fofr
/
flux-wrong
Flux lora, use “WRNG” to trigger image generation
- Public
- 701 runs
-
H100
Prediction
fofr/flux-wrong:272312c3c8b37dcf4944e1c039723583a108a767083461ff1e112b00d7c5bb34ID2bv80jfqtxrm40chcs09h6g3tmStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a WRNG photo of a cat
- lora_scale
- 1
- num_outputs
- 4
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 1.8
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a WRNG photo of a cat", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 1.8, "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-wrong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-wrong:272312c3c8b37dcf4944e1c039723583a108a767083461ff1e112b00d7c5bb34", { input: { model: "dev", prompt: "a WRNG photo of a cat", lora_scale: 1, num_outputs: 4, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 1.8, 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-wrong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-wrong:272312c3c8b37dcf4944e1c039723583a108a767083461ff1e112b00d7c5bb34", input={ "model": "dev", "prompt": "a WRNG photo of a cat", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 1.8, "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-wrong 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": "272312c3c8b37dcf4944e1c039723583a108a767083461ff1e112b00d7c5bb34", "input": { "model": "dev", "prompt": "a WRNG photo of a cat", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 1.8, "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": "2024-08-18T16:11:07.829833Z", "created_at": "2024-08-18T16:10:19.479000Z", "data_removed": false, "error": null, "id": "2bv80jfqtxrm40chcs09h6g3tm", "input": { "model": "dev", "prompt": "a WRNG photo of a cat", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 1.8, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 12461\nPrompt: a WRNG photo of a cat\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9565201522688\nDownloading weights\n2024-08-18T16:10:29Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/98b765fb53e3118e url=https://replicate.delivery/yhqm/ZSz5ywrZ9yqPCZIeSjEDdikTlG35seXg39HINRe2uLtlBunmA/trained_model.tar\n2024-08-18T16:10:30Z | INFO | [ Complete ] dest=/src/weights-cache/98b765fb53e3118e size=\"172 MB\" total_elapsed=1.542s url=https://replicate.delivery/yhqm/ZSz5ywrZ9yqPCZIeSjEDdikTlG35seXg39HINRe2uLtlBunmA/trained_model.tar\nb''\nDownloaded weights in 1.5688862800598145 seconds\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:01<00:27, 1.00s/it]\n 7%|▋ | 2/28 [00:01<00:22, 1.14it/s]\n 11%|█ | 3/28 [00:02<00:23, 1.07it/s]\n 14%|█▍ | 4/28 [00:03<00:23, 1.03it/s]\n 18%|█▊ | 5/28 [00:04<00:22, 1.02it/s]\n 21%|██▏ | 6/28 [00:05<00:21, 1.01it/s]\n 25%|██▌ | 7/28 [00:06<00:20, 1.00it/s]\n 29%|██▊ | 8/28 [00:07<00:20, 1.00s/it]\n 32%|███▏ | 9/28 [00:08<00:19, 1.00s/it]\n 36%|███▌ | 10/28 [00:09<00:18, 1.00s/it]\n 39%|███▉ | 11/28 [00:10<00:17, 1.00s/it]\n 43%|████▎ | 12/28 [00:11<00:16, 1.00s/it]\n 46%|████▋ | 13/28 [00:12<00:15, 1.01s/it]\n 50%|█████ | 14/28 [00:13<00:14, 1.01s/it]\n 54%|█████▎ | 15/28 [00:14<00:13, 1.01s/it]\n 57%|█████▋ | 16/28 [00:15<00:12, 1.01s/it]\n 61%|██████ | 17/28 [00:16<00:11, 1.01s/it]\n 64%|██████▍ | 18/28 [00:17<00:10, 1.01s/it]\n 68%|██████▊ | 19/28 [00:18<00:09, 1.01s/it]\n 71%|███████▏ | 20/28 [00:19<00:08, 1.01s/it]\n 75%|███████▌ | 21/28 [00:20<00:07, 1.01s/it]\n 79%|███████▊ | 22/28 [00:22<00:06, 1.01s/it]\n 82%|████████▏ | 23/28 [00:23<00:05, 1.01s/it]\n 86%|████████▌ | 24/28 [00:24<00:04, 1.01s/it]\n 89%|████████▉ | 25/28 [00:25<00:03, 1.02s/it]\n 93%|█████████▎| 26/28 [00:26<00:02, 1.02s/it]\n 96%|█████████▋| 27/28 [00:27<00:01, 1.01s/it]\n100%|██████████| 28/28 [00:28<00:00, 1.02s/it]\n100%|██████████| 28/28 [00:28<00:00, 1.00s/it]", "metrics": { "predict_time": 38.86313441, "total_time": 48.350833 }, "output": [ "https://replicate.delivery/yhqm/lIDYyRFWenX9Q63A7bSVXN0Tueh2fBliIbMJUj8gApO245nmA/out-0.webp", "https://replicate.delivery/yhqm/mG47jOXfzIXnF6COEWykqNYtRfWDdtyE3I3cjPWIVSf245nmA/out-1.webp", "https://replicate.delivery/yhqm/KEy7OJenhtQPWqmHuax9NQSafGesHVsANkC0sMlaZ7S245nmA/out-2.webp", "https://replicate.delivery/yhqm/At6waO5I1C6lDBracfCOrFWQME7BkegTmx0lgFyB27qb88TTA/out-3.webp" ], "started_at": "2024-08-18T16:10:28.966698Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/2bv80jfqtxrm40chcs09h6g3tm", "cancel": "https://api.replicate.com/v1/predictions/2bv80jfqtxrm40chcs09h6g3tm/cancel" }, "version": "272312c3c8b37dcf4944e1c039723583a108a767083461ff1e112b00d7c5bb34" }Generated inUsing seed: 12461 Prompt: a WRNG photo of a cat txt2img mode Using dev model Loading LoRA weights Ensuring enough disk space... Free disk space: 9565201522688 Downloading weights 2024-08-18T16:10:29Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/98b765fb53e3118e url=https://replicate.delivery/yhqm/ZSz5ywrZ9yqPCZIeSjEDdikTlG35seXg39HINRe2uLtlBunmA/trained_model.tar 2024-08-18T16:10:30Z | INFO | [ Complete ] dest=/src/weights-cache/98b765fb53e3118e size="172 MB" total_elapsed=1.542s url=https://replicate.delivery/yhqm/ZSz5ywrZ9yqPCZIeSjEDdikTlG35seXg39HINRe2uLtlBunmA/trained_model.tar b'' Downloaded weights in 1.5688862800598145 seconds LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:01<00:27, 1.00s/it] 7%|▋ | 2/28 [00:01<00:22, 1.14it/s] 11%|█ | 3/28 [00:02<00:23, 1.07it/s] 14%|█▍ | 4/28 [00:03<00:23, 1.03it/s] 18%|█▊ | 5/28 [00:04<00:22, 1.02it/s] 21%|██▏ | 6/28 [00:05<00:21, 1.01it/s] 25%|██▌ | 7/28 [00:06<00:20, 1.00it/s] 29%|██▊ | 8/28 [00:07<00:20, 1.00s/it] 32%|███▏ | 9/28 [00:08<00:19, 1.00s/it] 36%|███▌ | 10/28 [00:09<00:18, 1.00s/it] 39%|███▉ | 11/28 [00:10<00:17, 1.00s/it] 43%|████▎ | 12/28 [00:11<00:16, 1.00s/it] 46%|████▋ | 13/28 [00:12<00:15, 1.01s/it] 50%|█████ | 14/28 [00:13<00:14, 1.01s/it] 54%|█████▎ | 15/28 [00:14<00:13, 1.01s/it] 57%|█████▋ | 16/28 [00:15<00:12, 1.01s/it] 61%|██████ | 17/28 [00:16<00:11, 1.01s/it] 64%|██████▍ | 18/28 [00:17<00:10, 1.01s/it] 68%|██████▊ | 19/28 [00:18<00:09, 1.01s/it] 71%|███████▏ | 20/28 [00:19<00:08, 1.01s/it] 75%|███████▌ | 21/28 [00:20<00:07, 1.01s/it] 79%|███████▊ | 22/28 [00:22<00:06, 1.01s/it] 82%|████████▏ | 23/28 [00:23<00:05, 1.01s/it] 86%|████████▌ | 24/28 [00:24<00:04, 1.01s/it] 89%|████████▉ | 25/28 [00:25<00:03, 1.02s/it] 93%|█████████▎| 26/28 [00:26<00:02, 1.02s/it] 96%|█████████▋| 27/28 [00:27<00:01, 1.01s/it] 100%|██████████| 28/28 [00:28<00:00, 1.02s/it] 100%|██████████| 28/28 [00:28<00:00, 1.00s/it]Prediction
fofr/flux-wrong:272312c3c8b37dcf4944e1c039723583a108a767083461ff1e112b00d7c5bb34IDtxvq8nnv45rm00chcm790p2gkrStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a WRNG photo of a disfigured rabbit
- lora_scale
- 1
- num_outputs
- 4
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 1.8
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a WRNG photo of a disfigured rabbit", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 1.8, "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-wrong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-wrong:272312c3c8b37dcf4944e1c039723583a108a767083461ff1e112b00d7c5bb34", { input: { model: "dev", prompt: "a WRNG photo of a disfigured rabbit", lora_scale: 1, num_outputs: 4, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 1.8, 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-wrong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-wrong:272312c3c8b37dcf4944e1c039723583a108a767083461ff1e112b00d7c5bb34", input={ "model": "dev", "prompt": "a WRNG photo of a disfigured rabbit", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 1.8, "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-wrong 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": "272312c3c8b37dcf4944e1c039723583a108a767083461ff1e112b00d7c5bb34", "input": { "model": "dev", "prompt": "a WRNG photo of a disfigured rabbit", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 1.8, "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": "2024-08-18T10:36:20.021458Z", "created_at": "2024-08-18T10:35:49.921000Z", "data_removed": false, "error": null, "id": "txvq8nnv45rm00chcm790p2gkr", "input": { "model": "dev", "prompt": "a WRNG photo of a disfigured rabbit", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 1.8, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 43179\nPrompt: a WRNG photo of a disfigured rabbit\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nweights already loaded!\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:01<00:27, 1.01s/it]\n 7%|▋ | 2/28 [00:01<00:23, 1.13it/s]\n 11%|█ | 3/28 [00:02<00:23, 1.05it/s]\n 14%|█▍ | 4/28 [00:03<00:23, 1.02it/s]\n 18%|█▊ | 5/28 [00:04<00:22, 1.00it/s]\n 21%|██▏ | 6/28 [00:05<00:22, 1.01s/it]\n 25%|██▌ | 7/28 [00:06<00:21, 1.01s/it]\n 29%|██▊ | 8/28 [00:07<00:20, 1.02s/it]\n 32%|███▏ | 9/28 [00:08<00:19, 1.02s/it]\n 36%|███▌ | 10/28 [00:10<00:18, 1.02s/it]\n 39%|███▉ | 11/28 [00:11<00:17, 1.03s/it]\n 43%|████▎ | 12/28 [00:12<00:16, 1.02s/it]\n 46%|████▋ | 13/28 [00:13<00:15, 1.03s/it]\n 50%|█████ | 14/28 [00:14<00:14, 1.02s/it]\n 54%|█████▎ | 15/28 [00:15<00:13, 1.02s/it]\n 57%|█████▋ | 16/28 [00:16<00:12, 1.02s/it]\n 61%|██████ | 17/28 [00:17<00:11, 1.02s/it]\n 64%|██████▍ | 18/28 [00:18<00:10, 1.02s/it]\n 68%|██████▊ | 19/28 [00:19<00:09, 1.02s/it]\n 71%|███████▏ | 20/28 [00:20<00:08, 1.02s/it]\n 75%|███████▌ | 21/28 [00:21<00:07, 1.02s/it]\n 79%|███████▊ | 22/28 [00:22<00:06, 1.02s/it]\n 82%|████████▏ | 23/28 [00:23<00:05, 1.02s/it]\n 86%|████████▌ | 24/28 [00:24<00:04, 1.02s/it]\n 89%|████████▉ | 25/28 [00:25<00:03, 1.02s/it]\n 93%|█████████▎| 26/28 [00:26<00:02, 1.02s/it]\n 96%|█████████▋| 27/28 [00:27<00:01, 1.02s/it]\n100%|██████████| 28/28 [00:28<00:00, 1.02s/it]\n100%|██████████| 28/28 [00:28<00:00, 1.01s/it]", "metrics": { "predict_time": 30.065688607, "total_time": 30.100458 }, "output": [ "https://replicate.delivery/yhqm/j7QCOgTyBU5fcC5UuIXzLBpv6bjO6yeyarqMu13z6vtjC4TTA/out-0.webp", "https://replicate.delivery/yhqm/W5P2y6DcLtLcDxHZKIDBOmMgW8aVyLmE6yMBrYWQAaeRB8pJA/out-1.webp", "https://replicate.delivery/yhqm/Nd6jaK4Na3LSGRdkZzyfycGclfLP5idvQPuyaEFwxanjC4TTA/out-2.webp", "https://replicate.delivery/yhqm/GHq7T9MDuH4AB9HkBZbxiQbVEImYenafqezBIFdeSxAPKgPNB/out-3.webp" ], "started_at": "2024-08-18T10:35:49.955769Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/txvq8nnv45rm00chcm790p2gkr", "cancel": "https://api.replicate.com/v1/predictions/txvq8nnv45rm00chcm790p2gkr/cancel" }, "version": "272312c3c8b37dcf4944e1c039723583a108a767083461ff1e112b00d7c5bb34" }Generated inUsing seed: 43179 Prompt: a WRNG photo of a disfigured rabbit txt2img mode Using dev model Loading LoRA weights weights already loaded! 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:01<00:27, 1.01s/it] 7%|▋ | 2/28 [00:01<00:23, 1.13it/s] 11%|█ | 3/28 [00:02<00:23, 1.05it/s] 14%|█▍ | 4/28 [00:03<00:23, 1.02it/s] 18%|█▊ | 5/28 [00:04<00:22, 1.00it/s] 21%|██▏ | 6/28 [00:05<00:22, 1.01s/it] 25%|██▌ | 7/28 [00:06<00:21, 1.01s/it] 29%|██▊ | 8/28 [00:07<00:20, 1.02s/it] 32%|███▏ | 9/28 [00:08<00:19, 1.02s/it] 36%|███▌ | 10/28 [00:10<00:18, 1.02s/it] 39%|███▉ | 11/28 [00:11<00:17, 1.03s/it] 43%|████▎ | 12/28 [00:12<00:16, 1.02s/it] 46%|████▋ | 13/28 [00:13<00:15, 1.03s/it] 50%|█████ | 14/28 [00:14<00:14, 1.02s/it] 54%|█████▎ | 15/28 [00:15<00:13, 1.02s/it] 57%|█████▋ | 16/28 [00:16<00:12, 1.02s/it] 61%|██████ | 17/28 [00:17<00:11, 1.02s/it] 64%|██████▍ | 18/28 [00:18<00:10, 1.02s/it] 68%|██████▊ | 19/28 [00:19<00:09, 1.02s/it] 71%|███████▏ | 20/28 [00:20<00:08, 1.02s/it] 75%|███████▌ | 21/28 [00:21<00:07, 1.02s/it] 79%|███████▊ | 22/28 [00:22<00:06, 1.02s/it] 82%|████████▏ | 23/28 [00:23<00:05, 1.02s/it] 86%|████████▌ | 24/28 [00:24<00:04, 1.02s/it] 89%|████████▉ | 25/28 [00:25<00:03, 1.02s/it] 93%|█████████▎| 26/28 [00:26<00:02, 1.02s/it] 96%|█████████▋| 27/28 [00:27<00:01, 1.02s/it] 100%|██████████| 28/28 [00:28<00:00, 1.02s/it] 100%|██████████| 28/28 [00:28<00:00, 1.01s/it]Prediction
fofr/flux-wrong:272312c3c8b37dcf4944e1c039723583a108a767083461ff1e112b00d7c5bb34IDwkq572j32nrm20chcmeaw7ye7rStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a WRNG photo of a disfigured rabbit
- lora_scale
- 1
- num_outputs
- 4
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 1.4
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a WRNG photo of a disfigured rabbit", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 1.4, "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-wrong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-wrong:272312c3c8b37dcf4944e1c039723583a108a767083461ff1e112b00d7c5bb34", { input: { model: "dev", prompt: "a WRNG photo of a disfigured rabbit", lora_scale: 1, num_outputs: 4, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 1.4, 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-wrong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-wrong:272312c3c8b37dcf4944e1c039723583a108a767083461ff1e112b00d7c5bb34", input={ "model": "dev", "prompt": "a WRNG photo of a disfigured rabbit", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 1.4, "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-wrong 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": "272312c3c8b37dcf4944e1c039723583a108a767083461ff1e112b00d7c5bb34", "input": { "model": "dev", "prompt": "a WRNG photo of a disfigured rabbit", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 1.4, "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": "2024-08-18T10:51:50.897276Z", "created_at": "2024-08-18T10:50:36.693000Z", "data_removed": false, "error": null, "id": "wkq572j32nrm20chcmeaw7ye7r", "input": { "model": "dev", "prompt": "a WRNG photo of a disfigured rabbit", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 1.4, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 49312\nPrompt: a WRNG photo of a disfigured rabbit\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9636818944000\nDownloading weights\n2024-08-18T10:51:02Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/98b765fb53e3118e url=https://replicate.delivery/yhqm/ZSz5ywrZ9yqPCZIeSjEDdikTlG35seXg39HINRe2uLtlBunmA/trained_model.tar\n2024-08-18T10:51:03Z | INFO | [ Complete ] dest=/src/weights-cache/98b765fb53e3118e size=\"172 MB\" total_elapsed=1.272s url=https://replicate.delivery/yhqm/ZSz5ywrZ9yqPCZIeSjEDdikTlG35seXg39HINRe2uLtlBunmA/trained_model.tar\nb''\nDownloaded weights in 1.3075587749481201 seconds\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:01<00:27, 1.00s/it]\n 7%|▋ | 2/28 [00:01<00:22, 1.14it/s]\n 11%|█ | 3/28 [00:02<00:23, 1.07it/s]\n 14%|█▍ | 4/28 [00:03<00:23, 1.04it/s]\n 18%|█▊ | 5/28 [00:04<00:22, 1.02it/s]\n 21%|██▏ | 6/28 [00:05<00:21, 1.01it/s]\n 25%|██▌ | 7/28 [00:06<00:20, 1.01it/s]\n 29%|██▊ | 8/28 [00:07<00:19, 1.00it/s]\n 32%|███▏ | 9/28 [00:08<00:19, 1.00s/it]\n 36%|███▌ | 10/28 [00:09<00:18, 1.01s/it]\n 39%|███▉ | 11/28 [00:10<00:17, 1.01s/it]\n 43%|████▎ | 12/28 [00:11<00:16, 1.01s/it]\n 46%|████▋ | 13/28 [00:12<00:15, 1.01s/it]\n 50%|█████ | 14/28 [00:13<00:14, 1.01s/it]\n 54%|█████▎ | 15/28 [00:14<00:13, 1.01s/it]\n 57%|█████▋ | 16/28 [00:15<00:12, 1.01s/it]\n 61%|██████ | 17/28 [00:16<00:11, 1.01s/it]\n 64%|██████▍ | 18/28 [00:17<00:10, 1.01s/it]\n 68%|██████▊ | 19/28 [00:18<00:09, 1.01s/it]\n 71%|███████▏ | 20/28 [00:19<00:08, 1.01s/it]\n 75%|███████▌ | 21/28 [00:20<00:07, 1.01s/it]\n 79%|███████▊ | 22/28 [00:21<00:06, 1.01s/it]\n 82%|████████▏ | 23/28 [00:22<00:05, 1.01s/it]\n 86%|████████▌ | 24/28 [00:23<00:04, 1.01s/it]\n 89%|████████▉ | 25/28 [00:24<00:03, 1.01s/it]\n 93%|█████████▎| 26/28 [00:26<00:02, 1.01s/it]\n 96%|█████████▋| 27/28 [00:27<00:01, 1.01s/it]\n100%|██████████| 28/28 [00:28<00:00, 1.01s/it]\n100%|██████████| 28/28 [00:28<00:00, 1.00s/it]", "metrics": { "predict_time": 48.624243688, "total_time": 74.204276 }, "output": [ "https://replicate.delivery/yhqm/sVEE7mhH2IIZOJYsBqvgiRj47oRDUPVNUpL4SeUh2QfGR4TTA/out-0.webp", "https://replicate.delivery/yhqm/fQnQdLwYvjSpAC1MSHtpY2IHTtUb1Mk48h8IF7QEpieGR4TTA/out-1.webp", "https://replicate.delivery/yhqm/U8rtcR0qct7aBt4UYdevBAp8ah7QZPYhh2PeieXeIuQaEhPNB/out-2.webp", "https://replicate.delivery/yhqm/Zo0ifbb2eqsJo0VPSDdOo0fGDtVP5zHkzhnfI6cXtYNYEhPNB/out-3.webp" ], "started_at": "2024-08-18T10:51:02.273032Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wkq572j32nrm20chcmeaw7ye7r", "cancel": "https://api.replicate.com/v1/predictions/wkq572j32nrm20chcmeaw7ye7r/cancel" }, "version": "272312c3c8b37dcf4944e1c039723583a108a767083461ff1e112b00d7c5bb34" }Generated inUsing seed: 49312 Prompt: a WRNG photo of a disfigured rabbit txt2img mode Using dev model Loading LoRA weights Ensuring enough disk space... Free disk space: 9636818944000 Downloading weights 2024-08-18T10:51:02Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/98b765fb53e3118e url=https://replicate.delivery/yhqm/ZSz5ywrZ9yqPCZIeSjEDdikTlG35seXg39HINRe2uLtlBunmA/trained_model.tar 2024-08-18T10:51:03Z | INFO | [ Complete ] dest=/src/weights-cache/98b765fb53e3118e size="172 MB" total_elapsed=1.272s url=https://replicate.delivery/yhqm/ZSz5ywrZ9yqPCZIeSjEDdikTlG35seXg39HINRe2uLtlBunmA/trained_model.tar b'' Downloaded weights in 1.3075587749481201 seconds LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:01<00:27, 1.00s/it] 7%|▋ | 2/28 [00:01<00:22, 1.14it/s] 11%|█ | 3/28 [00:02<00:23, 1.07it/s] 14%|█▍ | 4/28 [00:03<00:23, 1.04it/s] 18%|█▊ | 5/28 [00:04<00:22, 1.02it/s] 21%|██▏ | 6/28 [00:05<00:21, 1.01it/s] 25%|██▌ | 7/28 [00:06<00:20, 1.01it/s] 29%|██▊ | 8/28 [00:07<00:19, 1.00it/s] 32%|███▏ | 9/28 [00:08<00:19, 1.00s/it] 36%|███▌ | 10/28 [00:09<00:18, 1.01s/it] 39%|███▉ | 11/28 [00:10<00:17, 1.01s/it] 43%|████▎ | 12/28 [00:11<00:16, 1.01s/it] 46%|████▋ | 13/28 [00:12<00:15, 1.01s/it] 50%|█████ | 14/28 [00:13<00:14, 1.01s/it] 54%|█████▎ | 15/28 [00:14<00:13, 1.01s/it] 57%|█████▋ | 16/28 [00:15<00:12, 1.01s/it] 61%|██████ | 17/28 [00:16<00:11, 1.01s/it] 64%|██████▍ | 18/28 [00:17<00:10, 1.01s/it] 68%|██████▊ | 19/28 [00:18<00:09, 1.01s/it] 71%|███████▏ | 20/28 [00:19<00:08, 1.01s/it] 75%|███████▌ | 21/28 [00:20<00:07, 1.01s/it] 79%|███████▊ | 22/28 [00:21<00:06, 1.01s/it] 82%|████████▏ | 23/28 [00:22<00:05, 1.01s/it] 86%|████████▌ | 24/28 [00:23<00:04, 1.01s/it] 89%|████████▉ | 25/28 [00:24<00:03, 1.01s/it] 93%|█████████▎| 26/28 [00:26<00:02, 1.01s/it] 96%|█████████▋| 27/28 [00:27<00:01, 1.01s/it] 100%|██████████| 28/28 [00:28<00:00, 1.01s/it] 100%|██████████| 28/28 [00:28<00:00, 1.00s/it]
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