afiaka87 / glid-3-xl
CompVis `latent-diffusion text2im` finetuned for inpainting.
This model uses a mask. Visit the new playground to try this model with our mask editing tool.
Prediction
afiaka87/glid-3-xl:742660111a36a544bd6de5acab0b46db0115eae9b091267d5ec48d885d564671IDn64nojk5qvgglgbqc4nbdu3zjeStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- steps
- 100
- width
- 256
- height
- 256
- prompt
- pikachu rendered in pixar
- batch_size
- 1
- guidance_scale
- 5
- aesthetic_rating
- 9
- aesthetic_weight
- 0.5
{ "seed": -1, "steps": 100, "width": 256, "height": 256, "prompt": "pikachu rendered in pixar", "batch_size": 1, "guidance_scale": 5, "aesthetic_rating": 9, "aesthetic_weight": 0.5 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/glid-3-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/glid-3-xl:742660111a36a544bd6de5acab0b46db0115eae9b091267d5ec48d885d564671", { input: { seed: -1, steps: 100, width: 256, height: 256, prompt: "pikachu rendered in pixar", batch_size: 1, guidance_scale: 5, aesthetic_rating: 9, aesthetic_weight: 0.5 } } ); 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 replicateImport the client:import replicateRun afiaka87/glid-3-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/glid-3-xl:742660111a36a544bd6de5acab0b46db0115eae9b091267d5ec48d885d564671", input={ "seed": -1, "steps": 100, "width": 256, "height": 256, "prompt": "pikachu rendered in pixar", "batch_size": 1, "guidance_scale": 5, "aesthetic_rating": 9, "aesthetic_weight": 0.5 } ) # The afiaka87/glid-3-xl model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/glid-3-xl/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/glid-3-xl 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": "afiaka87/glid-3-xl:742660111a36a544bd6de5acab0b46db0115eae9b091267d5ec48d885d564671", "input": { "seed": -1, "steps": 100, "width": 256, "height": 256, "prompt": "pikachu rendered in pixar", "batch_size": 1, "guidance_scale": 5, "aesthetic_rating": 9, "aesthetic_weight": 0.5 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-13T06:36:13.121933Z", "created_at": "2022-07-13T06:36:02.238585Z", "data_removed": false, "error": null, "id": "n64nojk5qvgglgbqc4nbdu3zje", "input": { "seed": -1, "steps": 100, "width": 256, "height": 256, "prompt": "pikachu rendered in pixar", "batch_size": 1, "guidance_scale": 5, "aesthetic_rating": 9, "aesthetic_weight": 0.5 }, "logs": "Using seed 2882092835\nRunning simulation for pikachu rendered in pixar\nEncoding text embeddings with pikachu rendered in pixar dimensions\nUsing aesthetic embedding 9 with weight 0.5\nRunning diffusion...\n\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:41, 2.37it/s]\n 2%|▏ | 2/100 [00:00<00:37, 2.64it/s]\n 3%|▎ | 3/100 [00:01<00:35, 2.74it/s]\n 5%|▌ | 5/100 [00:01<00:19, 4.79it/s]\n 7%|▋ | 7/100 [00:01<00:14, 6.47it/s]\n 9%|▉ | 9/100 [00:01<00:11, 7.79it/s]\n 11%|█ | 11/100 [00:01<00:10, 8.80it/s]\n 13%|█▎ | 13/100 [00:01<00:09, 9.56it/s]\n 15%|█▌ | 15/100 [00:02<00:08, 10.08it/s]\n 17%|█▋ | 17/100 [00:02<00:07, 10.42it/s]\n 19%|█▉ | 19/100 [00:02<00:07, 10.70it/s]\n 21%|██ | 21/100 [00:02<00:07, 10.84it/s]\n 23%|██▎ | 23/100 [00:02<00:06, 11.04it/s]\n 25%|██▌ | 25/100 [00:03<00:06, 11.11it/s]\n 27%|██▋ | 27/100 [00:03<00:06, 11.19it/s]\n 29%|██▉ | 29/100 [00:03<00:06, 11.25it/s]\n 31%|███ | 31/100 [00:03<00:06, 11.29it/s]\n 33%|███▎ | 33/100 [00:03<00:05, 11.31it/s]\n 35%|███▌ | 35/100 [00:03<00:05, 11.36it/s]\n 37%|███▋ | 37/100 [00:04<00:05, 11.34it/s]\n 39%|███▉ | 39/100 [00:04<00:05, 11.33it/s]\n 41%|████ | 41/100 [00:04<00:05, 11.32it/s]\n 43%|████▎ | 43/100 [00:04<00:05, 11.34it/s]\n 45%|████▌ | 45/100 [00:04<00:04, 11.32it/s]\n 47%|████▋ | 47/100 [00:04<00:04, 11.38it/s]\n 49%|████▉ | 49/100 [00:05<00:04, 11.40it/s]\n 51%|█████ | 51/100 [00:05<00:04, 11.36it/s]\n 53%|█████▎ | 53/100 [00:05<00:04, 11.37it/s]\n 55%|█████▌ | 55/100 [00:05<00:03, 11.39it/s]\n 57%|█████▋ | 57/100 [00:05<00:03, 11.37it/s]\n 59%|█████▉ | 59/100 [00:06<00:03, 11.36it/s]\n 61%|██████ | 61/100 [00:06<00:03, 11.39it/s]\n 63%|██████▎ | 63/100 [00:06<00:03, 11.36it/s]\n 65%|██████▌ | 65/100 [00:06<00:03, 11.38it/s]\n 67%|██████▋ | 67/100 [00:06<00:02, 11.36it/s]\n 69%|██████▉ | 69/100 [00:06<00:02, 11.35it/s]\n 71%|███████ | 71/100 [00:07<00:02, 11.33it/s]\n 73%|███████▎ | 73/100 [00:07<00:02, 11.32it/s]\n 75%|███████▌ | 75/100 [00:07<00:02, 11.35it/s]\n 77%|███████▋ | 77/100 [00:07<00:02, 11.31it/s]\n 79%|███████▉ | 79/100 [00:07<00:01, 11.30it/s]\n 81%|████████ | 81/100 [00:07<00:01, 11.30it/s]\n 83%|████████▎ | 83/100 [00:08<00:01, 11.30it/s]\n 85%|████████▌ | 85/100 [00:08<00:01, 11.28it/s]\n 87%|████████▋ | 87/100 [00:08<00:01, 11.31it/s]\n 89%|████████▉ | 89/100 [00:08<00:00, 11.28it/s]\n 91%|█████████ | 91/100 [00:08<00:00, 11.30it/s]\n 93%|█████████▎| 93/100 [00:09<00:00, 11.31it/s]\n 95%|█████████▌| 95/100 [00:09<00:00, 11.32it/s]\n 97%|█████████▋| 97/100 [00:09<00:00, 11.33it/s]\n 99%|█████████▉| 99/100 [00:09<00:00, 11.30it/s]\n100%|██████████| 100/100 [00:09<00:00, 10.34it/s]\nSaving final sample/s", "metrics": { "predict_time": 10.695204, "total_time": 10.883348 }, "output": [ [ "https://replicate.delivery/mgxm/804c6fe5-d74a-4796-8835-f411b3776dae/current_0.png" ] ], "started_at": "2022-07-13T06:36:02.426729Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/n64nojk5qvgglgbqc4nbdu3zje", "cancel": "https://api.replicate.com/v1/predictions/n64nojk5qvgglgbqc4nbdu3zje/cancel" }, "version": "742660111a36a544bd6de5acab0b46db0115eae9b091267d5ec48d885d564671" }Generated inUsing seed 2882092835 Running simulation for pikachu rendered in pixar Encoding text embeddings with pikachu rendered in pixar dimensions Using aesthetic embedding 9 with weight 0.5 Running diffusion... 0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:41, 2.37it/s] 2%|▏ | 2/100 [00:00<00:37, 2.64it/s] 3%|▎ | 3/100 [00:01<00:35, 2.74it/s] 5%|▌ | 5/100 [00:01<00:19, 4.79it/s] 7%|▋ | 7/100 [00:01<00:14, 6.47it/s] 9%|▉ | 9/100 [00:01<00:11, 7.79it/s] 11%|█ | 11/100 [00:01<00:10, 8.80it/s] 13%|█▎ | 13/100 [00:01<00:09, 9.56it/s] 15%|█▌ | 15/100 [00:02<00:08, 10.08it/s] 17%|█▋ | 17/100 [00:02<00:07, 10.42it/s] 19%|█▉ | 19/100 [00:02<00:07, 10.70it/s] 21%|██ | 21/100 [00:02<00:07, 10.84it/s] 23%|██▎ | 23/100 [00:02<00:06, 11.04it/s] 25%|██▌ | 25/100 [00:03<00:06, 11.11it/s] 27%|██▋ | 27/100 [00:03<00:06, 11.19it/s] 29%|██▉ | 29/100 [00:03<00:06, 11.25it/s] 31%|███ | 31/100 [00:03<00:06, 11.29it/s] 33%|███▎ | 33/100 [00:03<00:05, 11.31it/s] 35%|███▌ | 35/100 [00:03<00:05, 11.36it/s] 37%|███▋ | 37/100 [00:04<00:05, 11.34it/s] 39%|███▉ | 39/100 [00:04<00:05, 11.33it/s] 41%|████ | 41/100 [00:04<00:05, 11.32it/s] 43%|████▎ | 43/100 [00:04<00:05, 11.34it/s] 45%|████▌ | 45/100 [00:04<00:04, 11.32it/s] 47%|████▋ | 47/100 [00:04<00:04, 11.38it/s] 49%|████▉ | 49/100 [00:05<00:04, 11.40it/s] 51%|█████ | 51/100 [00:05<00:04, 11.36it/s] 53%|█████▎ | 53/100 [00:05<00:04, 11.37it/s] 55%|█████▌ | 55/100 [00:05<00:03, 11.39it/s] 57%|█████▋ | 57/100 [00:05<00:03, 11.37it/s] 59%|█████▉ | 59/100 [00:06<00:03, 11.36it/s] 61%|██████ | 61/100 [00:06<00:03, 11.39it/s] 63%|██████▎ | 63/100 [00:06<00:03, 11.36it/s] 65%|██████▌ | 65/100 [00:06<00:03, 11.38it/s] 67%|██████▋ | 67/100 [00:06<00:02, 11.36it/s] 69%|██████▉ | 69/100 [00:06<00:02, 11.35it/s] 71%|███████ | 71/100 [00:07<00:02, 11.33it/s] 73%|███████▎ | 73/100 [00:07<00:02, 11.32it/s] 75%|███████▌ | 75/100 [00:07<00:02, 11.35it/s] 77%|███████▋ | 77/100 [00:07<00:02, 11.31it/s] 79%|███████▉ | 79/100 [00:07<00:01, 11.30it/s] 81%|████████ | 81/100 [00:07<00:01, 11.30it/s] 83%|████████▎ | 83/100 [00:08<00:01, 11.30it/s] 85%|████████▌ | 85/100 [00:08<00:01, 11.28it/s] 87%|████████▋ | 87/100 [00:08<00:01, 11.31it/s] 89%|████████▉ | 89/100 [00:08<00:00, 11.28it/s] 91%|█████████ | 91/100 [00:08<00:00, 11.30it/s] 93%|█████████▎| 93/100 [00:09<00:00, 11.31it/s] 95%|█████████▌| 95/100 [00:09<00:00, 11.32it/s] 97%|█████████▋| 97/100 [00:09<00:00, 11.33it/s] 99%|█████████▉| 99/100 [00:09<00:00, 11.30it/s] 100%|██████████| 100/100 [00:09<00:00, 10.34it/s] Saving final sample/sPrediction
afiaka87/glid-3-xl:742660111a36a544bd6de5acab0b46db0115eae9b091267d5ec48d885d564671IDa5nwiv552ncpzo7xky7bpr42hqStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- -1
- steps
- 100
- width
- 256
- height
- 256
- prompt
- a giant cat sleeping on the beach
- batch_size
- 1
- guidance_scale
- 5
- aesthetic_rating
- 9
- aesthetic_weight
- 0.5
{ "seed": -1, "steps": 100, "width": 256, "height": 256, "prompt": "a giant cat sleeping on the beach", "batch_size": 1, "guidance_scale": 5, "aesthetic_rating": 9, "aesthetic_weight": 0.5 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run afiaka87/glid-3-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "afiaka87/glid-3-xl:742660111a36a544bd6de5acab0b46db0115eae9b091267d5ec48d885d564671", { input: { seed: -1, steps: 100, width: 256, height: 256, prompt: "a giant cat sleeping on the beach", batch_size: 1, guidance_scale: 5, aesthetic_rating: 9, aesthetic_weight: 0.5 } } ); 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 replicateImport the client:import replicateRun afiaka87/glid-3-xl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "afiaka87/glid-3-xl:742660111a36a544bd6de5acab0b46db0115eae9b091267d5ec48d885d564671", input={ "seed": -1, "steps": 100, "width": 256, "height": 256, "prompt": "a giant cat sleeping on the beach", "batch_size": 1, "guidance_scale": 5, "aesthetic_rating": 9, "aesthetic_weight": 0.5 } ) # The afiaka87/glid-3-xl model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/afiaka87/glid-3-xl/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run afiaka87/glid-3-xl 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": "afiaka87/glid-3-xl:742660111a36a544bd6de5acab0b46db0115eae9b091267d5ec48d885d564671", "input": { "seed": -1, "steps": 100, "width": 256, "height": 256, "prompt": "a giant cat sleeping on the beach", "batch_size": 1, "guidance_scale": 5, "aesthetic_rating": 9, "aesthetic_weight": 0.5 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-13T06:47:20.792835Z", "created_at": "2022-07-13T06:47:09.315226Z", "data_removed": false, "error": null, "id": "a5nwiv552ncpzo7xky7bpr42hq", "input": { "seed": -1, "steps": 100, "width": 256, "height": 256, "prompt": "a giant cat sleeping on the beach", "batch_size": 1, "guidance_scale": 5, "aesthetic_rating": 9, "aesthetic_weight": 0.5 }, "logs": "Using seed 3268308253\nRunning simulation for a giant cat sleeping on the beach\nEncoding text embeddings with a giant cat sleeping on the beach dimensions\nUsing aesthetic embedding 9 with weight 0.5\nRunning diffusion...\n\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:43, 2.29it/s]\n 2%|▏ | 2/100 [00:00<00:38, 2.51it/s]\n 3%|▎ | 3/100 [00:01<00:37, 2.58it/s]\n 5%|▌ | 5/100 [00:01<00:21, 4.48it/s]\n 7%|▋ | 7/100 [00:01<00:15, 6.05it/s]\n 9%|▉ | 9/100 [00:01<00:12, 7.30it/s]\n 11%|█ | 11/100 [00:01<00:10, 8.23it/s]\n 13%|█▎ | 13/100 [00:02<00:09, 8.88it/s]\n 15%|█▌ | 15/100 [00:02<00:09, 9.32it/s]\n 17%|█▋ 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61/100 [00:06<00:03, 10.50it/s]\n 63%|██████▎ | 63/100 [00:06<00:03, 10.53it/s]\n 65%|██████▌ | 65/100 [00:07<00:03, 10.50it/s]\n 67%|██████▋ | 67/100 [00:07<00:03, 10.53it/s]\n 69%|██████▉ | 69/100 [00:07<00:02, 10.49it/s]\n 71%|███████ | 71/100 [00:07<00:02, 10.50it/s]\n 73%|███████▎ | 73/100 [00:07<00:02, 10.52it/s]\n 75%|███████▌ | 75/100 [00:08<00:02, 10.50it/s]\n 77%|███████▋ | 77/100 [00:08<00:02, 10.50it/s]\n 79%|███████▉ | 79/100 [00:08<00:02, 10.50it/s]\n 81%|████████ | 81/100 [00:08<00:01, 10.51it/s]\n 83%|████████▎ | 83/100 [00:08<00:01, 10.51it/s]\n 85%|████████▌ | 85/100 [00:08<00:01, 10.51it/s]\n 87%|████████▋ | 87/100 [00:09<00:01, 10.51it/s]\n 89%|████████▉ | 89/100 [00:09<00:01, 10.50it/s]\n 91%|█████████ | 91/100 [00:09<00:00, 10.50it/s]\n 93%|█████████▎| 93/100 [00:09<00:00, 10.55it/s]\n 95%|█████████▌| 95/100 [00:09<00:00, 10.50it/s]\n 97%|█████████▋| 97/100 [00:10<00:00, 10.51it/s]\n 99%|█████████▉| 99/100 [00:10<00:00, 10.53it/s]\nSaving final sample/s\n100%|██████████| 100/100 [00:10<00:00, 9.62it/s]", "metrics": { "predict_time": 11.267158, "total_time": 11.477609 }, "output": [ [ "https://replicate.delivery/mgxm/1c887c1b-1022-4117-bba4-cf454ca07228/current_0.png" ] ], "started_at": "2022-07-13T06:47:09.525677Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/a5nwiv552ncpzo7xky7bpr42hq", "cancel": "https://api.replicate.com/v1/predictions/a5nwiv552ncpzo7xky7bpr42hq/cancel" }, "version": "742660111a36a544bd6de5acab0b46db0115eae9b091267d5ec48d885d564671" }Generated inUsing seed 3268308253 Running simulation for a giant cat sleeping on the beach Encoding text embeddings with a giant cat sleeping on the beach dimensions Using aesthetic embedding 9 with weight 0.5 Running diffusion... 0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:43, 2.29it/s] 2%|▏ | 2/100 [00:00<00:38, 2.51it/s] 3%|▎ | 3/100 [00:01<00:37, 2.58it/s] 5%|▌ | 5/100 [00:01<00:21, 4.48it/s] 7%|▋ | 7/100 [00:01<00:15, 6.05it/s] 9%|▉ | 9/100 [00:01<00:12, 7.30it/s] 11%|█ | 11/100 [00:01<00:10, 8.23it/s] 13%|█▎ | 13/100 [00:02<00:09, 8.88it/s] 15%|█▌ | 15/100 [00:02<00:09, 9.32it/s] 17%|█▋ | 17/100 [00:02<00:08, 9.68it/s] 19%|█▉ | 19/100 [00:02<00:08, 9.99it/s] 21%|██ | 21/100 [00:02<00:07, 10.20it/s] 23%|██▎ | 23/100 [00:03<00:07, 10.29it/s] 25%|██▌ | 25/100 [00:03<00:07, 10.31it/s] 27%|██▋ | 27/100 [00:03<00:07, 10.36it/s] 29%|██▉ | 29/100 [00:03<00:06, 10.45it/s] 31%|███ | 31/100 [00:03<00:06, 10.54it/s] 33%|███▎ | 33/100 [00:04<00:06, 10.58it/s] 35%|███▌ | 35/100 [00:04<00:06, 10.52it/s] 37%|███▋ | 37/100 [00:04<00:06, 10.48it/s] 39%|███▉ | 39/100 [00:04<00:05, 10.52it/s] 41%|████ | 41/100 [00:04<00:05, 10.57it/s] 43%|████▎ | 43/100 [00:04<00:05, 10.61it/s] 45%|████▌ | 45/100 [00:05<00:05, 10.53it/s] 47%|████▋ | 47/100 [00:05<00:05, 10.52it/s] 49%|████▉ | 49/100 [00:05<00:04, 10.51it/s] 51%|█████ | 51/100 [00:05<00:04, 10.54it/s] 53%|█████▎ | 53/100 [00:05<00:04, 10.56it/s] 55%|█████▌ | 55/100 [00:06<00:04, 10.53it/s] 57%|█████▋ | 57/100 [00:06<00:04, 10.50it/s] 59%|█████▉ | 59/100 [00:06<00:03, 10.50it/s] 61%|██████ | 61/100 [00:06<00:03, 10.50it/s] 63%|██████▎ | 63/100 [00:06<00:03, 10.53it/s] 65%|██████▌ | 65/100 [00:07<00:03, 10.50it/s] 67%|██████▋ | 67/100 [00:07<00:03, 10.53it/s] 69%|██████▉ | 69/100 [00:07<00:02, 10.49it/s] 71%|███████ | 71/100 [00:07<00:02, 10.50it/s] 73%|███████▎ | 73/100 [00:07<00:02, 10.52it/s] 75%|███████▌ | 75/100 [00:08<00:02, 10.50it/s] 77%|███████▋ | 77/100 [00:08<00:02, 10.50it/s] 79%|███████▉ | 79/100 [00:08<00:02, 10.50it/s] 81%|████████ | 81/100 [00:08<00:01, 10.51it/s] 83%|████████▎ | 83/100 [00:08<00:01, 10.51it/s] 85%|████████▌ | 85/100 [00:08<00:01, 10.51it/s] 87%|████████▋ | 87/100 [00:09<00:01, 10.51it/s] 89%|████████▉ | 89/100 [00:09<00:01, 10.50it/s] 91%|█████████ | 91/100 [00:09<00:00, 10.50it/s] 93%|█████████▎| 93/100 [00:09<00:00, 10.55it/s] 95%|█████████▌| 95/100 [00:09<00:00, 10.50it/s] 97%|█████████▋| 97/100 [00:10<00:00, 10.51it/s] 99%|█████████▉| 99/100 [00:10<00:00, 10.53it/s] Saving final sample/s 100%|██████████| 100/100 [00:10<00:00, 9.62it/s]
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