replicate/seamless-texture
seamless-texture
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replicate/seamless-texture
replicate/resnet
Classifies images with ResNet-50
replicate/dreambooth
Train your own custom Stable Diffusion model using a small set of images
replicate/flan-t5-xl
A language model by Google for tasks like classification, summarization, and more
replicate/llama-7b
Transformers implementation of the LLaMA language model
replicate/gpt-j-6b
A large language model by EleutherAI
replicate/all-mpnet-base-v2
This is a language model that can be used to obtain document embeddings suitable for downstream tasks like semantic search and clustering.
replicate/train-rvc-model
Train your own custom RVC model
replicate/goo
Generates goo
replicate/fast-flux-trainer
Train subjects or styles faster than ever
replicate/flux-2d-game-assets
Flux 2D Game Asset LoRA
Prediction
replicate/seamless-texture:9a59c0dce189bfe8a7fcb379c497713500ff959652c4e7874023f15983dec839IDa4341zfg8drm80cpzqk9erv8ncStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- FSTL A field of tall grass swaying in multiple directions, seamless texture
- go_fast
- lora_scale
- 1
- megapixels
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3
- output_quality
- 80
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "FSTL A field of tall grass swaying in multiple directions, seamless texture", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run replicate/seamless-texture using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "replicate/seamless-texture:9a59c0dce189bfe8a7fcb379c497713500ff959652c4e7874023f15983dec839", { input: { model: "dev", prompt: "FSTL A field of tall grass swaying in multiple directions, seamless texture", go_fast: false, lora_scale: 1, megapixels: "1", num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3, output_quality: 80, prompt_strength: 0.8, extra_lora_scale: 1, 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 replicate/seamless-texture using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "replicate/seamless-texture:9a59c0dce189bfe8a7fcb379c497713500ff959652c4e7874023f15983dec839", input={ "model": "dev", "prompt": "FSTL A field of tall grass swaying in multiple directions, seamless texture", "go_fast": False, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run replicate/seamless-texture 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": "replicate/seamless-texture:9a59c0dce189bfe8a7fcb379c497713500ff959652c4e7874023f15983dec839", "input": { "model": "dev", "prompt": "FSTL A field of tall grass swaying in multiple directions, seamless texture", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "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": "2025-05-23T16:08:27.833806Z", "created_at": "2025-05-23T16:08:12.611000Z", "data_removed": false, "error": null, "id": "a4341zfg8drm80cpzqk9erv8nc", "input": { "model": "dev", "prompt": "FSTL A field of tall grass swaying in multiple directions, seamless texture", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "free=26630810513408\nDownloading weights\n2025-05-23T16:08:18Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpakwz3fad/weights url=https://replicate.delivery/xezq/agJO5FSC0p7ICZIll2UFkPqK2XaEbWFDDICk3yYZ7HTUP5LF/flux-lora.tar\n2025-05-23T16:08:18Z | INFO | [ Cache Service ] enabled=true scheme=http target=hermes.services.svc.cluster.local\n2025-05-23T16:08:19Z | INFO | [ Complete ] dest=/tmp/tmpakwz3fad/weights size=\"172 MB\" total_elapsed=1.641s url=https://replicate.delivery/xezq/agJO5FSC0p7ICZIll2UFkPqK2XaEbWFDDICk3yYZ7HTUP5LF/flux-lora.tar\nDownloaded weights in 1.66s\nLoaded LoRAs in 2.23s\nUsing seed: 33274\nPrompt: FSTL A field of tall grass swaying in multiple directions, seamless texture\n[!] txt2img mode\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.82it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.32it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.08it/s]\n 14%|█▍ | 4/28 [00:00<00:06, 3.98it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 3.93it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.89it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.87it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.85it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 3.84it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.83it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.83it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.82it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.82it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.82it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.82it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.82it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.81it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.81it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 3.81it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.82it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.82it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.81it/s]\n 82%|████████▏ | 23/28 [00:05<00:01, 3.82it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.81it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.81it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.81it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.82it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.82it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.84it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 9.803095693, "total_time": 15.222806 }, "output": [ "https://replicate.delivery/xezq/abkZHWMux3K6AJpeCnepEUwwIBwQoAT8LhVF2fNxqzK37JfSB/out-0.webp" ], "started_at": "2025-05-23T16:08:18.030711Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-oe6ohgpflc3awldufsjaz5mycoo5pcocbgrj6i4st3obrdfal2ba", "get": "https://api.replicate.com/v1/predictions/a4341zfg8drm80cpzqk9erv8nc", "cancel": "https://api.replicate.com/v1/predictions/a4341zfg8drm80cpzqk9erv8nc/cancel" }, "version": "9a59c0dce189bfe8a7fcb379c497713500ff959652c4e7874023f15983dec839" }Generated infree=26630810513408 Downloading weights 2025-05-23T16:08:18Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpakwz3fad/weights url=https://replicate.delivery/xezq/agJO5FSC0p7ICZIll2UFkPqK2XaEbWFDDICk3yYZ7HTUP5LF/flux-lora.tar 2025-05-23T16:08:18Z | INFO | [ Cache Service ] enabled=true scheme=http target=hermes.services.svc.cluster.local 2025-05-23T16:08:19Z | INFO | [ Complete ] dest=/tmp/tmpakwz3fad/weights size="172 MB" total_elapsed=1.641s url=https://replicate.delivery/xezq/agJO5FSC0p7ICZIll2UFkPqK2XaEbWFDDICk3yYZ7HTUP5LF/flux-lora.tar Downloaded weights in 1.66s Loaded LoRAs in 2.23s Using seed: 33274 Prompt: FSTL A field of tall grass swaying in multiple directions, seamless texture [!] txt2img mode 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.82it/s] 7%|▋ | 2/28 [00:00<00:06, 4.32it/s] 11%|█ | 3/28 [00:00<00:06, 4.08it/s] 14%|█▍ | 4/28 [00:00<00:06, 3.98it/s] 18%|█▊ | 5/28 [00:01<00:05, 3.93it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.89it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.87it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.85it/s] 32%|███▏ | 9/28 [00:02<00:04, 3.84it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.83it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.83it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.82it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.82it/s] 50%|█████ | 14/28 [00:03<00:03, 3.82it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.82it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.82it/s] 61%|██████ | 17/28 [00:04<00:02, 3.81it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.81it/s] 68%|██████▊ | 19/28 [00:04<00:02, 3.81it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.82it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.82it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.81it/s] 82%|████████▏ | 23/28 [00:05<00:01, 3.82it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.81it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.81it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.81it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.82it/s] 100%|██████████| 28/28 [00:07<00:00, 3.82it/s] 100%|██████████| 28/28 [00:07<00:00, 3.84it/s] Total safe images: 1 out of 1Prediction
replicate/seamless-texture:9a59c0dce189bfe8a7fcb379c497713500ff959652c4e7874023f15983dec839IDs5y22r8ytdrmc0cpzqksb00hy0StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- FSTL A mosaic pattern of iridescent glass tiles in turquoise and purple, seamless texture
- go_fast
- lora_scale
- 1
- megapixels
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3
- output_quality
- 80
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "FSTL A mosaic pattern of iridescent glass tiles in turquoise and purple, seamless texture", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run replicate/seamless-texture using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "replicate/seamless-texture:9a59c0dce189bfe8a7fcb379c497713500ff959652c4e7874023f15983dec839", { input: { model: "dev", prompt: "FSTL A mosaic pattern of iridescent glass tiles in turquoise and purple, seamless texture", go_fast: false, lora_scale: 1, megapixels: "1", num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3, output_quality: 80, prompt_strength: 0.8, extra_lora_scale: 1, 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 replicate/seamless-texture using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "replicate/seamless-texture:9a59c0dce189bfe8a7fcb379c497713500ff959652c4e7874023f15983dec839", input={ "model": "dev", "prompt": "FSTL A mosaic pattern of iridescent glass tiles in turquoise and purple, seamless texture", "go_fast": False, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run replicate/seamless-texture 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": "replicate/seamless-texture:9a59c0dce189bfe8a7fcb379c497713500ff959652c4e7874023f15983dec839", "input": { "model": "dev", "prompt": "FSTL A mosaic pattern of iridescent glass tiles in turquoise and purple, seamless texture", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "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": "2025-05-23T16:08:34.238800Z", "created_at": "2025-05-23T16:08:24.531000Z", "data_removed": false, "error": null, "id": "s5y22r8ytdrmc0cpzqksb00hy0", "input": { "model": "dev", "prompt": "FSTL A mosaic pattern of iridescent glass tiles in turquoise and purple, seamless texture", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "free=27756272676864\nDownloading weights\n2025-05-23T16:08:24Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp3t0d1e53/weights url=https://replicate.delivery/xezq/agJO5FSC0p7ICZIll2UFkPqK2XaEbWFDDICk3yYZ7HTUP5LF/flux-lora.tar\n2025-05-23T16:08:24Z | INFO | [ Cache Service ] enabled=true scheme=http target=hermes.services.svc.cluster.local\n2025-05-23T16:08:26Z | INFO | [ Complete ] dest=/tmp/tmp3t0d1e53/weights size=\"172 MB\" total_elapsed=1.479s url=https://replicate.delivery/xezq/agJO5FSC0p7ICZIll2UFkPqK2XaEbWFDDICk3yYZ7HTUP5LF/flux-lora.tar\nDownloaded weights in 1.50s\nLoaded LoRAs in 2.03s\nUsing seed: 41554\nPrompt: FSTL A mosaic pattern of iridescent glass tiles in turquoise and purple, seamless texture\n[!] txt2img mode\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.82it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.33it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.08it/s]\n 14%|█▍ | 4/28 [00:00<00:06, 3.97it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 3.92it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.89it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.87it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.85it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 3.84it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.84it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.84it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.83it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.83it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.83it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.82it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.83it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.83it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.83it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 3.83it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.82it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.82it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.82it/s]\n 82%|████████▏ | 23/28 [00:05<00:01, 3.82it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.82it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.83it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.82it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.83it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.82it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.85it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 9.65689506, "total_time": 9.7078 }, "output": [ "https://replicate.delivery/xezq/M741K8ZZHPagCJGc6y4e2wVJphNhHaXvXMNA55chhEcBfkvUA/out-0.webp" ], "started_at": "2025-05-23T16:08:24.581905Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-gowwyvhogrvopfxyopt6v3wja7fq4fijriubhqvbuogoq6hu3lla", "get": "https://api.replicate.com/v1/predictions/s5y22r8ytdrmc0cpzqksb00hy0", "cancel": "https://api.replicate.com/v1/predictions/s5y22r8ytdrmc0cpzqksb00hy0/cancel" }, "version": "9a59c0dce189bfe8a7fcb379c497713500ff959652c4e7874023f15983dec839" }Generated infree=27756272676864 Downloading weights 2025-05-23T16:08:24Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp3t0d1e53/weights url=https://replicate.delivery/xezq/agJO5FSC0p7ICZIll2UFkPqK2XaEbWFDDICk3yYZ7HTUP5LF/flux-lora.tar 2025-05-23T16:08:24Z | INFO | [ Cache Service ] enabled=true scheme=http target=hermes.services.svc.cluster.local 2025-05-23T16:08:26Z | INFO | [ Complete ] dest=/tmp/tmp3t0d1e53/weights size="172 MB" total_elapsed=1.479s url=https://replicate.delivery/xezq/agJO5FSC0p7ICZIll2UFkPqK2XaEbWFDDICk3yYZ7HTUP5LF/flux-lora.tar Downloaded weights in 1.50s Loaded LoRAs in 2.03s Using seed: 41554 Prompt: FSTL A mosaic pattern of iridescent glass tiles in turquoise and purple, seamless texture [!] txt2img mode 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.82it/s] 7%|▋ | 2/28 [00:00<00:06, 4.33it/s] 11%|█ | 3/28 [00:00<00:06, 4.08it/s] 14%|█▍ | 4/28 [00:00<00:06, 3.97it/s] 18%|█▊ | 5/28 [00:01<00:05, 3.92it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.89it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.87it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.85it/s] 32%|███▏ | 9/28 [00:02<00:04, 3.84it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.84it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.84it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.83it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.83it/s] 50%|█████ | 14/28 [00:03<00:03, 3.83it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.82it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.83it/s] 61%|██████ | 17/28 [00:04<00:02, 3.83it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.83it/s] 68%|██████▊ | 19/28 [00:04<00:02, 3.83it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.82it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.82it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.82it/s] 82%|████████▏ | 23/28 [00:05<00:01, 3.82it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.82it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.83it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.82it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.83it/s] 100%|██████████| 28/28 [00:07<00:00, 3.82it/s] 100%|██████████| 28/28 [00:07<00:00, 3.85it/s] Total safe images: 1 out of 1Prediction
replicate/seamless-texture:9a59c0dce189bfe8a7fcb379c497713500ff959652c4e7874023f15983dec839IDrz27twbmcxrma0cpzqkvxkcn2rStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- FSTL A dense carpet of autumn leaves in red and orange hues, seamless texture
- go_fast
- lora_scale
- 1
- megapixels
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3
- output_quality
- 80
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "FSTL A dense carpet of autumn leaves in red and orange hues, seamless texture", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run replicate/seamless-texture using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "replicate/seamless-texture:9a59c0dce189bfe8a7fcb379c497713500ff959652c4e7874023f15983dec839", { input: { model: "dev", prompt: "FSTL A dense carpet of autumn leaves in red and orange hues, seamless texture", go_fast: false, lora_scale: 1, megapixels: "1", num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3, output_quality: 80, prompt_strength: 0.8, extra_lora_scale: 1, 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 replicate/seamless-texture using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "replicate/seamless-texture:9a59c0dce189bfe8a7fcb379c497713500ff959652c4e7874023f15983dec839", input={ "model": "dev", "prompt": "FSTL A dense carpet of autumn leaves in red and orange hues, seamless texture", "go_fast": False, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run replicate/seamless-texture 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": "replicate/seamless-texture:9a59c0dce189bfe8a7fcb379c497713500ff959652c4e7874023f15983dec839", "input": { "model": "dev", "prompt": "FSTL A dense carpet of autumn leaves in red and orange hues, seamless texture", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "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": "2025-05-23T16:08:57.381422Z", "created_at": "2025-05-23T16:08:46.439000Z", "data_removed": false, "error": null, "id": "rz27twbmcxrma0cpzqkvxkcn2r", "input": { "model": "dev", "prompt": "FSTL A dense carpet of autumn leaves in red and orange hues, seamless texture", "go_fast": false, "lora_scale": 1, "megapixels": "1", "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3, "output_quality": 80, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "free=27075716165632\nDownloading weights\n2025-05-23T16:08:46Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp2l4rk2mr/weights url=https://replicate.delivery/xezq/agJO5FSC0p7ICZIll2UFkPqK2XaEbWFDDICk3yYZ7HTUP5LF/flux-lora.tar\n2025-05-23T16:08:46Z | INFO | [ Cache Service ] enabled=true scheme=http target=hermes.services.svc.cluster.local\n2025-05-23T16:08:49Z | INFO | [ Complete ] dest=/tmp/tmp2l4rk2mr/weights size=\"172 MB\" total_elapsed=2.795s url=https://replicate.delivery/xezq/agJO5FSC0p7ICZIll2UFkPqK2XaEbWFDDICk3yYZ7HTUP5LF/flux-lora.tar\nDownloaded weights in 2.81s\nLoaded LoRAs in 3.37s\nUsing seed: 62762\nPrompt: FSTL A dense carpet of autumn leaves in red and orange hues, seamless texture\n[!] txt2img mode\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.81it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.32it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.08it/s]\n 14%|█▍ | 4/28 [00:00<00:06, 3.98it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 3.92it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.89it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.87it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.85it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 3.84it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.84it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.83it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.83it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.83it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.83it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.83it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.83it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.83it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.83it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 3.83it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.83it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.83it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.83it/s]\n 82%|████████▏ | 23/28 [00:05<00:01, 3.83it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.83it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.83it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.83it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.83it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.82it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.85it/s]\nTotal safe images: 1 out of 1", "metrics": { "predict_time": 10.922059604, "total_time": 10.942422 }, "output": [ "https://replicate.delivery/xezq/kfQ98chMORX4ZS6ezieyHASqLBMY0HiLxorscdoZjGPy8JfSB/out-0.webp" ], "started_at": "2025-05-23T16:08:46.459362Z", "status": "succeeded", "urls": { "stream": "https://stream.replicate.com/v1/files/bcwr-xpq2ychba37kx53oktwx4dfgmiwyr6ju33ojg66oh5u2f5efpssq", "get": "https://api.replicate.com/v1/predictions/rz27twbmcxrma0cpzqkvxkcn2r", "cancel": "https://api.replicate.com/v1/predictions/rz27twbmcxrma0cpzqkvxkcn2r/cancel" }, "version": "9a59c0dce189bfe8a7fcb379c497713500ff959652c4e7874023f15983dec839" }Generated infree=27075716165632 Downloading weights 2025-05-23T16:08:46Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp2l4rk2mr/weights url=https://replicate.delivery/xezq/agJO5FSC0p7ICZIll2UFkPqK2XaEbWFDDICk3yYZ7HTUP5LF/flux-lora.tar 2025-05-23T16:08:46Z | INFO | [ Cache Service ] enabled=true scheme=http target=hermes.services.svc.cluster.local 2025-05-23T16:08:49Z | INFO | [ Complete ] dest=/tmp/tmp2l4rk2mr/weights size="172 MB" total_elapsed=2.795s url=https://replicate.delivery/xezq/agJO5FSC0p7ICZIll2UFkPqK2XaEbWFDDICk3yYZ7HTUP5LF/flux-lora.tar Downloaded weights in 2.81s Loaded LoRAs in 3.37s Using seed: 62762 Prompt: FSTL A dense carpet of autumn leaves in red and orange hues, seamless texture [!] txt2img mode 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.81it/s] 7%|▋ | 2/28 [00:00<00:06, 4.32it/s] 11%|█ | 3/28 [00:00<00:06, 4.08it/s] 14%|█▍ | 4/28 [00:00<00:06, 3.98it/s] 18%|█▊ | 5/28 [00:01<00:05, 3.92it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.89it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.87it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.85it/s] 32%|███▏ | 9/28 [00:02<00:04, 3.84it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.84it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.83it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.83it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.83it/s] 50%|█████ | 14/28 [00:03<00:03, 3.83it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.83it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.83it/s] 61%|██████ | 17/28 [00:04<00:02, 3.83it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.83it/s] 68%|██████▊ | 19/28 [00:04<00:02, 3.83it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.83it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.83it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.83it/s] 82%|████████▏ | 23/28 [00:05<00:01, 3.83it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.83it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.83it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.83it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.83it/s] 100%|██████████| 28/28 [00:07<00:00, 3.82it/s] 100%|██████████| 28/28 [00:07<00:00, 3.85it/s] Total safe images: 1 out of 1
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