fofr
/
flux-cross-section
Flux lora, use "XSEC cross section" to trigger image generation
- Public
- 798 runs
-
H100
- Paper
Prediction
fofr/flux-cross-section:a9e1591dIDw794tf3c59rm60chc8pa799xerStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a detailed XSEC cross section illustration of a house
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 4:5
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a detailed XSEC cross section illustration of a house", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "4:5", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment 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-cross-section using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-cross-section:a9e1591da5dedf8d19838f31c4620ef622a7f876dfbe95e6f2f80701e4092b4c", { input: { model: "dev", prompt: "a detailed XSEC cross section illustration of a house", lora_scale: 1, num_outputs: 1, aspect_ratio: "4:5", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 28 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run fofr/flux-cross-section using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-cross-section:a9e1591da5dedf8d19838f31c4620ef622a7f876dfbe95e6f2f80701e4092b4c", input={ "model": "dev", "prompt": "a detailed XSEC cross section illustration of a house", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "4:5", "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.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run fofr/flux-cross-section 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": "a9e1591da5dedf8d19838f31c4620ef622a7f876dfbe95e6f2f80701e4092b4c", "input": { "model": "dev", "prompt": "a detailed XSEC cross section illustration of a house", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "4:5", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-17T21:09:31.773503Z", "created_at": "2024-08-17T21:09:24.138000Z", "data_removed": false, "error": null, "id": "w794tf3c59rm60chc8pa799xer", "input": { "model": "dev", "prompt": "a detailed XSEC cross section illustration of a house", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "4:5", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 51387\nPrompt: a detailed XSEC cross section illustration of a house\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nweights already loaded!\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:06, 3.94it/s]\n 7%|▋ | 2/28 [00:00<00:05, 4.53it/s]\n 11%|█ | 3/28 [00:00<00:05, 4.23it/s]\n 14%|█▍ | 4/28 [00:00<00:05, 4.10it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 4.03it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 4.00it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.98it/s]\n 29%|██▊ | 8/28 [00:01<00:05, 3.96it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 3.94it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.94it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.93it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.93it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.93it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.92it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.92it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.92it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.92it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.92it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 3.92it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.92it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.92it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.92it/s]\n 82%|████████▏ | 23/28 [00:05<00:01, 3.92it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.91it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.92it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.91it/s]\n 96%|█████████▋| 27/28 [00:06<00:00, 3.91it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.91it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.95it/s]", "metrics": { "predict_time": 7.600741296, "total_time": 7.635503 }, "output": [ "https://replicate.delivery/yhqm/IKqoOmLsH8oSJ5nkSMIfWboSE8n4BgREg5ApPProyH5FH2pJA/out-0.webp" ], "started_at": "2024-08-17T21:09:24.172761Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/w794tf3c59rm60chc8pa799xer", "cancel": "https://api.replicate.com/v1/predictions/w794tf3c59rm60chc8pa799xer/cancel" }, "version": "931760c5a46ba302fd64b68cd09c2eb1a2c98d929c36596ff45ee17a306b27f4" }
Generated inUsing seed: 51387 Prompt: a detailed XSEC cross section illustration of a house txt2img mode Using dev model Loading LoRA weights weights already loaded! 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:06, 3.94it/s] 7%|▋ | 2/28 [00:00<00:05, 4.53it/s] 11%|█ | 3/28 [00:00<00:05, 4.23it/s] 14%|█▍ | 4/28 [00:00<00:05, 4.10it/s] 18%|█▊ | 5/28 [00:01<00:05, 4.03it/s] 21%|██▏ | 6/28 [00:01<00:05, 4.00it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.98it/s] 29%|██▊ | 8/28 [00:01<00:05, 3.96it/s] 32%|███▏ | 9/28 [00:02<00:04, 3.94it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.94it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.93it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.93it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.93it/s] 50%|█████ | 14/28 [00:03<00:03, 3.92it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.92it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.92it/s] 61%|██████ | 17/28 [00:04<00:02, 3.92it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.92it/s] 68%|██████▊ | 19/28 [00:04<00:02, 3.92it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.92it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.92it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.92it/s] 82%|████████▏ | 23/28 [00:05<00:01, 3.92it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.91it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.92it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.91it/s] 96%|█████████▋| 27/28 [00:06<00:00, 3.91it/s] 100%|██████████| 28/28 [00:07<00:00, 3.91it/s] 100%|██████████| 28/28 [00:07<00:00, 3.95it/s]
Prediction
fofr/flux-cross-section:a9e1591dIDk0rkmf10x1rm20chc8ps7yhm50StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a detailed XSEC cross section illustration of an iphone
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 4:5
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a detailed XSEC cross section illustration of an iphone", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "4:5", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment 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-cross-section using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-cross-section:a9e1591da5dedf8d19838f31c4620ef622a7f876dfbe95e6f2f80701e4092b4c", { input: { model: "dev", prompt: "a detailed XSEC cross section illustration of an iphone", lora_scale: 1, num_outputs: 1, aspect_ratio: "4:5", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 28 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run fofr/flux-cross-section using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-cross-section:a9e1591da5dedf8d19838f31c4620ef622a7f876dfbe95e6f2f80701e4092b4c", input={ "model": "dev", "prompt": "a detailed XSEC cross section illustration of an iphone", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "4:5", "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.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run fofr/flux-cross-section 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": "a9e1591da5dedf8d19838f31c4620ef622a7f876dfbe95e6f2f80701e4092b4c", "input": { "model": "dev", "prompt": "a detailed XSEC cross section illustration of an iphone", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "4:5", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-17T21:10:56.886984Z", "created_at": "2024-08-17T21:10:10.408000Z", "data_removed": false, "error": null, "id": "k0rkmf10x1rm20chc8ps7yhm50", "input": { "model": "dev", "prompt": "a detailed XSEC cross section illustration of an iphone", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "4:5", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 52864\nPrompt: a detailed XSEC cross section illustration of an iphone\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9545110777856\nDownloading weights\n2024-08-17T21:10:37Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/ab887fdb500b3d25 url=https://replicate.delivery/yhqm/qZHChubCtjbsD9Qd6nGwX9lfYub5neNcffy3P0jCywkHowONB/trained_model.tar\n2024-08-17T21:10:39Z | INFO | [ Complete ] dest=/src/weights-cache/ab887fdb500b3d25 size=\"172 MB\" total_elapsed=1.438s url=https://replicate.delivery/yhqm/qZHChubCtjbsD9Qd6nGwX9lfYub5neNcffy3P0jCywkHowONB/trained_model.tar\nb''\nDownloaded weights in 1.4708430767059326 seconds\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:08, 3.08it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.94it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.88it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.87it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 3.86it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.85it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.84it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.84it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 3.84it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.83it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.83it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.83it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.84it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.83it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.83it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.83it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.83it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.82it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 3.82it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.83it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.83it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.83it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.82it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.83it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.83it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.82it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.82it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.83it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.82it/s]", "metrics": { "predict_time": 19.260566795, "total_time": 46.478984 }, "output": [ "https://replicate.delivery/yhqm/Lsz7CiG27T7iCV0tEP4c0GBJziMNQ63Euvdzy8gW9AM4D70E/out-0.webp" ], "started_at": "2024-08-17T21:10:37.626417Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/k0rkmf10x1rm20chc8ps7yhm50", "cancel": "https://api.replicate.com/v1/predictions/k0rkmf10x1rm20chc8ps7yhm50/cancel" }, "version": "931760c5a46ba302fd64b68cd09c2eb1a2c98d929c36596ff45ee17a306b27f4" }
Generated inUsing seed: 52864 Prompt: a detailed XSEC cross section illustration of an iphone txt2img mode Using dev model Loading LoRA weights Ensuring enough disk space... Free disk space: 9545110777856 Downloading weights 2024-08-17T21:10:37Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/ab887fdb500b3d25 url=https://replicate.delivery/yhqm/qZHChubCtjbsD9Qd6nGwX9lfYub5neNcffy3P0jCywkHowONB/trained_model.tar 2024-08-17T21:10:39Z | INFO | [ Complete ] dest=/src/weights-cache/ab887fdb500b3d25 size="172 MB" total_elapsed=1.438s url=https://replicate.delivery/yhqm/qZHChubCtjbsD9Qd6nGwX9lfYub5neNcffy3P0jCywkHowONB/trained_model.tar b'' Downloaded weights in 1.4708430767059326 seconds LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:08, 3.08it/s] 7%|▋ | 2/28 [00:00<00:06, 3.94it/s] 11%|█ | 3/28 [00:00<00:06, 3.88it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.87it/s] 18%|█▊ | 5/28 [00:01<00:05, 3.86it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.85it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.84it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.84it/s] 32%|███▏ | 9/28 [00:02<00:04, 3.84it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.83it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.83it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.83it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.84it/s] 50%|█████ | 14/28 [00:03<00:03, 3.83it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.83it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.83it/s] 61%|██████ | 17/28 [00:04<00:02, 3.83it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.82it/s] 68%|██████▊ | 19/28 [00:04<00:02, 3.82it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.83it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.83it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.83it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.82it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.83it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.83it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.82it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.82it/s] 100%|██████████| 28/28 [00:07<00:00, 3.83it/s] 100%|██████████| 28/28 [00:07<00:00, 3.82it/s]
Prediction
fofr/flux-cross-section:a9e1591dIDwkn595m105rm60chc8qaawkh8gStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a detailed XSEC cross section illustration of an iphone
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 4:5
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a detailed XSEC cross section illustration of an iphone", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "4:5", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }
npm install replicate
Set theREPLICATE_API_TOKEN
environment 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-cross-section using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-cross-section:a9e1591da5dedf8d19838f31c4620ef622a7f876dfbe95e6f2f80701e4092b4c", { input: { model: "dev", prompt: "a detailed XSEC cross section illustration of an iphone", lora_scale: 1, num_outputs: 1, aspect_ratio: "4:5", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 28 } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Import the clientimport replicate
Run fofr/flux-cross-section using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-cross-section:a9e1591da5dedf8d19838f31c4620ef622a7f876dfbe95e6f2f80701e4092b4c", input={ "model": "dev", "prompt": "a detailed XSEC cross section illustration of an iphone", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "4:5", "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.
Set theREPLICATE_API_TOKEN
environment variableexport REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run fofr/flux-cross-section 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": "a9e1591da5dedf8d19838f31c4620ef622a7f876dfbe95e6f2f80701e4092b4c", "input": { "model": "dev", "prompt": "a detailed XSEC cross section illustration of an iphone", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "4:5", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-17T21:12:26.849399Z", "created_at": "2024-08-17T21:11:40.545000Z", "data_removed": false, "error": null, "id": "wkn595m105rm60chc8qaawkh8g", "input": { "model": "dev", "prompt": "a detailed XSEC cross section illustration of an iphone", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "4:5", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 1938\nPrompt: a detailed XSEC cross section illustration of an iphone\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9397331750912\nDownloading weights\n2024-08-17T21:12:08Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/ab887fdb500b3d25 url=https://replicate.delivery/yhqm/qZHChubCtjbsD9Qd6nGwX9lfYub5neNcffy3P0jCywkHowONB/trained_model.tar\n2024-08-17T21:12:10Z | INFO | [ Complete ] dest=/src/weights-cache/ab887fdb500b3d25 size=\"172 MB\" total_elapsed=1.198s url=https://replicate.delivery/yhqm/qZHChubCtjbsD9Qd6nGwX9lfYub5neNcffy3P0jCywkHowONB/trained_model.tar\nb''\nDownloaded weights in 1.2252657413482666 seconds\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:06, 3.88it/s]\n 7%|▋ | 2/28 [00:00<00:05, 4.47it/s]\n 11%|█ | 3/28 [00:00<00:05, 4.18it/s]\n 14%|█▍ | 4/28 [00:00<00:05, 4.06it/s]\n 18%|█▊ | 5/28 [00:01<00:05, 4.00it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.96it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.94it/s]\n 29%|██▊ | 8/28 [00:01<00:05, 3.93it/s]\n 32%|███▏ | 9/28 [00:02<00:04, 3.91it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.90it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.90it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.90it/s]\n 46%|████▋ | 13/28 [00:03<00:03, 3.89it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.89it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.89it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.89it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.89it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.88it/s]\n 68%|██████▊ | 19/28 [00:04<00:02, 3.88it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.88it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.88it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.88it/s]\n 82%|████████▏ | 23/28 [00:05<00:01, 3.88it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.88it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.88it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.88it/s]\n 96%|█████████▋| 27/28 [00:06<00:00, 3.88it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.88it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.91it/s]", "metrics": { "predict_time": 18.097743599, "total_time": 46.304399 }, "output": [ "https://replicate.delivery/yhqm/vx2La4e4Nj0cJK4jSgmnGuoyBQkccVZ67AhpnZR1VjcdI2pJA/out-0.webp" ], "started_at": "2024-08-17T21:12:08.751656Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wkn595m105rm60chc8qaawkh8g", "cancel": "https://api.replicate.com/v1/predictions/wkn595m105rm60chc8qaawkh8g/cancel" }, "version": "931760c5a46ba302fd64b68cd09c2eb1a2c98d929c36596ff45ee17a306b27f4" }
Generated inUsing seed: 1938 Prompt: a detailed XSEC cross section illustration of an iphone txt2img mode Using dev model Loading LoRA weights Ensuring enough disk space... Free disk space: 9397331750912 Downloading weights 2024-08-17T21:12:08Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/ab887fdb500b3d25 url=https://replicate.delivery/yhqm/qZHChubCtjbsD9Qd6nGwX9lfYub5neNcffy3P0jCywkHowONB/trained_model.tar 2024-08-17T21:12:10Z | INFO | [ Complete ] dest=/src/weights-cache/ab887fdb500b3d25 size="172 MB" total_elapsed=1.198s url=https://replicate.delivery/yhqm/qZHChubCtjbsD9Qd6nGwX9lfYub5neNcffy3P0jCywkHowONB/trained_model.tar b'' Downloaded weights in 1.2252657413482666 seconds LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:06, 3.88it/s] 7%|▋ | 2/28 [00:00<00:05, 4.47it/s] 11%|█ | 3/28 [00:00<00:05, 4.18it/s] 14%|█▍ | 4/28 [00:00<00:05, 4.06it/s] 18%|█▊ | 5/28 [00:01<00:05, 4.00it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.96it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.94it/s] 29%|██▊ | 8/28 [00:01<00:05, 3.93it/s] 32%|███▏ | 9/28 [00:02<00:04, 3.91it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.90it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.90it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.90it/s] 46%|████▋ | 13/28 [00:03<00:03, 3.89it/s] 50%|█████ | 14/28 [00:03<00:03, 3.89it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.89it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.89it/s] 61%|██████ | 17/28 [00:04<00:02, 3.89it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.88it/s] 68%|██████▊ | 19/28 [00:04<00:02, 3.88it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.88it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.88it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.88it/s] 82%|████████▏ | 23/28 [00:05<00:01, 3.88it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.88it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.88it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.88it/s] 96%|█████████▋| 27/28 [00:06<00:00, 3.88it/s] 100%|██████████| 28/28 [00:07<00:00, 3.88it/s] 100%|██████████| 28/28 [00:07<00:00, 3.91it/s]
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