fofr
/
flux-brighton-west-pier
Flux lora, trigger image generation with "west pier"
- Public
- 89 runs
-
H100
- Paper
Prediction
fofr/flux-brighton-west-pier:d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160dID38kbera6e5rm20chew3vrdme7wStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a photo of Brighton’s west pier, long exposure, beautiful light
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 3:2
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a photo of Brighton’s west pier, long exposure, beautiful light", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "3:2", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }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 fofr/flux-brighton-west-pier using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-brighton-west-pier:d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d", { input: { model: "dev", prompt: "a photo of Brighton’s west pier, long exposure, beautiful light", lora_scale: 1, num_outputs: 1, aspect_ratio: "3:2", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 28 } } ); // 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 fofr/flux-brighton-west-pier using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-brighton-west-pier:d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d", input={ "model": "dev", "prompt": "a photo of Brighton’s west pier, long exposure, beautiful light", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "3:2", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fofr/flux-brighton-west-pier 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": "d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d", "input": { "model": "dev", "prompt": "a photo of Brighton’s west pier, long exposure, beautiful light", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "3:2", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-21T22:21:10.602503Z", "created_at": "2024-08-21T22:20:51.185000Z", "data_removed": false, "error": null, "id": "38kbera6e5rm20chew3vrdme7w", "input": { "model": "dev", "prompt": "a photo of Brighton’s west pier, long exposure, beautiful light", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "3:2", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 52531\nPrompt: a photo of Brighton’s west pier, long exposure, beautiful light\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9403074990080\nDownloading weights\n2024-08-21T22:20:51Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/5b35eeba647457c9 url=https://replicate.delivery/yhqm/xVA2Ba6zyMoGMdyz8hAvOHlzrXPj1h0SpeFf9xJTa1gTnBVTA/trained_model.tar\n2024-08-21T22:20:54Z | INFO | [ Complete ] dest=/src/weights-cache/5b35eeba647457c9 size=\"172 MB\" total_elapsed=3.636s url=https://replicate.delivery/yhqm/xVA2Ba6zyMoGMdyz8hAvOHlzrXPj1h0SpeFf9xJTa1gTnBVTA/trained_model.tar\nb''\nDownloaded weights in 3.66454815864563 seconds\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.79it/s]\n 7%|▋ | 2/28 [00:00<00:05, 4.36it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.06it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.93it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 3.88it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.85it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.82it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.80it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.79it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.79it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.78it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.77it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.77it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.78it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.77it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.76it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.77it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.77it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 3.77it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.76it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.77it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.77it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.77it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.76it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.77it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.77it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.77it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.76it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.80it/s]", "metrics": { "predict_time": 19.408434535, "total_time": 19.417503 }, "output": [ "https://replicate.delivery/yhqm/z8G0NhdvjoKEOhCzE6GkcdoAkfbtJDQxd15cLNy5GbeWpBVTA/out-0.webp" ], "started_at": "2024-08-21T22:20:51.194068Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/38kbera6e5rm20chew3vrdme7w", "cancel": "https://api.replicate.com/v1/predictions/38kbera6e5rm20chew3vrdme7w/cancel" }, "version": "d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d" }Generated inUsing seed: 52531 Prompt: a photo of Brighton’s west pier, long exposure, beautiful light txt2img mode Using dev model Loading LoRA weights Ensuring enough disk space... Free disk space: 9403074990080 Downloading weights 2024-08-21T22:20:51Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/5b35eeba647457c9 url=https://replicate.delivery/yhqm/xVA2Ba6zyMoGMdyz8hAvOHlzrXPj1h0SpeFf9xJTa1gTnBVTA/trained_model.tar 2024-08-21T22:20:54Z | INFO | [ Complete ] dest=/src/weights-cache/5b35eeba647457c9 size="172 MB" total_elapsed=3.636s url=https://replicate.delivery/yhqm/xVA2Ba6zyMoGMdyz8hAvOHlzrXPj1h0SpeFf9xJTa1gTnBVTA/trained_model.tar b'' Downloaded weights in 3.66454815864563 seconds LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.79it/s] 7%|▋ | 2/28 [00:00<00:05, 4.36it/s] 11%|█ | 3/28 [00:00<00:06, 4.06it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.93it/s] 18%|█▊ | 5/28 [00:01<00:05, 3.88it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.85it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.82it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.80it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.79it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.79it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.78it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.77it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.77it/s] 50%|█████ | 14/28 [00:03<00:03, 3.78it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.77it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.76it/s] 61%|██████ | 17/28 [00:04<00:02, 3.77it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.77it/s] 68%|██████▊ | 19/28 [00:04<00:02, 3.77it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.76it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.77it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.77it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.77it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.76it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.77it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.77it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.77it/s] 100%|██████████| 28/28 [00:07<00:00, 3.76it/s] 100%|██████████| 28/28 [00:07<00:00, 3.80it/s]Prediction
fofr/flux-brighton-west-pier:d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160dIDayhxeh223hrm00chew6s4c9a48StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a beautiful oil painting of Brighton’s west pier, brushstrokes, artwork, drawing, illustration, watercolor, monet
- lora_scale
- 0.7
- num_outputs
- 1
- aspect_ratio
- 3:2
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a beautiful oil painting of Brighton’s west pier, brushstrokes, artwork, drawing, illustration, watercolor, monet", "lora_scale": 0.7, "num_outputs": 1, "aspect_ratio": "3:2", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }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 fofr/flux-brighton-west-pier using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-brighton-west-pier:d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d", { input: { model: "dev", prompt: "a beautiful oil painting of Brighton’s west pier, brushstrokes, artwork, drawing, illustration, watercolor, monet", lora_scale: 0.7, num_outputs: 1, aspect_ratio: "3:2", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 28 } } ); // 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 fofr/flux-brighton-west-pier using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-brighton-west-pier:d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d", input={ "model": "dev", "prompt": "a beautiful oil painting of Brighton’s west pier, brushstrokes, artwork, drawing, illustration, watercolor, monet", "lora_scale": 0.7, "num_outputs": 1, "aspect_ratio": "3:2", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fofr/flux-brighton-west-pier 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": "d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d", "input": { "model": "dev", "prompt": "a beautiful oil painting of Brighton’s west pier, brushstrokes, artwork, drawing, illustration, watercolor, monet", "lora_scale": 0.7, "num_outputs": 1, "aspect_ratio": "3:2", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-21T22:28:18.925304Z", "created_at": "2024-08-21T22:27:23.292000Z", "data_removed": false, "error": null, "id": "ayhxeh223hrm00chew6s4c9a48", "input": { "model": "dev", "prompt": "a beautiful oil painting of Brighton’s west pier, brushstrokes, artwork, drawing, illustration, watercolor, monet", "lora_scale": 0.7, "num_outputs": 1, "aspect_ratio": "3:2", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 47191\nPrompt: a beautiful oil painting of Brighton’s west pier, brushstrokes, artwork, drawing, illustration, watercolor, monet\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.59it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.03it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.83it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.74it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.70it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.64it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.66it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.65it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.64it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.64it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.63it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.63it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.63it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.62it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.62it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.62it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.62it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.62it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.62it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.62it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.61it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.61it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.62it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.62it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.62it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.62it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.62it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.62it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.64it/s]", "metrics": { "predict_time": 16.719292853, "total_time": 55.633304 }, "output": [ "https://replicate.delivery/yhqm/hjWU99T8hexbWikN46MJnzjRSvAIBw93RoNGAKwXyqJB4gqJA/out-0.webp" ], "started_at": "2024-08-21T22:28:02.206011Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ayhxeh223hrm00chew6s4c9a48", "cancel": "https://api.replicate.com/v1/predictions/ayhxeh223hrm00chew6s4c9a48/cancel" }, "version": "d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d" }Generated inUsing seed: 47191 Prompt: a beautiful oil painting of Brighton’s west pier, brushstrokes, artwork, drawing, illustration, watercolor, monet txt2img mode Using dev model Loading LoRA weights LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.59it/s] 7%|▋ | 2/28 [00:00<00:06, 4.03it/s] 11%|█ | 3/28 [00:00<00:06, 3.83it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.74it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.70it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.64it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.66it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.65it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.64it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.64it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.63it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.63it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.63it/s] 50%|█████ | 14/28 [00:03<00:03, 3.62it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.62it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.62it/s] 61%|██████ | 17/28 [00:04<00:03, 3.62it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.62it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.62it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.62it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.61it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.61it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.62it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.62it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.62it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.62it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.62it/s] 100%|██████████| 28/28 [00:07<00:00, 3.62it/s] 100%|██████████| 28/28 [00:07<00:00, 3.64it/s]Prediction
fofr/flux-brighton-west-pier:d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160dIDhgpz8yj2k9rm00chew7sq5mermStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a beautiful long exposure photo of Brighton’s west pier, award winning
- lora_scale
- 0.9
- num_outputs
- 1
- aspect_ratio
- 3:2
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a beautiful long exposure photo of Brighton’s west pier, award winning", "lora_scale": 0.9, "num_outputs": 1, "aspect_ratio": "3:2", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }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 fofr/flux-brighton-west-pier using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-brighton-west-pier:d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d", { input: { model: "dev", prompt: "a beautiful long exposure photo of Brighton’s west pier, award winning", lora_scale: 0.9, num_outputs: 1, aspect_ratio: "3:2", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 28 } } ); // 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 fofr/flux-brighton-west-pier using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-brighton-west-pier:d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d", input={ "model": "dev", "prompt": "a beautiful long exposure photo of Brighton’s west pier, award winning", "lora_scale": 0.9, "num_outputs": 1, "aspect_ratio": "3:2", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fofr/flux-brighton-west-pier 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": "d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d", "input": { "model": "dev", "prompt": "a beautiful long exposure photo of Brighton’s west pier, award winning", "lora_scale": 0.9, "num_outputs": 1, "aspect_ratio": "3:2", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-21T22:31:16.309653Z", "created_at": "2024-08-21T22:29:34.490000Z", "data_removed": false, "error": null, "id": "hgpz8yj2k9rm00chew7sq5merm", "input": { "model": "dev", "prompt": "a beautiful long exposure photo of Brighton’s west pier, award winning", "lora_scale": 0.9, "num_outputs": 1, "aspect_ratio": "3:2", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 7146\nPrompt: a beautiful long exposure photo of Brighton’s west pier, award winning\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9449669246976\nDownloading weights\n2024-08-21T22:29:34Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/5b35eeba647457c9 url=https://replicate.delivery/yhqm/xVA2Ba6zyMoGMdyz8hAvOHlzrXPj1h0SpeFf9xJTa1gTnBVTA/trained_model.tar\n2024-08-21T22:29:35Z | INFO | [ Complete ] dest=/src/weights-cache/5b35eeba647457c9 size=\"172 MB\" total_elapsed=1.267s url=https://replicate.delivery/yhqm/xVA2Ba6zyMoGMdyz8hAvOHlzrXPj1h0SpeFf9xJTa1gTnBVTA/trained_model.tar\nb''\nDownloaded weights in 1.292743444442749 seconds\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.54it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.95it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.79it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.71it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.67it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.66it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.65it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.64it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.63it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.62it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.63it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.63it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.63it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.63it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.64it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.63it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.63it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.63it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.62it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.62it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.62it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.62it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.62it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.62it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.63it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.63it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.62it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.64it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.64it/s]", "metrics": { "predict_time": 101.699773422, "total_time": 101.819653 }, "output": [ "https://replicate.delivery/yhqm/J2kiN2Adcy6AIZMHHjeaornO4t7BqRvMCulmEN8YWiNa5gqJA/out-0.webp" ], "started_at": "2024-08-21T22:29:34.609879Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/hgpz8yj2k9rm00chew7sq5merm", "cancel": "https://api.replicate.com/v1/predictions/hgpz8yj2k9rm00chew7sq5merm/cancel" }, "version": "d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d" }Generated inUsing seed: 7146 Prompt: a beautiful long exposure photo of Brighton’s west pier, award winning txt2img mode Using dev model Loading LoRA weights Ensuring enough disk space... Free disk space: 9449669246976 Downloading weights 2024-08-21T22:29:34Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/5b35eeba647457c9 url=https://replicate.delivery/yhqm/xVA2Ba6zyMoGMdyz8hAvOHlzrXPj1h0SpeFf9xJTa1gTnBVTA/trained_model.tar 2024-08-21T22:29:35Z | INFO | [ Complete ] dest=/src/weights-cache/5b35eeba647457c9 size="172 MB" total_elapsed=1.267s url=https://replicate.delivery/yhqm/xVA2Ba6zyMoGMdyz8hAvOHlzrXPj1h0SpeFf9xJTa1gTnBVTA/trained_model.tar b'' Downloaded weights in 1.292743444442749 seconds LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.54it/s] 7%|▋ | 2/28 [00:00<00:06, 3.95it/s] 11%|█ | 3/28 [00:00<00:06, 3.79it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.71it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.67it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.66it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.65it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.64it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.63it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.62it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.63it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.63it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.63it/s] 50%|█████ | 14/28 [00:03<00:03, 3.63it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.64it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.63it/s] 61%|██████ | 17/28 [00:04<00:03, 3.63it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.63it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.62it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.62it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.62it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.62it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.62it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.62it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.63it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.63it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.62it/s] 100%|██████████| 28/28 [00:07<00:00, 3.64it/s] 100%|██████████| 28/28 [00:07<00:00, 3.64it/s]Prediction
fofr/flux-brighton-west-pier:d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160dIDjpj787r9z1rm00chew8rbnfdpmStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- an animated pixar film still of a man standing in front of Brighton’s west pier
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "an animated pixar film still of a man standing in front of Brighton’s west pier", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }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 fofr/flux-brighton-west-pier using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-brighton-west-pier:d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d", { input: { model: "dev", prompt: "an animated pixar film still of a man standing in front of Brighton’s west pier", lora_scale: 1, num_outputs: 1, aspect_ratio: "16:9", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 28 } } ); // 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 fofr/flux-brighton-west-pier using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-brighton-west-pier:d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d", input={ "model": "dev", "prompt": "an animated pixar film still of a man standing in front of Brighton’s west pier", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fofr/flux-brighton-west-pier 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": "d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d", "input": { "model": "dev", "prompt": "an animated pixar film still of a man standing in front of Brighton’s west pier", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-21T22:32:10.143193Z", "created_at": "2024-08-21T22:31:31.064000Z", "data_removed": false, "error": null, "id": "jpj787r9z1rm00chew8rbnfdpm", "input": { "model": "dev", "prompt": "an animated pixar film still of a man standing in front of Brighton’s west pier", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 60222\nPrompt: an animated pixar film still of a man standing in front of Brighton’s west pier\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9353538547712\nDownloading weights\n2024-08-21T22:31:48Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/5b35eeba647457c9 url=https://replicate.delivery/yhqm/xVA2Ba6zyMoGMdyz8hAvOHlzrXPj1h0SpeFf9xJTa1gTnBVTA/trained_model.tar\n2024-08-21T22:31:49Z | INFO | [ Complete ] dest=/src/weights-cache/5b35eeba647457c9 size=\"172 MB\" total_elapsed=1.777s url=https://replicate.delivery/yhqm/xVA2Ba6zyMoGMdyz8hAvOHlzrXPj1h0SpeFf9xJTa1gTnBVTA/trained_model.tar\nb''\nDownloaded weights in 1.8101046085357666 seconds\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.69it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.25it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.97it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.85it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.79it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.75it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.73it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.71it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.70it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.69it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.69it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.69it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.68it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.68it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.68it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.68it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.67it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.67it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.67it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.67it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.67it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.67it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.67it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.67it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.68it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.68it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.67it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.68it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.71it/s]", "metrics": { "predict_time": 22.187783622, "total_time": 39.079193 }, "output": [ "https://replicate.delivery/yhqm/Sh2mXgf7PYV7JiabQSiboqfEULWTKMc4WY3yH6DAWAeTnDqmA/out-0.webp" ], "started_at": "2024-08-21T22:31:47.955409Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jpj787r9z1rm00chew8rbnfdpm", "cancel": "https://api.replicate.com/v1/predictions/jpj787r9z1rm00chew8rbnfdpm/cancel" }, "version": "d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d" }Generated inUsing seed: 60222 Prompt: an animated pixar film still of a man standing in front of Brighton’s west pier txt2img mode Using dev model Loading LoRA weights Ensuring enough disk space... Free disk space: 9353538547712 Downloading weights 2024-08-21T22:31:48Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/5b35eeba647457c9 url=https://replicate.delivery/yhqm/xVA2Ba6zyMoGMdyz8hAvOHlzrXPj1h0SpeFf9xJTa1gTnBVTA/trained_model.tar 2024-08-21T22:31:49Z | INFO | [ Complete ] dest=/src/weights-cache/5b35eeba647457c9 size="172 MB" total_elapsed=1.777s url=https://replicate.delivery/yhqm/xVA2Ba6zyMoGMdyz8hAvOHlzrXPj1h0SpeFf9xJTa1gTnBVTA/trained_model.tar b'' Downloaded weights in 1.8101046085357666 seconds LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.69it/s] 7%|▋ | 2/28 [00:00<00:06, 4.25it/s] 11%|█ | 3/28 [00:00<00:06, 3.97it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.85it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.79it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.75it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.73it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.71it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.70it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.69it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.69it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.69it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.68it/s] 50%|█████ | 14/28 [00:03<00:03, 3.68it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.68it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.68it/s] 61%|██████ | 17/28 [00:04<00:02, 3.67it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.67it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.67it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.67it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.67it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.67it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.67it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.67it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.68it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.68it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.67it/s] 100%|██████████| 28/28 [00:07<00:00, 3.68it/s] 100%|██████████| 28/28 [00:07<00:00, 3.71it/s]Prediction
fofr/flux-brighton-west-pier:d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160dIDrhmxdpaajsrm20chew9rq6td84StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a photo of Brighton’s west pier on a desolate and barren Mars, rocky red ground, dust
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a photo of Brighton’s west pier on a desolate and barren Mars, rocky red ground, dust", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }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 fofr/flux-brighton-west-pier using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-brighton-west-pier:d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d", { input: { model: "dev", prompt: "a photo of Brighton’s west pier on a desolate and barren Mars, rocky red ground, dust", lora_scale: 1, num_outputs: 1, aspect_ratio: "16:9", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 28 } } ); // 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 fofr/flux-brighton-west-pier using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-brighton-west-pier:d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d", input={ "model": "dev", "prompt": "a photo of Brighton’s west pier on a desolate and barren Mars, rocky red ground, dust", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fofr/flux-brighton-west-pier 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": "d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d", "input": { "model": "dev", "prompt": "a photo of Brighton’s west pier on a desolate and barren Mars, rocky red ground, dust", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-21T22:34:28.700559Z", "created_at": "2024-08-21T22:33:58.678000Z", "data_removed": false, "error": null, "id": "rhmxdpaajsrm20chew9rq6td84", "input": { "model": "dev", "prompt": "a photo of Brighton’s west pier on a desolate and barren Mars, rocky red ground, dust", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 55891\nPrompt: a photo of Brighton’s west pier on a desolate and barren Mars, rocky red ground, dust\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nweights already loaded!\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.71it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.26it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.98it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.86it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.80it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.76it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.75it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.73it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.72it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.71it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.71it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.71it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.70it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.70it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.70it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.70it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.70it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.70it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.70it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.70it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.70it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.70it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.70it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.70it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.70it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.69it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.70it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.70it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.73it/s]", "metrics": { "predict_time": 8.058880574, "total_time": 30.022559 }, "output": [ "https://replicate.delivery/yhqm/H5gIEekRZfkKI0e3TpCpWQbnRawWpLerzfWc3kOywaRluOoaC/out-0.webp" ], "started_at": "2024-08-21T22:34:20.641679Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rhmxdpaajsrm20chew9rq6td84", "cancel": "https://api.replicate.com/v1/predictions/rhmxdpaajsrm20chew9rq6td84/cancel" }, "version": "d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d" }Generated inUsing seed: 55891 Prompt: a photo of Brighton’s west pier on a desolate and barren Mars, rocky red ground, dust txt2img mode Using dev model Loading LoRA weights weights already loaded! 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.71it/s] 7%|▋ | 2/28 [00:00<00:06, 4.26it/s] 11%|█ | 3/28 [00:00<00:06, 3.98it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.86it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.80it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.76it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.75it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.73it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.72it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.71it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.71it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.71it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.70it/s] 50%|█████ | 14/28 [00:03<00:03, 3.70it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.70it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.70it/s] 61%|██████ | 17/28 [00:04<00:02, 3.70it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.70it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.70it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.70it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.70it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.70it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.70it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.70it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.70it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.69it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.70it/s] 100%|██████████| 28/28 [00:07<00:00, 3.70it/s] 100%|██████████| 28/28 [00:07<00:00, 3.73it/s]Prediction
fofr/flux-brighton-west-pier:d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160dID06c11cg8nxrm60chew3b52vn08StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a photo of Brighton’s west pier
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 3:2
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a photo of Brighton’s west pier", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "3:2", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }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 fofr/flux-brighton-west-pier using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-brighton-west-pier:d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d", { input: { model: "dev", prompt: "a photo of Brighton’s west pier", lora_scale: 1, num_outputs: 1, aspect_ratio: "3:2", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 28 } } ); // 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 fofr/flux-brighton-west-pier using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-brighton-west-pier:d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d", input={ "model": "dev", "prompt": "a photo of Brighton’s west pier", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "3:2", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fofr/flux-brighton-west-pier 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": "d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d", "input": { "model": "dev", "prompt": "a photo of Brighton’s west pier", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "3:2", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-21T22:19:47.971117Z", "created_at": "2024-08-21T22:19:29.839000Z", "data_removed": false, "error": null, "id": "06c11cg8nxrm60chew3b52vn08", "input": { "model": "dev", "prompt": "a photo of Brighton’s west pier", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "3:2", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 43566\nPrompt: a photo of Brighton’s west pier\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9279086088192\nDownloading weights\n2024-08-21T22:19:29Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/5b35eeba647457c9 url=https://replicate.delivery/yhqm/xVA2Ba6zyMoGMdyz8hAvOHlzrXPj1h0SpeFf9xJTa1gTnBVTA/trained_model.tar\n2024-08-21T22:19:31Z | INFO | [ Complete ] dest=/src/weights-cache/5b35eeba647457c9 size=\"172 MB\" total_elapsed=1.961s url=https://replicate.delivery/yhqm/xVA2Ba6zyMoGMdyz8hAvOHlzrXPj1h0SpeFf9xJTa1gTnBVTA/trained_model.tar\nb''\nDownloaded weights in 2.055461883544922 seconds\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.60it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.24it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.00it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.90it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 3.85it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.82it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.80it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.78it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.78it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.77it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.76it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.76it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.76it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.76it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.76it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.76it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.76it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.77it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.76it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.76it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.76it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.76it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.76it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.76it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.76it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.76it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.76it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.76it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.78it/s]", "metrics": { "predict_time": 18.121050297, "total_time": 18.132117 }, "output": [ "https://replicate.delivery/yhqm/DrGcnm0nNw5iAFLfxeoGeSpUwfhQO3PBesjuy1BflfZ0B0gqJA/out-0.webp" ], "started_at": "2024-08-21T22:19:29.850067Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/06c11cg8nxrm60chew3b52vn08", "cancel": "https://api.replicate.com/v1/predictions/06c11cg8nxrm60chew3b52vn08/cancel" }, "version": "d9b0643f1a7fbec4ff655e3c39fcab9e6aaaa33a65102ce6cc24acddbb4f160d" }Generated inUsing seed: 43566 Prompt: a photo of Brighton’s west pier txt2img mode Using dev model Loading LoRA weights Ensuring enough disk space... Free disk space: 9279086088192 Downloading weights 2024-08-21T22:19:29Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/5b35eeba647457c9 url=https://replicate.delivery/yhqm/xVA2Ba6zyMoGMdyz8hAvOHlzrXPj1h0SpeFf9xJTa1gTnBVTA/trained_model.tar 2024-08-21T22:19:31Z | INFO | [ Complete ] dest=/src/weights-cache/5b35eeba647457c9 size="172 MB" total_elapsed=1.961s url=https://replicate.delivery/yhqm/xVA2Ba6zyMoGMdyz8hAvOHlzrXPj1h0SpeFf9xJTa1gTnBVTA/trained_model.tar b'' Downloaded weights in 2.055461883544922 seconds LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.60it/s] 7%|▋ | 2/28 [00:00<00:06, 4.24it/s] 11%|█ | 3/28 [00:00<00:06, 4.00it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.90it/s] 18%|█▊ | 5/28 [00:01<00:05, 3.85it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.82it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.80it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.78it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.78it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.77it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.76it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.76it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.76it/s] 50%|█████ | 14/28 [00:03<00:03, 3.76it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.76it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.76it/s] 61%|██████ | 17/28 [00:04<00:02, 3.76it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.77it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.76it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.76it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.76it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.76it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.76it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.76it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.76it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.76it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.76it/s] 100%|██████████| 28/28 [00:07<00:00, 3.76it/s] 100%|██████████| 28/28 [00:07<00:00, 3.78it/s]
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