oshtz
/
flux-plastic3d
flux.1 'plastic3d' style lora
- Public
- 768 runs
-
H100
- License
Prediction
oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3IDwf03hsyashrm00chk7a9zz3nygStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- plastic3d style, cyberpunk samurai riding a neon-lit hoverboard through a futuristic Tokyo
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- jpg
- guidance_scale
- 3.5
- output_quality
- 100
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "plastic3d style, cyberpunk samurai riding a neon-lit hoverboard through a futuristic Tokyo", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "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 oshtz/flux-plastic3d using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3", { input: { model: "dev", prompt: "plastic3d style, cyberpunk samurai riding a neon-lit hoverboard through a futuristic Tokyo", lora_scale: 1, num_outputs: 1, aspect_ratio: "16:9", output_format: "jpg", guidance_scale: 3.5, output_quality: 100, extra_lora_scale: 0.8, 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 oshtz/flux-plastic3d using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3", input={ "model": "dev", "prompt": "plastic3d style, cyberpunk samurai riding a neon-lit hoverboard through a futuristic Tokyo", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run oshtz/flux-plastic3d 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": "oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3", "input": { "model": "dev", "prompt": "plastic3d style, cyberpunk samurai riding a neon-lit hoverboard through a futuristic Tokyo", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "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": "2024-08-28T16:32:48.604893Z", "created_at": "2024-08-28T16:32:25.292000Z", "data_removed": false, "error": null, "id": "wf03hsyashrm00chk7a9zz3nyg", "input": { "model": "dev", "prompt": "plastic3d style, cyberpunk samurai riding a neon-lit hoverboard through a futuristic Tokyo", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 56864\nPrompt: plastic3d style, cyberpunk samurai riding a neon-lit hoverboard through a futuristic Tokyo\ntxt2img mode\nUsing dev model\nfree=9211621752832\nDownloading weights\n2024-08-28T16:32:25Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp5v1clzqn/weights url=https://replicate.delivery/yhqm/7uSfpiIuXA1ARKS3abytqHlOPApNcgkNTXpgC3fGaDERijVTA/trained_model.tar\n2024-08-28T16:32:31Z | INFO | [ Complete ] dest=/tmp/tmp5v1clzqn/weights size=\"172 MB\" total_elapsed=5.791s url=https://replicate.delivery/yhqm/7uSfpiIuXA1ARKS3abytqHlOPApNcgkNTXpgC3fGaDERijVTA/trained_model.tar\nDownloaded weights in 5.82s\nLoaded LoRAs in 15.35s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.72it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.29it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.01it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.90it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.83it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.80it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.77it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.76it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.74it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.74it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.74it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.73it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.73it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.73it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.73it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.72it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.72it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.72it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.73it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.72it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.72it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.72it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.73it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.72it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.72it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.72it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.72it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.72it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.75it/s]", "metrics": { "predict_time": 23.304397158, "total_time": 23.312893 }, "output": [ "https://replicate.delivery/yhqm/xPpQU6s8FjZmLhv6pnKZjPn06afXMqJANoGUErese4SgZgumA/out-0.jpg" ], "started_at": "2024-08-28T16:32:25.300495Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wf03hsyashrm00chk7a9zz3nyg", "cancel": "https://api.replicate.com/v1/predictions/wf03hsyashrm00chk7a9zz3nyg/cancel" }, "version": "b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3" }Generated inUsing seed: 56864 Prompt: plastic3d style, cyberpunk samurai riding a neon-lit hoverboard through a futuristic Tokyo txt2img mode Using dev model free=9211621752832 Downloading weights 2024-08-28T16:32:25Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp5v1clzqn/weights url=https://replicate.delivery/yhqm/7uSfpiIuXA1ARKS3abytqHlOPApNcgkNTXpgC3fGaDERijVTA/trained_model.tar 2024-08-28T16:32:31Z | INFO | [ Complete ] dest=/tmp/tmp5v1clzqn/weights size="172 MB" total_elapsed=5.791s url=https://replicate.delivery/yhqm/7uSfpiIuXA1ARKS3abytqHlOPApNcgkNTXpgC3fGaDERijVTA/trained_model.tar Downloaded weights in 5.82s Loaded LoRAs in 15.35s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.72it/s] 7%|▋ | 2/28 [00:00<00:06, 4.29it/s] 11%|█ | 3/28 [00:00<00:06, 4.01it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.90it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.83it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.80it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.77it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.76it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.74it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.74it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.74it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.73it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.73it/s] 50%|█████ | 14/28 [00:03<00:03, 3.73it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.73it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.72it/s] 61%|██████ | 17/28 [00:04<00:02, 3.72it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.72it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.73it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.72it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.72it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.72it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.73it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.72it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.72it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.72it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.72it/s] 100%|██████████| 28/28 [00:07<00:00, 3.72it/s] 100%|██████████| 28/28 [00:07<00:00, 3.75it/s]Prediction
oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3IDqfs2vd4d15rm40chk7bb3s4pc8StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- plastic3d style, steampunk octopus operating a vintage submarine in an underwater Victorian city
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- jpg
- guidance_scale
- 3.5
- output_quality
- 100
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "plastic3d style, steampunk octopus operating a vintage submarine in an underwater Victorian city", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "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 oshtz/flux-plastic3d using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3", { input: { model: "dev", prompt: "plastic3d style, steampunk octopus operating a vintage submarine in an underwater Victorian city", lora_scale: 1, num_outputs: 1, aspect_ratio: "16:9", output_format: "jpg", guidance_scale: 3.5, output_quality: 100, extra_lora_scale: 0.8, 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 oshtz/flux-plastic3d using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3", input={ "model": "dev", "prompt": "plastic3d style, steampunk octopus operating a vintage submarine in an underwater Victorian city", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run oshtz/flux-plastic3d 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": "oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3", "input": { "model": "dev", "prompt": "plastic3d style, steampunk octopus operating a vintage submarine in an underwater Victorian city", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "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": "2024-08-28T16:34:38.931359Z", "created_at": "2024-08-28T16:34:20.553000Z", "data_removed": false, "error": null, "id": "qfs2vd4d15rm40chk7bb3s4pc8", "input": { "model": "dev", "prompt": "plastic3d style, steampunk octopus operating a vintage submarine in an underwater Victorian city", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 37880\nPrompt: plastic3d style, steampunk octopus operating a vintage submarine in an underwater Victorian city\ntxt2img mode\nUsing dev model\nfree=9753289617408\nDownloading weights\n2024-08-28T16:34:20Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpd7lx_6n0/weights url=https://replicate.delivery/yhqm/7uSfpiIuXA1ARKS3abytqHlOPApNcgkNTXpgC3fGaDERijVTA/trained_model.tar\n2024-08-28T16:34:22Z | INFO | [ Complete ] dest=/tmp/tmpd7lx_6n0/weights size=\"172 MB\" total_elapsed=1.517s url=https://replicate.delivery/yhqm/7uSfpiIuXA1ARKS3abytqHlOPApNcgkNTXpgC3fGaDERijVTA/trained_model.tar\nDownloaded weights in 1.55s\nLoaded LoRAs in 10.36s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.72it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.27it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.99it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.88it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.81it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.78it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.76it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.74it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.74it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.73it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.73it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.72it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.72it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.71it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.71it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.71it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.71it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.71it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.71it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.71it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.71it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.72it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.72it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.72it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.72it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.71it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.70it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.70it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.74it/s]", "metrics": { "predict_time": 18.369847948, "total_time": 18.378359 }, "output": [ "https://replicate.delivery/yhqm/W51qeIXhSKWEPqdmqBqaIHC3vodCaagg3PtSh4eLGqDecgumA/out-0.jpg" ], "started_at": "2024-08-28T16:34:20.561511Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/qfs2vd4d15rm40chk7bb3s4pc8", "cancel": "https://api.replicate.com/v1/predictions/qfs2vd4d15rm40chk7bb3s4pc8/cancel" }, "version": "b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3" }Generated inUsing seed: 37880 Prompt: plastic3d style, steampunk octopus operating a vintage submarine in an underwater Victorian city txt2img mode Using dev model free=9753289617408 Downloading weights 2024-08-28T16:34:20Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpd7lx_6n0/weights url=https://replicate.delivery/yhqm/7uSfpiIuXA1ARKS3abytqHlOPApNcgkNTXpgC3fGaDERijVTA/trained_model.tar 2024-08-28T16:34:22Z | INFO | [ Complete ] dest=/tmp/tmpd7lx_6n0/weights size="172 MB" total_elapsed=1.517s url=https://replicate.delivery/yhqm/7uSfpiIuXA1ARKS3abytqHlOPApNcgkNTXpgC3fGaDERijVTA/trained_model.tar Downloaded weights in 1.55s Loaded LoRAs in 10.36s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.72it/s] 7%|▋ | 2/28 [00:00<00:06, 4.27it/s] 11%|█ | 3/28 [00:00<00:06, 3.99it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.88it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.81it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.78it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.76it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.74it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.74it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.73it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.73it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.72it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.72it/s] 50%|█████ | 14/28 [00:03<00:03, 3.71it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.71it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.71it/s] 61%|██████ | 17/28 [00:04<00:02, 3.71it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.71it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.71it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.71it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.71it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.72it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.72it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.72it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.72it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.71it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.70it/s] 100%|██████████| 28/28 [00:07<00:00, 3.70it/s] 100%|██████████| 28/28 [00:07<00:00, 3.74it/s]Prediction
oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3ID21gbj6g6exrm20chk7bvt10crwStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- plastic3d style, surreal treehouse library floating in a cosmic nebula with books orbiting like planets
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- jpg
- guidance_scale
- 3.5
- output_quality
- 100
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "plastic3d style, surreal treehouse library floating in a cosmic nebula with books orbiting like planets", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "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 oshtz/flux-plastic3d using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3", { input: { model: "dev", prompt: "plastic3d style, surreal treehouse library floating in a cosmic nebula with books orbiting like planets", lora_scale: 1, num_outputs: 1, aspect_ratio: "16:9", output_format: "jpg", guidance_scale: 3.5, output_quality: 100, extra_lora_scale: 0.8, 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 oshtz/flux-plastic3d using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3", input={ "model": "dev", "prompt": "plastic3d style, surreal treehouse library floating in a cosmic nebula with books orbiting like planets", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run oshtz/flux-plastic3d 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": "oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3", "input": { "model": "dev", "prompt": "plastic3d style, surreal treehouse library floating in a cosmic nebula with books orbiting like planets", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "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": "2024-08-28T16:35:09.111792Z", "created_at": "2024-08-28T16:34:51.639000Z", "data_removed": false, "error": null, "id": "21gbj6g6exrm20chk7bvt10crw", "input": { "model": "dev", "prompt": "plastic3d style, surreal treehouse library floating in a cosmic nebula with books orbiting like planets", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 20575\nPrompt: plastic3d style, surreal treehouse library floating in a cosmic nebula with books orbiting like planets\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 9.43s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.68it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.24it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.97it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.85it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.79it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.75it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.73it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.72it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.71it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.70it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.70it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.70it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.69it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.69it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.69it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.69it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.69it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.69it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.69it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.69it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.69it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.69it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.69it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.69it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.69it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.69it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.69it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.72it/s]", "metrics": { "predict_time": 17.463724331, "total_time": 17.472792 }, "output": [ "https://replicate.delivery/yhqm/ASd9s4ei0bSmWafBTCd39psI3fWyDC5p0PfePa4TcTcg3B6aC/out-0.jpg" ], "started_at": "2024-08-28T16:34:51.648068Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/21gbj6g6exrm20chk7bvt10crw", "cancel": "https://api.replicate.com/v1/predictions/21gbj6g6exrm20chk7bvt10crw/cancel" }, "version": "b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3" }Generated inUsing seed: 20575 Prompt: plastic3d style, surreal treehouse library floating in a cosmic nebula with books orbiting like planets txt2img mode Using dev model Loaded LoRAs in 9.43s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.68it/s] 7%|▋ | 2/28 [00:00<00:06, 4.24it/s] 11%|█ | 3/28 [00:00<00:06, 3.97it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.85it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.79it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.75it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.73it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.72it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.71it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.70it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.70it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.70it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.69it/s] 50%|█████ | 14/28 [00:03<00:03, 3.69it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.69it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.69it/s] 61%|██████ | 17/28 [00:04<00:02, 3.69it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.69it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.69it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.69it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.69it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.69it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.69it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.69it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.69it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.69it/s] 100%|██████████| 28/28 [00:07<00:00, 3.69it/s] 100%|██████████| 28/28 [00:07<00:00, 3.72it/s]Prediction
oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3IDz9bmmymat9rm60chk7btqaayygStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- plastic3d style, robotic chef preparing a gourmet meal in a kitchen made entirely of candy
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- jpg
- guidance_scale
- 3.5
- output_quality
- 100
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "plastic3d style, robotic chef preparing a gourmet meal in a kitchen made entirely of candy", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "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 oshtz/flux-plastic3d using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3", { input: { model: "dev", prompt: "plastic3d style, robotic chef preparing a gourmet meal in a kitchen made entirely of candy", lora_scale: 1, num_outputs: 1, aspect_ratio: "16:9", output_format: "jpg", guidance_scale: 3.5, output_quality: 100, extra_lora_scale: 0.8, 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 oshtz/flux-plastic3d using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3", input={ "model": "dev", "prompt": "plastic3d style, robotic chef preparing a gourmet meal in a kitchen made entirely of candy", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run oshtz/flux-plastic3d 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": "oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3", "input": { "model": "dev", "prompt": "plastic3d style, robotic chef preparing a gourmet meal in a kitchen made entirely of candy", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "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": "2024-08-28T16:35:41.124535Z", "created_at": "2024-08-28T16:35:25.522000Z", "data_removed": false, "error": null, "id": "z9bmmymat9rm60chk7btqaayyg", "input": { "model": "dev", "prompt": "plastic3d style, robotic chef preparing a gourmet meal in a kitchen made entirely of candy", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 8209\nPrompt: plastic3d style, robotic chef preparing a gourmet meal in a kitchen made entirely of candy\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 7.63s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.72it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.28it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.01it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.89it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.82it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.79it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.76it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.75it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.74it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.73it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.73it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.72it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.72it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.72it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.72it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.72it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.72it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.72it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.72it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.72it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.71it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.71it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.72it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.72it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.72it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.72it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.72it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.72it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.75it/s]", "metrics": { "predict_time": 15.594317956, "total_time": 15.602535 }, "output": [ "https://replicate.delivery/yhqm/xYL3UpClbyJeTqbkWa7Bl79eNWLYQowf0sq0EwQkEfxz9AdNB/out-0.jpg" ], "started_at": "2024-08-28T16:35:25.530217Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/z9bmmymat9rm60chk7btqaayyg", "cancel": "https://api.replicate.com/v1/predictions/z9bmmymat9rm60chk7btqaayyg/cancel" }, "version": "b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3" }Generated inUsing seed: 8209 Prompt: plastic3d style, robotic chef preparing a gourmet meal in a kitchen made entirely of candy txt2img mode Using dev model Loaded LoRAs in 7.63s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.72it/s] 7%|▋ | 2/28 [00:00<00:06, 4.28it/s] 11%|█ | 3/28 [00:00<00:06, 4.01it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.89it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.82it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.79it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.76it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.75it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.74it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.73it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.73it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.72it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.72it/s] 50%|█████ | 14/28 [00:03<00:03, 3.72it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.72it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.72it/s] 61%|██████ | 17/28 [00:04<00:02, 3.72it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.72it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.72it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.72it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.71it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.71it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.72it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.72it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.72it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.72it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.72it/s] 100%|██████████| 28/28 [00:07<00:00, 3.72it/s] 100%|██████████| 28/28 [00:07<00:00, 3.75it/s]Prediction
oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3ID8etbvaacx9rm20chk7c80004drStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- plastic3d style, post-apocalyptic greenhouse thriving on the moon's surface, tended by alien botanists
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- jpg
- guidance_scale
- 3.5
- output_quality
- 100
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "plastic3d style, post-apocalyptic greenhouse thriving on the moon's surface, tended by alien botanists", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "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 oshtz/flux-plastic3d using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3", { input: { model: "dev", prompt: "plastic3d style, post-apocalyptic greenhouse thriving on the moon's surface, tended by alien botanists", lora_scale: 1, num_outputs: 1, aspect_ratio: "16:9", output_format: "jpg", guidance_scale: 3.5, output_quality: 100, extra_lora_scale: 0.8, 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 oshtz/flux-plastic3d using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3", input={ "model": "dev", "prompt": "plastic3d style, post-apocalyptic greenhouse thriving on the moon's surface, tended by alien botanists", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run oshtz/flux-plastic3d 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": "oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3", "input": { "model": "dev", "prompt": "plastic3d style, post-apocalyptic greenhouse thriving on the moon\'s surface, tended by alien botanists", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "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": "2024-08-28T16:36:38.903104Z", "created_at": "2024-08-28T16:36:15.210000Z", "data_removed": false, "error": null, "id": "8etbvaacx9rm20chk7c80004dr", "input": { "model": "dev", "prompt": "plastic3d style, post-apocalyptic greenhouse thriving on the moon's surface, tended by alien botanists", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 60184\nPrompt: plastic3d style, post-apocalyptic greenhouse thriving on the moon's surface, tended by alien botanists\ntxt2img mode\nUsing dev model\nfree=9434060128256\nDownloading weights\n2024-08-28T16:36:20Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpamryb_3k/weights url=https://replicate.delivery/yhqm/7uSfpiIuXA1ARKS3abytqHlOPApNcgkNTXpgC3fGaDERijVTA/trained_model.tar\n2024-08-28T16:36:21Z | INFO | [ Complete ] dest=/tmp/tmpamryb_3k/weights size=\"172 MB\" total_elapsed=1.471s url=https://replicate.delivery/yhqm/7uSfpiIuXA1ARKS3abytqHlOPApNcgkNTXpgC3fGaDERijVTA/trained_model.tar\nDownloaded weights in 1.50s\nLoaded LoRAs in 10.45s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.68it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.23it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.97it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.87it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.80it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.76it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.74it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.73it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.71it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.70it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.70it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.70it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.70it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.69it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.69it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.70it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.69it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.69it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.69it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.70it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.69it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.69it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.69it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.69it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.69it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.69it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.70it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.72it/s]", "metrics": { "predict_time": 18.475668944, "total_time": 23.693104 }, "output": [ "https://replicate.delivery/yhqm/GPWz0pFfVny1Ay3jjdNWsetc3TN2izg14OkmK5IbAYgWQQXTA/out-0.jpg" ], "started_at": "2024-08-28T16:36:20.427435Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/8etbvaacx9rm20chk7c80004dr", "cancel": "https://api.replicate.com/v1/predictions/8etbvaacx9rm20chk7c80004dr/cancel" }, "version": "b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3" }Generated inUsing seed: 60184 Prompt: plastic3d style, post-apocalyptic greenhouse thriving on the moon's surface, tended by alien botanists txt2img mode Using dev model free=9434060128256 Downloading weights 2024-08-28T16:36:20Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpamryb_3k/weights url=https://replicate.delivery/yhqm/7uSfpiIuXA1ARKS3abytqHlOPApNcgkNTXpgC3fGaDERijVTA/trained_model.tar 2024-08-28T16:36:21Z | INFO | [ Complete ] dest=/tmp/tmpamryb_3k/weights size="172 MB" total_elapsed=1.471s url=https://replicate.delivery/yhqm/7uSfpiIuXA1ARKS3abytqHlOPApNcgkNTXpgC3fGaDERijVTA/trained_model.tar Downloaded weights in 1.50s Loaded LoRAs in 10.45s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.68it/s] 7%|▋ | 2/28 [00:00<00:06, 4.23it/s] 11%|█ | 3/28 [00:00<00:06, 3.97it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.87it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.80it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.76it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.74it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.73it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.71it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.70it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.70it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.70it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.70it/s] 50%|█████ | 14/28 [00:03<00:03, 3.69it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.69it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.70it/s] 61%|██████ | 17/28 [00:04<00:02, 3.69it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.69it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.69it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.70it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.69it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.69it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.69it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.69it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.69it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.69it/s] 100%|██████████| 28/28 [00:07<00:00, 3.70it/s] 100%|██████████| 28/28 [00:07<00:00, 3.72it/s]Prediction
oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3ID5h3zp8f17drm40chk7cajrrem8StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- plastic3d style, time-traveling jazz band performing for dinosaurs in a prehistoric jungle
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- jpg
- guidance_scale
- 3.5
- output_quality
- 100
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "plastic3d style, time-traveling jazz band performing for dinosaurs in a prehistoric jungle", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "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 oshtz/flux-plastic3d using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3", { input: { model: "dev", prompt: "plastic3d style, time-traveling jazz band performing for dinosaurs in a prehistoric jungle", lora_scale: 1, num_outputs: 1, aspect_ratio: "16:9", output_format: "jpg", guidance_scale: 3.5, output_quality: 100, extra_lora_scale: 0.8, 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 oshtz/flux-plastic3d using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3", input={ "model": "dev", "prompt": "plastic3d style, time-traveling jazz band performing for dinosaurs in a prehistoric jungle", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run oshtz/flux-plastic3d 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": "oshtz/flux-plastic3d:b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3", "input": { "model": "dev", "prompt": "plastic3d style, time-traveling jazz band performing for dinosaurs in a prehistoric jungle", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "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": "2024-08-28T16:37:08.821108Z", "created_at": "2024-08-28T16:36:53.179000Z", "data_removed": false, "error": null, "id": "5h3zp8f17drm40chk7cajrrem8", "input": { "model": "dev", "prompt": "plastic3d style, time-traveling jazz band performing for dinosaurs in a prehistoric jungle", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 19201\nPrompt: plastic3d style, time-traveling jazz band performing for dinosaurs in a prehistoric jungle\ntxt2img mode\nUsing dev model\nLoaded LoRAs in 7.66s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.72it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.29it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.01it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.89it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.83it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.79it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.77it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.75it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.74it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.74it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.74it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.73it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.73it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.73it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.73it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.72it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.72it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.72it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.72it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.71it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.71it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.71it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.71it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.71it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.71it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.71it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.71it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.71it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.75it/s]", "metrics": { "predict_time": 15.633560912, "total_time": 15.642108 }, "output": [ "https://replicate.delivery/yhqm/6IoOWsDxDFZkD9KzjoWuNBnhxAAuhSKrq7a2xesMezs0QQXTA/out-0.jpg" ], "started_at": "2024-08-28T16:36:53.187547Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5h3zp8f17drm40chk7cajrrem8", "cancel": "https://api.replicate.com/v1/predictions/5h3zp8f17drm40chk7cajrrem8/cancel" }, "version": "b8048bcf367adb3ecd9ac8721bdd2851849b4f9dcc134170bee99102042cabb3" }Generated inUsing seed: 19201 Prompt: plastic3d style, time-traveling jazz band performing for dinosaurs in a prehistoric jungle txt2img mode Using dev model Loaded LoRAs in 7.66s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.72it/s] 7%|▋ | 2/28 [00:00<00:06, 4.29it/s] 11%|█ | 3/28 [00:00<00:06, 4.01it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.89it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.83it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.79it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.77it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.75it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.74it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.74it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.74it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.73it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.73it/s] 50%|█████ | 14/28 [00:03<00:03, 3.73it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.73it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.72it/s] 61%|██████ | 17/28 [00:04<00:02, 3.72it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.72it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.72it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.71it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.71it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.71it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.71it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.71it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.71it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.71it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.71it/s] 100%|██████████| 28/28 [00:07<00:00, 3.71it/s] 100%|██████████| 28/28 [00:07<00:00, 3.75it/s]
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