jakedahn
/
flux-soviet-controlrooms
Flux.1 fine-tune on soviet-era controlrooms
- Public
- 133 runs
-
H100
Prediction
jakedahn/flux-soviet-controlrooms:10371959bab24ed449af0d5a03f36a4090fdd8468609ca1825824eb040988418Input
- model
- dev
- prompt
- a pink hello kitty SVCTR controlroom
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a pink hello kitty SVCTR controlroom", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "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 jakedahn/flux-soviet-controlrooms using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jakedahn/flux-soviet-controlrooms:10371959bab24ed449af0d5a03f36a4090fdd8468609ca1825824eb040988418", { input: { model: "dev", prompt: "a pink hello kitty SVCTR controlroom", lora_scale: 1, num_outputs: 1, aspect_ratio: "16:9", output_format: "webp", guidance_scale: 3.5, output_quality: 80, 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 jakedahn/flux-soviet-controlrooms using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jakedahn/flux-soviet-controlrooms:10371959bab24ed449af0d5a03f36a4090fdd8468609ca1825824eb040988418", input={ "model": "dev", "prompt": "a pink hello kitty SVCTR controlroom", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "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 jakedahn/flux-soviet-controlrooms 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": "10371959bab24ed449af0d5a03f36a4090fdd8468609ca1825824eb040988418", "input": { "model": "dev", "prompt": "a pink hello kitty SVCTR controlroom", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "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": "2024-08-25T20:23:31.131504Z", "created_at": "2024-08-25T20:22:59.861000Z", "data_removed": false, "error": null, "id": "2edv3veg2nrm20chhcta9cafmw", "input": { "model": "dev", "prompt": "a pink hello kitty SVCTR controlroom", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 21029\nPrompt: a pink hello kitty SVCTR controlroom\ntxt2img mode\nUsing dev model\nfree=9858535002112\nDownloading weights\n2024-08-25T20:23:10Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp0dqq0e9y/weights url=https://replicate.delivery/yhqm/3S2qu3TtEqrNEllNalsrYkQvlUH3GtceIJLpO4aIBYuTGDrJA/trained_model.tar\n2024-08-25T20:23:13Z | INFO | [ Complete ] dest=/tmp/tmp0dqq0e9y/weights size=\"172 MB\" total_elapsed=3.528s url=https://replicate.delivery/yhqm/3S2qu3TtEqrNEllNalsrYkQvlUH3GtceIJLpO4aIBYuTGDrJA/trained_model.tar\nDownloaded weights in 3.56s\nLoaded LoRAs in 12.83s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.71it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.27it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.98it/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, 3.77it/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, 3.72it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.71it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.71it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.71it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.70it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.70it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.70it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.70it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.70it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.70it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.70it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.70it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.70it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.70it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.70it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.70it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.70it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.70it/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, 3.73it/s]", "metrics": { "predict_time": 20.913431923, "total_time": 31.270504 }, "output": [ "https://replicate.delivery/yhqm/DQRY169u1P7qNZNBrFFHKlDsi4A408X5bdBwAho2jVlwEl1E/out-0.webp" ], "started_at": "2024-08-25T20:23:10.218072Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/2edv3veg2nrm20chhcta9cafmw", "cancel": "https://api.replicate.com/v1/predictions/2edv3veg2nrm20chhcta9cafmw/cancel" }, "version": "10371959bab24ed449af0d5a03f36a4090fdd8468609ca1825824eb040988418" }Generated inUsing seed: 21029 Prompt: a pink hello kitty SVCTR controlroom txt2img mode Using dev model free=9858535002112 Downloading weights 2024-08-25T20:23:10Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp0dqq0e9y/weights url=https://replicate.delivery/yhqm/3S2qu3TtEqrNEllNalsrYkQvlUH3GtceIJLpO4aIBYuTGDrJA/trained_model.tar 2024-08-25T20:23:13Z | INFO | [ Complete ] dest=/tmp/tmp0dqq0e9y/weights size="172 MB" total_elapsed=3.528s url=https://replicate.delivery/yhqm/3S2qu3TtEqrNEllNalsrYkQvlUH3GtceIJLpO4aIBYuTGDrJA/trained_model.tar Downloaded weights in 3.56s Loaded LoRAs in 12.83s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.71it/s] 7%|▋ | 2/28 [00:00<00:06, 4.27it/s] 11%|█ | 3/28 [00:00<00:06, 3.98it/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, 3.77it/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, 3.72it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.71it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.71it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.71it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.70it/s] 50%|█████ | 14/28 [00:03<00:03, 3.70it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.70it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.70it/s] 61%|██████ | 17/28 [00:04<00:02, 3.70it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.70it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.70it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.70it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.70it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.70it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.70it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.70it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.70it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.70it/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, 3.73it/s]Prediction
jakedahn/flux-soviet-controlrooms:10371959bab24ed449af0d5a03f36a4090fdd8468609ca1825824eb040988418Input
- model
- dev
- prompt
- a hello kitty SVCTR controlroom
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a hello kitty SVCTR controlroom", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "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 jakedahn/flux-soviet-controlrooms using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jakedahn/flux-soviet-controlrooms:10371959bab24ed449af0d5a03f36a4090fdd8468609ca1825824eb040988418", { input: { model: "dev", prompt: "a hello kitty SVCTR controlroom", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, 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 jakedahn/flux-soviet-controlrooms using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jakedahn/flux-soviet-controlrooms:10371959bab24ed449af0d5a03f36a4090fdd8468609ca1825824eb040988418", input={ "model": "dev", "prompt": "a hello kitty SVCTR controlroom", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "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 jakedahn/flux-soviet-controlrooms 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": "10371959bab24ed449af0d5a03f36a4090fdd8468609ca1825824eb040988418", "input": { "model": "dev", "prompt": "a hello kitty SVCTR controlroom", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "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": "2024-08-25T20:20:27.363357Z", "created_at": "2024-08-25T20:19:55.863000Z", "data_removed": false, "error": null, "id": "vhhbefr1axrm20chhcs9wpjz44", "input": { "model": "dev", "prompt": "a hello kitty SVCTR controlroom", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 10384\nPrompt: a hello kitty SVCTR controlroom\ntxt2img mode\nUsing dev model\nfree=9706140438528\nDownloading weights\n2024-08-25T20:20:08Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp9y6a6ro9/weights url=https://replicate.delivery/yhqm/3S2qu3TtEqrNEllNalsrYkQvlUH3GtceIJLpO4aIBYuTGDrJA/trained_model.tar\n2024-08-25T20:20:10Z | INFO | [ Complete ] dest=/tmp/tmp9y6a6ro9/weights size=\"172 MB\" total_elapsed=2.581s url=https://replicate.delivery/yhqm/3S2qu3TtEqrNEllNalsrYkQvlUH3GtceIJLpO4aIBYuTGDrJA/trained_model.tar\nDownloaded weights in 2.62s\nLoaded LoRAs in 11.32s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.69it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.24it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.96it/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, 3.69it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.69it/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, 3.68it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.68it/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, 3.68it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.68it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.68it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.69it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.68it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.68it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.68it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.71it/s]", "metrics": { "predict_time": 19.41321561, "total_time": 31.500357 }, "output": [ "https://replicate.delivery/yhqm/PeezAv5rTclGRULN3MgVeNifL3BKMnVq0fcwTUPJZwnaBiyaC/out-0.webp" ], "started_at": "2024-08-25T20:20:07.950142Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vhhbefr1axrm20chhcs9wpjz44", "cancel": "https://api.replicate.com/v1/predictions/vhhbefr1axrm20chhcs9wpjz44/cancel" }, "version": "10371959bab24ed449af0d5a03f36a4090fdd8468609ca1825824eb040988418" }Generated inUsing seed: 10384 Prompt: a hello kitty SVCTR controlroom txt2img mode Using dev model free=9706140438528 Downloading weights 2024-08-25T20:20:08Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp9y6a6ro9/weights url=https://replicate.delivery/yhqm/3S2qu3TtEqrNEllNalsrYkQvlUH3GtceIJLpO4aIBYuTGDrJA/trained_model.tar 2024-08-25T20:20:10Z | INFO | [ Complete ] dest=/tmp/tmp9y6a6ro9/weights size="172 MB" total_elapsed=2.581s url=https://replicate.delivery/yhqm/3S2qu3TtEqrNEllNalsrYkQvlUH3GtceIJLpO4aIBYuTGDrJA/trained_model.tar Downloaded weights in 2.62s Loaded LoRAs in 11.32s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.69it/s] 7%|▋ | 2/28 [00:00<00:06, 4.24it/s] 11%|█ | 3/28 [00:00<00:06, 3.96it/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, 3.69it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.69it/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, 3.68it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.68it/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, 3.68it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.68it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.68it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.69it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.68it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.68it/s] 100%|██████████| 28/28 [00:07<00:00, 3.68it/s] 100%|██████████| 28/28 [00:07<00:00, 3.71it/s]Prediction
jakedahn/flux-soviet-controlrooms:10371959bab24ed449af0d5a03f36a4090fdd8468609ca1825824eb040988418IDdkj4jk6y3xrm60chhcxvw70adgStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a fictional sci-fi movie SVCTR controlroom, in the style of disney pixar 3d animation
- lora_scale
- 0.6
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a fictional sci-fi movie SVCTR controlroom, in the style of disney pixar 3d animation", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "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 jakedahn/flux-soviet-controlrooms using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jakedahn/flux-soviet-controlrooms:10371959bab24ed449af0d5a03f36a4090fdd8468609ca1825824eb040988418", { input: { model: "dev", prompt: "a fictional sci-fi movie SVCTR controlroom, in the style of disney pixar 3d animation", lora_scale: 0.6, num_outputs: 1, aspect_ratio: "16:9", output_format: "webp", guidance_scale: 3.5, output_quality: 80, 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 jakedahn/flux-soviet-controlrooms using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jakedahn/flux-soviet-controlrooms:10371959bab24ed449af0d5a03f36a4090fdd8468609ca1825824eb040988418", input={ "model": "dev", "prompt": "a fictional sci-fi movie SVCTR controlroom, in the style of disney pixar 3d animation", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "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 jakedahn/flux-soviet-controlrooms 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": "10371959bab24ed449af0d5a03f36a4090fdd8468609ca1825824eb040988418", "input": { "model": "dev", "prompt": "a fictional sci-fi movie SVCTR controlroom, in the style of disney pixar 3d animation", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "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": "2024-08-25T20:31:21.963595Z", "created_at": "2024-08-25T20:30:42.207000Z", "data_removed": false, "error": null, "id": "dkj4jk6y3xrm60chhcxvw70adg", "input": { "model": "dev", "prompt": "a fictional sci-fi movie SVCTR controlroom, in the style of disney pixar 3d animation", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 53158\nPrompt: a fictional sci-fi movie SVCTR controlroom, in the style of disney pixar 3d animation\ntxt2img mode\nUsing dev model\nfree=9504810065920\nDownloading weights\n2024-08-25T20:31:04Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpsqgzplv2/weights url=https://replicate.delivery/yhqm/3S2qu3TtEqrNEllNalsrYkQvlUH3GtceIJLpO4aIBYuTGDrJA/trained_model.tar\n2024-08-25T20:31:05Z | INFO | [ Complete ] dest=/tmp/tmpsqgzplv2/weights size=\"172 MB\" total_elapsed=1.401s url=https://replicate.delivery/yhqm/3S2qu3TtEqrNEllNalsrYkQvlUH3GtceIJLpO4aIBYuTGDrJA/trained_model.tar\nDownloaded weights in 1.44s\nLoaded LoRAs in 9.34s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.58it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.00it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.78it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.68it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.65it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.62it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.60it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.59it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.59it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.58it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.58it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.57it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.57it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.57it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.57it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.57it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.57it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.57it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.57it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.57it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.57it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.57it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.57it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.57it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.57it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.57it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.57it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.57it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.59it/s]", "metrics": { "predict_time": 17.681269244, "total_time": 39.756595 }, "output": [ "https://replicate.delivery/yhqm/7bqYFIK5DK6xJl2gcRQg3HneJ2YJvwUz10bG0iAL5c5MNKrJA/out-0.webp" ], "started_at": "2024-08-25T20:31:04.282326Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/dkj4jk6y3xrm60chhcxvw70adg", "cancel": "https://api.replicate.com/v1/predictions/dkj4jk6y3xrm60chhcxvw70adg/cancel" }, "version": "10371959bab24ed449af0d5a03f36a4090fdd8468609ca1825824eb040988418" }Generated inUsing seed: 53158 Prompt: a fictional sci-fi movie SVCTR controlroom, in the style of disney pixar 3d animation txt2img mode Using dev model free=9504810065920 Downloading weights 2024-08-25T20:31:04Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpsqgzplv2/weights url=https://replicate.delivery/yhqm/3S2qu3TtEqrNEllNalsrYkQvlUH3GtceIJLpO4aIBYuTGDrJA/trained_model.tar 2024-08-25T20:31:05Z | INFO | [ Complete ] dest=/tmp/tmpsqgzplv2/weights size="172 MB" total_elapsed=1.401s url=https://replicate.delivery/yhqm/3S2qu3TtEqrNEllNalsrYkQvlUH3GtceIJLpO4aIBYuTGDrJA/trained_model.tar Downloaded weights in 1.44s Loaded LoRAs in 9.34s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.58it/s] 7%|▋ | 2/28 [00:00<00:06, 4.00it/s] 11%|█ | 3/28 [00:00<00:06, 3.78it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.68it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.65it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.62it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.60it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.59it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.59it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.58it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.58it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.57it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.57it/s] 50%|█████ | 14/28 [00:03<00:03, 3.57it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.57it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.57it/s] 61%|██████ | 17/28 [00:04<00:03, 3.57it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.57it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.57it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.57it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.57it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.57it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.57it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.57it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.57it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.57it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.57it/s] 100%|██████████| 28/28 [00:07<00:00, 3.57it/s] 100%|██████████| 28/28 [00:07<00:00, 3.59it/s]Prediction
jakedahn/flux-soviet-controlrooms:10371959bab24ed449af0d5a03f36a4090fdd8468609ca1825824eb040988418IDdkj4jk6y3xrm60chhcxvw70adgStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a fictional sci-fi movie SVCTR controlroom, in the style of disney pixar 3d animation
- lora_scale
- 0.6
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 0.8
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a fictional sci-fi movie SVCTR controlroom, in the style of disney pixar 3d animation", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "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 jakedahn/flux-soviet-controlrooms using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jakedahn/flux-soviet-controlrooms:10371959bab24ed449af0d5a03f36a4090fdd8468609ca1825824eb040988418", { input: { model: "dev", prompt: "a fictional sci-fi movie SVCTR controlroom, in the style of disney pixar 3d animation", lora_scale: 0.6, num_outputs: 1, aspect_ratio: "16:9", output_format: "webp", guidance_scale: 3.5, output_quality: 80, 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 jakedahn/flux-soviet-controlrooms using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jakedahn/flux-soviet-controlrooms:10371959bab24ed449af0d5a03f36a4090fdd8468609ca1825824eb040988418", input={ "model": "dev", "prompt": "a fictional sci-fi movie SVCTR controlroom, in the style of disney pixar 3d animation", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "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 jakedahn/flux-soviet-controlrooms 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": "10371959bab24ed449af0d5a03f36a4090fdd8468609ca1825824eb040988418", "input": { "model": "dev", "prompt": "a fictional sci-fi movie SVCTR controlroom, in the style of disney pixar 3d animation", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "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": "2024-08-25T20:31:21.963595Z", "created_at": "2024-08-25T20:30:42.207000Z", "data_removed": false, "error": null, "id": "dkj4jk6y3xrm60chhcxvw70adg", "input": { "model": "dev", "prompt": "a fictional sci-fi movie SVCTR controlroom, in the style of disney pixar 3d animation", "lora_scale": 0.6, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 0.8, "num_inference_steps": 28 }, "logs": "Using seed: 53158\nPrompt: a fictional sci-fi movie SVCTR controlroom, in the style of disney pixar 3d animation\ntxt2img mode\nUsing dev model\nfree=9504810065920\nDownloading weights\n2024-08-25T20:31:04Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpsqgzplv2/weights url=https://replicate.delivery/yhqm/3S2qu3TtEqrNEllNalsrYkQvlUH3GtceIJLpO4aIBYuTGDrJA/trained_model.tar\n2024-08-25T20:31:05Z | INFO | [ Complete ] dest=/tmp/tmpsqgzplv2/weights size=\"172 MB\" total_elapsed=1.401s url=https://replicate.delivery/yhqm/3S2qu3TtEqrNEllNalsrYkQvlUH3GtceIJLpO4aIBYuTGDrJA/trained_model.tar\nDownloaded weights in 1.44s\nLoaded LoRAs in 9.34s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.58it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.00it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.78it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.68it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.65it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.62it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.60it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.59it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.59it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.58it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.58it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.57it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.57it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.57it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.57it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.57it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.57it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.57it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.57it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.57it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.57it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.57it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.57it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.57it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.57it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.57it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.57it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.57it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.59it/s]", "metrics": { "predict_time": 17.681269244, "total_time": 39.756595 }, "output": [ "https://replicate.delivery/yhqm/7bqYFIK5DK6xJl2gcRQg3HneJ2YJvwUz10bG0iAL5c5MNKrJA/out-0.webp" ], "started_at": "2024-08-25T20:31:04.282326Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/dkj4jk6y3xrm60chhcxvw70adg", "cancel": "https://api.replicate.com/v1/predictions/dkj4jk6y3xrm60chhcxvw70adg/cancel" }, "version": "10371959bab24ed449af0d5a03f36a4090fdd8468609ca1825824eb040988418" }Generated inUsing seed: 53158 Prompt: a fictional sci-fi movie SVCTR controlroom, in the style of disney pixar 3d animation txt2img mode Using dev model free=9504810065920 Downloading weights 2024-08-25T20:31:04Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpsqgzplv2/weights url=https://replicate.delivery/yhqm/3S2qu3TtEqrNEllNalsrYkQvlUH3GtceIJLpO4aIBYuTGDrJA/trained_model.tar 2024-08-25T20:31:05Z | INFO | [ Complete ] dest=/tmp/tmpsqgzplv2/weights size="172 MB" total_elapsed=1.401s url=https://replicate.delivery/yhqm/3S2qu3TtEqrNEllNalsrYkQvlUH3GtceIJLpO4aIBYuTGDrJA/trained_model.tar Downloaded weights in 1.44s Loaded LoRAs in 9.34s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.58it/s] 7%|▋ | 2/28 [00:00<00:06, 4.00it/s] 11%|█ | 3/28 [00:00<00:06, 3.78it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.68it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.65it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.62it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.60it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.59it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.59it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.58it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.58it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.57it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.57it/s] 50%|█████ | 14/28 [00:03<00:03, 3.57it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.57it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.57it/s] 61%|██████ | 17/28 [00:04<00:03, 3.57it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.57it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.57it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.57it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.57it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.57it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.57it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.57it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.57it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.57it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.57it/s] 100%|██████████| 28/28 [00:07<00:00, 3.57it/s] 100%|██████████| 28/28 [00:07<00:00, 3.59it/s]
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