moayedhajiali / elasticdiffusion
ElasticDiffusion: Training-free Arbitrary Size Image Generation
Prediction
moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88IDtwjdctdb56gybyjoed23yqy5oaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 0
- prompt
- Envision a portrait of a horse, framed by a blue headscarf with muted tones of rust and cream. she has brown-colored eyes. Her attire, simple yet dignified
- img_width
- 1536
- rrg_scale
- 1000
- img_height
- 1536
- cosine_scale
- 10
- guidance_scale
- 10
- view_batch_size
- 16
- negative_prompts
- blurry, ugly, poorly drawn, deformed
- resampling_new_p
- 0.3
- resampling_steps
- 7
- num_inference_steps
- 50
{ "seed": 0, "prompt": "Envision a portrait of a horse, framed by a blue headscarf with muted tones of rust and cream. she has brown-colored eyes. Her attire, simple yet dignified", "img_width": 1536, "rrg_scale": 1000, "img_height": 1536, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", { input: { seed: 0, prompt: "Envision a portrait of a horse, framed by a blue headscarf with muted tones of rust and cream. she has brown-colored eyes. Her attire, simple yet dignified", img_width: 1536, rrg_scale: 1000, img_height: 1536, cosine_scale: 10, guidance_scale: 10, view_batch_size: 16, negative_prompts: "blurry, ugly, poorly drawn, deformed", resampling_new_p: 0.3, resampling_steps: 7, num_inference_steps: 50 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", 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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", input={ "seed": 0, "prompt": "Envision a portrait of a horse, framed by a blue headscarf with muted tones of rust and cream. she has brown-colored eyes. Her attire, simple yet dignified", "img_width": 1536, "rrg_scale": 1000, "img_height": 1536, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run moayedhajiali/elasticdiffusion 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": "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", "input": { "seed": 0, "prompt": "Envision a portrait of a horse, framed by a blue headscarf with muted tones of rust and cream. she has brown-colored eyes. Her attire, simple yet dignified", "img_width": 1536, "rrg_scale": 1000, "img_height": 1536, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "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": "2023-12-27T19:11:30.147480Z", "created_at": "2023-12-27T19:05:38.235713Z", "data_removed": false, "error": null, "id": "twjdctdb56gybyjoed23yqy5oa", "input": { "seed": 0, "prompt": "Envision a portrait of a horse, framed by a blue headscarf with muted tones of rust and cream. she has brown-colored eyes. Her attire, simple yet dignified", "img_width": 1536, "rrg_scale": 1000, "img_height": 1536, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "num_inference_steps": 50 }, "logs": "0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:07<05:43, 7.01s/it]\n 4%|▍ | 2/50 [00:13<05:35, 7.00s/it]\n 6%|▌ | 3/50 [00:20<05:28, 6.99s/it]\n 8%|▊ | 4/50 [00:27<05:21, 6.99s/it]\n 10%|█ | 5/50 [00:34<05:14, 6.99s/it]\n 12%|█▏ | 6/50 [00:41<05:07, 7.00s/it]\n 14%|█▍ | 7/50 [00:48<05:00, 7.00s/it]\n 16%|█▌ | 8/50 [00:55<04:53, 7.00s/it]\n 18%|█▊ | 9/50 [01:02<04:47, 7.00s/it]\n 20%|██ | 10/50 [01:09<04:40, 7.01s/it]\n 22%|██▏ | 11/50 [01:17<04:33, 7.01s/it]\n 24%|██▍ | 12/50 [01:24<04:27, 7.03s/it]\n 26%|██▌ | 13/50 [01:31<04:19, 7.03s/it]\n 28%|██▊ | 14/50 [01:38<04:12, 7.03s/it]\n 30%|███ | 15/50 [01:45<04:06, 7.03s/it]\n 32%|███▏ | 16/50 [01:52<03:58, 7.03s/it]\n 34%|███▍ | 17/50 [01:59<03:51, 7.03s/it]\n 36%|███▌ | 18/50 [02:06<03:44, 7.03s/it]\n 38%|███▊ | 19/50 [02:13<03:37, 7.03s/it]\n 40%|████ | 20/50 [02:20<03:30, 7.03s/it]\n 42%|████▏ | 21/50 [02:27<03:23, 7.03s/it]\n 44%|████▍ | 22/50 [02:34<03:16, 7.03s/it]\n 46%|████▌ | 23/50 [02:41<03:09, 7.02s/it]\n 48%|████▊ | 24/50 [02:48<03:02, 7.02s/it]\n 50%|█████ | 25/50 [02:55<02:55, 7.03s/it]\n 52%|█████▏ | 26/50 [03:02<02:48, 7.03s/it]\n 54%|█████▍ | 27/50 [03:09<02:41, 7.02s/it]\n 56%|█████▌ | 28/50 [03:16<02:34, 7.02s/it]\n 58%|█████▊ | 29/50 [03:23<02:27, 7.03s/it]\n 60%|██████ | 30/50 [03:30<02:20, 7.02s/it]\n 62%|██████▏ | 31/50 [03:37<02:13, 7.02s/it]\n 64%|██████▍ | 32/50 [03:44<02:06, 7.03s/it]\n 66%|██████▌ | 33/50 [03:51<01:59, 7.03s/it]\n 68%|██████▊ | 34/50 [03:58<01:52, 7.03s/it]\n 70%|███████ | 35/50 [04:05<01:45, 7.03s/it]\n 72%|███████▏ | 36/50 [04:12<01:38, 7.03s/it]\n 74%|███████▍ | 37/50 [04:19<01:31, 7.03s/it]\n 76%|███████▌ | 38/50 [04:26<01:24, 7.03s/it]\n 78%|███████▊ | 39/50 [04:33<01:17, 7.03s/it]\n 80%|████████ | 40/50 [04:40<01:10, 7.03s/it]\n 82%|████████▏ | 41/50 [04:47<01:03, 7.03s/it]\n 84%|████████▍ | 42/50 [04:54<00:56, 7.03s/it]\n 86%|████████▌ | 43/50 [05:01<00:49, 7.03s/it]\n 88%|████████▊ | 44/50 [05:08<00:42, 7.03s/it]\n 90%|█████████ | 45/50 [05:15<00:35, 7.03s/it]\n 92%|█████████▏| 46/50 [05:23<00:28, 7.03s/it]\n 94%|█████████▍| 47/50 [05:30<00:21, 7.03s/it]\n 96%|█████████▌| 48/50 [05:37<00:14, 7.03s/it]\n 98%|█████████▊| 49/50 [05:44<00:07, 7.03s/it]\n100%|██████████| 50/50 [05:49<00:00, 6.39s/it]\n100%|██████████| 50/50 [05:49<00:00, 6.98s/it]\n[INFO] Time taken: 350.0545735359192 seconds.", "metrics": { "predict_time": 351.855281, "total_time": 351.911767 }, "output": "https://replicate.delivery/pbxt/BdXvrZFI4d7gEV0YnfVQH9WmiB66i6WiMjIbB7Cc16xwRRDJA/result.png", "started_at": "2023-12-27T19:05:38.292199Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/twjdctdb56gybyjoed23yqy5oa", "cancel": "https://api.replicate.com/v1/predictions/twjdctdb56gybyjoed23yqy5oa/cancel" }, "version": "bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88" }Generated in0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:07<05:43, 7.01s/it] 4%|▍ | 2/50 [00:13<05:35, 7.00s/it] 6%|▌ | 3/50 [00:20<05:28, 6.99s/it] 8%|▊ | 4/50 [00:27<05:21, 6.99s/it] 10%|█ | 5/50 [00:34<05:14, 6.99s/it] 12%|█▏ | 6/50 [00:41<05:07, 7.00s/it] 14%|█▍ | 7/50 [00:48<05:00, 7.00s/it] 16%|█▌ | 8/50 [00:55<04:53, 7.00s/it] 18%|█▊ | 9/50 [01:02<04:47, 7.00s/it] 20%|██ | 10/50 [01:09<04:40, 7.01s/it] 22%|██▏ | 11/50 [01:17<04:33, 7.01s/it] 24%|██▍ | 12/50 [01:24<04:27, 7.03s/it] 26%|██▌ | 13/50 [01:31<04:19, 7.03s/it] 28%|██▊ | 14/50 [01:38<04:12, 7.03s/it] 30%|███ | 15/50 [01:45<04:06, 7.03s/it] 32%|███▏ | 16/50 [01:52<03:58, 7.03s/it] 34%|███▍ | 17/50 [01:59<03:51, 7.03s/it] 36%|███▌ | 18/50 [02:06<03:44, 7.03s/it] 38%|███▊ | 19/50 [02:13<03:37, 7.03s/it] 40%|████ | 20/50 [02:20<03:30, 7.03s/it] 42%|████▏ | 21/50 [02:27<03:23, 7.03s/it] 44%|████▍ | 22/50 [02:34<03:16, 7.03s/it] 46%|████▌ | 23/50 [02:41<03:09, 7.02s/it] 48%|████▊ | 24/50 [02:48<03:02, 7.02s/it] 50%|█████ | 25/50 [02:55<02:55, 7.03s/it] 52%|█████▏ | 26/50 [03:02<02:48, 7.03s/it] 54%|█████▍ | 27/50 [03:09<02:41, 7.02s/it] 56%|█████▌ | 28/50 [03:16<02:34, 7.02s/it] 58%|█████▊ | 29/50 [03:23<02:27, 7.03s/it] 60%|██████ | 30/50 [03:30<02:20, 7.02s/it] 62%|██████▏ | 31/50 [03:37<02:13, 7.02s/it] 64%|██████▍ | 32/50 [03:44<02:06, 7.03s/it] 66%|██████▌ | 33/50 [03:51<01:59, 7.03s/it] 68%|██████▊ | 34/50 [03:58<01:52, 7.03s/it] 70%|███████ | 35/50 [04:05<01:45, 7.03s/it] 72%|███████▏ | 36/50 [04:12<01:38, 7.03s/it] 74%|███████▍ | 37/50 [04:19<01:31, 7.03s/it] 76%|███████▌ | 38/50 [04:26<01:24, 7.03s/it] 78%|███████▊ | 39/50 [04:33<01:17, 7.03s/it] 80%|████████ | 40/50 [04:40<01:10, 7.03s/it] 82%|████████▏ | 41/50 [04:47<01:03, 7.03s/it] 84%|████████▍ | 42/50 [04:54<00:56, 7.03s/it] 86%|████████▌ | 43/50 [05:01<00:49, 7.03s/it] 88%|████████▊ | 44/50 [05:08<00:42, 7.03s/it] 90%|█████████ | 45/50 [05:15<00:35, 7.03s/it] 92%|█████████▏| 46/50 [05:23<00:28, 7.03s/it] 94%|█████████▍| 47/50 [05:30<00:21, 7.03s/it] 96%|█████████▌| 48/50 [05:37<00:14, 7.03s/it] 98%|█████████▊| 49/50 [05:44<00:07, 7.03s/it] 100%|██████████| 50/50 [05:49<00:00, 6.39s/it] 100%|██████████| 50/50 [05:49<00:00, 6.98s/it] [INFO] Time taken: 350.0545735359192 seconds.Prediction
moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88IDuxkyqgtbmleh2527ventavrzryStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 0
- prompt
- Envision a portrait of a cute corgi, framed by a red headscarf. his eyes are light brown. his attire is simple yet dignified
- img_width
- 1024
- rrg_scale
- 1000
- img_height
- 2048
- cosine_scale
- 10
- guidance_scale
- 10
- view_batch_size
- 16
- negative_prompts
- blurry, ugly, poorly drawn, deformed
- resampling_new_p
- 0.3
- resampling_steps
- 7
- num_inference_steps
- 50
{ "seed": 0, "prompt": "Envision a portrait of a cute corgi, framed by a red headscarf. his eyes are light brown. his attire is simple yet dignified", "img_width": 1024, "rrg_scale": 1000, "img_height": 2048, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", { input: { seed: 0, prompt: "Envision a portrait of a cute corgi, framed by a red headscarf. his eyes are light brown. his attire is simple yet dignified", img_width: 1024, rrg_scale: 1000, img_height: 2048, cosine_scale: 10, guidance_scale: 10, view_batch_size: 16, negative_prompts: "blurry, ugly, poorly drawn, deformed", resampling_new_p: 0.3, resampling_steps: 7, num_inference_steps: 50 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", 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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", input={ "seed": 0, "prompt": "Envision a portrait of a cute corgi, framed by a red headscarf. his eyes are light brown. his attire is simple yet dignified", "img_width": 1024, "rrg_scale": 1000, "img_height": 2048, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run moayedhajiali/elasticdiffusion 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": "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", "input": { "seed": 0, "prompt": "Envision a portrait of a cute corgi, framed by a red headscarf. his eyes are light brown. his attire is simple yet dignified", "img_width": 1024, "rrg_scale": 1000, "img_height": 2048, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "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": "2023-12-27T19:51:27.946539Z", "created_at": "2023-12-27T19:46:26.603455Z", "data_removed": false, "error": null, "id": "uxkyqgtbmleh2527ventavrzry", "input": { "seed": 0, "prompt": "Envision a portrait of a cute corgi, framed by a red headscarf. his eyes are light brown. his attire is simple yet dignified", "img_width": 1024, "rrg_scale": 1000, "img_height": 2048, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "num_inference_steps": 50 }, "logs": "0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:05<04:52, 5.96s/it]\n 4%|▍ | 2/50 [00:11<04:46, 5.97s/it]\n 6%|▌ | 3/50 [00:17<04:40, 5.98s/it]\n 8%|▊ | 4/50 [00:23<04:34, 5.97s/it]\n 10%|█ | 5/50 [00:29<04:28, 5.97s/it]\n 12%|█▏ | 6/50 [00:35<04:22, 5.97s/it]\n 14%|█▍ | 7/50 [00:41<04:16, 5.97s/it]\n 16%|█▌ | 8/50 [00:47<04:11, 5.99s/it]\n 18%|█▊ | 9/50 [00:53<04:05, 5.99s/it]\n 20%|██ | 10/50 [00:59<04:00, 6.00s/it]\n 22%|██▏ | 11/50 [01:05<03:53, 6.00s/it]\n 24%|██▍ | 12/50 [01:11<03:47, 5.99s/it]\n 26%|██▌ | 13/50 [01:17<03:41, 5.99s/it]\n 28%|██▊ | 14/50 [01:23<03:36, 6.00s/it]\n 30%|███ | 15/50 [01:29<03:29, 6.00s/it]\n 32%|███▏ | 16/50 [01:35<03:24, 6.00s/it]\n 34%|███▍ | 17/50 [01:41<03:18, 6.00s/it]\n 36%|███▌ | 18/50 [01:47<03:11, 6.00s/it]\n 38%|███▊ | 19/50 [01:53<03:05, 5.99s/it]\n 40%|████ | 20/50 [01:59<02:59, 5.99s/it]\n 42%|████▏ | 21/50 [02:05<02:53, 5.99s/it]\n 44%|████▍ | 22/50 [02:11<02:47, 5.99s/it]\n 46%|████▌ | 23/50 [02:17<02:41, 6.00s/it]\n 48%|████▊ | 24/50 [02:23<02:35, 6.00s/it]\n 50%|█████ | 25/50 [02:29<02:29, 6.00s/it]\n 52%|█████▏ | 26/50 [02:35<02:23, 6.00s/it]\n 54%|█████▍ | 27/50 [02:41<02:17, 6.00s/it]\n 56%|█████▌ | 28/50 [02:47<02:12, 6.00s/it]\n 58%|█████▊ | 29/50 [02:53<02:05, 6.00s/it]\n 60%|██████ | 30/50 [02:59<01:59, 6.00s/it]\n 62%|██████▏ | 31/50 [03:05<01:53, 6.00s/it]\n 64%|██████▍ | 32/50 [03:11<01:48, 6.01s/it]\n 66%|██████▌ | 33/50 [03:17<01:42, 6.00s/it]\n 68%|██████▊ | 34/50 [03:23<01:36, 6.00s/it]\n 70%|███████ | 35/50 [03:29<01:29, 6.00s/it]\n 72%|███████▏ | 36/50 [03:35<01:24, 6.00s/it]\n 74%|███████▍ | 37/50 [03:41<01:17, 6.00s/it]\n 76%|███████▌ | 38/50 [03:47<01:12, 6.00s/it]\n 78%|███████▊ | 39/50 [03:53<01:06, 6.00s/it]\n 80%|████████ | 40/50 [03:59<00:59, 6.00s/it]\n 82%|████████▏ | 41/50 [04:05<00:54, 6.00s/it]\n 84%|████████▍ | 42/50 [04:11<00:47, 6.00s/it]\n 86%|████████▌ | 43/50 [04:17<00:41, 6.00s/it]\n 88%|████████▊ | 44/50 [04:23<00:35, 6.00s/it]\n 90%|█████████ | 45/50 [04:29<00:29, 6.00s/it]\n 92%|█████████▏| 46/50 [04:35<00:23, 6.00s/it]\n 94%|█████████▍| 47/50 [04:41<00:18, 6.00s/it]\n 96%|█████████▌| 48/50 [04:47<00:12, 6.00s/it]\n 98%|█████████▊| 49/50 [04:53<00:06, 6.00s/it]\n100%|██████████| 50/50 [04:58<00:00, 5.63s/it]\n100%|██████████| 50/50 [04:58<00:00, 5.97s/it]\n[INFO] Time taken: 299.4873378276825 seconds.", "metrics": { "predict_time": 301.289303, "total_time": 301.343084 }, "output": "https://replicate.delivery/pbxt/6Fve0ZeSPEod10OBTMXBNhfb4T0pbYVepA1kOo4oAul8jMaIB/result.png", "started_at": "2023-12-27T19:46:26.657236Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/uxkyqgtbmleh2527ventavrzry", "cancel": "https://api.replicate.com/v1/predictions/uxkyqgtbmleh2527ventavrzry/cancel" }, "version": "bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88" }Generated in0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:05<04:52, 5.96s/it] 4%|▍ | 2/50 [00:11<04:46, 5.97s/it] 6%|▌ | 3/50 [00:17<04:40, 5.98s/it] 8%|▊ | 4/50 [00:23<04:34, 5.97s/it] 10%|█ | 5/50 [00:29<04:28, 5.97s/it] 12%|█▏ | 6/50 [00:35<04:22, 5.97s/it] 14%|█▍ | 7/50 [00:41<04:16, 5.97s/it] 16%|█▌ | 8/50 [00:47<04:11, 5.99s/it] 18%|█▊ | 9/50 [00:53<04:05, 5.99s/it] 20%|██ | 10/50 [00:59<04:00, 6.00s/it] 22%|██▏ | 11/50 [01:05<03:53, 6.00s/it] 24%|██▍ | 12/50 [01:11<03:47, 5.99s/it] 26%|██▌ | 13/50 [01:17<03:41, 5.99s/it] 28%|██▊ | 14/50 [01:23<03:36, 6.00s/it] 30%|███ | 15/50 [01:29<03:29, 6.00s/it] 32%|███▏ | 16/50 [01:35<03:24, 6.00s/it] 34%|███▍ | 17/50 [01:41<03:18, 6.00s/it] 36%|███▌ | 18/50 [01:47<03:11, 6.00s/it] 38%|███▊ | 19/50 [01:53<03:05, 5.99s/it] 40%|████ | 20/50 [01:59<02:59, 5.99s/it] 42%|████▏ | 21/50 [02:05<02:53, 5.99s/it] 44%|████▍ | 22/50 [02:11<02:47, 5.99s/it] 46%|████▌ | 23/50 [02:17<02:41, 6.00s/it] 48%|████▊ | 24/50 [02:23<02:35, 6.00s/it] 50%|█████ | 25/50 [02:29<02:29, 6.00s/it] 52%|█████▏ | 26/50 [02:35<02:23, 6.00s/it] 54%|█████▍ | 27/50 [02:41<02:17, 6.00s/it] 56%|█████▌ | 28/50 [02:47<02:12, 6.00s/it] 58%|█████▊ | 29/50 [02:53<02:05, 6.00s/it] 60%|██████ | 30/50 [02:59<01:59, 6.00s/it] 62%|██████▏ | 31/50 [03:05<01:53, 6.00s/it] 64%|██████▍ | 32/50 [03:11<01:48, 6.01s/it] 66%|██████▌ | 33/50 [03:17<01:42, 6.00s/it] 68%|██████▊ | 34/50 [03:23<01:36, 6.00s/it] 70%|███████ | 35/50 [03:29<01:29, 6.00s/it] 72%|███████▏ | 36/50 [03:35<01:24, 6.00s/it] 74%|███████▍ | 37/50 [03:41<01:17, 6.00s/it] 76%|███████▌ | 38/50 [03:47<01:12, 6.00s/it] 78%|███████▊ | 39/50 [03:53<01:06, 6.00s/it] 80%|████████ | 40/50 [03:59<00:59, 6.00s/it] 82%|████████▏ | 41/50 [04:05<00:54, 6.00s/it] 84%|████████▍ | 42/50 [04:11<00:47, 6.00s/it] 86%|████████▌ | 43/50 [04:17<00:41, 6.00s/it] 88%|████████▊ | 44/50 [04:23<00:35, 6.00s/it] 90%|█████████ | 45/50 [04:29<00:29, 6.00s/it] 92%|█████████▏| 46/50 [04:35<00:23, 6.00s/it] 94%|█████████▍| 47/50 [04:41<00:18, 6.00s/it] 96%|█████████▌| 48/50 [04:47<00:12, 6.00s/it] 98%|█████████▊| 49/50 [04:53<00:06, 6.00s/it] 100%|██████████| 50/50 [04:58<00:00, 5.63s/it] 100%|██████████| 50/50 [04:58<00:00, 5.97s/it] [INFO] Time taken: 299.4873378276825 seconds.Prediction
moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88IDdmcgtylbovd5hnfmtvhso7jdz4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 0
- prompt
- Envision a realistic portrait of a black woman, she has a white headscarf. Her eyes are dark black. her attire, simple yet delightful.
- img_width
- 1920
- rrg_scale
- 1000
- img_height
- 1080
- cosine_scale
- 10
- guidance_scale
- 10
- view_batch_size
- 16
- negative_prompts
- blurry, ugly, poorly drawn, deformed
- resampling_new_p
- 0.3
- resampling_steps
- 7
- num_inference_steps
- 50
{ "seed": 0, "prompt": "Envision a realistic portrait of a black woman, she has a white headscarf. Her eyes are dark black. her attire, simple yet delightful.", "img_width": 1920, "rrg_scale": 1000, "img_height": 1080, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", { input: { seed: 0, prompt: "Envision a realistic portrait of a black woman, she has a white headscarf. Her eyes are dark black. her attire, simple yet delightful.", img_width: 1920, rrg_scale: 1000, img_height: 1080, cosine_scale: 10, guidance_scale: 10, view_batch_size: 16, negative_prompts: "blurry, ugly, poorly drawn, deformed", resampling_new_p: 0.3, resampling_steps: 7, num_inference_steps: 50 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", 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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", input={ "seed": 0, "prompt": "Envision a realistic portrait of a black woman, she has a white headscarf. Her eyes are dark black. her attire, simple yet delightful.", "img_width": 1920, "rrg_scale": 1000, "img_height": 1080, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run moayedhajiali/elasticdiffusion 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": "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", "input": { "seed": 0, "prompt": "Envision a realistic portrait of a black woman, she has a white headscarf. Her eyes are dark black. her attire, simple yet delightful.", "img_width": 1920, "rrg_scale": 1000, "img_height": 1080, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "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": "2023-12-27T20:35:45.598767Z", "created_at": "2023-12-27T20:25:46.248480Z", "data_removed": false, "error": null, "id": "dmcgtylbovd5hnfmtvhso7jdz4", "input": { "seed": 0, "prompt": "Envision a realistic portrait of a black woman, she has a white headscarf. Her eyes are dark black. her attire, simple yet delightful.", "img_width": 1920, "rrg_scale": 1000, "img_height": 1080, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "num_inference_steps": 50 }, "logs": "0%| | 0/50 [00:00<?, ?it/s]/src/elastic_diffusion.py:502: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\nsampled_h = (idx_h * downsample_factor + random_indices.reshape(idx_h.shape[0], idx_h.shape[1]) // downsample_factor).view(-1)\n 2%|▏ | 1/50 [00:08<07:14, 8.86s/it]\n 4%|▍ | 2/50 [00:17<07:03, 8.82s/it]\n 6%|▌ | 3/50 [00:26<06:54, 8.82s/it]\n 8%|▊ | 4/50 [00:35<06:45, 8.82s/it]\n 10%|█ | 5/50 [00:44<06:37, 8.82s/it]\n 12%|█▏ | 6/50 [00:52<06:28, 8.82s/it]\n 14%|█▍ | 7/50 [01:01<06:19, 8.83s/it]\n 16%|█▌ | 8/50 [01:10<06:10, 8.83s/it]\n 18%|█▊ | 9/50 [01:19<06:01, 8.83s/it]\n 20%|██ | 10/50 [01:28<05:53, 8.83s/it]\n 22%|██▏ | 11/50 [01:37<05:44, 8.83s/it]\n 24%|██▍ | 12/50 [01:45<05:35, 8.84s/it]\n 26%|██▌ | 13/50 [01:54<05:27, 8.84s/it]\n 28%|██▊ | 14/50 [02:03<05:18, 8.84s/it]\n 30%|███ | 15/50 [02:12<05:09, 8.84s/it]\n 32%|███▏ | 16/50 [02:21<05:00, 8.84s/it]\n 34%|███▍ | 17/50 [02:30<04:51, 8.84s/it]\n 36%|███▌ | 18/50 [02:38<04:42, 8.84s/it]\n 38%|███▊ | 19/50 [02:47<04:33, 8.84s/it]\n 40%|████ | 20/50 [02:56<04:25, 8.84s/it]\n 42%|████▏ | 21/50 [03:05<04:16, 8.84s/it]\n 44%|████▍ | 22/50 [03:14<04:07, 8.84s/it]\n 46%|████▌ | 23/50 [03:23<03:58, 8.84s/it]\n 48%|████▊ | 24/50 [03:32<03:49, 8.84s/it]\n 50%|█████ | 25/50 [03:40<03:41, 8.84s/it]\n 52%|█████▏ | 26/50 [03:49<03:32, 8.85s/it]\n 54%|█████▍ | 27/50 [03:58<03:23, 8.84s/it]\n 56%|█████▌ | 28/50 [04:07<03:14, 8.84s/it]\n 58%|█████▊ | 29/50 [04:16<03:05, 8.84s/it]\n 60%|██████ | 30/50 [04:25<02:56, 8.85s/it]\n 62%|██████▏ | 31/50 [04:33<02:48, 8.85s/it]\n 64%|██████▍ | 32/50 [04:42<02:39, 8.85s/it]\n 66%|██████▌ | 33/50 [04:51<02:30, 8.85s/it]\n 68%|██████▊ | 34/50 [05:00<02:21, 8.85s/it]\n 70%|███████ | 35/50 [05:09<02:12, 8.84s/it]\n 72%|███████▏ | 36/50 [05:18<02:03, 8.85s/it]\n 74%|███████▍ | 37/50 [05:27<01:54, 8.84s/it]\n 76%|███████▌ | 38/50 [05:35<01:46, 8.85s/it]\n 78%|███████▊ | 39/50 [05:44<01:37, 8.85s/it]\n 80%|████████ | 40/50 [05:53<01:28, 8.85s/it]\n 82%|████████▏ | 41/50 [06:02<01:19, 8.85s/it]\n 84%|████████▍ | 42/50 [06:11<01:10, 8.85s/it]\n 86%|████████▌ | 43/50 [06:20<01:01, 8.85s/it]\n 88%|████████▊ | 44/50 [06:29<00:53, 8.85s/it]\n 90%|█████████ | 45/50 [06:37<00:44, 8.85s/it]\n 92%|█████████▏| 46/50 [06:46<00:35, 8.85s/it]\n 94%|█████████▍| 47/50 [06:55<00:26, 8.85s/it]\n 96%|█████████▌| 48/50 [07:04<00:17, 8.85s/it]\n 98%|█████████▊| 49/50 [07:13<00:08, 8.84s/it]\n100%|██████████| 50/50 [07:19<00:00, 8.02s/it]\n100%|██████████| 50/50 [07:19<00:00, 8.79s/it]\n[INFO] Time taken: 440.20098972320557 seconds.", "metrics": { "predict_time": 441.830478, "total_time": 599.350287 }, "output": "https://replicate.delivery/pbxt/juemLBMnZF1MZa1Noh1avKaUzDzVBL44fW6d07GrhWbgyjGSA/result.png", "started_at": "2023-12-27T20:28:23.768289Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/dmcgtylbovd5hnfmtvhso7jdz4", "cancel": "https://api.replicate.com/v1/predictions/dmcgtylbovd5hnfmtvhso7jdz4/cancel" }, "version": "bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88" }Generated in0%| | 0/50 [00:00<?, ?it/s]/src/elastic_diffusion.py:502: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). sampled_h = (idx_h * downsample_factor + random_indices.reshape(idx_h.shape[0], idx_h.shape[1]) // downsample_factor).view(-1) 2%|▏ | 1/50 [00:08<07:14, 8.86s/it] 4%|▍ | 2/50 [00:17<07:03, 8.82s/it] 6%|▌ | 3/50 [00:26<06:54, 8.82s/it] 8%|▊ | 4/50 [00:35<06:45, 8.82s/it] 10%|█ | 5/50 [00:44<06:37, 8.82s/it] 12%|█▏ | 6/50 [00:52<06:28, 8.82s/it] 14%|█▍ | 7/50 [01:01<06:19, 8.83s/it] 16%|█▌ | 8/50 [01:10<06:10, 8.83s/it] 18%|█▊ | 9/50 [01:19<06:01, 8.83s/it] 20%|██ | 10/50 [01:28<05:53, 8.83s/it] 22%|██▏ | 11/50 [01:37<05:44, 8.83s/it] 24%|██▍ | 12/50 [01:45<05:35, 8.84s/it] 26%|██▌ | 13/50 [01:54<05:27, 8.84s/it] 28%|██▊ | 14/50 [02:03<05:18, 8.84s/it] 30%|███ | 15/50 [02:12<05:09, 8.84s/it] 32%|███▏ | 16/50 [02:21<05:00, 8.84s/it] 34%|███▍ | 17/50 [02:30<04:51, 8.84s/it] 36%|███▌ | 18/50 [02:38<04:42, 8.84s/it] 38%|███▊ | 19/50 [02:47<04:33, 8.84s/it] 40%|████ | 20/50 [02:56<04:25, 8.84s/it] 42%|████▏ | 21/50 [03:05<04:16, 8.84s/it] 44%|████▍ | 22/50 [03:14<04:07, 8.84s/it] 46%|████▌ | 23/50 [03:23<03:58, 8.84s/it] 48%|████▊ | 24/50 [03:32<03:49, 8.84s/it] 50%|█████ | 25/50 [03:40<03:41, 8.84s/it] 52%|█████▏ | 26/50 [03:49<03:32, 8.85s/it] 54%|█████▍ | 27/50 [03:58<03:23, 8.84s/it] 56%|█████▌ | 28/50 [04:07<03:14, 8.84s/it] 58%|█████▊ | 29/50 [04:16<03:05, 8.84s/it] 60%|██████ | 30/50 [04:25<02:56, 8.85s/it] 62%|██████▏ | 31/50 [04:33<02:48, 8.85s/it] 64%|██████▍ | 32/50 [04:42<02:39, 8.85s/it] 66%|██████▌ | 33/50 [04:51<02:30, 8.85s/it] 68%|██████▊ | 34/50 [05:00<02:21, 8.85s/it] 70%|███████ | 35/50 [05:09<02:12, 8.84s/it] 72%|███████▏ | 36/50 [05:18<02:03, 8.85s/it] 74%|███████▍ | 37/50 [05:27<01:54, 8.84s/it] 76%|███████▌ | 38/50 [05:35<01:46, 8.85s/it] 78%|███████▊ | 39/50 [05:44<01:37, 8.85s/it] 80%|████████ | 40/50 [05:53<01:28, 8.85s/it] 82%|████████▏ | 41/50 [06:02<01:19, 8.85s/it] 84%|████████▍ | 42/50 [06:11<01:10, 8.85s/it] 86%|████████▌ | 43/50 [06:20<01:01, 8.85s/it] 88%|████████▊ | 44/50 [06:29<00:53, 8.85s/it] 90%|█████████ | 45/50 [06:37<00:44, 8.85s/it] 92%|█████████▏| 46/50 [06:46<00:35, 8.85s/it] 94%|█████████▍| 47/50 [06:55<00:26, 8.85s/it] 96%|█████████▌| 48/50 [07:04<00:17, 8.85s/it] 98%|█████████▊| 49/50 [07:13<00:08, 8.84s/it] 100%|██████████| 50/50 [07:19<00:00, 8.02s/it] 100%|██████████| 50/50 [07:19<00:00, 8.79s/it] [INFO] Time taken: 440.20098972320557 seconds.Prediction
moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88IDb2omdklbka53gppshvbmvgyupaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 0
- prompt
- A Cute Puppy with wings, Cartoon Drawings, high details
- img_width
- 2048
- rrg_scale
- 1500
- img_height
- 1536
- cosine_scale
- 10
- guidance_scale
- 10
- view_batch_size
- 16
- negative_prompts
- blurry, ugly, poorly drawn, deformed
- resampling_new_p
- 0.3
- resampling_steps
- 10
- num_inference_steps
- 50
{ "seed": 0, "prompt": "A Cute Puppy with wings, Cartoon Drawings, high details", "img_width": 2048, "rrg_scale": 1500, "img_height": 1536, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 10, "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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", { input: { seed: 0, prompt: "A Cute Puppy with wings, Cartoon Drawings, high details", img_width: 2048, rrg_scale: 1500, img_height: 1536, cosine_scale: 10, guidance_scale: 10, view_batch_size: 16, negative_prompts: "blurry, ugly, poorly drawn, deformed", resampling_new_p: 0.3, resampling_steps: 10, num_inference_steps: 50 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", 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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", input={ "seed": 0, "prompt": "A Cute Puppy with wings, Cartoon Drawings, high details", "img_width": 2048, "rrg_scale": 1500, "img_height": 1536, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 10, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run moayedhajiali/elasticdiffusion 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": "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", "input": { "seed": 0, "prompt": "A Cute Puppy with wings, Cartoon Drawings, high details", "img_width": 2048, "rrg_scale": 1500, "img_height": 1536, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 10, "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": "2023-12-27T21:06:21.652882Z", "created_at": "2023-12-27T20:54:06.329543Z", "data_removed": false, "error": null, "id": "b2omdklbka53gppshvbmvgyupa", "input": { "seed": 0, "prompt": "A Cute Puppy with wings, Cartoon Drawings, high details", "img_width": 2048, "rrg_scale": 1500, "img_height": 1536, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 10, "num_inference_steps": 50 }, "logs": "0%| | 0/50 [00:00<?, ?it/s]/src/elastic_diffusion.py:502: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\nsampled_h = (idx_h * downsample_factor + random_indices.reshape(idx_h.shape[0], idx_h.shape[1]) // downsample_factor).view(-1)\n 2%|▏ | 1/50 [00:09<08:05, 9.91s/it]\n 4%|▍ | 2/50 [00:19<07:55, 9.90s/it]\n 6%|▌ | 3/50 [00:29<07:44, 9.88s/it]\n 8%|▊ | 4/50 [00:39<07:34, 9.88s/it]\n 10%|█ | 5/50 [00:49<07:24, 9.88s/it]\n 12%|█▏ | 6/50 [00:59<07:14, 9.89s/it]\n 14%|█▍ | 7/50 [01:09<07:05, 9.89s/it]\n 16%|█▌ | 8/50 [01:19<06:55, 9.89s/it]\n 18%|█▊ | 9/50 [01:29<06:45, 9.90s/it]\n 20%|██ | 10/50 [01:38<06:36, 9.91s/it]\n 22%|██▏ | 11/50 [01:48<06:27, 9.94s/it]\n 24%|██▍ | 12/50 [01:58<06:17, 9.94s/it]\n 26%|██▌ | 13/50 [02:08<06:07, 9.94s/it]\n 28%|██▊ | 14/50 [02:19<06:10, 10.29s/it]\n 30%|███ | 15/50 [02:29<05:56, 10.17s/it]\n 32%|███▏ | 16/50 [02:39<05:43, 10.11s/it]\n 34%|███▍ | 17/50 [02:49<05:31, 10.05s/it]\n 36%|███▌ | 18/50 [02:59<05:20, 10.01s/it]\n 38%|███▊ | 19/50 [03:09<05:09, 9.98s/it]\n 40%|████ | 20/50 [03:19<04:58, 9.96s/it]\n 42%|████▏ | 21/50 [03:29<04:48, 9.95s/it]\n 44%|████▍ | 22/50 [03:39<04:38, 9.95s/it]\n 46%|████▌ | 23/50 [03:49<04:28, 9.94s/it]\n 48%|████▊ | 24/50 [03:59<04:18, 9.93s/it]\n 50%|█████ | 25/50 [04:09<04:08, 9.94s/it]\n 52%|█████▏ | 26/50 [04:19<03:58, 9.93s/it]\n 54%|█████▍ | 27/50 [04:28<03:48, 9.93s/it]\n 56%|█████▌ | 28/50 [04:38<03:38, 9.93s/it]\n 58%|█████▊ | 29/50 [04:48<03:28, 9.93s/it]\n 60%|██████ | 30/50 [04:58<03:18, 9.93s/it]\n 62%|██████▏ | 31/50 [05:08<03:08, 9.93s/it]\n 64%|██████▍ | 32/50 [05:18<02:58, 9.94s/it]\n 66%|██████▌ | 33/50 [05:28<02:48, 9.94s/it]\n 68%|██████▊ | 34/50 [05:38<02:39, 9.95s/it]\n 70%|███████ | 35/50 [05:48<02:29, 9.95s/it]\n 72%|███████▏ | 36/50 [05:58<02:19, 9.94s/it]\n 74%|███████▍ | 37/50 [06:08<02:09, 9.95s/it]\n 76%|███████▌ | 38/50 [06:18<01:59, 9.94s/it]\n 78%|███████▊ | 39/50 [06:28<01:49, 9.95s/it]\n 80%|████████ | 40/50 [06:38<01:39, 9.95s/it]\n 82%|████████▏ | 41/50 [06:48<01:29, 9.95s/it]\n 84%|████████▍ | 42/50 [06:58<01:20, 10.01s/it]\n 86%|████████▌ | 43/50 [07:08<01:09, 10.00s/it]\n 88%|████████▊ | 44/50 [07:18<00:59, 9.98s/it]\n 90%|█████████ | 45/50 [07:28<00:49, 9.97s/it]\n 92%|█████████▏| 46/50 [07:38<00:39, 9.97s/it]\n 94%|█████████▍| 47/50 [07:48<00:29, 9.96s/it]\n 96%|█████████▌| 48/50 [07:58<00:19, 9.96s/it]\n 98%|█████████▊| 49/50 [08:07<00:09, 9.96s/it]\n100%|██████████| 50/50 [08:15<00:00, 9.14s/it]\n100%|██████████| 50/50 [08:15<00:00, 9.90s/it]\n[INFO] Time taken: 497.21400928497314 seconds.", "metrics": { "predict_time": 499.347247, "total_time": 735.323339 }, "output": "https://replicate.delivery/pbxt/sbLKlR3UL3IoCNLJcc1ODkz1NT9SwDOgMUmfacoDkBYmHSDJA/result.png", "started_at": "2023-12-27T20:58:02.305635Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/b2omdklbka53gppshvbmvgyupa", "cancel": "https://api.replicate.com/v1/predictions/b2omdklbka53gppshvbmvgyupa/cancel" }, "version": "bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88" }Generated in0%| | 0/50 [00:00<?, ?it/s]/src/elastic_diffusion.py:502: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). sampled_h = (idx_h * downsample_factor + random_indices.reshape(idx_h.shape[0], idx_h.shape[1]) // downsample_factor).view(-1) 2%|▏ | 1/50 [00:09<08:05, 9.91s/it] 4%|▍ | 2/50 [00:19<07:55, 9.90s/it] 6%|▌ | 3/50 [00:29<07:44, 9.88s/it] 8%|▊ | 4/50 [00:39<07:34, 9.88s/it] 10%|█ | 5/50 [00:49<07:24, 9.88s/it] 12%|█▏ | 6/50 [00:59<07:14, 9.89s/it] 14%|█▍ | 7/50 [01:09<07:05, 9.89s/it] 16%|█▌ | 8/50 [01:19<06:55, 9.89s/it] 18%|█▊ | 9/50 [01:29<06:45, 9.90s/it] 20%|██ | 10/50 [01:38<06:36, 9.91s/it] 22%|██▏ | 11/50 [01:48<06:27, 9.94s/it] 24%|██▍ | 12/50 [01:58<06:17, 9.94s/it] 26%|██▌ | 13/50 [02:08<06:07, 9.94s/it] 28%|██▊ | 14/50 [02:19<06:10, 10.29s/it] 30%|███ | 15/50 [02:29<05:56, 10.17s/it] 32%|███▏ | 16/50 [02:39<05:43, 10.11s/it] 34%|███▍ | 17/50 [02:49<05:31, 10.05s/it] 36%|███▌ | 18/50 [02:59<05:20, 10.01s/it] 38%|███▊ | 19/50 [03:09<05:09, 9.98s/it] 40%|████ | 20/50 [03:19<04:58, 9.96s/it] 42%|████▏ | 21/50 [03:29<04:48, 9.95s/it] 44%|████▍ | 22/50 [03:39<04:38, 9.95s/it] 46%|████▌ | 23/50 [03:49<04:28, 9.94s/it] 48%|████▊ | 24/50 [03:59<04:18, 9.93s/it] 50%|█████ | 25/50 [04:09<04:08, 9.94s/it] 52%|█████▏ | 26/50 [04:19<03:58, 9.93s/it] 54%|█████▍ | 27/50 [04:28<03:48, 9.93s/it] 56%|█████▌ | 28/50 [04:38<03:38, 9.93s/it] 58%|█████▊ | 29/50 [04:48<03:28, 9.93s/it] 60%|██████ | 30/50 [04:58<03:18, 9.93s/it] 62%|██████▏ | 31/50 [05:08<03:08, 9.93s/it] 64%|██████▍ | 32/50 [05:18<02:58, 9.94s/it] 66%|██████▌ | 33/50 [05:28<02:48, 9.94s/it] 68%|██████▊ | 34/50 [05:38<02:39, 9.95s/it] 70%|███████ | 35/50 [05:48<02:29, 9.95s/it] 72%|███████▏ | 36/50 [05:58<02:19, 9.94s/it] 74%|███████▍ | 37/50 [06:08<02:09, 9.95s/it] 76%|███████▌ | 38/50 [06:18<01:59, 9.94s/it] 78%|███████▊ | 39/50 [06:28<01:49, 9.95s/it] 80%|████████ | 40/50 [06:38<01:39, 9.95s/it] 82%|████████▏ | 41/50 [06:48<01:29, 9.95s/it] 84%|████████▍ | 42/50 [06:58<01:20, 10.01s/it] 86%|████████▌ | 43/50 [07:08<01:09, 10.00s/it] 88%|████████▊ | 44/50 [07:18<00:59, 9.98s/it] 90%|█████████ | 45/50 [07:28<00:49, 9.97s/it] 92%|█████████▏| 46/50 [07:38<00:39, 9.97s/it] 94%|█████████▍| 47/50 [07:48<00:29, 9.96s/it] 96%|█████████▌| 48/50 [07:58<00:19, 9.96s/it] 98%|█████████▊| 49/50 [08:07<00:09, 9.96s/it] 100%|██████████| 50/50 [08:15<00:00, 9.14s/it] 100%|██████████| 50/50 [08:15<00:00, 9.90s/it] [INFO] Time taken: 497.21400928497314 seconds.Prediction
moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88IDrkktb63b7awabxgq6ic6dkqmbqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 0
- prompt
- A dramatic photo of a volcanic eruption, high details, sharp.
- img_width
- 768
- rrg_scale
- 1000
- img_height
- 2048
- cosine_scale
- 10
- guidance_scale
- 10
- view_batch_size
- 16
- negative_prompts
- blurry, ugly, poorly drawn, deformed
- resampling_new_p
- 0.3
- resampling_steps
- 7
- num_inference_steps
- 50
{ "seed": 0, "prompt": "A dramatic photo of a volcanic eruption, high details, sharp.", "img_width": 768, "rrg_scale": 1000, "img_height": 2048, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", { input: { seed: 0, prompt: "A dramatic photo of a volcanic eruption, high details, sharp.", img_width: 768, rrg_scale: 1000, img_height: 2048, cosine_scale: 10, guidance_scale: 10, view_batch_size: 16, negative_prompts: "blurry, ugly, poorly drawn, deformed", resampling_new_p: 0.3, resampling_steps: 7, num_inference_steps: 50 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", 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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", input={ "seed": 0, "prompt": "A dramatic photo of a volcanic eruption, high details, sharp.", "img_width": 768, "rrg_scale": 1000, "img_height": 2048, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run moayedhajiali/elasticdiffusion 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": "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", "input": { "seed": 0, "prompt": "A dramatic photo of a volcanic eruption, high details, sharp.", "img_width": 768, "rrg_scale": 1000, "img_height": 2048, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "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": "2023-12-28T03:44:57.895991Z", "created_at": "2023-12-28T03:37:07.869743Z", "data_removed": false, "error": null, "id": "rkktb63b7awabxgq6ic6dkqmbq", "input": { "seed": 0, "prompt": "A dramatic photo of a volcanic eruption, high details, sharp.", "img_width": 768, "rrg_scale": 1000, "img_height": 2048, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "num_inference_steps": 50 }, "logs": "0%| | 0/50 [00:00<?, ?it/s]/src/elastic_diffusion.py:502: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\nsampled_h = (idx_h * downsample_factor + random_indices.reshape(idx_h.shape[0], idx_h.shape[1]) // downsample_factor).view(-1)\n 2%|▏ | 1/50 [00:06<05:06, 6.25s/it]\n 4%|▍ | 2/50 [00:12<04:59, 6.23s/it]\n 6%|▌ | 3/50 [00:18<04:52, 6.23s/it]\n 8%|▊ | 4/50 [00:24<04:46, 6.23s/it]\n 10%|█ | 5/50 [00:31<04:40, 6.23s/it]\n 12%|█▏ | 6/50 [00:37<04:34, 6.23s/it]\n 14%|█▍ | 7/50 [00:43<04:28, 6.24s/it]\n 16%|█▌ | 8/50 [00:49<04:22, 6.26s/it]\n 18%|█▊ | 9/50 [00:56<04:16, 6.26s/it]\n 20%|██ | 10/50 [01:02<04:10, 6.27s/it]\n 22%|██▏ | 11/50 [01:08<04:04, 6.28s/it]\n 24%|██▍ | 12/50 [01:15<03:58, 6.28s/it]\n 26%|██▌ | 13/50 [01:21<03:52, 6.28s/it]\n 28%|██▊ | 14/50 [01:27<03:46, 6.28s/it]\n 30%|███ | 15/50 [01:34<03:40, 6.30s/it]\n 32%|███▏ | 16/50 [01:40<03:34, 6.30s/it]\n 34%|███▍ | 17/50 [01:46<03:27, 6.29s/it]\n 36%|███▌ | 18/50 [01:52<03:21, 6.29s/it]\n 38%|███▊ | 19/50 [01:59<03:15, 6.30s/it]\n 40%|████ | 20/50 [02:05<03:08, 6.30s/it]\n 42%|████▏ | 21/50 [02:11<03:02, 6.31s/it]\n 44%|████▍ | 22/50 [02:18<02:56, 6.31s/it]\n 46%|████▌ | 23/50 [02:24<02:50, 6.31s/it]\n 48%|████▊ | 24/50 [02:30<02:43, 6.31s/it]\n 50%|█████ | 25/50 [02:37<02:37, 6.30s/it]\n 52%|█████▏ | 26/50 [02:43<02:31, 6.30s/it]\n 54%|█████▍ | 27/50 [02:49<02:24, 6.30s/it]\n 56%|█████▌ | 28/50 [02:55<02:18, 6.30s/it]\n 58%|█████▊ | 29/50 [03:02<02:12, 6.30s/it]\n 60%|██████ | 30/50 [03:08<02:05, 6.29s/it]\n 62%|██████▏ | 31/50 [03:14<01:59, 6.28s/it]\n 64%|██████▍ | 32/50 [03:21<01:52, 6.28s/it]\n 66%|██████▌ | 33/50 [03:27<01:46, 6.27s/it]\n 68%|██████▊ | 34/50 [03:33<01:40, 6.27s/it]\n 70%|███████ | 35/50 [03:39<01:34, 6.27s/it]\n 72%|███████▏ | 36/50 [03:46<01:27, 6.27s/it]\n 74%|███████▍ | 37/50 [03:52<01:21, 6.26s/it]\n 76%|███████▌ | 38/50 [03:58<01:15, 6.26s/it]\n 78%|███████▊ | 39/50 [04:04<01:08, 6.26s/it]\n 80%|████████ | 40/50 [04:11<01:02, 6.26s/it]\n 82%|████████▏ | 41/50 [04:17<00:56, 6.26s/it]\n 84%|████████▍ | 42/50 [04:23<00:50, 6.27s/it]\n 86%|████████▌ | 43/50 [04:29<00:43, 6.28s/it]\n 88%|████████▊ | 44/50 [04:36<00:37, 6.27s/it]\n 90%|█████████ | 45/50 [04:42<00:31, 6.27s/it]\n 92%|█████████▏| 46/50 [04:48<00:25, 6.27s/it]\n 94%|█████████▍| 47/50 [04:55<00:18, 6.27s/it]\n 96%|█████████▌| 48/50 [05:01<00:12, 6.27s/it]\n 98%|█████████▊| 49/50 [05:07<00:06, 6.27s/it]\n100%|██████████| 50/50 [05:12<00:00, 5.87s/it]\n100%|██████████| 50/50 [05:12<00:00, 6.25s/it]\n[INFO] Time taken: 313.1778419017792 seconds.", "metrics": { "predict_time": 314.820496, "total_time": 470.026248 }, "output": "https://replicate.delivery/pbxt/qu6eBa5zVB2tD6GMydVo7DmMPP6VtvnDUgDEwLZ4gXxcCVDJA/result.png", "started_at": "2023-12-28T03:39:43.075495Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rkktb63b7awabxgq6ic6dkqmbq", "cancel": "https://api.replicate.com/v1/predictions/rkktb63b7awabxgq6ic6dkqmbq/cancel" }, "version": "bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88" }Generated in0%| | 0/50 [00:00<?, ?it/s]/src/elastic_diffusion.py:502: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). sampled_h = (idx_h * downsample_factor + random_indices.reshape(idx_h.shape[0], idx_h.shape[1]) // downsample_factor).view(-1) 2%|▏ | 1/50 [00:06<05:06, 6.25s/it] 4%|▍ | 2/50 [00:12<04:59, 6.23s/it] 6%|▌ | 3/50 [00:18<04:52, 6.23s/it] 8%|▊ | 4/50 [00:24<04:46, 6.23s/it] 10%|█ | 5/50 [00:31<04:40, 6.23s/it] 12%|█▏ | 6/50 [00:37<04:34, 6.23s/it] 14%|█▍ | 7/50 [00:43<04:28, 6.24s/it] 16%|█▌ | 8/50 [00:49<04:22, 6.26s/it] 18%|█▊ | 9/50 [00:56<04:16, 6.26s/it] 20%|██ | 10/50 [01:02<04:10, 6.27s/it] 22%|██▏ | 11/50 [01:08<04:04, 6.28s/it] 24%|██▍ | 12/50 [01:15<03:58, 6.28s/it] 26%|██▌ | 13/50 [01:21<03:52, 6.28s/it] 28%|██▊ | 14/50 [01:27<03:46, 6.28s/it] 30%|███ | 15/50 [01:34<03:40, 6.30s/it] 32%|███▏ | 16/50 [01:40<03:34, 6.30s/it] 34%|███▍ | 17/50 [01:46<03:27, 6.29s/it] 36%|███▌ | 18/50 [01:52<03:21, 6.29s/it] 38%|███▊ | 19/50 [01:59<03:15, 6.30s/it] 40%|████ | 20/50 [02:05<03:08, 6.30s/it] 42%|████▏ | 21/50 [02:11<03:02, 6.31s/it] 44%|████▍ | 22/50 [02:18<02:56, 6.31s/it] 46%|████▌ | 23/50 [02:24<02:50, 6.31s/it] 48%|████▊ | 24/50 [02:30<02:43, 6.31s/it] 50%|█████ | 25/50 [02:37<02:37, 6.30s/it] 52%|█████▏ | 26/50 [02:43<02:31, 6.30s/it] 54%|█████▍ | 27/50 [02:49<02:24, 6.30s/it] 56%|█████▌ | 28/50 [02:55<02:18, 6.30s/it] 58%|█████▊ | 29/50 [03:02<02:12, 6.30s/it] 60%|██████ | 30/50 [03:08<02:05, 6.29s/it] 62%|██████▏ | 31/50 [03:14<01:59, 6.28s/it] 64%|██████▍ | 32/50 [03:21<01:52, 6.28s/it] 66%|██████▌ | 33/50 [03:27<01:46, 6.27s/it] 68%|██████▊ | 34/50 [03:33<01:40, 6.27s/it] 70%|███████ | 35/50 [03:39<01:34, 6.27s/it] 72%|███████▏ | 36/50 [03:46<01:27, 6.27s/it] 74%|███████▍ | 37/50 [03:52<01:21, 6.26s/it] 76%|███████▌ | 38/50 [03:58<01:15, 6.26s/it] 78%|███████▊ | 39/50 [04:04<01:08, 6.26s/it] 80%|████████ | 40/50 [04:11<01:02, 6.26s/it] 82%|████████▏ | 41/50 [04:17<00:56, 6.26s/it] 84%|████████▍ | 42/50 [04:23<00:50, 6.27s/it] 86%|████████▌ | 43/50 [04:29<00:43, 6.28s/it] 88%|████████▊ | 44/50 [04:36<00:37, 6.27s/it] 90%|█████████ | 45/50 [04:42<00:31, 6.27s/it] 92%|█████████▏| 46/50 [04:48<00:25, 6.27s/it] 94%|█████████▍| 47/50 [04:55<00:18, 6.27s/it] 96%|█████████▌| 48/50 [05:01<00:12, 6.27s/it] 98%|█████████▊| 49/50 [05:07<00:06, 6.27s/it] 100%|██████████| 50/50 [05:12<00:00, 5.87s/it] 100%|██████████| 50/50 [05:12<00:00, 6.25s/it] [INFO] Time taken: 313.1778419017792 seconds.Prediction
moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88IDaqzsgctbz6ocog6hvv7zggzwb4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 0
- prompt
- A realistic bird-eye view of a island with palm tree on the side, simple, high details.
- img_width
- 512
- rrg_scale
- 2000
- img_height
- 2048
- cosine_scale
- 10
- guidance_scale
- 10
- view_batch_size
- 16
- negative_prompts
- blurry, ugly, poorly drawn, deformed
- resampling_new_p
- 0.3
- resampling_steps
- 7
- num_inference_steps
- 50
{ "seed": 0, "prompt": "A realistic bird-eye view of a island with palm tree on the side, simple, high details.", "img_width": 512, "rrg_scale": 2000, "img_height": 2048, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", { input: { seed: 0, prompt: "A realistic bird-eye view of a island with palm tree on the side, simple, high details.", img_width: 512, rrg_scale: 2000, img_height: 2048, cosine_scale: 10, guidance_scale: 10, view_batch_size: 16, negative_prompts: "blurry, ugly, poorly drawn, deformed", resampling_new_p: 0.3, resampling_steps: 7, num_inference_steps: 50 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", 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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", input={ "seed": 0, "prompt": "A realistic bird-eye view of a island with palm tree on the side, simple, high details.", "img_width": 512, "rrg_scale": 2000, "img_height": 2048, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run moayedhajiali/elasticdiffusion 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": "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", "input": { "seed": 0, "prompt": "A realistic bird-eye view of a island with palm tree on the side, simple, high details.", "img_width": 512, "rrg_scale": 2000, "img_height": 2048, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "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": "2023-12-28T04:37:28.685155Z", "created_at": "2023-12-28T04:30:11.104219Z", "data_removed": false, "error": null, "id": "aqzsgctbz6ocog6hvv7zggzwb4", "input": { "seed": 0, "prompt": "A realistic bird-eye view of a island with palm tree on the side, simple, high details.", "img_width": 512, "rrg_scale": 2000, "img_height": 2048, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "num_inference_steps": 50 }, "logs": "0%| | 0/50 [00:00<?, ?it/s]/src/elastic_diffusion.py:502: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\nsampled_h = (idx_h * downsample_factor + random_indices.reshape(idx_h.shape[0], idx_h.shape[1]) // downsample_factor).view(-1)\n 2%|▏ | 1/50 [00:06<05:21, 6.57s/it]\n 4%|▍ | 2/50 [00:13<05:13, 6.53s/it]\n 6%|▌ | 3/50 [00:19<05:06, 6.53s/it]\n 8%|▊ | 4/50 [00:26<05:00, 6.54s/it]\n 10%|█ | 5/50 [00:32<04:54, 6.53s/it]\n 12%|█▏ | 6/50 [00:39<04:47, 6.54s/it]\n 14%|█▍ | 7/50 [00:45<04:41, 6.54s/it]\n 16%|█▌ | 8/50 [00:52<04:34, 6.54s/it]\n 18%|█▊ | 9/50 [00:58<04:28, 6.54s/it]\n 20%|██ | 10/50 [01:05<04:21, 6.54s/it]\n 22%|██▏ | 11/50 [01:11<04:15, 6.55s/it]\n 24%|██▍ | 12/50 [01:18<04:08, 6.55s/it]\n 26%|██▌ | 13/50 [01:25<04:02, 6.55s/it]\n 28%|██▊ | 14/50 [01:31<03:55, 6.55s/it]\n 30%|███ | 15/50 [01:38<03:49, 6.55s/it]\n 32%|███▏ | 16/50 [01:44<03:42, 6.56s/it]\n 34%|███▍ | 17/50 [01:51<03:36, 6.56s/it]\n 36%|███▌ | 18/50 [01:57<03:29, 6.56s/it]\n 38%|███▊ | 19/50 [02:04<03:23, 6.56s/it]\n 40%|████ | 20/50 [02:11<03:17, 6.57s/it]\n 42%|████▏ | 21/50 [02:17<03:10, 6.57s/it]\n 44%|████▍ | 22/50 [02:24<03:03, 6.57s/it]\n 46%|████▌ | 23/50 [02:30<02:57, 6.57s/it]\n 48%|████▊ | 24/50 [02:37<02:50, 6.57s/it]\n 50%|█████ | 25/50 [02:43<02:44, 6.57s/it]\n 52%|█████▏ | 26/50 [02:50<02:37, 6.57s/it]\n 54%|█████▍ | 27/50 [02:57<02:31, 6.57s/it]\n 56%|█████▌ | 28/50 [03:03<02:24, 6.57s/it]\n 58%|█████▊ | 29/50 [03:10<02:18, 6.57s/it]\n 60%|██████ | 30/50 [03:16<02:11, 6.57s/it]\n 62%|██████▏ | 31/50 [03:23<02:04, 6.57s/it]\n 64%|██████▍ | 32/50 [03:29<01:58, 6.58s/it]\n 66%|██████▌ | 33/50 [03:36<01:51, 6.58s/it]\n 68%|██████▊ | 34/50 [03:43<01:45, 6.58s/it]\n 70%|███████ | 35/50 [03:49<01:38, 6.58s/it]\n 72%|███████▏ | 36/50 [03:56<01:32, 6.58s/it]\n 74%|███████▍ | 37/50 [04:02<01:25, 6.58s/it]\n 76%|███████▌ | 38/50 [04:09<01:18, 6.58s/it]\n 78%|███████▊ | 39/50 [04:15<01:12, 6.58s/it]\n 80%|████████ | 40/50 [04:22<01:05, 6.58s/it]\n 82%|████████▏ | 41/50 [04:29<00:59, 6.58s/it]\n 84%|████████▍ | 42/50 [04:35<00:52, 6.58s/it]\n 86%|████████▌ | 43/50 [04:42<00:46, 6.58s/it]\n 88%|████████▊ | 44/50 [04:48<00:39, 6.58s/it]\n 90%|█████████ | 45/50 [04:55<00:32, 6.58s/it]\n 92%|█████████▏| 46/50 [05:01<00:26, 6.58s/it]\n 94%|█████████▍| 47/50 [05:08<00:19, 6.57s/it]\n 96%|█████████▌| 48/50 [05:15<00:13, 6.57s/it]\n 98%|█████████▊| 49/50 [05:21<00:06, 6.57s/it]\n100%|██████████| 50/50 [05:26<00:00, 6.16s/it]\n100%|██████████| 50/50 [05:26<00:00, 6.54s/it]\n[INFO] Time taken: 327.4150733947754 seconds.", "metrics": { "predict_time": 328.806151, "total_time": 437.580936 }, "output": "https://replicate.delivery/pbxt/xiJoTZ00ehUTDSrtc7tWB2y1eg2m2dmQBJGX8vrgZHCH2qGSA/result.png", "started_at": "2023-12-28T04:31:59.879004Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/aqzsgctbz6ocog6hvv7zggzwb4", "cancel": "https://api.replicate.com/v1/predictions/aqzsgctbz6ocog6hvv7zggzwb4/cancel" }, "version": "bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88" }Generated in0%| | 0/50 [00:00<?, ?it/s]/src/elastic_diffusion.py:502: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). sampled_h = (idx_h * downsample_factor + random_indices.reshape(idx_h.shape[0], idx_h.shape[1]) // downsample_factor).view(-1) 2%|▏ | 1/50 [00:06<05:21, 6.57s/it] 4%|▍ | 2/50 [00:13<05:13, 6.53s/it] 6%|▌ | 3/50 [00:19<05:06, 6.53s/it] 8%|▊ | 4/50 [00:26<05:00, 6.54s/it] 10%|█ | 5/50 [00:32<04:54, 6.53s/it] 12%|█▏ | 6/50 [00:39<04:47, 6.54s/it] 14%|█▍ | 7/50 [00:45<04:41, 6.54s/it] 16%|█▌ | 8/50 [00:52<04:34, 6.54s/it] 18%|█▊ | 9/50 [00:58<04:28, 6.54s/it] 20%|██ | 10/50 [01:05<04:21, 6.54s/it] 22%|██▏ | 11/50 [01:11<04:15, 6.55s/it] 24%|██▍ | 12/50 [01:18<04:08, 6.55s/it] 26%|██▌ | 13/50 [01:25<04:02, 6.55s/it] 28%|██▊ | 14/50 [01:31<03:55, 6.55s/it] 30%|███ | 15/50 [01:38<03:49, 6.55s/it] 32%|███▏ | 16/50 [01:44<03:42, 6.56s/it] 34%|███▍ | 17/50 [01:51<03:36, 6.56s/it] 36%|███▌ | 18/50 [01:57<03:29, 6.56s/it] 38%|███▊ | 19/50 [02:04<03:23, 6.56s/it] 40%|████ | 20/50 [02:11<03:17, 6.57s/it] 42%|████▏ | 21/50 [02:17<03:10, 6.57s/it] 44%|████▍ | 22/50 [02:24<03:03, 6.57s/it] 46%|████▌ | 23/50 [02:30<02:57, 6.57s/it] 48%|████▊ | 24/50 [02:37<02:50, 6.57s/it] 50%|█████ | 25/50 [02:43<02:44, 6.57s/it] 52%|█████▏ | 26/50 [02:50<02:37, 6.57s/it] 54%|█████▍ | 27/50 [02:57<02:31, 6.57s/it] 56%|█████▌ | 28/50 [03:03<02:24, 6.57s/it] 58%|█████▊ | 29/50 [03:10<02:18, 6.57s/it] 60%|██████ | 30/50 [03:16<02:11, 6.57s/it] 62%|██████▏ | 31/50 [03:23<02:04, 6.57s/it] 64%|██████▍ | 32/50 [03:29<01:58, 6.58s/it] 66%|██████▌ | 33/50 [03:36<01:51, 6.58s/it] 68%|██████▊ | 34/50 [03:43<01:45, 6.58s/it] 70%|███████ | 35/50 [03:49<01:38, 6.58s/it] 72%|███████▏ | 36/50 [03:56<01:32, 6.58s/it] 74%|███████▍ | 37/50 [04:02<01:25, 6.58s/it] 76%|███████▌ | 38/50 [04:09<01:18, 6.58s/it] 78%|███████▊ | 39/50 [04:15<01:12, 6.58s/it] 80%|████████ | 40/50 [04:22<01:05, 6.58s/it] 82%|████████▏ | 41/50 [04:29<00:59, 6.58s/it] 84%|████████▍ | 42/50 [04:35<00:52, 6.58s/it] 86%|████████▌ | 43/50 [04:42<00:46, 6.58s/it] 88%|████████▊ | 44/50 [04:48<00:39, 6.58s/it] 90%|█████████ | 45/50 [04:55<00:32, 6.58s/it] 92%|█████████▏| 46/50 [05:01<00:26, 6.58s/it] 94%|█████████▍| 47/50 [05:08<00:19, 6.57s/it] 96%|█████████▌| 48/50 [05:15<00:13, 6.57s/it] 98%|█████████▊| 49/50 [05:21<00:06, 6.57s/it] 100%|██████████| 50/50 [05:26<00:00, 6.16s/it] 100%|██████████| 50/50 [05:26<00:00, 6.54s/it] [INFO] Time taken: 327.4150733947754 seconds.Prediction
moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88IDepwajtdbsw3vgzk7odton2nqnmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 0
- prompt
- Envision an ostrich in the dessert. she has a green scarf wrapping her body. her eyes are dark black. her attire, simple yet dignified
- img_width
- 2048
- rrg_scale
- 1000
- img_height
- 1024
- cosine_scale
- 10
- guidance_scale
- 10
- view_batch_size
- 16
- negative_prompts
- blurry, ugly, poorly drawn, deformed
- resampling_new_p
- 0.3
- resampling_steps
- 7
- num_inference_steps
- 50
{ "seed": 0, "prompt": "Envision an ostrich in the dessert. she has a green scarf wrapping her body. her eyes are dark black. her attire, simple yet dignified", "img_width": 2048, "rrg_scale": 1000, "img_height": 1024, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", { input: { seed: 0, prompt: "Envision an ostrich in the dessert. she has a green scarf wrapping her body. her eyes are dark black. her attire, simple yet dignified", img_width: 2048, rrg_scale: 1000, img_height: 1024, cosine_scale: 10, guidance_scale: 10, view_batch_size: 16, negative_prompts: "blurry, ugly, poorly drawn, deformed", resampling_new_p: 0.3, resampling_steps: 7, num_inference_steps: 50 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", 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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", input={ "seed": 0, "prompt": "Envision an ostrich in the dessert. she has a green scarf wrapping her body. her eyes are dark black. her attire, simple yet dignified", "img_width": 2048, "rrg_scale": 1000, "img_height": 1024, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run moayedhajiali/elasticdiffusion 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": "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", "input": { "seed": 0, "prompt": "Envision an ostrich in the dessert. she has a green scarf wrapping her body. her eyes are dark black. her attire, simple yet dignified", "img_width": 2048, "rrg_scale": 1000, "img_height": 1024, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "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": "2023-12-27T20:02:25.201417Z", "created_at": "2023-12-27T19:53:55.659596Z", "data_removed": false, "error": null, "id": "epwajtdbsw3vgzk7odton2nqnm", "input": { "seed": 0, "prompt": "Envision an ostrich in the dessert. she has a green scarf wrapping her body. her eyes are dark black. her attire, simple yet dignified", "img_width": 2048, "rrg_scale": 1000, "img_height": 1024, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "num_inference_steps": 50 }, "logs": "0%| | 0/50 [00:00<?, ?it/s]/src/elastic_diffusion.py:502: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\nsampled_h = (idx_h * downsample_factor + random_indices.reshape(idx_h.shape[0], idx_h.shape[1]) // downsample_factor).view(-1)\n 2%|▏ | 1/50 [00:05<04:52, 5.97s/it]\n 4%|▍ | 2/50 [00:11<04:45, 5.94s/it]\n 6%|▌ | 3/50 [00:17<04:38, 5.92s/it]\n 8%|▊ | 4/50 [00:23<04:32, 5.92s/it]\n 10%|█ | 5/50 [00:29<04:26, 5.92s/it]\n 12%|█▏ | 6/50 [00:35<04:20, 5.92s/it]\n 14%|█▍ | 7/50 [00:41<04:14, 5.92s/it]\n 16%|█▌ | 8/50 [00:47<04:08, 5.92s/it]\n 18%|█▊ | 9/50 [00:53<04:02, 5.92s/it]\n 20%|██ | 10/50 [00:59<03:57, 5.93s/it]\n 22%|██▏ | 11/50 [01:05<03:51, 5.93s/it]\n 24%|██▍ | 12/50 [01:11<03:45, 5.93s/it]\n 26%|██▌ | 13/50 [01:17<03:39, 5.93s/it]\n 28%|██▊ | 14/50 [01:22<03:33, 5.93s/it]\n 30%|███ | 15/50 [01:28<03:27, 5.93s/it]\n 32%|███▏ | 16/50 [01:34<03:21, 5.93s/it]\n 34%|███▍ | 17/50 [01:40<03:15, 5.93s/it]\n 36%|███▌ | 18/50 [01:46<03:09, 5.93s/it]\n 38%|███▊ | 19/50 [01:52<03:03, 5.93s/it]\n 40%|████ | 20/50 [01:58<02:58, 5.94s/it]\n 42%|████▏ | 21/50 [02:04<02:52, 5.94s/it]\n 44%|████▍ | 22/50 [02:10<02:46, 5.94s/it]\n 46%|████▌ | 23/50 [02:16<02:40, 5.94s/it]\n 48%|████▊ | 24/50 [02:22<02:34, 5.94s/it]\n 50%|█████ | 25/50 [02:28<02:28, 5.94s/it]\n 52%|█████▏ | 26/50 [02:34<02:22, 5.94s/it]\n 54%|█████▍ | 27/50 [02:40<02:16, 5.94s/it]\n 56%|█████▌ | 28/50 [02:46<02:10, 5.94s/it]\n 58%|█████▊ | 29/50 [02:52<02:04, 5.94s/it]\n 60%|██████ | 30/50 [02:58<01:58, 5.94s/it]\n 62%|██████▏ | 31/50 [03:03<01:52, 5.94s/it]\n 64%|██████▍ | 32/50 [03:09<01:46, 5.94s/it]\n 66%|██████▌ | 33/50 [03:15<01:41, 5.95s/it]\n 68%|██████▊ | 34/50 [03:21<01:35, 5.95s/it]\n 70%|███████ | 35/50 [03:27<01:29, 5.95s/it]\n 72%|███████▏ | 36/50 [03:33<01:23, 5.94s/it]\n 74%|███████▍ | 37/50 [03:39<01:17, 5.94s/it]\n 76%|███████▌ | 38/50 [03:45<01:11, 5.95s/it]\n 78%|███████▊ | 39/50 [03:51<01:05, 5.94s/it]\n 80%|████████ | 40/50 [03:57<00:59, 5.94s/it]\n 82%|████████▏ | 41/50 [04:03<00:53, 5.94s/it]\n 84%|████████▍ | 42/50 [04:09<00:47, 5.94s/it]\n 86%|████████▌ | 43/50 [04:15<00:41, 5.94s/it]\n 88%|████████▊ | 44/50 [04:21<00:35, 5.94s/it]\n 90%|█████████ | 45/50 [04:27<00:29, 5.94s/it]\n 92%|█████████▏| 46/50 [04:33<00:23, 5.94s/it]\n 94%|█████████▍| 47/50 [04:39<00:17, 5.94s/it]\n 96%|█████████▌| 48/50 [04:45<00:11, 5.94s/it]\n 98%|█████████▊| 49/50 [04:50<00:05, 5.94s/it]\n100%|██████████| 50/50 [04:55<00:00, 5.57s/it]\n100%|██████████| 50/50 [04:55<00:00, 5.91s/it]\n[INFO] Time taken: 296.53521728515625 seconds.", "metrics": { "predict_time": 298.256037, "total_time": 509.541821 }, "output": "https://replicate.delivery/pbxt/ZXaIgD8FnIYQMBBMAeVa59ew7vD2CoGSYKOTVweKtVeDNNaIB/result.png", "started_at": "2023-12-27T19:57:26.945380Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/epwajtdbsw3vgzk7odton2nqnm", "cancel": "https://api.replicate.com/v1/predictions/epwajtdbsw3vgzk7odton2nqnm/cancel" }, "version": "bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88" }Generated in0%| | 0/50 [00:00<?, ?it/s]/src/elastic_diffusion.py:502: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). sampled_h = (idx_h * downsample_factor + random_indices.reshape(idx_h.shape[0], idx_h.shape[1]) // downsample_factor).view(-1) 2%|▏ | 1/50 [00:05<04:52, 5.97s/it] 4%|▍ | 2/50 [00:11<04:45, 5.94s/it] 6%|▌ | 3/50 [00:17<04:38, 5.92s/it] 8%|▊ | 4/50 [00:23<04:32, 5.92s/it] 10%|█ | 5/50 [00:29<04:26, 5.92s/it] 12%|█▏ | 6/50 [00:35<04:20, 5.92s/it] 14%|█▍ | 7/50 [00:41<04:14, 5.92s/it] 16%|█▌ | 8/50 [00:47<04:08, 5.92s/it] 18%|█▊ | 9/50 [00:53<04:02, 5.92s/it] 20%|██ | 10/50 [00:59<03:57, 5.93s/it] 22%|██▏ | 11/50 [01:05<03:51, 5.93s/it] 24%|██▍ | 12/50 [01:11<03:45, 5.93s/it] 26%|██▌ | 13/50 [01:17<03:39, 5.93s/it] 28%|██▊ | 14/50 [01:22<03:33, 5.93s/it] 30%|███ | 15/50 [01:28<03:27, 5.93s/it] 32%|███▏ | 16/50 [01:34<03:21, 5.93s/it] 34%|███▍ | 17/50 [01:40<03:15, 5.93s/it] 36%|███▌ | 18/50 [01:46<03:09, 5.93s/it] 38%|███▊ | 19/50 [01:52<03:03, 5.93s/it] 40%|████ | 20/50 [01:58<02:58, 5.94s/it] 42%|████▏ | 21/50 [02:04<02:52, 5.94s/it] 44%|████▍ | 22/50 [02:10<02:46, 5.94s/it] 46%|████▌ | 23/50 [02:16<02:40, 5.94s/it] 48%|████▊ | 24/50 [02:22<02:34, 5.94s/it] 50%|█████ | 25/50 [02:28<02:28, 5.94s/it] 52%|█████▏ | 26/50 [02:34<02:22, 5.94s/it] 54%|█████▍ | 27/50 [02:40<02:16, 5.94s/it] 56%|█████▌ | 28/50 [02:46<02:10, 5.94s/it] 58%|█████▊ | 29/50 [02:52<02:04, 5.94s/it] 60%|██████ | 30/50 [02:58<01:58, 5.94s/it] 62%|██████▏ | 31/50 [03:03<01:52, 5.94s/it] 64%|██████▍ | 32/50 [03:09<01:46, 5.94s/it] 66%|██████▌ | 33/50 [03:15<01:41, 5.95s/it] 68%|██████▊ | 34/50 [03:21<01:35, 5.95s/it] 70%|███████ | 35/50 [03:27<01:29, 5.95s/it] 72%|███████▏ | 36/50 [03:33<01:23, 5.94s/it] 74%|███████▍ | 37/50 [03:39<01:17, 5.94s/it] 76%|███████▌ | 38/50 [03:45<01:11, 5.95s/it] 78%|███████▊ | 39/50 [03:51<01:05, 5.94s/it] 80%|████████ | 40/50 [03:57<00:59, 5.94s/it] 82%|████████▏ | 41/50 [04:03<00:53, 5.94s/it] 84%|████████▍ | 42/50 [04:09<00:47, 5.94s/it] 86%|████████▌ | 43/50 [04:15<00:41, 5.94s/it] 88%|████████▊ | 44/50 [04:21<00:35, 5.94s/it] 90%|█████████ | 45/50 [04:27<00:29, 5.94s/it] 92%|█████████▏| 46/50 [04:33<00:23, 5.94s/it] 94%|█████████▍| 47/50 [04:39<00:17, 5.94s/it] 96%|█████████▌| 48/50 [04:45<00:11, 5.94s/it] 98%|█████████▊| 49/50 [04:50<00:05, 5.94s/it] 100%|██████████| 50/50 [04:55<00:00, 5.57s/it] 100%|██████████| 50/50 [04:55<00:00, 5.91s/it] [INFO] Time taken: 296.53521728515625 seconds.Prediction
moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88IDtkoj34tbmo4zngbyfzhbteb33iStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 0
- prompt
- A professional photo of a rabbit riding a bike on a street in New York
- img_width
- 768
- rrg_scale
- 0
- img_height
- 768
- cosine_scale
- 10
- guidance_scale
- 10
- view_batch_size
- 16
- negative_prompts
- blurry, ugly, poorly drawn, deformed
- resampling_new_p
- 0.3
- resampling_steps
- 0
- num_inference_steps
- 50
{ "seed": 0, "prompt": "A professional photo of a rabbit riding a bike on a street in New York", "img_width": 768, "rrg_scale": 0, "img_height": 768, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 0, "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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", { input: { seed: 0, prompt: "A professional photo of a rabbit riding a bike on a street in New York", img_width: 768, rrg_scale: 0, img_height: 768, cosine_scale: 10, guidance_scale: 10, view_batch_size: 16, negative_prompts: "blurry, ugly, poorly drawn, deformed", resampling_new_p: 0.3, resampling_steps: 0, num_inference_steps: 50 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", 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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", input={ "seed": 0, "prompt": "A professional photo of a rabbit riding a bike on a street in New York", "img_width": 768, "rrg_scale": 0, "img_height": 768, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 0, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run moayedhajiali/elasticdiffusion 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": "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", "input": { "seed": 0, "prompt": "A professional photo of a rabbit riding a bike on a street in New York", "img_width": 768, "rrg_scale": 0, "img_height": 768, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 0, "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": "2023-12-28T04:08:07.436154Z", "created_at": "2023-12-28T04:07:26.997137Z", "data_removed": false, "error": null, "id": "tkoj34tbmo4zngbyfzhbteb33i", "input": { "seed": 0, "prompt": "A professional photo of a rabbit riding a bike on a street in New York", "img_width": 768, "rrg_scale": 0, "img_height": 768, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 0, "num_inference_steps": 50 }, "logs": "0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:39, 1.25it/s]\n 4%|▍ | 2/50 [00:01<00:37, 1.27it/s]\n 6%|▌ | 3/50 [00:02<00:36, 1.28it/s]\n 8%|▊ | 4/50 [00:03<00:35, 1.28it/s]\n 10%|█ | 5/50 [00:03<00:35, 1.28it/s]\n 12%|█▏ | 6/50 [00:04<00:34, 1.29it/s]\n 14%|█▍ | 7/50 [00:05<00:33, 1.29it/s]\n 16%|█▌ | 8/50 [00:06<00:32, 1.29it/s]\n 18%|█▊ | 9/50 [00:07<00:31, 1.29it/s]\n 20%|██ | 10/50 [00:07<00:31, 1.29it/s]\n 22%|██▏ | 11/50 [00:08<00:30, 1.29it/s]\n 24%|██▍ | 12/50 [00:09<00:29, 1.29it/s]\n 26%|██▌ | 13/50 [00:10<00:28, 1.29it/s]\n 28%|██▊ | 14/50 [00:10<00:27, 1.29it/s]\n 30%|███ | 15/50 [00:11<00:27, 1.29it/s]\n 32%|███▏ | 16/50 [00:12<00:26, 1.29it/s]\n 34%|███▍ | 17/50 [00:13<00:25, 1.29it/s]\n 36%|███▌ | 18/50 [00:14<00:24, 1.29it/s]\n 38%|███▊ | 19/50 [00:14<00:24, 1.29it/s]\n 40%|████ | 20/50 [00:15<00:23, 1.29it/s]\n 42%|████▏ | 21/50 [00:16<00:22, 1.29it/s]\n 44%|████▍ | 22/50 [00:17<00:21, 1.29it/s]\n 46%|████▌ | 23/50 [00:17<00:21, 1.29it/s]\n 48%|████▊ | 24/50 [00:18<00:20, 1.29it/s]\n 50%|█████ | 25/50 [00:19<00:19, 1.29it/s]\n 52%|█████▏ | 26/50 [00:20<00:18, 1.29it/s]\n 54%|█████▍ | 27/50 [00:20<00:17, 1.29it/s]\n 56%|█████▌ | 28/50 [00:21<00:17, 1.29it/s]\n 58%|█████▊ | 29/50 [00:22<00:16, 1.29it/s]\n 60%|██████ | 30/50 [00:23<00:15, 1.29it/s]\n 62%|██████▏ | 31/50 [00:24<00:14, 1.29it/s]\n 64%|██████▍ | 32/50 [00:24<00:14, 1.28it/s]\n 66%|██████▌ | 33/50 [00:25<00:13, 1.28it/s]\n 68%|██████▊ | 34/50 [00:26<00:12, 1.28it/s]\n 70%|███████ | 35/50 [00:27<00:11, 1.28it/s]\n 72%|███████▏ | 36/50 [00:28<00:10, 1.28it/s]\n 74%|███████▍ | 37/50 [00:28<00:10, 1.28it/s]\n 76%|███████▌ | 38/50 [00:29<00:09, 1.28it/s]\n 78%|███████▊ | 39/50 [00:30<00:08, 1.28it/s]\n 80%|████████ | 40/50 [00:31<00:07, 1.28it/s]\n 82%|████████▏ | 41/50 [00:31<00:07, 1.28it/s]\n 84%|████████▍ | 42/50 [00:32<00:06, 1.28it/s]\n 86%|████████▌ | 43/50 [00:33<00:05, 1.28it/s]\n 88%|████████▊ | 44/50 [00:34<00:04, 1.29it/s]\n 90%|█████████ | 45/50 [00:35<00:03, 1.29it/s]\n 92%|█████████▏| 46/50 [00:35<00:03, 1.29it/s]\n 94%|█████████▍| 47/50 [00:36<00:02, 1.29it/s]\n 96%|█████████▌| 48/50 [00:37<00:01, 1.29it/s]\n 98%|█████████▊| 49/50 [00:38<00:00, 1.29it/s]\n100%|██████████| 50/50 [00:38<00:00, 1.29it/s]\n100%|██████████| 50/50 [00:38<00:00, 1.29it/s]\n[INFO] Time taken: 39.205970764160156 seconds.", "metrics": { "predict_time": 40.402821, "total_time": 40.439017 }, "output": "https://replicate.delivery/pbxt/UZ274tDztOoPNl1nPTIBf2jYQOHZXBtsHJneDCoOJw7maqGSA/result.png", "started_at": "2023-12-28T04:07:27.033333Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/tkoj34tbmo4zngbyfzhbteb33i", "cancel": "https://api.replicate.com/v1/predictions/tkoj34tbmo4zngbyfzhbteb33i/cancel" }, "version": "bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88" }Generated in0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:39, 1.25it/s] 4%|▍ | 2/50 [00:01<00:37, 1.27it/s] 6%|▌ | 3/50 [00:02<00:36, 1.28it/s] 8%|▊ | 4/50 [00:03<00:35, 1.28it/s] 10%|█ | 5/50 [00:03<00:35, 1.28it/s] 12%|█▏ | 6/50 [00:04<00:34, 1.29it/s] 14%|█▍ | 7/50 [00:05<00:33, 1.29it/s] 16%|█▌ | 8/50 [00:06<00:32, 1.29it/s] 18%|█▊ | 9/50 [00:07<00:31, 1.29it/s] 20%|██ | 10/50 [00:07<00:31, 1.29it/s] 22%|██▏ | 11/50 [00:08<00:30, 1.29it/s] 24%|██▍ | 12/50 [00:09<00:29, 1.29it/s] 26%|██▌ | 13/50 [00:10<00:28, 1.29it/s] 28%|██▊ | 14/50 [00:10<00:27, 1.29it/s] 30%|███ | 15/50 [00:11<00:27, 1.29it/s] 32%|███▏ | 16/50 [00:12<00:26, 1.29it/s] 34%|███▍ | 17/50 [00:13<00:25, 1.29it/s] 36%|███▌ | 18/50 [00:14<00:24, 1.29it/s] 38%|███▊ | 19/50 [00:14<00:24, 1.29it/s] 40%|████ | 20/50 [00:15<00:23, 1.29it/s] 42%|████▏ | 21/50 [00:16<00:22, 1.29it/s] 44%|████▍ | 22/50 [00:17<00:21, 1.29it/s] 46%|████▌ | 23/50 [00:17<00:21, 1.29it/s] 48%|████▊ | 24/50 [00:18<00:20, 1.29it/s] 50%|█████ | 25/50 [00:19<00:19, 1.29it/s] 52%|█████▏ | 26/50 [00:20<00:18, 1.29it/s] 54%|█████▍ | 27/50 [00:20<00:17, 1.29it/s] 56%|█████▌ | 28/50 [00:21<00:17, 1.29it/s] 58%|█████▊ | 29/50 [00:22<00:16, 1.29it/s] 60%|██████ | 30/50 [00:23<00:15, 1.29it/s] 62%|██████▏ | 31/50 [00:24<00:14, 1.29it/s] 64%|██████▍ | 32/50 [00:24<00:14, 1.28it/s] 66%|██████▌ | 33/50 [00:25<00:13, 1.28it/s] 68%|██████▊ | 34/50 [00:26<00:12, 1.28it/s] 70%|███████ | 35/50 [00:27<00:11, 1.28it/s] 72%|███████▏ | 36/50 [00:28<00:10, 1.28it/s] 74%|███████▍ | 37/50 [00:28<00:10, 1.28it/s] 76%|███████▌ | 38/50 [00:29<00:09, 1.28it/s] 78%|███████▊ | 39/50 [00:30<00:08, 1.28it/s] 80%|████████ | 40/50 [00:31<00:07, 1.28it/s] 82%|████████▏ | 41/50 [00:31<00:07, 1.28it/s] 84%|████████▍ | 42/50 [00:32<00:06, 1.28it/s] 86%|████████▌ | 43/50 [00:33<00:05, 1.28it/s] 88%|████████▊ | 44/50 [00:34<00:04, 1.29it/s] 90%|█████████ | 45/50 [00:35<00:03, 1.29it/s] 92%|█████████▏| 46/50 [00:35<00:03, 1.29it/s] 94%|█████████▍| 47/50 [00:36<00:02, 1.29it/s] 96%|█████████▌| 48/50 [00:37<00:01, 1.29it/s] 98%|█████████▊| 49/50 [00:38<00:00, 1.29it/s] 100%|██████████| 50/50 [00:38<00:00, 1.29it/s] 100%|██████████| 50/50 [00:38<00:00, 1.29it/s] [INFO] Time taken: 39.205970764160156 seconds.Prediction
moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88IDzgnjx73b6df6ijoazfvz7txf74StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 0
- prompt
- A photo of the dolomites, highly detailed, sharp
- img_width
- 2048
- rrg_scale
- 1000
- img_height
- 768
- cosine_scale
- 10
- guidance_scale
- 10
- view_batch_size
- 16
- negative_prompts
- blurry, ugly, poorly drawn, deformed
- resampling_new_p
- 0.3
- resampling_steps
- 7
- num_inference_steps
- 50
{ "seed": 0, "prompt": "A photo of the dolomites, highly detailed, sharp", "img_width": 2048, "rrg_scale": 1000, "img_height": 768, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", { input: { seed: 0, prompt: "A photo of the dolomites, highly detailed, sharp", img_width: 2048, rrg_scale: 1000, img_height: 768, cosine_scale: 10, guidance_scale: 10, view_batch_size: 16, negative_prompts: "blurry, ugly, poorly drawn, deformed", resampling_new_p: 0.3, resampling_steps: 7, num_inference_steps: 50 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", 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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", input={ "seed": 0, "prompt": "A photo of the dolomites, highly detailed, sharp", "img_width": 2048, "rrg_scale": 1000, "img_height": 768, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run moayedhajiali/elasticdiffusion 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": "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", "input": { "seed": 0, "prompt": "A photo of the dolomites, highly detailed, sharp", "img_width": 2048, "rrg_scale": 1000, "img_height": 768, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "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": "2023-12-28T04:06:56.631561Z", "created_at": "2023-12-28T04:01:45.106688Z", "data_removed": false, "error": null, "id": "zgnjx73b6df6ijoazfvz7txf74", "input": { "seed": 0, "prompt": "A photo of the dolomites, highly detailed, sharp", "img_width": 2048, "rrg_scale": 1000, "img_height": 768, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "num_inference_steps": 50 }, "logs": "0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:06<05:04, 6.21s/it]\n 4%|▍ | 2/50 [00:12<04:57, 6.20s/it]\n 6%|▌ | 3/50 [00:18<04:51, 6.19s/it]\n 8%|▊ | 4/50 [00:24<04:44, 6.19s/it]\n 10%|█ | 5/50 [00:30<04:38, 6.20s/it]\n 12%|█▏ | 6/50 [00:37<04:32, 6.20s/it]\n 14%|█▍ | 7/50 [00:43<04:26, 6.20s/it]\n 16%|█▌ | 8/50 [00:49<04:20, 6.20s/it]\n 18%|█▊ | 9/50 [00:55<04:14, 6.20s/it]\n 20%|██ | 10/50 [01:01<04:07, 6.20s/it]\n 22%|██▏ | 11/50 [01:08<04:01, 6.20s/it]\n 24%|██▍ | 12/50 [01:14<03:55, 6.20s/it]\n 26%|██▌ | 13/50 [01:20<03:49, 6.20s/it]\n 28%|██▊ | 14/50 [01:26<03:43, 6.20s/it]\n 30%|███ | 15/50 [01:32<03:37, 6.20s/it]\n 32%|███▏ | 16/50 [01:39<03:31, 6.21s/it]\n 34%|███▍ | 17/50 [01:45<03:24, 6.21s/it]\n 36%|███▌ | 18/50 [01:51<03:18, 6.21s/it]\n 38%|███▊ | 19/50 [01:57<03:12, 6.20s/it]\n 40%|████ | 20/50 [02:04<03:06, 6.21s/it]\n 42%|████▏ | 21/50 [02:10<03:00, 6.21s/it]\n 44%|████▍ | 22/50 [02:16<02:53, 6.21s/it]\n 46%|████▌ | 23/50 [02:22<02:47, 6.21s/it]\n 48%|████▊ | 24/50 [02:28<02:41, 6.21s/it]\n 50%|█████ | 25/50 [02:35<02:35, 6.21s/it]\n 52%|█████▏ | 26/50 [02:41<02:29, 6.21s/it]\n 54%|█████▍ | 27/50 [02:47<02:22, 6.21s/it]\n 56%|█████▌ | 28/50 [02:53<02:16, 6.21s/it]\n 58%|█████▊ | 29/50 [02:59<02:10, 6.21s/it]\n 60%|██████ | 30/50 [03:06<02:04, 6.21s/it]\n 62%|██████▏ | 31/50 [03:12<01:57, 6.21s/it]\n 64%|██████▍ | 32/50 [03:18<01:51, 6.21s/it]\n 66%|██████▌ | 33/50 [03:24<01:45, 6.21s/it]\n 68%|██████▊ | 34/50 [03:30<01:39, 6.21s/it]\n 70%|███████ | 35/50 [03:37<01:33, 6.21s/it]\n 72%|███████▏ | 36/50 [03:43<01:26, 6.21s/it]\n 74%|███████▍ | 37/50 [03:49<01:20, 6.21s/it]\n 76%|███████▌ | 38/50 [03:55<01:14, 6.21s/it]\n 78%|███████▊ | 39/50 [04:02<01:08, 6.21s/it]\n 80%|████████ | 40/50 [04:08<01:02, 6.21s/it]\n 82%|████████▏ | 41/50 [04:14<00:55, 6.22s/it]\n 84%|████████▍ | 42/50 [04:20<00:49, 6.22s/it]\n 86%|████████▌ | 43/50 [04:26<00:43, 6.22s/it]\n 88%|████████▊ | 44/50 [04:33<00:37, 6.21s/it]\n 90%|█████████ | 45/50 [04:39<00:31, 6.21s/it]\n 92%|█████████▏| 46/50 [04:45<00:24, 6.21s/it]\n 94%|█████████▍| 47/50 [04:51<00:18, 6.21s/it]\n 96%|█████████▌| 48/50 [04:57<00:12, 6.22s/it]\n 98%|█████████▊| 49/50 [05:04<00:06, 6.22s/it]\n100%|██████████| 50/50 [05:09<00:00, 5.82s/it]\n100%|██████████| 50/50 [05:09<00:00, 6.18s/it]\n[INFO] Time taken: 309.7938311100006 seconds.", "metrics": { "predict_time": 311.491399, "total_time": 311.524873 }, "output": "https://replicate.delivery/pbxt/7K5JcAb1wQ7UAdNBRtj6suu8dERvJS73u0u6Q8I2h0wXmqhE/result.png", "started_at": "2023-12-28T04:01:45.140162Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zgnjx73b6df6ijoazfvz7txf74", "cancel": "https://api.replicate.com/v1/predictions/zgnjx73b6df6ijoazfvz7txf74/cancel" }, "version": "bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88" }Generated in0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:06<05:04, 6.21s/it] 4%|▍ | 2/50 [00:12<04:57, 6.20s/it] 6%|▌ | 3/50 [00:18<04:51, 6.19s/it] 8%|▊ | 4/50 [00:24<04:44, 6.19s/it] 10%|█ | 5/50 [00:30<04:38, 6.20s/it] 12%|█▏ | 6/50 [00:37<04:32, 6.20s/it] 14%|█▍ | 7/50 [00:43<04:26, 6.20s/it] 16%|█▌ | 8/50 [00:49<04:20, 6.20s/it] 18%|█▊ | 9/50 [00:55<04:14, 6.20s/it] 20%|██ | 10/50 [01:01<04:07, 6.20s/it] 22%|██▏ | 11/50 [01:08<04:01, 6.20s/it] 24%|██▍ | 12/50 [01:14<03:55, 6.20s/it] 26%|██▌ | 13/50 [01:20<03:49, 6.20s/it] 28%|██▊ | 14/50 [01:26<03:43, 6.20s/it] 30%|███ | 15/50 [01:32<03:37, 6.20s/it] 32%|███▏ | 16/50 [01:39<03:31, 6.21s/it] 34%|███▍ | 17/50 [01:45<03:24, 6.21s/it] 36%|███▌ | 18/50 [01:51<03:18, 6.21s/it] 38%|███▊ | 19/50 [01:57<03:12, 6.20s/it] 40%|████ | 20/50 [02:04<03:06, 6.21s/it] 42%|████▏ | 21/50 [02:10<03:00, 6.21s/it] 44%|████▍ | 22/50 [02:16<02:53, 6.21s/it] 46%|████▌ | 23/50 [02:22<02:47, 6.21s/it] 48%|████▊ | 24/50 [02:28<02:41, 6.21s/it] 50%|█████ | 25/50 [02:35<02:35, 6.21s/it] 52%|█████▏ | 26/50 [02:41<02:29, 6.21s/it] 54%|█████▍ | 27/50 [02:47<02:22, 6.21s/it] 56%|█████▌ | 28/50 [02:53<02:16, 6.21s/it] 58%|█████▊ | 29/50 [02:59<02:10, 6.21s/it] 60%|██████ | 30/50 [03:06<02:04, 6.21s/it] 62%|██████▏ | 31/50 [03:12<01:57, 6.21s/it] 64%|██████▍ | 32/50 [03:18<01:51, 6.21s/it] 66%|██████▌ | 33/50 [03:24<01:45, 6.21s/it] 68%|██████▊ | 34/50 [03:30<01:39, 6.21s/it] 70%|███████ | 35/50 [03:37<01:33, 6.21s/it] 72%|███████▏ | 36/50 [03:43<01:26, 6.21s/it] 74%|███████▍ | 37/50 [03:49<01:20, 6.21s/it] 76%|███████▌ | 38/50 [03:55<01:14, 6.21s/it] 78%|███████▊ | 39/50 [04:02<01:08, 6.21s/it] 80%|████████ | 40/50 [04:08<01:02, 6.21s/it] 82%|████████▏ | 41/50 [04:14<00:55, 6.22s/it] 84%|████████▍ | 42/50 [04:20<00:49, 6.22s/it] 86%|████████▌ | 43/50 [04:26<00:43, 6.22s/it] 88%|████████▊ | 44/50 [04:33<00:37, 6.21s/it] 90%|█████████ | 45/50 [04:39<00:31, 6.21s/it] 92%|█████████▏| 46/50 [04:45<00:24, 6.21s/it] 94%|█████████▍| 47/50 [04:51<00:18, 6.21s/it] 96%|█████████▌| 48/50 [04:57<00:12, 6.22s/it] 98%|█████████▊| 49/50 [05:04<00:06, 6.22s/it] 100%|██████████| 50/50 [05:09<00:00, 5.82s/it] 100%|██████████| 50/50 [05:09<00:00, 6.18s/it] [INFO] Time taken: 309.7938311100006 seconds.Prediction
moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88ID3o3quz3bq67inbfoyg7odf7g4uStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 0
- prompt
- Envision a portrait of a cute cat, her face is framed by a blue headscarf with muted tones of rust and cream. Her eyes are blue like faded denim. Her attire, simple yet dignified
- img_width
- 1080
- rrg_scale
- 1000
- img_height
- 1920
- cosine_scale
- 10
- guidance_scale
- 10
- view_batch_size
- 16
- negative_prompts
- blurry, ugly, poorly drawn, deformed
- resampling_new_p
- 0.3
- resampling_steps
- 7
- num_inference_steps
- 50
{ "seed": 0, "prompt": "Envision a portrait of a cute cat, her face is framed by a blue headscarf with muted tones of rust and cream. Her eyes are blue like faded denim. Her attire, simple yet dignified", "img_width": 1080, "rrg_scale": 1000, "img_height": 1920, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", { input: { seed: 0, prompt: "Envision a portrait of a cute cat, her face is framed by a blue headscarf with muted tones of rust and cream. Her eyes are blue like faded denim. Her attire, simple yet dignified", img_width: 1080, rrg_scale: 1000, img_height: 1920, cosine_scale: 10, guidance_scale: 10, view_batch_size: 16, negative_prompts: "blurry, ugly, poorly drawn, deformed", resampling_new_p: 0.3, resampling_steps: 7, num_inference_steps: 50 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", 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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", input={ "seed": 0, "prompt": "Envision a portrait of a cute cat, her face is framed by a blue headscarf with muted tones of rust and cream. Her eyes are blue like faded denim. Her attire, simple yet dignified", "img_width": 1080, "rrg_scale": 1000, "img_height": 1920, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run moayedhajiali/elasticdiffusion 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": "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", "input": { "seed": 0, "prompt": "Envision a portrait of a cute cat, her face is framed by a blue headscarf with muted tones of rust and cream. Her eyes are blue like faded denim. Her attire, simple yet dignified", "img_width": 1080, "rrg_scale": 1000, "img_height": 1920, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "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": "2023-12-27T20:12:01.223536Z", "created_at": "2023-12-27T20:04:33.339639Z", "data_removed": false, "error": null, "id": "3o3quz3bq67inbfoyg7odf7g4u", "input": { "seed": 0, "prompt": "Envision a portrait of a cute cat, her face is framed by a blue headscarf with muted tones of rust and cream. Her eyes are blue like faded denim. Her attire, simple yet dignified", "img_width": 1080, "rrg_scale": 1000, "img_height": 1920, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "num_inference_steps": 50 }, "logs": "0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:08<07:19, 8.97s/it]\n 4%|▍ | 2/50 [00:17<07:09, 8.94s/it]\n 6%|▌ | 3/50 [00:26<06:59, 8.93s/it]\n 8%|▊ | 4/50 [00:35<06:51, 8.95s/it]\n 10%|█ | 5/50 [00:44<06:42, 8.94s/it]\n 12%|█▏ | 6/50 [00:53<06:33, 8.94s/it]\n 14%|█▍ | 7/50 [01:02<06:24, 8.94s/it]\n 16%|█▌ | 8/50 [01:11<06:15, 8.94s/it]\n 18%|█▊ | 9/50 [01:20<06:06, 8.94s/it]\n 20%|██ | 10/50 [01:29<05:57, 8.94s/it]\n 22%|██▏ | 11/50 [01:38<05:48, 8.94s/it]\n 24%|██▍ | 12/50 [01:47<05:39, 8.94s/it]\n 26%|██▌ | 13/50 [01:56<05:30, 8.94s/it]\n 28%|██▊ | 14/50 [02:05<05:21, 8.94s/it]\n 30%|███ | 15/50 [02:14<05:12, 8.94s/it]\n 32%|███▏ | 16/50 [02:23<05:04, 8.94s/it]\n 34%|███▍ | 17/50 [02:32<04:55, 8.95s/it]\n 36%|███▌ | 18/50 [02:40<04:46, 8.94s/it]\n 38%|███▊ | 19/50 [02:49<04:37, 8.94s/it]\n 40%|████ | 20/50 [02:58<04:28, 8.94s/it]\n 42%|████▏ | 21/50 [03:07<04:19, 8.95s/it]\n 44%|████▍ | 22/50 [03:16<04:10, 8.95s/it]\n 46%|████▌ | 23/50 [03:25<04:01, 8.96s/it]\n 48%|████▊ | 24/50 [03:34<03:53, 8.97s/it]\n 50%|█████ | 25/50 [03:43<03:44, 8.97s/it]\n 52%|█████▏ | 26/50 [03:52<03:35, 8.96s/it]\n 54%|█████▍ | 27/50 [04:01<03:25, 8.96s/it]\n 56%|█████▌ | 28/50 [04:10<03:17, 8.96s/it]\n 58%|█████▊ | 29/50 [04:19<03:08, 8.96s/it]\n 60%|██████ | 30/50 [04:28<02:59, 8.96s/it]\n 62%|██████▏ | 31/50 [04:37<02:50, 8.96s/it]\n 64%|██████▍ | 32/50 [04:46<02:41, 8.96s/it]\n 66%|██████▌ | 33/50 [04:55<02:32, 8.96s/it]\n 68%|██████▊ | 34/50 [05:04<02:23, 8.97s/it]\n 70%|███████ | 35/50 [05:13<02:14, 8.96s/it]\n 72%|███████▏ | 36/50 [05:22<02:05, 8.96s/it]\n 74%|███████▍ | 37/50 [05:31<01:56, 8.97s/it]\n 76%|███████▌ | 38/50 [05:40<01:47, 8.97s/it]\n 78%|███████▊ | 39/50 [05:49<01:38, 8.96s/it]\n 80%|████████ | 40/50 [05:58<01:29, 8.97s/it]\n 82%|████████▏ | 41/50 [06:07<01:20, 8.97s/it]\n 84%|████████▍ | 42/50 [06:16<01:11, 8.96s/it]\n 86%|████████▌ | 43/50 [06:25<01:02, 8.97s/it]\n 88%|████████▊ | 44/50 [06:33<00:53, 8.96s/it]\n 90%|█████████ | 45/50 [06:42<00:44, 8.96s/it]\n 92%|█████████▏| 46/50 [06:51<00:35, 8.96s/it]\n 94%|█████████▍| 47/50 [07:00<00:26, 8.97s/it]\n 96%|█████████▌| 48/50 [07:09<00:17, 8.96s/it]\n 98%|█████████▊| 49/50 [07:18<00:08, 8.97s/it]\n100%|██████████| 50/50 [07:24<00:00, 8.13s/it]\n100%|██████████| 50/50 [07:24<00:00, 8.90s/it]\n[INFO] Time taken: 445.8617935180664 seconds.", "metrics": { "predict_time": 447.822146, "total_time": 447.883897 }, "output": "https://replicate.delivery/pbxt/cyVqzroYFOaqFdGKQes0HfkoRVfNrA4yA4hfS9KByeYBib0QC/result.png", "started_at": "2023-12-27T20:04:33.401390Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/3o3quz3bq67inbfoyg7odf7g4u", "cancel": "https://api.replicate.com/v1/predictions/3o3quz3bq67inbfoyg7odf7g4u/cancel" }, "version": "bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88" }Generated in0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:08<07:19, 8.97s/it] 4%|▍ | 2/50 [00:17<07:09, 8.94s/it] 6%|▌ | 3/50 [00:26<06:59, 8.93s/it] 8%|▊ | 4/50 [00:35<06:51, 8.95s/it] 10%|█ | 5/50 [00:44<06:42, 8.94s/it] 12%|█▏ | 6/50 [00:53<06:33, 8.94s/it] 14%|█▍ | 7/50 [01:02<06:24, 8.94s/it] 16%|█▌ | 8/50 [01:11<06:15, 8.94s/it] 18%|█▊ | 9/50 [01:20<06:06, 8.94s/it] 20%|██ | 10/50 [01:29<05:57, 8.94s/it] 22%|██▏ | 11/50 [01:38<05:48, 8.94s/it] 24%|██▍ | 12/50 [01:47<05:39, 8.94s/it] 26%|██▌ | 13/50 [01:56<05:30, 8.94s/it] 28%|██▊ | 14/50 [02:05<05:21, 8.94s/it] 30%|███ | 15/50 [02:14<05:12, 8.94s/it] 32%|███▏ | 16/50 [02:23<05:04, 8.94s/it] 34%|███▍ | 17/50 [02:32<04:55, 8.95s/it] 36%|███▌ | 18/50 [02:40<04:46, 8.94s/it] 38%|███▊ | 19/50 [02:49<04:37, 8.94s/it] 40%|████ | 20/50 [02:58<04:28, 8.94s/it] 42%|████▏ | 21/50 [03:07<04:19, 8.95s/it] 44%|████▍ | 22/50 [03:16<04:10, 8.95s/it] 46%|████▌ | 23/50 [03:25<04:01, 8.96s/it] 48%|████▊ | 24/50 [03:34<03:53, 8.97s/it] 50%|█████ | 25/50 [03:43<03:44, 8.97s/it] 52%|█████▏ | 26/50 [03:52<03:35, 8.96s/it] 54%|█████▍ | 27/50 [04:01<03:25, 8.96s/it] 56%|█████▌ | 28/50 [04:10<03:17, 8.96s/it] 58%|█████▊ | 29/50 [04:19<03:08, 8.96s/it] 60%|██████ | 30/50 [04:28<02:59, 8.96s/it] 62%|██████▏ | 31/50 [04:37<02:50, 8.96s/it] 64%|██████▍ | 32/50 [04:46<02:41, 8.96s/it] 66%|██████▌ | 33/50 [04:55<02:32, 8.96s/it] 68%|██████▊ | 34/50 [05:04<02:23, 8.97s/it] 70%|███████ | 35/50 [05:13<02:14, 8.96s/it] 72%|███████▏ | 36/50 [05:22<02:05, 8.96s/it] 74%|███████▍ | 37/50 [05:31<01:56, 8.97s/it] 76%|███████▌ | 38/50 [05:40<01:47, 8.97s/it] 78%|███████▊ | 39/50 [05:49<01:38, 8.96s/it] 80%|████████ | 40/50 [05:58<01:29, 8.97s/it] 82%|████████▏ | 41/50 [06:07<01:20, 8.97s/it] 84%|████████▍ | 42/50 [06:16<01:11, 8.96s/it] 86%|████████▌ | 43/50 [06:25<01:02, 8.97s/it] 88%|████████▊ | 44/50 [06:33<00:53, 8.96s/it] 90%|█████████ | 45/50 [06:42<00:44, 8.96s/it] 92%|█████████▏| 46/50 [06:51<00:35, 8.96s/it] 94%|█████████▍| 47/50 [07:00<00:26, 8.97s/it] 96%|█████████▌| 48/50 [07:09<00:17, 8.96s/it] 98%|█████████▊| 49/50 [07:18<00:08, 8.97s/it] 100%|██████████| 50/50 [07:24<00:00, 8.13s/it] 100%|██████████| 50/50 [07:24<00:00, 8.90s/it] [INFO] Time taken: 445.8617935180664 seconds.Prediction
moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88ID2mog35lbq2xztefdcmgjs7dprqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 0
- prompt
- An illustration of an astronaut riding a horse
- img_width
- 512
- rrg_scale
- 0
- img_height
- 512
- cosine_scale
- 10
- guidance_scale
- 10
- view_batch_size
- 16
- negative_prompts
- blurry, ugly, poorly drawn, deformed
- resampling_new_p
- 0.3
- resampling_steps
- 0
- num_inference_steps
- 50
{ "seed": 0, "prompt": "An illustration of an astronaut riding a horse", "img_width": 512, "rrg_scale": 0, "img_height": 512, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 0, "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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", { input: { seed: 0, prompt: "An illustration of an astronaut riding a horse", img_width: 512, rrg_scale: 0, img_height: 512, cosine_scale: 10, guidance_scale: 10, view_batch_size: 16, negative_prompts: "blurry, ugly, poorly drawn, deformed", resampling_new_p: 0.3, resampling_steps: 0, num_inference_steps: 50 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", 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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", input={ "seed": 0, "prompt": "An illustration of an astronaut riding a horse", "img_width": 512, "rrg_scale": 0, "img_height": 512, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 0, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run moayedhajiali/elasticdiffusion 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": "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", "input": { "seed": 0, "prompt": "An illustration of an astronaut riding a horse", "img_width": 512, "rrg_scale": 0, "img_height": 512, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 0, "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": "2023-12-28T04:08:59.940491Z", "created_at": "2023-12-28T04:08:14.157526Z", "data_removed": false, "error": null, "id": "2mog35lbq2xztefdcmgjs7dprq", "input": { "seed": 0, "prompt": "An illustration of an astronaut riding a horse", "img_width": 512, "rrg_scale": 0, "img_height": 512, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 0, "num_inference_steps": 50 }, "logs": "0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:44, 1.09it/s]\n 4%|▍ | 2/50 [00:01<00:43, 1.11it/s]\n 6%|▌ | 3/50 [00:02<00:42, 1.12it/s]\n 8%|▊ | 4/50 [00:03<00:41, 1.12it/s]\n 10%|█ | 5/50 [00:04<00:40, 1.12it/s]\n 12%|█▏ | 6/50 [00:05<00:39, 1.12it/s]\n 14%|█▍ | 7/50 [00:06<00:38, 1.12it/s]\n 16%|█▌ | 8/50 [00:07<00:37, 1.12it/s]\n 18%|█▊ | 9/50 [00:08<00:36, 1.12it/s]\n 20%|██ | 10/50 [00:08<00:35, 1.12it/s]\n 22%|██▏ | 11/50 [00:09<00:34, 1.12it/s]\n 24%|██▍ | 12/50 [00:10<00:33, 1.12it/s]\n 26%|██▌ | 13/50 [00:11<00:32, 1.12it/s]\n 28%|██▊ | 14/50 [00:12<00:32, 1.12it/s]\n 30%|███ | 15/50 [00:13<00:31, 1.12it/s]\n 32%|███▏ | 16/50 [00:14<00:30, 1.12it/s]\n 34%|███▍ | 17/50 [00:15<00:29, 1.12it/s]\n 36%|███▌ | 18/50 [00:16<00:28, 1.12it/s]\n 38%|███▊ | 19/50 [00:16<00:27, 1.12it/s]\n 40%|████ | 20/50 [00:17<00:26, 1.12it/s]\n 42%|████▏ | 21/50 [00:18<00:25, 1.12it/s]\n 44%|████▍ | 22/50 [00:19<00:24, 1.12it/s]\n 46%|████▌ | 23/50 [00:20<00:24, 1.12it/s]\n 48%|████▊ | 24/50 [00:21<00:23, 1.12it/s]\n 50%|█████ | 25/50 [00:22<00:22, 1.12it/s]\n 52%|█████▏ | 26/50 [00:23<00:21, 1.12it/s]\n 54%|█████▍ | 27/50 [00:24<00:20, 1.12it/s]\n 56%|█████▌ | 28/50 [00:24<00:19, 1.12it/s]\n 58%|█████▊ | 29/50 [00:25<00:18, 1.12it/s]\n 60%|██████ | 30/50 [00:26<00:17, 1.12it/s]\n 62%|██████▏ | 31/50 [00:27<00:16, 1.12it/s]\n 64%|██████▍ | 32/50 [00:28<00:16, 1.12it/s]\n 66%|██████▌ | 33/50 [00:29<00:15, 1.12it/s]\n 68%|██████▊ | 34/50 [00:30<00:14, 1.12it/s]\n 70%|███████ | 35/50 [00:31<00:13, 1.12it/s]\n 72%|███████▏ | 36/50 [00:32<00:12, 1.12it/s]\n 74%|███████▍ | 37/50 [00:32<00:11, 1.12it/s]\n 76%|███████▌ | 38/50 [00:33<00:10, 1.12it/s]\n 78%|███████▊ | 39/50 [00:34<00:09, 1.12it/s]\n 80%|████████ | 40/50 [00:35<00:08, 1.12it/s]\n 82%|████████▏ | 41/50 [00:36<00:08, 1.12it/s]\n 84%|████████▍ | 42/50 [00:37<00:07, 1.12it/s]\n 86%|████████▌ | 43/50 [00:38<00:06, 1.12it/s]\n 88%|████████▊ | 44/50 [00:39<00:05, 1.12it/s]\n 90%|█████████ | 45/50 [00:40<00:04, 1.12it/s]\n 92%|█████████▏| 46/50 [00:41<00:03, 1.12it/s]\n 94%|█████████▍| 47/50 [00:41<00:02, 1.12it/s]\n 96%|█████████▌| 48/50 [00:42<00:01, 1.12it/s]\n 98%|█████████▊| 49/50 [00:43<00:00, 1.12it/s]\n100%|██████████| 50/50 [00:44<00:00, 1.12it/s]\n100%|██████████| 50/50 [00:44<00:00, 1.12it/s]\n[INFO] Time taken: 44.77764296531677 seconds.", "metrics": { "predict_time": 45.750611, "total_time": 45.782965 }, "output": "https://replicate.delivery/pbxt/bFdwZjPFqc7GPhkgRAtqq6UlR2PC41JSM1vmVakRS7w2mqhE/result.png", "started_at": "2023-12-28T04:08:14.189880Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/2mog35lbq2xztefdcmgjs7dprq", "cancel": "https://api.replicate.com/v1/predictions/2mog35lbq2xztefdcmgjs7dprq/cancel" }, "version": "bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88" }Generated in0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:44, 1.09it/s] 4%|▍ | 2/50 [00:01<00:43, 1.11it/s] 6%|▌ | 3/50 [00:02<00:42, 1.12it/s] 8%|▊ | 4/50 [00:03<00:41, 1.12it/s] 10%|█ | 5/50 [00:04<00:40, 1.12it/s] 12%|█▏ | 6/50 [00:05<00:39, 1.12it/s] 14%|█▍ | 7/50 [00:06<00:38, 1.12it/s] 16%|█▌ | 8/50 [00:07<00:37, 1.12it/s] 18%|█▊ | 9/50 [00:08<00:36, 1.12it/s] 20%|██ | 10/50 [00:08<00:35, 1.12it/s] 22%|██▏ | 11/50 [00:09<00:34, 1.12it/s] 24%|██▍ | 12/50 [00:10<00:33, 1.12it/s] 26%|██▌ | 13/50 [00:11<00:32, 1.12it/s] 28%|██▊ | 14/50 [00:12<00:32, 1.12it/s] 30%|███ | 15/50 [00:13<00:31, 1.12it/s] 32%|███▏ | 16/50 [00:14<00:30, 1.12it/s] 34%|███▍ | 17/50 [00:15<00:29, 1.12it/s] 36%|███▌ | 18/50 [00:16<00:28, 1.12it/s] 38%|███▊ | 19/50 [00:16<00:27, 1.12it/s] 40%|████ | 20/50 [00:17<00:26, 1.12it/s] 42%|████▏ | 21/50 [00:18<00:25, 1.12it/s] 44%|████▍ | 22/50 [00:19<00:24, 1.12it/s] 46%|████▌ | 23/50 [00:20<00:24, 1.12it/s] 48%|████▊ | 24/50 [00:21<00:23, 1.12it/s] 50%|█████ | 25/50 [00:22<00:22, 1.12it/s] 52%|█████▏ | 26/50 [00:23<00:21, 1.12it/s] 54%|█████▍ | 27/50 [00:24<00:20, 1.12it/s] 56%|█████▌ | 28/50 [00:24<00:19, 1.12it/s] 58%|█████▊ | 29/50 [00:25<00:18, 1.12it/s] 60%|██████ | 30/50 [00:26<00:17, 1.12it/s] 62%|██████▏ | 31/50 [00:27<00:16, 1.12it/s] 64%|██████▍ | 32/50 [00:28<00:16, 1.12it/s] 66%|██████▌ | 33/50 [00:29<00:15, 1.12it/s] 68%|██████▊ | 34/50 [00:30<00:14, 1.12it/s] 70%|███████ | 35/50 [00:31<00:13, 1.12it/s] 72%|███████▏ | 36/50 [00:32<00:12, 1.12it/s] 74%|███████▍ | 37/50 [00:32<00:11, 1.12it/s] 76%|███████▌ | 38/50 [00:33<00:10, 1.12it/s] 78%|███████▊ | 39/50 [00:34<00:09, 1.12it/s] 80%|████████ | 40/50 [00:35<00:08, 1.12it/s] 82%|████████▏ | 41/50 [00:36<00:08, 1.12it/s] 84%|████████▍ | 42/50 [00:37<00:07, 1.12it/s] 86%|████████▌ | 43/50 [00:38<00:06, 1.12it/s] 88%|████████▊ | 44/50 [00:39<00:05, 1.12it/s] 90%|█████████ | 45/50 [00:40<00:04, 1.12it/s] 92%|█████████▏| 46/50 [00:41<00:03, 1.12it/s] 94%|█████████▍| 47/50 [00:41<00:02, 1.12it/s] 96%|█████████▌| 48/50 [00:42<00:01, 1.12it/s] 98%|█████████▊| 49/50 [00:43<00:00, 1.12it/s] 100%|██████████| 50/50 [00:44<00:00, 1.12it/s] 100%|██████████| 50/50 [00:44<00:00, 1.12it/s] [INFO] Time taken: 44.77764296531677 seconds.Prediction
moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88IDarekedtbs2ou7zjfjvbokqkssmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 0
- prompt
- A front view of a beautiful waterfall
- img_width
- 2048
- rrg_scale
- 1000
- img_height
- 512
- cosine_scale
- 10
- guidance_scale
- 10
- view_batch_size
- 16
- negative_prompts
- blurry, ugly, poorly drawn, deformed
- resampling_new_p
- 0.3
- resampling_steps
- 7
- num_inference_steps
- 50
{ "seed": 0, "prompt": "A front view of a beautiful waterfall", "img_width": 2048, "rrg_scale": 1000, "img_height": 512, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", { input: { seed: 0, prompt: "A front view of a beautiful waterfall", img_width: 2048, rrg_scale: 1000, img_height: 512, cosine_scale: 10, guidance_scale: 10, view_batch_size: 16, negative_prompts: "blurry, ugly, poorly drawn, deformed", resampling_new_p: 0.3, resampling_steps: 7, num_inference_steps: 50 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", 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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", input={ "seed": 0, "prompt": "A front view of a beautiful waterfall", "img_width": 2048, "rrg_scale": 1000, "img_height": 512, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run moayedhajiali/elasticdiffusion 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": "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", "input": { "seed": 0, "prompt": "A front view of a beautiful waterfall", "img_width": 2048, "rrg_scale": 1000, "img_height": 512, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "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": "2023-12-28T04:14:40.379623Z", "created_at": "2023-12-28T04:09:12.956407Z", "data_removed": false, "error": null, "id": "arekedtbs2ou7zjfjvbokqkssm", "input": { "seed": 0, "prompt": "A front view of a beautiful waterfall", "img_width": 2048, "rrg_scale": 1000, "img_height": 512, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 7, "num_inference_steps": 50 }, "logs": "0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:06<05:20, 6.54s/it]\n 4%|▍ | 2/50 [00:13<05:13, 6.53s/it]\n 6%|▌ | 3/50 [00:19<05:06, 6.53s/it]\n 8%|▊ | 4/50 [00:26<05:00, 6.53s/it]\n 10%|█ | 5/50 [00:32<04:53, 6.53s/it]\n 12%|█▏ | 6/50 [00:39<04:47, 6.53s/it]\n 14%|█▍ | 7/50 [00:45<04:40, 6.53s/it]\n 16%|█▌ | 8/50 [00:52<04:34, 6.54s/it]\n 18%|█▊ | 9/50 [00:58<04:28, 6.54s/it]\n 20%|██ | 10/50 [01:05<04:21, 6.54s/it]\n 22%|██▏ | 11/50 [01:11<04:14, 6.54s/it]\n 24%|██▍ | 12/50 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70%|███████ | 35/50 [03:48<01:38, 6.54s/it]\n 72%|███████▏ | 36/50 [03:55<01:31, 6.54s/it]\n 74%|███████▍ | 37/50 [04:01<01:25, 6.54s/it]\n 76%|███████▌ | 38/50 [04:08<01:18, 6.54s/it]\n 78%|███████▊ | 39/50 [04:14<01:11, 6.54s/it]\n 80%|████████ | 40/50 [04:21<01:05, 6.54s/it]\n 82%|████████▏ | 41/50 [04:28<00:58, 6.54s/it]\n 84%|████████▍ | 42/50 [04:34<00:52, 6.54s/it]\n 86%|████████▌ | 43/50 [04:41<00:45, 6.54s/it]\n 88%|████████▊ | 44/50 [04:47<00:39, 6.54s/it]\n 90%|█████████ | 45/50 [04:54<00:32, 6.54s/it]\n 92%|█████████▏| 46/50 [05:00<00:26, 6.54s/it]\n 94%|█████████▍| 47/50 [05:07<00:19, 6.54s/it]\n 96%|█████████▌| 48/50 [05:13<00:13, 6.54s/it]\n 98%|█████████▊| 49/50 [05:20<00:06, 6.54s/it]\n100%|██████████| 50/50 [05:25<00:00, 6.12s/it]\n100%|██████████| 50/50 [05:25<00:00, 6.51s/it]\n[INFO] Time taken: 326.00207567214966 seconds.", "metrics": { "predict_time": 327.390563, "total_time": 327.423216 }, "output": "https://replicate.delivery/pbxt/f2Jieqy7FPjbW0MM7xjU1SKZ8DUvlve594cxx29ySoe8CqaIB/result.png", "started_at": "2023-12-28T04:09:12.989060Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/arekedtbs2ou7zjfjvbokqkssm", "cancel": "https://api.replicate.com/v1/predictions/arekedtbs2ou7zjfjvbokqkssm/cancel" }, "version": "bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88" }Generated in0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:06<05:20, 6.54s/it] 4%|▍ | 2/50 [00:13<05:13, 6.53s/it] 6%|▌ | 3/50 [00:19<05:06, 6.53s/it] 8%|▊ | 4/50 [00:26<05:00, 6.53s/it] 10%|█ | 5/50 [00:32<04:53, 6.53s/it] 12%|█▏ | 6/50 [00:39<04:47, 6.53s/it] 14%|█▍ | 7/50 [00:45<04:40, 6.53s/it] 16%|█▌ | 8/50 [00:52<04:34, 6.54s/it] 18%|█▊ | 9/50 [00:58<04:28, 6.54s/it] 20%|██ | 10/50 [01:05<04:21, 6.54s/it] 22%|██▏ | 11/50 [01:11<04:14, 6.54s/it] 24%|██▍ | 12/50 [01:18<04:08, 6.54s/it] 26%|██▌ | 13/50 [01:24<04:01, 6.54s/it] 28%|██▊ | 14/50 [01:31<03:55, 6.54s/it] 30%|███ | 15/50 [01:38<03:48, 6.54s/it] 32%|███▏ | 16/50 [01:44<03:42, 6.54s/it] 34%|███▍ | 17/50 [01:51<03:35, 6.54s/it] 36%|███▌ | 18/50 [01:57<03:29, 6.54s/it] 38%|███▊ | 19/50 [02:04<03:22, 6.54s/it] 40%|████ | 20/50 [02:10<03:16, 6.54s/it] 42%|████▏ | 21/50 [02:17<03:09, 6.54s/it] 44%|████▍ | 22/50 [02:23<03:03, 6.54s/it] 46%|████▌ | 23/50 [02:30<02:56, 6.54s/it] 48%|████▊ | 24/50 [02:36<02:50, 6.54s/it] 50%|█████ | 25/50 [02:43<02:43, 6.54s/it] 52%|█████▏ | 26/50 [02:49<02:36, 6.54s/it] 54%|█████▍ | 27/50 [02:56<02:30, 6.54s/it] 56%|█████▌ | 28/50 [03:03<02:23, 6.54s/it] 58%|█████▊ | 29/50 [03:09<02:17, 6.54s/it] 60%|██████ | 30/50 [03:16<02:10, 6.54s/it] 62%|██████▏ | 31/50 [03:22<02:04, 6.54s/it] 64%|██████▍ | 32/50 [03:29<01:57, 6.54s/it] 66%|██████▌ | 33/50 [03:35<01:51, 6.54s/it] 68%|██████▊ | 34/50 [03:42<01:44, 6.54s/it] 70%|███████ | 35/50 [03:48<01:38, 6.54s/it] 72%|███████▏ | 36/50 [03:55<01:31, 6.54s/it] 74%|███████▍ | 37/50 [04:01<01:25, 6.54s/it] 76%|███████▌ | 38/50 [04:08<01:18, 6.54s/it] 78%|███████▊ | 39/50 [04:14<01:11, 6.54s/it] 80%|████████ | 40/50 [04:21<01:05, 6.54s/it] 82%|████████▏ | 41/50 [04:28<00:58, 6.54s/it] 84%|████████▍ | 42/50 [04:34<00:52, 6.54s/it] 86%|████████▌ | 43/50 [04:41<00:45, 6.54s/it] 88%|████████▊ | 44/50 [04:47<00:39, 6.54s/it] 90%|█████████ | 45/50 [04:54<00:32, 6.54s/it] 92%|█████████▏| 46/50 [05:00<00:26, 6.54s/it] 94%|█████████▍| 47/50 [05:07<00:19, 6.54s/it] 96%|█████████▌| 48/50 [05:13<00:13, 6.54s/it] 98%|█████████▊| 49/50 [05:20<00:06, 6.54s/it] 100%|██████████| 50/50 [05:25<00:00, 6.12s/it] 100%|██████████| 50/50 [05:25<00:00, 6.51s/it] [INFO] Time taken: 326.00207567214966 seconds.Prediction
moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88IDf21dafdzwxrgg0chwdh8bf912gStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 107
- prompt
- Envision a portrait of a cute scientist owl in blue and gray outfit announcing their latest breakthrough discovery. his eyes are light brown. his attire is simple yet dignified
- img_width
- 1024
- rrg_scale
- 750
- img_height
- 2048
- cosine_scale
- 10
- guidance_scale
- 10
- view_batch_size
- 16
- negative_prompts
- blurry, ugly, poorly drawn, deformed
- resampling_new_p
- 0.3
- resampling_steps
- 8
- num_inference_steps
- 50
{ "seed": 107, "prompt": "Envision a portrait of a cute scientist owl in blue and gray outfit announcing their latest breakthrough discovery. his eyes are light brown. his attire is simple yet dignified", "img_width": 1024, "rrg_scale": 750, "img_height": 2048, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", { input: { seed: 107, prompt: "Envision a portrait of a cute scientist owl in blue and gray outfit announcing their latest breakthrough discovery. his eyes are light brown. his attire is simple yet dignified", img_width: 1024, rrg_scale: 750, img_height: 2048, cosine_scale: 10, guidance_scale: 10, view_batch_size: 16, negative_prompts: "blurry, ugly, poorly drawn, deformed", resampling_new_p: 0.3, resampling_steps: 8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", 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 moayedhajiali/elasticdiffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", input={ "seed": 107, "prompt": "Envision a portrait of a cute scientist owl in blue and gray outfit announcing their latest breakthrough discovery. his eyes are light brown. his attire is simple yet dignified", "img_width": 1024, "rrg_scale": 750, "img_height": 2048, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run moayedhajiali/elasticdiffusion 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": "moayedhajiali/elasticdiffusion:bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88", "input": { "seed": 107, "prompt": "Envision a portrait of a cute scientist owl in blue and gray outfit announcing their latest breakthrough discovery. his eyes are light brown. his attire is simple yet dignified", "img_width": 1024, "rrg_scale": 750, "img_height": 2048, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 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": "2024-09-11T23:25:12.790185Z", "created_at": "2024-09-11T23:19:45.383000Z", "data_removed": false, "error": null, "id": "f21dafdzwxrgg0chwdh8bf912g", "input": { "seed": 107, "prompt": "Envision a portrait of a cute scientist owl in blue and gray outfit announcing their latest breakthrough discovery. his eyes are light brown. his attire is simple yet dignified", "img_width": 1024, "rrg_scale": 750, "img_height": 2048, "cosine_scale": 10, "guidance_scale": 10, "view_batch_size": 16, "negative_prompts": "blurry, ugly, poorly drawn, deformed", "resampling_new_p": 0.3, "resampling_steps": 8, "num_inference_steps": 50 }, "logs": "0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:06<05:17, 6.49s/it]\n 4%|▍ | 2/50 [00:12<05:11, 6.49s/it]\n 6%|▌ | 3/50 [00:19<05:04, 6.48s/it]\n 8%|▊ | 4/50 [00:26<04:59, 6.51s/it]\n 10%|█ | 5/50 [00:32<04:52, 6.50s/it]\n 12%|█▏ | 6/50 [00:38<04:45, 6.50s/it]\n 14%|█▍ | 7/50 [00:45<04:39, 6.50s/it]\n 16%|█▌ | 8/50 [00:51<04:32, 6.50s/it]\n 18%|█▊ | 9/50 [00:58<04:26, 6.49s/it]\n 20%|██ | 10/50 [01:04<04:19, 6.49s/it]\n 22%|██▏ | 11/50 [01:11<04:13, 6.49s/it]\n 24%|██▍ | 12/50 [01:17<04:06, 6.49s/it]\n 26%|██▌ | 13/50 [01:24<04:00, 6.49s/it]\n 28%|██▊ | 14/50 [01:30<03:53, 6.49s/it]\n 30%|███ | 15/50 [01:37<03:47, 6.49s/it]\n 32%|███▏ | 16/50 [01:43<03:40, 6.50s/it]\n 34%|███▍ | 17/50 [01:50<03:35, 6.52s/it]\n 36%|███▌ | 18/50 [01:57<03:28, 6.52s/it]\n 38%|███▊ | 19/50 [02:03<03:21, 6.51s/it]\n 40%|████ | 20/50 [02:10<03:15, 6.51s/it]\n 42%|████▏ | 21/50 [02:16<03:08, 6.52s/it]\n 44%|████▍ | 22/50 [02:23<03:02, 6.51s/it]\n 46%|████▌ | 23/50 [02:29<02:55, 6.51s/it]\n 48%|████▊ | 24/50 [02:36<02:49, 6.51s/it]\n 50%|█████ | 25/50 [02:42<02:42, 6.51s/it]\n 52%|█████▏ | 26/50 [02:49<02:36, 6.50s/it]\n 54%|█████▍ | 27/50 [02:55<02:29, 6.50s/it]\n 56%|█████▌ | 28/50 [03:02<02:22, 6.50s/it]\n 58%|█████▊ | 29/50 [03:08<02:16, 6.50s/it]\n 60%|██████ | 30/50 [03:15<02:09, 6.50s/it]\n 62%|██████▏ | 31/50 [03:21<02:03, 6.50s/it]\n 64%|██████▍ | 32/50 [03:28<01:56, 6.50s/it]\n 66%|██████▌ | 33/50 [03:34<01:50, 6.50s/it]\n 68%|██████▊ | 34/50 [03:41<01:43, 6.50s/it]\n 70%|███████ | 35/50 [03:47<01:37, 6.51s/it]\n 72%|███████▏ | 36/50 [03:54<01:31, 6.50s/it]\n 74%|███████▍ | 37/50 [04:00<01:24, 6.50s/it]\n 76%|███████▌ | 38/50 [04:07<01:18, 6.50s/it]\n 78%|███████▊ | 39/50 [04:13<01:11, 6.50s/it]\n 80%|████████ | 40/50 [04:20<01:05, 6.50s/it]\n 82%|████████▏ | 41/50 [04:26<00:58, 6.51s/it]\n 84%|████████▍ | 42/50 [04:33<00:52, 6.51s/it]\n 86%|████████▌ | 43/50 [04:39<00:45, 6.51s/it]\n 88%|████████▊ | 44/50 [04:46<00:39, 6.51s/it]\n 90%|█████████ | 45/50 [04:52<00:32, 6.54s/it]\n 92%|█████████▏| 46/50 [04:59<00:26, 6.53s/it]\n 94%|█████████▍| 47/50 [05:05<00:19, 6.53s/it]\n 96%|█████████▌| 48/50 [05:12<00:13, 6.52s/it]\n 98%|█████████▊| 49/50 [05:18<00:06, 6.52s/it]\n100%|██████████| 50/50 [05:24<00:00, 6.13s/it]\n100%|██████████| 50/50 [05:24<00:00, 6.48s/it]\n[INFO] Time taken: 324.91242361068726 seconds.", "metrics": { "predict_time": 327.301337936, "total_time": 327.407185 }, "output": "https://replicate.delivery/pbxt/cbb84nepXXQeDEs4vaFHfjYlKLd5xjSbMH9rgzadMfAcN2vNB/result.png", "started_at": "2024-09-11T23:19:45.488848Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/f21dafdzwxrgg0chwdh8bf912g", "cancel": "https://api.replicate.com/v1/predictions/f21dafdzwxrgg0chwdh8bf912g/cancel" }, "version": "bddc09369f9e622518f6d11daff26723a513714e08830ed053660d8ac44ffe88" }Generated in0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:06<05:17, 6.49s/it] 4%|▍ | 2/50 [00:12<05:11, 6.49s/it] 6%|▌ | 3/50 [00:19<05:04, 6.48s/it] 8%|▊ | 4/50 [00:26<04:59, 6.51s/it] 10%|█ | 5/50 [00:32<04:52, 6.50s/it] 12%|█▏ | 6/50 [00:38<04:45, 6.50s/it] 14%|█▍ | 7/50 [00:45<04:39, 6.50s/it] 16%|█▌ | 8/50 [00:51<04:32, 6.50s/it] 18%|█▊ | 9/50 [00:58<04:26, 6.49s/it] 20%|██ | 10/50 [01:04<04:19, 6.49s/it] 22%|██▏ | 11/50 [01:11<04:13, 6.49s/it] 24%|██▍ | 12/50 [01:17<04:06, 6.49s/it] 26%|██▌ | 13/50 [01:24<04:00, 6.49s/it] 28%|██▊ | 14/50 [01:30<03:53, 6.49s/it] 30%|███ | 15/50 [01:37<03:47, 6.49s/it] 32%|███▏ | 16/50 [01:43<03:40, 6.50s/it] 34%|███▍ | 17/50 [01:50<03:35, 6.52s/it] 36%|███▌ | 18/50 [01:57<03:28, 6.52s/it] 38%|███▊ | 19/50 [02:03<03:21, 6.51s/it] 40%|████ | 20/50 [02:10<03:15, 6.51s/it] 42%|████▏ | 21/50 [02:16<03:08, 6.52s/it] 44%|████▍ | 22/50 [02:23<03:02, 6.51s/it] 46%|████▌ | 23/50 [02:29<02:55, 6.51s/it] 48%|████▊ | 24/50 [02:36<02:49, 6.51s/it] 50%|█████ | 25/50 [02:42<02:42, 6.51s/it] 52%|█████▏ | 26/50 [02:49<02:36, 6.50s/it] 54%|█████▍ | 27/50 [02:55<02:29, 6.50s/it] 56%|█████▌ | 28/50 [03:02<02:22, 6.50s/it] 58%|█████▊ | 29/50 [03:08<02:16, 6.50s/it] 60%|██████ | 30/50 [03:15<02:09, 6.50s/it] 62%|██████▏ | 31/50 [03:21<02:03, 6.50s/it] 64%|██████▍ | 32/50 [03:28<01:56, 6.50s/it] 66%|██████▌ | 33/50 [03:34<01:50, 6.50s/it] 68%|██████▊ | 34/50 [03:41<01:43, 6.50s/it] 70%|███████ | 35/50 [03:47<01:37, 6.51s/it] 72%|███████▏ | 36/50 [03:54<01:31, 6.50s/it] 74%|███████▍ | 37/50 [04:00<01:24, 6.50s/it] 76%|███████▌ | 38/50 [04:07<01:18, 6.50s/it] 78%|███████▊ | 39/50 [04:13<01:11, 6.50s/it] 80%|████████ | 40/50 [04:20<01:05, 6.50s/it] 82%|████████▏ | 41/50 [04:26<00:58, 6.51s/it] 84%|████████▍ | 42/50 [04:33<00:52, 6.51s/it] 86%|████████▌ | 43/50 [04:39<00:45, 6.51s/it] 88%|████████▊ | 44/50 [04:46<00:39, 6.51s/it] 90%|█████████ | 45/50 [04:52<00:32, 6.54s/it] 92%|█████████▏| 46/50 [04:59<00:26, 6.53s/it] 94%|█████████▍| 47/50 [05:05<00:19, 6.53s/it] 96%|█████████▌| 48/50 [05:12<00:13, 6.52s/it] 98%|█████████▊| 49/50 [05:18<00:06, 6.52s/it] 100%|██████████| 50/50 [05:24<00:00, 6.13s/it] 100%|██████████| 50/50 [05:24<00:00, 6.48s/it] [INFO] Time taken: 324.91242361068726 seconds.
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