<00:03, 8.66it/s] 46%|████▌ | 23/50 [00:02<00:03, 8.69it/s] 48%|████▊ | 24/50 [00:02<00:02, 8.70it/s] 50%|█████ | 25/50 [00:02<00:02, 8.70it/s] 52%|█████▏ | 26/50 [00:03<00:02, 8.70it/s] 54%|█████▍ | 27/50 [00:03<00:02, 8.70it/s] 56%|█████▌ | 28/50 [00:03<00:02, 8.71it/s] 58%|█████▊ | 29/50 [00:03<00:02, 8.72it/s] 60%|██████ | 30/50 [00:03<00:02, 8.72it/s] 62%|██████▏ | 31/50 [00:03<00:02, 8.73it/s] 64%|██████▍ | 32/50 [00:03<00:02, 8.73it/s] 66%|██████▌ | 33/50 [00:03<00:01, 8.72it/s] 68%|██████▊ | 34/50 [00:03<00:01, 8.72it/s] 70%|███████ | 35/50 [00:04<00:01, 8.65it/s] 72%|███████▏ | 36/50 [00:04<00:01, 8.67it/s] 74%|███████▍ | 37/50 [00:04<00:01, 8.68it/s] 76%|███████▌ | 38/50 [00:04<00:01, 8.69it/s] 78%|███████▊ | 39/50 [00:04<00:01, 8.70it/s] 80%|████████ | 40/50 [00:04<00:01, 8.69it/s] 82%|████████▏ | 41/50 [00:04<00:01, 8.69it/s] 84%|████████▍ | 42/50 [00:04<00:00, 8.71it/s] 86%|████████▌ | 43/50 [00:04<00:00, 8.71it/s] 88%|████████▊ | 44/50 [00:05<00:00, 8.67it/s] 90%|█████████ | 45/50 [00:05<00:00, 8.67it/s] 92%|█████████▏| 46/50 [00:05<00:00, 8.67it/s] 94%|█████████▍| 47/50 [00:05<00:00, 8.68it/s] 96%|█████████▌| 48/50 [00:05<00:00, 8.69it/s] 98%|█████████▊| 49/50 [00:05<00:00, 8.67it/s] 100%|██████████| 50/50 [00:05<00:00, 8.44it/s] 100%|██████████| 50/50 [00:05<00:00, 8.63it/s]Prediction
cloneofsimo/lora:bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afcIDgtprrslu6rakbh5cbdrjsrr53qStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a photo of an astronaut riding a horse in the style of <1>
- lora_urls
- https://replicate.delivery/pbxt/lWeEmdjOxPTRTCXVLom7WEdW6Kgzh0Sfhb2pT2GhfNwU990gA/tmp4ht4wijisouth-park.safetensors
- scheduler
- DPMSolverMultistep
- lora_scales
- 0.6
- num_outputs
- "1"
- guidance_scale
- 7.5
- negative_prompt
- frame
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "a photo of an astronaut riding a horse in the style of <1>", "lora_urls": "https://replicate.delivery/pbxt/lWeEmdjOxPTRTCXVLom7WEdW6Kgzh0Sfhb2pT2GhfNwU990gA/tmp4ht4wijisouth-park.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": "1", "guidance_scale": 7.5, "negative_prompt": "frame", "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 cloneofsimo/lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cloneofsimo/lora:bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc", { input: { width: 512, height: 512, prompt: "a photo of an astronaut riding a horse in the style of <1>", lora_urls: "https://replicate.delivery/pbxt/lWeEmdjOxPTRTCXVLom7WEdW6Kgzh0Sfhb2pT2GhfNwU990gA/tmp4ht4wijisouth-park.safetensors", scheduler: "DPMSolverMultistep", lora_scales: "0.6", num_outputs: "1", guidance_scale: 7.5, negative_prompt: "frame", 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 cloneofsimo/lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cloneofsimo/lora:bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc", input={ "width": 512, "height": 512, "prompt": "a photo of an astronaut riding a horse in the style of <1>", "lora_urls": "https://replicate.delivery/pbxt/lWeEmdjOxPTRTCXVLom7WEdW6Kgzh0Sfhb2pT2GhfNwU990gA/tmp4ht4wijisouth-park.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": "1", "guidance_scale": 7.5, "negative_prompt": "frame", "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cloneofsimo/lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc", "input": { "width": 512, "height": 512, "prompt": "a photo of an astronaut riding a horse in the style of <1>", "lora_urls": "https://replicate.delivery/pbxt/lWeEmdjOxPTRTCXVLom7WEdW6Kgzh0Sfhb2pT2GhfNwU990gA/tmp4ht4wijisouth-park.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": "1", "guidance_scale": 7.5, "negative_prompt": "frame", "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": "2023-02-03T12:46:50.710524Z", "created_at": "2023-02-03T12:46:43.884137Z", "data_removed": false, "error": null, "id": "gtprrslu6rakbh5cbdrjsrr53q", "input": { "width": 512, "height": 512, "prompt": "a photo of an astronaut riding a horse in the style of <1>", "lora_urls": "https://replicate.delivery/pbxt/lWeEmdjOxPTRTCXVLom7WEdW6Kgzh0Sfhb2pT2GhfNwU990gA/tmp4ht4wijisouth-park.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": "1", "guidance_scale": 7.5, "negative_prompt": "frame", "num_inference_steps": 50 }, "logs": "Using seed: 9027\nThe requested LoRAs are loaded.\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 7.09it/s]\n 4%|▍ | 2/50 [00:00<00:06, 7.63it/s]\n 6%|▌ | 3/50 [00:00<00:05, 8.10it/s]\n 8%|▊ | 4/50 [00:00<00:05, 8.34it/s]\n 10%|█ | 5/50 [00:00<00:05, 8.48it/s]\n 12%|█▏ | 6/50 [00:00<00:05, 8.55it/s]\n 14%|█▍ | 7/50 [00:00<00:04, 8.61it/s]\n 16%|█▌ | 8/50 [00:00<00:04, 8.65it/s]\n 18%|█▊ | 9/50 [00:01<00:04, 8.68it/s]\n 20%|██ | 10/50 [00:01<00:04, 8.69it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 8.71it/s]\n 24%|██▍ | 12/50 [00:01<00:04, 8.71it/s]\n 26%|██▌ | 13/50 [00:01<00:04, 8.72it/s]\n 28%|██▊ | 14/50 [00:01<00:04, 8.73it/s]\n 30%|███ | 15/50 [00:01<00:04, 8.73it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 8.63it/s]\n 34%|███▍ | 17/50 [00:01<00:03, 8.67it/s]\n 36%|███▌ | 18/50 [00:02<00:03, 8.69it/s]\n 38%|███▊ | 19/50 [00:02<00:03, 8.71it/s]\n 40%|████ | 20/50 [00:02<00:03, 8.72it/s]\n 42%|████▏ | 21/50 [00:02<00:03, 8.72it/s]\n 44%|████▍ | 22/50 [00:02<00:03, 8.72it/s]\n 46%|████▌ | 23/50 [00:02<00:03, 8.72it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 8.73it/s]\n 50%|█████ | 25/50 [00:02<00:02, 8.73it/s]\n 52%|█████▏ | 26/50 [00:03<00:02, 8.74it/s]\n 54%|█████▍ | 27/50 [00:03<00:02, 8.71it/s]\n 56%|█████▌ | 28/50 [00:03<00:02, 8.72it/s]\n 58%|█████▊ | 29/50 [00:03<00:02, 8.73it/s]\n 60%|██████ | 30/50 [00:03<00:02, 8.74it/s]\n 62%|██████▏ | 31/50 [00:03<00:02, 8.75it/s]\n 64%|██████▍ | 32/50 [00:03<00:02, 8.75it/s]\n 66%|██████▌ | 33/50 [00:03<00:01, 8.62it/s]\n 68%|██████▊ | 34/50 [00:03<00:01, 8.65it/s]\n 70%|███████ | 35/50 [00:04<00:01, 8.68it/s]\n 72%|███████▏ | 36/50 [00:04<00:01, 8.69it/s]\n 74%|███████▍ | 37/50 [00:04<00:01, 8.71it/s]\n 76%|███████▌ | 38/50 [00:04<00:01, 8.73it/s]\n 78%|███████▊ | 39/50 [00:04<00:01, 8.74it/s]\n 80%|████████ | 40/50 [00:04<00:01, 8.75it/s]\n 82%|████████▏ | 41/50 [00:04<00:01, 8.75it/s]\n 84%|████████▍ | 42/50 [00:04<00:00, 8.72it/s]\n 86%|████████▌ | 43/50 [00:04<00:00, 8.73it/s]\n 88%|████████▊ | 44/50 [00:05<00:00, 8.74it/s]\n 90%|█████████ | 45/50 [00:05<00:00, 8.72it/s]\n 92%|█████████▏| 46/50 [00:05<00:00, 8.73it/s]\n 94%|█████████▍| 47/50 [00:05<00:00, 8.73it/s]\n 96%|█████████▌| 48/50 [00:05<00:00, 8.74it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 8.74it/s]\n100%|██████████| 50/50 [00:05<00:00, 8.73it/s]\n100%|██████████| 50/50 [00:05<00:00, 8.67it/s]", "metrics": { "predict_time": 6.746923, "total_time": 6.826387 }, "output": [ "https://replicate.delivery/pbxt/fsc6AWqFsvwCWaraWzSI1dGxf4uhkmpdfdsxtXNMS3J1hW1gA/out-0.png" ], "started_at": "2023-02-03T12:46:43.963601Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/gtprrslu6rakbh5cbdrjsrr53q", "cancel": "https://api.replicate.com/v1/predictions/gtprrslu6rakbh5cbdrjsrr53q/cancel" }, "version": "bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc" }Generated inUsing seed: 9027 The requested LoRAs are loaded. 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:06, 7.09it/s] 4%|▍ | 2/50 [00:00<00:06, 7.63it/s] 6%|▌ | 3/50 [00:00<00:05, 8.10it/s] 8%|▊ | 4/50 [00:00<00:05, 8.34it/s] 10%|█ | 5/50 [00:00<00:05, 8.48it/s] 12%|█▏ | 6/50 [00:00<00:05, 8.55it/s] 14%|█▍ | 7/50 [00:00<00:04, 8.61it/s] 16%|█▌ | 8/50 [00:00<00:04, 8.65it/s] 18%|█▊ | 9/50 [00:01<00:04, 8.68it/s] 20%|██ | 10/50 [00:01<00:04, 8.69it/s] 22%|██▏ | 11/50 [00:01<00:04, 8.71it/s] 24%|██▍ | 12/50 [00:01<00:04, 8.71it/s] 26%|██▌ | 13/50 [00:01<00:04, 8.72it/s] 28%|██▊ | 14/50 [00:01<00:04, 8.73it/s] 30%|███ | 15/50 [00:01<00:04, 8.73it/s] 32%|███▏ | 16/50 [00:01<00:03, 8.63it/s] 34%|███▍ | 17/50 [00:01<00:03, 8.67it/s] 36%|███▌ | 18/50 [00:02<00:03, 8.69it/s] 38%|███▊ | 19/50 [00:02<00:03, 8.71it/s] 40%|████ | 20/50 [00:02<00:03, 8.72it/s] 42%|████▏ | 21/50 [00:02<00:03, 8.72it/s] 44%|████▍ | 22/50 [00:02<00:03, 8.72it/s] 46%|████▌ | 23/50 [00:02<00:03, 8.72it/s] 48%|████▊ | 24/50 [00:02<00:02, 8.73it/s] 50%|█████ | 25/50 [00:02<00:02, 8.73it/s] 52%|█████▏ | 26/50 [00:03<00:02, 8.74it/s] 54%|█████▍ | 27/50 [00:03<00:02, 8.71it/s] 56%|█████▌ | 28/50 [00:03<00:02, 8.72it/s] 58%|█████▊ | 29/50 [00:03<00:02, 8.73it/s] 60%|██████ | 30/50 [00:03<00:02, 8.74it/s] 62%|██████▏ | 31/50 [00:03<00:02, 8.75it/s] 64%|██████▍ | 32/50 [00:03<00:02, 8.75it/s] 66%|██████▌ | 33/50 [00:03<00:01, 8.62it/s] 68%|██████▊ | 34/50 [00:03<00:01, 8.65it/s] 70%|███████ | 35/50 [00:04<00:01, 8.68it/s] 72%|███████▏ | 36/50 [00:04<00:01, 8.69it/s] 74%|███████▍ | 37/50 [00:04<00:01, 8.71it/s] 76%|███████▌ | 38/50 [00:04<00:01, 8.73it/s] 78%|███████▊ | 39/50 [00:04<00:01, 8.74it/s] 80%|████████ | 40/50 [00:04<00:01, 8.75it/s] 82%|████████▏ | 41/50 [00:04<00:01, 8.75it/s] 84%|████████▍ | 42/50 [00:04<00:00, 8.72it/s] 86%|████████▌ | 43/50 [00:04<00:00, 8.73it/s] 88%|████████▊ | 44/50 [00:05<00:00, 8.74it/s] 90%|█████████ | 45/50 [00:05<00:00, 8.72it/s] 92%|█████████▏| 46/50 [00:05<00:00, 8.73it/s] 94%|█████████▍| 47/50 [00:05<00:00, 8.73it/s] 96%|█████████▌| 48/50 [00:05<00:00, 8.74it/s] 98%|█████████▊| 49/50 [00:05<00:00, 8.74it/s] 100%|██████████| 50/50 [00:05<00:00, 8.73it/s] 100%|██████████| 50/50 [00:05<00:00, 8.67it/s]Prediction
cloneofsimo/lora:bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afcIDygb4a3eqrrabtjzm6nw7a4vc7eStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- an 18th century painting of <1> as a pirate wearing a pirate hat
- lora_urls
- https://replicate.delivery/pbxt/tLNfiG3fK2jZo0CrBG4cNTJNhEi7r117ANUBjWrLTkQRMraQA/tmpg9tq4is5me.safetensors
- scheduler
- DPMSolverMultistep
- lora_scales
- 0.5
- num_outputs
- "1"
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "an 18th century painting of <1> as a pirate wearing a pirate hat", "lora_urls": "https://replicate.delivery/pbxt/tLNfiG3fK2jZo0CrBG4cNTJNhEi7r117ANUBjWrLTkQRMraQA/tmpg9tq4is5me.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.5", "num_outputs": "1", "guidance_scale": 7.5, "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 cloneofsimo/lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cloneofsimo/lora:bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc", { input: { width: 512, height: 512, prompt: "an 18th century painting of <1> as a pirate wearing a pirate hat", lora_urls: "https://replicate.delivery/pbxt/tLNfiG3fK2jZo0CrBG4cNTJNhEi7r117ANUBjWrLTkQRMraQA/tmpg9tq4is5me.safetensors", scheduler: "DPMSolverMultistep", lora_scales: "0.5", num_outputs: "1", guidance_scale: 7.5, 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 cloneofsimo/lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cloneofsimo/lora:bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc", input={ "width": 512, "height": 512, "prompt": "an 18th century painting of <1> as a pirate wearing a pirate hat", "lora_urls": "https://replicate.delivery/pbxt/tLNfiG3fK2jZo0CrBG4cNTJNhEi7r117ANUBjWrLTkQRMraQA/tmpg9tq4is5me.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.5", "num_outputs": "1", "guidance_scale": 7.5, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cloneofsimo/lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc", "input": { "width": 512, "height": 512, "prompt": "an 18th century painting of <1> as a pirate wearing a pirate hat", "lora_urls": "https://replicate.delivery/pbxt/tLNfiG3fK2jZo0CrBG4cNTJNhEi7r117ANUBjWrLTkQRMraQA/tmpg9tq4is5me.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.5", "num_outputs": "1", "guidance_scale": 7.5, "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": "2023-02-03T12:57:02.245563Z", "created_at": "2023-02-03T12:56:54.935564Z", "data_removed": false, "error": null, "id": "ygb4a3eqrrabtjzm6nw7a4vc7e", "input": { "width": 512, "height": 512, "prompt": "an 18th century painting of <1> as a pirate wearing a pirate hat", "lora_urls": "https://replicate.delivery/pbxt/tLNfiG3fK2jZo0CrBG4cNTJNhEi7r117ANUBjWrLTkQRMraQA/tmpg9tq4is5me.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.5", "num_outputs": "1", "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Using seed: 39086\nThe requested LoRAs are loaded.\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 7.39it/s]\n 4%|▍ | 2/50 [00:00<00:06, 7.99it/s]\n 6%|▌ | 3/50 [00:00<00:05, 8.11it/s]\n 8%|▊ | 4/50 [00:00<00:05, 8.21it/s]\n 10%|█ | 5/50 [00:00<00:05, 8.22it/s]\n 12%|█▏ | 6/50 [00:00<00:05, 8.18it/s]\n 14%|█▍ | 7/50 [00:00<00:05, 8.24it/s]\n 16%|█▌ | 8/50 [00:00<00:05, 8.38it/s]\n 18%|█▊ | 9/50 [00:01<00:04, 8.27it/s]\n 20%|██ | 10/50 [00:01<00:04, 8.40it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 8.48it/s]\n 24%|██▍ | 12/50 [00:01<00:04, 8.48it/s]\n 26%|██▌ | 13/50 [00:01<00:04, 8.47it/s]\n 28%|██▊ | 14/50 [00:01<00:04, 8.18it/s]\n 30%|███ | 15/50 [00:01<00:04, 8.24it/s]\n 32%|███▏ | 16/50 [00:01<00:04, 8.32it/s]\n 34%|███▍ | 17/50 [00:02<00:03, 8.42it/s]\n 36%|███▌ | 18/50 [00:02<00:03, 8.49it/s]\n 38%|███▊ | 19/50 [00:02<00:03, 8.54it/s]\n 40%|████ | 20/50 [00:02<00:03, 8.58it/s]\n 42%|████▏ | 21/50 [00:02<00:03, 8.61it/s]\n 44%|████▍ | 22/50 [00:02<00:03, 8.63it/s]\n 46%|████▌ | 23/50 [00:02<00:03, 8.61it/s]\n 48%|████▊ | 24/50 [00:02<00:03, 8.64it/s]\n 50%|█████ | 25/50 [00:02<00:02, 8.64it/s]\n 52%|█████▏ | 26/50 [00:03<00:02, 8.65it/s]\n 54%|█████▍ | 27/50 [00:03<00:02, 8.61it/s]\n 56%|█████▌ | 28/50 [00:03<00:02, 8.63it/s]\n 58%|█████▊ | 29/50 [00:03<00:02, 8.65it/s]\n 60%|██████ | 30/50 [00:03<00:02, 8.66it/s]\n 62%|██████▏ | 31/50 [00:03<00:02, 8.67it/s]\n 64%|██████▍ | 32/50 [00:03<00:02, 8.67it/s]\n 66%|██████▌ | 33/50 [00:03<00:01, 8.68it/s]\n 68%|██████▊ | 34/50 [00:04<00:01, 8.68it/s]\n 70%|███████ | 35/50 [00:04<00:01, 8.66it/s]\n 72%|███████▏ | 36/50 [00:04<00:01, 8.67it/s]\n 74%|███████▍ | 37/50 [00:04<00:01, 8.68it/s]\n 76%|███████▌ | 38/50 [00:04<00:01, 8.68it/s]\n 78%|███████▊ | 39/50 [00:04<00:01, 8.66it/s]\n 80%|████████ | 40/50 [00:04<00:01, 8.43it/s]\n 82%|████████▏ | 41/50 [00:04<00:01, 8.31it/s]\n 84%|████████▍ | 42/50 [00:04<00:00, 8.30it/s]\n 86%|████████▌ | 43/50 [00:05<00:00, 8.10it/s]\n 88%|████████▊ | 44/50 [00:05<00:00, 7.99it/s]\n 90%|█████████ | 45/50 [00:05<00:00, 8.18it/s]\n 92%|█████████▏| 46/50 [00:05<00:00, 8.02it/s]\n 94%|█████████▍| 47/50 [00:05<00:00, 8.21it/s]\n 96%|█████████▌| 48/50 [00:05<00:00, 8.09it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 7.98it/s]\n100%|██████████| 50/50 [00:05<00:00, 8.11it/s]\n100%|██████████| 50/50 [00:05<00:00, 8.39it/s]", "metrics": { "predict_time": 7.237347, "total_time": 7.309999 }, "output": [ "https://replicate.delivery/pbxt/kumqhaepJLRjAKfSSDgEhAyGe1w4P2hsDB5So1R9KpN70W1gA/out-0.png" ], "started_at": "2023-02-03T12:56:55.008216Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ygb4a3eqrrabtjzm6nw7a4vc7e", "cancel": "https://api.replicate.com/v1/predictions/ygb4a3eqrrabtjzm6nw7a4vc7e/cancel" }, "version": "bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc" }Generated inUsing seed: 39086 The requested LoRAs are loaded. 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:06, 7.39it/s] 4%|▍ | 2/50 [00:00<00:06, 7.99it/s] 6%|▌ | 3/50 [00:00<00:05, 8.11it/s] 8%|▊ | 4/50 [00:00<00:05, 8.21it/s] 10%|█ | 5/50 [00:00<00:05, 8.22it/s] 12%|█▏ | 6/50 [00:00<00:05, 8.18it/s] 14%|█▍ | 7/50 [00:00<00:05, 8.24it/s] 16%|█▌ | 8/50 [00:00<00:05, 8.38it/s] 18%|█▊ | 9/50 [00:01<00:04, 8.27it/s] 20%|██ | 10/50 [00:01<00:04, 8.40it/s] 22%|██▏ | 11/50 [00:01<00:04, 8.48it/s] 24%|██▍ | 12/50 [00:01<00:04, 8.48it/s] 26%|██▌ | 13/50 [00:01<00:04, 8.47it/s] 28%|██▊ | 14/50 [00:01<00:04, 8.18it/s] 30%|███ | 15/50 [00:01<00:04, 8.24it/s] 32%|███▏ | 16/50 [00:01<00:04, 8.32it/s] 34%|███▍ | 17/50 [00:02<00:03, 8.42it/s] 36%|███▌ | 18/50 [00:02<00:03, 8.49it/s] 38%|███▊ | 19/50 [00:02<00:03, 8.54it/s] 40%|████ | 20/50 [00:02<00:03, 8.58it/s] 42%|████▏ | 21/50 [00:02<00:03, 8.61it/s] 44%|████▍ | 22/50 [00:02<00:03, 8.63it/s] 46%|████▌ | 23/50 [00:02<00:03, 8.61it/s] 48%|████▊ | 24/50 [00:02<00:03, 8.64it/s] 50%|█████ | 25/50 [00:02<00:02, 8.64it/s] 52%|█████▏ | 26/50 [00:03<00:02, 8.65it/s] 54%|█████▍ | 27/50 [00:03<00:02, 8.61it/s] 56%|█████▌ | 28/50 [00:03<00:02, 8.63it/s] 58%|█████▊ | 29/50 [00:03<00:02, 8.65it/s] 60%|██████ | 30/50 [00:03<00:02, 8.66it/s] 62%|██████▏ | 31/50 [00:03<00:02, 8.67it/s] 64%|██████▍ | 32/50 [00:03<00:02, 8.67it/s] 66%|██████▌ | 33/50 [00:03<00:01, 8.68it/s] 68%|██████▊ | 34/50 [00:04<00:01, 8.68it/s] 70%|███████ | 35/50 [00:04<00:01, 8.66it/s] 72%|███████▏ | 36/50 [00:04<00:01, 8.67it/s] 74%|███████▍ | 37/50 [00:04<00:01, 8.68it/s] 76%|███████▌ | 38/50 [00:04<00:01, 8.68it/s] 78%|███████▊ | 39/50 [00:04<00:01, 8.66it/s] 80%|████████ | 40/50 [00:04<00:01, 8.43it/s] 82%|████████▏ | 41/50 [00:04<00:01, 8.31it/s] 84%|████████▍ | 42/50 [00:04<00:00, 8.30it/s] 86%|████████▌ | 43/50 [00:05<00:00, 8.10it/s] 88%|████████▊ | 44/50 [00:05<00:00, 7.99it/s] 90%|█████████ | 45/50 [00:05<00:00, 8.18it/s] 92%|█████████▏| 46/50 [00:05<00:00, 8.02it/s] 94%|█████████▍| 47/50 [00:05<00:00, 8.21it/s] 96%|█████████▌| 48/50 [00:05<00:00, 8.09it/s] 98%|█████████▊| 49/50 [00:05<00:00, 7.98it/s] 100%|██████████| 50/50 [00:05<00:00, 8.11it/s] 100%|██████████| 50/50 [00:05<00:00, 8.39it/s]Prediction
cloneofsimo/lora:bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afcIDu7ewlgy4djawxg4bkteqjbw4pmStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a bronze statue of <1>
- lora_urls
- https://replicate.delivery/pbxt/tLNfiG3fK2jZo0CrBG4cNTJNhEi7r117ANUBjWrLTkQRMraQA/tmpg9tq4is5me.safetensors
- scheduler
- DPMSolverMultistep
- lora_scales
- 0.5
- num_outputs
- "1"
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "a bronze statue of <1>", "lora_urls": "https://replicate.delivery/pbxt/tLNfiG3fK2jZo0CrBG4cNTJNhEi7r117ANUBjWrLTkQRMraQA/tmpg9tq4is5me.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.5", "num_outputs": "1", "guidance_scale": 7.5, "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 cloneofsimo/lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cloneofsimo/lora:bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc", { input: { width: 512, height: 512, prompt: "a bronze statue of <1>", lora_urls: "https://replicate.delivery/pbxt/tLNfiG3fK2jZo0CrBG4cNTJNhEi7r117ANUBjWrLTkQRMraQA/tmpg9tq4is5me.safetensors", scheduler: "DPMSolverMultistep", lora_scales: "0.5", num_outputs: "1", guidance_scale: 7.5, 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 cloneofsimo/lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cloneofsimo/lora:bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc", input={ "width": 512, "height": 512, "prompt": "a bronze statue of <1>", "lora_urls": "https://replicate.delivery/pbxt/tLNfiG3fK2jZo0CrBG4cNTJNhEi7r117ANUBjWrLTkQRMraQA/tmpg9tq4is5me.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.5", "num_outputs": "1", "guidance_scale": 7.5, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cloneofsimo/lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc", "input": { "width": 512, "height": 512, "prompt": "a bronze statue of <1>", "lora_urls": "https://replicate.delivery/pbxt/tLNfiG3fK2jZo0CrBG4cNTJNhEi7r117ANUBjWrLTkQRMraQA/tmpg9tq4is5me.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.5", "num_outputs": "1", "guidance_scale": 7.5, "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": "2023-02-03T12:59:44.291061Z", "created_at": "2023-02-03T12:59:37.518281Z", "data_removed": false, "error": null, "id": "u7ewlgy4djawxg4bkteqjbw4pm", "input": { "width": 512, "height": 512, "prompt": "a bronze statue of <1>", "lora_urls": "https://replicate.delivery/pbxt/tLNfiG3fK2jZo0CrBG4cNTJNhEi7r117ANUBjWrLTkQRMraQA/tmpg9tq4is5me.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.5", "num_outputs": "1", "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Using seed: 7144\nThe requested LoRAs are loaded.\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 7.47it/s]\n 4%|▍ | 2/50 [00:00<00:05, 8.15it/s]\n 6%|▌ | 3/50 [00:00<00:05, 8.40it/s]\n 8%|▊ | 4/50 [00:00<00:05, 8.51it/s]\n 10%|█ | 5/50 [00:00<00:05, 8.59it/s]\n 12%|█▏ | 6/50 [00:00<00:05, 8.63it/s]\n 14%|█▍ | 7/50 [00:00<00:04, 8.66it/s]\n 16%|█▌ | 8/50 [00:00<00:04, 8.67it/s]\n 18%|█▊ | 9/50 [00:01<00:04, 8.69it/s]\n 20%|██ | 10/50 [00:01<00:04, 8.70it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 8.71it/s]\n 24%|██▍ | 12/50 [00:01<00:04, 8.71it/s]\n 26%|██▌ | 13/50 [00:01<00:04, 8.72it/s]\n 28%|██▊ | 14/50 [00:01<00:04, 8.72it/s]\n 30%|███ | 15/50 [00:01<00:04, 8.72it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 8.72it/s]\n 34%|███▍ | 17/50 [00:01<00:03, 8.72it/s]\n 36%|███▌ | 18/50 [00:02<00:03, 8.73it/s]\n 38%|███▊ | 19/50 [00:02<00:03, 8.64it/s]\n 40%|████ | 20/50 [00:02<00:03, 8.66it/s]\n 42%|████▏ | 21/50 [00:02<00:03, 8.68it/s]\n 44%|████▍ | 22/50 [00:02<00:03, 8.68it/s]\n 46%|████▌ | 23/50 [00:02<00:03, 8.70it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 8.71it/s]\n 50%|█████ | 25/50 [00:02<00:02, 8.71it/s]\n 52%|█████▏ | 26/50 [00:03<00:02, 8.71it/s]\n 54%|█████▍ | 27/50 [00:03<00:02, 8.72it/s]\n 56%|█████▌ | 28/50 [00:03<00:02, 8.69it/s]\n 58%|█████▊ | 29/50 [00:03<00:02, 8.69it/s]\n 60%|██████ | 30/50 [00:03<00:02, 8.68it/s]\n 62%|██████▏ | 31/50 [00:03<00:02, 8.68it/s]\n 64%|██████▍ | 32/50 [00:03<00:02, 8.70it/s]\n 66%|██████▌ | 33/50 [00:03<00:01, 8.70it/s]\n 68%|██████▊ | 34/50 [00:03<00:01, 8.70it/s]\n 70%|███████ | 35/50 [00:04<00:01, 8.71it/s]\n 72%|███████▏ | 36/50 [00:04<00:01, 8.71it/s]\n 74%|███████▍ | 37/50 [00:04<00:01, 8.71it/s]\n 76%|███████▌ | 38/50 [00:04<00:01, 8.69it/s]\n 78%|███████▊ | 39/50 [00:04<00:01, 8.70it/s]\n 80%|████████ | 40/50 [00:04<00:01, 8.70it/s]\n 82%|████████▏ | 41/50 [00:04<00:01, 8.69it/s]\n 84%|████████▍ | 42/50 [00:04<00:00, 8.70it/s]\n 86%|████████▌ | 43/50 [00:04<00:00, 8.71it/s]\n 88%|████████▊ | 44/50 [00:05<00:00, 8.72it/s]\n 90%|█████████ | 45/50 [00:05<00:00, 8.72it/s]\n 92%|█████████▏| 46/50 [00:05<00:00, 8.72it/s]\n 94%|█████████▍| 47/50 [00:05<00:00, 8.72it/s]\n 96%|█████████▌| 48/50 [00:05<00:00, 8.71it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 8.72it/s]\n100%|██████████| 50/50 [00:05<00:00, 8.72it/s]\n100%|██████████| 50/50 [00:05<00:00, 8.68it/s]", "metrics": { "predict_time": 6.703107, "total_time": 6.77278 }, "output": [ "https://replicate.delivery/pbxt/QAG3Y5x9LOL4LNwddQVl93ILLvkxNZ0WKuwHq9T6pI2P3qGE/out-0.png" ], "started_at": "2023-02-03T12:59:37.587954Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/u7ewlgy4djawxg4bkteqjbw4pm", "cancel": "https://api.replicate.com/v1/predictions/u7ewlgy4djawxg4bkteqjbw4pm/cancel" }, "version": "bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc" }Generated inUsing seed: 7144 The requested LoRAs are loaded. 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:06, 7.47it/s] 4%|▍ | 2/50 [00:00<00:05, 8.15it/s] 6%|▌ | 3/50 [00:00<00:05, 8.40it/s] 8%|▊ | 4/50 [00:00<00:05, 8.51it/s] 10%|█ | 5/50 [00:00<00:05, 8.59it/s] 12%|█▏ | 6/50 [00:00<00:05, 8.63it/s] 14%|█▍ | 7/50 [00:00<00:04, 8.66it/s] 16%|█▌ | 8/50 [00:00<00:04, 8.67it/s] 18%|█▊ | 9/50 [00:01<00:04, 8.69it/s] 20%|██ | 10/50 [00:01<00:04, 8.70it/s] 22%|██▏ | 11/50 [00:01<00:04, 8.71it/s] 24%|██▍ | 12/50 [00:01<00:04, 8.71it/s] 26%|██▌ | 13/50 [00:01<00:04, 8.72it/s] 28%|██▊ | 14/50 [00:01<00:04, 8.72it/s] 30%|███ | 15/50 [00:01<00:04, 8.72it/s] 32%|███▏ | 16/50 [00:01<00:03, 8.72it/s] 34%|███▍ | 17/50 [00:01<00:03, 8.72it/s] 36%|███▌ | 18/50 [00:02<00:03, 8.73it/s] 38%|███▊ | 19/50 [00:02<00:03, 8.64it/s] 40%|████ | 20/50 [00:02<00:03, 8.66it/s] 42%|████▏ | 21/50 [00:02<00:03, 8.68it/s] 44%|████▍ | 22/50 [00:02<00:03, 8.68it/s] 46%|████▌ | 23/50 [00:02<00:03, 8.70it/s] 48%|████▊ | 24/50 [00:02<00:02, 8.71it/s] 50%|█████ | 25/50 [00:02<00:02, 8.71it/s] 52%|█████▏ | 26/50 [00:03<00:02, 8.71it/s] 54%|█████▍ | 27/50 [00:03<00:02, 8.72it/s] 56%|█████▌ | 28/50 [00:03<00:02, 8.69it/s] 58%|█████▊ | 29/50 [00:03<00:02, 8.69it/s] 60%|██████ | 30/50 [00:03<00:02, 8.68it/s] 62%|██████▏ | 31/50 [00:03<00:02, 8.68it/s] 64%|██████▍ | 32/50 [00:03<00:02, 8.70it/s] 66%|██████▌ | 33/50 [00:03<00:01, 8.70it/s] 68%|██████▊ | 34/50 [00:03<00:01, 8.70it/s] 70%|███████ | 35/50 [00:04<00:01, 8.71it/s] 72%|███████▏ | 36/50 [00:04<00:01, 8.71it/s] 74%|███████▍ | 37/50 [00:04<00:01, 8.71it/s] 76%|███████▌ | 38/50 [00:04<00:01, 8.69it/s] 78%|███████▊ | 39/50 [00:04<00:01, 8.70it/s] 80%|████████ | 40/50 [00:04<00:01, 8.70it/s] 82%|████████▏ | 41/50 [00:04<00:01, 8.69it/s] 84%|████████▍ | 42/50 [00:04<00:00, 8.70it/s] 86%|████████▌ | 43/50 [00:04<00:00, 8.71it/s] 88%|████████▊ | 44/50 [00:05<00:00, 8.72it/s] 90%|█████████ | 45/50 [00:05<00:00, 8.72it/s] 92%|█████████▏| 46/50 [00:05<00:00, 8.72it/s] 94%|█████████▍| 47/50 [00:05<00:00, 8.72it/s] 96%|█████████▌| 48/50 [00:05<00:00, 8.71it/s] 98%|█████████▊| 49/50 [00:05<00:00, 8.72it/s] 100%|██████████| 50/50 [00:05<00:00, 8.72it/s] 100%|██████████| 50/50 [00:05<00:00, 8.68it/s]Prediction
cloneofsimo/lora:bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afcIDu5zelr7m5fakpf3wixuz67d4haStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a reconstruction of <1> on mars, futuristic sci-fi, dark, cgsociety, hyperrealism, hard lighting
- lora_urls
- https://replicate.delivery/pbxt/7zNSsYgOUVbDPNKZ1hBKppbNLQVfzZPSPJpIcLWQ0Tzd3VNIA/tmp9jr546jasagrada-familia.safetensors
- scheduler
- DPMSolverMultistep
- lora_scales
- 0.2
- num_outputs
- "1"
- guidance_scale
- 7.5
- negative_prompt
- grainy
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "a reconstruction of <1> on mars, futuristic sci-fi, dark, cgsociety, hyperrealism, hard lighting", "lora_urls": "https://replicate.delivery/pbxt/7zNSsYgOUVbDPNKZ1hBKppbNLQVfzZPSPJpIcLWQ0Tzd3VNIA/tmp9jr546jasagrada-familia.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.2", "num_outputs": "1", "guidance_scale": 7.5, "negative_prompt": "grainy", "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 cloneofsimo/lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cloneofsimo/lora:bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc", { input: { width: 512, height: 512, prompt: "a reconstruction of <1> on mars, futuristic sci-fi, dark, cgsociety, hyperrealism, hard lighting", lora_urls: "https://replicate.delivery/pbxt/7zNSsYgOUVbDPNKZ1hBKppbNLQVfzZPSPJpIcLWQ0Tzd3VNIA/tmp9jr546jasagrada-familia.safetensors", scheduler: "DPMSolverMultistep", lora_scales: "0.2", num_outputs: "1", guidance_scale: 7.5, negative_prompt: "grainy", 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 cloneofsimo/lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cloneofsimo/lora:bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc", input={ "width": 512, "height": 512, "prompt": "a reconstruction of <1> on mars, futuristic sci-fi, dark, cgsociety, hyperrealism, hard lighting", "lora_urls": "https://replicate.delivery/pbxt/7zNSsYgOUVbDPNKZ1hBKppbNLQVfzZPSPJpIcLWQ0Tzd3VNIA/tmp9jr546jasagrada-familia.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.2", "num_outputs": "1", "guidance_scale": 7.5, "negative_prompt": "grainy", "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cloneofsimo/lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc", "input": { "width": 512, "height": 512, "prompt": "a reconstruction of <1> on mars, futuristic sci-fi, dark, cgsociety, hyperrealism, hard lighting", "lora_urls": "https://replicate.delivery/pbxt/7zNSsYgOUVbDPNKZ1hBKppbNLQVfzZPSPJpIcLWQ0Tzd3VNIA/tmp9jr546jasagrada-familia.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.2", "num_outputs": "1", "guidance_scale": 7.5, "negative_prompt": "grainy", "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": "2023-02-03T13:32:48.023549Z", "created_at": "2023-02-03T13:32:39.389652Z", "data_removed": false, "error": null, "id": "u5zelr7m5fakpf3wixuz67d4ha", "input": { "width": 512, "height": 512, "prompt": "a reconstruction of <1> on mars, futuristic sci-fi, dark, cgsociety, hyperrealism, hard lighting", "lora_urls": "https://replicate.delivery/pbxt/7zNSsYgOUVbDPNKZ1hBKppbNLQVfzZPSPJpIcLWQ0Tzd3VNIA/tmp9jr546jasagrada-familia.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.2", "num_outputs": "1", "guidance_scale": 7.5, "negative_prompt": "grainy", "num_inference_steps": 50 }, "logs": "Using seed: 12780\nUsing disk cache...\nEmbedding <s0-0> replaced to <s0-0>\nEmbedding <s0-1> replaced to <s0-1>\nmerging time: 0.036347389221191406\n<s0-0>\nThe tokenizer already contains the token <s0-0>.\nReplacing <s0-0> embedding.\n<s0-1>\nThe tokenizer already contains the token <s0-1>.\nReplacing <s0-1> embedding.\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 7.16it/s]\n 4%|▍ | 2/50 [00:00<00:06, 7.59it/s]\n 6%|▌ | 3/50 [00:00<00:05, 8.07it/s]\n 8%|▊ | 4/50 [00:00<00:05, 8.31it/s]\n 10%|█ | 5/50 [00:00<00:05, 8.46it/s]\n 12%|█▏ | 6/50 [00:00<00:05, 8.56it/s]\n 14%|█▍ | 7/50 [00:00<00:04, 8.62it/s]\n 16%|█▌ | 8/50 [00:00<00:04, 8.66it/s]\n 18%|█▊ | 9/50 [00:01<00:04, 8.70it/s]\n 20%|██ | 10/50 [00:01<00:04, 8.68it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 8.70it/s]\n 24%|██▍ | 12/50 [00:01<00:04, 8.72it/s]\n 26%|██▌ | 13/50 [00:01<00:04, 8.72it/s]\n 28%|██▊ | 14/50 [00:01<00:04, 8.73it/s]\n 30%|███ | 15/50 [00:01<00:04, 8.74it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 8.74it/s]\n 34%|███▍ | 17/50 [00:01<00:03, 8.74it/s]\n 36%|███▌ | 18/50 [00:02<00:03, 8.74it/s]\n 38%|███▊ | 19/50 [00:02<00:03, 8.73it/s]\n 40%|████ | 20/50 [00:02<00:03, 8.74it/s]\n 42%|████▏ | 21/50 [00:02<00:03, 8.71it/s]\n 44%|████▍ | 22/50 [00:02<00:03, 8.57it/s]\n 46%|████▌ | 23/50 [00:02<00:03, 8.62it/s]\n 48%|████▊ | 24/50 [00:02<00:03, 8.66it/s]\n 50%|█████ | 25/50 [00:02<00:02, 8.68it/s]\n 52%|█████▏ | 26/50 [00:03<00:02, 8.49it/s]\n 54%|█████▍ | 27/50 [00:03<00:02, 8.57it/s]\n 56%|█████▌ | 28/50 [00:03<00:02, 8.55it/s]\n 58%|█████▊ | 29/50 [00:03<00:02, 8.60it/s]\n 60%|██████ | 30/50 [00:03<00:02, 8.60it/s]\n 62%|██████▏ | 31/50 [00:03<00:02, 8.65it/s]\n 64%|██████▍ | 32/50 [00:03<00:02, 8.67it/s]\n 66%|██████▌ | 33/50 [00:03<00:01, 8.70it/s]\n 68%|██████▊ | 34/50 [00:03<00:01, 8.69it/s]\n 70%|███████ | 35/50 [00:04<00:01, 8.70it/s]\n 72%|███████▏ | 36/50 [00:04<00:01, 8.71it/s]\n 74%|███████▍ | 37/50 [00:04<00:01, 8.72it/s]\n 76%|███████▌ | 38/50 [00:04<00:01, 8.71it/s]\n 78%|███████▊ | 39/50 [00:04<00:01, 8.71it/s]\n 80%|████████ | 40/50 [00:04<00:01, 8.73it/s]\n 82%|████████▏ | 41/50 [00:04<00:01, 8.74it/s]\n 84%|████████▍ | 42/50 [00:04<00:00, 8.74it/s]\n 86%|████████▌ | 43/50 [00:04<00:00, 8.75it/s]\n 88%|████████▊ | 44/50 [00:05<00:00, 8.74it/s]\n 90%|█████████ | 45/50 [00:05<00:00, 8.74it/s]\n 92%|█████████▏| 46/50 [00:05<00:00, 8.75it/s]\n 94%|█████████▍| 47/50 [00:05<00:00, 8.71it/s]\n 96%|█████████▌| 48/50 [00:05<00:00, 8.73it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 8.74it/s]\n100%|██████████| 50/50 [00:05<00:00, 8.75it/s]\n100%|██████████| 50/50 [00:05<00:00, 8.65it/s]", "metrics": { "predict_time": 8.559884, "total_time": 8.633897 }, "output": [ "https://replicate.delivery/pbxt/Y23MqrJWaewTeEb4CuVgBuQI7YbCnmd588msLJ6FVyeevvqBB/out-0.png" ], "started_at": "2023-02-03T13:32:39.463665Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/u5zelr7m5fakpf3wixuz67d4ha", "cancel": "https://api.replicate.com/v1/predictions/u5zelr7m5fakpf3wixuz67d4ha/cancel" }, "version": "bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc" }Generated inUsing seed: 12780 Using disk cache... Embedding <s0-0> replaced to <s0-0> Embedding <s0-1> replaced to <s0-1> merging time: 0.036347389221191406 <s0-0> The tokenizer already contains the token <s0-0>. Replacing <s0-0> embedding. <s0-1> The tokenizer already contains the token <s0-1>. Replacing <s0-1> embedding. 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:06, 7.16it/s] 4%|▍ | 2/50 [00:00<00:06, 7.59it/s] 6%|▌ | 3/50 [00:00<00:05, 8.07it/s] 8%|▊ | 4/50 [00:00<00:05, 8.31it/s] 10%|█ | 5/50 [00:00<00:05, 8.46it/s] 12%|█▏ | 6/50 [00:00<00:05, 8.56it/s] 14%|█▍ | 7/50 [00:00<00:04, 8.62it/s] 16%|█▌ | 8/50 [00:00<00:04, 8.66it/s] 18%|█▊ | 9/50 [00:01<00:04, 8.70it/s] 20%|██ | 10/50 [00:01<00:04, 8.68it/s] 22%|██▏ | 11/50 [00:01<00:04, 8.70it/s] 24%|██▍ | 12/50 [00:01<00:04, 8.72it/s] 26%|██▌ | 13/50 [00:01<00:04, 8.72it/s] 28%|██▊ | 14/50 [00:01<00:04, 8.73it/s] 30%|███ | 15/50 [00:01<00:04, 8.74it/s] 32%|███▏ | 16/50 [00:01<00:03, 8.74it/s] 34%|███▍ | 17/50 [00:01<00:03, 8.74it/s] 36%|███▌ | 18/50 [00:02<00:03, 8.74it/s] 38%|███▊ | 19/50 [00:02<00:03, 8.73it/s] 40%|████ | 20/50 [00:02<00:03, 8.74it/s] 42%|████▏ | 21/50 [00:02<00:03, 8.71it/s] 44%|████▍ | 22/50 [00:02<00:03, 8.57it/s] 46%|████▌ | 23/50 [00:02<00:03, 8.62it/s] 48%|████▊ | 24/50 [00:02<00:03, 8.66it/s] 50%|█████ | 25/50 [00:02<00:02, 8.68it/s] 52%|█████▏ | 26/50 [00:03<00:02, 8.49it/s] 54%|█████▍ | 27/50 [00:03<00:02, 8.57it/s] 56%|█████▌ | 28/50 [00:03<00:02, 8.55it/s] 58%|█████▊ | 29/50 [00:03<00:02, 8.60it/s] 60%|██████ | 30/50 [00:03<00:02, 8.60it/s] 62%|██████▏ | 31/50 [00:03<00:02, 8.65it/s] 64%|██████▍ | 32/50 [00:03<00:02, 8.67it/s] 66%|██████▌ | 33/50 [00:03<00:01, 8.70it/s] 68%|██████▊ | 34/50 [00:03<00:01, 8.69it/s] 70%|███████ | 35/50 [00:04<00:01, 8.70it/s] 72%|███████▏ | 36/50 [00:04<00:01, 8.71it/s] 74%|███████▍ | 37/50 [00:04<00:01, 8.72it/s] 76%|███████▌ | 38/50 [00:04<00:01, 8.71it/s] 78%|███████▊ | 39/50 [00:04<00:01, 8.71it/s] 80%|████████ | 40/50 [00:04<00:01, 8.73it/s] 82%|████████▏ | 41/50 [00:04<00:01, 8.74it/s] 84%|████████▍ | 42/50 [00:04<00:00, 8.74it/s] 86%|████████▌ | 43/50 [00:04<00:00, 8.75it/s] 88%|████████▊ | 44/50 [00:05<00:00, 8.74it/s] 90%|█████████ | 45/50 [00:05<00:00, 8.74it/s] 92%|█████████▏| 46/50 [00:05<00:00, 8.75it/s] 94%|█████████▍| 47/50 [00:05<00:00, 8.71it/s] 96%|█████████▌| 48/50 [00:05<00:00, 8.73it/s] 98%|█████████▊| 49/50 [00:05<00:00, 8.74it/s] 100%|██████████| 50/50 [00:05<00:00, 8.75it/s] 100%|██████████| 50/50 [00:05<00:00, 8.65it/s]Prediction
cloneofsimo/lora:bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afcIDbhfhvew2orgpxljj62dtgfyqimStatusSucceededSourceWebHardware–Total durationCreatedInput
- width
- 512
- height
- 512
- prompt
- a photo of an astronaut riding a horse in the style of <1>
- lora_urls
- https://replicate.delivery/pbxt/EdzdCihtoEKJPhsg2m2siciNUxGhy0dwXEyVRalfTmiGHWNIA/tmpc7q6wzxspokemon.safetensors
- scheduler
- DPMSolverMultistep
- lora_scales
- 0.2
- num_outputs
- "1"
- guidance_scale
- 7.5
- negative_prompt
- blurry
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "a photo of an astronaut riding a horse in the style of <1>", "lora_urls": "https://replicate.delivery/pbxt/EdzdCihtoEKJPhsg2m2siciNUxGhy0dwXEyVRalfTmiGHWNIA/tmpc7q6wzxspokemon.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.2", "num_outputs": "1", "guidance_scale": 7.5, "negative_prompt": "blurry", "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 cloneofsimo/lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cloneofsimo/lora:bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc", { input: { width: 512, height: 512, prompt: "a photo of an astronaut riding a horse in the style of <1>", lora_urls: "https://replicate.delivery/pbxt/EdzdCihtoEKJPhsg2m2siciNUxGhy0dwXEyVRalfTmiGHWNIA/tmpc7q6wzxspokemon.safetensors", scheduler: "DPMSolverMultistep", lora_scales: "0.2", num_outputs: "1", guidance_scale: 7.5, negative_prompt: "blurry", 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 cloneofsimo/lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cloneofsimo/lora:bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc", input={ "width": 512, "height": 512, "prompt": "a photo of an astronaut riding a horse in the style of <1>", "lora_urls": "https://replicate.delivery/pbxt/EdzdCihtoEKJPhsg2m2siciNUxGhy0dwXEyVRalfTmiGHWNIA/tmpc7q6wzxspokemon.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.2", "num_outputs": "1", "guidance_scale": 7.5, "negative_prompt": "blurry", "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cloneofsimo/lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc", "input": { "width": 512, "height": 512, "prompt": "a photo of an astronaut riding a horse in the style of <1>", "lora_urls": "https://replicate.delivery/pbxt/EdzdCihtoEKJPhsg2m2siciNUxGhy0dwXEyVRalfTmiGHWNIA/tmpc7q6wzxspokemon.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.2", "num_outputs": "1", "guidance_scale": 7.5, "negative_prompt": "blurry", "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": "2023-02-03T16:19:40.567921Z", "created_at": "2023-02-03T16:19:33.844240Z", "data_removed": false, "error": null, "id": "bhfhvew2orgpxljj62dtgfyqim", "input": { "width": 512, "height": 512, "prompt": "a photo of an astronaut riding a horse in the style of <1>", "lora_urls": "https://replicate.delivery/pbxt/EdzdCihtoEKJPhsg2m2siciNUxGhy0dwXEyVRalfTmiGHWNIA/tmpc7q6wzxspokemon.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.2", "num_outputs": "1", "guidance_scale": 7.5, "negative_prompt": "blurry", "num_inference_steps": 50 }, "logs": "Using seed: 12654\nThe requested LoRAs are loaded.\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 7.21it/s]\n 4%|▍ | 2/50 [00:00<00:05, 8.05it/s]\n 6%|▌ | 3/50 [00:00<00:05, 8.37it/s]\n 8%|▊ | 4/50 [00:00<00:05, 8.54it/s]\n 10%|█ | 5/50 [00:00<00:05, 8.63it/s]\n 12%|█▏ | 6/50 [00:00<00:05, 8.69it/s]\n 14%|█▍ | 7/50 [00:00<00:04, 8.70it/s]\n 16%|█▌ | 8/50 [00:00<00:04, 8.73it/s]\n 18%|█▊ | 9/50 [00:01<00:04, 8.75it/s]\n 20%|██ | 10/50 [00:01<00:04, 8.76it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 8.77it/s]\n 24%|██▍ | 12/50 [00:01<00:04, 8.78it/s]\n 26%|██▌ | 13/50 [00:01<00:04, 8.80it/s]\n 28%|██▊ | 14/50 [00:01<00:04, 8.80it/s]\n 30%|███ | 15/50 [00:01<00:03, 8.81it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 8.81it/s]\n 34%|███▍ | 17/50 [00:01<00:03, 8.81it/s]\n 36%|███▌ | 18/50 [00:02<00:03, 8.81it/s]\n 38%|███▊ | 19/50 [00:02<00:03, 8.81it/s]\n 40%|████ | 20/50 [00:02<00:03, 8.79it/s]\n 42%|████▏ | 21/50 [00:02<00:03, 8.80it/s]\n 44%|████▍ | 22/50 [00:02<00:03, 8.81it/s]\n 46%|████▌ | 23/50 [00:02<00:03, 8.81it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 8.81it/s]\n 50%|█████ | 25/50 [00:02<00:02, 8.80it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 8.80it/s]\n 54%|█████▍ | 27/50 [00:03<00:02, 8.81it/s]\n 56%|█████▌ | 28/50 [00:03<00:02, 8.81it/s]\n 58%|█████▊ | 29/50 [00:03<00:02, 8.80it/s]\n 60%|██████ | 30/50 [00:03<00:02, 8.79it/s]\n 62%|██████▏ | 31/50 [00:03<00:02, 8.80it/s]\n 64%|██████▍ | 32/50 [00:03<00:02, 8.81it/s]\n 66%|██████▌ | 33/50 [00:03<00:01, 8.81it/s]\n 68%|██████▊ | 34/50 [00:03<00:01, 8.81it/s]\n 70%|███████ | 35/50 [00:04<00:01, 8.79it/s]\n 72%|███████▏ | 36/50 [00:04<00:01, 8.79it/s]\n 74%|███████▍ | 37/50 [00:04<00:01, 8.80it/s]\n 76%|███████▌ | 38/50 [00:04<00:01, 8.80it/s]\n 78%|███████▊ | 39/50 [00:04<00:01, 8.81it/s]\n 80%|████████ | 40/50 [00:04<00:01, 8.79it/s]\n 82%|████████▏ | 41/50 [00:04<00:01, 8.80it/s]\n 84%|████████▍ | 42/50 [00:04<00:00, 8.80it/s]\n 86%|████████▌ | 43/50 [00:04<00:00, 8.81it/s]\n 88%|████████▊ | 44/50 [00:05<00:00, 8.81it/s]\n 90%|█████████ | 45/50 [00:05<00:00, 8.81it/s]\n 92%|█████████▏| 46/50 [00:05<00:00, 8.81it/s]\n 94%|█████████▍| 47/50 [00:05<00:00, 8.81it/s]\n 96%|█████████▌| 48/50 [00:05<00:00, 8.81it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 8.80it/s]\n100%|██████████| 50/50 [00:05<00:00, 8.81it/s]\n100%|██████████| 50/50 [00:05<00:00, 8.76it/s]", "metrics": { "predict_time": 6.655911, "total_time": 6.723681 }, "output": [ "https://replicate.delivery/pbxt/uoBf7xioxJ2UWahUFFPxNRsZqfV4WxhUehCSukp0W7t2wc1gA/out-0.png" ], "started_at": "2023-02-03T16:19:33.912010Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/bhfhvew2orgpxljj62dtgfyqim", "cancel": "https://api.replicate.com/v1/predictions/bhfhvew2orgpxljj62dtgfyqim/cancel" }, "version": "bb149dd20427beccf1b9f6332c7d5c233d914173fd463faa2c4a011080133afc" }Generated inUsing seed: 12654 The requested LoRAs are loaded. 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:06, 7.21it/s] 4%|▍ | 2/50 [00:00<00:05, 8.05it/s] 6%|▌ | 3/50 [00:00<00:05, 8.37it/s] 8%|▊ | 4/50 [00:00<00:05, 8.54it/s] 10%|█ | 5/50 [00:00<00:05, 8.63it/s] 12%|█▏ | 6/50 [00:00<00:05, 8.69it/s] 14%|█▍ | 7/50 [00:00<00:04, 8.70it/s] 16%|█▌ | 8/50 [00:00<00:04, 8.73it/s] 18%|█▊ | 9/50 [00:01<00:04, 8.75it/s] 20%|██ | 10/50 [00:01<00:04, 8.76it/s] 22%|██▏ | 11/50 [00:01<00:04, 8.77it/s] 24%|██▍ | 12/50 [00:01<00:04, 8.78it/s] 26%|██▌ | 13/50 [00:01<00:04, 8.80it/s] 28%|██▊ | 14/50 [00:01<00:04, 8.80it/s] 30%|███ | 15/50 [00:01<00:03, 8.81it/s] 32%|███▏ | 16/50 [00:01<00:03, 8.81it/s] 34%|███▍ | 17/50 [00:01<00:03, 8.81it/s] 36%|███▌ | 18/50 [00:02<00:03, 8.81it/s] 38%|███▊ | 19/50 [00:02<00:03, 8.81it/s] 40%|████ | 20/50 [00:02<00:03, 8.79it/s] 42%|████▏ | 21/50 [00:02<00:03, 8.80it/s] 44%|████▍ | 22/50 [00:02<00:03, 8.81it/s] 46%|████▌ | 23/50 [00:02<00:03, 8.81it/s] 48%|████▊ | 24/50 [00:02<00:02, 8.81it/s] 50%|█████ | 25/50 [00:02<00:02, 8.80it/s] 52%|█████▏ | 26/50 [00:02<00:02, 8.80it/s] 54%|█████▍ | 27/50 [00:03<00:02, 8.81it/s] 56%|█████▌ | 28/50 [00:03<00:02, 8.81it/s] 58%|█████▊ | 29/50 [00:03<00:02, 8.80it/s] 60%|██████ | 30/50 [00:03<00:02, 8.79it/s] 62%|██████▏ | 31/50 [00:03<00:02, 8.80it/s] 64%|██████▍ | 32/50 [00:03<00:02, 8.81it/s] 66%|██████▌ | 33/50 [00:03<00:01, 8.81it/s] 68%|██████▊ | 34/50 [00:03<00:01, 8.81it/s] 70%|███████ | 35/50 [00:04<00:01, 8.79it/s] 72%|███████▏ | 36/50 [00:04<00:01, 8.79it/s] 74%|███████▍ | 37/50 [00:04<00:01, 8.80it/s] 76%|███████▌ | 38/50 [00:04<00:01, 8.80it/s] 78%|███████▊ | 39/50 [00:04<00:01, 8.81it/s] 80%|████████ | 40/50 [00:04<00:01, 8.79it/s] 82%|████████▏ | 41/50 [00:04<00:01, 8.80it/s] 84%|████████▍ | 42/50 [00:04<00:00, 8.80it/s] 86%|████████▌ | 43/50 [00:04<00:00, 8.81it/s] 88%|████████▊ | 44/50 [00:05<00:00, 8.81it/s] 90%|█████████ | 45/50 [00:05<00:00, 8.81it/s] 92%|█████████▏| 46/50 [00:05<00:00, 8.81it/s] 94%|█████████▍| 47/50 [00:05<00:00, 8.81it/s] 96%|█████████▌| 48/50 [00:05<00:00, 8.81it/s] 98%|█████████▊| 49/50 [00:05<00:00, 8.80it/s] 100%|██████████| 50/50 [00:05<00:00, 8.81it/s] 100%|██████████| 50/50 [00:05<00:00, 8.76it/s]
Want to make some of these yourself?
Run this model