cbh123 / sdxl-hereditary
SDXL fine-tuned on Hereditary (Updated 1 year, 7 months ago)
- Public
- 133 runs
- SDXL fine-tune
Prediction
cbh123/sdxl-hereditary:535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64IDzpb2tvtbos6d7rsc7ajarszxgeStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- mask
- null
- seed
- null
- image
- null
- width
- 1024
- height
- 1024
- prompt
- a photo of a model of a house in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- refine_steps
- null
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "mask": null, "seed": null, "image": null, "width": 1024, "height": 1024, "prompt": "a photo of a model of a house in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "refine_steps": null, "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 cbh123/sdxl-hereditary using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cbh123/sdxl-hereditary:535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64", { input: { width: 1024, height: 1024, prompt: "a photo of a model of a house in the style of TOK", 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 cbh123/sdxl-hereditary using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cbh123/sdxl-hereditary:535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64", input={ "width": 1024, "height": 1024, "prompt": "a photo of a model of a house in the style of TOK", "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 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cbh123/sdxl-hereditary 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": "cbh123/sdxl-hereditary:535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64", "input": { "width": 1024, "height": 1024, "prompt": "a photo of a model of a house in the style of TOK", "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": "2023-10-30T15:53:22.834361Z", "created_at": "2023-10-30T15:52:56.667427Z", "data_removed": false, "error": null, "id": "zpb2tvtbos6d7rsc7ajarszxge", "input": { "mask": null, "seed": null, "image": null, "width": 1024, "height": 1024, "prompt": "a photo of a model of a house in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "refine_steps": null, "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: 37864\nEnsuring enough disk space...\nFree disk space: 1647081304064\nDownloading weights: https://pbxt.replicate.delivery/BayFVveQSRxsLq7yLpTTq7uatp4Ric0RQit92iLYVQXHdr5IA/trained_model.tar\nb'Downloaded 186 MB bytes in 4.852s (38 MB/s)\\nExtracted 186 MB in 0.063s (3.0 GB/s)\\n'\nDownloaded weights in 5.588767051696777 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a photo of a model of a house in the style of <s0><s1>\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, 3.64it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.63it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.62it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.62it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.62it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.62it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.62it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.62it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.62it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.62it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.61it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.61it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.61it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.61it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.61it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.61it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.61it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.61it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.61it/s]\n 42%|████▏ | 21/50 [00:05<00:08, 3.61it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.61it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.61it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.60it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.60it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.60it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.60it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.60it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.60it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.60it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.60it/s]\n 64%|██████▍ | 32/50 [00:08<00:05, 3.59it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.59it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.59it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.59it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.59it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.59it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.59it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.59it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.59it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.59it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.59it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.59it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.59it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.59it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.59it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.59it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.59it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.59it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.59it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.60it/s]", "metrics": { "predict_time": 22.077514, "total_time": 26.166934 }, "output": [ "https://pbxt.replicate.delivery/XMHbPMdQJlZRINexjldNDMQ28mLpidDNvrAOSvS08ht4Gs5IA/out-0.png" ], "started_at": "2023-10-30T15:53:00.756847Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zpb2tvtbos6d7rsc7ajarszxge", "cancel": "https://api.replicate.com/v1/predictions/zpb2tvtbos6d7rsc7ajarszxge/cancel" }, "version": "535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64" }Generated inUsing seed: 37864 Ensuring enough disk space... Free disk space: 1647081304064 Downloading weights: https://pbxt.replicate.delivery/BayFVveQSRxsLq7yLpTTq7uatp4Ric0RQit92iLYVQXHdr5IA/trained_model.tar b'Downloaded 186 MB bytes in 4.852s (38 MB/s)\nExtracted 186 MB in 0.063s (3.0 GB/s)\n' Downloaded weights in 5.588767051696777 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a photo of a model of a house in the style of <s0><s1> 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, 3.64it/s] 6%|▌ | 3/50 [00:00<00:12, 3.63it/s] 8%|▊ | 4/50 [00:01<00:12, 3.62it/s] 10%|█ | 5/50 [00:01<00:12, 3.62it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.62it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.62it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.62it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.62it/s] 20%|██ | 10/50 [00:02<00:11, 3.62it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.62it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.61it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.61it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.61it/s] 30%|███ | 15/50 [00:04<00:09, 3.61it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.61it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.61it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.61it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.61it/s] 40%|████ | 20/50 [00:05<00:08, 3.61it/s] 42%|████▏ | 21/50 [00:05<00:08, 3.61it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.61it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.61it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.60it/s] 50%|█████ | 25/50 [00:06<00:06, 3.60it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.60it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.60it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.60it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.60it/s] 60%|██████ | 30/50 [00:08<00:05, 3.60it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.60it/s] 64%|██████▍ | 32/50 [00:08<00:05, 3.59it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.59it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.59it/s] 70%|███████ | 35/50 [00:09<00:04, 3.59it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.59it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.59it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.59it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.59it/s] 80%|████████ | 40/50 [00:11<00:02, 3.59it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.59it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.59it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.59it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.59it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.59it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.59it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.59it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.59it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.59it/s] 100%|██████████| 50/50 [00:13<00:00, 3.59it/s] 100%|██████████| 50/50 [00:13<00:00, 3.60it/s]Prediction
cbh123/sdxl-hereditary:535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64IDrftn6llbx3ch6stzg4n2zkn2meStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @cbh123Input
- mask
- null
- seed
- null
- image
- null
- width
- 1024
- height
- 1024
- prompt
- a photo of a family in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- refine_steps
- null
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "mask": null, "seed": null, "image": null, "width": 1024, "height": 1024, "prompt": "a photo of a family in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "refine_steps": null, "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 cbh123/sdxl-hereditary using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cbh123/sdxl-hereditary:535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64", { input: { width: 1024, height: 1024, prompt: "a photo of a family in the style of TOK", 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 cbh123/sdxl-hereditary using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cbh123/sdxl-hereditary:535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64", input={ "width": 1024, "height": 1024, "prompt": "a photo of a family in the style of TOK", "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 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cbh123/sdxl-hereditary 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": "cbh123/sdxl-hereditary:535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64", "input": { "width": 1024, "height": 1024, "prompt": "a photo of a family in the style of TOK", "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": "2023-10-30T15:55:35.060458Z", "created_at": "2023-10-30T15:55:15.966946Z", "data_removed": false, "error": null, "id": "rftn6llbx3ch6stzg4n2zkn2me", "input": { "mask": null, "seed": null, "image": null, "width": 1024, "height": 1024, "prompt": "a photo of a family in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "refine_steps": null, "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: 35556\nEnsuring enough disk space...\nFree disk space: 1556107874304\nDownloading weights: https://pbxt.replicate.delivery/BayFVveQSRxsLq7yLpTTq7uatp4Ric0RQit92iLYVQXHdr5IA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.152s (1.2 GB/s)\\nExtracted 186 MB in 0.054s (3.4 GB/s)\\n'\nDownloaded weights in 0.2915680408477783 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a photo of a family in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:14, 3.48it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.58it/s]\n 6%|▌ | 3/50 [00:00<00:13, 3.60it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.62it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.62it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.62it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.62it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.62it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.62it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.63it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.62it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.62it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.62it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.62it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.62it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.62it/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, 3.64it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.64it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.64it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.64it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.64it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.64it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.64it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.63it/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, 3.64it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.64it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.63it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.63it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.63it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.63it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.63it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.63it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.63it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.63it/s]", "metrics": { "predict_time": 16.858774, "total_time": 19.093512 }, "output": [ "https://pbxt.replicate.delivery/3eH2tG7uha2lISKkZzP7Vb66iTQ1Nq77eRG1ASPRUKO2PYzRA/out-0.png" ], "started_at": "2023-10-30T15:55:18.201684Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rftn6llbx3ch6stzg4n2zkn2me", "cancel": "https://api.replicate.com/v1/predictions/rftn6llbx3ch6stzg4n2zkn2me/cancel" }, "version": "535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64" }Generated inUsing seed: 35556 Ensuring enough disk space... Free disk space: 1556107874304 Downloading weights: https://pbxt.replicate.delivery/BayFVveQSRxsLq7yLpTTq7uatp4Ric0RQit92iLYVQXHdr5IA/trained_model.tar b'Downloaded 186 MB bytes in 0.152s (1.2 GB/s)\nExtracted 186 MB in 0.054s (3.4 GB/s)\n' Downloaded weights in 0.2915680408477783 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a photo of a family in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:14, 3.48it/s] 4%|▍ | 2/50 [00:00<00:13, 3.58it/s] 6%|▌ | 3/50 [00:00<00:13, 3.60it/s] 8%|▊ | 4/50 [00:01<00:12, 3.62it/s] 10%|█ | 5/50 [00:01<00:12, 3.62it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.62it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.62it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s] 20%|██ | 10/50 [00:02<00:11, 3.62it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.62it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.63it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.62it/s] 30%|███ | 15/50 [00:04<00:09, 3.62it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.62it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.62it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.62it/s] 40%|████ | 20/50 [00:05<00:08, 3.62it/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, 3.64it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.64it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.64it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.64it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/s] 60%|██████ | 30/50 [00:08<00:05, 3.64it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.64it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.64it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.64it/s] 70%|███████ | 35/50 [00:09<00:04, 3.64it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.63it/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, 3.64it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.64it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.63it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.63it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.63it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.63it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.63it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.63it/s] 100%|██████████| 50/50 [00:13<00:00, 3.63it/s] 100%|██████████| 50/50 [00:13<00:00, 3.63it/s]Prediction
cbh123/sdxl-hereditary:535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64ID4lafzwtbj7zi2crssokg4w5zuaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @fofrInput
- mask
- null
- seed
- null
- image
- null
- width
- 1024
- height
- 1024
- prompt
- a closeup photo of a monster in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- refine_steps
- null
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "mask": null, "seed": null, "image": null, "width": 1024, "height": 1024, "prompt": "a closeup photo of a monster in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "refine_steps": null, "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 cbh123/sdxl-hereditary using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cbh123/sdxl-hereditary:535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64", { input: { width: 1024, height: 1024, prompt: "a closeup photo of a monster in the style of TOK", 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 cbh123/sdxl-hereditary using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cbh123/sdxl-hereditary:535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64", input={ "width": 1024, "height": 1024, "prompt": "a closeup photo of a monster in the style of TOK", "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 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cbh123/sdxl-hereditary 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": "cbh123/sdxl-hereditary:535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64", "input": { "width": 1024, "height": 1024, "prompt": "a closeup photo of a monster in the style of TOK", "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": "2023-10-30T16:00:27.155778Z", "created_at": "2023-10-30T16:00:03.639631Z", "data_removed": false, "error": null, "id": "4lafzwtbj7zi2crssokg4w5zua", "input": { "mask": null, "seed": null, "image": null, "width": 1024, "height": 1024, "prompt": "a closeup photo of a monster in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "refine_steps": null, "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: 62415\nEnsuring enough disk space...\nFree disk space: 1433793462272\nDownloading weights: https://pbxt.replicate.delivery/BayFVveQSRxsLq7yLpTTq7uatp4Ric0RQit92iLYVQXHdr5IA/trained_model.tar\nb'Downloaded 186 MB bytes in 5.814s (32 MB/s)\\nExtracted 186 MB in 0.068s (2.7 GB/s)\\n'\nDownloaded weights in 6.287867069244385 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a closeup photo of a monster in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.68it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.68it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.68it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.69it/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, 3.68it/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, 3.68it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.67it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.67it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.67it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.66it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.66it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.66it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.66it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]", "metrics": { "predict_time": 22.172555, "total_time": 23.516147 }, "output": [ "https://pbxt.replicate.delivery/IuciSLFJt6YJO5ooKFf2G0HulBxJoKN1RyAiEK7czSaNKs5IA/out-0.png" ], "started_at": "2023-10-30T16:00:04.983223Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4lafzwtbj7zi2crssokg4w5zua", "cancel": "https://api.replicate.com/v1/predictions/4lafzwtbj7zi2crssokg4w5zua/cancel" }, "version": "535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64" }Generated inUsing seed: 62415 Ensuring enough disk space... Free disk space: 1433793462272 Downloading weights: https://pbxt.replicate.delivery/BayFVveQSRxsLq7yLpTTq7uatp4Ric0RQit92iLYVQXHdr5IA/trained_model.tar b'Downloaded 186 MB bytes in 5.814s (32 MB/s)\nExtracted 186 MB in 0.068s (2.7 GB/s)\n' Downloaded weights in 6.287867069244385 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a closeup photo of a monster in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.68it/s] 4%|▍ | 2/50 [00:00<00:13, 3.68it/s] 6%|▌ | 3/50 [00:00<00:12, 3.68it/s] 8%|▊ | 4/50 [00:01<00:12, 3.69it/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, 3.68it/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, 3.68it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s] 50%|█████ | 25/50 [00:06<00:06, 3.67it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.67it/s] 60%|██████ | 30/50 [00:08<00:05, 3.67it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s] 70%|███████ | 35/50 [00:09<00:04, 3.67it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.66it/s] 80%|████████ | 40/50 [00:10<00:02, 3.66it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.66it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.66it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.66it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.66it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.66it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.66it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.66it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s]Prediction
cbh123/sdxl-hereditary:535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64IDuapa2m3bnuax6hieitrjvg6ijyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- mask
- null
- seed
- null
- image
- null
- width
- 1024
- height
- 1024
- prompt
- a macro photo of a model of a house in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- refine_steps
- null
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "mask": null, "seed": null, "image": null, "width": 1024, "height": 1024, "prompt": "a macro photo of a model of a house in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "refine_steps": null, "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 cbh123/sdxl-hereditary using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cbh123/sdxl-hereditary:535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64", { input: { width: 1024, height: 1024, prompt: "a macro photo of a model of a house in the style of TOK", 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 cbh123/sdxl-hereditary using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cbh123/sdxl-hereditary:535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64", input={ "width": 1024, "height": 1024, "prompt": "a macro photo of a model of a house in the style of TOK", "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 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cbh123/sdxl-hereditary 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": "cbh123/sdxl-hereditary:535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64", "input": { "width": 1024, "height": 1024, "prompt": "a macro photo of a model of a house in the style of TOK", "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": "2023-10-30T16:15:11.414025Z", "created_at": "2023-10-30T16:14:31.382055Z", "data_removed": false, "error": null, "id": "uapa2m3bnuax6hieitrjvg6ijy", "input": { "mask": null, "seed": null, "image": null, "width": 1024, "height": 1024, "prompt": "a macro photo of a model of a house in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "refine_steps": null, "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: 9856\nEnsuring enough disk space...\nFree disk space: 2283287437312\nDownloading weights: https://pbxt.replicate.delivery/BayFVveQSRxsLq7yLpTTq7uatp4Ric0RQit92iLYVQXHdr5IA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.287s (648 MB/s)\\nExtracted 186 MB in 0.075s (2.5 GB/s)\\n'\nDownloaded weights in 0.508732795715332 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a macro photo of a model of a house in the style of <s0><s1>\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, 3.67it/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, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.66it/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, 3.65it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.65it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.65it/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, 3.66it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.66it/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, 3.66it/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, 3.65it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.64it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.64it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/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, 3.65it/s]", "metrics": { "predict_time": 16.445701, "total_time": 40.03197 }, "output": [ "https://pbxt.replicate.delivery/acHNuKBwMyoELxW4HLw7AH8V2bhi38fVfweFkVgmkiMdExmjA/out-0.png" ], "started_at": "2023-10-30T16:14:54.968324Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/uapa2m3bnuax6hieitrjvg6ijy", "cancel": "https://api.replicate.com/v1/predictions/uapa2m3bnuax6hieitrjvg6ijy/cancel" }, "version": "535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64" }Generated inUsing seed: 9856 Ensuring enough disk space... Free disk space: 2283287437312 Downloading weights: https://pbxt.replicate.delivery/BayFVveQSRxsLq7yLpTTq7uatp4Ric0RQit92iLYVQXHdr5IA/trained_model.tar b'Downloaded 186 MB bytes in 0.287s (648 MB/s)\nExtracted 186 MB in 0.075s (2.5 GB/s)\n' Downloaded weights in 0.508732795715332 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a macro photo of a model of a house in the style of <s0><s1> 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, 3.67it/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, 3.67it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.66it/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, 3.65it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.65it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.65it/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, 3.66it/s] 50%|█████ | 25/50 [00:06<00:06, 3.66it/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, 3.66it/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, 3.65it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.64it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.64it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/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, 3.65it/s]Prediction
cbh123/sdxl-hereditary:535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64IDyxz2fhtbcmmzflobtyp7tsbhsqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @cbh123Input
- mask
- null
- seed
- null
- image
- null
- width
- 1024
- height
- 1024
- prompt
- a photograph of a treehouse in the style of TOK
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- refine_steps
- null
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "mask": null, "seed": null, "image": null, "width": 1024, "height": 1024, "prompt": "a photograph of a treehouse in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "refine_steps": null, "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 cbh123/sdxl-hereditary using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cbh123/sdxl-hereditary:535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64", { input: { width: 1024, height: 1024, prompt: "a photograph of a treehouse in the style of TOK", 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 cbh123/sdxl-hereditary using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cbh123/sdxl-hereditary:535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64", input={ "width": 1024, "height": 1024, "prompt": "a photograph of a treehouse in the style of TOK", "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 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run cbh123/sdxl-hereditary 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": "cbh123/sdxl-hereditary:535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64", "input": { "width": 1024, "height": 1024, "prompt": "a photograph of a treehouse in the style of TOK", "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": "2023-10-30T16:16:09.384058Z", "created_at": "2023-10-30T16:15:50.775771Z", "data_removed": false, "error": null, "id": "yxz2fhtbcmmzflobtyp7tsbhsq", "input": { "mask": null, "seed": null, "image": null, "width": 1024, "height": 1024, "prompt": "a photograph of a treehouse in the style of TOK", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "refine_steps": null, "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: 30398\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a photograph of a treehouse in the style of <s0><s1>\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.63it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.63it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.64it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.63it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.63it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.63it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.63it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.63it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.63it/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, 3.63it/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, 3.62it/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, 3.62it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.62it/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, 3.61it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.61it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.61it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.61it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.60it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.61it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.61it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.61it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.61it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.62it/s]", "metrics": { "predict_time": 16.600223, "total_time": 18.608287 }, "output": [ "https://pbxt.replicate.delivery/r6lPodeoThwcIyvkTJwcBMgGn7ppGPPPK8CWelpPPXqIjYzRA/out-0.png" ], "started_at": "2023-10-30T16:15:52.783835Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/yxz2fhtbcmmzflobtyp7tsbhsq", "cancel": "https://api.replicate.com/v1/predictions/yxz2fhtbcmmzflobtyp7tsbhsq/cancel" }, "version": "535fdb4d34d13e899f8a61c3172ef1698230bed3c2faa0a17708abde760a5f64" }Generated inUsing seed: 30398 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a photograph of a treehouse in the style of <s0><s1> txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.63it/s] 4%|▍ | 2/50 [00:00<00:13, 3.63it/s] 6%|▌ | 3/50 [00:00<00:12, 3.64it/s] 8%|▊ | 4/50 [00:01<00:12, 3.63it/s] 10%|█ | 5/50 [00:01<00:12, 3.63it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s] 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