jordancoult / sdxl-crossview
(Updated 1 year, 3 months ago)
- Public
- 88 runs
- SDXL fine-tune
Prediction
jordancoult/sdxl-crossview:e0d36fa4f27eda4aae33a8d8fe2333c115624368d88fdc609f3d382e496f9796IDo24zo3dbel3lvfeh3bz2ojocuuStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 1001
- width
- 1024
- height
- 1024
- prompt
- TOK crossview photo of a young woman in LA. Cross eye 3D photo. Two images, split down the middle. Perspect, depth, 3D
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.7
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- low quality, fake, 2D
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "seed": 1001, "width": 1024, "height": 1024, "prompt": "TOK crossview photo of a young woman in LA. Cross eye 3D photo. Two images, split down the middle. Perspect, depth, 3D", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.7, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "low quality, fake, 2D", "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 jordancoult/sdxl-crossview using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jordancoult/sdxl-crossview:e0d36fa4f27eda4aae33a8d8fe2333c115624368d88fdc609f3d382e496f9796", { input: { seed: 1001, width: 1024, height: 1024, prompt: "TOK crossview photo of a young woman in LA. Cross eye 3D photo. Two images, split down the middle. Perspect, depth, 3D", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.7, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "low quality, fake, 2D", 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 jordancoult/sdxl-crossview using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jordancoult/sdxl-crossview:e0d36fa4f27eda4aae33a8d8fe2333c115624368d88fdc609f3d382e496f9796", input={ "seed": 1001, "width": 1024, "height": 1024, "prompt": "TOK crossview photo of a young woman in LA. Cross eye 3D photo. Two images, split down the middle. Perspect, depth, 3D", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.7, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "low quality, fake, 2D", "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 jordancoult/sdxl-crossview 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": "jordancoult/sdxl-crossview:e0d36fa4f27eda4aae33a8d8fe2333c115624368d88fdc609f3d382e496f9796", "input": { "seed": 1001, "width": 1024, "height": 1024, "prompt": "TOK crossview photo of a young woman in LA. Cross eye 3D photo. Two images, split down the middle. Perspect, depth, 3D", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.7, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "low quality, fake, 2D", "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": "2024-03-22T02:29:37.848745Z", "created_at": "2024-03-22T02:28:58.128219Z", "data_removed": false, "error": null, "id": "o24zo3dbel3lvfeh3bz2ojocuu", "input": { "seed": 1001, "width": 1024, "height": 1024, "prompt": "TOK crossview photo of a young woman in LA. Cross eye 3D photo. Two images, split down the middle. Perspect, depth, 3D", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.7, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "low quality, fake, 2D", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 1001\nEnsuring enough disk space...\nFree disk space: 2051305418752\nDownloading weights: https://replicate.delivery/pbxt/k8neCRfpYyhtGkgsJJAnfzDSEZd79QDlgtMLnEed4jpduwHKB/trained_model.tar\n2024-03-22T02:29:17Z | INFO | [ Initiating ] dest=/src/weights-cache/7f32ccaf6dc0b197 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/k8neCRfpYyhtGkgsJJAnfzDSEZd79QDlgtMLnEed4jpduwHKB/trained_model.tar\n2024-03-22T02:29:22Z | INFO | [ Complete ] dest=/src/weights-cache/7f32ccaf6dc0b197 size=\"186 MB\" total_elapsed=4.763s url=https://replicate.delivery/pbxt/k8neCRfpYyhtGkgsJJAnfzDSEZd79QDlgtMLnEed4jpduwHKB/trained_model.tar\nb''\nDownloaded weights in 4.858321905136108 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1> crossview photo of a young woman in LA. Cross eye 3D photo. Two images, split down the middle. Perspect, depth, 3D\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.70it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.68it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.67it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.66it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.66it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.66it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.67it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.66it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.67it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.66it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.66it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.67it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.66it/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 [00:09<00:04, 3.66it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.66it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/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 [00:12<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, 3.66it/s]", "metrics": { "predict_time": 20.545245, "total_time": 39.720526 }, "output": [ "https://replicate.delivery/pbxt/FWHp7Owye1xOUaxhYFbseE0g8Flm8xpPpf9d95fhSp0HxnKKB/out-0.png" ], "started_at": "2024-03-22T02:29:17.303500Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/o24zo3dbel3lvfeh3bz2ojocuu", "cancel": "https://api.replicate.com/v1/predictions/o24zo3dbel3lvfeh3bz2ojocuu/cancel" }, "version": "e0d36fa4f27eda4aae33a8d8fe2333c115624368d88fdc609f3d382e496f9796" }Generated inUsing seed: 1001 Ensuring enough disk space... Free disk space: 2051305418752 Downloading weights: https://replicate.delivery/pbxt/k8neCRfpYyhtGkgsJJAnfzDSEZd79QDlgtMLnEed4jpduwHKB/trained_model.tar 2024-03-22T02:29:17Z | INFO | [ Initiating ] dest=/src/weights-cache/7f32ccaf6dc0b197 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/k8neCRfpYyhtGkgsJJAnfzDSEZd79QDlgtMLnEed4jpduwHKB/trained_model.tar 2024-03-22T02:29:22Z | INFO | [ Complete ] dest=/src/weights-cache/7f32ccaf6dc0b197 size="186 MB" total_elapsed=4.763s url=https://replicate.delivery/pbxt/k8neCRfpYyhtGkgsJJAnfzDSEZd79QDlgtMLnEed4jpduwHKB/trained_model.tar b'' Downloaded weights in 4.858321905136108 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: <s0><s1> crossview photo of a young woman in LA. Cross eye 3D photo. Two images, split down the middle. Perspect, depth, 3D txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.70it/s] 4%|▍ | 2/50 [00:00<00:13, 3.68it/s] 6%|▌ | 3/50 [00:00<00:12, 3.67it/s] 8%|▊ | 4/50 [00:01<00:12, 3.66it/s] 10%|█ | 5/50 [00:01<00:12, 3.66it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.66it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s] 20%|██ | 10/50 [00:02<00:10, 3.67it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.67it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.66it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s] 30%|███ | 15/50 [00:04<00:09, 3.67it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.66it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s] 40%|████ | 20/50 [00:05<00:08, 3.66it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.67it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.67it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.66it/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 [00:09<00:04, 3.66it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s] 70%|███████ | 35/50 [00:09<00:04, 3.66it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/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 [00:12<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, 3.66it/s]Prediction
jordancoult/sdxl-crossview:e0d36fa4f27eda4aae33a8d8fe2333c115624368d88fdc609f3d382e496f9796IDpqcnbnlbesmfvjv6o7ks6lldkeStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 1001
- width
- 1024
- height
- 1024
- prompt
- TOK crossview photo of a woman in a house. Cross eye 3D photo. Two images, split down the middle. Perspect, depth, 3D
- 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
- low quality, fake, 2D
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "seed": 1001, "width": 1024, "height": 1024, "prompt": "TOK crossview photo of a woman in a house. Cross eye 3D photo. Two images, split down the middle. Perspect, depth, 3D", "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": "low quality, fake, 2D", "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 jordancoult/sdxl-crossview using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jordancoult/sdxl-crossview:e0d36fa4f27eda4aae33a8d8fe2333c115624368d88fdc609f3d382e496f9796", { input: { seed: 1001, width: 1024, height: 1024, prompt: "TOK crossview photo of a woman in a house. Cross eye 3D photo. Two images, split down the middle. Perspect, depth, 3D", 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: "low quality, fake, 2D", 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 jordancoult/sdxl-crossview using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jordancoult/sdxl-crossview:e0d36fa4f27eda4aae33a8d8fe2333c115624368d88fdc609f3d382e496f9796", input={ "seed": 1001, "width": 1024, "height": 1024, "prompt": "TOK crossview photo of a woman in a house. Cross eye 3D photo. Two images, split down the middle. Perspect, depth, 3D", "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": "low quality, fake, 2D", "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 jordancoult/sdxl-crossview 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": "jordancoult/sdxl-crossview:e0d36fa4f27eda4aae33a8d8fe2333c115624368d88fdc609f3d382e496f9796", "input": { "seed": 1001, "width": 1024, "height": 1024, "prompt": "TOK crossview photo of a woman in a house. Cross eye 3D photo. Two images, split down the middle. Perspect, depth, 3D", "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": "low quality, fake, 2D", "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": "2024-03-19T22:42:36.044717Z", "created_at": "2024-03-19T22:42:17.818906Z", "data_removed": false, "error": null, "id": "pqcnbnlbesmfvjv6o7ks6lldke", "input": { "seed": 1001, "width": 1024, "height": 1024, "prompt": "TOK crossview photo of a woman in a house. Cross eye 3D photo. Two images, split down the middle. Perspect, depth, 3D", "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": "low quality, fake, 2D", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 1001\nEnsuring enough disk space...\nFree disk space: 2495617396736\nDownloading weights: https://replicate.delivery/pbxt/k8neCRfpYyhtGkgsJJAnfzDSEZd79QDlgtMLnEed4jpduwHKB/trained_model.tar\n2024-03-19T22:42:19Z | INFO | [ Initiating ] dest=/src/weights-cache/7f32ccaf6dc0b197 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/k8neCRfpYyhtGkgsJJAnfzDSEZd79QDlgtMLnEed4jpduwHKB/trained_model.tar\n2024-03-19T22:42:20Z | INFO | [ Complete ] dest=/src/weights-cache/7f32ccaf6dc0b197 size=\"186 MB\" total_elapsed=0.536s url=https://replicate.delivery/pbxt/k8neCRfpYyhtGkgsJJAnfzDSEZd79QDlgtMLnEed4jpduwHKB/trained_model.tar\nb''\nDownloaded weights in 0.6795947551727295 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1> crossview photo of a woman in a house. Cross eye 3D photo. Two images, split down the middle. Perspect, depth, 3D\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, 3.66it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.66it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.65it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.64it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.64it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.64it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.64it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.64it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.64it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.63it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.63it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.63it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.64it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.63it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.63it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.63it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.63it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.63it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.63it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.63it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.63it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.63it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.63it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.63it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.63it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.63it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.62it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.62it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.62it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.62it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.62it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.62it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.62it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/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, 3.62it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.62it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.62it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.62it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.63it/s]", "metrics": { "predict_time": 16.484642, "total_time": 18.225811 }, "output": [ "https://replicate.delivery/pbxt/81dyKxfcEnzsXqmvdP7Tjh4oFOhx5PA7JH610s3jNM2tNehSA/out-0.png" ], "started_at": "2024-03-19T22:42:19.560075Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/pqcnbnlbesmfvjv6o7ks6lldke", "cancel": "https://api.replicate.com/v1/predictions/pqcnbnlbesmfvjv6o7ks6lldke/cancel" }, "version": "e0d36fa4f27eda4aae33a8d8fe2333c115624368d88fdc609f3d382e496f9796" }Generated inUsing seed: 1001 Ensuring enough disk space... Free disk space: 2495617396736 Downloading weights: https://replicate.delivery/pbxt/k8neCRfpYyhtGkgsJJAnfzDSEZd79QDlgtMLnEed4jpduwHKB/trained_model.tar 2024-03-19T22:42:19Z | INFO | [ Initiating ] dest=/src/weights-cache/7f32ccaf6dc0b197 minimum_chunk_size=150M url=https://replicate.delivery/pbxt/k8neCRfpYyhtGkgsJJAnfzDSEZd79QDlgtMLnEed4jpduwHKB/trained_model.tar 2024-03-19T22:42:20Z | INFO | [ Complete ] dest=/src/weights-cache/7f32ccaf6dc0b197 size="186 MB" total_elapsed=0.536s url=https://replicate.delivery/pbxt/k8neCRfpYyhtGkgsJJAnfzDSEZd79QDlgtMLnEed4jpduwHKB/trained_model.tar b'' Downloaded weights in 0.6795947551727295 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: <s0><s1> crossview photo of a woman in a house. Cross eye 3D photo. Two images, split down the middle. Perspect, depth, 3D 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, 3.66it/s] 6%|▌ | 3/50 [00:00<00:12, 3.66it/s] 8%|▊ | 4/50 [00:01<00:12, 3.65it/s] 10%|█ | 5/50 [00:01<00:12, 3.64it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.63it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.64it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.64it/s] 20%|██ | 10/50 [00:02<00:10, 3.64it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.64it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s] 30%|███ | 15/50 [00:04<00:09, 3.64it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.63it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s] 40%|████ | 20/50 [00:05<00:08, 3.63it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.63it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.64it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.63it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.63it/s] 50%|█████ | 25/50 [00:06<00:06, 3.63it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.63it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.63it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.63it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.63it/s] 60%|██████ | 30/50 [00:08<00:05, 3.63it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.63it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.63it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.63it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s] 70%|███████ | 35/50 [00:09<00:04, 3.63it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.63it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.62it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.62it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.62it/s] 80%|████████ | 40/50 [00:11<00:02, 3.62it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.62it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.62it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.62it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/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, 3.62it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.62it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.62it/s] 100%|██████████| 50/50 [00:13<00:00, 3.62it/s] 100%|██████████| 50/50 [00:13<00:00, 3.63it/s]Prediction
jordancoult/sdxl-crossview:e0d36fa4f27eda4aae33a8d8fe2333c115624368d88fdc609f3d382e496f9796ID4qmud43bvmrkpirerq4fqn3e6eStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 1001
- width
- 1024
- height
- 1024
- prompt
- TOK crossview selfie of man in the street. Cross eye 3D photo.
- 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
- low quality, fake, 2D
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "seed": 1001, "width": 1024, "height": 1024, "prompt": "TOK crossview selfie of man in the street. Cross eye 3D photo.", "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": "low quality, fake, 2D", "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 jordancoult/sdxl-crossview using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jordancoult/sdxl-crossview:e0d36fa4f27eda4aae33a8d8fe2333c115624368d88fdc609f3d382e496f9796", { input: { seed: 1001, width: 1024, height: 1024, prompt: "TOK crossview selfie of man in the street. Cross eye 3D photo.", 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: "low quality, fake, 2D", 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 jordancoult/sdxl-crossview using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jordancoult/sdxl-crossview:e0d36fa4f27eda4aae33a8d8fe2333c115624368d88fdc609f3d382e496f9796", input={ "seed": 1001, "width": 1024, "height": 1024, "prompt": "TOK crossview selfie of man in the street. Cross eye 3D photo.", "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": "low quality, fake, 2D", "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 jordancoult/sdxl-crossview 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": "jordancoult/sdxl-crossview:e0d36fa4f27eda4aae33a8d8fe2333c115624368d88fdc609f3d382e496f9796", "input": { "seed": 1001, "width": 1024, "height": 1024, "prompt": "TOK crossview selfie of man in the street. Cross eye 3D photo.", "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": "low quality, fake, 2D", "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": "2024-03-19T22:52:53.785915Z", "created_at": "2024-03-19T22:52:30.606986Z", "data_removed": false, "error": null, "id": "4qmud43bvmrkpirerq4fqn3e6e", "input": { "seed": 1001, "width": 1024, "height": 1024, "prompt": "TOK crossview selfie of man in the street. Cross eye 3D photo.", "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": "low quality, fake, 2D", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 1001\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1> crossview selfie of man in the street. Cross eye 3D photo.\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:12, 3.83it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.82it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.80it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.79it/s]\n 10%|█ | 5/50 [00:01<00:11, 3.80it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.80it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.79it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.79it/s]\n 18%|█▊ | 9/50 [00:02<00:10, 3.79it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.79it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.79it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.78it/s]\n 26%|██▌ | 13/50 [00:03<00:09, 3.79it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.79it/s]\n 30%|███ | 15/50 [00:03<00:09, 3.78it/s]\n 32%|███▏ | 16/50 [00:04<00:08, 3.78it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.78it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.78it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.78it/s]\n 40%|████ | 20/50 [00:05<00:07, 3.78it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.78it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.78it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.78it/s]\n 48%|████▊ | 24/50 [00:06<00:06, 3.77it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.78it/s]\n 52%|█████▏ | 26/50 [00:06<00:06, 3.78it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.78it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.78it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.78it/s]\n 60%|██████ | 30/50 [00:07<00:05, 3.77it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.77it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.77it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.77it/s]\n 68%|██████▊ | 34/50 [00:08<00:04, 3.77it/s]\n 70%|███████ | 35/50 [00:09<00:03, 3.77it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.77it/s]\n 74%|███████▍ | 37/50 [00:09<00:03, 3.77it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.77it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.77it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.77it/s]\n 82%|████████▏ | 41/50 [00:10<00:02, 3.77it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.78it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.78it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.77it/s]\n 90%|█████████ | 45/50 [00:11<00:01, 3.77it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.77it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.77it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.77it/s]\n 98%|█████████▊| 49/50 [00:12<00:00, 3.77it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.77it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.78it/s]", "metrics": { "predict_time": 16.267485, "total_time": 23.178929 }, "output": [ "https://replicate.delivery/pbxt/ZBBDefr79noOAEZJ7W5Zotao1KX1KMTkJRt1xYEiRW8El8hSA/out-0.png" ], "started_at": "2024-03-19T22:52:37.518430Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4qmud43bvmrkpirerq4fqn3e6e", "cancel": "https://api.replicate.com/v1/predictions/4qmud43bvmrkpirerq4fqn3e6e/cancel" }, "version": "e0d36fa4f27eda4aae33a8d8fe2333c115624368d88fdc609f3d382e496f9796" }Generated inUsing seed: 1001 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: <s0><s1> crossview selfie of man in the street. Cross eye 3D photo. txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:12, 3.83it/s] 4%|▍ | 2/50 [00:00<00:12, 3.82it/s] 6%|▌ | 3/50 [00:00<00:12, 3.80it/s] 8%|▊ | 4/50 [00:01<00:12, 3.79it/s] 10%|█ | 5/50 [00:01<00:11, 3.80it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.80it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.79it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.79it/s] 18%|█▊ | 9/50 [00:02<00:10, 3.79it/s] 20%|██ | 10/50 [00:02<00:10, 3.79it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.79it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.78it/s] 26%|██▌ | 13/50 [00:03<00:09, 3.79it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.79it/s] 30%|███ | 15/50 [00:03<00:09, 3.78it/s] 32%|███▏ | 16/50 [00:04<00:08, 3.78it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.78it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.78it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.78it/s] 40%|████ | 20/50 [00:05<00:07, 3.78it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.78it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.78it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.78it/s] 48%|████▊ | 24/50 [00:06<00:06, 3.77it/s] 50%|█████ | 25/50 [00:06<00:06, 3.78it/s] 52%|█████▏ | 26/50 [00:06<00:06, 3.78it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.78it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.78it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.78it/s] 60%|██████ | 30/50 [00:07<00:05, 3.77it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.77it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.77it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.77it/s] 68%|██████▊ | 34/50 [00:08<00:04, 3.77it/s] 70%|███████ | 35/50 [00:09<00:03, 3.77it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.77it/s] 74%|███████▍ | 37/50 [00:09<00:03, 3.77it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.77it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.77it/s] 80%|████████ | 40/50 [00:10<00:02, 3.77it/s] 82%|████████▏ | 41/50 [00:10<00:02, 3.77it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.78it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.78it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.77it/s] 90%|█████████ | 45/50 [00:11<00:01, 3.77it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.77it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.77it/s] 96%|█████████▌| 48/50 [00:12<00:00, 3.77it/s] 98%|█████████▊| 49/50 [00:12<00:00, 3.77it/s] 100%|██████████| 50/50 [00:13<00:00, 3.77it/s] 100%|██████████| 50/50 [00:13<00:00, 3.78it/s]
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