shapestudio
/
portra-800-flux
Flux Lora inspired by Kodak Portra 800
- Public
- 191 runs
-
H100
Prediction
shapestudio/portra-800-flux:3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2IDbf5mhsznqhrm20chdz38q61f94StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- woman eating a ice cream, in the style of TOK
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 41
{ "model": "dev", "prompt": "woman eating a ice cream, in the style of TOK", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 41 }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 shapestudio/portra-800-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "shapestudio/portra-800-flux:3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2", { input: { model: "dev", prompt: "woman eating a ice cream, in the style of TOK", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 41 } } ); // 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 shapestudio/portra-800-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "shapestudio/portra-800-flux:3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2", input={ "model": "dev", "prompt": "woman eating a ice cream, in the style of TOK", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 41 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run shapestudio/portra-800-flux 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": "3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2", "input": { "model": "dev", "prompt": "woman eating a ice cream, in the style of TOK", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 41 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-20T12:33:35.175663Z", "created_at": "2024-08-20T12:33:15.708000Z", "data_removed": false, "error": null, "id": "bf5mhsznqhrm20chdz38q61f94", "input": { "model": "dev", "prompt": "woman eating a ice cream, in the style of TOK", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 41 }, "logs": "Using seed: 40761\nPrompt: woman eating a ice cream, in the style of TOK\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nLoRA weights loaded successfully\n 0%| | 0/41 [00:00<?, ?it/s]\n 2%|▏ | 1/41 [00:00<00:10, 3.67it/s]\n 5%|▍ | 2/41 [00:00<00:09, 4.23it/s]\n 7%|▋ | 3/41 [00:00<00:09, 3.96it/s]\n 10%|▉ | 4/41 [00:01<00:09, 3.83it/s]\n 12%|█▏ | 5/41 [00:01<00:09, 3.77it/s]\n 15%|█▍ | 6/41 [00:01<00:09, 3.74it/s]\n 17%|█▋ | 7/41 [00:01<00:09, 3.72it/s]\n 20%|█▉ | 8/41 [00:02<00:08, 3.69it/s]\n 22%|██▏ | 9/41 [00:02<00:08, 3.68it/s]\n 24%|██▍ | 10/41 [00:02<00:08, 3.68it/s]\n 27%|██▋ | 11/41 [00:02<00:08, 3.68it/s]\n 29%|██▉ | 12/41 [00:03<00:07, 3.67it/s]\n 32%|███▏ | 13/41 [00:03<00:07, 3.66it/s]\n 34%|███▍ | 14/41 [00:03<00:07, 3.67it/s]\n 37%|███▋ | 15/41 [00:04<00:07, 3.67it/s]\n 39%|███▉ | 16/41 [00:04<00:06, 3.66it/s]\n 41%|████▏ | 17/41 [00:04<00:06, 3.66it/s]\n 44%|████▍ | 18/41 [00:04<00:06, 3.66it/s]\n 46%|████▋ | 19/41 [00:05<00:06, 3.67it/s]\n 49%|████▉ | 20/41 [00:05<00:05, 3.66it/s]\n 51%|█████ | 21/41 [00:05<00:05, 3.66it/s]\n 54%|█████▎ | 22/41 [00:05<00:05, 3.66it/s]\n 56%|█████▌ | 23/41 [00:06<00:04, 3.67it/s]\n 59%|█████▊ | 24/41 [00:06<00:04, 3.66it/s]\n 61%|██████ | 25/41 [00:06<00:04, 3.66it/s]\n 63%|██████▎ | 26/41 [00:07<00:04, 3.66it/s]\n 66%|██████▌ | 27/41 [00:07<00:03, 3.67it/s]\n 68%|██████▊ | 28/41 [00:07<00:03, 3.66it/s]\n 71%|███████ | 29/41 [00:07<00:03, 3.66it/s]\n 73%|███████▎ | 30/41 [00:08<00:03, 3.66it/s]\n 76%|███████▌ | 31/41 [00:08<00:02, 3.67it/s]\n 78%|███████▊ | 32/41 [00:08<00:02, 3.66it/s]\n 80%|████████ | 33/41 [00:08<00:02, 3.66it/s]\n 83%|████████▎ | 34/41 [00:09<00:01, 3.66it/s]\n 85%|████████▌ | 35/41 [00:09<00:01, 3.66it/s]\n 88%|████████▊ | 36/41 [00:09<00:01, 3.66it/s]\n 90%|█████████ | 37/41 [00:10<00:01, 3.66it/s]\n 93%|█████████▎| 38/41 [00:10<00:00, 3.66it/s]\n 95%|█████████▌| 39/41 [00:10<00:00, 3.66it/s]\n 98%|█████████▊| 40/41 [00:10<00:00, 3.66it/s]\n100%|██████████| 41/41 [00:11<00:00, 3.66it/s]\n100%|██████████| 41/41 [00:11<00:00, 3.68it/s]", "metrics": { "predict_time": 19.445885826, "total_time": 19.467663 }, "output": [ "https://replicate.delivery/yhqm/d2NZwyoeKG3eqEOF7oYQxJBfGFyc11U6e5fdsLSGF5N5jfI1E/out-0.webp" ], "started_at": "2024-08-20T12:33:15.729777Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/bf5mhsznqhrm20chdz38q61f94", "cancel": "https://api.replicate.com/v1/predictions/bf5mhsznqhrm20chdz38q61f94/cancel" }, "version": "3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2" }Generated inUsing seed: 40761 Prompt: woman eating a ice cream, in the style of TOK txt2img mode Using dev model Loading LoRA weights LoRA weights loaded successfully 0%| | 0/41 [00:00<?, ?it/s] 2%|▏ | 1/41 [00:00<00:10, 3.67it/s] 5%|▍ | 2/41 [00:00<00:09, 4.23it/s] 7%|▋ | 3/41 [00:00<00:09, 3.96it/s] 10%|▉ | 4/41 [00:01<00:09, 3.83it/s] 12%|█▏ | 5/41 [00:01<00:09, 3.77it/s] 15%|█▍ | 6/41 [00:01<00:09, 3.74it/s] 17%|█▋ | 7/41 [00:01<00:09, 3.72it/s] 20%|█▉ | 8/41 [00:02<00:08, 3.69it/s] 22%|██▏ | 9/41 [00:02<00:08, 3.68it/s] 24%|██▍ | 10/41 [00:02<00:08, 3.68it/s] 27%|██▋ | 11/41 [00:02<00:08, 3.68it/s] 29%|██▉ | 12/41 [00:03<00:07, 3.67it/s] 32%|███▏ | 13/41 [00:03<00:07, 3.66it/s] 34%|███▍ | 14/41 [00:03<00:07, 3.67it/s] 37%|███▋ | 15/41 [00:04<00:07, 3.67it/s] 39%|███▉ | 16/41 [00:04<00:06, 3.66it/s] 41%|████▏ | 17/41 [00:04<00:06, 3.66it/s] 44%|████▍ | 18/41 [00:04<00:06, 3.66it/s] 46%|████▋ | 19/41 [00:05<00:06, 3.67it/s] 49%|████▉ | 20/41 [00:05<00:05, 3.66it/s] 51%|█████ | 21/41 [00:05<00:05, 3.66it/s] 54%|█████▎ | 22/41 [00:05<00:05, 3.66it/s] 56%|█████▌ | 23/41 [00:06<00:04, 3.67it/s] 59%|█████▊ | 24/41 [00:06<00:04, 3.66it/s] 61%|██████ | 25/41 [00:06<00:04, 3.66it/s] 63%|██████▎ | 26/41 [00:07<00:04, 3.66it/s] 66%|██████▌ | 27/41 [00:07<00:03, 3.67it/s] 68%|██████▊ | 28/41 [00:07<00:03, 3.66it/s] 71%|███████ | 29/41 [00:07<00:03, 3.66it/s] 73%|███████▎ | 30/41 [00:08<00:03, 3.66it/s] 76%|███████▌ | 31/41 [00:08<00:02, 3.67it/s] 78%|███████▊ | 32/41 [00:08<00:02, 3.66it/s] 80%|████████ | 33/41 [00:08<00:02, 3.66it/s] 83%|████████▎ | 34/41 [00:09<00:01, 3.66it/s] 85%|████████▌ | 35/41 [00:09<00:01, 3.66it/s] 88%|████████▊ | 36/41 [00:09<00:01, 3.66it/s] 90%|█████████ | 37/41 [00:10<00:01, 3.66it/s] 93%|█████████▎| 38/41 [00:10<00:00, 3.66it/s] 95%|█████████▌| 39/41 [00:10<00:00, 3.66it/s] 98%|█████████▊| 40/41 [00:10<00:00, 3.66it/s] 100%|██████████| 41/41 [00:11<00:00, 3.66it/s] 100%|██████████| 41/41 [00:11<00:00, 3.68it/s]Prediction
shapestudio/portra-800-flux:3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2IDcrjn7qd9pnrm00chdz4t17bx60StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- sun and shadows, in the style of TOK
- lora_scale
- 0.83
- num_outputs
- 4
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 41
{ "model": "dev", "prompt": "sun and shadows, in the style of TOK", "lora_scale": 0.83, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 41 }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 shapestudio/portra-800-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "shapestudio/portra-800-flux:3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2", { input: { model: "dev", prompt: "sun and shadows, in the style of TOK", lora_scale: 0.83, num_outputs: 4, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 41 } } ); // 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 shapestudio/portra-800-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "shapestudio/portra-800-flux:3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2", input={ "model": "dev", "prompt": "sun and shadows, in the style of TOK", "lora_scale": 0.83, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 41 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run shapestudio/portra-800-flux 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": "3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2", "input": { "model": "dev", "prompt": "sun and shadows, in the style of TOK", "lora_scale": 0.83, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 41 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-20T12:37:06.507839Z", "created_at": "2024-08-20T12:36:12.853000Z", "data_removed": false, "error": null, "id": "crjn7qd9pnrm00chdz4t17bx60", "input": { "model": "dev", "prompt": "sun and shadows, in the style of TOK", "lora_scale": 0.83, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 41 }, "logs": "Using seed: 19241\nPrompt: sun and shadows, in the style of TOK\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9717500735488\nDownloading weights\n2024-08-20T12:36:12Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/fd84441843d62451 url=https://replicate.delivery/yhqm/f9N0J6Rupa1BWafBihgj6CbHOqlvIyO2sfMymIxSNEJAoEpmA/trained_model.tar\n2024-08-20T12:36:14Z | INFO | [ Complete ] dest=/src/weights-cache/fd84441843d62451 size=\"172 MB\" total_elapsed=1.338s url=https://replicate.delivery/yhqm/f9N0J6Rupa1BWafBihgj6CbHOqlvIyO2sfMymIxSNEJAoEpmA/trained_model.tar\nb''\nDownloaded weights in 1.3716022968292236 seconds\nLoRA weights loaded successfully\n 0%| | 0/41 [00:00<?, ?it/s]\n 2%|▏ | 1/41 [00:01<00:40, 1.02s/it]\n 5%|▍ | 2/41 [00:01<00:35, 1.11it/s]\n 7%|▋ | 3/41 [00:02<00:36, 1.04it/s]\n 10%|▉ | 4/41 [00:03<00:36, 1.01it/s]\n 12%|█▏ | 5/41 [00:04<00:36, 1.00s/it]\n 15%|█▍ | 6/41 [00:05<00:35, 1.01s/it]\n 17%|█▋ | 7/41 [00:06<00:34, 1.02s/it]\n 20%|█▉ | 8/41 [00:08<00:33, 1.02s/it]\n 22%|██▏ | 9/41 [00:09<00:32, 1.02s/it]\n 24%|██▍ | 10/41 [00:10<00:31, 1.02s/it]\n 27%|██▋ | 11/41 [00:11<00:30, 1.03s/it]\n 29%|██▉ | 12/41 [00:12<00:29, 1.03s/it]\n 32%|███▏ | 13/41 [00:13<00:28, 1.03s/it]\n 34%|███▍ | 14/41 [00:14<00:27, 1.03s/it]\n 37%|███▋ | 15/41 [00:15<00:26, 1.03s/it]\n 39%|███▉ | 16/41 [00:16<00:25, 1.03s/it]\n 41%|████▏ | 17/41 [00:17<00:24, 1.03s/it]\n 44%|████▍ | 18/41 [00:18<00:23, 1.03s/it]\n 46%|████▋ | 19/41 [00:19<00:22, 1.03s/it]\n 49%|████▉ | 20/41 [00:20<00:21, 1.03s/it]\n 51%|█████ | 21/41 [00:21<00:20, 1.03s/it]\n 54%|█████▎ | 22/41 [00:22<00:19, 1.03s/it]\n 56%|█████▌ | 23/41 [00:23<00:18, 1.03s/it]\n 59%|█████▊ | 24/41 [00:24<00:17, 1.03s/it]\n 61%|██████ | 25/41 [00:25<00:16, 1.03s/it]\n 63%|██████▎ | 26/41 [00:26<00:15, 1.03s/it]\n 66%|██████▌ | 27/41 [00:27<00:14, 1.03s/it]\n 68%|██████▊ | 28/41 [00:28<00:13, 1.03s/it]\n 71%|███████ | 29/41 [00:29<00:12, 1.03s/it]\n 73%|███████▎ | 30/41 [00:30<00:11, 1.03s/it]\n 76%|███████▌ | 31/41 [00:31<00:10, 1.03s/it]\n 78%|███████▊ | 32/41 [00:32<00:09, 1.03s/it]\n 80%|████████ | 33/41 [00:33<00:08, 1.03s/it]\n 83%|████████▎ | 34/41 [00:34<00:07, 1.03s/it]\n 85%|████████▌ | 35/41 [00:35<00:06, 1.03s/it]\n 88%|████████▊ | 36/41 [00:36<00:05, 1.03s/it]\n 90%|█████████ | 37/41 [00:37<00:04, 1.03s/it]\n 93%|█████████▎| 38/41 [00:38<00:03, 1.03s/it]\n 95%|█████████▌| 39/41 [00:39<00:02, 1.03s/it]\n 98%|█████████▊| 40/41 [00:40<00:01, 1.03s/it]\n100%|██████████| 41/41 [00:42<00:00, 1.03s/it]\n100%|██████████| 41/41 [00:42<00:00, 1.02s/it]", "metrics": { "predict_time": 53.634901128, "total_time": 53.654839 }, "output": [ "https://replicate.delivery/yhqm/aaCailzb2XqYApeBWMAN7wnen1UOXGlZnGEEdbzPmFKyfHpmA/out-0.webp", "https://replicate.delivery/yhqm/4Gor0qWdSfQzKKoQNhVirSBufZuBRYsv8vliDAp8XHNyfHpmA/out-1.webp", "https://replicate.delivery/yhqm/VKXfzS7RrWX2DClW9B5SK8D5fheEnNaIRJe6VsBFi5jJffI1E/out-2.webp", "https://replicate.delivery/yhqm/xfg7CfrhaRughkyFYRnKNbD3Ep5JJNIKmKUEZqH76DKyfHpmA/out-3.webp" ], "started_at": "2024-08-20T12:36:12.872938Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/crjn7qd9pnrm00chdz4t17bx60", "cancel": "https://api.replicate.com/v1/predictions/crjn7qd9pnrm00chdz4t17bx60/cancel" }, "version": "3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2" }Generated inUsing seed: 19241 Prompt: sun and shadows, in the style of TOK txt2img mode Using dev model Loading LoRA weights Ensuring enough disk space... Free disk space: 9717500735488 Downloading weights 2024-08-20T12:36:12Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/fd84441843d62451 url=https://replicate.delivery/yhqm/f9N0J6Rupa1BWafBihgj6CbHOqlvIyO2sfMymIxSNEJAoEpmA/trained_model.tar 2024-08-20T12:36:14Z | INFO | [ Complete ] dest=/src/weights-cache/fd84441843d62451 size="172 MB" total_elapsed=1.338s url=https://replicate.delivery/yhqm/f9N0J6Rupa1BWafBihgj6CbHOqlvIyO2sfMymIxSNEJAoEpmA/trained_model.tar b'' Downloaded weights in 1.3716022968292236 seconds LoRA weights loaded successfully 0%| | 0/41 [00:00<?, ?it/s] 2%|▏ | 1/41 [00:01<00:40, 1.02s/it] 5%|▍ | 2/41 [00:01<00:35, 1.11it/s] 7%|▋ | 3/41 [00:02<00:36, 1.04it/s] 10%|▉ | 4/41 [00:03<00:36, 1.01it/s] 12%|█▏ | 5/41 [00:04<00:36, 1.00s/it] 15%|█▍ | 6/41 [00:05<00:35, 1.01s/it] 17%|█▋ | 7/41 [00:06<00:34, 1.02s/it] 20%|█▉ | 8/41 [00:08<00:33, 1.02s/it] 22%|██▏ | 9/41 [00:09<00:32, 1.02s/it] 24%|██▍ | 10/41 [00:10<00:31, 1.02s/it] 27%|██▋ | 11/41 [00:11<00:30, 1.03s/it] 29%|██▉ | 12/41 [00:12<00:29, 1.03s/it] 32%|███▏ | 13/41 [00:13<00:28, 1.03s/it] 34%|███▍ | 14/41 [00:14<00:27, 1.03s/it] 37%|███▋ | 15/41 [00:15<00:26, 1.03s/it] 39%|███▉ | 16/41 [00:16<00:25, 1.03s/it] 41%|████▏ | 17/41 [00:17<00:24, 1.03s/it] 44%|████▍ | 18/41 [00:18<00:23, 1.03s/it] 46%|████▋ | 19/41 [00:19<00:22, 1.03s/it] 49%|████▉ | 20/41 [00:20<00:21, 1.03s/it] 51%|█████ | 21/41 [00:21<00:20, 1.03s/it] 54%|█████▎ | 22/41 [00:22<00:19, 1.03s/it] 56%|█████▌ | 23/41 [00:23<00:18, 1.03s/it] 59%|█████▊ | 24/41 [00:24<00:17, 1.03s/it] 61%|██████ | 25/41 [00:25<00:16, 1.03s/it] 63%|██████▎ | 26/41 [00:26<00:15, 1.03s/it] 66%|██████▌ | 27/41 [00:27<00:14, 1.03s/it] 68%|██████▊ | 28/41 [00:28<00:13, 1.03s/it] 71%|███████ | 29/41 [00:29<00:12, 1.03s/it] 73%|███████▎ | 30/41 [00:30<00:11, 1.03s/it] 76%|███████▌ | 31/41 [00:31<00:10, 1.03s/it] 78%|███████▊ | 32/41 [00:32<00:09, 1.03s/it] 80%|████████ | 33/41 [00:33<00:08, 1.03s/it] 83%|████████▎ | 34/41 [00:34<00:07, 1.03s/it] 85%|████████▌ | 35/41 [00:35<00:06, 1.03s/it] 88%|████████▊ | 36/41 [00:36<00:05, 1.03s/it] 90%|█████████ | 37/41 [00:37<00:04, 1.03s/it] 93%|█████████▎| 38/41 [00:38<00:03, 1.03s/it] 95%|█████████▌| 39/41 [00:39<00:02, 1.03s/it] 98%|█████████▊| 40/41 [00:40<00:01, 1.03s/it] 100%|██████████| 41/41 [00:42<00:00, 1.03s/it] 100%|██████████| 41/41 [00:42<00:00, 1.02s/it]Prediction
shapestudio/portra-800-flux:3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2IDgz0ajg93esrm00chdzct52epncStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A detailed and intricate futuristic white android with a clear glass helmet piloted by a goldfish, swimming inside the helmet. The android and goldfish are looking out of a glass window in a futuristic building at a beautiful coral underwater city.
- lora_scale
- 0.86
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 38
{ "model": "dev", "prompt": "A detailed and intricate futuristic white android with a clear glass helmet piloted by a goldfish, swimming inside the helmet. The android and goldfish are looking out of a glass window in a futuristic building at a beautiful coral underwater city.", "lora_scale": 0.86, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 38 }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 shapestudio/portra-800-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "shapestudio/portra-800-flux:3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2", { input: { model: "dev", prompt: "A detailed and intricate futuristic white android with a clear glass helmet piloted by a goldfish, swimming inside the helmet. The android and goldfish are looking out of a glass window in a futuristic building at a beautiful coral underwater city.", lora_scale: 0.86, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 38 } } ); // 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 shapestudio/portra-800-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "shapestudio/portra-800-flux:3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2", input={ "model": "dev", "prompt": "A detailed and intricate futuristic white android with a clear glass helmet piloted by a goldfish, swimming inside the helmet. The android and goldfish are looking out of a glass window in a futuristic building at a beautiful coral underwater city.", "lora_scale": 0.86, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 38 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run shapestudio/portra-800-flux 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": "3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2", "input": { "model": "dev", "prompt": "A detailed and intricate futuristic white android with a clear glass helmet piloted by a goldfish, swimming inside the helmet. The android and goldfish are looking out of a glass window in a futuristic building at a beautiful coral underwater city.", "lora_scale": 0.86, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 38 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-20T12:53:42.961125Z", "created_at": "2024-08-20T12:53:07.062000Z", "data_removed": false, "error": null, "id": "gz0ajg93esrm00chdzct52epnc", "input": { "model": "dev", "prompt": "A detailed and intricate futuristic white android with a clear glass helmet piloted by a goldfish, swimming inside the helmet. The android and goldfish are looking out of a glass window in a futuristic building at a beautiful coral underwater city.", "lora_scale": 0.86, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 38 }, "logs": "Using seed: 11439\nPrompt: A detailed and intricate futuristic white android with a clear glass helmet piloted by a goldfish, swimming inside the helmet. The android and goldfish are looking out of a glass window in a futuristic building at a beautiful coral underwater city.\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nLoRA weights loaded successfully\n 0%| | 0/38 [00:00<?, ?it/s]\n 3%|▎ | 1/38 [00:00<00:10, 3.53it/s]\n 5%|▌ | 2/38 [00:00<00:09, 3.95it/s]\n 8%|▊ | 3/38 [00:00<00:09, 3.76it/s]\n 11%|█ | 4/38 [00:01<00:09, 3.68it/s]\n 13%|█▎ | 5/38 [00:01<00:09, 3.62it/s]\n 16%|█▌ | 6/38 [00:01<00:08, 3.60it/s]\n 18%|█▊ | 7/38 [00:01<00:08, 3.59it/s]\n 21%|██ | 8/38 [00:02<00:08, 3.58it/s]\n 24%|██▎ | 9/38 [00:02<00:08, 3.57it/s]\n 26%|██▋ | 10/38 [00:02<00:07, 3.55it/s]\n 29%|██▉ | 11/38 [00:03<00:07, 3.55it/s]\n 32%|███▏ | 12/38 [00:03<00:07, 3.55it/s]\n 34%|███▍ | 13/38 [00:03<00:07, 3.55it/s]\n 37%|███▋ | 14/38 [00:03<00:06, 3.54it/s]\n 39%|███▉ | 15/38 [00:04<00:06, 3.54it/s]\n 42%|████▏ | 16/38 [00:04<00:06, 3.55it/s]\n 45%|████▍ | 17/38 [00:04<00:05, 3.55it/s]\n 47%|████▋ | 18/38 [00:05<00:05, 3.54it/s]\n 50%|█████ | 19/38 [00:05<00:05, 3.55it/s]\n 53%|█████▎ | 20/38 [00:05<00:05, 3.55it/s]\n 55%|█████▌ | 21/38 [00:05<00:04, 3.55it/s]\n 58%|█████▊ | 22/38 [00:06<00:04, 3.54it/s]\n 61%|██████ | 23/38 [00:06<00:04, 3.54it/s]\n 63%|██████▎ | 24/38 [00:06<00:03, 3.54it/s]\n 66%|██████▌ | 25/38 [00:07<00:03, 3.54it/s]\n 68%|██████▊ | 26/38 [00:07<00:03, 3.53it/s]\n 71%|███████ | 27/38 [00:07<00:03, 3.54it/s]\n 74%|███████▎ | 28/38 [00:07<00:02, 3.54it/s]\n 76%|███████▋ | 29/38 [00:08<00:02, 3.54it/s]\n 79%|███████▉ | 30/38 [00:08<00:02, 3.54it/s]\n 82%|████████▏ | 31/38 [00:08<00:01, 3.55it/s]\n 84%|████████▍ | 32/38 [00:08<00:01, 3.55it/s]\n 87%|████████▋ | 33/38 [00:09<00:01, 3.54it/s]\n 89%|████████▉ | 34/38 [00:09<00:01, 3.54it/s]\n 92%|█████████▏| 35/38 [00:09<00:00, 3.55it/s]\n 95%|█████████▍| 36/38 [00:10<00:00, 3.54it/s]\n 97%|█████████▋| 37/38 [00:10<00:00, 3.53it/s]\n100%|██████████| 38/38 [00:10<00:00, 3.54it/s]\n100%|██████████| 38/38 [00:10<00:00, 3.56it/s]", "metrics": { "predict_time": 20.312455151, "total_time": 35.899125 }, "output": [ "https://replicate.delivery/yhqm/KY0N0V7nw5oCM9GAuDT2oWsKLyT4y9LVb80nlTUbBao1DJ1E/out-0.webp" ], "started_at": "2024-08-20T12:53:22.648670Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/gz0ajg93esrm00chdzct52epnc", "cancel": "https://api.replicate.com/v1/predictions/gz0ajg93esrm00chdzct52epnc/cancel" }, "version": "3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2" }Generated inUsing seed: 11439 Prompt: A detailed and intricate futuristic white android with a clear glass helmet piloted by a goldfish, swimming inside the helmet. The android and goldfish are looking out of a glass window in a futuristic building at a beautiful coral underwater city. txt2img mode Using dev model Loading LoRA weights LoRA weights loaded successfully 0%| | 0/38 [00:00<?, ?it/s] 3%|▎ | 1/38 [00:00<00:10, 3.53it/s] 5%|▌ | 2/38 [00:00<00:09, 3.95it/s] 8%|▊ | 3/38 [00:00<00:09, 3.76it/s] 11%|█ | 4/38 [00:01<00:09, 3.68it/s] 13%|█▎ | 5/38 [00:01<00:09, 3.62it/s] 16%|█▌ | 6/38 [00:01<00:08, 3.60it/s] 18%|█▊ | 7/38 [00:01<00:08, 3.59it/s] 21%|██ | 8/38 [00:02<00:08, 3.58it/s] 24%|██▎ | 9/38 [00:02<00:08, 3.57it/s] 26%|██▋ | 10/38 [00:02<00:07, 3.55it/s] 29%|██▉ | 11/38 [00:03<00:07, 3.55it/s] 32%|███▏ | 12/38 [00:03<00:07, 3.55it/s] 34%|███▍ | 13/38 [00:03<00:07, 3.55it/s] 37%|███▋ | 14/38 [00:03<00:06, 3.54it/s] 39%|███▉ | 15/38 [00:04<00:06, 3.54it/s] 42%|████▏ | 16/38 [00:04<00:06, 3.55it/s] 45%|████▍ | 17/38 [00:04<00:05, 3.55it/s] 47%|████▋ | 18/38 [00:05<00:05, 3.54it/s] 50%|█████ | 19/38 [00:05<00:05, 3.55it/s] 53%|█████▎ | 20/38 [00:05<00:05, 3.55it/s] 55%|█████▌ | 21/38 [00:05<00:04, 3.55it/s] 58%|█████▊ | 22/38 [00:06<00:04, 3.54it/s] 61%|██████ | 23/38 [00:06<00:04, 3.54it/s] 63%|██████▎ | 24/38 [00:06<00:03, 3.54it/s] 66%|██████▌ | 25/38 [00:07<00:03, 3.54it/s] 68%|██████▊ | 26/38 [00:07<00:03, 3.53it/s] 71%|███████ | 27/38 [00:07<00:03, 3.54it/s] 74%|███████▎ | 28/38 [00:07<00:02, 3.54it/s] 76%|███████▋ | 29/38 [00:08<00:02, 3.54it/s] 79%|███████▉ | 30/38 [00:08<00:02, 3.54it/s] 82%|████████▏ | 31/38 [00:08<00:01, 3.55it/s] 84%|████████▍ | 32/38 [00:08<00:01, 3.55it/s] 87%|████████▋ | 33/38 [00:09<00:01, 3.54it/s] 89%|████████▉ | 34/38 [00:09<00:01, 3.54it/s] 92%|█████████▏| 35/38 [00:09<00:00, 3.55it/s] 95%|█████████▍| 36/38 [00:10<00:00, 3.54it/s] 97%|█████████▋| 37/38 [00:10<00:00, 3.53it/s] 100%|██████████| 38/38 [00:10<00:00, 3.54it/s] 100%|██████████| 38/38 [00:10<00:00, 3.56it/s]Prediction
shapestudio/portra-800-flux:3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2IDngtcenjeg9rm20chdzdaa0qwwrStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A cup of coffee where the steam forms a cloud, raining tiny droplets back into the cup, in a self-sustaining cycle.
- lora_scale
- 0.86
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 38
{ "model": "dev", "prompt": "A cup of coffee where the steam forms a cloud, raining tiny droplets back into the cup, in a self-sustaining cycle.", "lora_scale": 0.86, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 38 }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 shapestudio/portra-800-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "shapestudio/portra-800-flux:3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2", { input: { model: "dev", prompt: "A cup of coffee where the steam forms a cloud, raining tiny droplets back into the cup, in a self-sustaining cycle.", lora_scale: 0.86, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 38 } } ); // 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 shapestudio/portra-800-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "shapestudio/portra-800-flux:3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2", input={ "model": "dev", "prompt": "A cup of coffee where the steam forms a cloud, raining tiny droplets back into the cup, in a self-sustaining cycle.", "lora_scale": 0.86, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 38 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run shapestudio/portra-800-flux 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": "3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2", "input": { "model": "dev", "prompt": "A cup of coffee where the steam forms a cloud, raining tiny droplets back into the cup, in a self-sustaining cycle.", "lora_scale": 0.86, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 38 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-20T12:54:55.535380Z", "created_at": "2024-08-20T12:54:23.618000Z", "data_removed": false, "error": null, "id": "ngtcenjeg9rm20chdzdaa0qwwr", "input": { "model": "dev", "prompt": "A cup of coffee where the steam forms a cloud, raining tiny droplets back into the cup, in a self-sustaining cycle.", "lora_scale": 0.86, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 38 }, "logs": "Using seed: 18640\nPrompt: A cup of coffee where the steam forms a cloud, raining tiny droplets back into the cup, in a self-sustaining cycle.\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nLoRA weights loaded successfully\n 0%| | 0/38 [00:00<?, ?it/s]\n 3%|▎ | 1/38 [00:00<00:10, 3.52it/s]\n 5%|▌ | 2/38 [00:00<00:09, 3.90it/s]\n 8%|▊ | 3/38 [00:00<00:09, 3.70it/s]\n 11%|█ | 4/38 [00:01<00:09, 3.62it/s]\n 13%|█▎ | 5/38 [00:01<00:09, 3.58it/s]\n 16%|█▌ | 6/38 [00:01<00:09, 3.55it/s]\n 18%|█▊ | 7/38 [00:01<00:08, 3.53it/s]\n 21%|██ | 8/38 [00:02<00:08, 3.53it/s]\n 24%|██▎ | 9/38 [00:02<00:08, 3.53it/s]\n 26%|██▋ | 10/38 [00:02<00:07, 3.52it/s]\n 29%|██▉ | 11/38 [00:03<00:07, 3.51it/s]\n 32%|███▏ | 12/38 [00:03<00:07, 3.51it/s]\n 34%|███▍ | 13/38 [00:03<00:07, 3.52it/s]\n 37%|███▋ | 14/38 [00:03<00:06, 3.51it/s]\n 39%|███▉ | 15/38 [00:04<00:06, 3.51it/s]\n 42%|████▏ | 16/38 [00:04<00:06, 3.51it/s]\n 45%|████▍ | 17/38 [00:04<00:05, 3.51it/s]\n 47%|████▋ | 18/38 [00:05<00:05, 3.51it/s]\n 50%|█████ | 19/38 [00:05<00:05, 3.50it/s]\n 53%|█████▎ | 20/38 [00:05<00:05, 3.51it/s]\n 55%|█████▌ | 21/38 [00:05<00:04, 3.51it/s]\n 58%|█████▊ | 22/38 [00:06<00:04, 3.51it/s]\n 61%|██████ | 23/38 [00:06<00:04, 3.50it/s]\n 63%|██████▎ | 24/38 [00:06<00:03, 3.51it/s]\n 66%|██████▌ | 25/38 [00:07<00:03, 3.51it/s]\n 68%|██████▊ | 26/38 [00:07<00:03, 3.51it/s]\n 71%|███████ | 27/38 [00:07<00:03, 3.50it/s]\n 74%|███████▎ | 28/38 [00:07<00:02, 3.50it/s]\n 76%|███████▋ | 29/38 [00:08<00:02, 3.51it/s]\n 79%|███████▉ | 30/38 [00:08<00:02, 3.51it/s]\n 82%|████████▏ | 31/38 [00:08<00:01, 3.51it/s]\n 84%|████████▍ | 32/38 [00:09<00:01, 3.51it/s]\n 87%|████████▋ | 33/38 [00:09<00:01, 3.51it/s]\n 89%|████████▉ | 34/38 [00:09<00:01, 3.51it/s]\n 92%|█████████▏| 35/38 [00:09<00:00, 3.51it/s]\n 95%|█████████▍| 36/38 [00:10<00:00, 3.51it/s]\n 97%|█████████▋| 37/38 [00:10<00:00, 3.51it/s]\n100%|██████████| 38/38 [00:10<00:00, 3.51it/s]\n100%|██████████| 38/38 [00:10<00:00, 3.52it/s]", "metrics": { "predict_time": 19.338690129, "total_time": 31.91738 }, "output": [ "https://replicate.delivery/yhqm/9M0L6kvWqfQ5YKhsVivNhpIia4F9ajrOjuOARbD1oc5PISqJA/out-0.webp" ], "started_at": "2024-08-20T12:54:36.196689Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ngtcenjeg9rm20chdzdaa0qwwr", "cancel": "https://api.replicate.com/v1/predictions/ngtcenjeg9rm20chdzdaa0qwwr/cancel" }, "version": "3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2" }Generated inUsing seed: 18640 Prompt: A cup of coffee where the steam forms a cloud, raining tiny droplets back into the cup, in a self-sustaining cycle. txt2img mode Using dev model Loading LoRA weights LoRA weights loaded successfully 0%| | 0/38 [00:00<?, ?it/s] 3%|▎ | 1/38 [00:00<00:10, 3.52it/s] 5%|▌ | 2/38 [00:00<00:09, 3.90it/s] 8%|▊ | 3/38 [00:00<00:09, 3.70it/s] 11%|█ | 4/38 [00:01<00:09, 3.62it/s] 13%|█▎ | 5/38 [00:01<00:09, 3.58it/s] 16%|█▌ | 6/38 [00:01<00:09, 3.55it/s] 18%|█▊ | 7/38 [00:01<00:08, 3.53it/s] 21%|██ | 8/38 [00:02<00:08, 3.53it/s] 24%|██▎ | 9/38 [00:02<00:08, 3.53it/s] 26%|██▋ | 10/38 [00:02<00:07, 3.52it/s] 29%|██▉ | 11/38 [00:03<00:07, 3.51it/s] 32%|███▏ | 12/38 [00:03<00:07, 3.51it/s] 34%|███▍ | 13/38 [00:03<00:07, 3.52it/s] 37%|███▋ | 14/38 [00:03<00:06, 3.51it/s] 39%|███▉ | 15/38 [00:04<00:06, 3.51it/s] 42%|████▏ | 16/38 [00:04<00:06, 3.51it/s] 45%|████▍ | 17/38 [00:04<00:05, 3.51it/s] 47%|████▋ | 18/38 [00:05<00:05, 3.51it/s] 50%|█████ | 19/38 [00:05<00:05, 3.50it/s] 53%|█████▎ | 20/38 [00:05<00:05, 3.51it/s] 55%|█████▌ | 21/38 [00:05<00:04, 3.51it/s] 58%|█████▊ | 22/38 [00:06<00:04, 3.51it/s] 61%|██████ | 23/38 [00:06<00:04, 3.50it/s] 63%|██████▎ | 24/38 [00:06<00:03, 3.51it/s] 66%|██████▌ | 25/38 [00:07<00:03, 3.51it/s] 68%|██████▊ | 26/38 [00:07<00:03, 3.51it/s] 71%|███████ | 27/38 [00:07<00:03, 3.50it/s] 74%|███████▎ | 28/38 [00:07<00:02, 3.50it/s] 76%|███████▋ | 29/38 [00:08<00:02, 3.51it/s] 79%|███████▉ | 30/38 [00:08<00:02, 3.51it/s] 82%|████████▏ | 31/38 [00:08<00:01, 3.51it/s] 84%|████████▍ | 32/38 [00:09<00:01, 3.51it/s] 87%|████████▋ | 33/38 [00:09<00:01, 3.51it/s] 89%|████████▉ | 34/38 [00:09<00:01, 3.51it/s] 92%|█████████▏| 35/38 [00:09<00:00, 3.51it/s] 95%|█████████▍| 36/38 [00:10<00:00, 3.51it/s] 97%|█████████▋| 37/38 [00:10<00:00, 3.51it/s] 100%|██████████| 38/38 [00:10<00:00, 3.51it/s] 100%|██████████| 38/38 [00:10<00:00, 3.52it/s]Prediction
shapestudio/portra-800-flux:3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2IDfq1xakzs1xrm60chdzg9d0bmwwStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a cocktail in a table with ice cubes falling into the glass and liquid spritzing outside
- lora_scale
- 0.86
- num_outputs
- 4
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 38
{ "model": "dev", "prompt": "a cocktail in a table with ice cubes falling into the glass and liquid spritzing outside", "lora_scale": 0.86, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 38 }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 shapestudio/portra-800-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "shapestudio/portra-800-flux:3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2", { input: { model: "dev", prompt: "a cocktail in a table with ice cubes falling into the glass and liquid spritzing outside", lora_scale: 0.86, num_outputs: 4, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 38 } } ); // 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 shapestudio/portra-800-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "shapestudio/portra-800-flux:3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2", input={ "model": "dev", "prompt": "a cocktail in a table with ice cubes falling into the glass and liquid spritzing outside", "lora_scale": 0.86, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 38 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run shapestudio/portra-800-flux 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": "3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2", "input": { "model": "dev", "prompt": "a cocktail in a table with ice cubes falling into the glass and liquid spritzing outside", "lora_scale": 0.86, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 38 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-20T13:02:28.558497Z", "created_at": "2024-08-20T13:01:40.495000Z", "data_removed": false, "error": null, "id": "fq1xakzs1xrm60chdzg9d0bmww", "input": { "model": "dev", "prompt": "a cocktail in a table with ice cubes falling into the glass and liquid spritzing outside", "lora_scale": 0.86, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 38 }, "logs": "Using seed: 7200\nPrompt: a cocktail in a table with ice cubes falling into the glass and liquid spritzing outside\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nLoRA weights loaded successfully\n 0%| | 0/38 [00:00<?, ?it/s]\n 3%|▎ | 1/38 [00:01<00:37, 1.02s/it]\n 5%|▌ | 2/38 [00:01<00:32, 1.11it/s]\n 8%|▊ | 3/38 [00:02<00:33, 1.04it/s]\n 11%|█ | 4/38 [00:03<00:33, 1.01it/s]\n 13%|█▎ | 5/38 [00:04<00:32, 1.00it/s]\n 16%|█▌ | 6/38 [00:05<00:32, 1.01s/it]\n 18%|█▊ | 7/38 [00:06<00:31, 1.02s/it]\n 21%|██ | 8/38 [00:08<00:30, 1.02s/it]\n 24%|██▎ | 9/38 [00:09<00:29, 1.02s/it]\n 26%|██▋ | 10/38 [00:10<00:28, 1.03s/it]\n 29%|██▉ | 11/38 [00:11<00:27, 1.03s/it]\n 32%|███▏ | 12/38 [00:12<00:26, 1.03s/it]\n 34%|███▍ | 13/38 [00:13<00:25, 1.03s/it]\n 37%|███▋ | 14/38 [00:14<00:24, 1.03s/it]\n 39%|███▉ | 15/38 [00:15<00:23, 1.03s/it]\n 42%|████▏ | 16/38 [00:16<00:22, 1.03s/it]\n 45%|████▍ | 17/38 [00:17<00:21, 1.03s/it]\n 47%|████▋ | 18/38 [00:18<00:20, 1.03s/it]\n 50%|█████ | 19/38 [00:19<00:19, 1.03s/it]\n 53%|█████▎ | 20/38 [00:20<00:18, 1.03s/it]\n 55%|█████▌ | 21/38 [00:21<00:17, 1.03s/it]\n 58%|█████▊ | 22/38 [00:22<00:16, 1.03s/it]\n 61%|██████ | 23/38 [00:23<00:15, 1.03s/it]\n 63%|██████▎ | 24/38 [00:24<00:14, 1.03s/it]\n 66%|██████▌ | 25/38 [00:25<00:13, 1.03s/it]\n 68%|██████▊ | 26/38 [00:26<00:12, 1.03s/it]\n 71%|███████ | 27/38 [00:27<00:11, 1.03s/it]\n 74%|███████▎ | 28/38 [00:28<00:10, 1.03s/it]\n 76%|███████▋ | 29/38 [00:29<00:09, 1.03s/it]\n 79%|███████▉ | 30/38 [00:30<00:08, 1.03s/it]\n 82%|████████▏ | 31/38 [00:31<00:07, 1.03s/it]\n 84%|████████▍ | 32/38 [00:32<00:06, 1.03s/it]\n 87%|████████▋ | 33/38 [00:33<00:05, 1.03s/it]\n 89%|████████▉ | 34/38 [00:34<00:04, 1.03s/it]\n 92%|█████████▏| 35/38 [00:35<00:03, 1.03s/it]\n 95%|█████████▍| 36/38 [00:36<00:02, 1.03s/it]\n 97%|█████████▋| 37/38 [00:37<00:01, 1.03s/it]\n100%|██████████| 38/38 [00:38<00:00, 1.03s/it]\n100%|██████████| 38/38 [00:38<00:00, 1.02s/it]", "metrics": { "predict_time": 48.042616991, "total_time": 48.063497 }, "output": [ "https://replicate.delivery/yhqm/E5SSJliNOyIhPBbVhCLIQv3EhiIN3ieU9iUXs9xDRsMyLSqJA/out-0.webp", "https://replicate.delivery/yhqm/pAnzQ640hvr7CJh6tdhMKNmLgEeenJHSEIgPOwVkv5XkXkUTA/out-1.webp", "https://replicate.delivery/yhqm/hGbwMIbefqj3akauIRHwH770TPK3OSGcL037AwieXTRJvIpmA/out-2.webp", "https://replicate.delivery/yhqm/x6iwsHse3el8bkheggFw4AF7OmlCi1FBQaLPKscSMwGIvIpmA/out-3.webp" ], "started_at": "2024-08-20T13:01:40.515880Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fq1xakzs1xrm60chdzg9d0bmww", "cancel": "https://api.replicate.com/v1/predictions/fq1xakzs1xrm60chdzg9d0bmww/cancel" }, "version": "3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2" }Generated inUsing seed: 7200 Prompt: a cocktail in a table with ice cubes falling into the glass and liquid spritzing outside txt2img mode Using dev model Loading LoRA weights LoRA weights loaded successfully 0%| | 0/38 [00:00<?, ?it/s] 3%|▎ | 1/38 [00:01<00:37, 1.02s/it] 5%|▌ | 2/38 [00:01<00:32, 1.11it/s] 8%|▊ | 3/38 [00:02<00:33, 1.04it/s] 11%|█ | 4/38 [00:03<00:33, 1.01it/s] 13%|█▎ | 5/38 [00:04<00:32, 1.00it/s] 16%|█▌ | 6/38 [00:05<00:32, 1.01s/it] 18%|█▊ | 7/38 [00:06<00:31, 1.02s/it] 21%|██ | 8/38 [00:08<00:30, 1.02s/it] 24%|██▎ | 9/38 [00:09<00:29, 1.02s/it] 26%|██▋ | 10/38 [00:10<00:28, 1.03s/it] 29%|██▉ | 11/38 [00:11<00:27, 1.03s/it] 32%|███▏ | 12/38 [00:12<00:26, 1.03s/it] 34%|███▍ | 13/38 [00:13<00:25, 1.03s/it] 37%|███▋ | 14/38 [00:14<00:24, 1.03s/it] 39%|███▉ | 15/38 [00:15<00:23, 1.03s/it] 42%|████▏ | 16/38 [00:16<00:22, 1.03s/it] 45%|████▍ | 17/38 [00:17<00:21, 1.03s/it] 47%|████▋ | 18/38 [00:18<00:20, 1.03s/it] 50%|█████ | 19/38 [00:19<00:19, 1.03s/it] 53%|█████▎ | 20/38 [00:20<00:18, 1.03s/it] 55%|█████▌ | 21/38 [00:21<00:17, 1.03s/it] 58%|█████▊ | 22/38 [00:22<00:16, 1.03s/it] 61%|██████ | 23/38 [00:23<00:15, 1.03s/it] 63%|██████▎ | 24/38 [00:24<00:14, 1.03s/it] 66%|██████▌ | 25/38 [00:25<00:13, 1.03s/it] 68%|██████▊ | 26/38 [00:26<00:12, 1.03s/it] 71%|███████ | 27/38 [00:27<00:11, 1.03s/it] 74%|███████▎ | 28/38 [00:28<00:10, 1.03s/it] 76%|███████▋ | 29/38 [00:29<00:09, 1.03s/it] 79%|███████▉ | 30/38 [00:30<00:08, 1.03s/it] 82%|████████▏ | 31/38 [00:31<00:07, 1.03s/it] 84%|████████▍ | 32/38 [00:32<00:06, 1.03s/it] 87%|████████▋ | 33/38 [00:33<00:05, 1.03s/it] 89%|████████▉ | 34/38 [00:34<00:04, 1.03s/it] 92%|█████████▏| 35/38 [00:35<00:03, 1.03s/it] 95%|█████████▍| 36/38 [00:36<00:02, 1.03s/it] 97%|█████████▋| 37/38 [00:37<00:01, 1.03s/it] 100%|██████████| 38/38 [00:38<00:00, 1.03s/it] 100%|██████████| 38/38 [00:38<00:00, 1.02s/it]Prediction
shapestudio/portra-800-flux:3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2IDbs8y9j90gnrm20chzz6a8f5nwwStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a lady in front of a green Mercedes convertible wearing a stylish leather bag
- lora_scale
- 0.86
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 38
{ "model": "dev", "prompt": "a lady in front of a green Mercedes convertible wearing a stylish leather bag", "lora_scale": 0.86, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 38 }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 shapestudio/portra-800-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "shapestudio/portra-800-flux:3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2", { input: { model: "dev", prompt: "a lady in front of a green Mercedes convertible wearing a stylish leather bag", lora_scale: 0.86, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 38 } } ); // 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 shapestudio/portra-800-flux using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "shapestudio/portra-800-flux:3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2", input={ "model": "dev", "prompt": "a lady in front of a green Mercedes convertible wearing a stylish leather bag", "lora_scale": 0.86, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 38 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run shapestudio/portra-800-flux 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": "3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2", "input": { "model": "dev", "prompt": "a lady in front of a green Mercedes convertible wearing a stylish leather bag", "lora_scale": 0.86, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 38 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-09-17T11:45:34.713314Z", "created_at": "2024-09-17T11:44:13.445000Z", "data_removed": false, "error": null, "id": "bs8y9j90gnrm20chzz6a8f5nww", "input": { "model": "dev", "prompt": "a lady in front of a green Mercedes convertible wearing a stylish leather bag", "lora_scale": 0.86, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 38 }, "logs": "Using seed: 39531\nPrompt: a lady in front of a green Mercedes convertible wearing a stylish leather bag\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 7.21s\n 0%| | 0/38 [00:00<?, ?it/s]\n 3%|▎ | 1/38 [00:00<00:10, 3.42it/s]\n 5%|▌ | 2/38 [00:00<00:09, 3.79it/s]\n 8%|▊ | 3/38 [00:00<00:09, 3.66it/s]\n 11%|█ | 4/38 [00:01<00:09, 3.60it/s]\n 13%|█▎ | 5/38 [00:01<00:09, 3.57it/s]\n 16%|█▌ | 6/38 [00:01<00:09, 3.55it/s]\n 18%|█▊ | 7/38 [00:01<00:08, 3.54it/s]\n 21%|██ | 8/38 [00:02<00:08, 3.53it/s]\n 24%|██▎ | 9/38 [00:02<00:08, 3.52it/s]\n 26%|██▋ | 10/38 [00:02<00:07, 3.52it/s]\n 29%|██▉ | 11/38 [00:03<00:07, 3.52it/s]\n 32%|███▏ | 12/38 [00:03<00:07, 3.52it/s]\n 34%|███▍ | 13/38 [00:03<00:07, 3.52it/s]\n 37%|███▋ | 14/38 [00:03<00:06, 3.52it/s]\n 39%|███▉ | 15/38 [00:04<00:06, 3.52it/s]\n 42%|████▏ | 16/38 [00:04<00:06, 3.51it/s]\n 45%|████▍ | 17/38 [00:04<00:05, 3.51it/s]\n 47%|████▋ | 18/38 [00:05<00:05, 3.51it/s]\n 50%|█████ | 19/38 [00:05<00:05, 3.51it/s]\n 53%|█████▎ | 20/38 [00:05<00:05, 3.51it/s]\n 55%|█████▌ | 21/38 [00:05<00:04, 3.51it/s]\n 58%|█████▊ | 22/38 [00:06<00:04, 3.51it/s]\n 61%|██████ | 23/38 [00:06<00:04, 3.51it/s]\n 63%|██████▎ | 24/38 [00:06<00:03, 3.52it/s]\n 66%|██████▌ | 25/38 [00:07<00:03, 3.52it/s]\n 68%|██████▊ | 26/38 [00:07<00:03, 3.51it/s]\n 71%|███████ | 27/38 [00:07<00:03, 3.51it/s]\n 74%|███████▎ | 28/38 [00:07<00:02, 3.51it/s]\n 76%|███████▋ | 29/38 [00:08<00:02, 3.51it/s]\n 79%|███████▉ | 30/38 [00:08<00:02, 3.52it/s]\n 82%|████████▏ | 31/38 [00:08<00:01, 3.51it/s]\n 84%|████████▍ | 32/38 [00:09<00:01, 3.51it/s]\n 87%|████████▋ | 33/38 [00:09<00:01, 3.51it/s]\n 89%|████████▉ | 34/38 [00:09<00:01, 3.51it/s]\n 92%|█████████▏| 35/38 [00:09<00:00, 3.51it/s]\n 95%|█████████▍| 36/38 [00:10<00:00, 3.51it/s]\n 97%|█████████▋| 37/38 [00:10<00:00, 3.52it/s]\n100%|██████████| 38/38 [00:10<00:00, 3.52it/s]\n100%|██████████| 38/38 [00:10<00:00, 3.52it/s]", "metrics": { "predict_time": 18.321872019, "total_time": 81.268314 }, "output": [ "https://replicate.delivery/yhqm/vNm8HtCelcVNE6POINdbR0eP26Qqx3ZBK7OzoizQViceuj7mA/out-0.webp" ], "started_at": "2024-09-17T11:45:16.391442Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/bs8y9j90gnrm20chzz6a8f5nww", "cancel": "https://api.replicate.com/v1/predictions/bs8y9j90gnrm20chzz6a8f5nww/cancel" }, "version": "3d616c81c96b5578e485988d51f260c4159b8ce7822cf13fa0af63a7e5c833c2" }Generated inUsing seed: 39531 Prompt: a lady in front of a green Mercedes convertible wearing a stylish leather bag [!] txt2img mode Using dev model Loaded LoRAs in 7.21s 0%| | 0/38 [00:00<?, ?it/s] 3%|▎ | 1/38 [00:00<00:10, 3.42it/s] 5%|▌ | 2/38 [00:00<00:09, 3.79it/s] 8%|▊ | 3/38 [00:00<00:09, 3.66it/s] 11%|█ | 4/38 [00:01<00:09, 3.60it/s] 13%|█▎ | 5/38 [00:01<00:09, 3.57it/s] 16%|█▌ | 6/38 [00:01<00:09, 3.55it/s] 18%|█▊ | 7/38 [00:01<00:08, 3.54it/s] 21%|██ | 8/38 [00:02<00:08, 3.53it/s] 24%|██▎ | 9/38 [00:02<00:08, 3.52it/s] 26%|██▋ | 10/38 [00:02<00:07, 3.52it/s] 29%|██▉ | 11/38 [00:03<00:07, 3.52it/s] 32%|███▏ | 12/38 [00:03<00:07, 3.52it/s] 34%|███▍ | 13/38 [00:03<00:07, 3.52it/s] 37%|███▋ | 14/38 [00:03<00:06, 3.52it/s] 39%|███▉ | 15/38 [00:04<00:06, 3.52it/s] 42%|████▏ | 16/38 [00:04<00:06, 3.51it/s] 45%|████▍ | 17/38 [00:04<00:05, 3.51it/s] 47%|████▋ | 18/38 [00:05<00:05, 3.51it/s] 50%|█████ | 19/38 [00:05<00:05, 3.51it/s] 53%|█████▎ | 20/38 [00:05<00:05, 3.51it/s] 55%|█████▌ | 21/38 [00:05<00:04, 3.51it/s] 58%|█████▊ | 22/38 [00:06<00:04, 3.51it/s] 61%|██████ | 23/38 [00:06<00:04, 3.51it/s] 63%|██████▎ | 24/38 [00:06<00:03, 3.52it/s] 66%|██████▌ | 25/38 [00:07<00:03, 3.52it/s] 68%|██████▊ | 26/38 [00:07<00:03, 3.51it/s] 71%|███████ | 27/38 [00:07<00:03, 3.51it/s] 74%|███████▎ | 28/38 [00:07<00:02, 3.51it/s] 76%|███████▋ | 29/38 [00:08<00:02, 3.51it/s] 79%|███████▉ | 30/38 [00:08<00:02, 3.52it/s] 82%|████████▏ | 31/38 [00:08<00:01, 3.51it/s] 84%|████████▍ | 32/38 [00:09<00:01, 3.51it/s] 87%|████████▋ | 33/38 [00:09<00:01, 3.51it/s] 89%|████████▉ | 34/38 [00:09<00:01, 3.51it/s] 92%|█████████▏| 35/38 [00:09<00:00, 3.51it/s] 95%|█████████▍| 36/38 [00:10<00:00, 3.51it/s] 97%|█████████▋| 37/38 [00:10<00:00, 3.52it/s] 100%|██████████| 38/38 [00:10<00:00, 3.52it/s] 100%|██████████| 38/38 [00:10<00:00, 3.52it/s]
Want to make some of these yourself?
Run this model