fofr
/
flux-drone-builds
Flux lora, use "DRNBUILD" to trigger image generation
- Public
- 510 runs
-
H100
Prediction
fofr/flux-drone-builds:564b4ba5IDa4vsy2ppy1rm00chsq5rjscgm0StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a photo of a DRNBUILD building reconstructed with drones, night, lights
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a photo of a DRNBUILD building reconstructed with drones, night, lights", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }npm install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/flux-drone-builds using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-drone-builds:564b4ba5f4aaa2aa5a2ff7b73fc93c1055141f1d26993229560e1cb081fb70d0", { input: { model: "dev", prompt: "a photo of a DRNBUILD building reconstructed with drones, night, lights", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the clientimport replicateRun fofr/flux-drone-builds using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-drone-builds:564b4ba5f4aaa2aa5a2ff7b73fc93c1055141f1d26993229560e1cb081fb70d0", input={ "model": "dev", "prompt": "a photo of a DRNBUILD building reconstructed with drones, night, lights", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/flux-drone-builds 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": "564b4ba5f4aaa2aa5a2ff7b73fc93c1055141f1d26993229560e1cb081fb70d0", "input": { "model": "dev", "prompt": "a photo of a DRNBUILD building reconstructed with drones, night, lights", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "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": "2024-09-07T18:43:12.683048Z", "created_at": "2024-09-07T18:42:53.808000Z", "data_removed": false, "error": null, "id": "a4vsy2ppy1rm00chsq5rjscgm0", "input": { "model": "dev", "prompt": "a photo of a DRNBUILD building reconstructed with drones, night, lights", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 56964\nPrompt: a photo of a DRNBUILD building reconstructed with drones, night, lights\n[!] txt2img mode\nUsing dev model\nfree=8036201402368\nDownloading weights\n2024-09-07T18:42:53Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpubofz8yo/weights url=https://replicate.delivery/yhqm/9vhFjFqI48rJAJfUPkiXxh2lRMCdDlAdhiGHyJnNuhVSfkaTA/trained_model.tar\n2024-09-07T18:42:56Z | INFO | [ Complete ] dest=/tmp/tmpubofz8yo/weights size=\"172 MB\" total_elapsed=2.930s url=https://replicate.delivery/yhqm/9vhFjFqI48rJAJfUPkiXxh2lRMCdDlAdhiGHyJnNuhVSfkaTA/trained_model.tar\nDownloaded weights in 2.96s\nLoaded LoRAs in 10.92s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.76it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.25it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.01it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.91it/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, 3.79it/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, 3.77it/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, 3.76it/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, 3.76it/s]\n 68%|██████▊ | 19/28 [00:04<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, 3.79it/s]", "metrics": { "predict_time": 18.866265576, "total_time": 18.875048 }, "output": [ "https://replicate.delivery/yhqm/H56zLFkRTjKKIJErKfJwY1vlqU26tiqUK0OguZYughBghStJA/out-0.webp" ], "started_at": "2024-09-07T18:42:53.816783Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/a4vsy2ppy1rm00chsq5rjscgm0", "cancel": "https://api.replicate.com/v1/predictions/a4vsy2ppy1rm00chsq5rjscgm0/cancel" }, "version": "564b4ba5f4aaa2aa5a2ff7b73fc93c1055141f1d26993229560e1cb081fb70d0" }Generated inUsing seed: 56964 Prompt: a photo of a DRNBUILD building reconstructed with drones, night, lights [!] txt2img mode Using dev model free=8036201402368 Downloading weights 2024-09-07T18:42:53Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpubofz8yo/weights url=https://replicate.delivery/yhqm/9vhFjFqI48rJAJfUPkiXxh2lRMCdDlAdhiGHyJnNuhVSfkaTA/trained_model.tar 2024-09-07T18:42:56Z | INFO | [ Complete ] dest=/tmp/tmpubofz8yo/weights size="172 MB" total_elapsed=2.930s url=https://replicate.delivery/yhqm/9vhFjFqI48rJAJfUPkiXxh2lRMCdDlAdhiGHyJnNuhVSfkaTA/trained_model.tar Downloaded weights in 2.96s Loaded LoRAs in 10.92s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.76it/s] 7%|▋ | 2/28 [00:00<00:06, 4.25it/s] 11%|█ | 3/28 [00:00<00:06, 4.01it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.91it/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, 3.79it/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, 3.77it/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, 3.76it/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, 3.76it/s] 68%|██████▊ | 19/28 [00:04<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, 3.79it/s]Prediction
fofr/flux-drone-builds:564b4ba5IDpbczhttbf5rm60chsq6s0zjgm0StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a photo of a DRNBUILD building reconstructed with drones, night, lights
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a photo of a DRNBUILD building reconstructed with drones, night, lights", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }npm install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/flux-drone-builds using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-drone-builds:564b4ba5f4aaa2aa5a2ff7b73fc93c1055141f1d26993229560e1cb081fb70d0", { input: { model: "dev", prompt: "a photo of a DRNBUILD building reconstructed with drones, night, lights", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the clientimport replicateRun fofr/flux-drone-builds using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-drone-builds:564b4ba5f4aaa2aa5a2ff7b73fc93c1055141f1d26993229560e1cb081fb70d0", input={ "model": "dev", "prompt": "a photo of a DRNBUILD building reconstructed with drones, night, lights", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/flux-drone-builds 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": "564b4ba5f4aaa2aa5a2ff7b73fc93c1055141f1d26993229560e1cb081fb70d0", "input": { "model": "dev", "prompt": "a photo of a DRNBUILD building reconstructed with drones, night, lights", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "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": "2024-09-07T18:44:47.297843Z", "created_at": "2024-09-07T18:44:29.177000Z", "data_removed": false, "error": null, "id": "pbczhttbf5rm60chsq6s0zjgm0", "input": { "model": "dev", "prompt": "a photo of a DRNBUILD building reconstructed with drones, night, lights", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 57385\nPrompt: a photo of a DRNBUILD building reconstructed with drones, night, lights\n[!] txt2img mode\nUsing dev model\nfree=8296327524352\nDownloading weights\n2024-09-07T18:44:29Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp1akynkq6/weights url=https://replicate.delivery/yhqm/9vhFjFqI48rJAJfUPkiXxh2lRMCdDlAdhiGHyJnNuhVSfkaTA/trained_model.tar\n2024-09-07T18:44:30Z | INFO | [ Complete ] dest=/tmp/tmp1akynkq6/weights size=\"172 MB\" total_elapsed=1.623s url=https://replicate.delivery/yhqm/9vhFjFqI48rJAJfUPkiXxh2lRMCdDlAdhiGHyJnNuhVSfkaTA/trained_model.tar\nDownloaded weights in 1.66s\nLoaded LoRAs in 10.14s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.76it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.25it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.02it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.92it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 3.86it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.83it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.81it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.79it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.78it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.78it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.77it/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, 3.77it/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, 3.76it/s]\n 68%|██████▊ | 19/28 [00:04<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, 3.79it/s]", "metrics": { "predict_time": 18.111558014, "total_time": 18.120843 }, "output": [ "https://replicate.delivery/yhqm/O8EqAcWuqS67Gt26afPqefUMnLlf8PQlKlOAvVYTkeP7joUbC/out-0.webp" ], "started_at": "2024-09-07T18:44:29.186285Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/pbczhttbf5rm60chsq6s0zjgm0", "cancel": "https://api.replicate.com/v1/predictions/pbczhttbf5rm60chsq6s0zjgm0/cancel" }, "version": "564b4ba5f4aaa2aa5a2ff7b73fc93c1055141f1d26993229560e1cb081fb70d0" }Generated inUsing seed: 57385 Prompt: a photo of a DRNBUILD building reconstructed with drones, night, lights [!] txt2img mode Using dev model free=8296327524352 Downloading weights 2024-09-07T18:44:29Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp1akynkq6/weights url=https://replicate.delivery/yhqm/9vhFjFqI48rJAJfUPkiXxh2lRMCdDlAdhiGHyJnNuhVSfkaTA/trained_model.tar 2024-09-07T18:44:30Z | INFO | [ Complete ] dest=/tmp/tmp1akynkq6/weights size="172 MB" total_elapsed=1.623s url=https://replicate.delivery/yhqm/9vhFjFqI48rJAJfUPkiXxh2lRMCdDlAdhiGHyJnNuhVSfkaTA/trained_model.tar Downloaded weights in 1.66s Loaded LoRAs in 10.14s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.76it/s] 7%|▋ | 2/28 [00:00<00:06, 4.25it/s] 11%|█ | 3/28 [00:00<00:06, 4.02it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.92it/s] 18%|█▊ | 5/28 [00:01<00:05, 3.86it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.83it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.81it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.79it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.78it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.78it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.77it/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, 3.77it/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, 3.76it/s] 68%|██████▊ | 19/28 [00:04<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, 3.79it/s]Prediction
fofr/flux-drone-builds:564b4ba5ID19e2wbggbhrm20chsq9bxwxq4wStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a photo of a DRNBUILD woman, night, lights
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a photo of a DRNBUILD woman, night, lights", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }npm install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/flux-drone-builds using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-drone-builds:564b4ba5f4aaa2aa5a2ff7b73fc93c1055141f1d26993229560e1cb081fb70d0", { input: { model: "dev", prompt: "a photo of a DRNBUILD woman, night, lights", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the clientimport replicateRun fofr/flux-drone-builds using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-drone-builds:564b4ba5f4aaa2aa5a2ff7b73fc93c1055141f1d26993229560e1cb081fb70d0", input={ "model": "dev", "prompt": "a photo of a DRNBUILD woman, night, lights", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/flux-drone-builds 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": "564b4ba5f4aaa2aa5a2ff7b73fc93c1055141f1d26993229560e1cb081fb70d0", "input": { "model": "dev", "prompt": "a photo of a DRNBUILD woman, night, lights", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "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": "2024-09-07T18:50:07.382145Z", "created_at": "2024-09-07T18:49:41.724000Z", "data_removed": false, "error": null, "id": "19e2wbggbhrm20chsq9bxwxq4w", "input": { "model": "dev", "prompt": "a photo of a DRNBUILD woman, night, lights", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 49913\nPrompt: a photo of a DRNBUILD woman, night, lights\n[!] txt2img mode\nUsing dev model\nfree=9275373805568\nDownloading weights\n2024-09-07T18:49:49Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpi9mquhyu/weights url=https://replicate.delivery/yhqm/9vhFjFqI48rJAJfUPkiXxh2lRMCdDlAdhiGHyJnNuhVSfkaTA/trained_model.tar\n2024-09-07T18:49:52Z | INFO | [ Complete ] dest=/tmp/tmpi9mquhyu/weights size=\"172 MB\" total_elapsed=3.090s url=https://replicate.delivery/yhqm/9vhFjFqI48rJAJfUPkiXxh2lRMCdDlAdhiGHyJnNuhVSfkaTA/trained_model.tar\nDownloaded weights in 3.12s\nLoaded LoRAs in 9.96s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.76it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.24it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.01it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.91it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 3.86it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.83it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.81it/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, 3.78it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.78it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.78it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.78it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.78it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.78it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.77it/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, 3.77it/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, 3.77it/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, 3.77it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.80it/s]", "metrics": { "predict_time": 17.886537629, "total_time": 25.658145 }, "output": [ "https://replicate.delivery/yhqm/fVR3bRUxTQV3GKpW5U6xEy6l1WfzbreMB2s5qKWmDLDelUqNB/out-0.webp" ], "started_at": "2024-09-07T18:49:49.495608Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/19e2wbggbhrm20chsq9bxwxq4w", "cancel": "https://api.replicate.com/v1/predictions/19e2wbggbhrm20chsq9bxwxq4w/cancel" }, "version": "564b4ba5f4aaa2aa5a2ff7b73fc93c1055141f1d26993229560e1cb081fb70d0" }Generated inUsing seed: 49913 Prompt: a photo of a DRNBUILD woman, night, lights [!] txt2img mode Using dev model free=9275373805568 Downloading weights 2024-09-07T18:49:49Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpi9mquhyu/weights url=https://replicate.delivery/yhqm/9vhFjFqI48rJAJfUPkiXxh2lRMCdDlAdhiGHyJnNuhVSfkaTA/trained_model.tar 2024-09-07T18:49:52Z | INFO | [ Complete ] dest=/tmp/tmpi9mquhyu/weights size="172 MB" total_elapsed=3.090s url=https://replicate.delivery/yhqm/9vhFjFqI48rJAJfUPkiXxh2lRMCdDlAdhiGHyJnNuhVSfkaTA/trained_model.tar Downloaded weights in 3.12s Loaded LoRAs in 9.96s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.76it/s] 7%|▋ | 2/28 [00:00<00:06, 4.24it/s] 11%|█ | 3/28 [00:00<00:06, 4.01it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.91it/s] 18%|█▊ | 5/28 [00:01<00:05, 3.86it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.83it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.81it/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, 3.78it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.78it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.78it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.78it/s] 50%|█████ | 14/28 [00:03<00:03, 3.78it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.78it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.77it/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, 3.77it/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, 3.77it/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, 3.77it/s] 100%|██████████| 28/28 [00:07<00:00, 3.80it/s]Prediction
fofr/flux-drone-builds:564b4ba5IDdvchngkk5hrm20chsqb9k3d5s4StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a photo of a DRNBUILD cyborg dog, night, lights
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a photo of a DRNBUILD cyborg dog, night, lights", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }npm install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the clientimport Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/flux-drone-builds using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-drone-builds:564b4ba5f4aaa2aa5a2ff7b73fc93c1055141f1d26993229560e1cb081fb70d0", { input: { model: "dev", prompt: "a photo of a DRNBUILD cyborg dog, night, lights", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 90, prompt_strength: 0.8, extra_lora_scale: 1, num_inference_steps: 28 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateSet theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the clientimport replicateRun fofr/flux-drone-builds using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-drone-builds:564b4ba5f4aaa2aa5a2ff7b73fc93c1055141f1d26993229560e1cb081fb70d0", input={ "model": "dev", "prompt": "a photo of a DRNBUILD cyborg dog, night, lights", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/flux-drone-builds 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": "564b4ba5f4aaa2aa5a2ff7b73fc93c1055141f1d26993229560e1cb081fb70d0", "input": { "model": "dev", "prompt": "a photo of a DRNBUILD cyborg dog, night, lights", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "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": "2024-09-07T18:54:58.785156Z", "created_at": "2024-09-07T18:54:29.164000Z", "data_removed": false, "error": null, "id": "dvchngkk5hrm20chsqb9k3d5s4", "input": { "model": "dev", "prompt": "a photo of a DRNBUILD cyborg dog, night, lights", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 41504\nPrompt: a photo of a DRNBUILD cyborg dog, night, lights\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 6.79s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.76it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.25it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.01it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.91it/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, 3.79it/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, 3.77it/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, 3.76it/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, 3.76it/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, 3.79it/s]", "metrics": { "predict_time": 14.74238088, "total_time": 29.621156 }, "output": [ "https://replicate.delivery/yhqm/BUFYoorzJKK6C5aJJ1kYV1PGw0bBMWmJeHivvcwDMLWBnStJA/out-0.webp" ], "started_at": "2024-09-07T18:54:44.042775Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/dvchngkk5hrm20chsqb9k3d5s4", "cancel": "https://api.replicate.com/v1/predictions/dvchngkk5hrm20chsqb9k3d5s4/cancel" }, "version": "564b4ba5f4aaa2aa5a2ff7b73fc93c1055141f1d26993229560e1cb081fb70d0" }Generated inUsing seed: 41504 Prompt: a photo of a DRNBUILD cyborg dog, night, lights [!] txt2img mode Using dev model Loaded LoRAs in 6.79s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.76it/s] 7%|▋ | 2/28 [00:00<00:06, 4.25it/s] 11%|█ | 3/28 [00:00<00:06, 4.01it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.91it/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, 3.79it/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, 3.77it/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, 3.76it/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, 3.76it/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, 3.79it/s]
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