bingbangboom-lab / flux-waterscape
Flux LoRA for creating watercolor styled illustrations. Use 'watercolor illustration in the style of WTRSCPE003' to trigger the model.
- Public
- 222 runs
-
H100
- Weights
Prediction
bingbangboom-lab/flux-waterscape:798f5ad1241db1e1069a647323845837a546a29a1c33b0b1b0e7d8dbc8e5f179IDfqr7x4fe8drm40cje4h8mnxnagStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a girl wearing a flower crown, standing in a garden ,watercolor illustration in the style of WTRSCPE003
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- png
- guidance_scale
- 3.5
- output_quality
- 100
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 25
{ "model": "dev", "prompt": "a girl wearing a flower crown, standing in a garden ,watercolor illustration in the style of WTRSCPE003", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 25 }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 bingbangboom-lab/flux-waterscape using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "bingbangboom-lab/flux-waterscape:798f5ad1241db1e1069a647323845837a546a29a1c33b0b1b0e7d8dbc8e5f179", { input: { model: "dev", prompt: "a girl wearing a flower crown, standing in a garden ,watercolor illustration in the style of WTRSCPE003", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "png", guidance_scale: 3.5, output_quality: 100, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 25 } } ); // 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 bingbangboom-lab/flux-waterscape using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "bingbangboom-lab/flux-waterscape:798f5ad1241db1e1069a647323845837a546a29a1c33b0b1b0e7d8dbc8e5f179", input={ "model": "dev", "prompt": "a girl wearing a flower crown, standing in a garden ,watercolor illustration in the style of WTRSCPE003", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 25 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run bingbangboom-lab/flux-waterscape 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": "bingbangboom-lab/flux-waterscape:798f5ad1241db1e1069a647323845837a546a29a1c33b0b1b0e7d8dbc8e5f179", "input": { "model": "dev", "prompt": "a girl wearing a flower crown, standing in a garden ,watercolor illustration in the style of WTRSCPE003", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-10-09T11:56:29.935324Z", "created_at": "2024-10-09T11:56:07.619000Z", "data_removed": false, "error": null, "id": "fqr7x4fe8drm40cje4h8mnxnag", "input": { "model": "dev", "prompt": "a girl wearing a flower crown, standing in a garden ,watercolor illustration in the style of WTRSCPE003", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 25 }, "logs": "Using seed: 50812\nPrompt: a girl wearing a flower crown, standing in a garden ,watercolor illustration in the style of WTRSCPE003\n[!] txt2img mode\nUsing dev model\nfree=7717417009152\nDownloading weights\n2024-10-09T11:56:17Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp1inbq6s4/weights url=https://replicate.delivery/yhqm/Azk0nsBV5CaOONEV7b0neRHMoNbeLd9KVOhAcBSeEqySfovNB/trained_model.tar\n2024-10-09T11:56:20Z | INFO | [ Complete ] dest=/tmp/tmp1inbq6s4/weights size=\"172 MB\" total_elapsed=2.382s url=https://replicate.delivery/yhqm/Azk0nsBV5CaOONEV7b0neRHMoNbeLd9KVOhAcBSeEqySfovNB/trained_model.tar\nDownloaded weights in 2.41s\nLoaded LoRAs in 3.11s\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:08, 2.86it/s]\n 8%|▊ | 2/25 [00:00<00:07, 3.19it/s]\n 12%|█▏ | 3/25 [00:00<00:07, 3.03it/s]\n 16%|█▌ | 4/25 [00:01<00:07, 2.96it/s]\n 20%|██ | 5/25 [00:01<00:06, 2.93it/s]\n 24%|██▍ | 6/25 [00:02<00:06, 2.91it/s]\n 28%|██▊ | 7/25 [00:02<00:06, 2.89it/s]\n 32%|███▏ | 8/25 [00:02<00:05, 2.88it/s]\n 36%|███▌ | 9/25 [00:03<00:05, 2.88it/s]\n 40%|████ | 10/25 [00:03<00:05, 2.87it/s]\n 44%|████▍ | 11/25 [00:03<00:04, 2.87it/s]\n 48%|████▊ | 12/25 [00:04<00:04, 2.87it/s]\n 52%|█████▏ | 13/25 [00:04<00:04, 2.87it/s]\n 56%|█████▌ | 14/25 [00:04<00:03, 2.87it/s]\n 60%|██████ | 15/25 [00:05<00:03, 2.87it/s]\n 64%|██████▍ | 16/25 [00:05<00:03, 2.87it/s]\n 68%|██████▊ | 17/25 [00:05<00:02, 2.86it/s]\n 72%|███████▏ | 18/25 [00:06<00:02, 2.86it/s]\n 76%|███████▌ | 19/25 [00:06<00:02, 2.86it/s]\n 80%|████████ | 20/25 [00:06<00:01, 2.86it/s]\n 84%|████████▍ | 21/25 [00:07<00:01, 2.86it/s]\n 88%|████████▊ | 22/25 [00:07<00:01, 2.86it/s]\n 92%|█████████▏| 23/25 [00:07<00:00, 2.86it/s]\n 96%|█████████▌| 24/25 [00:08<00:00, 2.86it/s]\n100%|██████████| 25/25 [00:08<00:00, 2.86it/s]\n100%|██████████| 25/25 [00:08<00:00, 2.88it/s]", "metrics": { "predict_time": 12.354242571, "total_time": 22.316324 }, "output": [ "https://replicate.delivery/yhqm/oC7fjNEylzTsSSseWvfLLdzfYKIVMetERRi7vNp1W8HptQocC/out-0.png" ], "started_at": "2024-10-09T11:56:17.581082Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fqr7x4fe8drm40cje4h8mnxnag", "cancel": "https://api.replicate.com/v1/predictions/fqr7x4fe8drm40cje4h8mnxnag/cancel" }, "version": "798f5ad1241db1e1069a647323845837a546a29a1c33b0b1b0e7d8dbc8e5f179" }Generated inUsing seed: 50812 Prompt: a girl wearing a flower crown, standing in a garden ,watercolor illustration in the style of WTRSCPE003 [!] txt2img mode Using dev model free=7717417009152 Downloading weights 2024-10-09T11:56:17Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp1inbq6s4/weights url=https://replicate.delivery/yhqm/Azk0nsBV5CaOONEV7b0neRHMoNbeLd9KVOhAcBSeEqySfovNB/trained_model.tar 2024-10-09T11:56:20Z | INFO | [ Complete ] dest=/tmp/tmp1inbq6s4/weights size="172 MB" total_elapsed=2.382s url=https://replicate.delivery/yhqm/Azk0nsBV5CaOONEV7b0neRHMoNbeLd9KVOhAcBSeEqySfovNB/trained_model.tar Downloaded weights in 2.41s Loaded LoRAs in 3.11s 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:08, 2.86it/s] 8%|▊ | 2/25 [00:00<00:07, 3.19it/s] 12%|█▏ | 3/25 [00:00<00:07, 3.03it/s] 16%|█▌ | 4/25 [00:01<00:07, 2.96it/s] 20%|██ | 5/25 [00:01<00:06, 2.93it/s] 24%|██▍ | 6/25 [00:02<00:06, 2.91it/s] 28%|██▊ | 7/25 [00:02<00:06, 2.89it/s] 32%|███▏ | 8/25 [00:02<00:05, 2.88it/s] 36%|███▌ | 9/25 [00:03<00:05, 2.88it/s] 40%|████ | 10/25 [00:03<00:05, 2.87it/s] 44%|████▍ | 11/25 [00:03<00:04, 2.87it/s] 48%|████▊ | 12/25 [00:04<00:04, 2.87it/s] 52%|█████▏ | 13/25 [00:04<00:04, 2.87it/s] 56%|█████▌ | 14/25 [00:04<00:03, 2.87it/s] 60%|██████ | 15/25 [00:05<00:03, 2.87it/s] 64%|██████▍ | 16/25 [00:05<00:03, 2.87it/s] 68%|██████▊ | 17/25 [00:05<00:02, 2.86it/s] 72%|███████▏ | 18/25 [00:06<00:02, 2.86it/s] 76%|███████▌ | 19/25 [00:06<00:02, 2.86it/s] 80%|████████ | 20/25 [00:06<00:01, 2.86it/s] 84%|████████▍ | 21/25 [00:07<00:01, 2.86it/s] 88%|████████▊ | 22/25 [00:07<00:01, 2.86it/s] 92%|█████████▏| 23/25 [00:07<00:00, 2.86it/s] 96%|█████████▌| 24/25 [00:08<00:00, 2.86it/s] 100%|██████████| 25/25 [00:08<00:00, 2.86it/s] 100%|██████████| 25/25 [00:08<00:00, 2.88it/s]Prediction
bingbangboom-lab/flux-waterscape:798f5ad1241db1e1069a647323845837a546a29a1c33b0b1b0e7d8dbc8e5f179IDws5md23yssrm00cje4ht1pckgcStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a white-haired young woman wearing a flower crown, a very large fiery dragon, castle in the background ,watercolor illustration in the style of WTRSCPE003
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- png
- guidance_scale
- 3.5
- output_quality
- 100
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 25
{ "model": "dev", "prompt": "a white-haired young woman wearing a flower crown, a very large fiery dragon, castle in the background ,watercolor illustration in the style of WTRSCPE003", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 25 }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 bingbangboom-lab/flux-waterscape using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "bingbangboom-lab/flux-waterscape:798f5ad1241db1e1069a647323845837a546a29a1c33b0b1b0e7d8dbc8e5f179", { input: { model: "dev", prompt: "a white-haired young woman wearing a flower crown, a very large fiery dragon, castle in the background ,watercolor illustration in the style of WTRSCPE003", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "png", guidance_scale: 3.5, output_quality: 100, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 25 } } ); // 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 bingbangboom-lab/flux-waterscape using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "bingbangboom-lab/flux-waterscape:798f5ad1241db1e1069a647323845837a546a29a1c33b0b1b0e7d8dbc8e5f179", input={ "model": "dev", "prompt": "a white-haired young woman wearing a flower crown, a very large fiery dragon, castle in the background ,watercolor illustration in the style of WTRSCPE003", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 25 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run bingbangboom-lab/flux-waterscape 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": "bingbangboom-lab/flux-waterscape:798f5ad1241db1e1069a647323845837a546a29a1c33b0b1b0e7d8dbc8e5f179", "input": { "model": "dev", "prompt": "a white-haired young woman wearing a flower crown, a very large fiery dragon, castle in the background ,watercolor illustration in the style of WTRSCPE003", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-10-09T11:58:27.234223Z", "created_at": "2024-10-09T11:56:44.622000Z", "data_removed": false, "error": null, "id": "ws5md23yssrm00cje4ht1pckgc", "input": { "model": "dev", "prompt": "a white-haired young woman wearing a flower crown, a very large fiery dragon, castle in the background ,watercolor illustration in the style of WTRSCPE003", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 25 }, "logs": "Using seed: 31477\nPrompt: a white-haired young woman wearing a flower crown, a very large fiery dragon, castle in the background ,watercolor illustration in the style of WTRSCPE003\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 0.74s\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:08, 2.86it/s]\n 8%|▊ | 2/25 [00:00<00:07, 3.19it/s]\n 12%|█▏ | 3/25 [00:00<00:07, 3.03it/s]\n 16%|█▌ | 4/25 [00:01<00:07, 2.96it/s]\n 20%|██ | 5/25 [00:01<00:06, 2.92it/s]\n 24%|██▍ | 6/25 [00:02<00:06, 2.90it/s]\n 28%|██▊ | 7/25 [00:02<00:06, 2.89it/s]\n 32%|███▏ | 8/25 [00:02<00:05, 2.88it/s]\n 36%|███▌ | 9/25 [00:03<00:05, 2.87it/s]\n 40%|████ | 10/25 [00:03<00:05, 2.87it/s]\n 44%|████▍ | 11/25 [00:03<00:04, 2.87it/s]\n 48%|████▊ | 12/25 [00:04<00:04, 2.87it/s]\n 52%|█████▏ | 13/25 [00:04<00:04, 2.86it/s]\n 56%|█████▌ | 14/25 [00:04<00:03, 2.86it/s]\n 60%|██████ | 15/25 [00:05<00:03, 2.86it/s]\n 64%|██████▍ | 16/25 [00:05<00:03, 2.86it/s]\n 68%|██████▊ | 17/25 [00:05<00:02, 2.86it/s]\n 72%|███████▏ | 18/25 [00:06<00:02, 2.86it/s]\n 76%|███████▌ | 19/25 [00:06<00:02, 2.86it/s]\n 80%|████████ | 20/25 [00:06<00:01, 2.86it/s]\n 84%|████████▍ | 21/25 [00:07<00:01, 2.86it/s]\n 88%|████████▊ | 22/25 [00:07<00:01, 2.86it/s]\n 92%|█████████▏| 23/25 [00:07<00:00, 2.86it/s]\n 96%|█████████▌| 24/25 [00:08<00:00, 2.86it/s]\n100%|██████████| 25/25 [00:08<00:00, 2.86it/s]\n100%|██████████| 25/25 [00:08<00:00, 2.88it/s]", "metrics": { "predict_time": 9.920985568999999, "total_time": 102.612223 }, "output": [ "https://replicate.delivery/yhqm/d0uVrf1Uw8xhJ6E6fvxa8pOl0ZKlGnNKtf1NGpzcWJxGPEKnA/out-0.png" ], "started_at": "2024-10-09T11:58:17.313237Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ws5md23yssrm00cje4ht1pckgc", "cancel": "https://api.replicate.com/v1/predictions/ws5md23yssrm00cje4ht1pckgc/cancel" }, "version": "798f5ad1241db1e1069a647323845837a546a29a1c33b0b1b0e7d8dbc8e5f179" }Generated inUsing seed: 31477 Prompt: a white-haired young woman wearing a flower crown, a very large fiery dragon, castle in the background ,watercolor illustration in the style of WTRSCPE003 [!] txt2img mode Using dev model Loaded LoRAs in 0.74s 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:08, 2.86it/s] 8%|▊ | 2/25 [00:00<00:07, 3.19it/s] 12%|█▏ | 3/25 [00:00<00:07, 3.03it/s] 16%|█▌ | 4/25 [00:01<00:07, 2.96it/s] 20%|██ | 5/25 [00:01<00:06, 2.92it/s] 24%|██▍ | 6/25 [00:02<00:06, 2.90it/s] 28%|██▊ | 7/25 [00:02<00:06, 2.89it/s] 32%|███▏ | 8/25 [00:02<00:05, 2.88it/s] 36%|███▌ | 9/25 [00:03<00:05, 2.87it/s] 40%|████ | 10/25 [00:03<00:05, 2.87it/s] 44%|████▍ | 11/25 [00:03<00:04, 2.87it/s] 48%|████▊ | 12/25 [00:04<00:04, 2.87it/s] 52%|█████▏ | 13/25 [00:04<00:04, 2.86it/s] 56%|█████▌ | 14/25 [00:04<00:03, 2.86it/s] 60%|██████ | 15/25 [00:05<00:03, 2.86it/s] 64%|██████▍ | 16/25 [00:05<00:03, 2.86it/s] 68%|██████▊ | 17/25 [00:05<00:02, 2.86it/s] 72%|███████▏ | 18/25 [00:06<00:02, 2.86it/s] 76%|███████▌ | 19/25 [00:06<00:02, 2.86it/s] 80%|████████ | 20/25 [00:06<00:01, 2.86it/s] 84%|████████▍ | 21/25 [00:07<00:01, 2.86it/s] 88%|████████▊ | 22/25 [00:07<00:01, 2.86it/s] 92%|█████████▏| 23/25 [00:07<00:00, 2.86it/s] 96%|█████████▌| 24/25 [00:08<00:00, 2.86it/s] 100%|██████████| 25/25 [00:08<00:00, 2.86it/s] 100%|██████████| 25/25 [00:08<00:00, 2.88it/s]Prediction
bingbangboom-lab/flux-waterscape:798f5ad1241db1e1069a647323845837a546a29a1c33b0b1b0e7d8dbc8e5f179ID87jg7t9nnhrm00cje4ha8t6jj0StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a cat in a field of lavender flowers, watercolor illustration in the style of WTRSCPE003
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- png
- guidance_scale
- 3.5
- output_quality
- 100
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 25
{ "model": "dev", "prompt": "a cat in a field of lavender flowers, watercolor illustration in the style of WTRSCPE003", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 25 }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 bingbangboom-lab/flux-waterscape using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "bingbangboom-lab/flux-waterscape:798f5ad1241db1e1069a647323845837a546a29a1c33b0b1b0e7d8dbc8e5f179", { input: { model: "dev", prompt: "a cat in a field of lavender flowers, watercolor illustration in the style of WTRSCPE003", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "png", guidance_scale: 3.5, output_quality: 100, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 25 } } ); // 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 bingbangboom-lab/flux-waterscape using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "bingbangboom-lab/flux-waterscape:798f5ad1241db1e1069a647323845837a546a29a1c33b0b1b0e7d8dbc8e5f179", input={ "model": "dev", "prompt": "a cat in a field of lavender flowers, watercolor illustration in the style of WTRSCPE003", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 25 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run bingbangboom-lab/flux-waterscape 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": "bingbangboom-lab/flux-waterscape:798f5ad1241db1e1069a647323845837a546a29a1c33b0b1b0e7d8dbc8e5f179", "input": { "model": "dev", "prompt": "a cat in a field of lavender flowers, watercolor illustration in the style of WTRSCPE003", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-10-09T11:55:36.649452Z", "created_at": "2024-10-09T11:55:20.364000Z", "data_removed": false, "error": null, "id": "87jg7t9nnhrm00cje4ha8t6jj0", "input": { "model": "dev", "prompt": "a cat in a field of lavender flowers, watercolor illustration in the style of WTRSCPE003", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 25 }, "logs": "Using seed: 57236\nPrompt: a cat in a field of lavender flowers, watercolor illustration in the style of WTRSCPE003\n[!] txt2img mode\nUsing dev model\nfree=8894652358656\nDownloading weights\n2024-10-09T11:55:25Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp57pchsn7/weights url=https://replicate.delivery/yhqm/Azk0nsBV5CaOONEV7b0neRHMoNbeLd9KVOhAcBSeEqySfovNB/trained_model.tar\n2024-10-09T11:55:27Z | INFO | [ Complete ] dest=/tmp/tmp57pchsn7/weights size=\"172 MB\" total_elapsed=1.671s url=https://replicate.delivery/yhqm/Azk0nsBV5CaOONEV7b0neRHMoNbeLd9KVOhAcBSeEqySfovNB/trained_model.tar\nDownloaded weights in 1.70s\nLoaded LoRAs in 2.29s\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:08, 2.89it/s]\n 8%|▊ | 2/25 [00:00<00:07, 3.23it/s]\n 12%|█▏ | 3/25 [00:00<00:07, 3.07it/s]\n 16%|█▌ | 4/25 [00:01<00:07, 3.00it/s]\n 20%|██ | 5/25 [00:01<00:06, 2.96it/s]\n 24%|██▍ | 6/25 [00:02<00:06, 2.94it/s]\n 28%|██▊ | 7/25 [00:02<00:06, 2.92it/s]\n 32%|███▏ | 8/25 [00:02<00:05, 2.91it/s]\n 36%|███▌ | 9/25 [00:03<00:05, 2.91it/s]\n 40%|████ | 10/25 [00:03<00:05, 2.90it/s]\n 44%|████▍ | 11/25 [00:03<00:04, 2.90it/s]\n 48%|████▊ | 12/25 [00:04<00:04, 2.90it/s]\n 52%|█████▏ | 13/25 [00:04<00:04, 2.90it/s]\n 56%|█████▌ | 14/25 [00:04<00:03, 2.90it/s]\n 60%|██████ | 15/25 [00:05<00:03, 2.89it/s]\n 64%|██████▍ | 16/25 [00:05<00:03, 2.89it/s]\n 68%|██████▊ | 17/25 [00:05<00:02, 2.89it/s]\n 72%|███████▏ | 18/25 [00:06<00:02, 2.89it/s]\n 76%|███████▌ | 19/25 [00:06<00:02, 2.89it/s]\n 80%|████████ | 20/25 [00:06<00:01, 2.89it/s]\n 84%|████████▍ | 21/25 [00:07<00:01, 2.89it/s]\n 88%|████████▊ | 22/25 [00:07<00:01, 2.90it/s]\n 92%|█████████▏| 23/25 [00:07<00:00, 2.90it/s]\n 96%|█████████▌| 24/25 [00:08<00:00, 2.90it/s]\n100%|██████████| 25/25 [00:08<00:00, 2.90it/s]\n100%|██████████| 25/25 [00:08<00:00, 2.91it/s]", "metrics": { "predict_time": 11.294471448, "total_time": 16.285452 }, "output": [ "https://replicate.delivery/yhqm/hZhfevuifRLtQokbTgx2lBtYIAGdEiINmPjH7lfpaybiTIUOB/out-0.png" ], "started_at": "2024-10-09T11:55:25.354980Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/87jg7t9nnhrm00cje4ha8t6jj0", "cancel": "https://api.replicate.com/v1/predictions/87jg7t9nnhrm00cje4ha8t6jj0/cancel" }, "version": "798f5ad1241db1e1069a647323845837a546a29a1c33b0b1b0e7d8dbc8e5f179" }Generated inUsing seed: 57236 Prompt: a cat in a field of lavender flowers, watercolor illustration in the style of WTRSCPE003 [!] txt2img mode Using dev model free=8894652358656 Downloading weights 2024-10-09T11:55:25Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp57pchsn7/weights url=https://replicate.delivery/yhqm/Azk0nsBV5CaOONEV7b0neRHMoNbeLd9KVOhAcBSeEqySfovNB/trained_model.tar 2024-10-09T11:55:27Z | INFO | [ Complete ] dest=/tmp/tmp57pchsn7/weights size="172 MB" total_elapsed=1.671s url=https://replicate.delivery/yhqm/Azk0nsBV5CaOONEV7b0neRHMoNbeLd9KVOhAcBSeEqySfovNB/trained_model.tar Downloaded weights in 1.70s Loaded LoRAs in 2.29s 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:08, 2.89it/s] 8%|▊ | 2/25 [00:00<00:07, 3.23it/s] 12%|█▏ | 3/25 [00:00<00:07, 3.07it/s] 16%|█▌ | 4/25 [00:01<00:07, 3.00it/s] 20%|██ | 5/25 [00:01<00:06, 2.96it/s] 24%|██▍ | 6/25 [00:02<00:06, 2.94it/s] 28%|██▊ | 7/25 [00:02<00:06, 2.92it/s] 32%|███▏ | 8/25 [00:02<00:05, 2.91it/s] 36%|███▌ | 9/25 [00:03<00:05, 2.91it/s] 40%|████ | 10/25 [00:03<00:05, 2.90it/s] 44%|████▍ | 11/25 [00:03<00:04, 2.90it/s] 48%|████▊ | 12/25 [00:04<00:04, 2.90it/s] 52%|█████▏ | 13/25 [00:04<00:04, 2.90it/s] 56%|█████▌ | 14/25 [00:04<00:03, 2.90it/s] 60%|██████ | 15/25 [00:05<00:03, 2.89it/s] 64%|██████▍ | 16/25 [00:05<00:03, 2.89it/s] 68%|██████▊ | 17/25 [00:05<00:02, 2.89it/s] 72%|███████▏ | 18/25 [00:06<00:02, 2.89it/s] 76%|███████▌ | 19/25 [00:06<00:02, 2.89it/s] 80%|████████ | 20/25 [00:06<00:01, 2.89it/s] 84%|████████▍ | 21/25 [00:07<00:01, 2.89it/s] 88%|████████▊ | 22/25 [00:07<00:01, 2.90it/s] 92%|█████████▏| 23/25 [00:07<00:00, 2.90it/s] 96%|█████████▌| 24/25 [00:08<00:00, 2.90it/s] 100%|██████████| 25/25 [00:08<00:00, 2.90it/s] 100%|██████████| 25/25 [00:08<00:00, 2.91it/s]Prediction
bingbangboom-lab/flux-waterscape:798f5ad1241db1e1069a647323845837a546a29a1c33b0b1b0e7d8dbc8e5f179IDayh66sm3y5rm60cje4k9ar69hrStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a big ferocious tiger, jungle, sunset, watercolor illustration in the style of WTRSCPE0033
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- png
- guidance_scale
- 3.5
- output_quality
- 100
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 25
{ "model": "dev", "prompt": "a big ferocious tiger, jungle, sunset, watercolor illustration in the style of WTRSCPE0033", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 25 }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 bingbangboom-lab/flux-waterscape using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "bingbangboom-lab/flux-waterscape:798f5ad1241db1e1069a647323845837a546a29a1c33b0b1b0e7d8dbc8e5f179", { input: { model: "dev", prompt: "a big ferocious tiger, jungle, sunset, watercolor illustration in the style of WTRSCPE0033", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "png", guidance_scale: 3.5, output_quality: 100, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 25 } } ); // 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 bingbangboom-lab/flux-waterscape using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "bingbangboom-lab/flux-waterscape:798f5ad1241db1e1069a647323845837a546a29a1c33b0b1b0e7d8dbc8e5f179", input={ "model": "dev", "prompt": "a big ferocious tiger, jungle, sunset, watercolor illustration in the style of WTRSCPE0033", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 25 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run bingbangboom-lab/flux-waterscape 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": "bingbangboom-lab/flux-waterscape:798f5ad1241db1e1069a647323845837a546a29a1c33b0b1b0e7d8dbc8e5f179", "input": { "model": "dev", "prompt": "a big ferocious tiger, jungle, sunset, watercolor illustration in the style of WTRSCPE0033", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-10-09T12:00:42.248475Z", "created_at": "2024-10-09T12:00:02.545000Z", "data_removed": false, "error": null, "id": "ayh66sm3y5rm60cje4k9ar69hr", "input": { "model": "dev", "prompt": "a big ferocious tiger, jungle, sunset, watercolor illustration in the style of WTRSCPE0033", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 100, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 25 }, "logs": "Using seed: 51062\nPrompt: a big ferocious tiger, jungle, sunset, watercolor illustration in the style of WTRSCPE0033\n[!] txt2img mode\nUsing dev model\nfree=6637246095360\nDownloading weights\n2024-10-09T12:00:30Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpwvpcufwq/weights url=https://replicate.delivery/yhqm/Azk0nsBV5CaOONEV7b0neRHMoNbeLd9KVOhAcBSeEqySfovNB/trained_model.tar\n2024-10-09T12:00:32Z | INFO | [ Complete ] dest=/tmp/tmpwvpcufwq/weights size=\"172 MB\" total_elapsed=1.448s url=https://replicate.delivery/yhqm/Azk0nsBV5CaOONEV7b0neRHMoNbeLd9KVOhAcBSeEqySfovNB/trained_model.tar\nDownloaded weights in 1.48s\nLoaded LoRAs in 2.22s\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:08, 2.90it/s]\n 8%|▊ | 2/25 [00:00<00:07, 3.23it/s]\n 12%|█▏ | 3/25 [00:00<00:07, 3.07it/s]\n 16%|█▌ | 4/25 [00:01<00:07, 3.00it/s]\n 20%|██ | 5/25 [00:01<00:06, 2.96it/s]\n 24%|██▍ | 6/25 [00:02<00:06, 2.94it/s]\n 28%|██▊ | 7/25 [00:02<00:06, 2.92it/s]\n 32%|███▏ | 8/25 [00:02<00:05, 2.92it/s]\n 36%|███▌ | 9/25 [00:03<00:05, 2.91it/s]\n 40%|████ | 10/25 [00:03<00:05, 2.91it/s]\n 44%|████▍ | 11/25 [00:03<00:04, 2.91it/s]\n 48%|████▊ | 12/25 [00:04<00:04, 2.90it/s]\n 52%|█████▏ | 13/25 [00:04<00:04, 2.90it/s]\n 56%|█████▌ | 14/25 [00:04<00:03, 2.90it/s]\n 60%|██████ | 15/25 [00:05<00:03, 2.90it/s]\n 64%|██████▍ | 16/25 [00:05<00:03, 2.90it/s]\n 68%|██████▊ | 17/25 [00:05<00:02, 2.90it/s]\n 72%|███████▏ | 18/25 [00:06<00:02, 2.90it/s]\n 76%|███████▌ | 19/25 [00:06<00:02, 2.90it/s]\n 80%|████████ | 20/25 [00:06<00:01, 2.90it/s]\n 84%|████████▍ | 21/25 [00:07<00:01, 2.90it/s]\n 88%|████████▊ | 22/25 [00:07<00:01, 2.90it/s]\n 92%|█████████▏| 23/25 [00:07<00:00, 2.90it/s]\n 96%|█████████▌| 24/25 [00:08<00:00, 2.90it/s]\n100%|██████████| 25/25 [00:08<00:00, 2.90it/s]\n100%|██████████| 25/25 [00:08<00:00, 2.92it/s]", "metrics": { "predict_time": 11.380412649, "total_time": 39.703475 }, "output": [ "https://replicate.delivery/yhqm/7pgTpFKp57aCIFLgEL0NCzEnFmd1PfJPZKEhELgpF0P1EhyJA/out-0.png" ], "started_at": "2024-10-09T12:00:30.868062Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ayh66sm3y5rm60cje4k9ar69hr", "cancel": "https://api.replicate.com/v1/predictions/ayh66sm3y5rm60cje4k9ar69hr/cancel" }, "version": "798f5ad1241db1e1069a647323845837a546a29a1c33b0b1b0e7d8dbc8e5f179" }Generated inUsing seed: 51062 Prompt: a big ferocious tiger, jungle, sunset, watercolor illustration in the style of WTRSCPE0033 [!] txt2img mode Using dev model free=6637246095360 Downloading weights 2024-10-09T12:00:30Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpwvpcufwq/weights url=https://replicate.delivery/yhqm/Azk0nsBV5CaOONEV7b0neRHMoNbeLd9KVOhAcBSeEqySfovNB/trained_model.tar 2024-10-09T12:00:32Z | INFO | [ Complete ] dest=/tmp/tmpwvpcufwq/weights size="172 MB" total_elapsed=1.448s url=https://replicate.delivery/yhqm/Azk0nsBV5CaOONEV7b0neRHMoNbeLd9KVOhAcBSeEqySfovNB/trained_model.tar Downloaded weights in 1.48s Loaded LoRAs in 2.22s 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:08, 2.90it/s] 8%|▊ | 2/25 [00:00<00:07, 3.23it/s] 12%|█▏ | 3/25 [00:00<00:07, 3.07it/s] 16%|█▌ | 4/25 [00:01<00:07, 3.00it/s] 20%|██ | 5/25 [00:01<00:06, 2.96it/s] 24%|██▍ | 6/25 [00:02<00:06, 2.94it/s] 28%|██▊ | 7/25 [00:02<00:06, 2.92it/s] 32%|███▏ | 8/25 [00:02<00:05, 2.92it/s] 36%|███▌ | 9/25 [00:03<00:05, 2.91it/s] 40%|████ | 10/25 [00:03<00:05, 2.91it/s] 44%|████▍ | 11/25 [00:03<00:04, 2.91it/s] 48%|████▊ | 12/25 [00:04<00:04, 2.90it/s] 52%|█████▏ | 13/25 [00:04<00:04, 2.90it/s] 56%|█████▌ | 14/25 [00:04<00:03, 2.90it/s] 60%|██████ | 15/25 [00:05<00:03, 2.90it/s] 64%|██████▍ | 16/25 [00:05<00:03, 2.90it/s] 68%|██████▊ | 17/25 [00:05<00:02, 2.90it/s] 72%|███████▏ | 18/25 [00:06<00:02, 2.90it/s] 76%|███████▌ | 19/25 [00:06<00:02, 2.90it/s] 80%|████████ | 20/25 [00:06<00:01, 2.90it/s] 84%|████████▍ | 21/25 [00:07<00:01, 2.90it/s] 88%|████████▊ | 22/25 [00:07<00:01, 2.90it/s] 92%|█████████▏| 23/25 [00:07<00:00, 2.90it/s] 96%|█████████▌| 24/25 [00:08<00:00, 2.90it/s] 100%|██████████| 25/25 [00:08<00:00, 2.90it/s] 100%|██████████| 25/25 [00:08<00:00, 2.92it/s]
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