fofr
/
flux-black-light
A flux lora fine-tuned on black light images
- Public
- 1.4M runs
-
H100
Prediction
fofr/flux-black-light:d0d48e298dcb51118c3f903817c833bba063936637a33ac52a8ffd6a94859af7Input
- model
- dev
- prompt
- a closeup BLKLGHT portrait photo of a cyberpunk
- lora_scale
- 0.75
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a closeup BLKLGHT portrait photo of a cyberpunk", "lora_scale": 0.75, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/flux-black-light using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-black-light:d0d48e298dcb51118c3f903817c833bba063936637a33ac52a8ffd6a94859af7", { input: { model: "dev", prompt: "a closeup BLKLGHT portrait photo of a cyberpunk", lora_scale: 0.75, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun fofr/flux-black-light using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-black-light:d0d48e298dcb51118c3f903817c833bba063936637a33ac52a8ffd6a94859af7", input={ "model": "dev", "prompt": "a closeup BLKLGHT portrait photo of a cyberpunk", "lora_scale": 0.75, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fofr/flux-black-light 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": "d0d48e298dcb51118c3f903817c833bba063936637a33ac52a8ffd6a94859af7", "input": { "model": "dev", "prompt": "a closeup BLKLGHT portrait photo of a cyberpunk", "lora_scale": 0.75, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-15T23:19:01.692922Z", "created_at": "2024-08-15T23:18:42.883000Z", "data_removed": false, "error": null, "id": "nj2psggqrdrm00chb1bapg9amm", "input": { "model": "dev", "prompt": "a closeup BLKLGHT portrait photo of a cyberpunk", "lora_scale": 0.75, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 52024\nPrompt: a closeup BLKLGHT portrait photo of a cyberpunk\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.51it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.93it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.74it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.66it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.61it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.59it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.57it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.55it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.56it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.55it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.55it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.55it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.53it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.54it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.54it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.54it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.49it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.55it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.55it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.55it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.55it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.53it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.55it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.55it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.55it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.54it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.54it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.53it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.56it/s]", "metrics": { "predict_time": 17.536645147, "total_time": 18.809922 }, "output": [ "https://replicate.delivery/yhqm/ZehHgBlLml30R64OxKegcYEHlzGgd0rcIrf5ejXaFK5VuPMNB/out-0.webp" ], "started_at": "2024-08-15T23:18:44.156277Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/nj2psggqrdrm00chb1bapg9amm", "cancel": "https://api.replicate.com/v1/predictions/nj2psggqrdrm00chb1bapg9amm/cancel" }, "version": "d0d48e298dcb51118c3f903817c833bba063936637a33ac52a8ffd6a94859af7" }Generated inUsing seed: 52024 Prompt: a closeup BLKLGHT portrait photo of a cyberpunk txt2img mode Using dev model Loading LoRA weights LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.51it/s] 7%|▋ | 2/28 [00:00<00:06, 3.93it/s] 11%|█ | 3/28 [00:00<00:06, 3.74it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.66it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.61it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.59it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.57it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.55it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.56it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.55it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.55it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.55it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.53it/s] 50%|█████ | 14/28 [00:03<00:03, 3.54it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.54it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.54it/s] 61%|██████ | 17/28 [00:04<00:03, 3.49it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.55it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.55it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.55it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.55it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.53it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.55it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.55it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.55it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.54it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.54it/s] 100%|██████████| 28/28 [00:07<00:00, 3.53it/s] 100%|██████████| 28/28 [00:07<00:00, 3.56it/s]Prediction
fofr/flux-black-light:d0d48e298dcb51118c3f903817c833bba063936637a33ac52a8ffd6a94859af7IDf1qece16r9rm20chb1da4gzy70StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a closeup BLKLGHT portrait photo of a car
- lora_scale
- 0.75
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a closeup BLKLGHT portrait photo of a car", "lora_scale": 0.75, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/flux-black-light using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-black-light:d0d48e298dcb51118c3f903817c833bba063936637a33ac52a8ffd6a94859af7", { input: { model: "dev", prompt: "a closeup BLKLGHT portrait photo of a car", lora_scale: 0.75, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun fofr/flux-black-light using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-black-light:d0d48e298dcb51118c3f903817c833bba063936637a33ac52a8ffd6a94859af7", input={ "model": "dev", "prompt": "a closeup BLKLGHT portrait photo of a car", "lora_scale": 0.75, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fofr/flux-black-light 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": "d0d48e298dcb51118c3f903817c833bba063936637a33ac52a8ffd6a94859af7", "input": { "model": "dev", "prompt": "a closeup BLKLGHT portrait photo of a car", "lora_scale": 0.75, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-15T23:23:28.579050Z", "created_at": "2024-08-15T23:23:08.866000Z", "data_removed": false, "error": null, "id": "f1qece16r9rm20chb1da4gzy70", "input": { "model": "dev", "prompt": "a closeup BLKLGHT portrait photo of a car", "lora_scale": 0.75, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 3875\nPrompt: a closeup BLKLGHT portrait photo of a car\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.51it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.94it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.74it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.64it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.61it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.58it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.57it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.55it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.54it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.54it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.54it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.53it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.53it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.53it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.53it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.53it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.53it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.52it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.53it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.53it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.53it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.53it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.53it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.52it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.53it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.51it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.52it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.52it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.55it/s]", "metrics": { "predict_time": 17.164136112, "total_time": 19.71305 }, "output": [ "https://replicate.delivery/yhqm/fhWIsG0Vk8TMaS9BLCTBGnfaL5T0m4092QNMZez2EfDBffw0E/out-0.webp" ], "started_at": "2024-08-15T23:23:11.414914Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/f1qece16r9rm20chb1da4gzy70", "cancel": "https://api.replicate.com/v1/predictions/f1qece16r9rm20chb1da4gzy70/cancel" }, "version": "d0d48e298dcb51118c3f903817c833bba063936637a33ac52a8ffd6a94859af7" }Generated inUsing seed: 3875 Prompt: a closeup BLKLGHT portrait photo of a car txt2img mode Using dev model Loading LoRA weights LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.51it/s] 7%|▋ | 2/28 [00:00<00:06, 3.94it/s] 11%|█ | 3/28 [00:00<00:06, 3.74it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.64it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.61it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.58it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.57it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.55it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.54it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.54it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.54it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.53it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.53it/s] 50%|█████ | 14/28 [00:03<00:03, 3.53it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.53it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.53it/s] 61%|██████ | 17/28 [00:04<00:03, 3.53it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.52it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.53it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.53it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.53it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.53it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.53it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.52it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.53it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.51it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.52it/s] 100%|██████████| 28/28 [00:07<00:00, 3.52it/s] 100%|██████████| 28/28 [00:07<00:00, 3.55it/s]Prediction
fofr/flux-black-light:d0d48e298dcb51118c3f903817c833bba063936637a33ac52a8ffd6a94859af7IDtg9x4bc7j9rm40chb1e89xgrcmStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a neon BLKLGHT landscape photo
- lora_scale
- 0.75
- num_outputs
- 4
- aspect_ratio
- 3:2
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a neon BLKLGHT landscape photo", "lora_scale": 0.75, "num_outputs": 4, "aspect_ratio": "3:2", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/flux-black-light using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-black-light:d0d48e298dcb51118c3f903817c833bba063936637a33ac52a8ffd6a94859af7", { input: { model: "dev", prompt: "a neon BLKLGHT landscape photo", lora_scale: 0.75, num_outputs: 4, aspect_ratio: "3:2", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 28 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun fofr/flux-black-light using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-black-light:d0d48e298dcb51118c3f903817c833bba063936637a33ac52a8ffd6a94859af7", input={ "model": "dev", "prompt": "a neon BLKLGHT landscape photo", "lora_scale": 0.75, "num_outputs": 4, "aspect_ratio": "3:2", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run fofr/flux-black-light 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": "d0d48e298dcb51118c3f903817c833bba063936637a33ac52a8ffd6a94859af7", "input": { "model": "dev", "prompt": "a neon BLKLGHT landscape photo", "lora_scale": 0.75, "num_outputs": 4, "aspect_ratio": "3:2", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-15T23:26:24.733361Z", "created_at": "2024-08-15T23:25:44.722000Z", "data_removed": false, "error": null, "id": "tg9x4bc7j9rm40chb1e89xgrcm", "input": { "model": "dev", "prompt": "a neon BLKLGHT landscape photo", "lora_scale": 0.75, "num_outputs": 4, "aspect_ratio": "3:2", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 32641\nPrompt: a neon BLKLGHT landscape photo\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:26, 1.02it/s]\n 7%|▋ | 2/28 [00:01<00:22, 1.15it/s]\n 11%|█ | 3/28 [00:02<00:23, 1.09it/s]\n 14%|█▍ | 4/28 [00:03<00:22, 1.06it/s]\n 18%|█▊ | 5/28 [00:04<00:22, 1.04it/s]\n 21%|██▏ | 6/28 [00:05<00:21, 1.03it/s]\n 25%|██▌ | 7/28 [00:06<00:20, 1.02it/s]\n 29%|██▊ | 8/28 [00:07<00:19, 1.02it/s]\n 32%|███▏ | 9/28 [00:08<00:18, 1.02it/s]\n 36%|███▌ | 10/28 [00:09<00:17, 1.02it/s]\n 39%|███▉ | 11/28 [00:10<00:16, 1.01it/s]\n 43%|████▎ | 12/28 [00:11<00:15, 1.01it/s]\n 46%|████▋ | 13/28 [00:12<00:14, 1.01it/s]\n 50%|█████ | 14/28 [00:13<00:13, 1.01it/s]\n 54%|█████▎ | 15/28 [00:14<00:12, 1.01it/s]\n 57%|█████▋ | 16/28 [00:15<00:11, 1.01it/s]\n 61%|██████ | 17/28 [00:16<00:10, 1.01it/s]\n 64%|██████▍ | 18/28 [00:17<00:09, 1.01it/s]\n 68%|██████▊ | 19/28 [00:18<00:08, 1.01it/s]\n 71%|███████▏ | 20/28 [00:19<00:07, 1.01it/s]\n 75%|███████▌ | 21/28 [00:20<00:06, 1.01it/s]\n 79%|███████▊ | 22/28 [00:21<00:05, 1.01it/s]\n 82%|████████▏ | 23/28 [00:22<00:04, 1.01it/s]\n 86%|████████▌ | 24/28 [00:23<00:03, 1.01it/s]\n 89%|████████▉ | 25/28 [00:24<00:02, 1.01it/s]\n 93%|█████████▎| 26/28 [00:25<00:01, 1.01it/s]\n 96%|█████████▋| 27/28 [00:26<00:00, 1.01it/s]\n100%|██████████| 28/28 [00:27<00:00, 1.01it/s]\n100%|██████████| 28/28 [00:27<00:00, 1.02it/s]", "metrics": { "predict_time": 36.65203678, "total_time": 40.011361 }, "output": [ "https://replicate.delivery/yhqm/R4rhSxNRIZoAKdYC3WeFBe2Il9hncUv3lVtrtrXHVnjgCETTA/out-0.webp", "https://replicate.delivery/yhqm/oK29AWROPdJyI9u7567a9WuaIzars2UWIUWaGQzUA7BoAx0E/out-1.webp", "https://replicate.delivery/yhqm/JwUkOcoqhAraHxsGRBI1jKsaweYV5zKq80qgUYN2BkCQBipJA/out-2.webp", "https://replicate.delivery/yhqm/3pfS0fm8CvvfxocmbKNytGkmWhfI1f3Qc4BXYfLf9TxWQBipJA/out-3.webp" ], "started_at": "2024-08-15T23:25:48.081325Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/tg9x4bc7j9rm40chb1e89xgrcm", "cancel": "https://api.replicate.com/v1/predictions/tg9x4bc7j9rm40chb1e89xgrcm/cancel" }, "version": "d0d48e298dcb51118c3f903817c833bba063936637a33ac52a8ffd6a94859af7" }Generated inUsing seed: 32641 Prompt: a neon BLKLGHT landscape photo txt2img mode Using dev model Loading LoRA weights LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:26, 1.02it/s] 7%|▋ | 2/28 [00:01<00:22, 1.15it/s] 11%|█ | 3/28 [00:02<00:23, 1.09it/s] 14%|█▍ | 4/28 [00:03<00:22, 1.06it/s] 18%|█▊ | 5/28 [00:04<00:22, 1.04it/s] 21%|██▏ | 6/28 [00:05<00:21, 1.03it/s] 25%|██▌ | 7/28 [00:06<00:20, 1.02it/s] 29%|██▊ | 8/28 [00:07<00:19, 1.02it/s] 32%|███▏ | 9/28 [00:08<00:18, 1.02it/s] 36%|███▌ | 10/28 [00:09<00:17, 1.02it/s] 39%|███▉ | 11/28 [00:10<00:16, 1.01it/s] 43%|████▎ | 12/28 [00:11<00:15, 1.01it/s] 46%|████▋ | 13/28 [00:12<00:14, 1.01it/s] 50%|█████ | 14/28 [00:13<00:13, 1.01it/s] 54%|█████▎ | 15/28 [00:14<00:12, 1.01it/s] 57%|█████▋ | 16/28 [00:15<00:11, 1.01it/s] 61%|██████ | 17/28 [00:16<00:10, 1.01it/s] 64%|██████▍ | 18/28 [00:17<00:09, 1.01it/s] 68%|██████▊ | 19/28 [00:18<00:08, 1.01it/s] 71%|███████▏ | 20/28 [00:19<00:07, 1.01it/s] 75%|███████▌ | 21/28 [00:20<00:06, 1.01it/s] 79%|███████▊ | 22/28 [00:21<00:05, 1.01it/s] 82%|████████▏ | 23/28 [00:22<00:04, 1.01it/s] 86%|████████▌ | 24/28 [00:23<00:03, 1.01it/s] 89%|████████▉ | 25/28 [00:24<00:02, 1.01it/s] 93%|█████████▎| 26/28 [00:25<00:01, 1.01it/s] 96%|█████████▋| 27/28 [00:26<00:00, 1.01it/s] 100%|██████████| 28/28 [00:27<00:00, 1.01it/s] 100%|██████████| 28/28 [00:27<00:00, 1.02it/s]
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