hudsongraeme
/
highland
Tesla Model 3 Highland SDXL fine-tune
- Public
- 304 runs
-
L40S
- SDXL fine-tune
Prediction
hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddfIDd23ss5tbu5axqmaqqqepx676jaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of a green TOK parked in London England
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo of a green TOK parked in London England", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "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 hudsongraeme/highland using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", { input: { width: 1024, height: 1024, prompt: "A photo of a green TOK parked in London England", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, 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 hudsongraeme/highland using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", input={ "width": 1024, "height": 1024, "prompt": "A photo of a green TOK parked in London England", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "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 hudsongraeme/highland 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": "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a green TOK parked in London England", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "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": "2023-10-14T17:00:36.940260Z", "created_at": "2023-10-14T16:59:38.480280Z", "data_removed": false, "error": null, "id": "d23ss5tbu5axqmaqqqepx676ja", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a green TOK parked in London England", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 27168\nskipping loading .. weights already loaded\nPrompt: A photo of a green <s0><s1> parked in London England\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<00:50, 1.04s/it]\n 4%|▍ | 2/50 [00:02<00:50, 1.04s/it]\n 6%|▌ | 3/50 [00:03<00:49, 1.05s/it]\n 8%|▊ | 4/50 [00:04<00:48, 1.05s/it]\n 10%|█ | 5/50 [00:05<00:47, 1.05s/it]\n 12%|█▏ | 6/50 [00:06<00:46, 1.05s/it]\n 14%|█▍ | 7/50 [00:07<00:45, 1.05s/it]\n 16%|█▌ | 8/50 [00:08<00:44, 1.05s/it]\n 18%|█▊ | 9/50 [00:09<00:43, 1.05s/it]\n 20%|██ | 10/50 [00:10<00:41, 1.05s/it]\n 22%|██▏ | 11/50 [00:11<00:40, 1.05s/it]\n 24%|██▍ | 12/50 [00:12<00:39, 1.05s/it]\n 26%|██▌ | 13/50 [00:13<00:38, 1.05s/it]\n 28%|██▊ | 14/50 [00:14<00:37, 1.05s/it]\n 30%|███ | 15/50 [00:15<00:36, 1.05s/it]\n 32%|███▏ | 16/50 [00:16<00:35, 1.05s/it]\n 34%|███▍ | 17/50 [00:17<00:34, 1.05s/it]\n 36%|███▌ | 18/50 [00:18<00:33, 1.05s/it]\n 38%|███▊ | 19/50 [00:19<00:32, 1.05s/it]\n 40%|████ | 20/50 [00:20<00:31, 1.05s/it]\n 42%|████▏ | 21/50 [00:22<00:30, 1.05s/it]\n 44%|████▍ | 22/50 [00:23<00:29, 1.05s/it]\n 46%|████▌ | 23/50 [00:24<00:28, 1.05s/it]\n 48%|████▊ | 24/50 [00:25<00:27, 1.05s/it]\n 50%|█████ | 25/50 [00:26<00:26, 1.05s/it]\n 52%|█████▏ | 26/50 [00:27<00:25, 1.05s/it]\n 54%|█████▍ | 27/50 [00:28<00:24, 1.05s/it]\n 56%|█████▌ | 28/50 [00:29<00:23, 1.05s/it]\n 58%|█████▊ | 29/50 [00:30<00:22, 1.05s/it]\n 60%|██████ | 30/50 [00:31<00:21, 1.05s/it]\n 62%|██████▏ | 31/50 [00:32<00:19, 1.05s/it]\n 64%|██████▍ | 32/50 [00:33<00:18, 1.05s/it]\n 66%|██████▌ | 33/50 [00:34<00:17, 1.05s/it]\n 68%|██████▊ | 34/50 [00:35<00:16, 1.05s/it]\n 70%|███████ | 35/50 [00:36<00:15, 1.05s/it]\n 72%|███████▏ | 36/50 [00:37<00:14, 1.05s/it]\n 74%|███████▍ | 37/50 [00:38<00:13, 1.05s/it]\n 76%|███████▌ | 38/50 [00:39<00:12, 1.05s/it]\n 78%|███████▊ | 39/50 [00:40<00:11, 1.05s/it]\n 80%|████████ | 40/50 [00:42<00:10, 1.05s/it]\n 82%|████████▏ | 41/50 [00:43<00:09, 1.05s/it]\n 84%|████████▍ | 42/50 [00:44<00:08, 1.05s/it]\n 86%|████████▌ | 43/50 [00:45<00:07, 1.05s/it]\n 88%|████████▊ | 44/50 [00:46<00:06, 1.05s/it]\n 90%|█████████ | 45/50 [00:47<00:05, 1.05s/it]\n 92%|█████████▏| 46/50 [00:48<00:04, 1.05s/it]\n 94%|█████████▍| 47/50 [00:49<00:03, 1.05s/it]\n 96%|█████████▌| 48/50 [00:50<00:02, 1.05s/it]\n 98%|█████████▊| 49/50 [00:51<00:01, 1.05s/it]\n100%|██████████| 50/50 [00:52<00:00, 1.05s/it]\n100%|██████████| 50/50 [00:52<00:00, 1.05s/it]", "metrics": { "predict_time": 58.260371, "total_time": 58.45998 }, "output": [ "https://pbxt.replicate.delivery/hoeNTNmvDpSKYynnNNUvBrTHpODAfXSifezfhyxiuRdfM7hbE/out-0.png", "https://pbxt.replicate.delivery/MIaQmMut9xIvN1nxtIB55VcatKOXqtnIiokhRfxvnOKa2D3IA/out-1.png", "https://pbxt.replicate.delivery/aNXslFSQqL77C9vKfSUXeNyfRChrWKbWxqPN5GJOdRJoZPcjA/out-2.png", "https://pbxt.replicate.delivery/7xBXgsNunHaND9ZbCevHYls8gXBmHC3nwCp1j6jb0SJa2D3IA/out-3.png" ], "started_at": "2023-10-14T16:59:38.679889Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/d23ss5tbu5axqmaqqqepx676ja", "cancel": "https://api.replicate.com/v1/predictions/d23ss5tbu5axqmaqqqepx676ja/cancel" }, "version": "0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf" }Generated inUsing seed: 27168 skipping loading .. weights already loaded Prompt: A photo of a green <s0><s1> parked in London England txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<00:50, 1.04s/it] 4%|▍ | 2/50 [00:02<00:50, 1.04s/it] 6%|▌ | 3/50 [00:03<00:49, 1.05s/it] 8%|▊ | 4/50 [00:04<00:48, 1.05s/it] 10%|█ | 5/50 [00:05<00:47, 1.05s/it] 12%|█▏ | 6/50 [00:06<00:46, 1.05s/it] 14%|█▍ | 7/50 [00:07<00:45, 1.05s/it] 16%|█▌ | 8/50 [00:08<00:44, 1.05s/it] 18%|█▊ | 9/50 [00:09<00:43, 1.05s/it] 20%|██ | 10/50 [00:10<00:41, 1.05s/it] 22%|██▏ | 11/50 [00:11<00:40, 1.05s/it] 24%|██▍ | 12/50 [00:12<00:39, 1.05s/it] 26%|██▌ | 13/50 [00:13<00:38, 1.05s/it] 28%|██▊ | 14/50 [00:14<00:37, 1.05s/it] 30%|███ | 15/50 [00:15<00:36, 1.05s/it] 32%|███▏ | 16/50 [00:16<00:35, 1.05s/it] 34%|███▍ | 17/50 [00:17<00:34, 1.05s/it] 36%|███▌ | 18/50 [00:18<00:33, 1.05s/it] 38%|███▊ | 19/50 [00:19<00:32, 1.05s/it] 40%|████ | 20/50 [00:20<00:31, 1.05s/it] 42%|████▏ | 21/50 [00:22<00:30, 1.05s/it] 44%|████▍ | 22/50 [00:23<00:29, 1.05s/it] 46%|████▌ | 23/50 [00:24<00:28, 1.05s/it] 48%|████▊ | 24/50 [00:25<00:27, 1.05s/it] 50%|█████ | 25/50 [00:26<00:26, 1.05s/it] 52%|█████▏ | 26/50 [00:27<00:25, 1.05s/it] 54%|█████▍ | 27/50 [00:28<00:24, 1.05s/it] 56%|█████▌ 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[00:52<00:00, 1.05s/it] 100%|██████████| 50/50 [00:52<00:00, 1.05s/it]Prediction
hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddfIDoq24czlbvo4cjufk2fv6esuuhyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of TOK driving in deep water
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo of TOK driving in deep water", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "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 hudsongraeme/highland using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", { input: { width: 1024, height: 1024, prompt: "A photo of TOK driving in deep water", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, 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 hudsongraeme/highland using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", input={ "width": 1024, "height": 1024, "prompt": "A photo of TOK driving in deep water", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "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 hudsongraeme/highland 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": "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", "input": { "width": 1024, "height": 1024, "prompt": "A photo of TOK driving in deep water", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "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": "2023-10-14T17:18:18.142124Z", "created_at": "2023-10-14T17:17:19.520173Z", "data_removed": false, "error": null, "id": "oq24czlbvo4cjufk2fv6esuuhy", "input": { "width": 1024, "height": 1024, "prompt": "A photo of TOK driving in deep water", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 31318\nskipping loading .. weights already loaded\nPrompt: A photo of <s0><s1> driving in deep water\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<00:51, 1.04s/it]\n 4%|▍ | 2/50 [00:02<00:50, 1.05s/it]\n 6%|▌ | 3/50 [00:03<00:49, 1.05s/it]\n 8%|▊ | 4/50 [00:04<00:48, 1.05s/it]\n 10%|█ | 5/50 [00:05<00:47, 1.05s/it]\n 12%|█▏ | 6/50 [00:06<00:46, 1.05s/it]\n 14%|█▍ | 7/50 [00:07<00:45, 1.05s/it]\n 16%|█▌ | 8/50 [00:08<00:44, 1.05s/it]\n 18%|█▊ | 9/50 [00:09<00:43, 1.05s/it]\n 20%|██ | 10/50 [00:10<00:42, 1.05s/it]\n 22%|██▏ | 11/50 [00:11<00:40, 1.05s/it]\n 24%|██▍ | 12/50 [00:12<00:39, 1.05s/it]\n 26%|██▌ | 13/50 [00:13<00:38, 1.05s/it]\n 28%|██▊ | 14/50 [00:14<00:37, 1.05s/it]\n 30%|███ | 15/50 [00:15<00:36, 1.05s/it]\n 32%|███▏ | 16/50 [00:16<00:35, 1.05s/it]\n 34%|███▍ | 17/50 [00:17<00:34, 1.05s/it]\n 36%|███▌ | 18/50 [00:18<00:33, 1.05s/it]\n 38%|███▊ | 19/50 [00:19<00:32, 1.05s/it]\n 40%|████ | 20/50 [00:20<00:31, 1.05s/it]\n 42%|████▏ | 21/50 [00:22<00:30, 1.05s/it]\n 44%|████▍ | 22/50 [00:23<00:29, 1.05s/it]\n 46%|████▌ | 23/50 [00:24<00:28, 1.05s/it]\n 48%|████▊ | 24/50 [00:25<00:27, 1.05s/it]\n 50%|█████ | 25/50 [00:26<00:26, 1.05s/it]\n 52%|█████▏ | 26/50 [00:27<00:25, 1.05s/it]\n 54%|█████▍ | 27/50 [00:28<00:24, 1.05s/it]\n 56%|█████▌ | 28/50 [00:29<00:23, 1.05s/it]\n 58%|█████▊ | 29/50 [00:30<00:22, 1.05s/it]\n 60%|██████ | 30/50 [00:31<00:21, 1.05s/it]\n 62%|██████▏ | 31/50 [00:32<00:19, 1.05s/it]\n 64%|██████▍ | 32/50 [00:33<00:18, 1.05s/it]\n 66%|██████▌ | 33/50 [00:34<00:17, 1.05s/it]\n 68%|██████▊ | 34/50 [00:35<00:16, 1.05s/it]\n 70%|███████ | 35/50 [00:36<00:15, 1.05s/it]\n 72%|███████▏ | 36/50 [00:37<00:14, 1.05s/it]\n 74%|███████▍ | 37/50 [00:38<00:13, 1.05s/it]\n 76%|███████▌ | 38/50 [00:39<00:12, 1.05s/it]\n 78%|███████▊ | 39/50 [00:40<00:11, 1.05s/it]\n 80%|████████ | 40/50 [00:42<00:10, 1.06s/it]\n 82%|████████▏ | 41/50 [00:43<00:09, 1.05s/it]\n 84%|████████▍ | 42/50 [00:44<00:08, 1.06s/it]\n 86%|████████▌ | 43/50 [00:45<00:07, 1.05s/it]\n 88%|████████▊ | 44/50 [00:46<00:06, 1.05s/it]\n 90%|█████████ | 45/50 [00:47<00:05, 1.05s/it]\n 92%|█████████▏| 46/50 [00:48<00:04, 1.05s/it]\n 94%|█████████▍| 47/50 [00:49<00:03, 1.06s/it]\n 96%|█████████▌| 48/50 [00:50<00:02, 1.05s/it]\n 98%|█████████▊| 49/50 [00:51<00:01, 1.05s/it]\n100%|██████████| 50/50 [00:52<00:00, 1.06s/it]\n100%|██████████| 50/50 [00:52<00:00, 1.05s/it]", "metrics": { "predict_time": 58.460466, "total_time": 58.621951 }, "output": [ "https://pbxt.replicate.delivery/CXFHIjYlwH4lI1XTY3chGkQV1x9GFnzfiflYSwuzeg5x6PcjA/out-0.png", "https://pbxt.replicate.delivery/7e4GEVTxenlJ6EfiBdhs6tzfIQn5nBrPBClstJrDMDjk1fwNC/out-1.png", "https://pbxt.replicate.delivery/6eUUpLPiQTQWRi2fBGTjWL7014WSpDGYt5xcHKGU9WjZ9HuRA/out-2.png", "https://pbxt.replicate.delivery/7E2lFPULNMLdGBN7fA7eTNeyjbO2gzVWQYGiRqTAmeol1fwNC/out-3.png" ], "started_at": "2023-10-14T17:17:19.681658Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/oq24czlbvo4cjufk2fv6esuuhy", "cancel": "https://api.replicate.com/v1/predictions/oq24czlbvo4cjufk2fv6esuuhy/cancel" }, "version": "0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf" }Generated inUsing seed: 31318 skipping loading .. weights already loaded Prompt: A photo of <s0><s1> driving in deep water txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<00:51, 1.04s/it] 4%|▍ | 2/50 [00:02<00:50, 1.05s/it] 6%|▌ | 3/50 [00:03<00:49, 1.05s/it] 8%|▊ | 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100%|██████████| 50/50 [00:52<00:00, 1.05s/it]Prediction
hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddfID44btvvtbjrajj4ltosmu6box5eStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- image
- width
- 1024
- height
- 1024
- prompt
- A photo of black TOK parked in a dark garage, headlights
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.75
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "image": "https://replicate.delivery/pbxt/Jj0I2xaZiapNmdgzZd6X1ajK1FavXNyjIXTu85HbI1pMTJ71/1d052dc9dc5f0479b2aad37b283bf9dc.jpg", "width": 1024, "height": 1024, "prompt": "A photo of black TOK parked in a dark garage, headlights", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.75, "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 hudsongraeme/highland using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", { input: { image: "https://replicate.delivery/pbxt/Jj0I2xaZiapNmdgzZd6X1ajK1FavXNyjIXTu85HbI1pMTJ71/1d052dc9dc5f0479b2aad37b283bf9dc.jpg", width: 1024, height: 1024, prompt: "A photo of black TOK parked in a dark garage, headlights", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.75, 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 hudsongraeme/highland using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", input={ "image": "https://replicate.delivery/pbxt/Jj0I2xaZiapNmdgzZd6X1ajK1FavXNyjIXTu85HbI1pMTJ71/1d052dc9dc5f0479b2aad37b283bf9dc.jpg", "width": 1024, "height": 1024, "prompt": "A photo of black TOK parked in a dark garage, headlights", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.75, "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 hudsongraeme/highland 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": "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", "input": { "image": "https://replicate.delivery/pbxt/Jj0I2xaZiapNmdgzZd6X1ajK1FavXNyjIXTu85HbI1pMTJ71/1d052dc9dc5f0479b2aad37b283bf9dc.jpg", "width": 1024, "height": 1024, "prompt": "A photo of black TOK parked in a dark garage, headlights", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.75, "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": "2023-10-19T05:04:45.290705Z", "created_at": "2023-10-19T05:04:15.107113Z", "data_removed": false, "error": null, "id": "44btvvtbjrajj4ltosmu6box5e", "input": { "image": "https://replicate.delivery/pbxt/Jj0I2xaZiapNmdgzZd6X1ajK1FavXNyjIXTu85HbI1pMTJ71/1d052dc9dc5f0479b2aad37b283bf9dc.jpg", "width": 1024, "height": 1024, "prompt": "A photo of black TOK parked in a dark garage, headlights", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.75, "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: 2045\nEnsuring enough disk space...\nFree disk space: 1657469538304\nDownloading weights: https://pbxt.replicate.delivery/Z86EzxrueJVxMKoFopqrMx55nUBCOzv7eFlhgDSHeTl5HPcjA/trained_model.tar\nb'Downloaded 186 MB bytes in 1.317s (141 MB/s)\\nExtracted 186 MB in 0.063s (2.9 GB/s)\\n'\nDownloaded weights in 1.6889147758483887 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of black <s0><s1> parked in a dark garage, headlights\nimg2img mode\n/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 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:07, 5.21it/s]\n 5%|▌ | 2/40 [00:00<00:05, 7.04it/s]\n 8%|▊ | 3/40 [00:00<00:04, 7.99it/s]\n 10%|█ | 4/40 [00:00<00:04, 8.54it/s]\n 12%|█▎ | 5/40 [00:00<00:03, 8.89it/s]\n 15%|█▌ | 6/40 [00:00<00:03, 9.11it/s]\n 18%|█▊ | 7/40 [00:00<00:03, 9.29it/s]\n 20%|██ | 8/40 [00:00<00:03, 9.35it/s]\n 22%|██▎ | 9/40 [00:01<00:03, 9.40it/s]\n 25%|██▌ | 10/40 [00:01<00:03, 9.41it/s]\n 28%|██▊ | 11/40 [00:01<00:03, 9.39it/s]\n 30%|███ | 12/40 [00:01<00:02, 9.46it/s]\n 32%|███▎ | 13/40 [00:01<00:02, 9.52it/s]\n 35%|███▌ | 14/40 [00:01<00:02, 9.56it/s]\n 38%|███▊ | 15/40 [00:01<00:02, 9.55it/s]\n 40%|████ | 16/40 [00:01<00:02, 9.56it/s]\n 42%|████▎ | 17/40 [00:01<00:02, 9.59it/s]\n 45%|████▌ | 18/40 [00:01<00:02, 9.60it/s]\n 48%|████▊ | 19/40 [00:02<00:02, 9.61it/s]\n 50%|█████ | 20/40 [00:02<00:02, 9.36it/s]\n 52%|█████▎ | 21/40 [00:02<00:02, 9.42it/s]\n 55%|█████▌ | 22/40 [00:02<00:01, 9.50it/s]\n 57%|█████▊ | 23/40 [00:02<00:01, 9.54it/s]\n 60%|██████ | 24/40 [00:02<00:01, 9.57it/s]\n 62%|██████▎ | 25/40 [00:02<00:01, 9.48it/s]\n 65%|██████▌ | 26/40 [00:02<00:01, 9.44it/s]\n 68%|██████▊ | 27/40 [00:02<00:01, 9.39it/s]\n 70%|███████ | 28/40 [00:03<00:01, 9.36it/s]\n 72%|███████▎ | 29/40 [00:03<00:01, 9.44it/s]\n 75%|███████▌ | 30/40 [00:03<00:01, 9.31it/s]\n 78%|███████▊ | 31/40 [00:03<00:00, 9.34it/s]\n 80%|████████ | 32/40 [00:03<00:00, 9.39it/s]\n 82%|████████▎ | 33/40 [00:03<00:00, 9.42it/s]\n 85%|████████▌ | 34/40 [00:03<00:00, 9.35it/s]\n 88%|████████▊ | 35/40 [00:03<00:00, 9.36it/s]\n 90%|█████████ | 36/40 [00:03<00:00, 9.35it/s]\n 92%|█████████▎| 37/40 [00:03<00:00, 9.29it/s]\n 95%|█████████▌| 38/40 [00:04<00:00, 9.30it/s]\n 98%|█████████▊| 39/40 [00:04<00:00, 9.29it/s]\n100%|██████████| 40/40 [00:04<00:00, 9.24it/s]\n100%|██████████| 40/40 [00:04<00:00, 9.25it/s]", "metrics": { "predict_time": 9.342711, "total_time": 30.183592 }, "output": [ "https://pbxt.replicate.delivery/IfF0KceFvFtLS0ifqeED89K0I7vY3SUZpULY0w4QYHPyuaeNC/out-0.png" ], "started_at": "2023-10-19T05:04:35.947994Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/44btvvtbjrajj4ltosmu6box5e", "cancel": "https://api.replicate.com/v1/predictions/44btvvtbjrajj4ltosmu6box5e/cancel" }, "version": "0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf" }Generated inUsing seed: 2045 Ensuring enough disk space... Free disk space: 1657469538304 Downloading weights: https://pbxt.replicate.delivery/Z86EzxrueJVxMKoFopqrMx55nUBCOzv7eFlhgDSHeTl5HPcjA/trained_model.tar b'Downloaded 186 MB bytes in 1.317s (141 MB/s)\nExtracted 186 MB in 0.063s (2.9 GB/s)\n' Downloaded weights in 1.6889147758483887 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A photo of black <s0><s1> parked in a dark garage, headlights img2img mode /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, 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:07, 5.21it/s] 5%|▌ | 2/40 [00:00<00:05, 7.04it/s] 8%|▊ | 3/40 [00:00<00:04, 7.99it/s] 10%|█ | 4/40 [00:00<00:04, 8.54it/s] 12%|█▎ | 5/40 [00:00<00:03, 8.89it/s] 15%|█▌ | 6/40 [00:00<00:03, 9.11it/s] 18%|█▊ | 7/40 [00:00<00:03, 9.29it/s] 20%|██ | 8/40 [00:00<00:03, 9.35it/s] 22%|██▎ | 9/40 [00:01<00:03, 9.40it/s] 25%|██▌ | 10/40 [00:01<00:03, 9.41it/s] 28%|██▊ | 11/40 [00:01<00:03, 9.39it/s] 30%|███ | 12/40 [00:01<00:02, 9.46it/s] 32%|███▎ | 13/40 [00:01<00:02, 9.52it/s] 35%|███▌ | 14/40 [00:01<00:02, 9.56it/s] 38%|███▊ | 15/40 [00:01<00:02, 9.55it/s] 40%|████ | 16/40 [00:01<00:02, 9.56it/s] 42%|████▎ | 17/40 [00:01<00:02, 9.59it/s] 45%|████▌ | 18/40 [00:01<00:02, 9.60it/s] 48%|████▊ | 19/40 [00:02<00:02, 9.61it/s] 50%|█████ | 20/40 [00:02<00:02, 9.36it/s] 52%|█████▎ | 21/40 [00:02<00:02, 9.42it/s] 55%|█████▌ | 22/40 [00:02<00:01, 9.50it/s] 57%|█████▊ | 23/40 [00:02<00:01, 9.54it/s] 60%|██████ | 24/40 [00:02<00:01, 9.57it/s] 62%|██████▎ | 25/40 [00:02<00:01, 9.48it/s] 65%|██████▌ | 26/40 [00:02<00:01, 9.44it/s] 68%|██████▊ | 27/40 [00:02<00:01, 9.39it/s] 70%|███████ | 28/40 [00:03<00:01, 9.36it/s] 72%|███████▎ | 29/40 [00:03<00:01, 9.44it/s] 75%|███████▌ | 30/40 [00:03<00:01, 9.31it/s] 78%|███████▊ | 31/40 [00:03<00:00, 9.34it/s] 80%|████████ | 32/40 [00:03<00:00, 9.39it/s] 82%|████████▎ | 33/40 [00:03<00:00, 9.42it/s] 85%|████████▌ | 34/40 [00:03<00:00, 9.35it/s] 88%|████████▊ | 35/40 [00:03<00:00, 9.36it/s] 90%|█████████ | 36/40 [00:03<00:00, 9.35it/s] 92%|█████████▎| 37/40 [00:03<00:00, 9.29it/s] 95%|█████████▌| 38/40 [00:04<00:00, 9.30it/s] 98%|█████████▊| 39/40 [00:04<00:00, 9.29it/s] 100%|██████████| 40/40 [00:04<00:00, 9.24it/s] 100%|██████████| 40/40 [00:04<00:00, 9.25it/s]Prediction
hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddfIDch7dg2tbutnqeptuaplvzcoufeStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- image
- width
- 1024
- height
- 1024
- prompt
- A photo of black TOK parked in a dark garage, side view
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.75
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "image": "https://replicate.delivery/pbxt/Jj0KaNf2POzVZbR1TZGb0F8smOvHDEu0HAhn4MVeTZD2Ayae/1d052dc9dc5f0479b2aad37b283bf9dc.jpg", "width": 1024, "height": 1024, "prompt": "A photo of black TOK parked in a dark garage, side view", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.75, "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 hudsongraeme/highland using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", { input: { image: "https://replicate.delivery/pbxt/Jj0KaNf2POzVZbR1TZGb0F8smOvHDEu0HAhn4MVeTZD2Ayae/1d052dc9dc5f0479b2aad37b283bf9dc.jpg", width: 1024, height: 1024, prompt: "A photo of black TOK parked in a dark garage, side view", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.75, 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 hudsongraeme/highland using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", input={ "image": "https://replicate.delivery/pbxt/Jj0KaNf2POzVZbR1TZGb0F8smOvHDEu0HAhn4MVeTZD2Ayae/1d052dc9dc5f0479b2aad37b283bf9dc.jpg", "width": 1024, "height": 1024, "prompt": "A photo of black TOK parked in a dark garage, side view", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.75, "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 hudsongraeme/highland 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": "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", "input": { "image": "https://replicate.delivery/pbxt/Jj0KaNf2POzVZbR1TZGb0F8smOvHDEu0HAhn4MVeTZD2Ayae/1d052dc9dc5f0479b2aad37b283bf9dc.jpg", "width": 1024, "height": 1024, "prompt": "A photo of black TOK parked in a dark garage, side view", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.75, "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": "2023-10-19T05:07:01.766698Z", "created_at": "2023-10-19T05:06:56.457289Z", "data_removed": false, "error": null, "id": "ch7dg2tbutnqeptuaplvzcoufe", "input": { "image": "https://replicate.delivery/pbxt/Jj0KaNf2POzVZbR1TZGb0F8smOvHDEu0HAhn4MVeTZD2Ayae/1d052dc9dc5f0479b2aad37b283bf9dc.jpg", "width": 1024, "height": 1024, "prompt": "A photo of black TOK parked in a dark garage, side view", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.75, "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: 28443\nskipping loading .. weights already loaded\nPrompt: A photo of black <s0><s1> parked in a dark garage, side view\nimg2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:04, 8.52it/s]\n 5%|▌ | 2/40 [00:00<00:04, 8.63it/s]\n 8%|▊ | 3/40 [00:00<00:04, 8.68it/s]\n 10%|█ | 4/40 [00:00<00:04, 8.88it/s]\n 12%|█▎ | 5/40 [00:00<00:03, 9.05it/s]\n 15%|█▌ | 6/40 [00:00<00:03, 9.20it/s]\n 18%|█▊ | 7/40 [00:00<00:03, 9.29it/s]\n 20%|██ | 8/40 [00:00<00:03, 9.36it/s]\n 22%|██▎ | 9/40 [00:00<00:03, 9.35it/s]\n 25%|██▌ | 10/40 [00:01<00:03, 9.23it/s]\n 28%|██▊ | 11/40 [00:01<00:03, 9.04it/s]\n 30%|███ | 12/40 [00:01<00:03, 8.93it/s]\n 32%|███▎ | 13/40 [00:01<00:02, 9.04it/s]\n 35%|███▌ | 14/40 [00:01<00:02, 9.10it/s]\n 38%|███▊ | 15/40 [00:01<00:02, 9.22it/s]\n 40%|████ | 16/40 [00:01<00:02, 9.31it/s]\n 42%|████▎ | 17/40 [00:01<00:02, 9.38it/s]\n 45%|████▌ | 18/40 [00:01<00:02, 9.17it/s]\n 48%|████▊ | 19/40 [00:02<00:02, 9.11it/s]\n 50%|█████ | 20/40 [00:02<00:02, 8.98it/s]\n 52%|█████▎ | 21/40 [00:02<00:02, 8.91it/s]\n 55%|█████▌ | 22/40 [00:02<00:02, 8.84it/s]\n 57%|█████▊ | 23/40 [00:02<00:01, 8.76it/s]\n 60%|██████ | 24/40 [00:02<00:01, 8.96it/s]\n 62%|██████▎ | 25/40 [00:02<00:01, 9.11it/s]\n 65%|██████▌ | 26/40 [00:02<00:01, 9.23it/s]\n 68%|██████▊ | 27/40 [00:02<00:01, 9.31it/s]\n 70%|███████ | 28/40 [00:03<00:01, 9.33it/s]\n 72%|███████▎ | 29/40 [00:03<00:01, 9.23it/s]\n 75%|███████▌ | 30/40 [00:03<00:01, 9.08it/s]\n 78%|███████▊ | 31/40 [00:03<00:00, 9.18it/s]\n 80%|████████ | 32/40 [00:03<00:00, 9.00it/s]\n 82%|████████▎ | 33/40 [00:03<00:00, 9.04it/s]\n 85%|████████▌ | 34/40 [00:03<00:00, 9.14it/s]\n 88%|████████▊ | 35/40 [00:03<00:00, 9.22it/s]\n 90%|█████████ | 36/40 [00:03<00:00, 9.12it/s]\n 92%|█████████▎| 37/40 [00:04<00:00, 9.15it/s]\n 95%|█████████▌| 38/40 [00:04<00:00, 9.04it/s]\n 98%|█████████▊| 39/40 [00:04<00:00, 9.17it/s]\n100%|██████████| 40/40 [00:04<00:00, 9.28it/s]\n100%|██████████| 40/40 [00:04<00:00, 9.12it/s]", "metrics": { "predict_time": 5.365755, "total_time": 5.309409 }, "output": [ "https://pbxt.replicate.delivery/lFxfMqlGvFWiWKdO4E0E3GqWm2uTqohf4jNWz3ANs7erbNfGB/out-0.png" ], "started_at": "2023-10-19T05:06:56.400943Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ch7dg2tbutnqeptuaplvzcoufe", "cancel": "https://api.replicate.com/v1/predictions/ch7dg2tbutnqeptuaplvzcoufe/cancel" }, "version": "0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf" }Generated inUsing seed: 28443 skipping loading .. weights already loaded Prompt: A photo of black <s0><s1> parked in a dark garage, side view img2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:04, 8.52it/s] 5%|▌ | 2/40 [00:00<00:04, 8.63it/s] 8%|▊ | 3/40 [00:00<00:04, 8.68it/s] 10%|█ | 4/40 [00:00<00:04, 8.88it/s] 12%|█▎ | 5/40 [00:00<00:03, 9.05it/s] 15%|█▌ | 6/40 [00:00<00:03, 9.20it/s] 18%|█▊ | 7/40 [00:00<00:03, 9.29it/s] 20%|██ | 8/40 [00:00<00:03, 9.36it/s] 22%|██▎ | 9/40 [00:00<00:03, 9.35it/s] 25%|██▌ | 10/40 [00:01<00:03, 9.23it/s] 28%|██▊ | 11/40 [00:01<00:03, 9.04it/s] 30%|███ | 12/40 [00:01<00:03, 8.93it/s] 32%|███▎ | 13/40 [00:01<00:02, 9.04it/s] 35%|███▌ | 14/40 [00:01<00:02, 9.10it/s] 38%|███▊ | 15/40 [00:01<00:02, 9.22it/s] 40%|████ | 16/40 [00:01<00:02, 9.31it/s] 42%|████▎ | 17/40 [00:01<00:02, 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100%|██████████| 40/40 [00:04<00:00, 9.28it/s] 100%|██████████| 40/40 [00:04<00:00, 9.12it/s]Prediction
hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddfIDnqefrtlbojhx3x2676b4tpfezmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- image
- width
- 1024
- height
- 1024
- prompt
- A photo of black TOK parked in a dark garage, glare
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.75
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "image": "https://replicate.delivery/pbxt/Jj0LG9S7LcO5KLEejBEyGLBRWwbGtKV2UD8EjFoO1UQStegs/1d052dc9dc5f0479b2aad37b283bf9dc.jpg", "width": 1024, "height": 1024, "prompt": "A photo of black TOK parked in a dark garage, glare", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.75, "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 hudsongraeme/highland using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", { input: { image: "https://replicate.delivery/pbxt/Jj0LG9S7LcO5KLEejBEyGLBRWwbGtKV2UD8EjFoO1UQStegs/1d052dc9dc5f0479b2aad37b283bf9dc.jpg", width: 1024, height: 1024, prompt: "A photo of black TOK parked in a dark garage, glare", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.75, 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 hudsongraeme/highland using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", input={ "image": "https://replicate.delivery/pbxt/Jj0LG9S7LcO5KLEejBEyGLBRWwbGtKV2UD8EjFoO1UQStegs/1d052dc9dc5f0479b2aad37b283bf9dc.jpg", "width": 1024, "height": 1024, "prompt": "A photo of black TOK parked in a dark garage, glare", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.75, "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 hudsongraeme/highland 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": "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", "input": { "image": "https://replicate.delivery/pbxt/Jj0LG9S7LcO5KLEejBEyGLBRWwbGtKV2UD8EjFoO1UQStegs/1d052dc9dc5f0479b2aad37b283bf9dc.jpg", "width": 1024, "height": 1024, "prompt": "A photo of black TOK parked in a dark garage, glare", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.75, "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": "2023-10-19T05:07:45.041999Z", "created_at": "2023-10-19T05:07:38.606242Z", "data_removed": false, "error": null, "id": "nqefrtlbojhx3x2676b4tpfezm", "input": { "image": "https://replicate.delivery/pbxt/Jj0LG9S7LcO5KLEejBEyGLBRWwbGtKV2UD8EjFoO1UQStegs/1d052dc9dc5f0479b2aad37b283bf9dc.jpg", "width": 1024, "height": 1024, "prompt": "A photo of black TOK parked in a dark garage, glare", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.75, "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: 29966\nskipping loading .. weights already loaded\nPrompt: A photo of black <s0><s1> parked in a dark garage, glare\nimg2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 2%|▎ | 1/40 [00:00<00:04, 8.91it/s]\n 5%|▌ | 2/40 [00:00<00:04, 9.14it/s]\n 8%|▊ | 3/40 [00:00<00:03, 9.29it/s]\n 10%|█ | 4/40 [00:00<00:03, 9.39it/s]\n 12%|█▎ | 5/40 [00:00<00:03, 9.43it/s]\n 15%|█▌ | 6/40 [00:00<00:03, 9.48it/s]\n 18%|█▊ | 7/40 [00:00<00:03, 9.47it/s]\n 20%|██ | 8/40 [00:00<00:03, 9.23it/s]\n 22%|██▎ | 9/40 [00:00<00:03, 9.00it/s]\n 25%|██▌ | 10/40 [00:01<00:03, 8.82it/s]\n 28%|██▊ | 11/40 [00:01<00:03, 8.68it/s]\n 30%|███ | 12/40 [00:01<00:03, 8.90it/s]\n 32%|███▎ | 13/40 [00:01<00:02, 9.09it/s]\n 35%|███▌ | 14/40 [00:01<00:02, 9.23it/s]\n 38%|███▊ | 15/40 [00:01<00:02, 9.33it/s]\n 40%|████ | 16/40 [00:01<00:02, 9.29it/s]\n 42%|████▎ | 17/40 [00:01<00:02, 9.07it/s]\n 45%|████▌ | 18/40 [00:01<00:02, 9.02it/s]\n 48%|████▊ | 19/40 [00:02<00:02, 9.14it/s]\n 50%|█████ | 20/40 [00:02<00:02, 9.02it/s]\n 52%|█████▎ | 21/40 [00:02<00:02, 8.96it/s]\n 55%|█████▌ | 22/40 [00:02<00:01, 9.13it/s]\n 57%|█████▊ | 23/40 [00:02<00:01, 9.27it/s]\n 60%|██████ | 24/40 [00:02<00:01, 9.37it/s]\n 62%|██████▎ | 25/40 [00:02<00:01, 9.43it/s]\n 65%|██████▌ | 26/40 [00:02<00:01, 9.24it/s]\n 68%|██████▊ | 27/40 [00:02<00:01, 9.12it/s]\n 70%|███████ | 28/40 [00:03<00:01, 9.04it/s]\n 72%|███████▎ | 29/40 [00:03<00:01, 8.95it/s]\n 75%|███████▌ | 30/40 [00:03<00:01, 8.90it/s]\n 78%|███████▊ | 31/40 [00:03<00:00, 9.05it/s]\n 80%|████████ | 32/40 [00:03<00:00, 9.20it/s]\n 82%|████████▎ | 33/40 [00:03<00:00, 9.31it/s]\n 85%|████████▌ | 34/40 [00:03<00:00, 9.40it/s]\n 88%|████████▊ | 35/40 [00:03<00:00, 9.25it/s]\n 90%|█████████ | 36/40 [00:03<00:00, 9.12it/s]\n 92%|█████████▎| 37/40 [00:04<00:00, 9.19it/s]\n 95%|█████████▌| 38/40 [00:04<00:00, 9.09it/s]\n 98%|█████████▊| 39/40 [00:04<00:00, 8.97it/s]\n100%|██████████| 40/40 [00:04<00:00, 8.92it/s]\n100%|██████████| 40/40 [00:04<00:00, 9.13it/s]", "metrics": { "predict_time": 5.328752, "total_time": 6.435757 }, "output": [ "https://pbxt.replicate.delivery/iV8NPA7UCMLfM64rHZ6YUSPe1t8VTLOlA7QC33dABm4gumvRA/out-0.png" ], "started_at": "2023-10-19T05:07:39.713247Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/nqefrtlbojhx3x2676b4tpfezm", "cancel": "https://api.replicate.com/v1/predictions/nqefrtlbojhx3x2676b4tpfezm/cancel" }, "version": "0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf" }Generated inUsing seed: 29966 skipping loading .. weights already loaded Prompt: A photo of black <s0><s1> parked in a dark garage, glare img2img mode 0%| | 0/40 [00:00<?, ?it/s] 2%|▎ | 1/40 [00:00<00:04, 8.91it/s] 5%|▌ | 2/40 [00:00<00:04, 9.14it/s] 8%|▊ | 3/40 [00:00<00:03, 9.29it/s] 10%|█ | 4/40 [00:00<00:03, 9.39it/s] 12%|█▎ | 5/40 [00:00<00:03, 9.43it/s] 15%|█▌ | 6/40 [00:00<00:03, 9.48it/s] 18%|█▊ | 7/40 [00:00<00:03, 9.47it/s] 20%|██ | 8/40 [00:00<00:03, 9.23it/s] 22%|██▎ | 9/40 [00:00<00:03, 9.00it/s] 25%|██▌ | 10/40 [00:01<00:03, 8.82it/s] 28%|██▊ | 11/40 [00:01<00:03, 8.68it/s] 30%|███ | 12/40 [00:01<00:03, 8.90it/s] 32%|███▎ | 13/40 [00:01<00:02, 9.09it/s] 35%|███▌ | 14/40 [00:01<00:02, 9.23it/s] 38%|███▊ | 15/40 [00:01<00:02, 9.33it/s] 40%|████ | 16/40 [00:01<00:02, 9.29it/s] 42%|████▎ | 17/40 [00:01<00:02, 9.07it/s] 45%|████▌ | 18/40 [00:01<00:02, 9.02it/s] 48%|████▊ | 19/40 [00:02<00:02, 9.14it/s] 50%|█████ | 20/40 [00:02<00:02, 9.02it/s] 52%|█████▎ | 21/40 [00:02<00:02, 8.96it/s] 55%|█████▌ | 22/40 [00:02<00:01, 9.13it/s] 57%|█████▊ | 23/40 [00:02<00:01, 9.27it/s] 60%|██████ | 24/40 [00:02<00:01, 9.37it/s] 62%|██████▎ | 25/40 [00:02<00:01, 9.43it/s] 65%|██████▌ | 26/40 [00:02<00:01, 9.24it/s] 68%|██████▊ | 27/40 [00:02<00:01, 9.12it/s] 70%|███████ | 28/40 [00:03<00:01, 9.04it/s] 72%|███████▎ | 29/40 [00:03<00:01, 8.95it/s] 75%|███████▌ | 30/40 [00:03<00:01, 8.90it/s] 78%|███████▊ | 31/40 [00:03<00:00, 9.05it/s] 80%|████████ | 32/40 [00:03<00:00, 9.20it/s] 82%|████████▎ | 33/40 [00:03<00:00, 9.31it/s] 85%|████████▌ | 34/40 [00:03<00:00, 9.40it/s] 88%|████████▊ | 35/40 [00:03<00:00, 9.25it/s] 90%|█████████ | 36/40 [00:03<00:00, 9.12it/s] 92%|█████████▎| 37/40 [00:04<00:00, 9.19it/s] 95%|█████████▌| 38/40 [00:04<00:00, 9.09it/s] 98%|█████████▊| 39/40 [00:04<00:00, 8.97it/s] 100%|██████████| 40/40 [00:04<00:00, 8.92it/s] 100%|██████████| 40/40 [00:04<00:00, 9.13it/s]Prediction
hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddfID5d5ccxdbicstcvgwlgoatncgueStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- image
- width
- 1024
- height
- 1024
- prompt
- A photo of TOK parked near autumn trees, bright
- 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
- 0.9
- num_inference_steps
- 50
{ "image": "https://replicate.delivery/pbxt/Jj0dlwNWKeg5pVrAhN6Lnn1GnLx25x10dJ8QXTqC99e3rFNZ/empty-autumn-road-with-trees-in-a-row-on-the-edges-photo.jpg", "width": 1024, "height": 1024, "prompt": "A photo of TOK parked near autumn trees, bright", "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": 0.9, "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 hudsongraeme/highland using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", { input: { image: "https://replicate.delivery/pbxt/Jj0dlwNWKeg5pVrAhN6Lnn1GnLx25x10dJ8QXTqC99e3rFNZ/empty-autumn-road-with-trees-in-a-row-on-the-edges-photo.jpg", width: 1024, height: 1024, prompt: "A photo of TOK parked near autumn trees, bright", 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: 0.9, 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 hudsongraeme/highland using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", input={ "image": "https://replicate.delivery/pbxt/Jj0dlwNWKeg5pVrAhN6Lnn1GnLx25x10dJ8QXTqC99e3rFNZ/empty-autumn-road-with-trees-in-a-row-on-the-edges-photo.jpg", "width": 1024, "height": 1024, "prompt": "A photo of TOK parked near autumn trees, bright", "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": 0.9, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run hudsongraeme/highland 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": "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", "input": { "image": "https://replicate.delivery/pbxt/Jj0dlwNWKeg5pVrAhN6Lnn1GnLx25x10dJ8QXTqC99e3rFNZ/empty-autumn-road-with-trees-in-a-row-on-the-edges-photo.jpg", "width": 1024, "height": 1024, "prompt": "A photo of TOK parked near autumn trees, bright", "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": 0.9, "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": "2023-10-19T05:27:24.731302Z", "created_at": "2023-10-19T05:27:10.807809Z", "data_removed": false, "error": null, "id": "5d5ccxdbicstcvgwlgoatncgue", "input": { "image": "https://replicate.delivery/pbxt/Jj0dlwNWKeg5pVrAhN6Lnn1GnLx25x10dJ8QXTqC99e3rFNZ/empty-autumn-road-with-trees-in-a-row-on-the-edges-photo.jpg", "width": 1024, "height": 1024, "prompt": "A photo of TOK parked near autumn trees, bright", "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": 0.9, "num_inference_steps": 50 }, "logs": "Using seed: 38313\nskipping loading .. weights already loaded\nPrompt: A photo of <s0><s1> parked near autumn trees, bright\nimg2img mode\n 0%| | 0/45 [00:00<?, ?it/s]\n 2%|▏ | 1/45 [00:00<00:09, 4.86it/s]\n 4%|▍ | 2/45 [00:00<00:08, 4.85it/s]\n 7%|▋ | 3/45 [00:00<00:08, 4.86it/s]\n 9%|▉ | 4/45 [00:00<00:08, 4.87it/s]\n 11%|█ | 5/45 [00:01<00:08, 4.87it/s]\n 13%|█▎ | 6/45 [00:01<00:07, 4.88it/s]\n 16%|█▌ | 7/45 [00:01<00:07, 4.88it/s]\n 18%|█▊ | 8/45 [00:01<00:07, 4.88it/s]\n 20%|██ | 9/45 [00:01<00:07, 4.88it/s]\n 22%|██▏ | 10/45 [00:02<00:07, 4.88it/s]\n 24%|██▍ | 11/45 [00:02<00:06, 4.88it/s]\n 27%|██▋ | 12/45 [00:02<00:06, 4.88it/s]\n 29%|██▉ | 13/45 [00:02<00:06, 4.89it/s]\n 31%|███ | 14/45 [00:02<00:06, 4.89it/s]\n 33%|███▎ | 15/45 [00:03<00:06, 4.89it/s]\n 36%|███▌ | 16/45 [00:03<00:05, 4.89it/s]\n 38%|███▊ | 17/45 [00:03<00:05, 4.88it/s]\n 40%|████ | 18/45 [00:03<00:05, 4.88it/s]\n 42%|████▏ | 19/45 [00:03<00:05, 4.88it/s]\n 44%|████▍ | 20/45 [00:04<00:05, 4.88it/s]\n 47%|████▋ | 21/45 [00:04<00:04, 4.88it/s]\n 49%|████▉ | 22/45 [00:04<00:04, 4.88it/s]\n 51%|█████ | 23/45 [00:04<00:04, 4.88it/s]\n 53%|█████▎ | 24/45 [00:04<00:04, 4.88it/s]\n 56%|█████▌ | 25/45 [00:05<00:04, 4.88it/s]\n 58%|█████▊ | 26/45 [00:05<00:03, 4.88it/s]\n 60%|██████ | 27/45 [00:05<00:03, 4.88it/s]\n 62%|██████▏ | 28/45 [00:05<00:03, 4.88it/s]\n 64%|██████▍ | 29/45 [00:05<00:03, 4.88it/s]\n 67%|██████▋ | 30/45 [00:06<00:03, 4.88it/s]\n 69%|██████▉ | 31/45 [00:06<00:02, 4.88it/s]\n 71%|███████ | 32/45 [00:06<00:02, 4.88it/s]\n 73%|███████▎ | 33/45 [00:06<00:02, 4.87it/s]\n 76%|███████▌ | 34/45 [00:06<00:02, 4.87it/s]\n 78%|███████▊ | 35/45 [00:07<00:02, 4.87it/s]\n 80%|████████ | 36/45 [00:07<00:01, 4.87it/s]\n 82%|████████▏ | 37/45 [00:07<00:01, 4.87it/s]\n 84%|████████▍ | 38/45 [00:07<00:01, 4.87it/s]\n 87%|████████▋ | 39/45 [00:07<00:01, 4.87it/s]\n 89%|████████▉ | 40/45 [00:08<00:01, 4.87it/s]\n 91%|█████████ | 41/45 [00:08<00:00, 4.86it/s]\n 93%|█████████▎| 42/45 [00:08<00:00, 4.86it/s]\n 96%|█████████▌| 43/45 [00:08<00:00, 4.86it/s]\n 98%|█████████▊| 44/45 [00:09<00:00, 4.86it/s]\n100%|██████████| 45/45 [00:09<00:00, 4.86it/s]\n100%|██████████| 45/45 [00:09<00:00, 4.87it/s]", "metrics": { "predict_time": 12.288079, "total_time": 13.923493 }, "output": [ "https://pbxt.replicate.delivery/wrVj9XtBipZlEhglMUFuyfvIZsaO8rX6FUY3818Qbxrdgz3IA/out-0.png" ], "started_at": "2023-10-19T05:27:12.443223Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5d5ccxdbicstcvgwlgoatncgue", "cancel": "https://api.replicate.com/v1/predictions/5d5ccxdbicstcvgwlgoatncgue/cancel" }, "version": "0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf" }Generated inUsing seed: 38313 skipping loading .. weights already loaded Prompt: A photo of <s0><s1> parked near autumn trees, bright img2img mode 0%| | 0/45 [00:00<?, ?it/s] 2%|▏ | 1/45 [00:00<00:09, 4.86it/s] 4%|▍ | 2/45 [00:00<00:08, 4.85it/s] 7%|▋ | 3/45 [00:00<00:08, 4.86it/s] 9%|▉ | 4/45 [00:00<00:08, 4.87it/s] 11%|█ | 5/45 [00:01<00:08, 4.87it/s] 13%|█▎ | 6/45 [00:01<00:07, 4.88it/s] 16%|█▌ | 7/45 [00:01<00:07, 4.88it/s] 18%|█▊ | 8/45 [00:01<00:07, 4.88it/s] 20%|██ | 9/45 [00:01<00:07, 4.88it/s] 22%|██▏ | 10/45 [00:02<00:07, 4.88it/s] 24%|██▍ | 11/45 [00:02<00:06, 4.88it/s] 27%|██▋ | 12/45 [00:02<00:06, 4.88it/s] 29%|██▉ | 13/45 [00:02<00:06, 4.89it/s] 31%|███ | 14/45 [00:02<00:06, 4.89it/s] 33%|███▎ | 15/45 [00:03<00:06, 4.89it/s] 36%|███▌ | 16/45 [00:03<00:05, 4.89it/s] 38%|███▊ | 17/45 [00:03<00:05, 4.88it/s] 40%|████ | 18/45 [00:03<00:05, 4.88it/s] 42%|████▏ | 19/45 [00:03<00:05, 4.88it/s] 44%|████▍ | 20/45 [00:04<00:05, 4.88it/s] 47%|████▋ | 21/45 [00:04<00:04, 4.88it/s] 49%|████▉ | 22/45 [00:04<00:04, 4.88it/s] 51%|█████ | 23/45 [00:04<00:04, 4.88it/s] 53%|█████▎ | 24/45 [00:04<00:04, 4.88it/s] 56%|█████▌ | 25/45 [00:05<00:04, 4.88it/s] 58%|█████▊ | 26/45 [00:05<00:03, 4.88it/s] 60%|██████ | 27/45 [00:05<00:03, 4.88it/s] 62%|██████▏ | 28/45 [00:05<00:03, 4.88it/s] 64%|██████▍ | 29/45 [00:05<00:03, 4.88it/s] 67%|██████▋ | 30/45 [00:06<00:03, 4.88it/s] 69%|██████▉ | 31/45 [00:06<00:02, 4.88it/s] 71%|███████ | 32/45 [00:06<00:02, 4.88it/s] 73%|███████▎ | 33/45 [00:06<00:02, 4.87it/s] 76%|███████▌ | 34/45 [00:06<00:02, 4.87it/s] 78%|███████▊ | 35/45 [00:07<00:02, 4.87it/s] 80%|████████ | 36/45 [00:07<00:01, 4.87it/s] 82%|████████▏ | 37/45 [00:07<00:01, 4.87it/s] 84%|████████▍ | 38/45 [00:07<00:01, 4.87it/s] 87%|████████▋ | 39/45 [00:07<00:01, 4.87it/s] 89%|████████▉ | 40/45 [00:08<00:01, 4.87it/s] 91%|█████████ | 41/45 [00:08<00:00, 4.86it/s] 93%|█████████▎| 42/45 [00:08<00:00, 4.86it/s] 96%|█████████▌| 43/45 [00:08<00:00, 4.86it/s] 98%|█████████▊| 44/45 [00:09<00:00, 4.86it/s] 100%|██████████| 45/45 [00:09<00:00, 4.86it/s] 100%|██████████| 45/45 [00:09<00:00, 4.87it/s]Prediction
hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddfIDlhnzyb3b5zs4otpispmj2bejliStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- image
- width
- 1024
- height
- 1024
- prompt
- A photo of dark red TOK parked near autumn trees, bright
- 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
- 0.9
- num_inference_steps
- 50
{ "image": "https://replicate.delivery/pbxt/Jj0ek2oFHnDiI8JIcbonXLnIavOzcIknC0kyQcUe78nlLEyR/empty-autumn-road-with-trees-in-a-row-on-the-edges-photo.jpg", "width": 1024, "height": 1024, "prompt": "A photo of dark red TOK parked near autumn trees, bright", "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": 0.9, "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 hudsongraeme/highland using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", { input: { image: "https://replicate.delivery/pbxt/Jj0ek2oFHnDiI8JIcbonXLnIavOzcIknC0kyQcUe78nlLEyR/empty-autumn-road-with-trees-in-a-row-on-the-edges-photo.jpg", width: 1024, height: 1024, prompt: "A photo of dark red TOK parked near autumn trees, bright", 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: 0.9, 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 hudsongraeme/highland using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", input={ "image": "https://replicate.delivery/pbxt/Jj0ek2oFHnDiI8JIcbonXLnIavOzcIknC0kyQcUe78nlLEyR/empty-autumn-road-with-trees-in-a-row-on-the-edges-photo.jpg", "width": 1024, "height": 1024, "prompt": "A photo of dark red TOK parked near autumn trees, bright", "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": 0.9, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run hudsongraeme/highland 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": "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", "input": { "image": "https://replicate.delivery/pbxt/Jj0ek2oFHnDiI8JIcbonXLnIavOzcIknC0kyQcUe78nlLEyR/empty-autumn-road-with-trees-in-a-row-on-the-edges-photo.jpg", "width": 1024, "height": 1024, "prompt": "A photo of dark red TOK parked near autumn trees, bright", "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": 0.9, "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": "2023-10-19T05:28:25.558136Z", "created_at": "2023-10-19T05:28:12.844259Z", "data_removed": false, "error": null, "id": "lhnzyb3b5zs4otpispmj2bejli", "input": { "image": "https://replicate.delivery/pbxt/Jj0ek2oFHnDiI8JIcbonXLnIavOzcIknC0kyQcUe78nlLEyR/empty-autumn-road-with-trees-in-a-row-on-the-edges-photo.jpg", "width": 1024, "height": 1024, "prompt": "A photo of dark red TOK parked near autumn trees, bright", "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": 0.9, "num_inference_steps": 50 }, "logs": "Using seed: 8504\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of dark red <s0><s1> parked near autumn trees, bright\nimg2img mode\n 0%| | 0/45 [00:00<?, ?it/s]\n 2%|▏ | 1/45 [00:00<00:09, 4.85it/s]\n 4%|▍ | 2/45 [00:00<00:08, 4.87it/s]\n 7%|▋ | 3/45 [00:00<00:08, 4.88it/s]\n 9%|▉ | 4/45 [00:00<00:08, 4.88it/s]\n 11%|█ | 5/45 [00:01<00:08, 4.88it/s]\n 13%|█▎ | 6/45 [00:01<00:07, 4.88it/s]\n 16%|█▌ | 7/45 [00:01<00:07, 4.88it/s]\n 18%|█▊ | 8/45 [00:01<00:07, 4.88it/s]\n 20%|██ | 9/45 [00:01<00:07, 4.88it/s]\n 22%|██▏ | 10/45 [00:02<00:07, 4.88it/s]\n 24%|██▍ | 11/45 [00:02<00:06, 4.88it/s]\n 27%|██▋ | 12/45 [00:02<00:06, 4.88it/s]\n 29%|██▉ | 13/45 [00:02<00:06, 4.88it/s]\n 31%|███ | 14/45 [00:02<00:06, 4.88it/s]\n 33%|███▎ | 15/45 [00:03<00:06, 4.88it/s]\n 36%|███▌ | 16/45 [00:03<00:05, 4.88it/s]\n 38%|███▊ | 17/45 [00:03<00:05, 4.88it/s]\n 40%|████ | 18/45 [00:03<00:05, 4.88it/s]\n 42%|████▏ | 19/45 [00:03<00:05, 4.88it/s]\n 44%|████▍ | 20/45 [00:04<00:05, 4.88it/s]\n 47%|████▋ | 21/45 [00:04<00:04, 4.88it/s]\n 49%|████▉ | 22/45 [00:04<00:04, 4.87it/s]\n 51%|█████ | 23/45 [00:04<00:04, 4.88it/s]\n 53%|█████▎ | 24/45 [00:04<00:04, 4.88it/s]\n 56%|█████▌ | 25/45 [00:05<00:04, 4.87it/s]\n 58%|█████▊ | 26/45 [00:05<00:03, 4.87it/s]\n 60%|██████ | 27/45 [00:05<00:03, 4.87it/s]\n 62%|██████▏ | 28/45 [00:05<00:03, 4.87it/s]\n 64%|██████▍ | 29/45 [00:05<00:03, 4.87it/s]\n 67%|██████▋ | 30/45 [00:06<00:03, 4.87it/s]\n 69%|██████▉ | 31/45 [00:06<00:02, 4.87it/s]\n 71%|███████ | 32/45 [00:06<00:02, 4.87it/s]\n 73%|███████▎ | 33/45 [00:06<00:02, 4.87it/s]\n 76%|███████▌ | 34/45 [00:06<00:02, 4.87it/s]\n 78%|███████▊ | 35/45 [00:07<00:02, 4.87it/s]\n 80%|████████ | 36/45 [00:07<00:01, 4.87it/s]\n 82%|████████▏ | 37/45 [00:07<00:01, 4.87it/s]\n 84%|████████▍ | 38/45 [00:07<00:01, 4.87it/s]\n 87%|████████▋ | 39/45 [00:08<00:01, 4.87it/s]\n 89%|████████▉ | 40/45 [00:08<00:01, 4.87it/s]\n 91%|█████████ | 41/45 [00:08<00:00, 4.87it/s]\n 93%|█████████▎| 42/45 [00:08<00:00, 4.87it/s]\n 96%|█████████▌| 43/45 [00:08<00:00, 4.87it/s]\n 98%|█████████▊| 44/45 [00:09<00:00, 4.87it/s]\n100%|██████████| 45/45 [00:09<00:00, 4.87it/s]\n100%|██████████| 45/45 [00:09<00:00, 4.87it/s]", "metrics": { "predict_time": 11.753762, "total_time": 12.713877 }, "output": [ "https://pbxt.replicate.delivery/P6NeNjDfSVtBbUJw4y6EZCEPvMZzCI87eTg7i3uwj9czDOfGB/out-0.png" ], "started_at": "2023-10-19T05:28:13.804374Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/lhnzyb3b5zs4otpispmj2bejli", "cancel": "https://api.replicate.com/v1/predictions/lhnzyb3b5zs4otpispmj2bejli/cancel" }, "version": "0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf" }Generated inUsing seed: 8504 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A photo of dark red <s0><s1> parked near autumn trees, bright img2img mode 0%| | 0/45 [00:00<?, ?it/s] 2%|▏ | 1/45 [00:00<00:09, 4.85it/s] 4%|▍ | 2/45 [00:00<00:08, 4.87it/s] 7%|▋ | 3/45 [00:00<00:08, 4.88it/s] 9%|▉ | 4/45 [00:00<00:08, 4.88it/s] 11%|█ | 5/45 [00:01<00:08, 4.88it/s] 13%|█▎ | 6/45 [00:01<00:07, 4.88it/s] 16%|█▌ | 7/45 [00:01<00:07, 4.88it/s] 18%|█▊ | 8/45 [00:01<00:07, 4.88it/s] 20%|██ | 9/45 [00:01<00:07, 4.88it/s] 22%|██▏ | 10/45 [00:02<00:07, 4.88it/s] 24%|██▍ | 11/45 [00:02<00:06, 4.88it/s] 27%|██▋ | 12/45 [00:02<00:06, 4.88it/s] 29%|██▉ | 13/45 [00:02<00:06, 4.88it/s] 31%|███ | 14/45 [00:02<00:06, 4.88it/s] 33%|███▎ | 15/45 [00:03<00:06, 4.88it/s] 36%|███▌ | 16/45 [00:03<00:05, 4.88it/s] 38%|███▊ | 17/45 [00:03<00:05, 4.88it/s] 40%|████ | 18/45 [00:03<00:05, 4.88it/s] 42%|████▏ | 19/45 [00:03<00:05, 4.88it/s] 44%|████▍ | 20/45 [00:04<00:05, 4.88it/s] 47%|████▋ | 21/45 [00:04<00:04, 4.88it/s] 49%|████▉ | 22/45 [00:04<00:04, 4.87it/s] 51%|█████ | 23/45 [00:04<00:04, 4.88it/s] 53%|█████▎ | 24/45 [00:04<00:04, 4.88it/s] 56%|█████▌ | 25/45 [00:05<00:04, 4.87it/s] 58%|█████▊ | 26/45 [00:05<00:03, 4.87it/s] 60%|██████ | 27/45 [00:05<00:03, 4.87it/s] 62%|██████▏ | 28/45 [00:05<00:03, 4.87it/s] 64%|██████▍ | 29/45 [00:05<00:03, 4.87it/s] 67%|██████▋ | 30/45 [00:06<00:03, 4.87it/s] 69%|██████▉ | 31/45 [00:06<00:02, 4.87it/s] 71%|███████ | 32/45 [00:06<00:02, 4.87it/s] 73%|███████▎ | 33/45 [00:06<00:02, 4.87it/s] 76%|███████▌ | 34/45 [00:06<00:02, 4.87it/s] 78%|███████▊ | 35/45 [00:07<00:02, 4.87it/s] 80%|████████ | 36/45 [00:07<00:01, 4.87it/s] 82%|████████▏ | 37/45 [00:07<00:01, 4.87it/s] 84%|████████▍ | 38/45 [00:07<00:01, 4.87it/s] 87%|████████▋ | 39/45 [00:08<00:01, 4.87it/s] 89%|████████▉ | 40/45 [00:08<00:01, 4.87it/s] 91%|█████████ | 41/45 [00:08<00:00, 4.87it/s] 93%|█████████▎| 42/45 [00:08<00:00, 4.87it/s] 96%|█████████▌| 43/45 [00:08<00:00, 4.87it/s] 98%|█████████▊| 44/45 [00:09<00:00, 4.87it/s] 100%|██████████| 45/45 [00:09<00:00, 4.87it/s] 100%|██████████| 45/45 [00:09<00:00, 4.87it/s]Prediction
hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddfIDwnvp5jtbhqds4qbbvgm33xybzuStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 2938382
- image
- width
- 1024
- height
- 1024
- prompt
- A photo of TOK parked near autumn trees, bright
- 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
- 0.9
- num_inference_steps
- 50
{ "seed": 2938382, "image": "https://replicate.delivery/pbxt/Jj0hwQ8UK8qVzy3sqBuVRPlvvGK92qAMJWRnBEeSGCqhKDei/empty-autumn-road-with-trees-in-a-row-on-the-edges-photo.jpg", "width": 1024, "height": 1024, "prompt": "A photo of TOK parked near autumn trees, bright", "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": 0.9, "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 hudsongraeme/highland using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", { input: { seed: 2938382, image: "https://replicate.delivery/pbxt/Jj0hwQ8UK8qVzy3sqBuVRPlvvGK92qAMJWRnBEeSGCqhKDei/empty-autumn-road-with-trees-in-a-row-on-the-edges-photo.jpg", width: 1024, height: 1024, prompt: "A photo of TOK parked near autumn trees, bright", 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: 0.9, 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 hudsongraeme/highland using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", input={ "seed": 2938382, "image": "https://replicate.delivery/pbxt/Jj0hwQ8UK8qVzy3sqBuVRPlvvGK92qAMJWRnBEeSGCqhKDei/empty-autumn-road-with-trees-in-a-row-on-the-edges-photo.jpg", "width": 1024, "height": 1024, "prompt": "A photo of TOK parked near autumn trees, bright", "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": 0.9, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run hudsongraeme/highland 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": "hudsongraeme/highland:0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf", "input": { "seed": 2938382, "image": "https://replicate.delivery/pbxt/Jj0hwQ8UK8qVzy3sqBuVRPlvvGK92qAMJWRnBEeSGCqhKDei/empty-autumn-road-with-trees-in-a-row-on-the-edges-photo.jpg", "width": 1024, "height": 1024, "prompt": "A photo of TOK parked near autumn trees, bright", "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": 0.9, "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": "2023-10-19T05:31:47.935849Z", "created_at": "2023-10-19T05:31:35.308835Z", "data_removed": false, "error": null, "id": "wnvp5jtbhqds4qbbvgm33xybzu", "input": { "seed": 2938382, "image": "https://replicate.delivery/pbxt/Jj0hwQ8UK8qVzy3sqBuVRPlvvGK92qAMJWRnBEeSGCqhKDei/empty-autumn-road-with-trees-in-a-row-on-the-edges-photo.jpg", "width": 1024, "height": 1024, "prompt": "A photo of TOK parked near autumn trees, bright", "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": 0.9, "num_inference_steps": 50 }, "logs": "Using seed: 2938382\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of <s0><s1> parked near autumn trees, bright\nimg2img mode\n 0%| | 0/45 [00:00<?, ?it/s]\n 2%|▏ | 1/45 [00:00<00:08, 4.91it/s]\n 4%|▍ | 2/45 [00:00<00:08, 4.90it/s]\n 7%|▋ | 3/45 [00:00<00:08, 4.90it/s]\n 9%|▉ | 4/45 [00:00<00:08, 4.90it/s]\n 11%|█ | 5/45 [00:01<00:08, 4.89it/s]\n 13%|█▎ | 6/45 [00:01<00:07, 4.89it/s]\n 16%|█▌ | 7/45 [00:01<00:07, 4.89it/s]\n 18%|█▊ | 8/45 [00:01<00:07, 4.89it/s]\n 20%|██ | 9/45 [00:01<00:07, 4.89it/s]\n 22%|██▏ | 10/45 [00:02<00:07, 4.88it/s]\n 24%|██▍ | 11/45 [00:02<00:06, 4.88it/s]\n 27%|██▋ | 12/45 [00:02<00:06, 4.88it/s]\n 29%|██▉ | 13/45 [00:02<00:06, 4.88it/s]\n 31%|███ | 14/45 [00:02<00:06, 4.87it/s]\n 33%|███▎ | 15/45 [00:03<00:06, 4.87it/s]\n 36%|███▌ | 16/45 [00:03<00:05, 4.87it/s]\n 38%|███▊ | 17/45 [00:03<00:05, 4.87it/s]\n 40%|████ | 18/45 [00:03<00:05, 4.87it/s]\n 42%|████▏ | 19/45 [00:03<00:05, 4.87it/s]\n 44%|████▍ | 20/45 [00:04<00:05, 4.87it/s]\n 47%|████▋ | 21/45 [00:04<00:04, 4.87it/s]\n 49%|████▉ | 22/45 [00:04<00:04, 4.87it/s]\n 51%|█████ | 23/45 [00:04<00:04, 4.87it/s]\n 53%|█████▎ | 24/45 [00:04<00:04, 4.87it/s]\n 56%|█████▌ | 25/45 [00:05<00:04, 4.87it/s]\n 58%|█████▊ | 26/45 [00:05<00:03, 4.87it/s]\n 60%|██████ | 27/45 [00:05<00:03, 4.87it/s]\n 62%|██████▏ | 28/45 [00:05<00:03, 4.87it/s]\n 64%|██████▍ | 29/45 [00:05<00:03, 4.87it/s]\n 67%|██████▋ | 30/45 [00:06<00:03, 4.87it/s]\n 69%|██████▉ | 31/45 [00:06<00:02, 4.87it/s]\n 71%|███████ | 32/45 [00:06<00:02, 4.87it/s]\n 73%|███████▎ | 33/45 [00:06<00:02, 4.87it/s]\n 76%|███████▌ | 34/45 [00:06<00:02, 4.87it/s]\n 78%|███████▊ | 35/45 [00:07<00:02, 4.87it/s]\n 80%|████████ | 36/45 [00:07<00:01, 4.87it/s]\n 82%|████████▏ | 37/45 [00:07<00:01, 4.87it/s]\n 84%|████████▍ | 38/45 [00:07<00:01, 4.87it/s]\n 87%|████████▋ | 39/45 [00:08<00:01, 4.87it/s]\n 89%|████████▉ | 40/45 [00:08<00:01, 4.87it/s]\n 91%|█████████ | 41/45 [00:08<00:00, 4.86it/s]\n 93%|█████████▎| 42/45 [00:08<00:00, 4.86it/s]\n 96%|█████████▌| 43/45 [00:08<00:00, 4.86it/s]\n 98%|█████████▊| 44/45 [00:09<00:00, 4.86it/s]\n100%|██████████| 45/45 [00:09<00:00, 4.87it/s]\n100%|██████████| 45/45 [00:09<00:00, 4.87it/s]", "metrics": { "predict_time": 11.581396, "total_time": 12.627014 }, "output": [ "https://pbxt.replicate.delivery/m1eRkqFo3QTegkDbNuuRgcGvWeNf6Yfvp7EvOVGdvkRbo48NC/out-0.png" ], "started_at": "2023-10-19T05:31:36.354453Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wnvp5jtbhqds4qbbvgm33xybzu", "cancel": "https://api.replicate.com/v1/predictions/wnvp5jtbhqds4qbbvgm33xybzu/cancel" }, "version": "0700d54b3b15b59948cbcaaaa6419952b9f1e7a5e7b06d39e6f146a2f3f57ddf" }Generated inUsing seed: 2938382 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A photo of <s0><s1> parked near autumn trees, bright img2img mode 0%| | 0/45 [00:00<?, ?it/s] 2%|▏ | 1/45 [00:00<00:08, 4.91it/s] 4%|▍ | 2/45 [00:00<00:08, 4.90it/s] 7%|▋ | 3/45 [00:00<00:08, 4.90it/s] 9%|▉ | 4/45 [00:00<00:08, 4.90it/s] 11%|█ | 5/45 [00:01<00:08, 4.89it/s] 13%|█▎ | 6/45 [00:01<00:07, 4.89it/s] 16%|█▌ | 7/45 [00:01<00:07, 4.89it/s] 18%|█▊ | 8/45 [00:01<00:07, 4.89it/s] 20%|██ | 9/45 [00:01<00:07, 4.89it/s] 22%|██▏ | 10/45 [00:02<00:07, 4.88it/s] 24%|██▍ | 11/45 [00:02<00:06, 4.88it/s] 27%|██▋ | 12/45 [00:02<00:06, 4.88it/s] 29%|██▉ | 13/45 [00:02<00:06, 4.88it/s] 31%|███ | 14/45 [00:02<00:06, 4.87it/s] 33%|███▎ | 15/45 [00:03<00:06, 4.87it/s] 36%|███▌ | 16/45 [00:03<00:05, 4.87it/s] 38%|███▊ | 17/45 [00:03<00:05, 4.87it/s] 40%|████ | 18/45 [00:03<00:05, 4.87it/s] 42%|████▏ | 19/45 [00:03<00:05, 4.87it/s] 44%|████▍ | 20/45 [00:04<00:05, 4.87it/s] 47%|████▋ | 21/45 [00:04<00:04, 4.87it/s] 49%|████▉ | 22/45 [00:04<00:04, 4.87it/s] 51%|█████ | 23/45 [00:04<00:04, 4.87it/s] 53%|█████▎ | 24/45 [00:04<00:04, 4.87it/s] 56%|█████▌ | 25/45 [00:05<00:04, 4.87it/s] 58%|█████▊ | 26/45 [00:05<00:03, 4.87it/s] 60%|██████ | 27/45 [00:05<00:03, 4.87it/s] 62%|██████▏ | 28/45 [00:05<00:03, 4.87it/s] 64%|██████▍ | 29/45 [00:05<00:03, 4.87it/s] 67%|██████▋ | 30/45 [00:06<00:03, 4.87it/s] 69%|██████▉ | 31/45 [00:06<00:02, 4.87it/s] 71%|███████ | 32/45 [00:06<00:02, 4.87it/s] 73%|███████▎ | 33/45 [00:06<00:02, 4.87it/s] 76%|███████▌ | 34/45 [00:06<00:02, 4.87it/s] 78%|███████▊ | 35/45 [00:07<00:02, 4.87it/s] 80%|████████ | 36/45 [00:07<00:01, 4.87it/s] 82%|████████▏ | 37/45 [00:07<00:01, 4.87it/s] 84%|████████▍ | 38/45 [00:07<00:01, 4.87it/s] 87%|████████▋ | 39/45 [00:08<00:01, 4.87it/s] 89%|████████▉ | 40/45 [00:08<00:01, 4.87it/s] 91%|█████████ | 41/45 [00:08<00:00, 4.86it/s] 93%|█████████▎| 42/45 [00:08<00:00, 4.86it/s] 96%|█████████▌| 43/45 [00:08<00:00, 4.86it/s] 98%|█████████▊| 44/45 [00:09<00:00, 4.86it/s] 100%|██████████| 45/45 [00:09<00:00, 4.87it/s] 100%|██████████| 45/45 [00:09<00:00, 4.87it/s]Prediction
hudsongraeme/highland:74449961ceb51ee4cf61b0577efc19c5a566cefef24de3a3c37e17d48b5989bcIDr3ndxxdblpp7m65weyzoid7xdiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- image
- width
- 1024
- height
- 1024
- prompt
- A photo of dark red Tesla Model 3 highland parked near autumn trees, bright
- 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
- 0.9
- num_inference_steps
- 50
{ "image": "https://replicate.delivery/pbxt/Jj0ek2oFHnDiI8JIcbonXLnIavOzcIknC0kyQcUe78nlLEyR/empty-autumn-road-with-trees-in-a-row-on-the-edges-photo.jpg", "width": 1024, "height": 1024, "prompt": "A photo of dark red Tesla Model 3 highland parked near autumn trees, bright", "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": 0.9, "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 hudsongraeme/highland using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hudsongraeme/highland:74449961ceb51ee4cf61b0577efc19c5a566cefef24de3a3c37e17d48b5989bc", { input: { image: "https://replicate.delivery/pbxt/Jj0ek2oFHnDiI8JIcbonXLnIavOzcIknC0kyQcUe78nlLEyR/empty-autumn-road-with-trees-in-a-row-on-the-edges-photo.jpg", width: 1024, height: 1024, prompt: "A photo of dark red Tesla Model 3 highland parked near autumn trees, bright", 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: 0.9, 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 hudsongraeme/highland using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hudsongraeme/highland:74449961ceb51ee4cf61b0577efc19c5a566cefef24de3a3c37e17d48b5989bc", input={ "image": "https://replicate.delivery/pbxt/Jj0ek2oFHnDiI8JIcbonXLnIavOzcIknC0kyQcUe78nlLEyR/empty-autumn-road-with-trees-in-a-row-on-the-edges-photo.jpg", "width": 1024, "height": 1024, "prompt": "A photo of dark red Tesla Model 3 highland parked near autumn trees, bright", "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": 0.9, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run hudsongraeme/highland 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": "hudsongraeme/highland:74449961ceb51ee4cf61b0577efc19c5a566cefef24de3a3c37e17d48b5989bc", "input": { "image": "https://replicate.delivery/pbxt/Jj0ek2oFHnDiI8JIcbonXLnIavOzcIknC0kyQcUe78nlLEyR/empty-autumn-road-with-trees-in-a-row-on-the-edges-photo.jpg", "width": 1024, "height": 1024, "prompt": "A photo of dark red Tesla Model 3 highland parked near autumn trees, bright", "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": 0.9, "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": "2023-10-24T03:28:31.191512Z", "created_at": "2023-10-24T03:28:19.152181Z", "data_removed": false, "error": null, "id": "r3ndxxdblpp7m65weyzoid7xdi", "input": { "image": "https://replicate.delivery/pbxt/Jj0ek2oFHnDiI8JIcbonXLnIavOzcIknC0kyQcUe78nlLEyR/empty-autumn-road-with-trees-in-a-row-on-the-edges-photo.jpg", "width": 1024, "height": 1024, "prompt": "A photo of dark red Tesla Model 3 highland parked near autumn trees, bright", "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": 0.9, "num_inference_steps": 50 }, "logs": "Using seed: 61391\nskipping loading .. weights already loaded\nPrompt: A photo of dark red Tesla Model 3 <s0><s1> parked near autumn trees, bright\nimg2img mode\n 0%| | 0/45 [00:00<?, ?it/s]\n 2%|▏ | 1/45 [00:00<00:08, 4.90it/s]\n 4%|▍ | 2/45 [00:00<00:08, 4.91it/s]\n 7%|▋ | 3/45 [00:00<00:08, 4.92it/s]\n 9%|▉ | 4/45 [00:00<00:08, 4.92it/s]\n 11%|█ | 5/45 [00:01<00:08, 4.90it/s]\n 13%|█▎ | 6/45 [00:01<00:07, 4.91it/s]\n 16%|█▌ | 7/45 [00:01<00:07, 4.91it/s]\n 18%|█▊ | 8/45 [00:01<00:07, 4.91it/s]\n 20%|██ | 9/45 [00:01<00:07, 4.91it/s]\n 22%|██▏ | 10/45 [00:02<00:07, 4.91it/s]\n 24%|██▍ | 11/45 [00:02<00:06, 4.90it/s]\n 27%|██▋ | 12/45 [00:02<00:06, 4.91it/s]\n 29%|██▉ | 13/45 [00:02<00:06, 4.91it/s]\n 31%|███ | 14/45 [00:02<00:06, 4.91it/s]\n 33%|███▎ | 15/45 [00:03<00:06, 4.90it/s]\n 36%|███▌ | 16/45 [00:03<00:05, 4.90it/s]\n 38%|███▊ | 17/45 [00:03<00:05, 4.90it/s]\n 40%|████ | 18/45 [00:03<00:05, 4.90it/s]\n 42%|████▏ | 19/45 [00:03<00:05, 4.90it/s]\n 44%|████▍ | 20/45 [00:04<00:05, 4.90it/s]\n 47%|████▋ | 21/45 [00:04<00:04, 4.90it/s]\n 49%|████▉ | 22/45 [00:04<00:04, 4.90it/s]\n 51%|█████ | 23/45 [00:04<00:04, 4.90it/s]\n 53%|█████▎ | 24/45 [00:04<00:04, 4.90it/s]\n 56%|█████▌ | 25/45 [00:05<00:04, 4.90it/s]\n 58%|█████▊ | 26/45 [00:05<00:03, 4.89it/s]\n 60%|██████ | 27/45 [00:05<00:03, 4.90it/s]\n 62%|██████▏ | 28/45 [00:05<00:03, 4.89it/s]\n 64%|██████▍ | 29/45 [00:05<00:03, 4.89it/s]\n 67%|██████▋ | 30/45 [00:06<00:03, 4.89it/s]\n 69%|██████▉ | 31/45 [00:06<00:02, 4.89it/s]\n 71%|███████ | 32/45 [00:06<00:02, 4.89it/s]\n 73%|███████▎ | 33/45 [00:06<00:02, 4.89it/s]\n 76%|███████▌ | 34/45 [00:06<00:02, 4.89it/s]\n 78%|███████▊ | 35/45 [00:07<00:02, 4.88it/s]\n 80%|████████ | 36/45 [00:07<00:01, 4.88it/s]\n 82%|████████▏ | 37/45 [00:07<00:01, 4.88it/s]\n 84%|████████▍ | 38/45 [00:07<00:01, 4.88it/s]\n 87%|████████▋ | 39/45 [00:07<00:01, 4.88it/s]\n 89%|████████▉ | 40/45 [00:08<00:01, 4.88it/s]\n 91%|█████████ | 41/45 [00:08<00:00, 4.88it/s]\n 93%|█████████▎| 42/45 [00:08<00:00, 4.88it/s]\n 96%|█████████▌| 43/45 [00:08<00:00, 4.88it/s]\n 98%|█████████▊| 44/45 [00:08<00:00, 4.87it/s]\n100%|██████████| 45/45 [00:09<00:00, 4.88it/s]\n100%|██████████| 45/45 [00:09<00:00, 4.89it/s]", "metrics": { "predict_time": 12.056649, "total_time": 12.039331 }, "output": [ "https://replicate.delivery/pbxt/Lv6fHDP8J0RvXCDxJPuLAaTSvttSCAf0iRMlXmdc3oCee6EHB/out-0.png" ], "started_at": "2023-10-24T03:28:19.134863Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/r3ndxxdblpp7m65weyzoid7xdi", "cancel": "https://api.replicate.com/v1/predictions/r3ndxxdblpp7m65weyzoid7xdi/cancel" }, "version": "74449961ceb51ee4cf61b0577efc19c5a566cefef24de3a3c37e17d48b5989bc" }Generated inUsing seed: 61391 skipping loading .. weights already loaded Prompt: A photo of dark red Tesla Model 3 <s0><s1> parked near autumn trees, bright img2img mode 0%| | 0/45 [00:00<?, ?it/s] 2%|▏ | 1/45 [00:00<00:08, 4.90it/s] 4%|▍ | 2/45 [00:00<00:08, 4.91it/s] 7%|▋ | 3/45 [00:00<00:08, 4.92it/s] 9%|▉ | 4/45 [00:00<00:08, 4.92it/s] 11%|█ | 5/45 [00:01<00:08, 4.90it/s] 13%|█▎ | 6/45 [00:01<00:07, 4.91it/s] 16%|█▌ | 7/45 [00:01<00:07, 4.91it/s] 18%|█▊ | 8/45 [00:01<00:07, 4.91it/s] 20%|██ | 9/45 [00:01<00:07, 4.91it/s] 22%|██▏ | 10/45 [00:02<00:07, 4.91it/s] 24%|██▍ | 11/45 [00:02<00:06, 4.90it/s] 27%|██▋ | 12/45 [00:02<00:06, 4.91it/s] 29%|██▉ | 13/45 [00:02<00:06, 4.91it/s] 31%|███ | 14/45 [00:02<00:06, 4.91it/s] 33%|███▎ | 15/45 [00:03<00:06, 4.90it/s] 36%|███▌ | 16/45 [00:03<00:05, 4.90it/s] 38%|███▊ | 17/45 [00:03<00:05, 4.90it/s] 40%|████ | 18/45 [00:03<00:05, 4.90it/s] 42%|████▏ | 19/45 [00:03<00:05, 4.90it/s] 44%|████▍ | 20/45 [00:04<00:05, 4.90it/s] 47%|████▋ | 21/45 [00:04<00:04, 4.90it/s] 49%|████▉ | 22/45 [00:04<00:04, 4.90it/s] 51%|█████ | 23/45 [00:04<00:04, 4.90it/s] 53%|█████▎ | 24/45 [00:04<00:04, 4.90it/s] 56%|█████▌ | 25/45 [00:05<00:04, 4.90it/s] 58%|█████▊ | 26/45 [00:05<00:03, 4.89it/s] 60%|██████ | 27/45 [00:05<00:03, 4.90it/s] 62%|██████▏ | 28/45 [00:05<00:03, 4.89it/s] 64%|██████▍ | 29/45 [00:05<00:03, 4.89it/s] 67%|██████▋ | 30/45 [00:06<00:03, 4.89it/s] 69%|██████▉ | 31/45 [00:06<00:02, 4.89it/s] 71%|███████ | 32/45 [00:06<00:02, 4.89it/s] 73%|███████▎ | 33/45 [00:06<00:02, 4.89it/s] 76%|███████▌ | 34/45 [00:06<00:02, 4.89it/s] 78%|███████▊ | 35/45 [00:07<00:02, 4.88it/s] 80%|████████ | 36/45 [00:07<00:01, 4.88it/s] 82%|████████▏ | 37/45 [00:07<00:01, 4.88it/s] 84%|████████▍ | 38/45 [00:07<00:01, 4.88it/s] 87%|████████▋ | 39/45 [00:07<00:01, 4.88it/s] 89%|████████▉ | 40/45 [00:08<00:01, 4.88it/s] 91%|█████████ | 41/45 [00:08<00:00, 4.88it/s] 93%|█████████▎| 42/45 [00:08<00:00, 4.88it/s] 96%|█████████▌| 43/45 [00:08<00:00, 4.88it/s] 98%|█████████▊| 44/45 [00:08<00:00, 4.87it/s] 100%|██████████| 45/45 [00:09<00:00, 4.88it/s] 100%|██████████| 45/45 [00:09<00:00, 4.89it/s]Prediction
hudsongraeme/highland:74449961ceb51ee4cf61b0577efc19c5a566cefef24de3a3c37e17d48b5989bcIDjahbfrdb7jrlr66ixtigd74hc4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of Tesla Model 3 highland driving in deep water
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo of Tesla Model 3 highland driving in deep water", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "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 hudsongraeme/highland using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "hudsongraeme/highland:74449961ceb51ee4cf61b0577efc19c5a566cefef24de3a3c37e17d48b5989bc", { input: { width: 1024, height: 1024, prompt: "A photo of Tesla Model 3 highland driving in deep water", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, 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 hudsongraeme/highland using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "hudsongraeme/highland:74449961ceb51ee4cf61b0577efc19c5a566cefef24de3a3c37e17d48b5989bc", input={ "width": 1024, "height": 1024, "prompt": "A photo of Tesla Model 3 highland driving in deep water", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "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 hudsongraeme/highland 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": "hudsongraeme/highland:74449961ceb51ee4cf61b0577efc19c5a566cefef24de3a3c37e17d48b5989bc", "input": { "width": 1024, "height": 1024, "prompt": "A photo of Tesla Model 3 highland driving in deep water", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "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": "2023-10-24T03:32:38.910593Z", "created_at": "2023-10-24T03:31:33.016457Z", "data_removed": false, "error": null, "id": "jahbfrdb7jrlr66ixtigd74hc4", "input": { "width": 1024, "height": 1024, "prompt": "A photo of Tesla Model 3 highland driving in deep water", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 3689\nEnsuring enough disk space...\nFree disk space: 1877974945792\nDownloading weights: https://pbxt.replicate.delivery/flqWaPStjaSwTygkP5swhA1LISkM28QCb23TonmFsZ02Tn4IA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.742s (250 MB/s)\\nExtracted 186 MB in 0.058s (3.2 GB/s)\\n'\nDownloaded weights in 1.1947174072265625 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of Tesla Model 3 <s0><s1> driving in deep water\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<00:51, 1.06s/it]\n 4%|▍ | 2/50 [00:02<00:50, 1.06s/it]\n 6%|▌ | 3/50 [00:03<00:49, 1.06s/it]\n 8%|▊ | 4/50 [00:04<00:48, 1.06s/it]\n 10%|█ | 5/50 [00:05<00:47, 1.06s/it]\n 12%|█▏ | 6/50 [00:06<00:46, 1.06s/it]\n 14%|█▍ | 7/50 [00:07<00:45, 1.06s/it]\n 16%|█▌ | 8/50 [00:08<00:44, 1.07s/it]\n 18%|█▊ | 9/50 [00:09<00:43, 1.07s/it]\n 20%|██ | 10/50 [00:10<00:42, 1.07s/it]\n 22%|██▏ | 11/50 [00:11<00:41, 1.07s/it]\n 24%|██▍ | 12/50 [00:12<00:40, 1.07s/it]\n 26%|██▌ | 13/50 [00:13<00:39, 1.06s/it]\n 28%|██▊ | 14/50 [00:14<00:38, 1.07s/it]\n 30%|███ | 15/50 [00:15<00:37, 1.07s/it]\n 32%|███▏ | 16/50 [00:17<00:36, 1.07s/it]\n 34%|███▍ | 17/50 [00:18<00:35, 1.07s/it]\n 36%|███▌ | 18/50 [00:19<00:34, 1.07s/it]\n 38%|███▊ | 19/50 [00:20<00:33, 1.07s/it]\n 40%|████ | 20/50 [00:21<00:32, 1.07s/it]\n 42%|████▏ | 21/50 [00:22<00:30, 1.07s/it]\n 44%|████▍ | 22/50 [00:23<00:29, 1.07s/it]\n 46%|████▌ | 23/50 [00:24<00:28, 1.07s/it]\n 48%|████▊ | 24/50 [00:25<00:27, 1.07s/it]\n 50%|█████ | 25/50 [00:26<00:26, 1.07s/it]\n 52%|█████▏ | 26/50 [00:27<00:25, 1.07s/it]\n 54%|█████▍ | 27/50 [00:28<00:24, 1.07s/it]\n 56%|█████▌ | 28/50 [00:29<00:23, 1.07s/it]\n 58%|█████▊ | 29/50 [00:30<00:22, 1.07s/it]\n 60%|██████ | 30/50 [00:31<00:21, 1.07s/it]\n 62%|██████▏ | 31/50 [00:33<00:20, 1.07s/it]\n 64%|██████▍ | 32/50 [00:34<00:19, 1.07s/it]\n 66%|██████▌ | 33/50 [00:35<00:18, 1.07s/it]\n 68%|██████▊ | 34/50 [00:36<00:17, 1.07s/it]\n 70%|███████ | 35/50 [00:37<00:16, 1.07s/it]\n 72%|███████▏ | 36/50 [00:38<00:14, 1.07s/it]\n 74%|███████▍ | 37/50 [00:39<00:13, 1.07s/it]\n 76%|███████▌ | 38/50 [00:40<00:12, 1.07s/it]\n 78%|███████▊ | 39/50 [00:41<00:11, 1.07s/it]\n 80%|████████ | 40/50 [00:42<00:10, 1.07s/it]\n 82%|████████▏ | 41/50 [00:43<00:09, 1.07s/it]\n 84%|████████▍ | 42/50 [00:44<00:08, 1.07s/it]\n 86%|████████▌ | 43/50 [00:45<00:07, 1.07s/it]\n 88%|████████▊ | 44/50 [00:46<00:06, 1.07s/it]\n 90%|█████████ | 45/50 [00:48<00:05, 1.07s/it]\n 92%|█████████▏| 46/50 [00:49<00:04, 1.07s/it]\n 94%|█████████▍| 47/50 [00:50<00:03, 1.07s/it]\n 96%|█████████▌| 48/50 [00:51<00:02, 1.07s/it]\n 98%|█████████▊| 49/50 [00:52<00:01, 1.07s/it]\n100%|██████████| 50/50 [00:53<00:00, 1.07s/it]\n100%|██████████| 50/50 [00:53<00:00, 1.07s/it]", "metrics": { "predict_time": 62.915086, "total_time": 65.894136 }, "output": [ "https://replicate.delivery/pbxt/HYFqVwTRd64hOBoW7QH0PvlAmvgvd2YDX4W9RxrGlLx0sTcE/out-0.png", "https://replicate.delivery/pbxt/U1ky6LOFDprBNZuHRJgfqfxoE9kstPpxwkcjkotjbQQVzOxRA/out-1.png", "https://replicate.delivery/pbxt/EA6s0FEj5fUET66UJELSgpVraYxGW11o1QrRPPqGfhiVzOxRA/out-2.png", "https://replicate.delivery/pbxt/1x2vzI6gZV6kMxDbQiFbIMsetfCBYN7LeGMqBPmeoFxbN7EHB/out-3.png" ], "started_at": "2023-10-24T03:31:35.995507Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jahbfrdb7jrlr66ixtigd74hc4", "cancel": "https://api.replicate.com/v1/predictions/jahbfrdb7jrlr66ixtigd74hc4/cancel" }, "version": "74449961ceb51ee4cf61b0577efc19c5a566cefef24de3a3c37e17d48b5989bc" }Generated inUsing seed: 3689 Ensuring enough disk space... Free disk space: 1877974945792 Downloading weights: https://pbxt.replicate.delivery/flqWaPStjaSwTygkP5swhA1LISkM28QCb23TonmFsZ02Tn4IA/trained_model.tar b'Downloaded 186 MB bytes in 0.742s (250 MB/s)\nExtracted 186 MB in 0.058s (3.2 GB/s)\n' Downloaded weights in 1.1947174072265625 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A photo of Tesla Model 3 <s0><s1> driving in deep water txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<00:51, 1.06s/it] 4%|▍ | 2/50 [00:02<00:50, 1.06s/it] 6%|▌ | 3/50 [00:03<00:49, 1.06s/it] 8%|▊ | 4/50 [00:04<00:48, 1.06s/it] 10%|█ | 5/50 [00:05<00:47, 1.06s/it] 12%|█▏ | 6/50 [00:06<00:46, 1.06s/it] 14%|█▍ | 7/50 [00:07<00:45, 1.06s/it] 16%|█▌ | 8/50 [00:08<00:44, 1.07s/it] 18%|█▊ | 9/50 [00:09<00:43, 1.07s/it] 20%|██ | 10/50 [00:10<00:42, 1.07s/it] 22%|██▏ | 11/50 [00:11<00:41, 1.07s/it] 24%|██▍ | 12/50 [00:12<00:40, 1.07s/it] 26%|██▌ | 13/50 [00:13<00:39, 1.06s/it] 28%|██▊ | 14/50 [00:14<00:38, 1.07s/it] 30%|███ | 15/50 [00:15<00:37, 1.07s/it] 32%|███▏ | 16/50 [00:17<00:36, 1.07s/it] 34%|███▍ | 17/50 [00:18<00:35, 1.07s/it] 36%|███▌ | 18/50 [00:19<00:34, 1.07s/it] 38%|███▊ | 19/50 [00:20<00:33, 1.07s/it] 40%|████ | 20/50 [00:21<00:32, 1.07s/it] 42%|████▏ | 21/50 [00:22<00:30, 1.07s/it] 44%|████▍ | 22/50 [00:23<00:29, 1.07s/it] 46%|████▌ | 23/50 [00:24<00:28, 1.07s/it] 48%|████▊ | 24/50 [00:25<00:27, 1.07s/it] 50%|█████ | 25/50 [00:26<00:26, 1.07s/it] 52%|█████▏ | 26/50 [00:27<00:25, 1.07s/it] 54%|█████▍ | 27/50 [00:28<00:24, 1.07s/it] 56%|█████▌ | 28/50 [00:29<00:23, 1.07s/it] 58%|█████▊ | 29/50 [00:30<00:22, 1.07s/it] 60%|██████ | 30/50 [00:31<00:21, 1.07s/it] 62%|██████▏ | 31/50 [00:33<00:20, 1.07s/it] 64%|██████▍ | 32/50 [00:34<00:19, 1.07s/it] 66%|██████▌ | 33/50 [00:35<00:18, 1.07s/it] 68%|██████▊ | 34/50 [00:36<00:17, 1.07s/it] 70%|███████ | 35/50 [00:37<00:16, 1.07s/it] 72%|███████▏ | 36/50 [00:38<00:14, 1.07s/it] 74%|███████▍ | 37/50 [00:39<00:13, 1.07s/it] 76%|███████▌ | 38/50 [00:40<00:12, 1.07s/it] 78%|███████▊ | 39/50 [00:41<00:11, 1.07s/it] 80%|████████ | 40/50 [00:42<00:10, 1.07s/it] 82%|████████▏ | 41/50 [00:43<00:09, 1.07s/it] 84%|████████▍ | 42/50 [00:44<00:08, 1.07s/it] 86%|████████▌ | 43/50 [00:45<00:07, 1.07s/it] 88%|████████▊ | 44/50 [00:46<00:06, 1.07s/it] 90%|█████████ | 45/50 [00:48<00:05, 1.07s/it] 92%|█████████▏| 46/50 [00:49<00:04, 1.07s/it] 94%|█████████▍| 47/50 [00:50<00:03, 1.07s/it] 96%|█████████▌| 48/50 [00:51<00:02, 1.07s/it] 98%|█████████▊| 49/50 [00:52<00:01, 1.07s/it] 100%|██████████| 50/50 [00:53<00:00, 1.07s/it] 100%|██████████| 50/50 [00:53<00:00, 1.07s/it]
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