fermatresearch
/
sdxl-outpainting-lora
An improved outpainting model that supports LoRA urls. This model uses PatchMatch to improve the mask quality.
- Public
- 73.7K runs
-
L40S
- GitHub
Prediction
fermatresearch/sdxl-outpainting-lora:a542ccf3IDnnpqpq3bdhpiacmadbwjn45hu4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- image
- prompt
- a rocket taking off
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- outpaint_size
- 384
- guidance_scale
- 7.5
- apply_watermark
- condition_scale
- 0.25
- negative_prompt
- outpaint_direction
- up
{ "image": "https://replicate.delivery/pbxt/JxMPqburslwiAuLm53BfmsT1FUKwj95mNuXU7x2MCYLohRmQ/out-0%20%281%29.png", "prompt": "a rocket taking off", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "outpaint_size": 384, "guidance_scale": 7.5, "apply_watermark": false, "condition_scale": 0.25, "negative_prompt": "", "outpaint_direction": "up" }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 fermatresearch/sdxl-outpainting-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fermatresearch/sdxl-outpainting-lora:a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804", { input: { image: "https://replicate.delivery/pbxt/JxMPqburslwiAuLm53BfmsT1FUKwj95mNuXU7x2MCYLohRmQ/out-0%20%281%29.png", prompt: "a rocket taking off", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, condition_scale: 0.25, negative_prompt: "" } } ); 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 fermatresearch/sdxl-outpainting-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fermatresearch/sdxl-outpainting-lora:a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804", input={ "image": "https://replicate.delivery/pbxt/JxMPqburslwiAuLm53BfmsT1FUKwj95mNuXU7x2MCYLohRmQ/out-0%20%281%29.png", "prompt": "a rocket taking off", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "condition_scale": 0.25, "negative_prompt": "" } ) 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 fermatresearch/sdxl-outpainting-lora 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": "a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804", "input": { "image": "https://replicate.delivery/pbxt/JxMPqburslwiAuLm53BfmsT1FUKwj95mNuXU7x2MCYLohRmQ/out-0%20%281%29.png", "prompt": "a rocket taking off", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "condition_scale": 0.25, "negative_prompt": "" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-11-28T15:48:20.625855Z", "created_at": "2023-11-28T15:47:43.852871Z", "data_removed": false, "error": null, "id": "nnpqpq3bdhpiacmadbwjn45hu4", "input": { "image": "https://replicate.delivery/pbxt/JxMPqburslwiAuLm53BfmsT1FUKwj95mNuXU7x2MCYLohRmQ/out-0%20%281%29.png", "prompt": "a rocket taking off", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "outpaint_size": 384, "guidance_scale": 7.5, "apply_watermark": false, "condition_scale": 0.25, "negative_prompt": "", "outpaint_direction": "up" }, "logs": "Using seed: 57989\nApplying smart preprocessing...\n 0%| | 0/24 [00:00<?, ?it/s]\n 4%|▍ | 1/24 [00:00<00:13, 1.73it/s]\n 8%|▊ | 2/24 [00:01<00:12, 1.76it/s]\n 12%|█▎ | 3/24 [00:01<00:11, 1.77it/s]\n 17%|█▋ | 4/24 [00:02<00:11, 1.77it/s]\n 21%|██ | 5/24 [00:02<00:10, 1.77it/s]\n 25%|██▌ | 6/24 [00:03<00:10, 1.77it/s]\n 29%|██▉ | 7/24 [00:03<00:09, 1.77it/s]\n 33%|███▎ | 8/24 [00:04<00:09, 1.77it/s]\n 38%|███▊ | 9/24 [00:05<00:08, 1.77it/s]\n 42%|████▏ | 10/24 [00:05<00:07, 1.77it/s]\n 46%|████▌ | 11/24 [00:06<00:07, 1.77it/s]\n 50%|█████ | 12/24 [00:06<00:06, 1.77it/s]\n 54%|█████▍ | 13/24 [00:07<00:06, 1.77it/s]\n 58%|█████▊ | 14/24 [00:07<00:05, 1.77it/s]\n 62%|██████▎ | 15/24 [00:08<00:05, 1.77it/s]\n 67%|██████▋ | 16/24 [00:09<00:04, 1.77it/s]\n 71%|███████ | 17/24 [00:09<00:03, 1.76it/s]\n 75%|███████▌ | 18/24 [00:10<00:03, 1.77it/s]\n 79%|███████▉ | 19/24 [00:10<00:02, 1.76it/s]\n 83%|████████▎ | 20/24 [00:11<00:02, 1.76it/s]\n 88%|████████▊ | 21/24 [00:11<00:01, 1.76it/s]\n 92%|█████████▏| 22/24 [00:12<00:01, 1.76it/s]\n 96%|█████████▌| 23/24 [00:13<00:00, 1.76it/s]\n100%|██████████| 24/24 [00:13<00:00, 1.76it/s]\n100%|██████████| 24/24 [00:13<00:00, 1.77it/s]", "metrics": { "predict_time": 36.766624, "total_time": 36.772984 }, "output": [ "https://replicate.delivery/pbxt/WYFfYrzreUpna07rEmES5xlD4QWLpGBMENP6DFfT2dOIu35jA/out-0.png" ], "started_at": "2023-11-28T15:47:43.859231Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/nnpqpq3bdhpiacmadbwjn45hu4", "cancel": "https://api.replicate.com/v1/predictions/nnpqpq3bdhpiacmadbwjn45hu4/cancel" }, "version": "9f8633e47599f8614d480947c96497f088ddfeac80690d20ffcc78eb42955c7f" }Generated inUsing seed: 57989 Applying smart preprocessing... 0%| | 0/24 [00:00<?, ?it/s] 4%|▍ | 1/24 [00:00<00:13, 1.73it/s] 8%|▊ | 2/24 [00:01<00:12, 1.76it/s] 12%|█▎ | 3/24 [00:01<00:11, 1.77it/s] 17%|█▋ | 4/24 [00:02<00:11, 1.77it/s] 21%|██ | 5/24 [00:02<00:10, 1.77it/s] 25%|██▌ | 6/24 [00:03<00:10, 1.77it/s] 29%|██▉ | 7/24 [00:03<00:09, 1.77it/s] 33%|███▎ | 8/24 [00:04<00:09, 1.77it/s] 38%|███▊ | 9/24 [00:05<00:08, 1.77it/s] 42%|████▏ | 10/24 [00:05<00:07, 1.77it/s] 46%|████▌ | 11/24 [00:06<00:07, 1.77it/s] 50%|█████ | 12/24 [00:06<00:06, 1.77it/s] 54%|█████▍ | 13/24 [00:07<00:06, 1.77it/s] 58%|█████▊ | 14/24 [00:07<00:05, 1.77it/s] 62%|██████▎ | 15/24 [00:08<00:05, 1.77it/s] 67%|██████▋ | 16/24 [00:09<00:04, 1.77it/s] 71%|███████ | 17/24 [00:09<00:03, 1.76it/s] 75%|███████▌ | 18/24 [00:10<00:03, 1.77it/s] 79%|███████▉ | 19/24 [00:10<00:02, 1.76it/s] 83%|████████▎ | 20/24 [00:11<00:02, 1.76it/s] 88%|████████▊ | 21/24 [00:11<00:01, 1.76it/s] 92%|█████████▏| 22/24 [00:12<00:01, 1.76it/s] 96%|█████████▌| 23/24 [00:13<00:00, 1.76it/s] 100%|██████████| 24/24 [00:13<00:00, 1.76it/s] 100%|██████████| 24/24 [00:13<00:00, 1.77it/s]Prediction
fermatresearch/sdxl-outpainting-lora:a542ccf3IDelyr4ytbddjjoiwgbb5lbjevtqStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- image
- prompt
- a woman wearing a colorful suit in Milano
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- outpaint_size
- 128
- guidance_scale
- 7.5
- apply_watermark
- condition_scale
- 0.25
- negative_prompt
- outpaint_direction
- right
{ "image": "https://replicate.delivery/pbxt/JxMAAcnTVc2m2ckv52hyZQPUnOQCFut7OSrqNH99YIzJqXBp/test.jpeg", "prompt": "a woman wearing a colorful suit in Milano", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "outpaint_size": 128, "guidance_scale": 7.5, "apply_watermark": false, "condition_scale": 0.25, "negative_prompt": "", "outpaint_direction": "right" }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 fermatresearch/sdxl-outpainting-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fermatresearch/sdxl-outpainting-lora:a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804", { input: { image: "https://replicate.delivery/pbxt/JxMAAcnTVc2m2ckv52hyZQPUnOQCFut7OSrqNH99YIzJqXBp/test.jpeg", prompt: "a woman wearing a colorful suit in Milano", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, condition_scale: 0.25, negative_prompt: "" } } ); 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 fermatresearch/sdxl-outpainting-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fermatresearch/sdxl-outpainting-lora:a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804", input={ "image": "https://replicate.delivery/pbxt/JxMAAcnTVc2m2ckv52hyZQPUnOQCFut7OSrqNH99YIzJqXBp/test.jpeg", "prompt": "a woman wearing a colorful suit in Milano", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "condition_scale": 0.25, "negative_prompt": "" } ) 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 fermatresearch/sdxl-outpainting-lora 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": "a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804", "input": { "image": "https://replicate.delivery/pbxt/JxMAAcnTVc2m2ckv52hyZQPUnOQCFut7OSrqNH99YIzJqXBp/test.jpeg", "prompt": "a woman wearing a colorful suit in Milano", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "condition_scale": 0.25, "negative_prompt": "" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-11-28T15:31:51.784612Z", "created_at": "2023-11-28T15:31:11.749311Z", "data_removed": false, "error": null, "id": "elyr4ytbddjjoiwgbb5lbjevtq", "input": { "image": "https://replicate.delivery/pbxt/JxMAAcnTVc2m2ckv52hyZQPUnOQCFut7OSrqNH99YIzJqXBp/test.jpeg", "prompt": "a woman wearing a colorful suit in Milano", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "outpaint_size": 128, "guidance_scale": 7.5, "apply_watermark": false, "condition_scale": 0.25, "negative_prompt": "", "outpaint_direction": "right" }, "logs": "Using seed: 30702\nApplying smart preprocessing...\n 0%| | 0/24 [00:00<?, ?it/s]\n 4%|▍ | 1/24 [00:00<00:08, 2.71it/s]\n 8%|▊ | 2/24 [00:00<00:07, 2.79it/s]\n 12%|█▎ | 3/24 [00:01<00:07, 2.81it/s]\n 17%|█▋ | 4/24 [00:01<00:07, 2.83it/s]\n 21%|██ | 5/24 [00:01<00:06, 2.84it/s]\n 25%|██▌ | 6/24 [00:02<00:06, 2.85it/s]\n 29%|██▉ | 7/24 [00:02<00:05, 2.86it/s]\n 33%|███▎ | 8/24 [00:02<00:05, 2.86it/s]\n 38%|███▊ | 9/24 [00:03<00:05, 2.86it/s]\n 42%|████▏ | 10/24 [00:03<00:04, 2.86it/s]\n 46%|████▌ | 11/24 [00:03<00:04, 2.86it/s]\n 50%|█████ | 12/24 [00:04<00:04, 2.86it/s]\n 54%|█████▍ | 13/24 [00:04<00:03, 2.86it/s]\n 58%|█████▊ | 14/24 [00:04<00:03, 2.86it/s]\n 62%|██████▎ | 15/24 [00:05<00:03, 2.86it/s]\n 67%|██████▋ | 16/24 [00:05<00:02, 2.86it/s]\n 71%|███████ | 17/24 [00:05<00:02, 2.86it/s]\n 75%|███████▌ | 18/24 [00:06<00:02, 2.86it/s]\n 79%|███████▉ | 19/24 [00:06<00:01, 2.85it/s]\n 83%|████████▎ | 20/24 [00:07<00:01, 2.85it/s]\n 88%|████████▊ | 21/24 [00:07<00:01, 2.85it/s]\n 92%|█████████▏| 22/24 [00:07<00:00, 2.85it/s]\n 96%|█████████▌| 23/24 [00:08<00:00, 2.85it/s]\n100%|██████████| 24/24 [00:08<00:00, 2.85it/s]\n100%|██████████| 24/24 [00:08<00:00, 2.85it/s]", "metrics": { "predict_time": 23.829508, "total_time": 40.035301 }, "output": [ "https://replicate.delivery/pbxt/rPTMocqeEHV0YCrDtWq5aRmM4OwY4AX7DXmhTROHh5MzzdeRA/out-0.png" ], "started_at": "2023-11-28T15:31:27.955104Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/elyr4ytbddjjoiwgbb5lbjevtq", "cancel": "https://api.replicate.com/v1/predictions/elyr4ytbddjjoiwgbb5lbjevtq/cancel" }, "version": "9f8633e47599f8614d480947c96497f088ddfeac80690d20ffcc78eb42955c7f" }Generated inUsing seed: 30702 Applying smart preprocessing... 0%| | 0/24 [00:00<?, ?it/s] 4%|▍ | 1/24 [00:00<00:08, 2.71it/s] 8%|▊ | 2/24 [00:00<00:07, 2.79it/s] 12%|█▎ | 3/24 [00:01<00:07, 2.81it/s] 17%|█▋ | 4/24 [00:01<00:07, 2.83it/s] 21%|██ | 5/24 [00:01<00:06, 2.84it/s] 25%|██▌ | 6/24 [00:02<00:06, 2.85it/s] 29%|██▉ | 7/24 [00:02<00:05, 2.86it/s] 33%|███▎ | 8/24 [00:02<00:05, 2.86it/s] 38%|███▊ | 9/24 [00:03<00:05, 2.86it/s] 42%|████▏ | 10/24 [00:03<00:04, 2.86it/s] 46%|████▌ | 11/24 [00:03<00:04, 2.86it/s] 50%|█████ | 12/24 [00:04<00:04, 2.86it/s] 54%|█████▍ | 13/24 [00:04<00:03, 2.86it/s] 58%|█████▊ | 14/24 [00:04<00:03, 2.86it/s] 62%|██████▎ | 15/24 [00:05<00:03, 2.86it/s] 67%|██████▋ | 16/24 [00:05<00:02, 2.86it/s] 71%|███████ | 17/24 [00:05<00:02, 2.86it/s] 75%|███████▌ | 18/24 [00:06<00:02, 2.86it/s] 79%|███████▉ | 19/24 [00:06<00:01, 2.85it/s] 83%|████████▎ | 20/24 [00:07<00:01, 2.85it/s] 88%|████████▊ | 21/24 [00:07<00:01, 2.85it/s] 92%|█████████▏| 22/24 [00:07<00:00, 2.85it/s] 96%|█████████▌| 23/24 [00:08<00:00, 2.85it/s] 100%|██████████| 24/24 [00:08<00:00, 2.85it/s] 100%|██████████| 24/24 [00:08<00:00, 2.85it/s]Prediction
fermatresearch/sdxl-outpainting-lora:a542ccf3IDzlo7polbcxrmq6wuqwdoyer66mStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- image
- prompt
- a beautiful beach
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- outpaint_size
- 512
- guidance_scale
- 7.5
- apply_watermark
- condition_scale
- 0.25
- negative_prompt
- outpaint_direction
- left
{ "image": "https://replicate.delivery/pbxt/JxMD8uqMrtHU3DKOB4IogIVDZZ78zc607oWpWLsRQVTxBZWl/out-0.png", "prompt": "a beautiful beach", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "outpaint_size": 512, "guidance_scale": 7.5, "apply_watermark": false, "condition_scale": 0.25, "negative_prompt": "", "outpaint_direction": "left" }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 fermatresearch/sdxl-outpainting-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fermatresearch/sdxl-outpainting-lora:a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804", { input: { image: "https://replicate.delivery/pbxt/JxMD8uqMrtHU3DKOB4IogIVDZZ78zc607oWpWLsRQVTxBZWl/out-0.png", prompt: "a beautiful beach", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, condition_scale: 0.25, negative_prompt: "" } } ); 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 fermatresearch/sdxl-outpainting-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fermatresearch/sdxl-outpainting-lora:a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804", input={ "image": "https://replicate.delivery/pbxt/JxMD8uqMrtHU3DKOB4IogIVDZZ78zc607oWpWLsRQVTxBZWl/out-0.png", "prompt": "a beautiful beach", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "condition_scale": 0.25, "negative_prompt": "" } ) 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 fermatresearch/sdxl-outpainting-lora 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": "a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804", "input": { "image": "https://replicate.delivery/pbxt/JxMD8uqMrtHU3DKOB4IogIVDZZ78zc607oWpWLsRQVTxBZWl/out-0.png", "prompt": "a beautiful beach", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "condition_scale": 0.25, "negative_prompt": "" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-11-28T15:35:52.102551Z", "created_at": "2023-11-28T15:34:20.125215Z", "data_removed": false, "error": null, "id": "zlo7polbcxrmq6wuqwdoyer66m", "input": { "image": "https://replicate.delivery/pbxt/JxMD8uqMrtHU3DKOB4IogIVDZZ78zc607oWpWLsRQVTxBZWl/out-0.png", "prompt": "a beautiful beach", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "outpaint_size": 512, "guidance_scale": 7.5, "apply_watermark": false, "condition_scale": 0.25, "negative_prompt": "", "outpaint_direction": "left" }, "logs": "Using seed: 20863\nApplying smart preprocessing...\n 0%| | 0/24 [00:00<?, ?it/s]\n 4%|▍ | 1/24 [00:00<00:11, 1.99it/s]\n 8%|▊ | 2/24 [00:00<00:10, 2.05it/s]\n 12%|█▎ | 3/24 [00:01<00:10, 2.07it/s]\n 17%|█▋ | 4/24 [00:01<00:09, 2.08it/s]\n 21%|██ | 5/24 [00:02<00:09, 2.08it/s]\n 25%|██▌ | 6/24 [00:02<00:08, 2.09it/s]\n 29%|██▉ | 7/24 [00:03<00:08, 2.09it/s]\n 33%|███▎ | 8/24 [00:03<00:07, 2.09it/s]\n 38%|███▊ | 9/24 [00:04<00:07, 2.09it/s]\n 42%|████▏ | 10/24 [00:04<00:06, 2.09it/s]\n 46%|████▌ | 11/24 [00:05<00:06, 2.09it/s]\n 50%|█████ | 12/24 [00:05<00:05, 2.09it/s]\n 54%|█████▍ | 13/24 [00:06<00:05, 2.09it/s]\n 58%|█████▊ | 14/24 [00:06<00:04, 2.08it/s]\n 62%|██████▎ | 15/24 [00:07<00:04, 2.08it/s]\n 67%|██████▋ | 16/24 [00:07<00:03, 2.08it/s]\n 71%|███████ | 17/24 [00:08<00:03, 2.08it/s]\n 75%|███████▌ | 18/24 [00:08<00:02, 2.08it/s]\n 79%|███████▉ | 19/24 [00:09<00:02, 2.08it/s]\n 83%|████████▎ | 20/24 [00:09<00:01, 2.08it/s]\n 88%|████████▊ | 21/24 [00:10<00:01, 2.08it/s]\n 92%|█████████▏| 22/24 [00:10<00:00, 2.08it/s]\n 96%|█████████▌| 23/24 [00:11<00:00, 2.08it/s]\n100%|██████████| 24/24 [00:11<00:00, 2.08it/s]\n100%|██████████| 24/24 [00:11<00:00, 2.08it/s]", "metrics": { "predict_time": 48.497606, "total_time": 91.977336 }, "output": [ "https://replicate.delivery/pbxt/zK0UY5F3Gp5HF9Y7bIXeKAWrmOtI5bzvte1UpJktRZ0Xr78RA/out-0.png" ], "started_at": "2023-11-28T15:35:03.604945Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zlo7polbcxrmq6wuqwdoyer66m", "cancel": "https://api.replicate.com/v1/predictions/zlo7polbcxrmq6wuqwdoyer66m/cancel" }, "version": "9f8633e47599f8614d480947c96497f088ddfeac80690d20ffcc78eb42955c7f" }Generated inUsing seed: 20863 Applying smart preprocessing... 0%| | 0/24 [00:00<?, ?it/s] 4%|▍ | 1/24 [00:00<00:11, 1.99it/s] 8%|▊ | 2/24 [00:00<00:10, 2.05it/s] 12%|█▎ | 3/24 [00:01<00:10, 2.07it/s] 17%|█▋ | 4/24 [00:01<00:09, 2.08it/s] 21%|██ | 5/24 [00:02<00:09, 2.08it/s] 25%|██▌ | 6/24 [00:02<00:08, 2.09it/s] 29%|██▉ | 7/24 [00:03<00:08, 2.09it/s] 33%|███▎ | 8/24 [00:03<00:07, 2.09it/s] 38%|███▊ | 9/24 [00:04<00:07, 2.09it/s] 42%|████▏ | 10/24 [00:04<00:06, 2.09it/s] 46%|████▌ | 11/24 [00:05<00:06, 2.09it/s] 50%|█████ | 12/24 [00:05<00:05, 2.09it/s] 54%|█████▍ | 13/24 [00:06<00:05, 2.09it/s] 58%|█████▊ | 14/24 [00:06<00:04, 2.08it/s] 62%|██████▎ | 15/24 [00:07<00:04, 2.08it/s] 67%|██████▋ | 16/24 [00:07<00:03, 2.08it/s] 71%|███████ | 17/24 [00:08<00:03, 2.08it/s] 75%|███████▌ | 18/24 [00:08<00:02, 2.08it/s] 79%|███████▉ | 19/24 [00:09<00:02, 2.08it/s] 83%|████████▎ | 20/24 [00:09<00:01, 2.08it/s] 88%|████████▊ | 21/24 [00:10<00:01, 2.08it/s] 92%|█████████▏| 22/24 [00:10<00:00, 2.08it/s] 96%|█████████▌| 23/24 [00:11<00:00, 2.08it/s] 100%|██████████| 24/24 [00:11<00:00, 2.08it/s] 100%|██████████| 24/24 [00:11<00:00, 2.08it/s]Prediction
fermatresearch/sdxl-outpainting-lora:a542ccf3IDnsuivltbba5xft62mzkm4pm5wiStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- image
- prompt
- cyberpunk night
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- outpaint_size
- 255
- guidance_scale
- 7.5
- apply_watermark
- condition_scale
- 0.25
- negative_prompt
- outpaint_direction
- up
{ "image": "https://replicate.delivery/pbxt/JxMICETVXAuIoROXOhiNQM4YotSsTQpEw9D5R2uzjuaZ6eJq/590c008a-25dc-4e82-81cb-bccb508f1312.jpeg", "prompt": "cyberpunk night", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "outpaint_size": 255, "guidance_scale": 7.5, "apply_watermark": false, "condition_scale": 0.25, "negative_prompt": "", "outpaint_direction": "up" }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 fermatresearch/sdxl-outpainting-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fermatresearch/sdxl-outpainting-lora:a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804", { input: { image: "https://replicate.delivery/pbxt/JxMICETVXAuIoROXOhiNQM4YotSsTQpEw9D5R2uzjuaZ6eJq/590c008a-25dc-4e82-81cb-bccb508f1312.jpeg", prompt: "cyberpunk night", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, condition_scale: 0.25, negative_prompt: "" } } ); 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 fermatresearch/sdxl-outpainting-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fermatresearch/sdxl-outpainting-lora:a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804", input={ "image": "https://replicate.delivery/pbxt/JxMICETVXAuIoROXOhiNQM4YotSsTQpEw9D5R2uzjuaZ6eJq/590c008a-25dc-4e82-81cb-bccb508f1312.jpeg", "prompt": "cyberpunk night", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "condition_scale": 0.25, "negative_prompt": "" } ) 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 fermatresearch/sdxl-outpainting-lora 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": "a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804", "input": { "image": "https://replicate.delivery/pbxt/JxMICETVXAuIoROXOhiNQM4YotSsTQpEw9D5R2uzjuaZ6eJq/590c008a-25dc-4e82-81cb-bccb508f1312.jpeg", "prompt": "cyberpunk night", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "condition_scale": 0.25, "negative_prompt": "" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-11-28T15:40:07.565582Z", "created_at": "2023-11-28T15:39:39.944666Z", "data_removed": false, "error": null, "id": "nsuivltbba5xft62mzkm4pm5wi", "input": { "image": "https://replicate.delivery/pbxt/JxMICETVXAuIoROXOhiNQM4YotSsTQpEw9D5R2uzjuaZ6eJq/590c008a-25dc-4e82-81cb-bccb508f1312.jpeg", "prompt": "cyberpunk night", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "outpaint_size": 255, "guidance_scale": 7.5, "apply_watermark": false, "condition_scale": 0.25, "negative_prompt": "", "outpaint_direction": "up" }, "logs": "Using seed: 131\nApplying smart preprocessing...\n 0%| | 0/24 [00:00<?, ?it/s]\n 4%|▍ | 1/24 [00:00<00:09, 2.43it/s]\n 8%|▊ | 2/24 [00:00<00:08, 2.49it/s]\n 12%|█▎ | 3/24 [00:01<00:08, 2.50it/s]\n 17%|█▋ | 4/24 [00:01<00:07, 2.51it/s]\n 21%|██ | 5/24 [00:01<00:07, 2.52it/s]\n 25%|██▌ | 6/24 [00:02<00:07, 2.52it/s]\n 29%|██▉ | 7/24 [00:02<00:06, 2.52it/s]\n 33%|███▎ | 8/24 [00:03<00:06, 2.52it/s]\n 38%|███▊ | 9/24 [00:03<00:05, 2.52it/s]\n 42%|████▏ | 10/24 [00:03<00:05, 2.52it/s]\n 46%|████▌ | 11/24 [00:04<00:05, 2.51it/s]\n 50%|█████ | 12/24 [00:04<00:04, 2.51it/s]\n 54%|█████▍ | 13/24 [00:05<00:04, 2.51it/s]\n 58%|█████▊ | 14/24 [00:05<00:03, 2.51it/s]\n 62%|██████▎ | 15/24 [00:05<00:03, 2.51it/s]\n 67%|██████▋ | 16/24 [00:06<00:03, 2.51it/s]\n 71%|███████ | 17/24 [00:06<00:02, 2.51it/s]\n 75%|███████▌ | 18/24 [00:07<00:02, 2.51it/s]\n 79%|███████▉ | 19/24 [00:07<00:01, 2.51it/s]\n 83%|████████▎ | 20/24 [00:07<00:01, 2.51it/s]\n 88%|████████▊ | 21/24 [00:08<00:01, 2.51it/s]\n 92%|█████████▏| 22/24 [00:08<00:00, 2.51it/s]\n 96%|█████████▌| 23/24 [00:09<00:00, 2.51it/s]\n100%|██████████| 24/24 [00:09<00:00, 2.51it/s]\n100%|██████████| 24/24 [00:09<00:00, 2.51it/s]", "metrics": { "predict_time": 27.585161, "total_time": 27.620916 }, "output": [ "https://replicate.delivery/pbxt/a03XgQ4OeF2uLqCZsuCtYvpeH9fJjKGRNEZiM2XDIpKseuzHB/out-0.png" ], "started_at": "2023-11-28T15:39:39.980421Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/nsuivltbba5xft62mzkm4pm5wi", "cancel": "https://api.replicate.com/v1/predictions/nsuivltbba5xft62mzkm4pm5wi/cancel" }, "version": "9f8633e47599f8614d480947c96497f088ddfeac80690d20ffcc78eb42955c7f" }Generated inUsing seed: 131 Applying smart preprocessing... 0%| | 0/24 [00:00<?, ?it/s] 4%|▍ | 1/24 [00:00<00:09, 2.43it/s] 8%|▊ | 2/24 [00:00<00:08, 2.49it/s] 12%|█▎ | 3/24 [00:01<00:08, 2.50it/s] 17%|█▋ | 4/24 [00:01<00:07, 2.51it/s] 21%|██ | 5/24 [00:01<00:07, 2.52it/s] 25%|██▌ | 6/24 [00:02<00:07, 2.52it/s] 29%|██▉ | 7/24 [00:02<00:06, 2.52it/s] 33%|███▎ | 8/24 [00:03<00:06, 2.52it/s] 38%|███▊ | 9/24 [00:03<00:05, 2.52it/s] 42%|████▏ | 10/24 [00:03<00:05, 2.52it/s] 46%|████▌ | 11/24 [00:04<00:05, 2.51it/s] 50%|█████ | 12/24 [00:04<00:04, 2.51it/s] 54%|█████▍ | 13/24 [00:05<00:04, 2.51it/s] 58%|█████▊ | 14/24 [00:05<00:03, 2.51it/s] 62%|██████▎ | 15/24 [00:05<00:03, 2.51it/s] 67%|██████▋ | 16/24 [00:06<00:03, 2.51it/s] 71%|███████ | 17/24 [00:06<00:02, 2.51it/s] 75%|███████▌ | 18/24 [00:07<00:02, 2.51it/s] 79%|███████▉ | 19/24 [00:07<00:01, 2.51it/s] 83%|████████▎ | 20/24 [00:07<00:01, 2.51it/s] 88%|████████▊ | 21/24 [00:08<00:01, 2.51it/s] 92%|█████████▏| 22/24 [00:08<00:00, 2.51it/s] 96%|█████████▌| 23/24 [00:09<00:00, 2.51it/s] 100%|██████████| 24/24 [00:09<00:00, 2.51it/s] 100%|██████████| 24/24 [00:09<00:00, 2.51it/s]Prediction
fermatresearch/sdxl-outpainting-lora:a542ccf3ID95r3xqswshrgp0cfx3rsr9q67mStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- image
- prompt
- beautiful european city with dramatic light
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- outpaint_up
- 0
- outpaint_down
- 0
- outpaint_left
- 256
- guidance_scale
- 7.5
- outpaint_right
- 256
- apply_watermark
- condition_scale
- 0.25
- negative_prompt
{ "image": "https://replicate.delivery/pbxt/L2nC0t0m7YTGI4vUfOxZUSHgKNilHMJUVO67rqC8qo5lYC9J/fermat_app_a_realistic_image_of_a_street_from_paris_e9db015b-1068-4c6c-9884-0265d939dcb2.png", "prompt": "beautiful european city with dramatic light", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "outpaint_up": 0, "outpaint_down": 0, "outpaint_left": 256, "guidance_scale": 7.5, "outpaint_right": 256, "apply_watermark": false, "condition_scale": 0.25, "negative_prompt": "" }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 fermatresearch/sdxl-outpainting-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fermatresearch/sdxl-outpainting-lora:a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804", { input: { image: "https://replicate.delivery/pbxt/L2nC0t0m7YTGI4vUfOxZUSHgKNilHMJUVO67rqC8qo5lYC9J/fermat_app_a_realistic_image_of_a_street_from_paris_e9db015b-1068-4c6c-9884-0265d939dcb2.png", prompt: "beautiful european city with dramatic light", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, outpaint_up: 0, outpaint_down: 0, outpaint_left: 256, guidance_scale: 7.5, outpaint_right: 256, apply_watermark: false, condition_scale: 0.25, negative_prompt: "" } } ); 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 fermatresearch/sdxl-outpainting-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fermatresearch/sdxl-outpainting-lora:a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804", input={ "image": "https://replicate.delivery/pbxt/L2nC0t0m7YTGI4vUfOxZUSHgKNilHMJUVO67rqC8qo5lYC9J/fermat_app_a_realistic_image_of_a_street_from_paris_e9db015b-1068-4c6c-9884-0265d939dcb2.png", "prompt": "beautiful european city with dramatic light", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "outpaint_up": 0, "outpaint_down": 0, "outpaint_left": 256, "guidance_scale": 7.5, "outpaint_right": 256, "apply_watermark": False, "condition_scale": 0.25, "negative_prompt": "" } ) 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 fermatresearch/sdxl-outpainting-lora 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": "a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804", "input": { "image": "https://replicate.delivery/pbxt/L2nC0t0m7YTGI4vUfOxZUSHgKNilHMJUVO67rqC8qo5lYC9J/fermat_app_a_realistic_image_of_a_street_from_paris_e9db015b-1068-4c6c-9884-0265d939dcb2.png", "prompt": "beautiful european city with dramatic light", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "outpaint_up": 0, "outpaint_down": 0, "outpaint_left": 256, "guidance_scale": 7.5, "outpaint_right": 256, "apply_watermark": false, "condition_scale": 0.25, "negative_prompt": "" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-06-05T15:15:14.447491Z", "created_at": "2024-06-05T15:07:54.956000Z", "data_removed": false, "error": null, "id": "95r3xqswshrgp0cfx3rsr9q67m", "input": { "image": "https://replicate.delivery/pbxt/L2nC0t0m7YTGI4vUfOxZUSHgKNilHMJUVO67rqC8qo5lYC9J/fermat_app_a_realistic_image_of_a_street_from_paris_e9db015b-1068-4c6c-9884-0265d939dcb2.png", "prompt": "beautiful european city with dramatic light", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "outpaint_up": 0, "outpaint_down": 0, "outpaint_left": 256, "guidance_scale": 7.5, "outpaint_right": 256, "apply_watermark": false, "condition_scale": 0.25, "negative_prompt": "" }, "logs": "Using seed: 34499\nApplying smart preprocessing...\nRunning PatchMatch\nPatchMatch completed, time taken: 12332.704345703125 ms\nRunning PatchMatch\nPatchMatch completed, time taken: 21385.12060546875 ms\n 0%| | 0/19 [00:00<?, ?it/s]\n 5%|▌ | 1/19 [00:00<00:10, 1.77it/s]\n 11%|█ | 2/19 [00:00<00:07, 2.42it/s]\n 16%|█▌ | 3/19 [00:01<00:07, 2.27it/s]\n 21%|██ | 4/19 [00:01<00:06, 2.20it/s]\n 26%|██▋ | 5/19 [00:02<00:06, 2.16it/s]\n 32%|███▏ | 6/19 [00:02<00:06, 2.14it/s]\n 37%|███▋ | 7/19 [00:03<00:05, 2.13it/s]\n 42%|████▏ | 8/19 [00:03<00:05, 2.12it/s]\n 47%|████▋ | 9/19 [00:04<00:04, 2.11it/s]\n 53%|█████▎ | 10/19 [00:04<00:04, 2.11it/s]\n 58%|█████▊ | 11/19 [00:05<00:03, 2.10it/s]\n 63%|██████▎ | 12/19 [00:05<00:03, 2.10it/s]\n 68%|██████▊ | 13/19 [00:06<00:02, 2.10it/s]\n 74%|███████▎ | 14/19 [00:06<00:02, 2.10it/s]\n 79%|███████▉ | 15/19 [00:07<00:01, 2.10it/s]\n 84%|████████▍ | 16/19 [00:07<00:01, 2.09it/s]\n 89%|████████▉ | 17/19 [00:08<00:00, 2.09it/s]\n 95%|█████████▍| 18/19 [00:08<00:00, 2.09it/s]\n100%|██████████| 19/19 [00:08<00:00, 2.09it/s]\n100%|██████████| 19/19 [00:08<00:00, 2.12it/s]", "metrics": { "predict_time": 47.387431, "total_time": 439.491491 }, "output": [ "https://replicate.delivery/pbxt/ijEcNuS5hnIBMFstWsEkcPeINwiyvS0ZaeepJePZwZfHgZcXC/out-0.png" ], "started_at": "2024-06-05T15:14:27.060060Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/95r3xqswshrgp0cfx3rsr9q67m", "cancel": "https://api.replicate.com/v1/predictions/95r3xqswshrgp0cfx3rsr9q67m/cancel" }, "version": "a542ccf352995f3c41f0bcfaef641daa3058bf2b00e08e04feb0295334ab9804" }Generated inUsing seed: 34499 Applying smart preprocessing... Running PatchMatch PatchMatch completed, time taken: 12332.704345703125 ms Running PatchMatch PatchMatch completed, time taken: 21385.12060546875 ms 0%| | 0/19 [00:00<?, ?it/s] 5%|▌ | 1/19 [00:00<00:10, 1.77it/s] 11%|█ | 2/19 [00:00<00:07, 2.42it/s] 16%|█▌ | 3/19 [00:01<00:07, 2.27it/s] 21%|██ | 4/19 [00:01<00:06, 2.20it/s] 26%|██▋ | 5/19 [00:02<00:06, 2.16it/s] 32%|███▏ | 6/19 [00:02<00:06, 2.14it/s] 37%|███▋ | 7/19 [00:03<00:05, 2.13it/s] 42%|████▏ | 8/19 [00:03<00:05, 2.12it/s] 47%|████▋ | 9/19 [00:04<00:04, 2.11it/s] 53%|█████▎ | 10/19 [00:04<00:04, 2.11it/s] 58%|█████▊ | 11/19 [00:05<00:03, 2.10it/s] 63%|██████▎ | 12/19 [00:05<00:03, 2.10it/s] 68%|██████▊ | 13/19 [00:06<00:02, 2.10it/s] 74%|███████▎ | 14/19 [00:06<00:02, 2.10it/s] 79%|███████▉ | 15/19 [00:07<00:01, 2.10it/s] 84%|████████▍ | 16/19 [00:07<00:01, 2.09it/s] 89%|████████▉ | 17/19 [00:08<00:00, 2.09it/s] 95%|█████████▍| 18/19 [00:08<00:00, 2.09it/s] 100%|██████████| 19/19 [00:08<00:00, 2.09it/s] 100%|██████████| 19/19 [00:08<00:00, 2.12it/s]
Want to make some of these yourself?
Run this model