fofr / flux-messy-desktop
- Public
- 381 runs
-
H100
Prediction
fofr/flux-messy-desktop:a344a39cb42d636a6d602ad94cb3b1b3fcc924d000de1c87adff10a6f72731f6ID3dptmxtr4xrj60cj97wtxvj128StatusSucceededSourceWebHardwareA100 (80GB)Total durationCreatedInput
- model
- dev
- prompt
- a portrait photo of a MESSY_DESKTOP woman
- 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 portrait photo of a MESSY_DESKTOP woman", "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 }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 fofr/flux-messy-desktop using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-messy-desktop:a344a39cb42d636a6d602ad94cb3b1b3fcc924d000de1c87adff10a6f72731f6", { input: { model: "dev", prompt: "a portrait photo of a MESSY_DESKTOP woman", 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 } } ); // 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-messy-desktop using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-messy-desktop:a344a39cb42d636a6d602ad94cb3b1b3fcc924d000de1c87adff10a6f72731f6", input={ "model": "dev", "prompt": "a portrait photo of a MESSY_DESKTOP woman", "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.
Run fofr/flux-messy-desktop 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": "fofr/flux-messy-desktop:a344a39cb42d636a6d602ad94cb3b1b3fcc924d000de1c87adff10a6f72731f6", "input": { "model": "dev", "prompt": "a portrait photo of a MESSY_DESKTOP woman", "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": "2024-10-01T21:27:22.461102Z", "created_at": "2024-10-01T21:25:30.791000Z", "data_removed": false, "error": null, "id": "3dptmxtr4xrj60cj97wtxvj128", "input": { "model": "dev", "prompt": "a portrait photo of a MESSY_DESKTOP woman", "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: 56440\nPrompt: a portrait photo of a MESSY_DESKTOP woman\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 0.84s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:17, 1.55it/s]\n 7%|▋ | 2/28 [00:01<00:14, 1.75it/s]\n 11%|█ | 3/28 [00:01<00:15, 1.65it/s]\n 14%|█▍ | 4/28 [00:02<00:14, 1.61it/s]\n 18%|█▊ | 5/28 [00:03<00:14, 1.59it/s]\n 21%|██▏ | 6/28 [00:03<00:13, 1.57it/s]\n 25%|██▌ | 7/28 [00:04<00:13, 1.57it/s]\n 29%|██▊ | 8/28 [00:05<00:12, 1.56it/s]\n 32%|███▏ | 9/28 [00:05<00:12, 1.56it/s]\n 36%|███▌ | 10/28 [00:06<00:11, 1.55it/s]\n 39%|███▉ | 11/28 [00:06<00:10, 1.55it/s]\n 43%|████▎ | 12/28 [00:07<00:10, 1.55it/s]\n 46%|████▋ | 13/28 [00:08<00:09, 1.55it/s]\n 50%|█████ | 14/28 [00:08<00:09, 1.55it/s]\n 54%|█████▎ | 15/28 [00:09<00:08, 1.55it/s]\n 57%|█████▋ | 16/28 [00:10<00:07, 1.55it/s]\n 61%|██████ | 17/28 [00:10<00:07, 1.55it/s]\n 64%|██████▍ | 18/28 [00:11<00:06, 1.55it/s]\n 68%|██████▊ | 19/28 [00:12<00:05, 1.55it/s]\n 71%|███████▏ | 20/28 [00:12<00:05, 1.55it/s]\n 75%|███████▌ | 21/28 [00:13<00:04, 1.55it/s]\n 79%|███████▊ | 22/28 [00:14<00:03, 1.55it/s]\n 82%|████████▏ | 23/28 [00:14<00:03, 1.55it/s]\n 86%|████████▌ | 24/28 [00:15<00:02, 1.55it/s]\n 89%|████████▉ | 25/28 [00:16<00:01, 1.55it/s]\n 93%|█████████▎| 26/28 [00:16<00:01, 1.55it/s]\n 96%|█████████▋| 27/28 [00:17<00:00, 1.55it/s]\n100%|██████████| 28/28 [00:17<00:00, 1.55it/s]\n100%|██████████| 28/28 [00:17<00:00, 1.56it/s]", "metrics": { "predict_time": 19.563654813, "total_time": 111.670102 }, "output": [ "https://replicate.delivery/yhqm/svkE7EqbA4JwGdgvff1uVNRk6Rn93sd3tIRCaLCQqRI6shiTA/out-0.webp" ], "started_at": "2024-10-01T21:27:02.897447Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/3dptmxtr4xrj60cj97wtxvj128", "cancel": "https://api.replicate.com/v1/predictions/3dptmxtr4xrj60cj97wtxvj128/cancel" }, "version": "a344a39cb42d636a6d602ad94cb3b1b3fcc924d000de1c87adff10a6f72731f6" }Generated inUsing seed: 56440 Prompt: a portrait photo of a MESSY_DESKTOP woman [!] txt2img mode Using dev model Loaded LoRAs in 0.84s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:17, 1.55it/s] 7%|▋ | 2/28 [00:01<00:14, 1.75it/s] 11%|█ | 3/28 [00:01<00:15, 1.65it/s] 14%|█▍ | 4/28 [00:02<00:14, 1.61it/s] 18%|█▊ | 5/28 [00:03<00:14, 1.59it/s] 21%|██▏ | 6/28 [00:03<00:13, 1.57it/s] 25%|██▌ | 7/28 [00:04<00:13, 1.57it/s] 29%|██▊ | 8/28 [00:05<00:12, 1.56it/s] 32%|███▏ | 9/28 [00:05<00:12, 1.56it/s] 36%|███▌ | 10/28 [00:06<00:11, 1.55it/s] 39%|███▉ | 11/28 [00:06<00:10, 1.55it/s] 43%|████▎ | 12/28 [00:07<00:10, 1.55it/s] 46%|████▋ | 13/28 [00:08<00:09, 1.55it/s] 50%|█████ | 14/28 [00:08<00:09, 1.55it/s] 54%|█████▎ | 15/28 [00:09<00:08, 1.55it/s] 57%|█████▋ | 16/28 [00:10<00:07, 1.55it/s] 61%|██████ | 17/28 [00:10<00:07, 1.55it/s] 64%|██████▍ | 18/28 [00:11<00:06, 1.55it/s] 68%|██████▊ | 19/28 [00:12<00:05, 1.55it/s] 71%|███████▏ | 20/28 [00:12<00:05, 1.55it/s] 75%|███████▌ | 21/28 [00:13<00:04, 1.55it/s] 79%|███████▊ | 22/28 [00:14<00:03, 1.55it/s] 82%|████████▏ | 23/28 [00:14<00:03, 1.55it/s] 86%|████████▌ | 24/28 [00:15<00:02, 1.55it/s] 89%|████████▉ | 25/28 [00:16<00:01, 1.55it/s] 93%|█████████▎| 26/28 [00:16<00:01, 1.55it/s] 96%|█████████▋| 27/28 [00:17<00:00, 1.55it/s] 100%|██████████| 28/28 [00:17<00:00, 1.55it/s] 100%|██████████| 28/28 [00:17<00:00, 1.56it/s]Prediction
fofr/flux-messy-desktop:a344a39cb42d636a6d602ad94cb3b1b3fcc924d000de1c87adff10a6f72731f6ID18zxqke1gsrj00cj987vahtcbcStatusSucceededSourceWebHardwareA100 (80GB)Total durationCreatedInput
- model
- dev
- prompt
- an extreme closeup portrait photo of a MESSY_DESKTOP living room interior, captions and collage
- lora_scale
- 1
- num_outputs
- 4
- aspect_ratio
- 3:4
- output_format
- webp
- guidance_scale
- 2.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "an extreme closeup portrait photo of a MESSY_DESKTOP living room interior, captions and collage", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "3:4", "output_format": "webp", "guidance_scale": 2.5, "output_quality": 90, "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 fofr/flux-messy-desktop using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-messy-desktop:a344a39cb42d636a6d602ad94cb3b1b3fcc924d000de1c87adff10a6f72731f6", { input: { model: "dev", prompt: "an extreme closeup portrait photo of a MESSY_DESKTOP living room interior, captions and collage", lora_scale: 1, num_outputs: 4, aspect_ratio: "3:4", output_format: "webp", guidance_scale: 2.5, output_quality: 90, 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 fofr/flux-messy-desktop using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-messy-desktop:a344a39cb42d636a6d602ad94cb3b1b3fcc924d000de1c87adff10a6f72731f6", input={ "model": "dev", "prompt": "an extreme closeup portrait photo of a MESSY_DESKTOP living room interior, captions and collage", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "3:4", "output_format": "webp", "guidance_scale": 2.5, "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.
Run fofr/flux-messy-desktop 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": "fofr/flux-messy-desktop:a344a39cb42d636a6d602ad94cb3b1b3fcc924d000de1c87adff10a6f72731f6", "input": { "model": "dev", "prompt": "an extreme closeup portrait photo of a MESSY_DESKTOP living room interior, captions and collage", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "3:4", "output_format": "webp", "guidance_scale": 2.5, "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": "2024-10-01T21:51:13.252489Z", "created_at": "2024-10-01T21:49:59.558000Z", "data_removed": false, "error": null, "id": "18zxqke1gsrj00cj987vahtcbc", "input": { "model": "dev", "prompt": "an extreme closeup portrait photo of a MESSY_DESKTOP living room interior, captions and collage", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "3:4", "output_format": "webp", "guidance_scale": 2.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 44140\nPrompt: an extreme closeup portrait photo of a MESSY_DESKTOP living room interior, captions and collage\n[!] txt2img mode\nUsing dev model\nfree=5449057923072\nDownloading weights\n2024-10-01T21:49:59Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpfolepuvg/weights url=https://replicate.delivery/yhqm/8h2pgWpN8fzzAy8NYNfVvIPo5ZfNRqVQwzDVL2xZeuLvoFKOB/trained_model.tar\n2024-10-01T21:50:00Z | INFO | [ Complete ] dest=/tmp/tmpfolepuvg/weights size=\"172 MB\" total_elapsed=1.051s url=https://replicate.delivery/yhqm/8h2pgWpN8fzzAy8NYNfVvIPo5ZfNRqVQwzDVL2xZeuLvoFKOB/trained_model.tar\nDownloaded weights in 1.16s\nLoaded LoRAs in 1.98s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:02<01:06, 2.46s/it]\n 7%|▋ | 2/28 [00:04<00:56, 2.18s/it]\n 11%|█ | 3/28 [00:06<00:57, 2.31s/it]\n 14%|█▍ | 4/28 [00:09<00:56, 2.37s/it]\n 18%|█▊ | 5/28 [00:11<00:55, 2.41s/it]\n 21%|██▏ | 6/28 [00:14<00:53, 2.43s/it]\n 25%|██▌ | 7/28 [00:16<00:51, 2.45s/it]\n 29%|██▊ | 8/28 [00:19<00:49, 2.46s/it]\n 32%|███▏ | 9/28 [00:21<00:46, 2.46s/it]\n 36%|███▌ | 10/28 [00:24<00:44, 2.47s/it]\n 39%|███▉ | 11/28 [00:26<00:42, 2.47s/it]\n 43%|████▎ | 12/28 [00:29<00:39, 2.47s/it]\n 46%|████▋ | 13/28 [00:31<00:37, 2.48s/it]\n 50%|█████ | 14/28 [00:34<00:34, 2.48s/it]\n 54%|█████▎ | 15/28 [00:36<00:32, 2.48s/it]\n 57%|█████▋ | 16/28 [00:39<00:29, 2.48s/it]\n 61%|██████ | 17/28 [00:41<00:27, 2.48s/it]\n 64%|██████▍ | 18/28 [00:44<00:24, 2.48s/it]\n 68%|██████▊ | 19/28 [00:46<00:22, 2.48s/it]\n 71%|███████▏ | 20/28 [00:49<00:19, 2.48s/it]\n 75%|███████▌ | 21/28 [00:51<00:17, 2.48s/it]\n 79%|███████▊ | 22/28 [00:54<00:14, 2.48s/it]\n 82%|████████▏ | 23/28 [00:56<00:12, 2.48s/it]\n 86%|████████▌ | 24/28 [00:58<00:09, 2.48s/it]\n 89%|████████▉ | 25/28 [01:01<00:07, 2.48s/it]\n 93%|█████████▎| 26/28 [01:03<00:04, 2.48s/it]\n 96%|█████████▋| 27/28 [01:06<00:02, 2.49s/it]\n100%|██████████| 28/28 [01:08<00:00, 2.49s/it]\n100%|██████████| 28/28 [01:08<00:00, 2.46s/it]", "metrics": { "predict_time": 73.684865422, "total_time": 73.694489 }, "output": [ "https://replicate.delivery/yhqm/UzDTF4af3gTvBa8aUo22RYVMoXB33PdY3AdNeTATzKiQDiiTA/out-0.webp", "https://replicate.delivery/yhqm/y6FGbRcuqF4YE1G9OKlPAYzeDaqRcMDTNqzKULf6esagGEFnA/out-1.webp", "https://replicate.delivery/yhqm/MaATfdFrCxxkWKBfeb6DZyz460kke0y53TvVetv5cuREaQUcC/out-2.webp", "https://replicate.delivery/yhqm/DxActq7r7tbfWq98Dqzu5gf6HcRAKMKwrR8CP8NTjeFjGEFnA/out-3.webp" ], "started_at": "2024-10-01T21:49:59.567623Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/18zxqke1gsrj00cj987vahtcbc", "cancel": "https://api.replicate.com/v1/predictions/18zxqke1gsrj00cj987vahtcbc/cancel" }, "version": "a344a39cb42d636a6d602ad94cb3b1b3fcc924d000de1c87adff10a6f72731f6" }Generated inUsing seed: 44140 Prompt: an extreme closeup portrait photo of a MESSY_DESKTOP living room interior, captions and collage [!] txt2img mode Using dev model free=5449057923072 Downloading weights 2024-10-01T21:49:59Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpfolepuvg/weights url=https://replicate.delivery/yhqm/8h2pgWpN8fzzAy8NYNfVvIPo5ZfNRqVQwzDVL2xZeuLvoFKOB/trained_model.tar 2024-10-01T21:50:00Z | INFO | [ Complete ] dest=/tmp/tmpfolepuvg/weights size="172 MB" total_elapsed=1.051s url=https://replicate.delivery/yhqm/8h2pgWpN8fzzAy8NYNfVvIPo5ZfNRqVQwzDVL2xZeuLvoFKOB/trained_model.tar Downloaded weights in 1.16s Loaded LoRAs in 1.98s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:02<01:06, 2.46s/it] 7%|▋ | 2/28 [00:04<00:56, 2.18s/it] 11%|█ | 3/28 [00:06<00:57, 2.31s/it] 14%|█▍ | 4/28 [00:09<00:56, 2.37s/it] 18%|█▊ | 5/28 [00:11<00:55, 2.41s/it] 21%|██▏ | 6/28 [00:14<00:53, 2.43s/it] 25%|██▌ | 7/28 [00:16<00:51, 2.45s/it] 29%|██▊ | 8/28 [00:19<00:49, 2.46s/it] 32%|███▏ | 9/28 [00:21<00:46, 2.46s/it] 36%|███▌ | 10/28 [00:24<00:44, 2.47s/it] 39%|███▉ | 11/28 [00:26<00:42, 2.47s/it] 43%|████▎ | 12/28 [00:29<00:39, 2.47s/it] 46%|████▋ | 13/28 [00:31<00:37, 2.48s/it] 50%|█████ | 14/28 [00:34<00:34, 2.48s/it] 54%|█████▎ | 15/28 [00:36<00:32, 2.48s/it] 57%|█████▋ | 16/28 [00:39<00:29, 2.48s/it] 61%|██████ | 17/28 [00:41<00:27, 2.48s/it] 64%|██████▍ | 18/28 [00:44<00:24, 2.48s/it] 68%|██████▊ | 19/28 [00:46<00:22, 2.48s/it] 71%|███████▏ | 20/28 [00:49<00:19, 2.48s/it] 75%|███████▌ | 21/28 [00:51<00:17, 2.48s/it] 79%|███████▊ | 22/28 [00:54<00:14, 2.48s/it] 82%|████████▏ | 23/28 [00:56<00:12, 2.48s/it] 86%|████████▌ | 24/28 [00:58<00:09, 2.48s/it] 89%|████████▉ | 25/28 [01:01<00:07, 2.48s/it] 93%|█████████▎| 26/28 [01:03<00:04, 2.48s/it] 96%|█████████▋| 27/28 [01:06<00:02, 2.49s/it] 100%|██████████| 28/28 [01:08<00:00, 2.49s/it] 100%|██████████| 28/28 [01:08<00:00, 2.46s/it]Prediction
fofr/flux-messy-desktop:a344a39cb42d636a6d602ad94cb3b1b3fcc924d000de1c87adff10a6f72731f6IDr6xyejv141rj00cj9a1sjjnjkwStatusSucceededSourceWebHardwareA100 (80GB)Total durationCreatedInput
- model
- dev
- prompt
- portrait with a MESSY jacket
- lora_scale
- 1.3
- num_outputs
- 4
- aspect_ratio
- 3:4
- output_format
- webp
- guidance_scale
- 2.5
- output_quality
- 90
- prompt_strength
- 0.8
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "dev", "prompt": "portrait with a MESSY jacket", "lora_scale": 1.3, "num_outputs": 4, "aspect_ratio": "3:4", "output_format": "webp", "guidance_scale": 2.5, "output_quality": 90, "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 fofr/flux-messy-desktop using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-messy-desktop:a344a39cb42d636a6d602ad94cb3b1b3fcc924d000de1c87adff10a6f72731f6", { input: { model: "dev", prompt: "portrait with a MESSY jacket", lora_scale: 1.3, num_outputs: 4, aspect_ratio: "3:4", output_format: "webp", guidance_scale: 2.5, output_quality: 90, 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 fofr/flux-messy-desktop using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-messy-desktop:a344a39cb42d636a6d602ad94cb3b1b3fcc924d000de1c87adff10a6f72731f6", input={ "model": "dev", "prompt": "portrait with a MESSY jacket", "lora_scale": 1.3, "num_outputs": 4, "aspect_ratio": "3:4", "output_format": "webp", "guidance_scale": 2.5, "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.
Run fofr/flux-messy-desktop 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": "fofr/flux-messy-desktop:a344a39cb42d636a6d602ad94cb3b1b3fcc924d000de1c87adff10a6f72731f6", "input": { "model": "dev", "prompt": "portrait with a MESSY jacket", "lora_scale": 1.3, "num_outputs": 4, "aspect_ratio": "3:4", "output_format": "webp", "guidance_scale": 2.5, "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": "2024-10-01T23:57:28.378162Z", "created_at": "2024-10-01T23:56:17.056000Z", "data_removed": false, "error": null, "id": "r6xyejv141rj00cj9a1sjjnjkw", "input": { "model": "dev", "prompt": "portrait with a MESSY jacket", "lora_scale": 1.3, "num_outputs": 4, "aspect_ratio": "3:4", "output_format": "webp", "guidance_scale": 2.5, "output_quality": 90, "prompt_strength": 0.8, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 61875\nPrompt: portrait with a MESSY jacket\n[!] txt2img mode\nUsing dev model\nLoaded LoRAs in 0.80s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:02<01:06, 2.46s/it]\n 7%|▋ | 2/28 [00:04<00:56, 2.18s/it]\n 11%|█ | 3/28 [00:06<00:57, 2.31s/it]\n 14%|█▍ | 4/28 [00:09<00:56, 2.37s/it]\n 18%|█▊ | 5/28 [00:11<00:55, 2.40s/it]\n 21%|██▏ | 6/28 [00:14<00:53, 2.42s/it]\n 25%|██▌ | 7/28 [00:16<00:51, 2.44s/it]\n 29%|██▊ | 8/28 [00:19<00:48, 2.45s/it]\n 32%|███▏ | 9/28 [00:21<00:46, 2.45s/it]\n 36%|███▌ | 10/28 [00:24<00:44, 2.46s/it]\n 39%|███▉ | 11/28 [00:26<00:41, 2.46s/it]\n 43%|████▎ | 12/28 [00:29<00:39, 2.46s/it]\n 46%|████▋ | 13/28 [00:31<00:36, 2.46s/it]\n 50%|█████ | 14/28 [00:34<00:34, 2.46s/it]\n 54%|█████▎ | 15/28 [00:36<00:32, 2.46s/it]\n 57%|█████▋ | 16/28 [00:38<00:29, 2.46s/it]\n 61%|██████ | 17/28 [00:41<00:27, 2.46s/it]\n 64%|██████▍ | 18/28 [00:43<00:24, 2.46s/it]\n 68%|██████▊ | 19/28 [00:46<00:22, 2.47s/it]\n 71%|███████▏ | 20/28 [00:48<00:19, 2.47s/it]\n 75%|███████▌ | 21/28 [00:51<00:17, 2.47s/it]\n 79%|███████▊ | 22/28 [00:53<00:14, 2.47s/it]\n 82%|████████▏ | 23/28 [00:56<00:12, 2.47s/it]\n 86%|████████▌ | 24/28 [00:58<00:09, 2.47s/it]\n 89%|████████▉ | 25/28 [01:01<00:07, 2.47s/it]\n 93%|█████████▎| 26/28 [01:03<00:04, 2.47s/it]\n 96%|█████████▋| 27/28 [01:06<00:02, 2.47s/it]\n100%|██████████| 28/28 [01:08<00:00, 2.47s/it]\n100%|██████████| 28/28 [01:08<00:00, 2.45s/it]", "metrics": { "predict_time": 71.311292579, "total_time": 71.322162 }, "output": [ "https://replicate.delivery/yhqm/8VZwHx4HRioffk8e6FgWnGR8NW8qE7HjWB4QulRCtDtQzHFnA/out-0.webp", "https://replicate.delivery/yhqm/64eGjIBvjEQzKaKUUCBeGC54Es4WgjQ7xPX0DHqCdfrQzHFnA/out-1.webp", "https://replicate.delivery/yhqm/vZIusYMInwraCFn1kyx1urdMzLiCfPehrVGz2X3MoxOo5jiTA/out-2.webp", "https://replicate.delivery/yhqm/5e4TDTWeMOhImkExAzb3vu2F3L3Lk8BCWAM5rHOBhCAo5jiTA/out-3.webp" ], "started_at": "2024-10-01T23:56:17.066869Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/r6xyejv141rj00cj9a1sjjnjkw", "cancel": "https://api.replicate.com/v1/predictions/r6xyejv141rj00cj9a1sjjnjkw/cancel" }, "version": "a344a39cb42d636a6d602ad94cb3b1b3fcc924d000de1c87adff10a6f72731f6" }Generated inUsing seed: 61875 Prompt: portrait with a MESSY jacket [!] txt2img mode Using dev model Loaded LoRAs in 0.80s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:02<01:06, 2.46s/it] 7%|▋ | 2/28 [00:04<00:56, 2.18s/it] 11%|█ | 3/28 [00:06<00:57, 2.31s/it] 14%|█▍ | 4/28 [00:09<00:56, 2.37s/it] 18%|█▊ | 5/28 [00:11<00:55, 2.40s/it] 21%|██▏ | 6/28 [00:14<00:53, 2.42s/it] 25%|██▌ | 7/28 [00:16<00:51, 2.44s/it] 29%|██▊ | 8/28 [00:19<00:48, 2.45s/it] 32%|███▏ | 9/28 [00:21<00:46, 2.45s/it] 36%|███▌ | 10/28 [00:24<00:44, 2.46s/it] 39%|███▉ | 11/28 [00:26<00:41, 2.46s/it] 43%|████▎ | 12/28 [00:29<00:39, 2.46s/it] 46%|████▋ | 13/28 [00:31<00:36, 2.46s/it] 50%|█████ | 14/28 [00:34<00:34, 2.46s/it] 54%|█████▎ | 15/28 [00:36<00:32, 2.46s/it] 57%|█████▋ | 16/28 [00:38<00:29, 2.46s/it] 61%|██████ | 17/28 [00:41<00:27, 2.46s/it] 64%|██████▍ | 18/28 [00:43<00:24, 2.46s/it] 68%|██████▊ | 19/28 [00:46<00:22, 2.47s/it] 71%|███████▏ | 20/28 [00:48<00:19, 2.47s/it] 75%|███████▌ | 21/28 [00:51<00:17, 2.47s/it] 79%|███████▊ | 22/28 [00:53<00:14, 2.47s/it] 82%|████████▏ | 23/28 [00:56<00:12, 2.47s/it] 86%|████████▌ | 24/28 [00:58<00:09, 2.47s/it] 89%|████████▉ | 25/28 [01:01<00:07, 2.47s/it] 93%|█████████▎| 26/28 [01:03<00:04, 2.47s/it] 96%|█████████▋| 27/28 [01:06<00:02, 2.47s/it] 100%|██████████| 28/28 [01:08<00:00, 2.47s/it] 100%|██████████| 28/28 [01:08<00:00, 2.45s/it]
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