ferluht / fomenko
SDXL tuned on Anatoly Fomenko paintings
- Public
- 335 runs
-
L40S
- SDXL fine-tune
Prediction
ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8bID67anm4lbtuhmhzvo3yhclp2htiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- in style of fomenko a black and white drawing of a monolith floating in space surrounded by geometric abstractions
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "in style of fomenko a black and white drawing of a monolith floating in space surrounded by geometric abstractions", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run ferluht/fomenko using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b", { input: { width: 1024, height: 1024, prompt: "in style of fomenko a black and white drawing of a monolith floating in space surrounded by geometric abstractions", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // 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 ferluht/fomenko using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b", input={ "width": 1024, "height": 1024, "prompt": "in style of fomenko a black and white drawing of a monolith floating in space surrounded by geometric abstractions", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run ferluht/fomenko 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": "ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b", "input": { "width": 1024, "height": 1024, "prompt": "in style of fomenko a black and white drawing of a monolith floating in space surrounded by geometric abstractions", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-11-20T18:26:06.899858Z", "created_at": "2023-11-20T18:25:38.081987Z", "data_removed": false, "error": null, "id": "67anm4lbtuhmhzvo3yhclp2hti", "input": { "width": 1024, "height": 1024, "prompt": "in style of fomenko a black and white drawing of a monolith floating in space surrounded by geometric abstractions", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 55597\nEnsuring enough disk space...\nFree disk space: 1457255284736\nDownloading weights: https://replicate.delivery/pbxt/lTwrmd1DVlbeNKby72nDCPdqigYq10efXOMST53OtWgYxq0jA/trained_model.tar\nb'Downloaded 186 MB bytes in 4.915s (38 MB/s)\\nExtracted 186 MB in 0.073s (2.6 GB/s)\\n'\nDownloaded weights in 5.257249593734741 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: in style of fomenko a black and white drawing of a monolith floating in space surrounded by geometric abstractions\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.66it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.65it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.65it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.65it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.65it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.64it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.64it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.64it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.64it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.64it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.63it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.63it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.63it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.63it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.63it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.62it/s]\n 42%|████▏ | 21/50 [00:05<00:08, 3.62it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.62it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.62it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.61it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.62it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.62it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.62it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.62it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.62it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.62it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.62it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.62it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.63it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.64it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.64it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.64it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.64it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.64it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.64it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.64it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.64it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.63it/s]", "metrics": { "predict_time": 21.042482, "total_time": 28.817871 }, "output": [ "https://replicate.delivery/pbxt/RGzd6zRVe2TQFStIMFTbiOeiAOknOfEUwVuBA6zfVHx7rVpHB/out-0.png" ], "started_at": "2023-11-20T18:25:45.857376Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/67anm4lbtuhmhzvo3yhclp2hti", "cancel": "https://api.replicate.com/v1/predictions/67anm4lbtuhmhzvo3yhclp2hti/cancel" }, "version": "bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b" }Generated inUsing seed: 55597 Ensuring enough disk space... Free disk space: 1457255284736 Downloading weights: https://replicate.delivery/pbxt/lTwrmd1DVlbeNKby72nDCPdqigYq10efXOMST53OtWgYxq0jA/trained_model.tar b'Downloaded 186 MB bytes in 4.915s (38 MB/s)\nExtracted 186 MB in 0.073s (2.6 GB/s)\n' Downloaded weights in 5.257249593734741 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: in style of fomenko a black and white drawing of a monolith floating in space surrounded by geometric abstractions txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.66it/s] 4%|▍ | 2/50 [00:00<00:13, 3.65it/s] 6%|▌ | 3/50 [00:00<00:12, 3.65it/s] 8%|▊ | 4/50 [00:01<00:12, 3.65it/s] 10%|█ | 5/50 [00:01<00:12, 3.65it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.64it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.64it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.64it/s] 20%|██ | 10/50 [00:02<00:11, 3.64it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.64it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s] 30%|███ | 15/50 [00:04<00:09, 3.63it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.63it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.63it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.63it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.63it/s] 40%|████ | 20/50 [00:05<00:08, 3.62it/s] 42%|████▏ | 21/50 [00:05<00:08, 3.62it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.62it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.62it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.61it/s] 50%|█████ | 25/50 [00:06<00:06, 3.62it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.62it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.62it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.62it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.62it/s] 60%|██████ | 30/50 [00:08<00:05, 3.62it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.62it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.62it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.63it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s] 70%|███████ | 35/50 [00:09<00:04, 3.64it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.64it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.64it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.64it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.64it/s] 80%|████████ | 40/50 [00:11<00:02, 3.64it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.64it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.64it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.64it/s] 100%|██████████| 50/50 [00:13<00:00, 3.63it/s]Prediction
ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8bIDhrckd3dbhu3larhx7f73d3tpnuStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- in style of fomenko a black and white drawing of a woman standing on a planet looking into the sky with stars and geometric abstractions
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "in style of fomenko a black and white drawing of a woman standing on a planet looking into the sky with stars and geometric abstractions", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run ferluht/fomenko using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b", { input: { width: 1024, height: 1024, prompt: "in style of fomenko a black and white drawing of a woman standing on a planet looking into the sky with stars and geometric abstractions", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // 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 ferluht/fomenko using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b", input={ "width": 1024, "height": 1024, "prompt": "in style of fomenko a black and white drawing of a woman standing on a planet looking into the sky with stars and geometric abstractions", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run ferluht/fomenko 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": "ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b", "input": { "width": 1024, "height": 1024, "prompt": "in style of fomenko a black and white drawing of a woman standing on a planet looking into the sky with stars and geometric abstractions", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-11-20T18:31:35.399945Z", "created_at": "2023-11-20T18:31:14.213826Z", "data_removed": false, "error": null, "id": "hrckd3dbhu3larhx7f73d3tpnu", "input": { "width": 1024, "height": 1024, "prompt": "in style of fomenko a black and white drawing of a woman standing on a planet looking into the sky with stars and geometric abstractions", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 33568\nEnsuring enough disk space...\nFree disk space: 1417705848832\nDownloading weights: https://replicate.delivery/pbxt/lTwrmd1DVlbeNKby72nDCPdqigYq10efXOMST53OtWgYxq0jA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.384s (485 MB/s)\\nExtracted 186 MB in 0.054s (3.4 GB/s)\\n'\nDownloaded weights in 0.5584640502929688 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: in style of fomenko a black and white drawing of a woman standing on a planet looking into the sky with stars and geometric abstractions\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.65it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.63it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.63it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.64it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.64it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.64it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.64it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.64it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.64it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.64it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.64it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.64it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.63it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.63it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.63it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.63it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.63it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.63it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.63it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.63it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.63it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.63it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.63it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.63it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.63it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.62it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.62it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.62it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.62it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.62it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.62it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.62it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.62it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.62it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.62it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.62it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.62it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.62it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.62it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.62it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.62it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.63it/s]", "metrics": { "predict_time": 16.957601, "total_time": 21.186119 }, "output": [ "https://replicate.delivery/pbxt/LsO6dxiUqu7UN1ZYeCiMxzP17UwyoSkWlrapteXjjw6GgV6RA/out-0.png" ], "started_at": "2023-11-20T18:31:18.442344Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/hrckd3dbhu3larhx7f73d3tpnu", "cancel": "https://api.replicate.com/v1/predictions/hrckd3dbhu3larhx7f73d3tpnu/cancel" }, "version": "bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b" }Generated inUsing seed: 33568 Ensuring enough disk space... Free disk space: 1417705848832 Downloading weights: https://replicate.delivery/pbxt/lTwrmd1DVlbeNKby72nDCPdqigYq10efXOMST53OtWgYxq0jA/trained_model.tar b'Downloaded 186 MB bytes in 0.384s (485 MB/s)\nExtracted 186 MB in 0.054s (3.4 GB/s)\n' Downloaded weights in 0.5584640502929688 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: in style of fomenko a black and white drawing of a woman standing on a planet looking into the sky with stars and geometric abstractions txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.65it/s] 4%|▍ | 2/50 [00:00<00:13, 3.63it/s] 6%|▌ | 3/50 [00:00<00:12, 3.63it/s] 8%|▊ | 4/50 [00:01<00:12, 3.64it/s] 10%|█ | 5/50 [00:01<00:12, 3.64it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.64it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.64it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.64it/s] 20%|██ | 10/50 [00:02<00:10, 3.64it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.64it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s] 30%|███ | 15/50 [00:04<00:09, 3.64it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s] 40%|████ | 20/50 [00:05<00:08, 3.64it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.63it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.63it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.63it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.63it/s] 50%|█████ | 25/50 [00:06<00:06, 3.63it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.63it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.63it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.63it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.63it/s] 60%|██████ | 30/50 [00:08<00:05, 3.63it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.63it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.63it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.63it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s] 70%|███████ | 35/50 [00:09<00:04, 3.62it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.62it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.62it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.62it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.62it/s] 80%|████████ | 40/50 [00:11<00:02, 3.62it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.62it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.62it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.62it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.62it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.62it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.62it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.62it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.62it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.62it/s] 100%|██████████| 50/50 [00:13<00:00, 3.62it/s] 100%|██████████| 50/50 [00:13<00:00, 3.63it/s]Prediction
ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8bIDvmgpjldbtigjxoc6o44ech62xiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- in style of fomenko a black and white drawing of a scientist looking at surrealistic ruins on the other planet
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 2
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "in style of fomenko a black and white drawing of a scientist looking at surrealistic ruins on the other planet", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 2, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run ferluht/fomenko using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b", { input: { width: 1024, height: 1024, prompt: "in style of fomenko a black and white drawing of a scientist looking at surrealistic ruins on the other planet", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 2, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // 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 ferluht/fomenko using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b", input={ "width": 1024, "height": 1024, "prompt": "in style of fomenko a black and white drawing of a scientist looking at surrealistic ruins on the other planet", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 2, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run ferluht/fomenko 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": "ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b", "input": { "width": 1024, "height": 1024, "prompt": "in style of fomenko a black and white drawing of a scientist looking at surrealistic ruins on the other planet", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 2, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-11-20T18:50:35.518645Z", "created_at": "2023-11-20T18:49:55.000277Z", "data_removed": false, "error": null, "id": "vmgpjldbtigjxoc6o44ech62xi", "input": { "width": 1024, "height": 1024, "prompt": "in style of fomenko a black and white drawing of a scientist looking at surrealistic ruins on the other planet", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 2, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 27214\nEnsuring enough disk space...\nFree disk space: 1473215037440\nDownloading weights: https://replicate.delivery/pbxt/lTwrmd1DVlbeNKby72nDCPdqigYq10efXOMST53OtWgYxq0jA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.255s (729 MB/s)\\nExtracted 186 MB in 0.058s (3.2 GB/s)\\n'\nDownloaded weights in 0.4075162410736084 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: in style of fomenko a black and white drawing of a scientist looking at surrealistic ruins on the other planet\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:26, 1.85it/s]\n 4%|▍ | 2/50 [00:01<00:25, 1.87it/s]\n 6%|▌ | 3/50 [00:01<00:25, 1.87it/s]\n 8%|▊ | 4/50 [00:02<00:24, 1.87it/s]\n 10%|█ | 5/50 [00:02<00:24, 1.87it/s]\n 12%|█▏ | 6/50 [00:03<00:23, 1.87it/s]\n 14%|█▍ | 7/50 [00:03<00:22, 1.87it/s]\n 16%|█▌ | 8/50 [00:04<00:22, 1.87it/s]\n 18%|█▊ | 9/50 [00:04<00:21, 1.87it/s]\n 20%|██ | 10/50 [00:05<00:21, 1.87it/s]\n 22%|██▏ | 11/50 [00:05<00:20, 1.87it/s]\n 24%|██▍ | 12/50 [00:06<00:20, 1.87it/s]\n 26%|██▌ | 13/50 [00:06<00:19, 1.87it/s]\n 28%|██▊ | 14/50 [00:07<00:19, 1.87it/s]\n 30%|███ | 15/50 [00:08<00:18, 1.88it/s]\n 32%|███▏ | 16/50 [00:08<00:18, 1.88it/s]\n 34%|███▍ | 17/50 [00:09<00:17, 1.88it/s]\n 36%|███▌ | 18/50 [00:09<00:17, 1.88it/s]\n 38%|███▊ | 19/50 [00:10<00:16, 1.88it/s]\n 40%|████ | 20/50 [00:10<00:15, 1.88it/s]\n 42%|████▏ | 21/50 [00:11<00:15, 1.88it/s]\n 44%|████▍ | 22/50 [00:11<00:14, 1.88it/s]\n 46%|████▌ | 23/50 [00:12<00:14, 1.88it/s]\n 48%|████▊ | 24/50 [00:12<00:13, 1.87it/s]\n 50%|█████ | 25/50 [00:13<00:13, 1.87it/s]\n 52%|█████▏ | 26/50 [00:13<00:12, 1.87it/s]\n 54%|█████▍ | 27/50 [00:14<00:12, 1.87it/s]\n 56%|█████▌ | 28/50 [00:14<00:11, 1.87it/s]\n 58%|█████▊ | 29/50 [00:15<00:11, 1.87it/s]\n 60%|██████ | 30/50 [00:16<00:10, 1.87it/s]\n 62%|██████▏ | 31/50 [00:16<00:10, 1.87it/s]\n 64%|██████▍ | 32/50 [00:17<00:09, 1.87it/s]\n 66%|██████▌ | 33/50 [00:17<00:09, 1.87it/s]\n 68%|██████▊ | 34/50 [00:18<00:08, 1.87it/s]\n 70%|███████ | 35/50 [00:18<00:08, 1.87it/s]\n 72%|███████▏ | 36/50 [00:19<00:07, 1.87it/s]\n 74%|███████▍ | 37/50 [00:19<00:06, 1.87it/s]\n 76%|███████▌ | 38/50 [00:20<00:06, 1.87it/s]\n 78%|███████▊ | 39/50 [00:20<00:05, 1.87it/s]\n 80%|████████ | 40/50 [00:21<00:05, 1.87it/s]\n 82%|████████▏ | 41/50 [00:21<00:04, 1.87it/s]\n 84%|████████▍ | 42/50 [00:22<00:04, 1.87it/s]\n 86%|████████▌ | 43/50 [00:22<00:03, 1.87it/s]\n 88%|████████▊ | 44/50 [00:23<00:03, 1.87it/s]\n 90%|█████████ | 45/50 [00:24<00:02, 1.87it/s]\n 92%|█████████▏| 46/50 [00:24<00:02, 1.87it/s]\n 94%|█████████▍| 47/50 [00:25<00:01, 1.87it/s]\n 96%|█████████▌| 48/50 [00:25<00:01, 1.86it/s]\n 98%|█████████▊| 49/50 [00:26<00:00, 1.86it/s]\n100%|██████████| 50/50 [00:26<00:00, 1.86it/s]\n100%|██████████| 50/50 [00:26<00:00, 1.87it/s]", "metrics": { "predict_time": 31.482446, "total_time": 40.518368 }, "output": [ "https://replicate.delivery/pbxt/4XSVOeroM3VUECKCm2Id7EphSnS3kz2RkNApOR3ylb084K9IA/out-0.png", "https://replicate.delivery/pbxt/4ebOxB5N5Y0mcinCpwAeovPDOrQ4sTVn9SFsYvsObh47xV6RA/out-1.png" ], "started_at": "2023-11-20T18:50:04.036199Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vmgpjldbtigjxoc6o44ech62xi", "cancel": "https://api.replicate.com/v1/predictions/vmgpjldbtigjxoc6o44ech62xi/cancel" }, "version": "bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b" }Generated inUsing seed: 27214 Ensuring enough disk space... Free disk space: 1473215037440 Downloading weights: https://replicate.delivery/pbxt/lTwrmd1DVlbeNKby72nDCPdqigYq10efXOMST53OtWgYxq0jA/trained_model.tar b'Downloaded 186 MB bytes in 0.255s (729 MB/s)\nExtracted 186 MB in 0.058s (3.2 GB/s)\n' Downloaded weights in 0.4075162410736084 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: in style of fomenko a black and white drawing of a scientist looking at surrealistic ruins on the other planet txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:26, 1.85it/s] 4%|▍ | 2/50 [00:01<00:25, 1.87it/s] 6%|▌ | 3/50 [00:01<00:25, 1.87it/s] 8%|▊ | 4/50 [00:02<00:24, 1.87it/s] 10%|█ | 5/50 [00:02<00:24, 1.87it/s] 12%|█▏ | 6/50 [00:03<00:23, 1.87it/s] 14%|█▍ | 7/50 [00:03<00:22, 1.87it/s] 16%|█▌ | 8/50 [00:04<00:22, 1.87it/s] 18%|█▊ | 9/50 [00:04<00:21, 1.87it/s] 20%|██ | 10/50 [00:05<00:21, 1.87it/s] 22%|██▏ | 11/50 [00:05<00:20, 1.87it/s] 24%|██▍ | 12/50 [00:06<00:20, 1.87it/s] 26%|██▌ | 13/50 [00:06<00:19, 1.87it/s] 28%|██▊ | 14/50 [00:07<00:19, 1.87it/s] 30%|███ | 15/50 [00:08<00:18, 1.88it/s] 32%|███▏ | 16/50 [00:08<00:18, 1.88it/s] 34%|███▍ | 17/50 [00:09<00:17, 1.88it/s] 36%|███▌ | 18/50 [00:09<00:17, 1.88it/s] 38%|███▊ | 19/50 [00:10<00:16, 1.88it/s] 40%|████ | 20/50 [00:10<00:15, 1.88it/s] 42%|████▏ | 21/50 [00:11<00:15, 1.88it/s] 44%|████▍ | 22/50 [00:11<00:14, 1.88it/s] 46%|████▌ | 23/50 [00:12<00:14, 1.88it/s] 48%|████▊ | 24/50 [00:12<00:13, 1.87it/s] 50%|█████ | 25/50 [00:13<00:13, 1.87it/s] 52%|█████▏ | 26/50 [00:13<00:12, 1.87it/s] 54%|█████▍ | 27/50 [00:14<00:12, 1.87it/s] 56%|█████▌ | 28/50 [00:14<00:11, 1.87it/s] 58%|█████▊ | 29/50 [00:15<00:11, 1.87it/s] 60%|██████ | 30/50 [00:16<00:10, 1.87it/s] 62%|██████▏ | 31/50 [00:16<00:10, 1.87it/s] 64%|██████▍ | 32/50 [00:17<00:09, 1.87it/s] 66%|██████▌ | 33/50 [00:17<00:09, 1.87it/s] 68%|██████▊ | 34/50 [00:18<00:08, 1.87it/s] 70%|███████ | 35/50 [00:18<00:08, 1.87it/s] 72%|███████▏ | 36/50 [00:19<00:07, 1.87it/s] 74%|███████▍ | 37/50 [00:19<00:06, 1.87it/s] 76%|███████▌ | 38/50 [00:20<00:06, 1.87it/s] 78%|███████▊ | 39/50 [00:20<00:05, 1.87it/s] 80%|████████ | 40/50 [00:21<00:05, 1.87it/s] 82%|████████▏ | 41/50 [00:21<00:04, 1.87it/s] 84%|████████▍ | 42/50 [00:22<00:04, 1.87it/s] 86%|████████▌ | 43/50 [00:22<00:03, 1.87it/s] 88%|████████▊ | 44/50 [00:23<00:03, 1.87it/s] 90%|█████████ | 45/50 [00:24<00:02, 1.87it/s] 92%|█████████▏| 46/50 [00:24<00:02, 1.87it/s] 94%|█████████▍| 47/50 [00:25<00:01, 1.87it/s] 96%|█████████▌| 48/50 [00:25<00:01, 1.86it/s] 98%|█████████▊| 49/50 [00:26<00:00, 1.86it/s] 100%|██████████| 50/50 [00:26<00:00, 1.86it/s] 100%|██████████| 50/50 [00:26<00:00, 1.87it/s]Prediction
ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8bIDuktbiblbad5qcm56thaolflwciStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- in style of fomenko a black and white drawing of a man head looking at monolith floating in space surrounded by planets and geometric abstractions
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "in style of fomenko a black and white drawing of a man head looking at monolith floating in space surrounded by planets and geometric abstractions", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run ferluht/fomenko using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b", { input: { width: 1024, height: 1024, prompt: "in style of fomenko a black and white drawing of a man head looking at monolith floating in space surrounded by planets and geometric abstractions", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // 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 ferluht/fomenko using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b", input={ "width": 1024, "height": 1024, "prompt": "in style of fomenko a black and white drawing of a man head looking at monolith floating in space surrounded by planets and geometric abstractions", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run ferluht/fomenko 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": "ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b", "input": { "width": 1024, "height": 1024, "prompt": "in style of fomenko a black and white drawing of a man head looking at monolith floating in space surrounded by planets and geometric abstractions", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-11-20T18:28:05.986630Z", "created_at": "2023-11-20T18:27:45.967092Z", "data_removed": false, "error": null, "id": "uktbiblbad5qcm56thaolflwci", "input": { "width": 1024, "height": 1024, "prompt": "in style of fomenko a black and white drawing of a man head looking at monolith floating in space surrounded by planets and geometric abstractions", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 8421\nEnsuring enough disk space...\nFree disk space: 1734228365312\nDownloading weights: https://replicate.delivery/pbxt/lTwrmd1DVlbeNKby72nDCPdqigYq10efXOMST53OtWgYxq0jA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.242s (767 MB/s)\\nExtracted 186 MB in 0.062s (3.0 GB/s)\\n'\nDownloaded weights in 0.45479393005371094 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: in style of fomenko a black and white drawing of a man head looking at monolith floating in space surrounded by planets and geometric abstractions\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.72it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.71it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.71it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.70it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.69it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.69it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.68it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.68it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.68it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.68it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.68it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.68it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.69it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.69it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.70it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.70it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.70it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.71it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.70it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.70it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.70it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.70it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.70it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.71it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.70it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.70it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.70it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.70it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.70it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.70it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.69it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.70it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.69it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.70it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.69it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.69it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.69it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.69it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.70it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.70it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.70it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.70it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.70it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.69it/s]", "metrics": { "predict_time": 15.897175, "total_time": 20.019538 }, "output": [ "https://replicate.delivery/pbxt/eMYvXlm38L0SDq8JecXQl7kGCpiZXH2zifpfRPSQq5LXzVpHB/out-0.png" ], "started_at": "2023-11-20T18:27:50.089455Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/uktbiblbad5qcm56thaolflwci", "cancel": "https://api.replicate.com/v1/predictions/uktbiblbad5qcm56thaolflwci/cancel" }, "version": "bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b" }Generated inUsing seed: 8421 Ensuring enough disk space... Free disk space: 1734228365312 Downloading weights: https://replicate.delivery/pbxt/lTwrmd1DVlbeNKby72nDCPdqigYq10efXOMST53OtWgYxq0jA/trained_model.tar b'Downloaded 186 MB bytes in 0.242s (767 MB/s)\nExtracted 186 MB in 0.062s (3.0 GB/s)\n' Downloaded weights in 0.45479393005371094 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: in style of fomenko a black and white drawing of a man head looking at monolith floating in space surrounded by planets and geometric abstractions txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.72it/s] 4%|▍ | 2/50 [00:00<00:12, 3.71it/s] 6%|▌ | 3/50 [00:00<00:12, 3.71it/s] 8%|▊ | 4/50 [00:01<00:12, 3.70it/s] 10%|█ | 5/50 [00:01<00:12, 3.69it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s] 20%|██ | 10/50 [00:02<00:10, 3.69it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.68it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.68it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.68it/s] 30%|███ | 15/50 [00:04<00:09, 3.68it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.68it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.68it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.68it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s] 40%|████ | 20/50 [00:05<00:08, 3.69it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.69it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.70it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.70it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.70it/s] 50%|█████ | 25/50 [00:06<00:06, 3.71it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.70it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.70it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.70it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.70it/s] 60%|██████ | 30/50 [00:08<00:05, 3.70it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.71it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.70it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.70it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.70it/s] 70%|███████ | 35/50 [00:09<00:04, 3.70it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.70it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.70it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.69it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.70it/s] 80%|████████ | 40/50 [00:10<00:02, 3.69it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.70it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.69it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.69it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.69it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.69it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.70it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.70it/s] 96%|█████████▌| 48/50 [00:12<00:00, 3.70it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.70it/s] 100%|██████████| 50/50 [00:13<00:00, 3.70it/s] 100%|██████████| 50/50 [00:13<00:00, 3.69it/s]Prediction
ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8bID63hykelbribzjuzydkdl5x4rieStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- in style of fomenko a color drawing of a cat walking through geometric abstractions and surrealistic space ruins
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "in style of fomenko a color drawing of a cat walking through geometric abstractions and surrealistic space ruins", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run ferluht/fomenko using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b", { input: { width: 1024, height: 1024, prompt: "in style of fomenko a color drawing of a cat walking through geometric abstractions and surrealistic space ruins", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // 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 ferluht/fomenko using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b", input={ "width": 1024, "height": 1024, "prompt": "in style of fomenko a color drawing of a cat walking through geometric abstractions and surrealistic space ruins", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run ferluht/fomenko 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": "ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b", "input": { "width": 1024, "height": 1024, "prompt": "in style of fomenko a color drawing of a cat walking through geometric abstractions and surrealistic space ruins", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-11-20T18:33:47.844969Z", "created_at": "2023-11-20T18:33:23.854984Z", "data_removed": false, "error": null, "id": "63hykelbribzjuzydkdl5x4rie", "input": { "width": 1024, "height": 1024, "prompt": "in style of fomenko a color drawing of a cat walking through geometric abstractions and surrealistic space ruins", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 2653\nEnsuring enough disk space...\nFree disk space: 1856327561216\nDownloading weights: https://replicate.delivery/pbxt/lTwrmd1DVlbeNKby72nDCPdqigYq10efXOMST53OtWgYxq0jA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.333s (558 MB/s)\\nExtracted 186 MB in 0.055s (3.4 GB/s)\\n'\nDownloaded weights in 0.5325040817260742 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: in style of fomenko a color drawing of a cat walking through geometric abstractions and surrealistic space ruins\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.67it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.67it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.67it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.67it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.67it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.67it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.66it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.66it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.66it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.66it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.66it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.66it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.65it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.65it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.65it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.65it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.65it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.65it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.65it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.65it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.65it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.65it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.65it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.64it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.64it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.64it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]", "metrics": { "predict_time": 16.912795, "total_time": 23.989985 }, "output": [ "https://replicate.delivery/pbxt/efNiFADOZuo6MEfuEorKsXoe31vE6Kq8efJVizxsUxAmiYleIA/out-0.png" ], "started_at": "2023-11-20T18:33:30.932174Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/63hykelbribzjuzydkdl5x4rie", "cancel": "https://api.replicate.com/v1/predictions/63hykelbribzjuzydkdl5x4rie/cancel" }, "version": "bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b" }Generated inUsing seed: 2653 Ensuring enough disk space... Free disk space: 1856327561216 Downloading weights: https://replicate.delivery/pbxt/lTwrmd1DVlbeNKby72nDCPdqigYq10efXOMST53OtWgYxq0jA/trained_model.tar b'Downloaded 186 MB bytes in 0.333s (558 MB/s)\nExtracted 186 MB in 0.055s (3.4 GB/s)\n' Downloaded weights in 0.5325040817260742 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: in style of fomenko a color drawing of a cat walking through geometric abstractions and surrealistic space ruins txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.67it/s] 4%|▍ | 2/50 [00:00<00:13, 3.67it/s] 6%|▌ | 3/50 [00:00<00:12, 3.67it/s] 8%|▊ | 4/50 [00:01<00:12, 3.67it/s] 10%|█ | 5/50 [00:01<00:12, 3.67it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s] 20%|██ | 10/50 [00:02<00:10, 3.67it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.66it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s] 30%|███ | 15/50 [00:04<00:09, 3.66it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.66it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s] 40%|████ | 20/50 [00:05<00:08, 3.66it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.66it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.66it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.65it/s] 50%|█████ | 25/50 [00:06<00:06, 3.65it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.65it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.65it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.65it/s] 60%|██████ | 30/50 [00:08<00:05, 3.65it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.65it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.65it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s] 70%|███████ | 35/50 [00:09<00:04, 3.65it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.65it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s] 80%|████████ | 40/50 [00:10<00:02, 3.65it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.64it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.64it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.64it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.64it/s] 100%|██████████| 50/50 [00:13<00:00, 3.64it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s]Prediction
ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8bIDrmfbtglbgt5axue6hszgmynyaaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- in style of fomenko a color drawing of a scientist looking at a butterfly surrounded by geometric abstractions with strong perspective
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 2
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "in style of fomenko a color drawing of a scientist looking at a butterfly surrounded by geometric abstractions with strong perspective", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 2, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run ferluht/fomenko using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b", { input: { width: 1024, height: 1024, prompt: "in style of fomenko a color drawing of a scientist looking at a butterfly surrounded by geometric abstractions with strong perspective", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 2, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); // 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 ferluht/fomenko using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b", input={ "width": 1024, "height": 1024, "prompt": "in style of fomenko a color drawing of a scientist looking at a butterfly surrounded by geometric abstractions with strong perspective", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 2, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run ferluht/fomenko 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": "ferluht/fomenko:bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b", "input": { "width": 1024, "height": 1024, "prompt": "in style of fomenko a color drawing of a scientist looking at a butterfly surrounded by geometric abstractions with strong perspective", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 2, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-11-20T18:52:50.504061Z", "created_at": "2023-11-20T18:52:14.573134Z", "data_removed": false, "error": null, "id": "rmfbtglbgt5axue6hszgmynyaa", "input": { "width": 1024, "height": 1024, "prompt": "in style of fomenko a color drawing of a scientist looking at a butterfly surrounded by geometric abstractions with strong perspective", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 2, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 46389\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: in style of fomenko a color drawing of a scientist looking at a butterfly surrounded by geometric abstractions with strong perspective\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:26, 1.86it/s]\n 4%|▍ | 2/50 [00:01<00:25, 1.87it/s]\n 6%|▌ | 3/50 [00:01<00:25, 1.88it/s]\n 8%|▊ | 4/50 [00:02<00:24, 1.88it/s]\n 10%|█ | 5/50 [00:02<00:23, 1.88it/s]\n 12%|█▏ | 6/50 [00:03<00:23, 1.88it/s]\n 14%|█▍ | 7/50 [00:03<00:22, 1.88it/s]\n 16%|█▌ | 8/50 [00:04<00:22, 1.88it/s]\n 18%|█▊ | 9/50 [00:04<00:21, 1.88it/s]\n 20%|██ | 10/50 [00:05<00:21, 1.88it/s]\n 22%|██▏ | 11/50 [00:05<00:20, 1.88it/s]\n 24%|██▍ | 12/50 [00:06<00:20, 1.88it/s]\n 26%|██▌ | 13/50 [00:06<00:19, 1.88it/s]\n 28%|██▊ | 14/50 [00:07<00:19, 1.88it/s]\n 30%|███ | 15/50 [00:07<00:18, 1.88it/s]\n 32%|███▏ | 16/50 [00:08<00:18, 1.88it/s]\n 34%|███▍ | 17/50 [00:09<00:17, 1.88it/s]\n 36%|███▌ | 18/50 [00:09<00:17, 1.88it/s]\n 38%|███▊ | 19/50 [00:10<00:16, 1.88it/s]\n 40%|████ | 20/50 [00:10<00:16, 1.87it/s]\n 42%|████▏ | 21/50 [00:11<00:15, 1.87it/s]\n 44%|████▍ | 22/50 [00:11<00:14, 1.87it/s]\n 46%|████▌ | 23/50 [00:12<00:14, 1.87it/s]\n 48%|████▊ | 24/50 [00:12<00:13, 1.87it/s]\n 50%|█████ | 25/50 [00:13<00:13, 1.87it/s]\n 52%|█████▏ | 26/50 [00:13<00:12, 1.87it/s]\n 54%|█████▍ | 27/50 [00:14<00:12, 1.87it/s]\n 56%|█████▌ | 28/50 [00:14<00:11, 1.87it/s]\n 58%|█████▊ | 29/50 [00:15<00:11, 1.87it/s]\n 60%|██████ | 30/50 [00:15<00:10, 1.87it/s]\n 62%|██████▏ | 31/50 [00:16<00:10, 1.87it/s]\n 64%|██████▍ | 32/50 [00:17<00:09, 1.87it/s]\n 66%|██████▌ | 33/50 [00:17<00:09, 1.87it/s]\n 68%|██████▊ | 34/50 [00:18<00:08, 1.87it/s]\n 70%|███████ | 35/50 [00:18<00:08, 1.87it/s]\n 72%|███████▏ | 36/50 [00:19<00:07, 1.87it/s]\n 74%|███████▍ | 37/50 [00:19<00:06, 1.87it/s]\n 76%|███████▌ | 38/50 [00:20<00:06, 1.87it/s]\n 78%|███████▊ | 39/50 [00:20<00:05, 1.87it/s]\n 80%|████████ | 40/50 [00:21<00:05, 1.87it/s]\n 82%|████████▏ | 41/50 [00:21<00:04, 1.87it/s]\n 84%|████████▍ | 42/50 [00:22<00:04, 1.87it/s]\n 86%|████████▌ | 43/50 [00:22<00:03, 1.87it/s]\n 88%|████████▊ | 44/50 [00:23<00:03, 1.87it/s]\n 90%|█████████ | 45/50 [00:24<00:02, 1.87it/s]\n 92%|█████████▏| 46/50 [00:24<00:02, 1.87it/s]\n 94%|█████████▍| 47/50 [00:25<00:01, 1.87it/s]\n 96%|█████████▌| 48/50 [00:25<00:01, 1.87it/s]\n 98%|█████████▊| 49/50 [00:26<00:00, 1.87it/s]\n100%|██████████| 50/50 [00:26<00:00, 1.87it/s]\n100%|██████████| 50/50 [00:26<00:00, 1.87it/s]", "metrics": { "predict_time": 30.87772, "total_time": 35.930927 }, "output": [ "https://replicate.delivery/pbxt/EBPmfea1OpphKEs7ksGUyxrpS4E7al0ffHvLkgB6ZQ2DQXpHB/out-0.png", "https://replicate.delivery/pbxt/gmg3JXv1BJbSChRcB08WHDkQk4PM39cCze3GOGUlLeJC0V6RA/out-1.png" ], "started_at": "2023-11-20T18:52:19.626341Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rmfbtglbgt5axue6hszgmynyaa", "cancel": "https://api.replicate.com/v1/predictions/rmfbtglbgt5axue6hszgmynyaa/cancel" }, "version": "bd789c96c564d8e6e04f1a5addf6d7462c8ffcafdaab91661b4a6bcfa7155e8b" }Generated inUsing seed: 46389 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: in style of fomenko a color drawing of a scientist looking at a butterfly surrounded by geometric abstractions with strong perspective txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:26, 1.86it/s] 4%|▍ | 2/50 [00:01<00:25, 1.87it/s] 6%|▌ | 3/50 [00:01<00:25, 1.88it/s] 8%|▊ | 4/50 [00:02<00:24, 1.88it/s] 10%|█ | 5/50 [00:02<00:23, 1.88it/s] 12%|█▏ | 6/50 [00:03<00:23, 1.88it/s] 14%|█▍ | 7/50 [00:03<00:22, 1.88it/s] 16%|█▌ | 8/50 [00:04<00:22, 1.88it/s] 18%|█▊ | 9/50 [00:04<00:21, 1.88it/s] 20%|██ | 10/50 [00:05<00:21, 1.88it/s] 22%|██▏ | 11/50 [00:05<00:20, 1.88it/s] 24%|██▍ | 12/50 [00:06<00:20, 1.88it/s] 26%|██▌ | 13/50 [00:06<00:19, 1.88it/s] 28%|██▊ | 14/50 [00:07<00:19, 1.88it/s] 30%|███ | 15/50 [00:07<00:18, 1.88it/s] 32%|███▏ | 16/50 [00:08<00:18, 1.88it/s] 34%|███▍ | 17/50 [00:09<00:17, 1.88it/s] 36%|███▌ | 18/50 [00:09<00:17, 1.88it/s] 38%|███▊ | 19/50 [00:10<00:16, 1.88it/s] 40%|████ | 20/50 [00:10<00:16, 1.87it/s] 42%|████▏ | 21/50 [00:11<00:15, 1.87it/s] 44%|████▍ | 22/50 [00:11<00:14, 1.87it/s] 46%|████▌ | 23/50 [00:12<00:14, 1.87it/s] 48%|████▊ | 24/50 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