cloneofsimo
/
gta5_lora
- Public
- 11.2K runs
-
T4
Prediction
cloneofsimo/gta5_lora:510cd14734751629cdd247aa8f46bdbed91043302a3a4c9fd0d02b71b4f42dd1Input
- width
- 512
- height
- 512
- prompt
- a photo of <1> gtav style
- lora_urls
- https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors
- scheduler
- DPMSolverMultistep
- lora_scales
- 0.3
- num_outputs
- 1
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "width": 512, "height": 512, "prompt": "a photo of <1> gtav style", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.3", "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/gta5_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cloneofsimo/gta5_lora:510cd14734751629cdd247aa8f46bdbed91043302a3a4c9fd0d02b71b4f42dd1", { input: { width: 512, height: 512, prompt: "a photo of <1> gtav style", lora_urls: "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", scheduler: "DPMSolverMultistep", lora_scales: "0.3", 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/gta5_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cloneofsimo/gta5_lora:510cd14734751629cdd247aa8f46bdbed91043302a3a4c9fd0d02b71b4f42dd1", input={ "width": 512, "height": 512, "prompt": "a photo of <1> gtav style", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.3", "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/gta5_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": "510cd14734751629cdd247aa8f46bdbed91043302a3a4c9fd0d02b71b4f42dd1", "input": { "width": 512, "height": 512, "prompt": "a photo of <1> gtav style", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.3", "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": "2023-02-07T12:23:47.003961Z", "created_at": "2023-02-07T12:18:39.363405Z", "data_removed": false, "error": null, "id": "326cs4pgdbhxbku3acyd4tvlna", "input": { "width": 512, "height": 512, "prompt": "a photo of <1> gtav style", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.3", "num_outputs": 1, "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Using seed: 34243\nDownloading LoRA model... from https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors\nEmbedding <s1> replaced to <s0-0>\nEmbedding <s2> replaced to <s0-1>\nSaved at a19abd9d90155444e77213ffdb9df509104c355cf2070e66c289ca10190d119b7247ec08de67cee71cbafc02e4d35a005a68eba909b4cce697fe7b695de4b355.safetensors\nmerging time: 0.04404568672180176\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<00:13, 3.71it/s]\n 4%|▍ | 2/50 [00:00<00:10, 4.37it/s]\n 6%|▌ | 3/50 [00:00<00:10, 4.68it/s]\n 8%|▊ | 4/50 [00:00<00:09, 4.86it/s]\n 10%|█ | 5/50 [00:01<00:09, 4.93it/s]\n 12%|█▏ | 6/50 [00:01<00:08, 4.96it/s]\n 14%|█▍ | 7/50 [00:01<00:08, 5.00it/s]\n 16%|█▌ | 8/50 [00:01<00:08, 5.02it/s]\n 18%|█▊ | 9/50 [00:01<00:08, 5.05it/s]\n 20%|██ | 10/50 [00:02<00:07, 5.07it/s]\n 22%|██▏ | 11/50 [00:02<00:07, 5.06it/s]\n 24%|██▍ | 12/50 [00:02<00:07, 5.05it/s]\n 26%|██▌ | 13/50 [00:02<00:07, 5.07it/s]\n 28%|██▊ | 14/50 [00:02<00:07, 5.08it/s]\n 30%|███ | 15/50 [00:03<00:06, 5.08it/s]\n 32%|███▏ | 16/50 [00:03<00:06, 5.08it/s]\n 34%|███▍ | 17/50 [00:03<00:06, 5.07it/s]\n 36%|███▌ | 18/50 [00:03<00:06, 5.06it/s]\n 38%|███▊ | 19/50 [00:03<00:06, 5.06it/s]\n 40%|████ | 20/50 [00:04<00:05, 5.05it/s]\n 42%|████▏ | 21/50 [00:04<00:05, 5.06it/s]\n 44%|████▍ | 22/50 [00:04<00:05, 5.06it/s]\n 46%|████▌ | 23/50 [00:04<00:05, 5.05it/s]\n 48%|████▊ | 24/50 [00:04<00:05, 5.04it/s]\n 50%|█████ | 25/50 [00:05<00:04, 5.06it/s]\n 52%|█████▏ | 26/50 [00:05<00:04, 5.06it/s]\n 54%|█████▍ | 27/50 [00:05<00:04, 5.06it/s]\n 56%|█████▌ | 28/50 [00:05<00:04, 5.04it/s]\n 58%|█████▊ | 29/50 [00:05<00:04, 5.04it/s]\n 60%|██████ | 30/50 [00:05<00:03, 5.06it/s]\n 62%|██████▏ | 31/50 [00:06<00:03, 5.07it/s]\n 64%|██████▍ | 32/50 [00:06<00:03, 5.05it/s]\n 66%|██████▌ | 33/50 [00:06<00:03, 5.05it/s]\n 68%|██████▊ | 34/50 [00:06<00:03, 5.07it/s]\n 70%|███████ | 35/50 [00:06<00:02, 5.07it/s]\n 72%|███████▏ | 36/50 [00:07<00:02, 5.07it/s]\n 74%|███████▍ | 37/50 [00:07<00:02, 5.08it/s]\n 76%|███████▌ | 38/50 [00:07<00:02, 5.08it/s]\n 78%|███████▊ | 39/50 [00:07<00:02, 5.06it/s]\n 80%|████████ | 40/50 [00:07<00:01, 5.07it/s]\n 82%|████████▏ | 41/50 [00:08<00:01, 5.08it/s]\n 84%|████████▍ | 42/50 [00:08<00:01, 5.07it/s]\n 86%|████████▌ | 43/50 [00:08<00:01, 5.07it/s]\n 88%|████████▊ | 44/50 [00:08<00:01, 5.05it/s]\n 90%|█████████ | 45/50 [00:08<00:00, 5.06it/s]\n 92%|█████████▏| 46/50 [00:09<00:00, 5.07it/s]\n 94%|█████████▍| 47/50 [00:09<00:00, 5.06it/s]\n 96%|█████████▌| 48/50 [00:09<00:00, 5.05it/s]\n 98%|█████████▊| 49/50 [00:09<00:00, 5.03it/s]\n100%|██████████| 50/50 [00:09<00:00, 5.04it/s]\n100%|██████████| 50/50 [00:09<00:00, 5.03it/s]", "metrics": { "predict_time": 13.025214, "total_time": 307.640556 }, "output": [ "https://replicate.delivery/pbxt/cSmt4kc1qALpLReNU1OS67BskmlogVo8trBzjktCAf9STf3gA/out-0.png" ], "started_at": "2023-02-07T12:23:33.978747Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/326cs4pgdbhxbku3acyd4tvlna", "cancel": "https://api.replicate.com/v1/predictions/326cs4pgdbhxbku3acyd4tvlna/cancel" }, "version": "510cd14734751629cdd247aa8f46bdbed91043302a3a4c9fd0d02b71b4f42dd1" }Generated inUsing seed: 34243 Downloading LoRA model... from https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors Embedding <s1> replaced to <s0-0> Embedding <s2> replaced to <s0-1> Saved at a19abd9d90155444e77213ffdb9df509104c355cf2070e66c289ca10190d119b7247ec08de67cee71cbafc02e4d35a005a68eba909b4cce697fe7b695de4b355.safetensors merging time: 0.04404568672180176 <s0-0> The tokenizer already contains the token <s0-0>. Replacing <s0-0> embedding. <s0-1> The tokenizer already contains the token <s0-1>. 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