fofr
/
sdxl-upside-down
SDXL fine-tuned on upside down photos of people
- Public
- 170 runs
-
L40S
- SDXL fine-tune
Prediction
fofr/sdxl-upside-down:96ca22eeID6ekwgplbjgafhaweygtdqiemyaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @fofrInput
- width
- 1024
- height
- 1024
- prompt
- A TOK photo of a person
- 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": "A TOK photo of a person", "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 replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/sdxl-upside-down using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-upside-down:96ca22ee353563144869b523f36a05692f8f00a90557654f2b75be17a37bad94", { input: { width: 1024, height: 1024, prompt: "A TOK photo of a person", 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 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fofr/sdxl-upside-down using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-upside-down:96ca22ee353563144869b523f36a05692f8f00a90557654f2b75be17a37bad94", input={ "width": 1024, "height": 1024, "prompt": "A TOK photo of a person", "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.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/sdxl-upside-down 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": "96ca22ee353563144869b523f36a05692f8f00a90557654f2b75be17a37bad94", "input": { "width": 1024, "height": 1024, "prompt": "A TOK photo of a person", "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": "2023-11-03T22:21:44.383615Z", "created_at": "2023-11-03T22:21:21.591161Z", "data_removed": false, "error": null, "id": "6ekwgplbjgafhaweygtdqiemya", "input": { "width": 1024, "height": 1024, "prompt": "A TOK photo of a person", "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: 3563\nEnsuring enough disk space...\nFree disk space: 1881561550848\nDownloading weights: https://replicate.delivery/pbxt/cWUOCX3ltYZaIN6ccTYYpH9GpnklLwa2mKg4imoHavT6jMdE/trained_model.tar\nb'Downloaded 186 MB bytes in 3.557s (52 MB/s)\\nExtracted 186 MB in 0.057s (3.3 GB/s)\\n'\nDownloaded weights in 4.00781774520874 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A <s0><s1> photo of a person\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.70it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.70it/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, 3.69it/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, 3.67it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.67it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.67it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.67it/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<00:02, 3.67it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.67it/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, 3.67it/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, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]", "metrics": { "predict_time": 20.78414, "total_time": 22.792454 }, "output": [ "https://replicate.delivery/pbxt/qYTj7uYe3eobL0cucLja3HTuZMfCIwdkceYu2QUpEsf6OSmOC/out-0.png" ], "started_at": "2023-11-03T22:21:23.599475Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6ekwgplbjgafhaweygtdqiemya", "cancel": "https://api.replicate.com/v1/predictions/6ekwgplbjgafhaweygtdqiemya/cancel" }, "version": "96ca22ee353563144869b523f36a05692f8f00a90557654f2b75be17a37bad94" }Generated inUsing seed: 3563 Ensuring enough disk space... Free disk space: 1881561550848 Downloading weights: https://replicate.delivery/pbxt/cWUOCX3ltYZaIN6ccTYYpH9GpnklLwa2mKg4imoHavT6jMdE/trained_model.tar b'Downloaded 186 MB bytes in 3.557s (52 MB/s)\nExtracted 186 MB in 0.057s (3.3 GB/s)\n' Downloaded weights in 4.00781774520874 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A <s0><s1> photo of a person txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.69it/s] 4%|▍ | 2/50 [00:00<00:12, 3.70it/s] 6%|▌ | 3/50 [00:00<00:12, 3.70it/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, 3.69it/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, 3.67it/s] 40%|████ | 20/50 [00:05<00:08, 3.67it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.67it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.67it/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<00:02, 3.67it/s] 80%|████████ | 40/50 [00:10<00:02, 3.67it/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, 3.67it/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, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s]Prediction
fofr/sdxl-upside-down:96ca22eeIDsjhc3dlboa67xrxzd2zwkuptyyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A TOK photo of a beautiful person, unreal engine
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 1
- 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": "A TOK photo of a beautiful person, unreal engine", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "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 replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/sdxl-upside-down using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-upside-down:96ca22ee353563144869b523f36a05692f8f00a90557654f2b75be17a37bad94", { input: { width: 1024, height: 1024, prompt: "A TOK photo of a beautiful person, unreal engine", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 1, 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 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fofr/sdxl-upside-down using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-upside-down:96ca22ee353563144869b523f36a05692f8f00a90557654f2b75be17a37bad94", input={ "width": 1024, "height": 1024, "prompt": "A TOK photo of a beautiful person, unreal engine", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "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.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/sdxl-upside-down 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": "96ca22ee353563144869b523f36a05692f8f00a90557654f2b75be17a37bad94", "input": { "width": 1024, "height": 1024, "prompt": "A TOK photo of a beautiful person, unreal engine", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "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": "2023-11-03T22:32:46.997988Z", "created_at": "2023-11-03T22:32:31.059694Z", "data_removed": false, "error": null, "id": "sjhc3dlboa67xrxzd2zwkuptyy", "input": { "width": 1024, "height": 1024, "prompt": "A TOK photo of a beautiful person, unreal engine", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "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: 59476\nskipping loading .. weights already loaded\nPrompt: A <s0><s1> photo of a beautiful person, unreal engine\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, 3.64it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.63it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.63it/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<00:11, 3.63it/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, 3.63it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.63it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.62it/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, 3.62it/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, 3.62it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.62it/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, 3.63it/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, 3.61it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.61it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.61it/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, 3.61it/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": 15.970765, "total_time": 15.938294 }, "output": [ "https://replicate.delivery/pbxt/ZeFNimKFAbzAU6YDt7G0VOkyidO0SYMv7KhjYIxbbPTHOZ6IA/out-0.png" ], "started_at": "2023-11-03T22:32:31.027223Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/sjhc3dlboa67xrxzd2zwkuptyy", "cancel": "https://api.replicate.com/v1/predictions/sjhc3dlboa67xrxzd2zwkuptyy/cancel" }, "version": "96ca22ee353563144869b523f36a05692f8f00a90557654f2b75be17a37bad94" }Generated inUsing seed: 59476 skipping loading .. weights already loaded Prompt: A <s0><s1> photo of a beautiful person, unreal engine 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, 3.64it/s] 6%|▌ | 3/50 [00:00<00:12, 3.63it/s] 8%|▊ | 4/50 [00:01<00:12, 3.63it/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<00:11, 3.63it/s] 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3.62it/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, 3.61it/s] 80%|████████ | 40/50 [00:11<00:02, 3.61it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.61it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.61it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.61it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.61it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.61it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.61it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.61it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.61it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.61it/s] 100%|██████████| 50/50 [00:13<00:00, 3.61it/s] 100%|██████████| 50/50 [00:13<00:00, 3.62it/s]Prediction
fofr/sdxl-upside-down:96ca22eeID4dagaptbzcox5et64thpgiuwjeStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1152
- height
- 768
- prompt
- A TOK landscape photo
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 1
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- broken, distorted, ugly
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1152, "height": 768, "prompt": "A TOK landscape photo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "broken, distorted, ugly", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/sdxl-upside-down using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-upside-down:96ca22ee353563144869b523f36a05692f8f00a90557654f2b75be17a37bad94", { input: { width: 1152, height: 768, prompt: "A TOK landscape photo", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 1, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "broken, distorted, ugly", prompt_strength: 0.8, num_inference_steps: 50 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fofr/sdxl-upside-down using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-upside-down:96ca22ee353563144869b523f36a05692f8f00a90557654f2b75be17a37bad94", input={ "width": 1152, "height": 768, "prompt": "A TOK landscape photo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "broken, distorted, ugly", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/sdxl-upside-down 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": "96ca22ee353563144869b523f36a05692f8f00a90557654f2b75be17a37bad94", "input": { "width": 1152, "height": 768, "prompt": "A TOK landscape photo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "broken, distorted, ugly", "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": "2023-11-03T22:34:50.008879Z", "created_at": "2023-11-03T22:34:33.593867Z", "data_removed": false, "error": null, "id": "4dagaptbzcox5et64thpgiuwje", "input": { "width": 1152, "height": 768, "prompt": "A TOK landscape photo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "broken, distorted, ugly", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 40838\nEnsuring enough disk space...\nFree disk space: 1567556841472\nDownloading weights: https://replicate.delivery/pbxt/cWUOCX3ltYZaIN6ccTYYpH9GpnklLwa2mKg4imoHavT6jMdE/trained_model.tar\nb'Downloaded 186 MB bytes in 0.237s (785 MB/s)\\nExtracted 186 MB in 0.057s (3.3 GB/s)\\n'\nDownloaded weights in 0.39359593391418457 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A <s0><s1> landscape photo\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:11, 4.32it/s]\n 4%|▍ | 2/50 [00:00<00:11, 4.31it/s]\n 6%|▌ | 3/50 [00:00<00:10, 4.31it/s]\n 8%|▊ | 4/50 [00:00<00:10, 4.30it/s]\n 10%|█ | 5/50 [00:01<00:10, 4.28it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.28it/s]\n 14%|█▍ | 7/50 [00:01<00:10, 4.28it/s]\n 16%|█▌ | 8/50 [00:01<00:09, 4.27it/s]\n 18%|█▊ | 9/50 [00:02<00:09, 4.27it/s]\n 20%|██ | 10/50 [00:02<00:09, 4.27it/s]\n 22%|██▏ | 11/50 [00:02<00:09, 4.27it/s]\n 24%|██▍ | 12/50 [00:02<00:08, 4.27it/s]\n 26%|██▌ | 13/50 [00:03<00:08, 4.27it/s]\n 28%|██▊ | 14/50 [00:03<00:08, 4.27it/s]\n 30%|███ | 15/50 [00:03<00:08, 4.27it/s]\n 32%|███▏ | 16/50 [00:03<00:07, 4.26it/s]\n 34%|███▍ | 17/50 [00:03<00:07, 4.26it/s]\n 36%|███▌ | 18/50 [00:04<00:07, 4.26it/s]\n 38%|███▊ | 19/50 [00:04<00:07, 4.26it/s]\n 40%|████ | 20/50 [00:04<00:07, 4.26it/s]\n 42%|████▏ | 21/50 [00:04<00:06, 4.26it/s]\n 44%|████▍ | 22/50 [00:05<00:06, 4.26it/s]\n 46%|████▌ | 23/50 [00:05<00:06, 4.26it/s]\n 48%|████▊ | 24/50 [00:05<00:06, 4.26it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.26it/s]\n 52%|█████▏ | 26/50 [00:06<00:05, 4.26it/s]\n 54%|█████▍ | 27/50 [00:06<00:05, 4.26it/s]\n 56%|█████▌ | 28/50 [00:06<00:05, 4.26it/s]\n 58%|█████▊ | 29/50 [00:06<00:04, 4.26it/s]\n 60%|██████ | 30/50 [00:07<00:04, 4.25it/s]\n 62%|██████▏ | 31/50 [00:07<00:04, 4.25it/s]\n 64%|██████▍ | 32/50 [00:07<00:04, 4.25it/s]\n 66%|██████▌ | 33/50 [00:07<00:03, 4.26it/s]\n 68%|██████▊ | 34/50 [00:07<00:03, 4.25it/s]\n 70%|███████ | 35/50 [00:08<00:03, 4.25it/s]\n 72%|███████▏ | 36/50 [00:08<00:03, 4.25it/s]\n 74%|███████▍ | 37/50 [00:08<00:03, 4.25it/s]\n 76%|███████▌ | 38/50 [00:08<00:02, 4.25it/s]\n 78%|███████▊ | 39/50 [00:09<00:02, 4.25it/s]\n 80%|████████ | 40/50 [00:09<00:02, 4.25it/s]\n 82%|████████▏ | 41/50 [00:09<00:02, 4.25it/s]\n 84%|████████▍ | 42/50 [00:09<00:01, 4.25it/s]\n 86%|████████▌ | 43/50 [00:10<00:01, 4.25it/s]\n 88%|████████▊ | 44/50 [00:10<00:01, 4.25it/s]\n 90%|█████████ | 45/50 [00:10<00:01, 4.25it/s]\n 92%|█████████▏| 46/50 [00:10<00:00, 4.25it/s]\n 94%|█████████▍| 47/50 [00:11<00:00, 4.25it/s]\n 96%|█████████▌| 48/50 [00:11<00:00, 4.25it/s]\n 98%|█████████▊| 49/50 [00:11<00:00, 4.25it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.25it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.26it/s]", "metrics": { "predict_time": 14.919925, "total_time": 16.415012 }, "output": [ "https://replicate.delivery/pbxt/vAXuaTXiq6qTHx9ENOO42Jlnh0SXPDYra9GrDA2fnMvEPZ6IA/out-0.png" ], "started_at": "2023-11-03T22:34:35.088954Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4dagaptbzcox5et64thpgiuwje", "cancel": "https://api.replicate.com/v1/predictions/4dagaptbzcox5et64thpgiuwje/cancel" }, "version": "96ca22ee353563144869b523f36a05692f8f00a90557654f2b75be17a37bad94" }Generated inUsing seed: 40838 Ensuring enough disk space... Free disk space: 1567556841472 Downloading weights: https://replicate.delivery/pbxt/cWUOCX3ltYZaIN6ccTYYpH9GpnklLwa2mKg4imoHavT6jMdE/trained_model.tar b'Downloaded 186 MB bytes in 0.237s (785 MB/s)\nExtracted 186 MB in 0.057s (3.3 GB/s)\n' Downloaded weights in 0.39359593391418457 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A <s0><s1> landscape photo txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:11, 4.32it/s] 4%|▍ | 2/50 [00:00<00:11, 4.31it/s] 6%|▌ | 3/50 [00:00<00:10, 4.31it/s] 8%|▊ | 4/50 [00:00<00:10, 4.30it/s] 10%|█ | 5/50 [00:01<00:10, 4.28it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.28it/s] 14%|█▍ | 7/50 [00:01<00:10, 4.28it/s] 16%|█▌ | 8/50 [00:01<00:09, 4.27it/s] 18%|█▊ | 9/50 [00:02<00:09, 4.27it/s] 20%|██ | 10/50 [00:02<00:09, 4.27it/s] 22%|██▏ | 11/50 [00:02<00:09, 4.27it/s] 24%|██▍ | 12/50 [00:02<00:08, 4.27it/s] 26%|██▌ | 13/50 [00:03<00:08, 4.27it/s] 28%|██▊ | 14/50 [00:03<00:08, 4.27it/s] 30%|███ | 15/50 [00:03<00:08, 4.27it/s] 32%|███▏ | 16/50 [00:03<00:07, 4.26it/s] 34%|███▍ | 17/50 [00:03<00:07, 4.26it/s] 36%|███▌ | 18/50 [00:04<00:07, 4.26it/s] 38%|███▊ | 19/50 [00:04<00:07, 4.26it/s] 40%|████ | 20/50 [00:04<00:07, 4.26it/s] 42%|████▏ | 21/50 [00:04<00:06, 4.26it/s] 44%|████▍ | 22/50 [00:05<00:06, 4.26it/s] 46%|████▌ | 23/50 [00:05<00:06, 4.26it/s] 48%|████▊ | 24/50 [00:05<00:06, 4.26it/s] 50%|█████ | 25/50 [00:05<00:05, 4.26it/s] 52%|█████▏ | 26/50 [00:06<00:05, 4.26it/s] 54%|█████▍ | 27/50 [00:06<00:05, 4.26it/s] 56%|█████▌ | 28/50 [00:06<00:05, 4.26it/s] 58%|█████▊ | 29/50 [00:06<00:04, 4.26it/s] 60%|██████ | 30/50 [00:07<00:04, 4.25it/s] 62%|██████▏ | 31/50 [00:07<00:04, 4.25it/s] 64%|██████▍ | 32/50 [00:07<00:04, 4.25it/s] 66%|██████▌ | 33/50 [00:07<00:03, 4.26it/s] 68%|██████▊ | 34/50 [00:07<00:03, 4.25it/s] 70%|███████ | 35/50 [00:08<00:03, 4.25it/s] 72%|███████▏ | 36/50 [00:08<00:03, 4.25it/s] 74%|███████▍ | 37/50 [00:08<00:03, 4.25it/s] 76%|███████▌ | 38/50 [00:08<00:02, 4.25it/s] 78%|███████▊ | 39/50 [00:09<00:02, 4.25it/s] 80%|████████ | 40/50 [00:09<00:02, 4.25it/s] 82%|████████▏ | 41/50 [00:09<00:02, 4.25it/s] 84%|████████▍ | 42/50 [00:09<00:01, 4.25it/s] 86%|████████▌ | 43/50 [00:10<00:01, 4.25it/s] 88%|████████▊ | 44/50 [00:10<00:01, 4.25it/s] 90%|█████████ | 45/50 [00:10<00:01, 4.25it/s] 92%|█████████▏| 46/50 [00:10<00:00, 4.25it/s] 94%|█████████▍| 47/50 [00:11<00:00, 4.25it/s] 96%|█████████▌| 48/50 [00:11<00:00, 4.25it/s] 98%|█████████▊| 49/50 [00:11<00:00, 4.25it/s] 100%|██████████| 50/50 [00:11<00:00, 4.25it/s] 100%|██████████| 50/50 [00:11<00:00, 4.26it/s]Prediction
fofr/sdxl-upside-down:96ca22eeIDzujzwd3bivektjzl62qid6d334StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @fofrInput
- width
- 1152
- height
- 768
- prompt
- A TOK cityscape photo
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 1
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- broken, distorted, ugly
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1152, "height": 768, "prompt": "A TOK cityscape photo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "broken, distorted, ugly", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/sdxl-upside-down using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-upside-down:96ca22ee353563144869b523f36a05692f8f00a90557654f2b75be17a37bad94", { input: { width: 1152, height: 768, prompt: "A TOK cityscape photo", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 1, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "broken, distorted, ugly", prompt_strength: 0.8, num_inference_steps: 50 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fofr/sdxl-upside-down using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-upside-down:96ca22ee353563144869b523f36a05692f8f00a90557654f2b75be17a37bad94", input={ "width": 1152, "height": 768, "prompt": "A TOK cityscape photo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "broken, distorted, ugly", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/sdxl-upside-down 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": "96ca22ee353563144869b523f36a05692f8f00a90557654f2b75be17a37bad94", "input": { "width": 1152, "height": 768, "prompt": "A TOK cityscape photo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "broken, distorted, ugly", "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": "2023-11-03T22:37:13.599783Z", "created_at": "2023-11-03T22:36:59.682875Z", "data_removed": false, "error": null, "id": "zujzwd3bivektjzl62qid6d334", "input": { "width": 1152, "height": 768, "prompt": "A TOK cityscape photo", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "broken, distorted, ugly", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 42564\nskipping loading .. weights already loaded\nPrompt: A <s0><s1> cityscape photo\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:11, 4.35it/s]\n 4%|▍ | 2/50 [00:00<00:11, 4.33it/s]\n 6%|▌ | 3/50 [00:00<00:10, 4.33it/s]\n 8%|▊ | 4/50 [00:00<00:10, 4.32it/s]\n 10%|█ | 5/50 [00:01<00:10, 4.31it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.31it/s]\n 14%|█▍ | 7/50 [00:01<00:09, 4.31it/s]\n 16%|█▌ | 8/50 [00:01<00:09, 4.31it/s]\n 18%|█▊ | 9/50 [00:02<00:09, 4.30it/s]\n 20%|██ | 10/50 [00:02<00:09, 4.30it/s]\n 22%|██▏ | 11/50 [00:02<00:09, 4.30it/s]\n 24%|██▍ | 12/50 [00:02<00:08, 4.30it/s]\n 26%|██▌ | 13/50 [00:03<00:08, 4.30it/s]\n 28%|██▊ | 14/50 [00:03<00:08, 4.30it/s]\n 30%|███ | 15/50 [00:03<00:08, 4.30it/s]\n 32%|███▏ | 16/50 [00:03<00:07, 4.30it/s]\n 34%|███▍ | 17/50 [00:03<00:07, 4.30it/s]\n 36%|███▌ | 18/50 [00:04<00:07, 4.30it/s]\n 38%|███▊ | 19/50 [00:04<00:07, 4.30it/s]\n 40%|████ | 20/50 [00:04<00:06, 4.30it/s]\n 42%|████▏ | 21/50 [00:04<00:06, 4.30it/s]\n 44%|████▍ | 22/50 [00:05<00:06, 4.30it/s]\n 46%|████▌ | 23/50 [00:05<00:06, 4.30it/s]\n 48%|████▊ | 24/50 [00:05<00:06, 4.30it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.29it/s]\n 52%|█████▏ | 26/50 [00:06<00:05, 4.29it/s]\n 54%|█████▍ | 27/50 [00:06<00:05, 4.29it/s]\n 56%|█████▌ | 28/50 [00:06<00:05, 4.29it/s]\n 58%|█████▊ | 29/50 [00:06<00:04, 4.29it/s]\n 60%|██████ | 30/50 [00:06<00:04, 4.29it/s]\n 62%|██████▏ | 31/50 [00:07<00:04, 4.29it/s]\n 64%|██████▍ | 32/50 [00:07<00:04, 4.29it/s]\n 66%|██████▌ | 33/50 [00:07<00:03, 4.29it/s]\n 68%|██████▊ | 34/50 [00:07<00:03, 4.29it/s]\n 70%|███████ | 35/50 [00:08<00:03, 4.29it/s]\n 72%|███████▏ | 36/50 [00:08<00:03, 4.29it/s]\n 74%|███████▍ | 37/50 [00:08<00:03, 4.29it/s]\n 76%|███████▌ | 38/50 [00:08<00:02, 4.29it/s]\n 78%|███████▊ | 39/50 [00:09<00:02, 4.29it/s]\n 80%|████████ | 40/50 [00:09<00:02, 4.29it/s]\n 82%|████████▏ | 41/50 [00:09<00:02, 4.29it/s]\n 84%|████████▍ | 42/50 [00:09<00:01, 4.29it/s]\n 86%|████████▌ | 43/50 [00:10<00:01, 4.29it/s]\n 88%|████████▊ | 44/50 [00:10<00:01, 4.29it/s]\n 90%|█████████ | 45/50 [00:10<00:01, 4.29it/s]\n 92%|█████████▏| 46/50 [00:10<00:00, 4.29it/s]\n 94%|█████████▍| 47/50 [00:10<00:00, 4.28it/s]\n 96%|█████████▌| 48/50 [00:11<00:00, 4.28it/s]\n 98%|█████████▊| 49/50 [00:11<00:00, 4.29it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.29it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.29it/s]", "metrics": { "predict_time": 13.936731, "total_time": 13.916908 }, "output": [ "https://replicate.delivery/pbxt/acReeeFezUUeeNhfw1IkrH4zQiesHN0J3M4k5ahUlQ84Ygy0RA/out-0.png" ], "started_at": "2023-11-03T22:36:59.663052Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zujzwd3bivektjzl62qid6d334", "cancel": "https://api.replicate.com/v1/predictions/zujzwd3bivektjzl62qid6d334/cancel" }, "version": "96ca22ee353563144869b523f36a05692f8f00a90557654f2b75be17a37bad94" }Generated inUsing seed: 42564 skipping loading .. weights already loaded Prompt: A <s0><s1> cityscape photo txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:11, 4.35it/s] 4%|▍ | 2/50 [00:00<00:11, 4.33it/s] 6%|▌ | 3/50 [00:00<00:10, 4.33it/s] 8%|▊ | 4/50 [00:00<00:10, 4.32it/s] 10%|█ | 5/50 [00:01<00:10, 4.31it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.31it/s] 14%|█▍ | 7/50 [00:01<00:09, 4.31it/s] 16%|█▌ | 8/50 [00:01<00:09, 4.31it/s] 18%|█▊ | 9/50 [00:02<00:09, 4.30it/s] 20%|██ | 10/50 [00:02<00:09, 4.30it/s] 22%|██▏ | 11/50 [00:02<00:09, 4.30it/s] 24%|██▍ | 12/50 [00:02<00:08, 4.30it/s] 26%|██▌ | 13/50 [00:03<00:08, 4.30it/s] 28%|██▊ | 14/50 [00:03<00:08, 4.30it/s] 30%|███ | 15/50 [00:03<00:08, 4.30it/s] 32%|███▏ | 16/50 [00:03<00:07, 4.30it/s] 34%|███▍ | 17/50 [00:03<00:07, 4.30it/s] 36%|███▌ | 18/50 [00:04<00:07, 4.30it/s] 38%|███▊ | 19/50 [00:04<00:07, 4.30it/s] 40%|████ | 20/50 [00:04<00:06, 4.30it/s] 42%|████▏ | 21/50 [00:04<00:06, 4.30it/s] 44%|████▍ | 22/50 [00:05<00:06, 4.30it/s] 46%|████▌ | 23/50 [00:05<00:06, 4.30it/s] 48%|████▊ | 24/50 [00:05<00:06, 4.30it/s] 50%|█████ | 25/50 [00:05<00:05, 4.29it/s] 52%|█████▏ | 26/50 [00:06<00:05, 4.29it/s] 54%|█████▍ | 27/50 [00:06<00:05, 4.29it/s] 56%|█████▌ | 28/50 [00:06<00:05, 4.29it/s] 58%|█████▊ | 29/50 [00:06<00:04, 4.29it/s] 60%|██████ | 30/50 [00:06<00:04, 4.29it/s] 62%|██████▏ | 31/50 [00:07<00:04, 4.29it/s] 64%|██████▍ | 32/50 [00:07<00:04, 4.29it/s] 66%|██████▌ | 33/50 [00:07<00:03, 4.29it/s] 68%|██████▊ | 34/50 [00:07<00:03, 4.29it/s] 70%|███████ | 35/50 [00:08<00:03, 4.29it/s] 72%|███████▏ | 36/50 [00:08<00:03, 4.29it/s] 74%|███████▍ | 37/50 [00:08<00:03, 4.29it/s] 76%|███████▌ | 38/50 [00:08<00:02, 4.29it/s] 78%|███████▊ | 39/50 [00:09<00:02, 4.29it/s] 80%|████████ | 40/50 [00:09<00:02, 4.29it/s] 82%|████████▏ | 41/50 [00:09<00:02, 4.29it/s] 84%|████████▍ | 42/50 [00:09<00:01, 4.29it/s] 86%|████████▌ | 43/50 [00:10<00:01, 4.29it/s] 88%|████████▊ | 44/50 [00:10<00:01, 4.29it/s] 90%|█████████ | 45/50 [00:10<00:01, 4.29it/s] 92%|█████████▏| 46/50 [00:10<00:00, 4.29it/s] 94%|█████████▍| 47/50 [00:10<00:00, 4.28it/s] 96%|█████████▌| 48/50 [00:11<00:00, 4.28it/s] 98%|█████████▊| 49/50 [00:11<00:00, 4.29it/s] 100%|██████████| 50/50 [00:11<00:00, 4.29it/s] 100%|██████████| 50/50 [00:11<00:00, 4.29it/s]Prediction
fofr/sdxl-upside-down:96ca22eeIDcm32crlbgmekntrd4liagnnjquStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1152
- height
- 768
- prompt
- A TOK cityscape photo
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- broken, distorted, ugly
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1152, "height": 768, "prompt": "A TOK cityscape photo", "refine": "expert_ensemble_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": "broken, distorted, ugly", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/sdxl-upside-down using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-upside-down:96ca22ee353563144869b523f36a05692f8f00a90557654f2b75be17a37bad94", { input: { width: 1152, height: 768, prompt: "A TOK cityscape photo", refine: "expert_ensemble_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: "broken, distorted, ugly", prompt_strength: 0.8, num_inference_steps: 50 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fofr/sdxl-upside-down using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-upside-down:96ca22ee353563144869b523f36a05692f8f00a90557654f2b75be17a37bad94", input={ "width": 1152, "height": 768, "prompt": "A TOK cityscape photo", "refine": "expert_ensemble_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": "broken, distorted, ugly", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/sdxl-upside-down 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": "96ca22ee353563144869b523f36a05692f8f00a90557654f2b75be17a37bad94", "input": { "width": 1152, "height": 768, "prompt": "A TOK cityscape photo", "refine": "expert_ensemble_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": "broken, distorted, ugly", "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": "2023-11-03T22:38:59.716409Z", "created_at": "2023-11-03T22:38:48.176670Z", "data_removed": false, "error": null, "id": "cm32crlbgmekntrd4liagnnjqu", "input": { "width": 1152, "height": 768, "prompt": "A TOK cityscape photo", "refine": "expert_ensemble_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": "broken, distorted, ugly", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 27961\nskipping loading .. weights already loaded\nPrompt: A <s0><s1> cityscape photo\ntxt2img mode\n 0%| | 0/32 [00:00<?, ?it/s]\n 3%|▎ | 1/32 [00:00<00:07, 4.38it/s]\n 6%|▋ | 2/32 [00:00<00:06, 4.35it/s]\n 9%|▉ | 3/32 [00:00<00:06, 4.34it/s]\n 12%|█▎ | 4/32 [00:00<00:06, 4.33it/s]\n 16%|█▌ | 5/32 [00:01<00:06, 4.32it/s]\n 19%|█▉ | 6/32 [00:01<00:06, 4.32it/s]\n 22%|██▏ | 7/32 [00:01<00:05, 4.32it/s]\n 25%|██▌ | 8/32 [00:01<00:05, 4.32it/s]\n 28%|██▊ | 9/32 [00:02<00:05, 4.32it/s]\n 31%|███▏ | 10/32 [00:02<00:05, 4.31it/s]\n 34%|███▍ | 11/32 [00:02<00:04, 4.31it/s]\n 38%|███▊ | 12/32 [00:02<00:04, 4.31it/s]\n 41%|████ | 13/32 [00:03<00:04, 4.31it/s]\n 44%|████▍ | 14/32 [00:03<00:04, 4.31it/s]\n 47%|████▋ | 15/32 [00:03<00:03, 4.30it/s]\n 50%|█████ | 16/32 [00:03<00:03, 4.30it/s]\n 53%|█████▎ | 17/32 [00:03<00:03, 4.30it/s]\n 56%|█████▋ | 18/32 [00:04<00:03, 4.30it/s]\n 59%|█████▉ | 19/32 [00:04<00:03, 4.30it/s]\n 62%|██████▎ | 20/32 [00:04<00:02, 4.30it/s]\n 66%|██████▌ | 21/32 [00:04<00:02, 4.30it/s]\n 69%|██████▉ | 22/32 [00:05<00:02, 4.30it/s]\n 72%|███████▏ | 23/32 [00:05<00:02, 4.30it/s]\n 75%|███████▌ | 24/32 [00:05<00:01, 4.30it/s]\n 78%|███████▊ | 25/32 [00:05<00:01, 4.30it/s]\n 81%|████████▏ | 26/32 [00:06<00:01, 4.30it/s]\n 84%|████████▍ | 27/32 [00:06<00:01, 4.29it/s]\n 88%|████████▊ | 28/32 [00:06<00:00, 4.29it/s]\n 91%|█████████ | 29/32 [00:06<00:00, 4.29it/s]\n 94%|█████████▍| 30/32 [00:06<00:00, 4.29it/s]\n 97%|█████████▋| 31/32 [00:07<00:00, 4.29it/s]\n100%|██████████| 32/32 [00:07<00:00, 4.29it/s]\n100%|██████████| 32/32 [00:07<00:00, 4.30it/s]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:01, 5.49it/s]\n 20%|██ | 2/10 [00:00<00:01, 5.44it/s]\n 30%|███ | 3/10 [00:00<00:01, 5.43it/s]\n 40%|████ | 4/10 [00:00<00:01, 5.44it/s]\n 50%|█████ | 5/10 [00:00<00:00, 5.42it/s]\n 60%|██████ | 6/10 [00:01<00:00, 5.41it/s]\n 70%|███████ | 7/10 [00:01<00:00, 5.40it/s]\n 80%|████████ | 8/10 [00:01<00:00, 5.41it/s]\n 90%|█████████ | 9/10 [00:01<00:00, 5.41it/s]\n100%|██████████| 10/10 [00:01<00:00, 5.40it/s]\n100%|██████████| 10/10 [00:01<00:00, 5.41it/s]", "metrics": { "predict_time": 11.55844, "total_time": 11.539739 }, "output": [ "https://replicate.delivery/pbxt/1EMA9qLNgwbLCli6zb7eOERWJfWVaCWGAgfTgzGeDvXJIKTHB/out-0.png" ], "started_at": "2023-11-03T22:38:48.157969Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/cm32crlbgmekntrd4liagnnjqu", "cancel": "https://api.replicate.com/v1/predictions/cm32crlbgmekntrd4liagnnjqu/cancel" }, "version": "96ca22ee353563144869b523f36a05692f8f00a90557654f2b75be17a37bad94" }Generated inUsing seed: 27961 skipping loading .. weights already loaded Prompt: A <s0><s1> cityscape photo txt2img mode 0%| | 0/32 [00:00<?, ?it/s] 3%|▎ | 1/32 [00:00<00:07, 4.38it/s] 6%|▋ | 2/32 [00:00<00:06, 4.35it/s] 9%|▉ | 3/32 [00:00<00:06, 4.34it/s] 12%|█▎ | 4/32 [00:00<00:06, 4.33it/s] 16%|█▌ | 5/32 [00:01<00:06, 4.32it/s] 19%|█▉ | 6/32 [00:01<00:06, 4.32it/s] 22%|██▏ | 7/32 [00:01<00:05, 4.32it/s] 25%|██▌ | 8/32 [00:01<00:05, 4.32it/s] 28%|██▊ | 9/32 [00:02<00:05, 4.32it/s] 31%|███▏ | 10/32 [00:02<00:05, 4.31it/s] 34%|███▍ | 11/32 [00:02<00:04, 4.31it/s] 38%|███▊ | 12/32 [00:02<00:04, 4.31it/s] 41%|████ | 13/32 [00:03<00:04, 4.31it/s] 44%|████▍ | 14/32 [00:03<00:04, 4.31it/s] 47%|████▋ | 15/32 [00:03<00:03, 4.30it/s] 50%|█████ | 16/32 [00:03<00:03, 4.30it/s] 53%|█████▎ | 17/32 [00:03<00:03, 4.30it/s] 56%|█████▋ | 18/32 [00:04<00:03, 4.30it/s] 59%|█████▉ | 19/32 [00:04<00:03, 4.30it/s] 62%|██████▎ | 20/32 [00:04<00:02, 4.30it/s] 66%|██████▌ | 21/32 [00:04<00:02, 4.30it/s] 69%|██████▉ | 22/32 [00:05<00:02, 4.30it/s] 72%|███████▏ | 23/32 [00:05<00:02, 4.30it/s] 75%|███████▌ | 24/32 [00:05<00:01, 4.30it/s] 78%|███████▊ | 25/32 [00:05<00:01, 4.30it/s] 81%|████████▏ | 26/32 [00:06<00:01, 4.30it/s] 84%|████████▍ | 27/32 [00:06<00:01, 4.29it/s] 88%|████████▊ | 28/32 [00:06<00:00, 4.29it/s] 91%|█████████ | 29/32 [00:06<00:00, 4.29it/s] 94%|█████████▍| 30/32 [00:06<00:00, 4.29it/s] 97%|█████████▋| 31/32 [00:07<00:00, 4.29it/s] 100%|██████████| 32/32 [00:07<00:00, 4.29it/s] 100%|██████████| 32/32 [00:07<00:00, 4.30it/s] 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:01, 5.49it/s] 20%|██ | 2/10 [00:00<00:01, 5.44it/s] 30%|███ | 3/10 [00:00<00:01, 5.43it/s] 40%|████ | 4/10 [00:00<00:01, 5.44it/s] 50%|█████ | 5/10 [00:00<00:00, 5.42it/s] 60%|██████ | 6/10 [00:01<00:00, 5.41it/s] 70%|███████ | 7/10 [00:01<00:00, 5.40it/s] 80%|████████ | 8/10 [00:01<00:00, 5.41it/s] 90%|█████████ | 9/10 [00:01<00:00, 5.41it/s] 100%|██████████| 10/10 [00:01<00:00, 5.40it/s] 100%|██████████| 10/10 [00:01<00:00, 5.41it/s]
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