laion-ai / erlich
Generate a logo using text.
Prediction
laion-ai/erlich:d937c50007a1cacbdbd1e0a71ccaeac2b383a20f5579ea9b3f5ad46fcb2e2607IDr5koa5tzs5f3fgrwdtek55uoaqStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- "-1"
- steps
- "75"
- width
- "256"
- height
- "256"
- prompt
- led zeppelin
- batch_size
- "6"
- guidance_scale
- "5"
- aesthetic_rating
- 9
- aesthetic_weight
- 0.5
{ "seed": "-1", "steps": "75", "width": "256", "height": "256", "prompt": "led zeppelin", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.5 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run laion-ai/erlich using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "laion-ai/erlich:d937c50007a1cacbdbd1e0a71ccaeac2b383a20f5579ea9b3f5ad46fcb2e2607", { input: { seed: "-1", steps: "75", width: "256", height: "256", prompt: "led zeppelin", batch_size: "6", guidance_scale: "5", aesthetic_rating: 9, aesthetic_weight: 0.5 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun laion-ai/erlich using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "laion-ai/erlich:d937c50007a1cacbdbd1e0a71ccaeac2b383a20f5579ea9b3f5ad46fcb2e2607", input={ "seed": "-1", "steps": "75", "width": "256", "height": "256", "prompt": "led zeppelin", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.5 } ) # The laion-ai/erlich model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/laion-ai/erlich/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run laion-ai/erlich 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": "laion-ai/erlich:d937c50007a1cacbdbd1e0a71ccaeac2b383a20f5579ea9b3f5ad46fcb2e2607", "input": { "seed": "-1", "steps": "75", "width": "256", "height": "256", "prompt": "led zeppelin", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.5 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-06-24T23:10:32.870474Z", "created_at": "2022-06-24T23:08:13.014348Z", "data_removed": false, "error": null, "id": "r5koa5tzs5f3fgrwdtek55uoaq", "input": { "seed": "-1", "steps": "75", "width": "256", "height": "256", "prompt": "led zeppelin", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.5 }, "logs": "Using seed 944961095\nRunning simulation for led zeppelin\nEncoding text embeddings with led zeppelin dimensions\nUsing aesthetic embedding 9 with weight 0.5\nUsing inpaint model but no image is provided. Initializing with zeros.\nRunning diffusion...\n\n0it [00:00, ?it/s]\n\nTimestep 0 - saving sample\n 0%| | 0/75 [00:00<?, ?it/s]\u001b[A\n1it [00:03, 3.27s/it]\n\n 1%|▏ | 1/75 [00:03<04:02, 3.27s/it]\u001b[A\n2it [00:05, 2.83s/it]\n\n 3%|▎ | 2/75 [00:05<03:26, 2.83s/it]\u001b[A\n3it [00:08, 2.69s/it]\n\n 4%|▍ | 3/75 [00:08<03:13, 2.69s/it]\u001b[A\n4it [00:08, 1.88s/it]\n\n 5%|▌ | 4/75 [00:08<02:13, 1.88s/it]\u001b[A\n5it [00:09, 1.43s/it]\n\n 7%|▋ | 5/75 [00:09<01:39, 1.43s/it]\u001b[A\n6it [00:10, 1.16s/it]\n\n 8%|▊ | 6/75 [00:10<01:19, 1.16s/it]\u001b[A\n7it [00:10, 1.01it/s]\n\n 9%|▉ | 7/75 [00:10<01:07, 1.01it/s]\u001b[A\n8it [00:11, 1.14it/s]\n\n 11%|█ | 8/75 [00:11<00:58, 1.14it/s]\u001b[A\n9it [00:12, 1.25it/s]\n\n 12%|█▏ | 9/75 [00:12<00:52, 1.25it/s]\u001b[A\n10it [00:12, 1.34it/s]\n\nTimestep 10 - saving sample\n 13%|█▎ | 10/75 [00:12<00:48, 1.34it/s]\u001b[A\n11it [00:14, 1.08it/s]\n\n 15%|█▍ | 11/75 [00:14<00:59, 1.08it/s]\u001b[A\n12it [00:14, 1.20it/s]\n\n 16%|█▌ | 12/75 [00:14<00:52, 1.20it/s]\u001b[A\n13it [00:15, 1.29it/s]\n\n 17%|█▋ | 13/75 [00:15<00:48, 1.29it/s]\u001b[A\n14it [00:15, 1.36it/s]\n\n 19%|█▊ | 14/75 [00:15<00:44, 1.36it/s]\u001b[A\n15it [00:16, 1.42it/s]\n\n 20%|██ | 15/75 [00:16<00:42, 1.42it/s]\u001b[A\n16it [00:17, 1.46it/s]\n\n 21%|██▏ | 16/75 [00:17<00:40, 1.46it/s]\u001b[A\n17it [00:17, 1.49it/s]\n\n 23%|██▎ | 17/75 [00:17<00:38, 1.49it/s]\u001b[A\n18it [00:18, 1.51it/s]\n\n 24%|██▍ | 18/75 [00:18<00:37, 1.51it/s]\u001b[A\n19it [00:19, 1.53it/s]\n\n 25%|██▌ | 19/75 [00:19<00:36, 1.53it/s]\u001b[A\n20it [00:19, 1.54it/s]\n\nTimestep 20 - saving sample\n 27%|██▋ | 20/75 [00:19<00:35, 1.54it/s]\u001b[A\n21it [00:21, 1.17it/s]\n\n 28%|██▊ | 21/75 [00:21<00:46, 1.17it/s]\u001b[A\n22it [00:21, 1.27it/s]\n\n 29%|██▉ | 22/75 [00:21<00:41, 1.27it/s]\u001b[A\n23it [00:22, 1.35it/s]\n\n 31%|███ | 23/75 [00:22<00:38, 1.35it/s]\u001b[A\n24it [00:23, 1.41it/s]\n\n 32%|███▏ | 24/75 [00:23<00:36, 1.41it/s]\u001b[A\n25it [00:23, 1.45it/s]\n\n 33%|███▎ | 25/75 [00:23<00:34, 1.45it/s]\u001b[A\n26it [00:24, 1.48it/s]\n\n 35%|███▍ | 26/75 [00:24<00:33, 1.48it/s]\u001b[A\n27it [00:24, 1.51it/s]\n\n 36%|███▌ | 27/75 [00:24<00:31, 1.51it/s]\u001b[A\n28it [00:25, 1.52it/s]\n\n 37%|███▋ | 28/75 [00:25<00:30, 1.52it/s]\u001b[A\n29it [00:26, 1.53it/s]\n\n 39%|███▊ | 29/75 [00:26<00:30, 1.53it/s]\u001b[A\n30it [00:26, 1.54it/s]\n\nTimestep 30 - saving sample\n 40%|████ | 30/75 [00:26<00:29, 1.54it/s]\u001b[A\n31it [00:28, 1.16it/s]\n\n 41%|████▏ | 31/75 [00:28<00:37, 1.16it/s]\u001b[A\n32it [00:28, 1.27it/s]\n\n 43%|████▎ | 32/75 [00:28<00:33, 1.27it/s]\u001b[A\n33it [00:29, 1.34it/s]\n\n 44%|████▍ | 33/75 [00:29<00:31, 1.34it/s]\u001b[A\n34it [00:30, 1.40it/s]\n\n 45%|████▌ | 34/75 [00:30<00:29, 1.40it/s]\u001b[A\n35it [00:30, 1.44it/s]\n\n 47%|████▋ | 35/75 [00:30<00:27, 1.44it/s]\u001b[A\n36it [00:31, 1.47it/s]\n\n 48%|████▊ | 36/75 [00:31<00:26, 1.47it/s]\u001b[A\n37it [00:32, 1.49it/s]\n\n 49%|████▉ | 37/75 [00:32<00:25, 1.49it/s]\u001b[A\n38it [00:32, 1.51it/s]\n\n 51%|█████ | 38/75 [00:32<00:24, 1.51it/s]\u001b[A\n39it [00:33, 1.52it/s]\n\n 52%|█████▏ | 39/75 [00:33<00:23, 1.52it/s]\u001b[A\n40it [00:34, 1.52it/s]\n\nTimestep 40 - saving sample\n 53%|█████▎ | 40/75 [00:34<00:22, 1.52it/s]\u001b[A\n41it [00:35, 1.16it/s]\n\n 55%|█████▍ | 41/75 [00:35<00:29, 1.16it/s]\u001b[A\n42it [00:36, 1.26it/s]\n\n 56%|█████▌ | 42/75 [00:36<00:26, 1.26it/s]\u001b[A\n43it [00:36, 1.33it/s]\n\n 57%|█████▋ | 43/75 [00:36<00:24, 1.33it/s]\u001b[A\n44it [00:37, 1.39it/s]\n\n 59%|█████▊ | 44/75 [00:37<00:22, 1.39it/s]\u001b[A\n45it [00:37, 1.43it/s]\n\n 60%|██████ | 45/75 [00:37<00:20, 1.43it/s]\u001b[A\n46it [00:38, 1.46it/s]\n\n 61%|██████▏ | 46/75 [00:38<00:19, 1.46it/s]\u001b[A\n47it [00:39, 1.49it/s]\n\n 63%|██████▎ | 47/75 [00:39<00:18, 1.49it/s]\u001b[A\n48it [00:39, 1.50it/s]\n\n 64%|██████▍ | 48/75 [00:39<00:17, 1.50it/s]\u001b[A\n49it [00:40, 1.51it/s]\n\n 65%|██████▌ | 49/75 [00:40<00:17, 1.51it/s]\u001b[A\n50it [00:41, 1.52it/s]\n\nTimestep 50 - saving sample\n 67%|██████▋ | 50/75 [00:41<00:16, 1.52it/s]\u001b[A\n51it [00:42, 1.15it/s]\n\n 68%|██████▊ | 51/75 [00:42<00:20, 1.15it/s]\u001b[A\n52it [00:43, 1.25it/s]\n\n 69%|██████▉ | 52/75 [00:43<00:18, 1.25it/s]\u001b[A\n53it [00:43, 1.32it/s]\n\n 71%|███████ | 53/75 [00:43<00:16, 1.32it/s]\u001b[A\n54it [00:44, 1.38it/s]\n\n 72%|███████▏ | 54/75 [00:44<00:15, 1.38it/s]\u001b[A\n55it [00:45, 1.42it/s]\n\n 73%|███████▎ | 55/75 [00:45<00:14, 1.42it/s]\u001b[A\n56it [00:45, 1.45it/s]\n\n 75%|███████▍ | 56/75 [00:45<00:13, 1.45it/s]\u001b[A\n57it [00:46, 1.47it/s]\n\n 76%|███████▌ | 57/75 [00:46<00:12, 1.47it/s]\u001b[A\n58it [00:47, 1.48it/s]\n\n 77%|███████▋ | 58/75 [00:47<00:11, 1.48it/s]\u001b[A\n59it [00:47, 1.49it/s]\n\n 79%|███████▊ | 59/75 [00:47<00:10, 1.49it/s]\u001b[A\n60it [00:48, 1.50it/s]\n\nTimestep 60 - saving sample\n 80%|████████ | 60/75 [00:48<00:10, 1.50it/s]\u001b[A\n61it [00:49, 1.14it/s]\n\n 81%|████████▏ | 61/75 [00:49<00:12, 1.14it/s]\u001b[A\n62it [00:50, 1.24it/s]\n\n 83%|████████▎ | 62/75 [00:50<00:10, 1.24it/s]\u001b[A\n63it [00:51, 1.31it/s]\n\n 84%|████████▍ | 63/75 [00:51<00:09, 1.31it/s]\u001b[A\n64it [00:51, 1.37it/s]\n\n 85%|████████▌ | 64/75 [00:51<00:08, 1.37it/s]\u001b[A\n65it [00:52, 1.41it/s]\n\n 87%|████████▋ | 65/75 [00:52<00:07, 1.41it/s]\u001b[A\n66it [00:53, 1.44it/s]\n\n 88%|████████▊ | 66/75 [00:53<00:06, 1.44it/s]\u001b[A\n67it [00:53, 1.46it/s]\n\n 89%|████████▉ | 67/75 [00:53<00:05, 1.46it/s]\u001b[A\n68it [00:54, 1.48it/s]\n\n 91%|█████████ | 68/75 [00:54<00:04, 1.48it/s]\u001b[A\n69it [00:55, 1.49it/s]\n\n 92%|█████████▏| 69/75 [00:55<00:04, 1.49it/s]\u001b[A\n70it [00:55, 1.49it/s]\n\nTimestep 70 - saving sample\n 93%|█████████▎| 70/75 [00:55<00:03, 1.49it/s]\u001b[A\n71it [00:57, 1.14it/s]\n\n 95%|█████████▍| 71/75 [00:57<00:03, 1.14it/s]\u001b[A\n72it [00:57, 1.23it/s]\n\n 96%|█████████▌| 72/75 [00:57<00:02, 1.23it/s]\u001b[A\n73it [00:58, 1.30it/s]\n\n 97%|█████████▋| 73/75 [00:58<00:01, 1.30it/s]\u001b[A\n74it [00:59, 1.35it/s]\n\nTimestep 74 - saving final sample\n 99%|█████████▊| 74/75 [00:59<00:00, 1.35it/s]\u001b[A\n75it [01:00, 1.08it/s]\n\n100%|██████████| 75/75 [01:00<00:00, 1.08it/s]\u001b[A\n100%|██████████| 75/75 [01:00<00:00, 1.24it/s]\n\n75it [01:00, 1.24it/s]", "metrics": { "predict_time": 65.193993, "total_time": 139.856126 }, "output": [ [ "https://replicate.delivery/mgxm/1c498631-870c-406a-a025-bd149a463551/current_0.jpg", "https://replicate.delivery/mgxm/c65d0815-8da5-4e57-ae3f-56e2fa20cc3b/current_1.jpg", "https://replicate.delivery/mgxm/7d0656ed-c8bd-449a-b83f-65acf63e480e/current_2.jpg", "https://replicate.delivery/mgxm/85af7b2d-2945-4053-8e19-1f5c1d9f0d8e/current_3.jpg", "https://replicate.delivery/mgxm/73d89b4c-85c0-4dc7-90d6-2af31c4d12d6/current_4.jpg", "https://replicate.delivery/mgxm/70a1f94a-7feb-4144-8f8f-b8f7885a45d5/current_5.jpg" ], [ "https://replicate.delivery/mgxm/93a73cd7-47ed-459f-993a-88dbc29964e5/current_0.jpg", "https://replicate.delivery/mgxm/bd67f2bd-eda5-4956-85ea-b986b357f494/current_1.jpg", "https://replicate.delivery/mgxm/ab80dc75-39b0-49cd-893b-e7cfeb8c2a33/current_2.jpg", "https://replicate.delivery/mgxm/c8191fda-2adc-409c-b2d4-e29789c922bc/current_3.jpg", "https://replicate.delivery/mgxm/f690645f-b280-4994-83d9-832bdf8f1026/current_4.jpg", "https://replicate.delivery/mgxm/5d0038e7-b396-425e-b6b4-91ecca23557e/current_5.jpg" ], [ "https://replicate.delivery/mgxm/e135163c-cb3a-4fbe-94c7-caf75d6db47c/current_0.jpg", "https://replicate.delivery/mgxm/b7afaf95-4914-4758-beaa-9702ccb53789/current_1.jpg", "https://replicate.delivery/mgxm/9c50cfe9-6e34-438f-9458-5273b8857a80/current_2.jpg", "https://replicate.delivery/mgxm/bb45d61f-a21a-448c-9f8f-cebb1f584027/current_3.jpg", "https://replicate.delivery/mgxm/4286d049-0421-485b-a3bf-0f083a90109e/current_4.jpg", "https://replicate.delivery/mgxm/90d75832-bca1-40de-aee8-e4f10c88c6f7/current_5.jpg" ], [ "https://replicate.delivery/mgxm/53a8fa12-d2d5-43e3-9a6d-20a9a2488ca3/current_0.jpg", "https://replicate.delivery/mgxm/2ce8e69b-090e-46db-92db-aad501731b87/current_1.jpg", "https://replicate.delivery/mgxm/d14e461c-3e86-4a38-b98f-4fccf00e0fb6/current_2.jpg", "https://replicate.delivery/mgxm/7f3edbda-8b9f-42ef-8117-a06059e08030/current_3.jpg", "https://replicate.delivery/mgxm/0c1eee6f-6f69-46ad-8358-47046615d451/current_4.jpg", "https://replicate.delivery/mgxm/0f1e3303-17bd-4824-9bca-c8edd322dfa8/current_5.jpg" ], [ "https://replicate.delivery/mgxm/354c4f6c-a88e-4d03-8a38-f3f25fb0e052/current_0.jpg", "https://replicate.delivery/mgxm/38eb771a-bd17-4c81-a3ab-2afe79e67ebe/current_1.jpg", "https://replicate.delivery/mgxm/494e5fb6-bb40-4c18-adf2-48a7829b58d4/current_2.jpg", "https://replicate.delivery/mgxm/9e0e5ba1-1229-450a-ba0d-cfd7bddc859f/current_3.jpg", "https://replicate.delivery/mgxm/b43a919b-6199-488d-8660-4ad5e953484f/current_4.jpg", "https://replicate.delivery/mgxm/ec53f571-93b0-40dd-9815-19e32edf4ec0/current_5.jpg" ], [ "https://replicate.delivery/mgxm/48a41eeb-8ec7-46a5-9609-d52a4ab9e0ad/current_0.jpg", "https://replicate.delivery/mgxm/20eead7c-6784-494f-ac09-3a053ebecccf/current_1.jpg", "https://replicate.delivery/mgxm/0d1723bd-4fbd-4b48-bdc2-9b0847dd5629/current_2.jpg", "https://replicate.delivery/mgxm/639d1511-db2c-4a39-981c-e04f110ac9aa/current_3.jpg", "https://replicate.delivery/mgxm/82c793a2-049b-49b6-aee4-83353a58e5fe/current_4.jpg", "https://replicate.delivery/mgxm/3403739f-9b8c-4325-aee5-7f8300a082a2/current_5.jpg" ], [ "https://replicate.delivery/mgxm/0b0c0cb0-0253-41e6-a72c-291fa4549932/current_0.jpg", "https://replicate.delivery/mgxm/887ec673-0193-44e5-baba-4ea780be1d61/current_1.jpg", "https://replicate.delivery/mgxm/deb9caf9-3cc1-4d25-b3a0-d9c2f9d4446e/current_2.jpg", "https://replicate.delivery/mgxm/c6c296bf-412c-4695-8f34-88033bfe5153/current_3.jpg", "https://replicate.delivery/mgxm/f19a5dd3-ed7b-4984-bf89-c14f81e55666/current_4.jpg", "https://replicate.delivery/mgxm/8fe70da1-ad6c-4b5e-92b5-a7182bb53399/current_5.jpg" ], [ "https://replicate.delivery/mgxm/0b779308-36e1-4e34-934e-a645ac188ea5/current_0.jpg", "https://replicate.delivery/mgxm/29e2b21e-cb35-4c97-9a81-05b0ebb69dbc/current_1.jpg", "https://replicate.delivery/mgxm/ed43ed37-1f8e-4722-a724-436ed31fec2e/current_2.jpg", "https://replicate.delivery/mgxm/1b69fc21-4c6c-45e7-bacf-de6d4eafa1a1/current_3.jpg", "https://replicate.delivery/mgxm/ec2da6cc-b784-4c8e-b58c-d9d3951a62c3/current_4.jpg", "https://replicate.delivery/mgxm/298a98ee-8d1f-470d-aa1d-c7d0c81155a4/current_5.jpg" ], [ "https://replicate.delivery/mgxm/ff49fa28-6ed5-4979-b165-a12796dde961/current_0.jpg", "https://replicate.delivery/mgxm/6b4639d8-4f12-486e-9c7c-624eaa2a74c8/current_1.jpg", "https://replicate.delivery/mgxm/ed075811-c996-4512-a7b4-1d3c2832a4b8/current_2.jpg", "https://replicate.delivery/mgxm/d4ac32a4-fa9a-4c7c-b38a-8cce7510e272/current_3.jpg", "https://replicate.delivery/mgxm/3a533a0f-9ba3-4363-b7cb-a8be42d8caa5/current_4.jpg", "https://replicate.delivery/mgxm/a5be196c-457c-4878-ac36-cb06275b8bbc/current_5.jpg" ] ], "started_at": "2022-06-24T23:09:27.676481Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/r5koa5tzs5f3fgrwdtek55uoaq", "cancel": "https://api.replicate.com/v1/predictions/r5koa5tzs5f3fgrwdtek55uoaq/cancel" }, "version": "d937c50007a1cacbdbd1e0a71ccaeac2b383a20f5579ea9b3f5ad46fcb2e2607" }Generated inUsing seed 944961095 Running simulation for led zeppelin Encoding text embeddings with led zeppelin dimensions Using aesthetic embedding 9 with weight 0.5 Using inpaint model but no image is provided. Initializing with zeros. Running diffusion... 0it [00:00, ?it/s] Timestep 0 - saving sample 0%| | 0/75 [00:00<?, ?it/s] 1it [00:03, 3.27s/it] 1%|▏ | 1/75 [00:03<04:02, 3.27s/it] 2it [00:05, 2.83s/it] 3%|▎ | 2/75 [00:05<03:26, 2.83s/it] 3it [00:08, 2.69s/it] 4%|▍ | 3/75 [00:08<03:13, 2.69s/it] 4it [00:08, 1.88s/it] 5%|▌ | 4/75 [00:08<02:13, 1.88s/it] 5it [00:09, 1.43s/it] 7%|▋ | 5/75 [00:09<01:39, 1.43s/it] 6it [00:10, 1.16s/it] 8%|▊ | 6/75 [00:10<01:19, 1.16s/it] 7it [00:10, 1.01it/s] 9%|▉ | 7/75 [00:10<01:07, 1.01it/s] 8it [00:11, 1.14it/s] 11%|█ | 8/75 [00:11<00:58, 1.14it/s] 9it [00:12, 1.25it/s] 12%|█▏ | 9/75 [00:12<00:52, 1.25it/s] 10it [00:12, 1.34it/s] Timestep 10 - saving sample 13%|█▎ | 10/75 [00:12<00:48, 1.34it/s] 11it [00:14, 1.08it/s] 15%|█▍ | 11/75 [00:14<00:59, 1.08it/s] 12it [00:14, 1.20it/s] 16%|█▌ | 12/75 [00:14<00:52, 1.20it/s] 13it [00:15, 1.29it/s] 17%|█▋ | 13/75 [00:15<00:48, 1.29it/s] 14it [00:15, 1.36it/s] 19%|█▊ | 14/75 [00:15<00:44, 1.36it/s] 15it [00:16, 1.42it/s] 20%|██ | 15/75 [00:16<00:42, 1.42it/s] 16it [00:17, 1.46it/s] 21%|██▏ | 16/75 [00:17<00:40, 1.46it/s] 17it [00:17, 1.49it/s] 23%|██▎ | 17/75 [00:17<00:38, 1.49it/s] 18it [00:18, 1.51it/s] 24%|██▍ | 18/75 [00:18<00:37, 1.51it/s] 19it [00:19, 1.53it/s] 25%|██▌ | 19/75 [00:19<00:36, 1.53it/s] 20it [00:19, 1.54it/s] Timestep 20 - saving sample 27%|██▋ | 20/75 [00:19<00:35, 1.54it/s] 21it [00:21, 1.17it/s] 28%|██▊ | 21/75 [00:21<00:46, 1.17it/s] 22it [00:21, 1.27it/s] 29%|██▉ | 22/75 [00:21<00:41, 1.27it/s] 23it [00:22, 1.35it/s] 31%|███ | 23/75 [00:22<00:38, 1.35it/s] 24it [00:23, 1.41it/s] 32%|███▏ | 24/75 [00:23<00:36, 1.41it/s] 25it [00:23, 1.45it/s] 33%|███▎ | 25/75 [00:23<00:34, 1.45it/s] 26it [00:24, 1.48it/s] 35%|███▍ | 26/75 [00:24<00:33, 1.48it/s] 27it [00:24, 1.51it/s] 36%|███▌ | 27/75 [00:24<00:31, 1.51it/s] 28it [00:25, 1.52it/s] 37%|███▋ | 28/75 [00:25<00:30, 1.52it/s] 29it [00:26, 1.53it/s] 39%|███▊ | 29/75 [00:26<00:30, 1.53it/s] 30it [00:26, 1.54it/s] Timestep 30 - saving sample 40%|████ | 30/75 [00:26<00:29, 1.54it/s] 31it [00:28, 1.16it/s] 41%|████▏ | 31/75 [00:28<00:37, 1.16it/s] 32it [00:28, 1.27it/s] 43%|████▎ | 32/75 [00:28<00:33, 1.27it/s] 33it [00:29, 1.34it/s] 44%|████▍ | 33/75 [00:29<00:31, 1.34it/s] 34it [00:30, 1.40it/s] 45%|████▌ | 34/75 [00:30<00:29, 1.40it/s] 35it [00:30, 1.44it/s] 47%|████▋ | 35/75 [00:30<00:27, 1.44it/s] 36it [00:31, 1.47it/s] 48%|████▊ | 36/75 [00:31<00:26, 1.47it/s] 37it [00:32, 1.49it/s] 49%|████▉ | 37/75 [00:32<00:25, 1.49it/s] 38it [00:32, 1.51it/s] 51%|█████ | 38/75 [00:32<00:24, 1.51it/s] 39it [00:33, 1.52it/s] 52%|█████▏ | 39/75 [00:33<00:23, 1.52it/s] 40it [00:34, 1.52it/s] Timestep 40 - saving sample 53%|█████▎ | 40/75 [00:34<00:22, 1.52it/s] 41it [00:35, 1.16it/s] 55%|█████▍ | 41/75 [00:35<00:29, 1.16it/s] 42it [00:36, 1.26it/s] 56%|█████▌ | 42/75 [00:36<00:26, 1.26it/s] 43it [00:36, 1.33it/s] 57%|█████▋ | 43/75 [00:36<00:24, 1.33it/s] 44it [00:37, 1.39it/s] 59%|█████▊ | 44/75 [00:37<00:22, 1.39it/s] 45it [00:37, 1.43it/s] 60%|██████ | 45/75 [00:37<00:20, 1.43it/s] 46it [00:38, 1.46it/s] 61%|██████▏ | 46/75 [00:38<00:19, 1.46it/s] 47it [00:39, 1.49it/s] 63%|██████▎ | 47/75 [00:39<00:18, 1.49it/s] 48it [00:39, 1.50it/s] 64%|██████▍ | 48/75 [00:39<00:17, 1.50it/s] 49it [00:40, 1.51it/s] 65%|██████▌ | 49/75 [00:40<00:17, 1.51it/s] 50it [00:41, 1.52it/s] Timestep 50 - saving sample 67%|██████▋ | 50/75 [00:41<00:16, 1.52it/s] 51it [00:42, 1.15it/s] 68%|██████▊ | 51/75 [00:42<00:20, 1.15it/s] 52it [00:43, 1.25it/s] 69%|██████▉ | 52/75 [00:43<00:18, 1.25it/s] 53it [00:43, 1.32it/s] 71%|███████ | 53/75 [00:43<00:16, 1.32it/s] 54it [00:44, 1.38it/s] 72%|███████▏ | 54/75 [00:44<00:15, 1.38it/s] 55it [00:45, 1.42it/s] 73%|███████▎ | 55/75 [00:45<00:14, 1.42it/s] 56it [00:45, 1.45it/s] 75%|███████▍ | 56/75 [00:45<00:13, 1.45it/s] 57it [00:46, 1.47it/s] 76%|███████▌ | 57/75 [00:46<00:12, 1.47it/s] 58it [00:47, 1.48it/s] 77%|███████▋ | 58/75 [00:47<00:11, 1.48it/s] 59it [00:47, 1.49it/s] 79%|███████▊ | 59/75 [00:47<00:10, 1.49it/s] 60it [00:48, 1.50it/s] Timestep 60 - saving sample 80%|████████ | 60/75 [00:48<00:10, 1.50it/s] 61it [00:49, 1.14it/s] 81%|████████▏ | 61/75 [00:49<00:12, 1.14it/s] 62it [00:50, 1.24it/s] 83%|████████▎ | 62/75 [00:50<00:10, 1.24it/s] 63it [00:51, 1.31it/s] 84%|████████▍ | 63/75 [00:51<00:09, 1.31it/s] 64it [00:51, 1.37it/s] 85%|████████▌ | 64/75 [00:51<00:08, 1.37it/s] 65it [00:52, 1.41it/s] 87%|████████▋ | 65/75 [00:52<00:07, 1.41it/s] 66it [00:53, 1.44it/s] 88%|████████▊ | 66/75 [00:53<00:06, 1.44it/s] 67it [00:53, 1.46it/s] 89%|████████▉ | 67/75 [00:53<00:05, 1.46it/s] 68it [00:54, 1.48it/s] 91%|█████████ | 68/75 [00:54<00:04, 1.48it/s] 69it [00:55, 1.49it/s] 92%|█████████▏| 69/75 [00:55<00:04, 1.49it/s] 70it [00:55, 1.49it/s] Timestep 70 - saving sample 93%|█████████▎| 70/75 [00:55<00:03, 1.49it/s] 71it [00:57, 1.14it/s] 95%|█████████▍| 71/75 [00:57<00:03, 1.14it/s] 72it [00:57, 1.23it/s] 96%|█████████▌| 72/75 [00:57<00:02, 1.23it/s] 73it [00:58, 1.30it/s] 97%|█████████▋| 73/75 [00:58<00:01, 1.30it/s] 74it [00:59, 1.35it/s] Timestep 74 - saving final sample 99%|█████████▊| 74/75 [00:59<00:00, 1.35it/s] 75it [01:00, 1.08it/s] 100%|██████████| 75/75 [01:00<00:00, 1.08it/s] 100%|██████████| 75/75 [01:00<00:00, 1.24it/s] 75it [01:00, 1.24it/s]Prediction
laion-ai/erlich:d937c50007a1cacbdbd1e0a71ccaeac2b383a20f5579ea9b3f5ad46fcb2e2607IDv2onktt7yjalpglsu4apogpcnmStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- "-1"
- steps
- "75"
- width
- "256"
- height
- "256"
- prompt
- pink floyd
- batch_size
- "6"
- guidance_scale
- "5"
- aesthetic_rating
- 9
- aesthetic_weight
- 0.5
{ "seed": "-1", "steps": "75", "width": "256", "height": "256", "prompt": "pink floyd", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.5 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run laion-ai/erlich using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "laion-ai/erlich:d937c50007a1cacbdbd1e0a71ccaeac2b383a20f5579ea9b3f5ad46fcb2e2607", { input: { seed: "-1", steps: "75", width: "256", height: "256", prompt: "pink floyd", batch_size: "6", guidance_scale: "5", aesthetic_rating: 9, aesthetic_weight: 0.5 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun laion-ai/erlich using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "laion-ai/erlich:d937c50007a1cacbdbd1e0a71ccaeac2b383a20f5579ea9b3f5ad46fcb2e2607", input={ "seed": "-1", "steps": "75", "width": "256", "height": "256", "prompt": "pink floyd", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.5 } ) # The laion-ai/erlich model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/laion-ai/erlich/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run laion-ai/erlich 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": "laion-ai/erlich:d937c50007a1cacbdbd1e0a71ccaeac2b383a20f5579ea9b3f5ad46fcb2e2607", "input": { "seed": "-1", "steps": "75", "width": "256", "height": "256", "prompt": "pink floyd", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.5 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-06-24T23:17:04.794145Z", "created_at": "2022-06-24T23:14:03.423862Z", "data_removed": false, "error": null, "id": "v2onktt7yjalpglsu4apogpcnm", "input": { "seed": "-1", "steps": "75", "width": "256", "height": "256", "prompt": "pink floyd", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.5 }, "logs": "Using seed 2401128644\nRunning simulation for pink floyd\nEncoding text embeddings with pink floyd dimensions\nUsing aesthetic embedding 9 with weight 0.5\nUsing inpaint model but no image is provided. Initializing with zeros.\nRunning diffusion...\n\n0it [00:00, ?it/s]\n\nTimestep 0 - saving sample\n 0%| | 0/75 [00:00<?, ?it/s]\u001b[A\n1it [00:03, 3.37s/it]\n\n 1%|▏ | 1/75 [00:03<04:09, 3.37s/it]\u001b[A\n2it [00:05, 2.90s/it]\n\n 3%|▎ | 2/75 [00:05<03:32, 2.90s/it]\u001b[A\n3it [00:08, 2.76s/it]\n\n 4%|▍ | 3/75 [00:08<03:18, 2.76s/it]\u001b[A\n4it [00:09, 1.93s/it]\n\n 5%|▌ | 4/75 [00:09<02:16, 1.93s/it]\u001b[A\n5it [00:09, 1.47s/it]\n\n 7%|▋ | 5/75 [00:09<01:42, 1.47s/it]\u001b[A\n6it [00:10, 1.19s/it]\n\n 8%|▊ | 6/75 [00:10<01:22, 1.19s/it]\u001b[A\n7it [00:11, 1.01s/it]\n\n 9%|▉ | 7/75 [00:11<01:08, 1.01s/it]\u001b[A\n8it [00:11, 1.11it/s]\n\n 11%|█ | 8/75 [00:11<01:00, 1.11it/s]\u001b[A\n9it [00:12, 1.22it/s]\n\n 12%|█▏ | 9/75 [00:12<00:54, 1.22it/s]\u001b[A\n10it [00:13, 1.30it/s]\n\nTimestep 10 - saving sample\n 13%|█▎ | 10/75 [00:13<00:49, 1.30it/s]\u001b[A\n11it [00:14, 1.05it/s]\n\n 15%|█▍ | 11/75 [00:14<01:00, 1.05it/s]\u001b[A\n12it [00:15, 1.17it/s]\n\n 16%|█▌ | 12/75 [00:15<00:53, 1.17it/s]\u001b[A\n13it [00:15, 1.26it/s]\n\n 17%|█▋ | 13/75 [00:15<00:49, 1.26it/s]\u001b[A\n14it [00:16, 1.33it/s]\n\n 19%|█▊ | 14/75 [00:16<00:45, 1.33it/s]\u001b[A\n15it [00:17, 1.38it/s]\n\n 20%|██ | 15/75 [00:17<00:43, 1.38it/s]\u001b[A\n16it [00:17, 1.42it/s]\n\n 21%|██▏ | 16/75 [00:17<00:41, 1.42it/s]\u001b[A\n17it [00:18, 1.45it/s]\n\n 23%|██▎ | 17/75 [00:18<00:40, 1.45it/s]\u001b[A\n18it [00:19, 1.47it/s]\n\n 24%|██▍ | 18/75 [00:19<00:38, 1.47it/s]\u001b[A\n19it [00:19, 1.48it/s]\n\n 25%|██▌ | 19/75 [00:19<00:37, 1.48it/s]\u001b[A\n20it [00:20, 1.49it/s]\n\nTimestep 20 - saving sample\n 27%|██▋ | 20/75 [00:20<00:36, 1.49it/s]\u001b[A\n21it [00:21, 1.14it/s]\n\n 28%|██▊ | 21/75 [00:21<00:47, 1.14it/s]\u001b[A\n22it [00:22, 1.24it/s]\n\n 29%|██▉ | 22/75 [00:22<00:42, 1.24it/s]\u001b[A\n23it [00:23, 1.31it/s]\n\n 31%|███ | 23/75 [00:23<00:39, 1.31it/s]\u001b[A\n24it [00:23, 1.37it/s]\n\n 32%|███▏ | 24/75 [00:23<00:37, 1.37it/s]\u001b[A\n25it [00:24, 1.41it/s]\n\n 33%|███▎ | 25/75 [00:24<00:35, 1.41it/s]\u001b[A\n26it [00:24, 1.44it/s]\n\n 35%|███▍ | 26/75 [00:24<00:34, 1.44it/s]\u001b[A\n27it [00:25, 1.46it/s]\n\n 36%|███▌ | 27/75 [00:25<00:32, 1.46it/s]\u001b[A\n28it [00:26, 1.48it/s]\n\n 37%|███▋ | 28/75 [00:26<00:31, 1.48it/s]\u001b[A\n29it [00:26, 1.49it/s]\n\n 39%|███▊ | 29/75 [00:26<00:30, 1.49it/s]\u001b[A\n30it [00:27, 1.50it/s]\n\nTimestep 30 - saving sample\n 40%|████ | 30/75 [00:27<00:30, 1.50it/s]\u001b[A\n31it [00:28, 1.14it/s]\n\n 41%|████▏ | 31/75 [00:28<00:38, 1.14it/s]\u001b[A\n32it [00:29, 1.23it/s]\n\n 43%|████▎ | 32/75 [00:29<00:34, 1.23it/s]\u001b[A\n33it [00:30, 1.30it/s]\n\n 44%|████▍ | 33/75 [00:30<00:32, 1.30it/s]\u001b[A\n34it [00:30, 1.36it/s]\n\n 45%|████▌ | 34/75 [00:30<00:30, 1.36it/s]\u001b[A\n35it [00:31, 1.40it/s]\n\n 47%|████▋ | 35/75 [00:31<00:28, 1.40it/s]\u001b[A\n36it [00:32, 1.43it/s]\n\n 48%|████▊ | 36/75 [00:32<00:27, 1.43it/s]\u001b[A\n37it [00:32, 1.45it/s]\n\n 49%|████▉ | 37/75 [00:32<00:26, 1.45it/s]\u001b[A\n38it [00:33, 1.47it/s]\n\n 51%|█████ | 38/75 [00:33<00:25, 1.47it/s]\u001b[A\n39it [00:34, 1.47it/s]\n\n 52%|█████▏ | 39/75 [00:34<00:24, 1.47it/s]\u001b[A\n40it [00:34, 1.48it/s]\n\nTimestep 40 - saving sample\n 53%|█████▎ | 40/75 [00:34<00:23, 1.48it/s]\u001b[A\n41it [00:36, 1.11it/s]\n\n 55%|█████▍ | 41/75 [00:36<00:30, 1.11it/s]\u001b[A\n42it [00:37, 1.21it/s]\n\n 56%|█████▌ | 42/75 [00:37<00:27, 1.21it/s]\u001b[A\n43it [00:37, 1.28it/s]\n\n 57%|█████▋ | 43/75 [00:37<00:25, 1.28it/s]\u001b[A\n44it [00:38, 1.33it/s]\n\n 59%|█████▊ | 44/75 [00:38<00:23, 1.33it/s]\u001b[A\n45it [00:39, 1.38it/s]\n\n 60%|██████ | 45/75 [00:39<00:21, 1.38it/s]\u001b[A\n46it [00:39, 1.41it/s]\n\n 61%|██████▏ | 46/75 [00:39<00:20, 1.41it/s]\u001b[A\n47it [00:40, 1.43it/s]\n\n 63%|██████▎ | 47/75 [00:40<00:19, 1.43it/s]\u001b[A\n48it [00:41, 1.45it/s]\n\n 64%|██████▍ | 48/75 [00:41<00:18, 1.45it/s]\u001b[A\n49it [00:41, 1.46it/s]\n\n 65%|██████▌ | 49/75 [00:41<00:17, 1.46it/s]\u001b[A\n50it [00:42, 1.46it/s]\n\nTimestep 50 - saving sample\n 67%|██████▋ | 50/75 [00:42<00:17, 1.46it/s]\u001b[A\n51it [00:44, 1.00s/it]\n\n 68%|██████▊ | 51/75 [00:44<00:24, 1.00s/it]\u001b[A\n52it [00:44, 1.12it/s]\n\n 69%|██████▉ | 52/75 [00:44<00:20, 1.12it/s]\u001b[A\n53it [00:45, 1.21it/s]\n\n 71%|███████ | 53/75 [00:45<00:18, 1.21it/s]\u001b[A\n54it [00:46, 1.28it/s]\n\n 72%|███████▏ | 54/75 [00:46<00:16, 1.28it/s]\u001b[A\n55it [00:46, 1.34it/s]\n\n 73%|███████▎ | 55/75 [00:46<00:14, 1.34it/s]\u001b[A\n56it [00:47, 1.38it/s]\n\n 75%|███████▍ | 56/75 [00:47<00:13, 1.38it/s]\u001b[A\n57it [00:48, 1.41it/s]\n\n 76%|███████▌ | 57/75 [00:48<00:12, 1.41it/s]\u001b[A\n58it [00:48, 1.43it/s]\n\n 77%|███████▋ | 58/75 [00:48<00:11, 1.43it/s]\u001b[A\n59it [00:49, 1.45it/s]\n\n 79%|███████▊ | 59/75 [00:49<00:11, 1.45it/s]\u001b[A\n60it [00:50, 1.46it/s]\n\nTimestep 60 - saving sample\n 80%|████████ | 60/75 [00:50<00:10, 1.46it/s]\u001b[A\n61it [00:51, 1.03it/s]\n\n 81%|████████▏ | 61/75 [00:51<00:13, 1.03it/s]\u001b[A\n62it [00:52, 1.14it/s]\n\n 83%|████████▎ | 62/75 [00:52<00:11, 1.14it/s]\u001b[A\n63it [00:53, 1.22it/s]\n\n 84%|████████▍ | 63/75 [00:53<00:09, 1.22it/s]\u001b[A\n64it [00:53, 1.28it/s]\n\n 85%|████████▌ | 64/75 [00:53<00:08, 1.28it/s]\u001b[A\n65it [00:54, 1.34it/s]\n\n 87%|████████▋ | 65/75 [00:54<00:07, 1.34it/s]\u001b[A\n66it [00:55, 1.37it/s]\n\n 88%|████████▊ | 66/75 [00:55<00:06, 1.37it/s]\u001b[A\n67it [00:55, 1.39it/s]\n\n 89%|████████▉ | 67/75 [00:55<00:05, 1.39it/s]\u001b[A\n68it [00:56, 1.42it/s]\n\n 91%|█████████ | 68/75 [00:56<00:04, 1.42it/s]\u001b[A\n69it [00:57, 1.43it/s]\n\n 92%|█████████▏| 69/75 [00:57<00:04, 1.43it/s]\u001b[A\n70it [00:57, 1.44it/s]\n\nTimestep 70 - saving sample\n 93%|█████████▎| 70/75 [00:57<00:03, 1.44it/s]\u001b[A\n71it [00:59, 1.02it/s]\n\n 95%|█████████▍| 71/75 [00:59<00:03, 1.02it/s]\u001b[A\n72it [01:00, 1.14it/s]\n\n 96%|█████████▌| 72/75 [01:00<00:02, 1.14it/s]\u001b[A\n73it [01:00, 1.20it/s]\n\n 97%|█████████▋| 73/75 [01:00<00:01, 1.20it/s]\u001b[A\n74it [01:01, 1.26it/s]\n\nTimestep 74 - saving final sample\n 99%|█████████▊| 74/75 [01:01<00:00, 1.26it/s]\u001b[A\n75it [01:03, 1.06s/it]\n\n100%|██████████| 75/75 [01:03<00:00, 1.06s/it]\u001b[A\n100%|██████████| 75/75 [01:03<00:00, 1.18it/s]\n\n75it [01:03, 1.18it/s]", "metrics": { "predict_time": 68.094431, "total_time": 181.370283 }, "output": [ [ "https://replicate.delivery/mgxm/db5f4b35-ec70-4659-b915-ed3994f15f5b/current_0.jpg", "https://replicate.delivery/mgxm/031638d7-e18d-4b9c-ab74-88b81a52f87d/current_1.jpg", "https://replicate.delivery/mgxm/42bc7b01-ec71-40e2-82ba-86430b43cc34/current_2.jpg", "https://replicate.delivery/mgxm/77295f24-2599-4b9e-a7c0-90b22d1d5f94/current_3.jpg", "https://replicate.delivery/mgxm/a9c9d85c-7643-40bb-b907-7974a0fd3254/current_4.jpg", "https://replicate.delivery/mgxm/ff59763d-2ec0-4cec-8b33-4b0a154ff7d6/current_5.jpg" ], [ "https://replicate.delivery/mgxm/ce97a926-ab91-45dd-82af-09869de41048/current_0.jpg", "https://replicate.delivery/mgxm/c16273ae-ce9c-44c9-b236-63fbae22aaca/current_1.jpg", "https://replicate.delivery/mgxm/e4419965-98d7-44cd-a24f-23a1986f2a75/current_2.jpg", "https://replicate.delivery/mgxm/62832539-8338-4e7a-b4ae-f45d7cf84555/current_3.jpg", "https://replicate.delivery/mgxm/9d4ac715-5fe2-4915-b76f-220238a2ecac/current_4.jpg", "https://replicate.delivery/mgxm/7596c11e-2b1e-4d85-b8e2-42142db4d2a9/current_5.jpg" ], [ "https://replicate.delivery/mgxm/d906a739-239a-4df8-9b90-5ed76cdd3df0/current_0.jpg", "https://replicate.delivery/mgxm/42dca56c-b4ff-4cce-b8d4-6fd56c3f10fd/current_1.jpg", "https://replicate.delivery/mgxm/8bdeba9c-9a99-4852-ba2a-606b6f03b72c/current_2.jpg", "https://replicate.delivery/mgxm/26342173-0d0e-4a0e-bb4e-e51c4dfc3926/current_3.jpg", "https://replicate.delivery/mgxm/da58b363-eecd-47f5-ae1e-7e9301970130/current_4.jpg", "https://replicate.delivery/mgxm/132c0086-560a-43f9-9dcd-bebff2733832/current_5.jpg" ], [ "https://replicate.delivery/mgxm/3320b654-4f60-4c42-a457-22398cb15f82/current_0.jpg", "https://replicate.delivery/mgxm/9f27a862-f46d-4d4e-8a69-f8ab89b92927/current_1.jpg", "https://replicate.delivery/mgxm/c86c8885-37f3-42d0-be32-9cd8d9bcde48/current_2.jpg", "https://replicate.delivery/mgxm/2f2c13e0-7c90-4c01-aa29-4f1c3d7f031e/current_3.jpg", "https://replicate.delivery/mgxm/5066a84f-8058-4cc8-ae48-cf6fe35df93c/current_4.jpg", "https://replicate.delivery/mgxm/c2f15f02-c3de-4711-982e-65347e3cc559/current_5.jpg" ], [ "https://replicate.delivery/mgxm/e46d2fff-66f5-473c-838c-2e816e3a18be/current_0.jpg", "https://replicate.delivery/mgxm/0561ae73-07d1-4203-b45e-76025909509c/current_1.jpg", "https://replicate.delivery/mgxm/7c3c5bb6-e0c3-4a61-9537-552a94176350/current_2.jpg", "https://replicate.delivery/mgxm/77bcf163-98b2-4ef4-b31d-684a652d92ed/current_3.jpg", "https://replicate.delivery/mgxm/1b8d3cdc-63e9-4707-a8df-a0af4a63e798/current_4.jpg", "https://replicate.delivery/mgxm/929d7ab3-97d9-4b50-b3ab-a35dde593af0/current_5.jpg" ], [ "https://replicate.delivery/mgxm/acb7bb06-6444-4352-b570-8d2a84b95442/current_0.jpg", "https://replicate.delivery/mgxm/5fb0e81d-feb9-4ad3-bfb3-140af12c5a83/current_1.jpg", "https://replicate.delivery/mgxm/70b7cb09-e777-41e2-ab3c-0d6cb429ff2e/current_2.jpg", "https://replicate.delivery/mgxm/740a1f1f-597e-4202-9fb5-1442e326b16d/current_3.jpg", "https://replicate.delivery/mgxm/ad78e4cc-1604-4523-a35b-f564313adb90/current_4.jpg", "https://replicate.delivery/mgxm/5cad09f2-8259-4ec8-9f1a-c32451f3ad2d/current_5.jpg" ], [ "https://replicate.delivery/mgxm/25932139-98f0-4a41-a5c6-d4246d6c8e6e/current_0.jpg", "https://replicate.delivery/mgxm/6318cb0e-8e7f-4687-9182-b51f8761d484/current_1.jpg", "https://replicate.delivery/mgxm/77a9daf1-215d-477d-b85f-c863ea622b9a/current_2.jpg", "https://replicate.delivery/mgxm/053e9908-fd68-4dbc-a232-7ffa5bdb5177/current_3.jpg", "https://replicate.delivery/mgxm/877084f3-eec4-44f6-b260-bff7d380cf55/current_4.jpg", "https://replicate.delivery/mgxm/14610c57-8793-4af2-939b-f42381a9d696/current_5.jpg" ], [ "https://replicate.delivery/mgxm/1017e565-c8ee-49bf-be72-e22b2a6caf17/current_0.jpg", "https://replicate.delivery/mgxm/781ad353-f9d5-414a-b8ae-98a91545dacb/current_1.jpg", "https://replicate.delivery/mgxm/24a1dfa5-badb-4691-b1e1-91a92bad990a/current_2.jpg", "https://replicate.delivery/mgxm/0fa7a63b-2c69-47a1-9ae7-e39b5ddcb279/current_3.jpg", "https://replicate.delivery/mgxm/9b6c2b82-94fd-44cf-82f8-97eb69129c18/current_4.jpg", "https://replicate.delivery/mgxm/fb97992a-3439-480c-9bf0-1d8e5f80fa5d/current_5.jpg" ], [ "https://replicate.delivery/mgxm/4355028d-e9a6-4471-8111-b98c510242bd/current_0.jpg", "https://replicate.delivery/mgxm/f9b14f53-9436-447b-847e-0019da6bfe34/current_1.jpg", "https://replicate.delivery/mgxm/2ff36ef0-30fb-466d-959d-6b0aa348b99f/current_2.jpg", "https://replicate.delivery/mgxm/2784e100-e50b-47b0-bfcf-5f23de2821a4/current_3.jpg", "https://replicate.delivery/mgxm/1573f979-53e4-49d4-9746-d1d3ad02f1c7/current_4.jpg", "https://replicate.delivery/mgxm/fba54869-9b0a-44eb-880b-33ddb7fe3501/current_5.jpg" ] ], "started_at": "2022-06-24T23:15:56.699714Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/v2onktt7yjalpglsu4apogpcnm", "cancel": "https://api.replicate.com/v1/predictions/v2onktt7yjalpglsu4apogpcnm/cancel" }, "version": "d937c50007a1cacbdbd1e0a71ccaeac2b383a20f5579ea9b3f5ad46fcb2e2607" }Generated inUsing seed 2401128644 Running simulation for pink floyd Encoding text embeddings with pink floyd dimensions Using aesthetic embedding 9 with weight 0.5 Using inpaint model but no image is provided. Initializing with zeros. Running diffusion... 0it [00:00, ?it/s] Timestep 0 - saving sample 0%| | 0/75 [00:00<?, ?it/s] 1it [00:03, 3.37s/it] 1%|▏ | 1/75 [00:03<04:09, 3.37s/it] 2it [00:05, 2.90s/it] 3%|▎ | 2/75 [00:05<03:32, 2.90s/it] 3it [00:08, 2.76s/it] 4%|▍ | 3/75 [00:08<03:18, 2.76s/it] 4it [00:09, 1.93s/it] 5%|▌ | 4/75 [00:09<02:16, 1.93s/it] 5it [00:09, 1.47s/it] 7%|▋ | 5/75 [00:09<01:42, 1.47s/it] 6it [00:10, 1.19s/it] 8%|▊ | 6/75 [00:10<01:22, 1.19s/it] 7it [00:11, 1.01s/it] 9%|▉ | 7/75 [00:11<01:08, 1.01s/it] 8it [00:11, 1.11it/s] 11%|█ | 8/75 [00:11<01:00, 1.11it/s] 9it [00:12, 1.22it/s] 12%|█▏ | 9/75 [00:12<00:54, 1.22it/s] 10it [00:13, 1.30it/s] Timestep 10 - saving sample 13%|█▎ | 10/75 [00:13<00:49, 1.30it/s] 11it [00:14, 1.05it/s] 15%|█▍ | 11/75 [00:14<01:00, 1.05it/s] 12it [00:15, 1.17it/s] 16%|█▌ | 12/75 [00:15<00:53, 1.17it/s] 13it [00:15, 1.26it/s] 17%|█▋ | 13/75 [00:15<00:49, 1.26it/s] 14it [00:16, 1.33it/s] 19%|█▊ | 14/75 [00:16<00:45, 1.33it/s] 15it [00:17, 1.38it/s] 20%|██ | 15/75 [00:17<00:43, 1.38it/s] 16it [00:17, 1.42it/s] 21%|██▏ | 16/75 [00:17<00:41, 1.42it/s] 17it [00:18, 1.45it/s] 23%|██▎ | 17/75 [00:18<00:40, 1.45it/s] 18it [00:19, 1.47it/s] 24%|██▍ | 18/75 [00:19<00:38, 1.47it/s] 19it [00:19, 1.48it/s] 25%|██▌ | 19/75 [00:19<00:37, 1.48it/s] 20it [00:20, 1.49it/s] Timestep 20 - saving sample 27%|██▋ | 20/75 [00:20<00:36, 1.49it/s] 21it [00:21, 1.14it/s] 28%|██▊ | 21/75 [00:21<00:47, 1.14it/s] 22it [00:22, 1.24it/s] 29%|██▉ | 22/75 [00:22<00:42, 1.24it/s] 23it [00:23, 1.31it/s] 31%|███ | 23/75 [00:23<00:39, 1.31it/s] 24it [00:23, 1.37it/s] 32%|███▏ | 24/75 [00:23<00:37, 1.37it/s] 25it [00:24, 1.41it/s] 33%|███▎ | 25/75 [00:24<00:35, 1.41it/s] 26it [00:24, 1.44it/s] 35%|███▍ | 26/75 [00:24<00:34, 1.44it/s] 27it [00:25, 1.46it/s] 36%|███▌ | 27/75 [00:25<00:32, 1.46it/s] 28it [00:26, 1.48it/s] 37%|███▋ | 28/75 [00:26<00:31, 1.48it/s] 29it [00:26, 1.49it/s] 39%|███▊ | 29/75 [00:26<00:30, 1.49it/s] 30it [00:27, 1.50it/s] Timestep 30 - saving sample 40%|████ | 30/75 [00:27<00:30, 1.50it/s] 31it [00:28, 1.14it/s] 41%|████▏ | 31/75 [00:28<00:38, 1.14it/s] 32it [00:29, 1.23it/s] 43%|████▎ | 32/75 [00:29<00:34, 1.23it/s] 33it [00:30, 1.30it/s] 44%|████▍ | 33/75 [00:30<00:32, 1.30it/s] 34it [00:30, 1.36it/s] 45%|████▌ | 34/75 [00:30<00:30, 1.36it/s] 35it [00:31, 1.40it/s] 47%|████▋ | 35/75 [00:31<00:28, 1.40it/s] 36it [00:32, 1.43it/s] 48%|████▊ | 36/75 [00:32<00:27, 1.43it/s] 37it [00:32, 1.45it/s] 49%|████▉ | 37/75 [00:32<00:26, 1.45it/s] 38it [00:33, 1.47it/s] 51%|█████ | 38/75 [00:33<00:25, 1.47it/s] 39it [00:34, 1.47it/s] 52%|█████▏ | 39/75 [00:34<00:24, 1.47it/s] 40it [00:34, 1.48it/s] Timestep 40 - saving sample 53%|█████▎ | 40/75 [00:34<00:23, 1.48it/s] 41it [00:36, 1.11it/s] 55%|█████▍ | 41/75 [00:36<00:30, 1.11it/s] 42it [00:37, 1.21it/s] 56%|█████▌ | 42/75 [00:37<00:27, 1.21it/s] 43it [00:37, 1.28it/s] 57%|█████▋ | 43/75 [00:37<00:25, 1.28it/s] 44it [00:38, 1.33it/s] 59%|█████▊ | 44/75 [00:38<00:23, 1.33it/s] 45it [00:39, 1.38it/s] 60%|██████ | 45/75 [00:39<00:21, 1.38it/s] 46it [00:39, 1.41it/s] 61%|██████▏ | 46/75 [00:39<00:20, 1.41it/s] 47it [00:40, 1.43it/s] 63%|██████▎ | 47/75 [00:40<00:19, 1.43it/s] 48it [00:41, 1.45it/s] 64%|██████▍ | 48/75 [00:41<00:18, 1.45it/s] 49it [00:41, 1.46it/s] 65%|██████▌ | 49/75 [00:41<00:17, 1.46it/s] 50it [00:42, 1.46it/s] Timestep 50 - saving sample 67%|██████▋ | 50/75 [00:42<00:17, 1.46it/s] 51it [00:44, 1.00s/it] 68%|██████▊ | 51/75 [00:44<00:24, 1.00s/it] 52it [00:44, 1.12it/s] 69%|██████▉ | 52/75 [00:44<00:20, 1.12it/s] 53it [00:45, 1.21it/s] 71%|███████ | 53/75 [00:45<00:18, 1.21it/s] 54it [00:46, 1.28it/s] 72%|███████▏ | 54/75 [00:46<00:16, 1.28it/s] 55it [00:46, 1.34it/s] 73%|███████▎ | 55/75 [00:46<00:14, 1.34it/s] 56it [00:47, 1.38it/s] 75%|███████▍ | 56/75 [00:47<00:13, 1.38it/s] 57it [00:48, 1.41it/s] 76%|███████▌ | 57/75 [00:48<00:12, 1.41it/s] 58it [00:48, 1.43it/s] 77%|███████▋ | 58/75 [00:48<00:11, 1.43it/s] 59it [00:49, 1.45it/s] 79%|███████▊ | 59/75 [00:49<00:11, 1.45it/s] 60it [00:50, 1.46it/s] Timestep 60 - saving sample 80%|████████ | 60/75 [00:50<00:10, 1.46it/s] 61it [00:51, 1.03it/s] 81%|████████▏ | 61/75 [00:51<00:13, 1.03it/s] 62it [00:52, 1.14it/s] 83%|████████▎ | 62/75 [00:52<00:11, 1.14it/s] 63it [00:53, 1.22it/s] 84%|████████▍ | 63/75 [00:53<00:09, 1.22it/s] 64it [00:53, 1.28it/s] 85%|████████▌ | 64/75 [00:53<00:08, 1.28it/s] 65it [00:54, 1.34it/s] 87%|████████▋ | 65/75 [00:54<00:07, 1.34it/s] 66it [00:55, 1.37it/s] 88%|████████▊ | 66/75 [00:55<00:06, 1.37it/s] 67it [00:55, 1.39it/s] 89%|████████▉ | 67/75 [00:55<00:05, 1.39it/s] 68it [00:56, 1.42it/s] 91%|█████████ | 68/75 [00:56<00:04, 1.42it/s] 69it [00:57, 1.43it/s] 92%|█████████▏| 69/75 [00:57<00:04, 1.43it/s] 70it [00:57, 1.44it/s] Timestep 70 - saving sample 93%|█████████▎| 70/75 [00:57<00:03, 1.44it/s] 71it [00:59, 1.02it/s] 95%|█████████▍| 71/75 [00:59<00:03, 1.02it/s] 72it [01:00, 1.14it/s] 96%|█████████▌| 72/75 [01:00<00:02, 1.14it/s] 73it [01:00, 1.20it/s] 97%|█████████▋| 73/75 [01:00<00:01, 1.20it/s] 74it [01:01, 1.26it/s] Timestep 74 - saving final sample 99%|█████████▊| 74/75 [01:01<00:00, 1.26it/s] 75it [01:03, 1.06s/it] 100%|██████████| 75/75 [01:03<00:00, 1.06s/it] 100%|██████████| 75/75 [01:03<00:00, 1.18it/s] 75it [01:03, 1.18it/s]Prediction
laion-ai/erlich:548faf80d48dec72e11457b7368015ed9232f66d6e4ee2619de2c871a8085449IDgpzwvhiwhbelpml3lpgdtarcmyStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- "-1"
- steps
- "50"
- width
- "256"
- height
- "256"
- prompt
- low poly "laion" logo of a lion-symbol. psychedlic colors.
- batch_size
- "4"
- guidance_scale
- "5"
- aesthetic_rating
- 9
- aesthetic_weight
- 0.1
{ "seed": "-1", "steps": "50", "width": "256", "height": "256", "prompt": "low poly \"laion\" logo of a lion-symbol. psychedlic colors.", "batch_size": "4", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.1 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run laion-ai/erlich using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "laion-ai/erlich:548faf80d48dec72e11457b7368015ed9232f66d6e4ee2619de2c871a8085449", { input: { seed: "-1", steps: "50", width: "256", height: "256", prompt: "low poly \"laion\" logo of a lion-symbol. psychedlic colors.", batch_size: "4", guidance_scale: "5", aesthetic_rating: 9, aesthetic_weight: 0.1 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun laion-ai/erlich using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "laion-ai/erlich:548faf80d48dec72e11457b7368015ed9232f66d6e4ee2619de2c871a8085449", input={ "seed": "-1", "steps": "50", "width": "256", "height": "256", "prompt": "low poly \"laion\" logo of a lion-symbol. psychedlic colors.", "batch_size": "4", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.1 } ) # The laion-ai/erlich model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/laion-ai/erlich/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run laion-ai/erlich 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": "laion-ai/erlich:548faf80d48dec72e11457b7368015ed9232f66d6e4ee2619de2c871a8085449", "input": { "seed": "-1", "steps": "50", "width": "256", "height": "256", "prompt": "low poly \\"laion\\" logo of a lion-symbol. psychedlic colors.", "batch_size": "4", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.1 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-06-28T15:12:36.833287Z", "created_at": "2022-06-28T15:12:20.418454Z", "data_removed": false, "error": null, "id": "gpzwvhiwhbelpml3lpgdtarcmy", "input": { "seed": "-1", "steps": "50", "width": "256", "height": "256", "prompt": "low poly \"laion\" logo of a lion-symbol. psychedlic colors.", "batch_size": "4", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.1 }, "logs": "Using seed 3576313590\nRunning simulation for low poly \"laion\" logo of a lion-symbol. psychedlic colors.\nEncoding text embeddings with low poly \"laion\" logo of a lion-symbol. psychedlic colors. dimensions\nUsing aesthetic embedding 9 with weight 0.1\nUsing inpaint model but no image is provided. Initializing with zeros.\nRunning diffusion...\n\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:45, 1.07it/s]\n 4%|▍ | 2/50 [00:01<00:43, 1.11it/s]\n 6%|▌ | 3/50 [00:02<00:41, 1.12it/s]\n 8%|▊ | 4/50 [00:02<00:28, 1.60it/s]\n 10%|█ | 5/50 [00:03<00:21, 2.09it/s]\n 12%|█▏ | 6/50 [00:03<00:17, 2.56it/s]\n 14%|█▍ | 7/50 [00:03<00:14, 2.99it/s]\n 16%|█▌ | 8/50 [00:03<00:12, 3.35it/s]\n 18%|█▊ | 9/50 [00:04<00:11, 3.65it/s]\n 20%|██ | 10/50 [00:04<00:10, 3.87it/s]\n 22%|██▏ | 11/50 [00:04<00:09, 4.05it/s]\n 24%|██▍ | 12/50 [00:04<00:09, 4.19it/s]\n 26%|██▌ | 13/50 [00:04<00:08, 4.29it/s]\n 28%|██▊ | 14/50 [00:05<00:08, 4.37it/s]\n 30%|███ | 15/50 [00:05<00:07, 4.42it/s]\n 32%|███▏ | 16/50 [00:05<00:07, 4.46it/s]\n 34%|███▍ | 17/50 [00:05<00:07, 4.49it/s]\n 36%|███▌ | 18/50 [00:05<00:07, 4.49it/s]\n 38%|███▊ | 19/50 [00:06<00:06, 4.49it/s]\n 40%|████ | 20/50 [00:06<00:06, 4.49it/s]\n 42%|████▏ | 21/50 [00:06<00:06, 4.51it/s]\n 44%|████▍ | 22/50 [00:06<00:06, 4.50it/s]\n 46%|████▌ | 23/50 [00:07<00:05, 4.50it/s]\n 48%|████▊ | 24/50 [00:07<00:05, 4.49it/s]\n 50%|█████ | 25/50 [00:07<00:05, 4.49it/s]\n 52%|█████▏ | 26/50 [00:07<00:05, 4.50it/s]\n 54%|█████▍ | 27/50 [00:07<00:05, 4.50it/s]\n 56%|█████▌ | 28/50 [00:08<00:04, 4.50it/s]\n 58%|█████▊ | 29/50 [00:08<00:04, 4.49it/s]\n 60%|██████ | 30/50 [00:08<00:04, 4.49it/s]\n 62%|██████▏ | 31/50 [00:08<00:04, 4.49it/s]\n 64%|██████▍ | 32/50 [00:09<00:04, 4.49it/s]\n 66%|██████▌ | 33/50 [00:09<00:03, 4.49it/s]\n 68%|██████▊ | 34/50 [00:09<00:03, 4.48it/s]\n 70%|███████ | 35/50 [00:09<00:03, 4.47it/s]\n 72%|███████▏ | 36/50 [00:10<00:03, 4.47it/s]\n 74%|███████▍ | 37/50 [00:10<00:02, 4.48it/s]\n 76%|███████▌ | 38/50 [00:10<00:02, 4.48it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 4.48it/s]\n 80%|████████ | 40/50 [00:10<00:02, 4.48it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 4.49it/s]\n 84%|████████▍ | 42/50 [00:11<00:01, 4.48it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 4.48it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 4.47it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 4.46it/s]\n 92%|█████████▏| 46/50 [00:12<00:00, 4.46it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 4.46it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 4.46it/s]\nSaving final sample/s\n 98%|█████████▊| 49/50 [00:12<00:00, 4.46it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.01it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.71it/s]", "metrics": { "predict_time": 16.233225, "total_time": 16.414833 }, "output": [ [ "https://replicate.delivery/mgxm/40bce360-eb75-44c9-b8e7-217fe0f33232/current_0.png", "https://replicate.delivery/mgxm/67f76b4e-51d4-4f4a-8773-38ccdee9dfb8/current_1.png", "https://replicate.delivery/mgxm/3fd90365-5592-463c-80d6-c51394fc0cc3/current_2.png", "https://replicate.delivery/mgxm/2ef9cafb-d92f-4154-b2be-69f4a9bec09a/current_3.png" ] ], "started_at": "2022-06-28T15:12:20.600062Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/gpzwvhiwhbelpml3lpgdtarcmy", "cancel": "https://api.replicate.com/v1/predictions/gpzwvhiwhbelpml3lpgdtarcmy/cancel" }, "version": "548faf80d48dec72e11457b7368015ed9232f66d6e4ee2619de2c871a8085449" }Generated inUsing seed 3576313590 Running simulation for low poly "laion" logo of a lion-symbol. psychedlic colors. Encoding text embeddings with low poly "laion" logo of a lion-symbol. psychedlic colors. dimensions Using aesthetic embedding 9 with weight 0.1 Using inpaint model but no image is provided. Initializing with zeros. Running diffusion... 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:45, 1.07it/s] 4%|▍ | 2/50 [00:01<00:43, 1.11it/s] 6%|▌ | 3/50 [00:02<00:41, 1.12it/s] 8%|▊ | 4/50 [00:02<00:28, 1.60it/s] 10%|█ | 5/50 [00:03<00:21, 2.09it/s] 12%|█▏ | 6/50 [00:03<00:17, 2.56it/s] 14%|█▍ | 7/50 [00:03<00:14, 2.99it/s] 16%|█▌ | 8/50 [00:03<00:12, 3.35it/s] 18%|█▊ | 9/50 [00:04<00:11, 3.65it/s] 20%|██ | 10/50 [00:04<00:10, 3.87it/s] 22%|██▏ | 11/50 [00:04<00:09, 4.05it/s] 24%|██▍ | 12/50 [00:04<00:09, 4.19it/s] 26%|██▌ | 13/50 [00:04<00:08, 4.29it/s] 28%|██▊ | 14/50 [00:05<00:08, 4.37it/s] 30%|███ | 15/50 [00:05<00:07, 4.42it/s] 32%|███▏ | 16/50 [00:05<00:07, 4.46it/s] 34%|███▍ | 17/50 [00:05<00:07, 4.49it/s] 36%|███▌ | 18/50 [00:05<00:07, 4.49it/s] 38%|███▊ | 19/50 [00:06<00:06, 4.49it/s] 40%|████ | 20/50 [00:06<00:06, 4.49it/s] 42%|████▏ | 21/50 [00:06<00:06, 4.51it/s] 44%|████▍ | 22/50 [00:06<00:06, 4.50it/s] 46%|████▌ | 23/50 [00:07<00:05, 4.50it/s] 48%|████▊ | 24/50 [00:07<00:05, 4.49it/s] 50%|█████ | 25/50 [00:07<00:05, 4.49it/s] 52%|█████▏ | 26/50 [00:07<00:05, 4.50it/s] 54%|█████▍ | 27/50 [00:07<00:05, 4.50it/s] 56%|█████▌ | 28/50 [00:08<00:04, 4.50it/s] 58%|█████▊ | 29/50 [00:08<00:04, 4.49it/s] 60%|██████ | 30/50 [00:08<00:04, 4.49it/s] 62%|██████▏ | 31/50 [00:08<00:04, 4.49it/s] 64%|██████▍ | 32/50 [00:09<00:04, 4.49it/s] 66%|██████▌ | 33/50 [00:09<00:03, 4.49it/s] 68%|██████▊ | 34/50 [00:09<00:03, 4.48it/s] 70%|███████ | 35/50 [00:09<00:03, 4.47it/s] 72%|███████▏ | 36/50 [00:10<00:03, 4.47it/s] 74%|███████▍ | 37/50 [00:10<00:02, 4.48it/s] 76%|███████▌ | 38/50 [00:10<00:02, 4.48it/s] 78%|███████▊ | 39/50 [00:10<00:02, 4.48it/s] 80%|████████ | 40/50 [00:10<00:02, 4.48it/s] 82%|████████▏ | 41/50 [00:11<00:02, 4.49it/s] 84%|████████▍ | 42/50 [00:11<00:01, 4.48it/s] 86%|████████▌ | 43/50 [00:11<00:01, 4.48it/s] 88%|████████▊ | 44/50 [00:11<00:01, 4.47it/s] 90%|█████████ | 45/50 [00:12<00:01, 4.46it/s] 92%|█████████▏| 46/50 [00:12<00:00, 4.46it/s] 94%|█████████▍| 47/50 [00:12<00:00, 4.46it/s] 96%|█████████▌| 48/50 [00:12<00:00, 4.46it/s] Saving final sample/s 98%|█████████▊| 49/50 [00:12<00:00, 4.46it/s] 100%|██████████| 50/50 [00:13<00:00, 3.01it/s] 100%|██████████| 50/50 [00:13<00:00, 3.71it/s]Prediction
laion-ai/erlich:a51ce279c0131991c5a143a9c6a3ec6de146e765d9311cff7435b1db1190faaaID2d56rgiq4vcphcvzi6xeercinyStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- "-1"
- steps
- "75"
- width
- "256"
- height
- "256"
- prompt
- stranger things
- batch_size
- "6"
- guidance_scale
- "5"
- aesthetic_rating
- 9
- aesthetic_weight
- 0.5
{ "seed": "-1", "steps": "75", "width": "256", "height": "256", "prompt": "stranger things", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.5 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run laion-ai/erlich using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "laion-ai/erlich:a51ce279c0131991c5a143a9c6a3ec6de146e765d9311cff7435b1db1190faaa", { input: { seed: "-1", steps: "75", width: "256", height: "256", prompt: "stranger things", batch_size: "6", guidance_scale: "5", aesthetic_rating: 9, aesthetic_weight: 0.5 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun laion-ai/erlich using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "laion-ai/erlich:a51ce279c0131991c5a143a9c6a3ec6de146e765d9311cff7435b1db1190faaa", input={ "seed": "-1", "steps": "75", "width": "256", "height": "256", "prompt": "stranger things", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.5 } ) # The laion-ai/erlich model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/laion-ai/erlich/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run laion-ai/erlich 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": "laion-ai/erlich:a51ce279c0131991c5a143a9c6a3ec6de146e765d9311cff7435b1db1190faaa", "input": { "seed": "-1", "steps": "75", "width": "256", "height": "256", "prompt": "stranger things", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.5 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-10T10:30:50.098614Z", "created_at": "2022-07-10T10:30:17.358982Z", "data_removed": false, "error": null, "id": "2d56rgiq4vcphcvzi6xeerciny", "input": { "seed": "-1", "steps": "75", "width": "256", "height": "256", "prompt": "stranger things", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.5 }, "logs": "Using seed 3269048179\nRunning simulation for stranger things\nEncoding text embeddings with stranger things dimensions\nUsing aesthetic embedding 9 with weight 0.5\nRunning diffusion...\n\n 0%| | 0/75 [00:00<?, ?it/s]\n 1%|▏ | 1/75 [00:01<01:33, 1.26s/it]\n 3%|▎ | 2/75 [00:02<01:30, 1.25s/it]\n 4%|▍ | 3/75 [00:03<01:29, 1.24s/it]\n 5%|▌ | 4/75 [00:04<01:01, 1.15it/s]\n 7%|▋ | 5/75 [00:04<00:47, 1.49it/s]\n 8%|▊ | 6/75 [00:04<00:37, 1.82it/s]\n 9%|▉ | 7/75 [00:04<00:31, 2.13it/s]\n 11%|█ | 8/75 [00:05<00:28, 2.37it/s]\n 12%|█▏ | 9/75 [00:05<00:25, 2.58it/s]\n 13%|█▎ | 10/75 [00:05<00:23, 2.74it/s]\n 15%|█▍ | 11/75 [00:06<00:22, 2.86it/s]\n 16%|█▌ | 12/75 [00:06<00:21, 2.94it/s]\n 17%|█▋ | 13/75 [00:06<00:20, 3.01it/s]\n 19%|█▊ | 14/75 [00:07<00:19, 3.06it/s]\n 20%|██ | 15/75 [00:07<00:19, 3.07it/s]\n 21%|██▏ | 16/75 [00:07<00:19, 3.09it/s]\n 23%|██▎ | 17/75 [00:08<00:18, 3.12it/s]\n 24%|██▍ | 18/75 [00:08<00:18, 3.11it/s]\n 25%|██▌ | 19/75 [00:08<00:17, 3.12it/s]\n 27%|██▋ | 20/75 [00:09<00:17, 3.12it/s]\n 28%|██▊ | 21/75 [00:09<00:17, 3.14it/s]\n 29%|██▉ | 22/75 [00:09<00:17, 3.11it/s]\n 31%|███ | 23/75 [00:10<00:16, 3.12it/s]\n 32%|███▏ | 24/75 [00:10<00:16, 3.11it/s]\n 33%|███▎ | 25/75 [00:10<00:16, 3.09it/s]\n 35%|███▍ | 26/75 [00:11<00:15, 3.10it/s]\n 36%|███▌ | 27/75 [00:11<00:15, 3.09it/s]\n 37%|███▋ | 28/75 [00:11<00:15, 3.08it/s]\n 39%|███▊ | 29/75 [00:12<00:14, 3.08it/s]\n 40%|████ | 30/75 [00:12<00:14, 3.08it/s]\n 41%|████▏ | 31/75 [00:12<00:14, 3.07it/s]\n 43%|████▎ | 32/75 [00:12<00:14, 3.06it/s]\n 44%|████▍ | 33/75 [00:13<00:13, 3.07it/s]\n 45%|████▌ | 34/75 [00:13<00:13, 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2.97it/s]\n 76%|███████▌ | 57/75 [00:21<00:06, 2.96it/s]\n 77%|███████▋ | 58/75 [00:21<00:05, 2.97it/s]\n 79%|███████▊ | 59/75 [00:22<00:05, 2.95it/s]\n 80%|████████ | 60/75 [00:22<00:05, 2.94it/s]\n 81%|████████▏ | 61/75 [00:22<00:04, 2.95it/s]\n 83%|████████▎ | 62/75 [00:23<00:04, 2.95it/s]\n 84%|████████▍ | 63/75 [00:23<00:04, 2.94it/s]\n 85%|████████▌ | 64/75 [00:23<00:03, 2.95it/s]\n 87%|████████▋ | 65/75 [00:24<00:03, 2.94it/s]\n 88%|████████▊ | 66/75 [00:24<00:03, 2.93it/s]\n 89%|████████▉ | 67/75 [00:24<00:02, 2.93it/s]\n 91%|█████████ | 68/75 [00:25<00:02, 2.94it/s]\n 92%|█████████▏| 69/75 [00:25<00:02, 2.93it/s]\n 93%|█████████▎| 70/75 [00:25<00:01, 2.93it/s]\n 95%|█████████▍| 71/75 [00:26<00:01, 2.93it/s]\n 96%|█████████▌| 72/75 [00:26<00:01, 2.93it/s]\n 97%|█████████▋| 73/75 [00:26<00:00, 2.94it/s]\n 99%|█████████▊| 74/75 [00:27<00:00, 2.94it/s]\n100%|██████████| 75/75 [00:27<00:00, 2.93it/s]\n100%|██████████| 75/75 [00:27<00:00, 2.73it/s]\nSaving final sample/s", "metrics": { "predict_time": 32.566438, "total_time": 32.739632 }, "output": [ [ "https://replicate.delivery/mgxm/3b5a16cc-52e4-47fb-ac9b-c945c60b14d6/current_0.png", "https://replicate.delivery/mgxm/6678501a-2111-47e7-924c-981497bd7a6d/current_1.png", "https://replicate.delivery/mgxm/ff3c2321-566e-4bd2-8fec-d943eadc58dd/current_2.png", "https://replicate.delivery/mgxm/36b570a4-8859-423d-9175-3b0b6920d8da/current_3.png", "https://replicate.delivery/mgxm/8075cd49-2169-444a-a346-c9d1b6a49f54/current_4.png", "https://replicate.delivery/mgxm/8d77d879-4084-4204-a9e9-a98306aeb251/current_5.png" ] ], "started_at": "2022-07-10T10:30:17.532176Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/2d56rgiq4vcphcvzi6xeerciny", "cancel": "https://api.replicate.com/v1/predictions/2d56rgiq4vcphcvzi6xeerciny/cancel" }, "version": "a51ce279c0131991c5a143a9c6a3ec6de146e765d9311cff7435b1db1190faaa" }Generated inUsing seed 3269048179 Running simulation for stranger things Encoding text embeddings with stranger things dimensions Using aesthetic embedding 9 with weight 0.5 Running diffusion... 0%| | 0/75 [00:00<?, ?it/s] 1%|▏ | 1/75 [00:01<01:33, 1.26s/it] 3%|▎ | 2/75 [00:02<01:30, 1.25s/it] 4%|▍ | 3/75 [00:03<01:29, 1.24s/it] 5%|▌ | 4/75 [00:04<01:01, 1.15it/s] 7%|▋ | 5/75 [00:04<00:47, 1.49it/s] 8%|▊ | 6/75 [00:04<00:37, 1.82it/s] 9%|▉ | 7/75 [00:04<00:31, 2.13it/s] 11%|█ | 8/75 [00:05<00:28, 2.37it/s] 12%|█▏ | 9/75 [00:05<00:25, 2.58it/s] 13%|█▎ | 10/75 [00:05<00:23, 2.74it/s] 15%|█▍ | 11/75 [00:06<00:22, 2.86it/s] 16%|█▌ | 12/75 [00:06<00:21, 2.94it/s] 17%|█▋ | 13/75 [00:06<00:20, 3.01it/s] 19%|█▊ | 14/75 [00:07<00:19, 3.06it/s] 20%|██ | 15/75 [00:07<00:19, 3.07it/s] 21%|██▏ | 16/75 [00:07<00:19, 3.09it/s] 23%|██▎ | 17/75 [00:08<00:18, 3.12it/s] 24%|██▍ | 18/75 [00:08<00:18, 3.11it/s] 25%|██▌ | 19/75 [00:08<00:17, 3.12it/s] 27%|██▋ | 20/75 [00:09<00:17, 3.12it/s] 28%|██▊ | 21/75 [00:09<00:17, 3.14it/s] 29%|██▉ | 22/75 [00:09<00:17, 3.11it/s] 31%|███ | 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92%|█████████▏| 69/75 [00:25<00:02, 2.93it/s] 93%|█████████▎| 70/75 [00:25<00:01, 2.93it/s] 95%|█████████▍| 71/75 [00:26<00:01, 2.93it/s] 96%|█████████▌| 72/75 [00:26<00:01, 2.93it/s] 97%|█████████▋| 73/75 [00:26<00:00, 2.94it/s] 99%|█████████▊| 74/75 [00:27<00:00, 2.94it/s] 100%|██████████| 75/75 [00:27<00:00, 2.93it/s] 100%|██████████| 75/75 [00:27<00:00, 2.73it/s] Saving final sample/sPrediction
laion-ai/erlich:a51ce279c0131991c5a143a9c6a3ec6de146e765d9311cff7435b1db1190faaaID6ur4mbq6bvgq7ahfr6yiywsqbiStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- "-1"
- steps
- "100"
- width
- "256"
- height
- "256"
- prompt
- stranger things
- batch_size
- "6"
- guidance_scale
- "5"
- aesthetic_rating
- 9
- aesthetic_weight
- 0.1
{ "seed": "-1", "steps": "100", "width": "256", "height": "256", "prompt": "stranger things", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.1 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run laion-ai/erlich using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "laion-ai/erlich:a51ce279c0131991c5a143a9c6a3ec6de146e765d9311cff7435b1db1190faaa", { input: { seed: "-1", steps: "100", width: "256", height: "256", prompt: "stranger things", batch_size: "6", guidance_scale: "5", aesthetic_rating: 9, aesthetic_weight: 0.1 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun laion-ai/erlich using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "laion-ai/erlich:a51ce279c0131991c5a143a9c6a3ec6de146e765d9311cff7435b1db1190faaa", input={ "seed": "-1", "steps": "100", "width": "256", "height": "256", "prompt": "stranger things", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.1 } ) # The laion-ai/erlich model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/laion-ai/erlich/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run laion-ai/erlich 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": "laion-ai/erlich:a51ce279c0131991c5a143a9c6a3ec6de146e765d9311cff7435b1db1190faaa", "input": { "seed": "-1", "steps": "100", "width": "256", "height": "256", "prompt": "stranger things", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.1 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-10T10:32:07.773099Z", "created_at": "2022-07-10T10:31:27.474112Z", "data_removed": false, "error": null, "id": "6ur4mbq6bvgq7ahfr6yiywsqbi", "input": { "seed": "-1", "steps": "100", "width": "256", "height": "256", "prompt": "stranger things", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.1 }, "logs": "Using seed 2877705998\nRunning simulation for stranger things\nEncoding text embeddings with stranger things dimensions\nUsing aesthetic embedding 9 with weight 0.1\nRunning diffusion...\n\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:01<02:03, 1.25s/it]\n 2%|▏ | 2/100 [00:02<02:00, 1.23s/it]\n 3%|▎ | 3/100 [00:03<01:59, 1.23s/it]\n 4%|▍ | 4/100 [00:04<01:23, 1.16it/s]\n 5%|▌ | 5/100 [00:04<01:03, 1.50it/s]\n 6%|▌ | 6/100 [00:04<00:50, 1.84it/s]\n 7%|▋ | 7/100 [00:04<00:43, 2.15it/s]\n 8%|▊ | 8/100 [00:05<00:38, 2.41it/s]\n 9%|▉ | 9/100 [00:05<00:34, 2.61it/s]\n 10%|█ | 10/100 [00:05<00:32, 2.78it/s]\n 11%|█ | 11/100 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2.99it/s]\n 99%|█████████▉| 99/100 [00:34<00:00, 3.00it/s]\n100%|██████████| 100/100 [00:35<00:00, 3.00it/s]\n100%|██████████| 100/100 [00:35<00:00, 2.84it/s]\nSaving final sample/s", "metrics": { "predict_time": 40.089363, "total_time": 40.298987 }, "output": [ [ "https://replicate.delivery/mgxm/18798d43-04ea-4a39-ae1d-9e1bcee8bf89/current_0.png", "https://replicate.delivery/mgxm/5c6a268e-950c-4f71-acdc-07aebd5fdad5/current_1.png", "https://replicate.delivery/mgxm/f8154499-129a-4919-bc9c-8f65a8b32a3e/current_2.png", "https://replicate.delivery/mgxm/d8a9bbd1-e184-4148-9737-70dc0c2c1fd3/current_3.png", "https://replicate.delivery/mgxm/e10d03c1-3cd2-4f83-b644-78900be39631/current_4.png", "https://replicate.delivery/mgxm/e37899be-7c9e-4083-a68b-e1c92233fe0b/current_5.png" ] ], "started_at": "2022-07-10T10:31:27.683736Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6ur4mbq6bvgq7ahfr6yiywsqbi", "cancel": "https://api.replicate.com/v1/predictions/6ur4mbq6bvgq7ahfr6yiywsqbi/cancel" }, "version": "a51ce279c0131991c5a143a9c6a3ec6de146e765d9311cff7435b1db1190faaa" }Generated inUsing seed 2877705998 Running simulation for stranger things Encoding text embeddings with stranger things dimensions Using aesthetic embedding 9 with weight 0.1 Running diffusion... 0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:01<02:03, 1.25s/it] 2%|▏ | 2/100 [00:02<02:00, 1.23s/it] 3%|▎ | 3/100 [00:03<01:59, 1.23s/it] 4%|▍ | 4/100 [00:04<01:23, 1.16it/s] 5%|▌ | 5/100 [00:04<01:03, 1.50it/s] 6%|▌ | 6/100 [00:04<00:50, 1.84it/s] 7%|▋ | 7/100 [00:04<00:43, 2.15it/s] 8%|▊ | 8/100 [00:05<00:38, 2.41it/s] 9%|▉ | 9/100 [00:05<00:34, 2.61it/s] 10%|█ | 10/100 [00:05<00:32, 2.78it/s] 11%|█ | 11/100 [00:06<00:30, 2.91it/s] 12%|█▏ | 12/100 [00:06<00:29, 3.00it/s] 13%|█▎ | 13/100 [00:06<00:28, 3.06it/s] 14%|█▍ | 14/100 [00:07<00:27, 3.11it/s] 15%|█▌ | 15/100 [00:07<00:27, 3.15it/s] 16%|█▌ | 16/100 [00:07<00:26, 3.16it/s] 17%|█▋ 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laion-ai/erlich:a51ce279c0131991c5a143a9c6a3ec6de146e765d9311cff7435b1db1190faaaInput
- seed
- "-1"
- steps
- "100"
- width
- "256"
- height
- "256"
- prompt
- vector logo, owl with wings spread on fire, purple fire, psychedelic, symbol
- batch_size
- "6"
- guidance_scale
- "5"
- aesthetic_rating
- 9
- aesthetic_weight
- 0.1
{ "seed": "-1", "steps": "100", "width": "256", "height": "256", "prompt": "vector logo, owl with wings spread on fire, purple fire, psychedelic, symbol", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.1 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run laion-ai/erlich using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "laion-ai/erlich:a51ce279c0131991c5a143a9c6a3ec6de146e765d9311cff7435b1db1190faaa", { input: { seed: "-1", steps: "100", width: "256", height: "256", prompt: "vector logo, owl with wings spread on fire, purple fire, psychedelic, symbol", batch_size: "6", guidance_scale: "5", aesthetic_rating: 9, aesthetic_weight: 0.1 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun laion-ai/erlich using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "laion-ai/erlich:a51ce279c0131991c5a143a9c6a3ec6de146e765d9311cff7435b1db1190faaa", input={ "seed": "-1", "steps": "100", "width": "256", "height": "256", "prompt": "vector logo, owl with wings spread on fire, purple fire, psychedelic, symbol", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.1 } ) # The laion-ai/erlich model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/laion-ai/erlich/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run laion-ai/erlich 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": "laion-ai/erlich:a51ce279c0131991c5a143a9c6a3ec6de146e765d9311cff7435b1db1190faaa", "input": { "seed": "-1", "steps": "100", "width": "256", "height": "256", "prompt": "vector logo, owl with wings spread on fire, purple fire, psychedelic, symbol", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.1 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-21T19:32:46Z", "created_at": "2022-07-21T19:32:09.128980Z", "data_removed": false, "error": "", "id": "ammwcg4ktzavxcxefr4mrtfxuu", "input": { "seed": "-1", "steps": "100", "width": "256", "height": "256", "prompt": "vector logo, owl with wings spread on fire, purple fire, psychedelic, symbol", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.1 }, "logs": "Using seed 16158454\r\nRunning simulation for vector logo, owl with wings spread on fire, purple fire, psychedelic, symbol\r\nEncoding text embeddings with vector logo, owl with wings spread on fire, purple fire, psychedelic, symbol dimensions\r\nUsing aesthetic embedding 9 with weight 0.1\r\nRunning diffusion...\r\n\r\n 0%| | 0/100 [00:00<?, ?it/s]\r\n 1%| | 1/100 [00:01<01:58, 1.20s/it]\r\n 2%|▏ | 2/100 [00:02<01:54, 1.17s/it]\r\n 3%|▎ | 3/100 [00:03<01:52, 1.16s/it]\r\n 4%|▍ | 4/100 [00:03<01:18, 1.23it/s]\r\n 5%|▌ | 5/100 [00:04<00:59, 1.60it/s]\r\n 6%|▌ | 6/100 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91%|█████████ | 91/100 [00:29<00:02, 3.40it/s]\r\n 92%|█████████▏| 92/100 [00:29<00:02, 3.39it/s]\r\n 93%|█████████▎| 93/100 [00:29<00:02, 3.39it/s]\r\n 94%|█████████▍| 94/100 [00:30<00:01, 3.39it/s]\r\n 95%|█████████▌| 95/100 [00:30<00:01, 3.40it/s]\r\n 96%|█████████▌| 96/100 [00:30<00:01, 3.39it/s]\r\n 97%|█████████▋| 97/100 [00:30<00:00, 3.39it/s]\r\n 98%|█████████▊| 98/100 [00:31<00:00, 3.38it/s]\r\n 99%|█████████▉| 99/100 [00:31<00:00, 3.39it/s]\r\n100%|██████████| 100/100 [00:31<00:00, 3.39it/s]\r\n100%|██████████| 100/100 [00:31<00:00, 3.14it/s]\r\nSaving final sample/s", "metrics": { "predict_time": 37, "total_time": 36.87102 }, "output": [ [ "https://replicate.delivery/mgxm/0a305d40-edc8-48a3-a801-e4ed6e37d0c8/current_0.png", "https://replicate.delivery/mgxm/13dc984f-aadc-4af7-8071-d32e03873d47/current_1.png", "https://replicate.delivery/mgxm/4c4c03b9-1b4b-495f-9203-9a6a6e0da708/current_2.png", "https://replicate.delivery/mgxm/d1c8d266-4eb9-4096-9947-620ac22175cc/current_3.png", "https://replicate.delivery/mgxm/1f348c2d-c4c7-48ab-8372-3c913ecbe2b5/current_4.png", "https://replicate.delivery/mgxm/a56af400-8cb1-457d-8395-65d355b46e03/current_5.png" ] ], "started_at": "2022-07-21T19:32:09Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ammwcg4ktzavxcxefr4mrtfxuu", "cancel": "https://api.replicate.com/v1/predictions/ammwcg4ktzavxcxefr4mrtfxuu/cancel" }, "version": "a51ce279c0131991c5a143a9c6a3ec6de146e765d9311cff7435b1db1190faaa" }Generated inUsing seed 16158454 Running simulation for vector logo, owl with wings spread on fire, purple fire, psychedelic, symbol Encoding text embeddings with vector logo, owl with wings spread on fire, purple fire, psychedelic, symbol dimensions Using aesthetic embedding 9 with weight 0.1 Running diffusion... 0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:01<01:58, 1.20s/it] 2%|▏ | 2/100 [00:02<01:54, 1.17s/it] 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3.40it/s] 96%|█████████▌| 96/100 [00:30<00:01, 3.39it/s] 97%|█████████▋| 97/100 [00:30<00:00, 3.39it/s] 98%|█████████▊| 98/100 [00:31<00:00, 3.38it/s] 99%|█████████▉| 99/100 [00:31<00:00, 3.39it/s] 100%|██████████| 100/100 [00:31<00:00, 3.39it/s] 100%|██████████| 100/100 [00:31<00:00, 3.14it/s] Saving final sample/sPrediction
laion-ai/erlich:a51ce279c0131991c5a143a9c6a3ec6de146e765d9311cff7435b1db1190faaaID5mychegnafh73mrc2zmbdvxfqiStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- "-1"
- steps
- "100"
- width
- "256"
- height
- "256"
- prompt
- paper plane logo with shadow of plane flying around the world, logo, vector
- batch_size
- "6"
- guidance_scale
- "5"
- aesthetic_rating
- 9
- aesthetic_weight
- 0.1
{ "seed": "-1", "steps": "100", "width": "256", "height": "256", "prompt": "paper plane logo with shadow of plane flying around the world, logo, vector", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.1 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run laion-ai/erlich using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "laion-ai/erlich:a51ce279c0131991c5a143a9c6a3ec6de146e765d9311cff7435b1db1190faaa", { input: { seed: "-1", steps: "100", width: "256", height: "256", prompt: "paper plane logo with shadow of plane flying around the world, logo, vector", batch_size: "6", guidance_scale: "5", aesthetic_rating: 9, aesthetic_weight: 0.1 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun laion-ai/erlich using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "laion-ai/erlich:a51ce279c0131991c5a143a9c6a3ec6de146e765d9311cff7435b1db1190faaa", input={ "seed": "-1", "steps": "100", "width": "256", "height": "256", "prompt": "paper plane logo with shadow of plane flying around the world, logo, vector", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.1 } ) # The laion-ai/erlich model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/laion-ai/erlich/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run laion-ai/erlich 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": "laion-ai/erlich:a51ce279c0131991c5a143a9c6a3ec6de146e765d9311cff7435b1db1190faaa", "input": { "seed": "-1", "steps": "100", "width": "256", "height": "256", "prompt": "paper plane logo with shadow of plane flying around the world, logo, vector", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.1 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-07-21T19:34:51.200560Z", "created_at": "2022-07-21T19:34:12.690573Z", "data_removed": false, "error": null, "id": "5mychegnafh73mrc2zmbdvxfqi", "input": { "seed": "-1", "steps": "100", "width": "256", "height": "256", "prompt": "paper plane logo with shadow of plane flying around the world, logo, vector", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.1 }, "logs": "Using seed 2312414383\nRunning simulation for paper plane logo with shadow of plane flying around the world, logo, vector\nEncoding text embeddings with paper plane logo with shadow of plane flying around the world, logo, vector dimensions\nUsing aesthetic embedding 9 with weight 0.1\nRunning diffusion...\n\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:01<02:01, 1.23s/it]\n 2%|▏ | 2/100 [00:02<01:53, 1.16s/it]\n 3%|▎ | 3/100 [00:03<01:50, 1.14s/it]\n 4%|▍ | 4/100 [00:03<01:16, 1.25it/s]\n 5%|▌ | 5/100 [00:04<00:58, 1.63it/s]\n 6%|▌ | 6/100 [00:04<00:46, 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[00:29<00:01, 3.52it/s]\n 95%|█████████▌| 95/100 [00:29<00:01, 3.52it/s]\n 96%|█████████▌| 96/100 [00:29<00:01, 3.52it/s]\n 97%|█████████▋| 97/100 [00:29<00:00, 3.52it/s]\n 98%|█████████▊| 98/100 [00:30<00:00, 3.52it/s]\n 99%|█████████▉| 99/100 [00:30<00:00, 3.52it/s]\n100%|██████████| 100/100 [00:30<00:00, 3.52it/s]\n100%|██████████| 100/100 [00:30<00:00, 3.25it/s]\nSaving final sample/s", "metrics": { "predict_time": 38.342397, "total_time": 38.509987 }, "output": [ [ "https://replicate.delivery/mgxm/271db70b-9e90-4825-9596-b759af8e6d28/current_0.png", "https://replicate.delivery/mgxm/27c035e6-9ad6-43b5-8b83-b87a9e132c08/current_1.png", "https://replicate.delivery/mgxm/976054e7-e18d-41f6-a73a-16a7ef356e10/current_2.png", "https://replicate.delivery/mgxm/7a1f4d4f-8fc9-4624-baeb-f4ddcf644958/current_3.png", "https://replicate.delivery/mgxm/153f966e-43dc-4bf6-b8fc-69b3cc27bf33/current_4.png", "https://replicate.delivery/mgxm/ec51a2db-283b-4668-9010-b3ea5a2a34d2/current_5.png" ] ], "started_at": "2022-07-21T19:34:12.858163Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5mychegnafh73mrc2zmbdvxfqi", "cancel": "https://api.replicate.com/v1/predictions/5mychegnafh73mrc2zmbdvxfqi/cancel" }, "version": "a51ce279c0131991c5a143a9c6a3ec6de146e765d9311cff7435b1db1190faaa" }Generated inUsing seed 2312414383 Running simulation for paper plane logo with shadow of plane flying around the world, logo, vector Encoding text embeddings with paper plane logo with shadow of plane flying around the world, logo, vector dimensions Using aesthetic embedding 9 with weight 0.1 Running diffusion... 0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:01<02:01, 1.23s/it] 2%|▏ | 2/100 [00:02<01:53, 1.16s/it] 3%|▎ | 3/100 [00:03<01:50, 1.14s/it] 4%|▍ | 4/100 [00:03<01:16, 1.25it/s] 5%|▌ | 5/100 [00:04<00:58, 1.63it/s] 6%|▌ | 6/100 [00:04<00:46, 2.01it/s] 7%|▋ | 7/100 [00:04<00:39, 2.34it/s] 8%|▊ | 8/100 [00:04<00:34, 2.63it/s] 9%|▉ | 9/100 [00:05<00:31, 2.87it/s] 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3.52it/s] 100%|██████████| 100/100 [00:30<00:00, 3.25it/s] Saving final sample/sPrediction
laion-ai/erlich:92fa143ccefeed01534d5d6648bd47796ef06847a6bc55c0e5c5b6975f2dcdfbIDkjo2hwofv5ak3ffw2673cdnlnyStatusSucceededSourceWebHardware–Total durationCreatedInput
- seed
- "-1"
- steps
- "100"
- width
- "256"
- height
- "256"
- prompt
- paper plane logo with shadow of plane flying around the world, logo, digital art
- batch_size
- "6"
- guidance_scale
- "5"
- aesthetic_rating
- 9
- aesthetic_weight
- 0.1
{ "seed": "-1", "steps": "100", "width": "256", "height": "256", "prompt": "paper plane logo with shadow of plane flying around the world, logo, digital art", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.1 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run laion-ai/erlich using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "laion-ai/erlich:92fa143ccefeed01534d5d6648bd47796ef06847a6bc55c0e5c5b6975f2dcdfb", { input: { seed: "-1", steps: "100", width: "256", height: "256", prompt: "paper plane logo with shadow of plane flying around the world, logo, digital art", batch_size: "6", guidance_scale: "5", aesthetic_rating: 9, aesthetic_weight: 0.1 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun laion-ai/erlich using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "laion-ai/erlich:92fa143ccefeed01534d5d6648bd47796ef06847a6bc55c0e5c5b6975f2dcdfb", input={ "seed": "-1", "steps": "100", "width": "256", "height": "256", "prompt": "paper plane logo with shadow of plane flying around the world, logo, digital art", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.1 } ) # The laion-ai/erlich model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/laion-ai/erlich/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run laion-ai/erlich 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": "laion-ai/erlich:92fa143ccefeed01534d5d6648bd47796ef06847a6bc55c0e5c5b6975f2dcdfb", "input": { "seed": "-1", "steps": "100", "width": "256", "height": "256", "prompt": "paper plane logo with shadow of plane flying around the world, logo, digital art", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.1 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-05T14:40:21.390360Z", "created_at": "2022-08-05T14:33:58.632235Z", "data_removed": false, "error": null, "id": "kjo2hwofv5ak3ffw2673cdnlny", "input": { "seed": "-1", "steps": "100", "width": "256", "height": "256", "prompt": "paper plane logo with shadow of plane flying around the world, logo, digital art", "batch_size": "6", "guidance_scale": "5", "aesthetic_rating": 9, "aesthetic_weight": 0.1 }, "logs": "Using seed 4158679567\nUsing preloaded models\nEncoding text embeddings with paper plane logo with shadow of plane flying around the world, logo, digital art dimensions\nUsing aesthetic embedding 9 with weight 0.1\nRunning diffusion...\n\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:01<02:28, 1.50s/it]\n 2%|▏ | 2/100 [00:02<02:08, 1.31s/it]\n 3%|▎ | 3/100 [00:03<02:01, 1.25s/it]\n 4%|▍ | 4/100 [00:04<01:23, 1.14it/s]\n 5%|▌ | 5/100 [00:04<01:03, 1.50it/s]\n 6%|▌ | 6/100 [00:04<00:50, 1.86it/s]\n 7%|▋ | 7/100 [00:05<00:42, 2.18it/s]\n 8%|▊ | 8/100 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[00:25<00:07, 3.33it/s]\n 76%|███████▌ | 76/100 [00:25<00:07, 3.33it/s]\n 77%|███████▋ | 77/100 [00:25<00:06, 3.34it/s]\n 78%|███████▊ | 78/100 [00:26<00:06, 3.33it/s]\n 79%|███████▉ | 79/100 [00:26<00:06, 3.34it/s]\n 80%|████████ | 80/100 [00:26<00:05, 3.33it/s]\n 81%|████████ | 81/100 [00:27<00:05, 3.33it/s]\n 82%|████████▏ | 82/100 [00:27<00:05, 3.33it/s]\n 83%|████████▎ | 83/100 [00:27<00:05, 3.33it/s]\n 84%|████████▍ | 84/100 [00:27<00:04, 3.33it/s]\n 85%|████████▌ | 85/100 [00:28<00:04, 3.33it/s]\n 86%|████████▌ | 86/100 [00:28<00:04, 3.32it/s]\n 87%|████████▋ | 87/100 [00:28<00:03, 3.32it/s]\n 88%|████████▊ | 88/100 [00:29<00:03, 3.32it/s]\n 89%|████████▉ | 89/100 [00:29<00:03, 3.32it/s]\n 90%|█████████ | 90/100 [00:29<00:03, 3.32it/s]\n 91%|█████████ | 91/100 [00:30<00:02, 3.32it/s]\n 92%|█████████▏| 92/100 [00:30<00:02, 3.32it/s]\n 93%|█████████▎| 93/100 [00:30<00:02, 3.32it/s]\n 94%|█████████▍| 94/100 [00:31<00:01, 3.32it/s]\n 95%|█████████▌| 95/100 [00:31<00:01, 3.32it/s]\n 96%|█████████▌| 96/100 [00:31<00:01, 3.33it/s]\n 97%|█████████▋| 97/100 [00:31<00:00, 3.32it/s]\n 98%|█████████▊| 98/100 [00:32<00:00, 3.33it/s]\n 99%|█████████▉| 99/100 [00:32<00:00, 3.32it/s]\n100%|██████████| 100/100 [00:32<00:00, 3.32it/s]\n100%|██████████| 100/100 [00:32<00:00, 3.05it/s]\nSaving final sample/s", "metrics": { "predict_time": 37.033327, "total_time": 382.758125 }, "output": [ [ "https://replicate.delivery/mgxm/f71aaee1-8937-403c-b497-9d05921cba95/ts_99-batch_0.png", "https://replicate.delivery/mgxm/0dae8f90-d30f-4bc6-a21d-98d2e6918ba5/ts_99-batch_1.png", "https://replicate.delivery/mgxm/3d7569c9-e1d1-400e-a7ad-1ab97c98c97c/ts_99-batch_2.png", "https://replicate.delivery/mgxm/264565dc-6828-4fa1-8521-614ff5c236fa/ts_99-batch_3.png", "https://replicate.delivery/mgxm/8022084b-a249-4979-ab0f-c774f687cf77/ts_99-batch_4.png", "https://replicate.delivery/mgxm/bcfb9294-1da5-4762-b761-dd14ad601f03/ts_99-batch_5.png" ] ], "started_at": "2022-08-05T14:39:44.357033Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kjo2hwofv5ak3ffw2673cdnlny", "cancel": "https://api.replicate.com/v1/predictions/kjo2hwofv5ak3ffw2673cdnlny/cancel" }, "version": "92fa143ccefeed01534d5d6648bd47796ef06847a6bc55c0e5c5b6975f2dcdfb" }Generated inUsing seed 4158679567 Using preloaded models Encoding text embeddings with paper plane logo with shadow of plane flying around the world, logo, digital art dimensions Using aesthetic embedding 9 with weight 0.1 Running diffusion... 0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:01<02:28, 1.50s/it] 2%|▏ | 2/100 [00:02<02:08, 1.31s/it] 3%|▎ | 3/100 [00:03<02:01, 1.25s/it] 4%|▍ | 4/100 [00:04<01:23, 1.14it/s] 5%|▌ | 5/100 [00:04<01:03, 1.50it/s] 6%|▌ | 6/100 [00:04<00:50, 1.86it/s] 7%|▋ | 7/100 [00:05<00:42, 2.18it/s] 8%|▊ | 8/100 [00:05<00:37, 2.46it/s] 9%|▉ | 9/100 [00:05<00:33, 2.69it/s] 10%|█ | 10/100 [00:05<00:31, 2.87it/s] 11%|█ | 11/100 [00:06<00:29, 3.01it/s] 12%|█▏ | 12/100 [00:06<00:28, 3.11it/s] 13%|█▎ 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