chamuditha4 / anime_diff-oolong
AnimateDiff text to video from your imagination!
- Public
- 338 runs
-
L40S
Prediction
chamuditha4/anime_diff-oolong:bb6a407222e755b8d3dd92d776522156bcd7cab1760c80c24a0b2ef995d62223IDywbmhc47chrgj0cha0sbbahfrwStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- seed
- 13204175718326964000
- prompt
- masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes
- num_frames
- 24
- guidance_scale
- 7.5
- negative_prompt
- badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth
- num_inference_steps
- 25
{ "seed": 13204175718326964000, "prompt": "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 }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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chamuditha4/anime_diff-oolong:bb6a407222e755b8d3dd92d776522156bcd7cab1760c80c24a0b2ef995d62223", { input: { seed: 13204175718326964000, prompt: "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes", num_frames: 24, guidance_scale: 7.5, negative_prompt: "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", num_inference_steps: 25 } } ); // 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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chamuditha4/anime_diff-oolong:bb6a407222e755b8d3dd92d776522156bcd7cab1760c80c24a0b2ef995d62223", input={ "seed": 13204175718326964000, "prompt": "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run chamuditha4/anime_diff-oolong 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": "chamuditha4/anime_diff-oolong:bb6a407222e755b8d3dd92d776522156bcd7cab1760c80c24a0b2ef995d62223", "input": { "seed": 13204175718326964000, "prompt": "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-14T09:33:39.248248Z", "created_at": "2024-08-14T09:22:54.436000Z", "data_removed": false, "error": null, "id": "ywbmhc47chrgj0cha0sbbahfrw", "input": { "seed": 13204175718326964000, "prompt": "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 }, "logs": "Using seed: 13204175718326964000\n 0%| | 0/8 [00:00<?, ?it/s]\n 12%|█▎ | 1/8 [00:01<00:10, 1.44s/it]\n 25%|██▌ | 2/8 [00:02<00:07, 1.17s/it]\n 38%|███▊ | 3/8 [00:03<00:05, 1.09s/it]\n 50%|█████ | 4/8 [00:04<00:04, 1.05s/it]\n 62%|██████▎ | 5/8 [00:05<00:03, 1.03s/it]\n 75%|███████▌ | 6/8 [00:06<00:02, 1.02s/it]\n 88%|████████▊ | 7/8 [00:07<00:01, 1.01s/it]\n100%|██████████| 8/8 [00:08<00:00, 1.01s/it]\n100%|██████████| 8/8 [00:08<00:00, 1.05s/it]\n 0%| | 0/16 [00:00<?, ?it/s]\n 6%|▋ | 1/16 [00:00<00:14, 1.01it/s]\n 12%|█▎ | 2/16 [00:01<00:13, 1.01it/s]\n 19%|█▉ | 3/16 [00:02<00:12, 1.01it/s]\n 25%|██▌ | 4/16 [00:03<00:11, 1.01it/s]\n 31%|███▏ | 5/16 [00:04<00:10, 1.01it/s]\n 38%|███▊ | 6/16 [00:05<00:09, 1.01it/s]\n 44%|████▍ | 7/16 [00:06<00:08, 1.01it/s]\n 50%|█████ | 8/16 [00:07<00:07, 1.01it/s]\n 56%|█████▋ | 9/16 [00:08<00:06, 1.01it/s]\n 62%|██████▎ | 10/16 [00:09<00:05, 1.01it/s]\n 69%|██████▉ | 11/16 [00:10<00:04, 1.01it/s]\n 75%|███████▌ | 12/16 [00:11<00:03, 1.01it/s]\n 81%|████████▏ | 13/16 [00:12<00:02, 1.01it/s]\n 88%|████████▊ | 14/16 [00:13<00:01, 1.01it/s]\n 94%|█████████▍| 15/16 [00:14<00:00, 1.01it/s]\n100%|██████████| 16/16 [00:15<00:00, 1.01it/s]\n100%|██████████| 16/16 [00:15<00:00, 1.01it/s]\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:23, 1.01it/s]\n 8%|▊ | 2/25 [00:01<00:22, 1.01it/s]\n 12%|█▏ | 3/25 [00:02<00:21, 1.01it/s]\n 16%|█▌ | 4/25 [00:03<00:20, 1.01it/s]\n 20%|██ | 5/25 [00:04<00:19, 1.01it/s]\n 24%|██▍ | 6/25 [00:05<00:18, 1.00it/s]\n 28%|██▊ | 7/25 [00:06<00:17, 1.00it/s]\n 32%|███▏ | 8/25 [00:07<00:16, 1.00it/s]\n 36%|███▌ | 9/25 [00:08<00:15, 1.00it/s]\n 40%|████ | 10/25 [00:09<00:14, 1.00it/s]\n 44%|████▍ | 11/25 [00:10<00:13, 1.00it/s]\n 48%|████▊ | 12/25 [00:11<00:12, 1.00it/s]\n 52%|█████▏ | 13/25 [00:12<00:11, 1.00it/s]\n 56%|█████▌ | 14/25 [00:13<00:10, 1.00it/s]\n 60%|██████ | 15/25 [00:14<00:09, 1.00it/s]\n 64%|██████▍ | 16/25 [00:15<00:08, 1.00it/s]\n 68%|██████▊ | 17/25 [00:16<00:07, 1.00it/s]\n 72%|███████▏ | 18/25 [00:17<00:06, 1.00it/s]\n 76%|███████▌ | 19/25 [00:18<00:06, 1.00s/it]\n 80%|████████ | 20/25 [00:19<00:05, 1.00s/it]\n 84%|████████▍ | 21/25 [00:20<00:04, 1.00s/it]\n 88%|████████▊ | 22/25 [00:21<00:03, 1.00s/it]\n 92%|█████████▏| 23/25 [00:22<00:02, 1.00s/it]\n 96%|█████████▌| 24/25 [00:23<00:01, 1.00s/it]\n100%|██████████| 25/25 [00:24<00:00, 1.00it/s]\n100%|██████████| 25/25 [00:24<00:00, 1.00it/s]", "metrics": { "predict_time": 58.113464988, "total_time": 644.812248 }, "output": "https://replicate.delivery/pbxt/SlNXyuU5fLRJDaxlg4j3TA5g3ibf1b9Hf1eYj5pysqfMero0E/output.gif", "started_at": "2024-08-14T09:32:41.134784Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ywbmhc47chrgj0cha0sbbahfrw", "cancel": "https://api.replicate.com/v1/predictions/ywbmhc47chrgj0cha0sbbahfrw/cancel" }, "version": "bb6a407222e755b8d3dd92d776522156bcd7cab1760c80c24a0b2ef995d62223" }Generated inUsing seed: 13204175718326964000 0%| | 0/8 [00:00<?, ?it/s] 12%|█▎ | 1/8 [00:01<00:10, 1.44s/it] 25%|██▌ | 2/8 [00:02<00:07, 1.17s/it] 38%|███▊ | 3/8 [00:03<00:05, 1.09s/it] 50%|█████ | 4/8 [00:04<00:04, 1.05s/it] 62%|██████▎ | 5/8 [00:05<00:03, 1.03s/it] 75%|███████▌ | 6/8 [00:06<00:02, 1.02s/it] 88%|████████▊ | 7/8 [00:07<00:01, 1.01s/it] 100%|██████████| 8/8 [00:08<00:00, 1.01s/it] 100%|██████████| 8/8 [00:08<00:00, 1.05s/it] 0%| | 0/16 [00:00<?, ?it/s] 6%|▋ | 1/16 [00:00<00:14, 1.01it/s] 12%|█▎ | 2/16 [00:01<00:13, 1.01it/s] 19%|█▉ | 3/16 [00:02<00:12, 1.01it/s] 25%|██▌ | 4/16 [00:03<00:11, 1.01it/s] 31%|███▏ | 5/16 [00:04<00:10, 1.01it/s] 38%|███▊ | 6/16 [00:05<00:09, 1.01it/s] 44%|████▍ | 7/16 [00:06<00:08, 1.01it/s] 50%|█████ | 8/16 [00:07<00:07, 1.01it/s] 56%|█████▋ | 9/16 [00:08<00:06, 1.01it/s] 62%|██████▎ | 10/16 [00:09<00:05, 1.01it/s] 69%|██████▉ | 11/16 [00:10<00:04, 1.01it/s] 75%|███████▌ | 12/16 [00:11<00:03, 1.01it/s] 81%|████████▏ | 13/16 [00:12<00:02, 1.01it/s] 88%|████████▊ | 14/16 [00:13<00:01, 1.01it/s] 94%|█████████▍| 15/16 [00:14<00:00, 1.01it/s] 100%|██████████| 16/16 [00:15<00:00, 1.01it/s] 100%|██████████| 16/16 [00:15<00:00, 1.01it/s] 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:23, 1.01it/s] 8%|▊ | 2/25 [00:01<00:22, 1.01it/s] 12%|█▏ | 3/25 [00:02<00:21, 1.01it/s] 16%|█▌ | 4/25 [00:03<00:20, 1.01it/s] 20%|██ | 5/25 [00:04<00:19, 1.01it/s] 24%|██▍ | 6/25 [00:05<00:18, 1.00it/s] 28%|██▊ | 7/25 [00:06<00:17, 1.00it/s] 32%|███▏ | 8/25 [00:07<00:16, 1.00it/s] 36%|███▌ | 9/25 [00:08<00:15, 1.00it/s] 40%|████ | 10/25 [00:09<00:14, 1.00it/s] 44%|████▍ | 11/25 [00:10<00:13, 1.00it/s] 48%|████▊ | 12/25 [00:11<00:12, 1.00it/s] 52%|█████▏ | 13/25 [00:12<00:11, 1.00it/s] 56%|█████▌ | 14/25 [00:13<00:10, 1.00it/s] 60%|██████ | 15/25 [00:14<00:09, 1.00it/s] 64%|██████▍ | 16/25 [00:15<00:08, 1.00it/s] 68%|██████▊ | 17/25 [00:16<00:07, 1.00it/s] 72%|███████▏ | 18/25 [00:17<00:06, 1.00it/s] 76%|███████▌ | 19/25 [00:18<00:06, 1.00s/it] 80%|████████ | 20/25 [00:19<00:05, 1.00s/it] 84%|████████▍ | 21/25 [00:20<00:04, 1.00s/it] 88%|████████▊ | 22/25 [00:21<00:03, 1.00s/it] 92%|█████████▏| 23/25 [00:22<00:02, 1.00s/it] 96%|█████████▌| 24/25 [00:23<00:01, 1.00s/it] 100%|██████████| 25/25 [00:24<00:00, 1.00it/s] 100%|██████████| 25/25 [00:24<00:00, 1.00it/s]Prediction
chamuditha4/anime_diff-oolong:63c0a7088c834739b4bcbe0d8771bf2eef2970dc5d0e5093e792c82020ccf9f2IDz8wgmf7871rgj0ch97jryd3m1gStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- seed
- 13204175718326964000
- prompt
- a panda playing a guitar, on a boat, in the ocean, high quality
- num_frames
- 24
- guidance_scale
- 7.5
- negative_prompt
- bad quality, worse quality
- num_inference_steps
- 25
{ "seed": 13204175718326964000, "prompt": "a panda playing a guitar, on a boat, in the ocean, high quality", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "bad quality, worse quality", "num_inference_steps": 25 }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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chamuditha4/anime_diff-oolong:63c0a7088c834739b4bcbe0d8771bf2eef2970dc5d0e5093e792c82020ccf9f2", { input: { seed: 13204175718326964000, prompt: "a panda playing a guitar, on a boat, in the ocean, high quality", num_frames: 24, guidance_scale: 7.5, negative_prompt: "bad quality, worse quality", num_inference_steps: 25 } } ); // 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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chamuditha4/anime_diff-oolong:63c0a7088c834739b4bcbe0d8771bf2eef2970dc5d0e5093e792c82020ccf9f2", input={ "seed": 13204175718326964000, "prompt": "a panda playing a guitar, on a boat, in the ocean, high quality", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "bad quality, worse quality", "num_inference_steps": 25 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run chamuditha4/anime_diff-oolong 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": "chamuditha4/anime_diff-oolong:63c0a7088c834739b4bcbe0d8771bf2eef2970dc5d0e5093e792c82020ccf9f2", "input": { "seed": 13204175718326964000, "prompt": "a panda playing a guitar, on a boat, in the ocean, high quality", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "bad quality, worse quality", "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-13T04:07:46.659656Z", "created_at": "2024-08-13T04:01:29.656000Z", "data_removed": false, "error": null, "id": "z8wgmf7871rgj0ch97jryd3m1g", "input": { "seed": 13204175718326964000, "prompt": "a panda playing a guitar, on a boat, in the ocean, high quality", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "bad quality, worse quality", "num_inference_steps": 25 }, "logs": "0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:04<01:45, 4.40s/it]\n 8%|▊ | 2/25 [00:05<00:55, 2.39s/it]\n 12%|█▏ | 3/25 [00:06<00:38, 1.75s/it]\n 16%|█▌ | 4/25 [00:07<00:30, 1.45s/it]\n 20%|██ | 5/25 [00:08<00:25, 1.28s/it]\n 24%|██▍ | 6/25 [00:09<00:22, 1.18s/it]\n 28%|██▊ | 7/25 [00:10<00:20, 1.12s/it]\n 32%|███▏ | 8/25 [00:11<00:18, 1.08s/it]\n 36%|███▌ | 9/25 [00:12<00:16, 1.05s/it]\n 40%|████ | 10/25 [00:13<00:15, 1.04s/it]\n 44%|████▍ | 11/25 [00:14<00:14, 1.02s/it]\n 48%|████▊ | 12/25 [00:15<00:13, 1.01s/it]\n 52%|█████▏ | 13/25 [00:16<00:12, 1.01s/it]\n 56%|█████▌ | 14/25 [00:17<00:11, 1.01s/it]\n 60%|██████ | 15/25 [00:18<00:10, 1.00s/it]\n 64%|██████▍ | 16/25 [00:19<00:08, 1.00it/s]\n 68%|██████▊ | 17/25 [00:20<00:07, 1.00it/s]\n 72%|███████▏ | 18/25 [00:21<00:06, 1.01it/s]\n 76%|███████▌ | 19/25 [00:22<00:05, 1.01it/s]\n 80%|████████ | 20/25 [00:23<00:04, 1.01it/s]\n 84%|████████▍ | 21/25 [00:24<00:03, 1.01it/s]\n 88%|████████▊ | 22/25 [00:25<00:02, 1.01it/s]\n 92%|█████████▏| 23/25 [00:26<00:01, 1.01it/s]\n 96%|█████████▌| 24/25 [00:27<00:00, 1.01it/s]\n100%|██████████| 25/25 [00:28<00:00, 1.01it/s]\n100%|██████████| 25/25 [00:28<00:00, 1.13s/it]", "metrics": { "predict_time": 33.675792711, "total_time": 377.003656 }, "output": "https://replicate.delivery/pbxt/j8twfkQXp4xhParDBa7rhAUdYwzI5MqJs2ecnUVomOhS4ISTA/output.gif", "started_at": "2024-08-13T04:07:12.983863Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/z8wgmf7871rgj0ch97jryd3m1g", "cancel": "https://api.replicate.com/v1/predictions/z8wgmf7871rgj0ch97jryd3m1g/cancel" }, "version": "63c0a7088c834739b4bcbe0d8771bf2eef2970dc5d0e5093e792c82020ccf9f2" }Generated in0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:04<01:45, 4.40s/it] 8%|▊ | 2/25 [00:05<00:55, 2.39s/it] 12%|█▏ | 3/25 [00:06<00:38, 1.75s/it] 16%|█▌ | 4/25 [00:07<00:30, 1.45s/it] 20%|██ | 5/25 [00:08<00:25, 1.28s/it] 24%|██▍ | 6/25 [00:09<00:22, 1.18s/it] 28%|██▊ | 7/25 [00:10<00:20, 1.12s/it] 32%|███▏ | 8/25 [00:11<00:18, 1.08s/it] 36%|███▌ | 9/25 [00:12<00:16, 1.05s/it] 40%|████ | 10/25 [00:13<00:15, 1.04s/it] 44%|████▍ | 11/25 [00:14<00:14, 1.02s/it] 48%|████▊ | 12/25 [00:15<00:13, 1.01s/it] 52%|█████▏ | 13/25 [00:16<00:12, 1.01s/it] 56%|█████▌ | 14/25 [00:17<00:11, 1.01s/it] 60%|██████ | 15/25 [00:18<00:10, 1.00s/it] 64%|██████▍ | 16/25 [00:19<00:08, 1.00it/s] 68%|██████▊ | 17/25 [00:20<00:07, 1.00it/s] 72%|███████▏ | 18/25 [00:21<00:06, 1.01it/s] 76%|███████▌ | 19/25 [00:22<00:05, 1.01it/s] 80%|████████ | 20/25 [00:23<00:04, 1.01it/s] 84%|████████▍ | 21/25 [00:24<00:03, 1.01it/s] 88%|████████▊ | 22/25 [00:25<00:02, 1.01it/s] 92%|█████████▏| 23/25 [00:26<00:01, 1.01it/s] 96%|█████████▌| 24/25 [00:27<00:00, 1.01it/s] 100%|██████████| 25/25 [00:28<00:00, 1.01it/s] 100%|██████████| 25/25 [00:28<00:00, 1.13s/it]Prediction
chamuditha4/anime_diff-oolong:bb6a407222e755b8d3dd92d776522156bcd7cab1760c80c24a0b2ef995d62223IDccjxs7e3jnrgj0cha0yrcz05ycStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- seed
- 0
- prompt
- masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes
- num_frames
- 32
- guidance_scale
- 7.5
- negative_prompt
- badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth
- num_inference_steps
- 25
{ "seed": 0, "prompt": "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes", "num_frames": 32, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 }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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chamuditha4/anime_diff-oolong:bb6a407222e755b8d3dd92d776522156bcd7cab1760c80c24a0b2ef995d62223", { input: { seed: 0, prompt: "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes", num_frames: 32, guidance_scale: 7.5, negative_prompt: "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", num_inference_steps: 25 } } ); // 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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chamuditha4/anime_diff-oolong:bb6a407222e755b8d3dd92d776522156bcd7cab1760c80c24a0b2ef995d62223", input={ "seed": 0, "prompt": "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes", "num_frames": 32, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run chamuditha4/anime_diff-oolong 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": "chamuditha4/anime_diff-oolong:bb6a407222e755b8d3dd92d776522156bcd7cab1760c80c24a0b2ef995d62223", "input": { "seed": 0, "prompt": "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes", "num_frames": 32, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-14T09:35:51.668958Z", "created_at": "2024-08-14T09:35:10.741000Z", "data_removed": false, "error": null, "id": "ccjxs7e3jnrgj0cha0yrcz05yc", "input": { "seed": 0, "prompt": "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes", "num_frames": 32, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 }, "logs": "Using seed: 702704429\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:01<00:40, 1.71s/it]\n 8%|▊ | 2/25 [00:03<00:34, 1.48s/it]\n 12%|█▏ | 3/25 [00:04<00:31, 1.41s/it]\n 16%|█▌ | 4/25 [00:05<00:28, 1.38s/it]\n 20%|██ | 5/25 [00:07<00:27, 1.36s/it]\n 24%|██▍ | 6/25 [00:08<00:25, 1.35s/it]\n 28%|██▊ | 7/25 [00:09<00:24, 1.34s/it]\n 32%|███▏ | 8/25 [00:10<00:22, 1.34s/it]\n 36%|███▌ | 9/25 [00:12<00:21, 1.33s/it]\n 40%|████ | 10/25 [00:13<00:19, 1.33s/it]\n 44%|████▍ | 11/25 [00:14<00:18, 1.33s/it]\n 48%|████▊ | 12/25 [00:16<00:17, 1.33s/it]\n 52%|█████▏ | 13/25 [00:17<00:15, 1.33s/it]\n 56%|█████▌ | 14/25 [00:18<00:14, 1.33s/it]\n 60%|██████ | 15/25 [00:20<00:13, 1.33s/it]\n 64%|██████▍ | 16/25 [00:21<00:11, 1.33s/it]\n 68%|██████▊ | 17/25 [00:22<00:10, 1.33s/it]\n 72%|███████▏ | 18/25 [00:24<00:09, 1.33s/it]\n 76%|███████▌ | 19/25 [00:25<00:07, 1.33s/it]\n 80%|████████ | 20/25 [00:26<00:06, 1.33s/it]\n 84%|████████▍ | 21/25 [00:28<00:05, 1.33s/it]\n 88%|████████▊ | 22/25 [00:29<00:03, 1.33s/it]\n 92%|█████████▏| 23/25 [00:30<00:02, 1.33s/it]\n 96%|█████████▌| 24/25 [00:32<00:01, 1.33s/it]\n100%|██████████| 25/25 [00:33<00:00, 1.33s/it]\n100%|██████████| 25/25 [00:33<00:00, 1.34s/it]", "metrics": { "predict_time": 40.804185810999996, "total_time": 40.927958 }, "output": "https://replicate.delivery/pbxt/piH9wOr06cbeQKcfNcfFVQv3MZPpQBto2JMXiPTyUzsrjFlmA/output.gif", "started_at": "2024-08-14T09:35:10.864772Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ccjxs7e3jnrgj0cha0yrcz05yc", "cancel": "https://api.replicate.com/v1/predictions/ccjxs7e3jnrgj0cha0yrcz05yc/cancel" }, "version": "bb6a407222e755b8d3dd92d776522156bcd7cab1760c80c24a0b2ef995d62223" }Generated inUsing seed: 702704429 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:01<00:40, 1.71s/it] 8%|▊ | 2/25 [00:03<00:34, 1.48s/it] 12%|█▏ | 3/25 [00:04<00:31, 1.41s/it] 16%|█▌ | 4/25 [00:05<00:28, 1.38s/it] 20%|██ | 5/25 [00:07<00:27, 1.36s/it] 24%|██▍ | 6/25 [00:08<00:25, 1.35s/it] 28%|██▊ | 7/25 [00:09<00:24, 1.34s/it] 32%|███▏ | 8/25 [00:10<00:22, 1.34s/it] 36%|███▌ | 9/25 [00:12<00:21, 1.33s/it] 40%|████ | 10/25 [00:13<00:19, 1.33s/it] 44%|████▍ | 11/25 [00:14<00:18, 1.33s/it] 48%|████▊ | 12/25 [00:16<00:17, 1.33s/it] 52%|█████▏ | 13/25 [00:17<00:15, 1.33s/it] 56%|█████▌ | 14/25 [00:18<00:14, 1.33s/it] 60%|██████ | 15/25 [00:20<00:13, 1.33s/it] 64%|██████▍ | 16/25 [00:21<00:11, 1.33s/it] 68%|██████▊ | 17/25 [00:22<00:10, 1.33s/it] 72%|███████▏ | 18/25 [00:24<00:09, 1.33s/it] 76%|███████▌ | 19/25 [00:25<00:07, 1.33s/it] 80%|████████ | 20/25 [00:26<00:06, 1.33s/it] 84%|████████▍ | 21/25 [00:28<00:05, 1.33s/it] 88%|████████▊ | 22/25 [00:29<00:03, 1.33s/it] 92%|█████████▏| 23/25 [00:30<00:02, 1.33s/it] 96%|█████████▌| 24/25 [00:32<00:01, 1.33s/it] 100%|██████████| 25/25 [00:33<00:00, 1.33s/it] 100%|██████████| 25/25 [00:33<00:00, 1.34s/it]Prediction
chamuditha4/anime_diff-oolong:a4be06ddb54c1fed336bb1bd22d317d0f2ee3b8f712dbb150aad4c7053b2493eID1pw39brdyxrgp0cha2889vvwg0StatusSucceededSourceWebHardwareA40Total durationCreatedInput
- prompt
- masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes
- num_frames
- 24
- guidance_scale
- 7.5
- negative_prompt
- badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth
- num_inference_steps
- 25
{ "prompt": "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 }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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chamuditha4/anime_diff-oolong:a4be06ddb54c1fed336bb1bd22d317d0f2ee3b8f712dbb150aad4c7053b2493e", { input: { prompt: "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes", num_frames: 24, guidance_scale: 7.5, negative_prompt: "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", num_inference_steps: 25 } } ); // 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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chamuditha4/anime_diff-oolong:a4be06ddb54c1fed336bb1bd22d317d0f2ee3b8f712dbb150aad4c7053b2493e", input={ "prompt": "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run chamuditha4/anime_diff-oolong 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": "chamuditha4/anime_diff-oolong:a4be06ddb54c1fed336bb1bd22d317d0f2ee3b8f712dbb150aad4c7053b2493e", "input": { "prompt": "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-14T11:09:24.710217Z", "created_at": "2024-08-14T11:05:03.735000Z", "data_removed": false, "error": null, "id": "1pw39brdyxrgp0cha2889vvwg0", "input": { "prompt": "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 }, "logs": "Using seed: 3885112135\n 0%| | 0/8 [00:00<?, ?it/s]\n 12%|█▎ | 1/8 [00:01<00:10, 1.56s/it]\n 25%|██▌ | 2/8 [00:02<00:07, 1.22s/it]\n 38%|███▊ | 3/8 [00:03<00:05, 1.11s/it]\n 50%|█████ | 4/8 [00:04<00:04, 1.06s/it]\n 62%|██████▎ | 5/8 [00:05<00:03, 1.03s/it]\n 75%|███████▌ | 6/8 [00:06<00:02, 1.02s/it]\n 88%|████████▊ | 7/8 [00:07<00:01, 1.01s/it]\n100%|██████████| 8/8 [00:08<00:00, 1.00s/it]\n100%|██████████| 8/8 [00:08<00:00, 1.06s/it]\n 0%| | 0/16 [00:00<?, ?it/s]\n 6%|▋ | 1/16 [00:00<00:14, 1.01it/s]\n 12%|█▎ | 2/16 [00:01<00:13, 1.01it/s]\n 19%|█▉ | 3/16 [00:02<00:12, 1.01it/s]\n 25%|██▌ | 4/16 [00:03<00:11, 1.01it/s]\n 31%|███▏ | 5/16 [00:04<00:10, 1.01it/s]\n 38%|███▊ | 6/16 [00:05<00:09, 1.01it/s]\n 44%|████▍ | 7/16 [00:06<00:08, 1.01it/s]\n 50%|█████ | 8/16 [00:07<00:07, 1.01it/s]\n 56%|█████▋ | 9/16 [00:08<00:06, 1.01it/s]\n 62%|██████▎ | 10/16 [00:09<00:05, 1.01it/s]\n 69%|██████▉ | 11/16 [00:10<00:04, 1.01it/s]\n 75%|███████▌ | 12/16 [00:11<00:03, 1.01it/s]\n 81%|████████▏ | 13/16 [00:12<00:02, 1.01it/s]\n 88%|████████▊ | 14/16 [00:13<00:01, 1.01it/s]\n 94%|█████████▍| 15/16 [00:14<00:00, 1.01it/s]\n100%|██████████| 16/16 [00:15<00:00, 1.01it/s]\n100%|██████████| 16/16 [00:15<00:00, 1.01it/s]\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:23, 1.01it/s]\n 8%|▊ | 2/25 [00:01<00:22, 1.01it/s]\n 12%|█▏ | 3/25 [00:02<00:21, 1.01it/s]\n 16%|█▌ | 4/25 [00:03<00:20, 1.01it/s]\n 20%|██ | 5/25 [00:04<00:19, 1.01it/s]\n 24%|██▍ | 6/25 [00:05<00:18, 1.01it/s]\n 28%|██▊ | 7/25 [00:06<00:17, 1.01it/s]\n 32%|███▏ | 8/25 [00:07<00:16, 1.01it/s]\n 36%|███▌ | 9/25 [00:08<00:15, 1.01it/s]\n 40%|████ | 10/25 [00:09<00:14, 1.01it/s]\n 44%|████▍ | 11/25 [00:10<00:13, 1.01it/s]\n 48%|████▊ | 12/25 [00:11<00:12, 1.01it/s]\n 52%|█████▏ | 13/25 [00:12<00:11, 1.01it/s]\n 56%|█████▌ | 14/25 [00:13<00:10, 1.01it/s]\n 60%|██████ | 15/25 [00:14<00:09, 1.01it/s]\n 64%|██████▍ | 16/25 [00:15<00:08, 1.01it/s]\n 68%|██████▊ | 17/25 [00:16<00:07, 1.01it/s]\n 72%|███████▏ | 18/25 [00:17<00:06, 1.01it/s]\n 76%|███████▌ | 19/25 [00:18<00:05, 1.00it/s]\n 80%|████████ | 20/25 [00:19<00:04, 1.00it/s]\n 84%|████████▍ | 21/25 [00:20<00:03, 1.00it/s]\n 88%|████████▊ | 22/25 [00:21<00:02, 1.00it/s]\n 92%|█████████▏| 23/25 [00:22<00:01, 1.00it/s]\n 96%|█████████▌| 24/25 [00:23<00:00, 1.00it/s]\n100%|██████████| 25/25 [00:24<00:00, 1.00it/s]\n100%|██████████| 25/25 [00:24<00:00, 1.01it/s]", "metrics": { "predict_time": 58.562372966, "total_time": 260.975217 }, "output": "https://replicate.delivery/pbxt/XlcmteAav9R8QaskFAm3TqB7e2ZMUQzqeree3kYxytDdMhUaC/output.gif", "started_at": "2024-08-14T11:08:26.147844Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/1pw39brdyxrgp0cha2889vvwg0", "cancel": "https://api.replicate.com/v1/predictions/1pw39brdyxrgp0cha2889vvwg0/cancel" }, "version": "a4be06ddb54c1fed336bb1bd22d317d0f2ee3b8f712dbb150aad4c7053b2493e" }Generated inUsing seed: 3885112135 0%| | 0/8 [00:00<?, ?it/s] 12%|█▎ | 1/8 [00:01<00:10, 1.56s/it] 25%|██▌ | 2/8 [00:02<00:07, 1.22s/it] 38%|███▊ | 3/8 [00:03<00:05, 1.11s/it] 50%|█████ | 4/8 [00:04<00:04, 1.06s/it] 62%|██████▎ | 5/8 [00:05<00:03, 1.03s/it] 75%|███████▌ | 6/8 [00:06<00:02, 1.02s/it] 88%|████████▊ | 7/8 [00:07<00:01, 1.01s/it] 100%|██████████| 8/8 [00:08<00:00, 1.00s/it] 100%|██████████| 8/8 [00:08<00:00, 1.06s/it] 0%| | 0/16 [00:00<?, ?it/s] 6%|▋ | 1/16 [00:00<00:14, 1.01it/s] 12%|█▎ | 2/16 [00:01<00:13, 1.01it/s] 19%|█▉ | 3/16 [00:02<00:12, 1.01it/s] 25%|██▌ | 4/16 [00:03<00:11, 1.01it/s] 31%|███▏ | 5/16 [00:04<00:10, 1.01it/s] 38%|███▊ | 6/16 [00:05<00:09, 1.01it/s] 44%|████▍ | 7/16 [00:06<00:08, 1.01it/s] 50%|█████ | 8/16 [00:07<00:07, 1.01it/s] 56%|█████▋ | 9/16 [00:08<00:06, 1.01it/s] 62%|██████▎ | 10/16 [00:09<00:05, 1.01it/s] 69%|██████▉ | 11/16 [00:10<00:04, 1.01it/s] 75%|███████▌ | 12/16 [00:11<00:03, 1.01it/s] 81%|████████▏ | 13/16 [00:12<00:02, 1.01it/s] 88%|████████▊ | 14/16 [00:13<00:01, 1.01it/s] 94%|█████████▍| 15/16 [00:14<00:00, 1.01it/s] 100%|██████████| 16/16 [00:15<00:00, 1.01it/s] 100%|██████████| 16/16 [00:15<00:00, 1.01it/s] 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:23, 1.01it/s] 8%|▊ | 2/25 [00:01<00:22, 1.01it/s] 12%|█▏ | 3/25 [00:02<00:21, 1.01it/s] 16%|█▌ | 4/25 [00:03<00:20, 1.01it/s] 20%|██ | 5/25 [00:04<00:19, 1.01it/s] 24%|██▍ | 6/25 [00:05<00:18, 1.01it/s] 28%|██▊ | 7/25 [00:06<00:17, 1.01it/s] 32%|███▏ | 8/25 [00:07<00:16, 1.01it/s] 36%|███▌ | 9/25 [00:08<00:15, 1.01it/s] 40%|████ | 10/25 [00:09<00:14, 1.01it/s] 44%|████▍ | 11/25 [00:10<00:13, 1.01it/s] 48%|████▊ | 12/25 [00:11<00:12, 1.01it/s] 52%|█████▏ | 13/25 [00:12<00:11, 1.01it/s] 56%|█████▌ | 14/25 [00:13<00:10, 1.01it/s] 60%|██████ | 15/25 [00:14<00:09, 1.01it/s] 64%|██████▍ | 16/25 [00:15<00:08, 1.01it/s] 68%|██████▊ | 17/25 [00:16<00:07, 1.01it/s] 72%|███████▏ | 18/25 [00:17<00:06, 1.01it/s] 76%|███████▌ | 19/25 [00:18<00:05, 1.00it/s] 80%|████████ | 20/25 [00:19<00:04, 1.00it/s] 84%|████████▍ | 21/25 [00:20<00:03, 1.00it/s] 88%|████████▊ | 22/25 [00:21<00:02, 1.00it/s] 92%|█████████▏| 23/25 [00:22<00:01, 1.00it/s] 96%|█████████▌| 24/25 [00:23<00:00, 1.00it/s] 100%|██████████| 25/25 [00:24<00:00, 1.00it/s] 100%|██████████| 25/25 [00:24<00:00, 1.01it/s]Prediction
chamuditha4/anime_diff-oolong:1edabe2555f533b910a57e4843b73ea16d2ff71caebcd71c19832f5d94a6c796ID3krc7r422drgp0ch9m4bv7m5j0StatusSucceededSourceWebHardwareA40Total durationCreatedInput
- prompt
- a panda playing a guitar, on a boat, in the ocean, high quality
- num_frames
- 24
- guidance_scale
- 7.5
- negative_prompt
- bad quality, worse quality
- num_inference_steps
- 25
{ "prompt": "a panda playing a guitar, on a boat, in the ocean, high quality", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "bad quality, worse quality", "num_inference_steps": 25 }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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chamuditha4/anime_diff-oolong:1edabe2555f533b910a57e4843b73ea16d2ff71caebcd71c19832f5d94a6c796", { input: { prompt: "a panda playing a guitar, on a boat, in the ocean, high quality", num_frames: 24, guidance_scale: 7.5, negative_prompt: "bad quality, worse quality", num_inference_steps: 25 } } ); // 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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chamuditha4/anime_diff-oolong:1edabe2555f533b910a57e4843b73ea16d2ff71caebcd71c19832f5d94a6c796", input={ "prompt": "a panda playing a guitar, on a boat, in the ocean, high quality", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "bad quality, worse quality", "num_inference_steps": 25 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run chamuditha4/anime_diff-oolong 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": "chamuditha4/anime_diff-oolong:1edabe2555f533b910a57e4843b73ea16d2ff71caebcd71c19832f5d94a6c796", "input": { "prompt": "a panda playing a guitar, on a boat, in the ocean, high quality", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "bad quality, worse quality", "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-13T18:48:20.455945Z", "created_at": "2024-08-13T18:38:08.915000Z", "data_removed": false, "error": null, "id": "3krc7r422drgp0ch9m4bv7m5j0", "input": { "prompt": "a panda playing a guitar, on a boat, in the ocean, high quality", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "bad quality, worse quality", "num_inference_steps": 25 }, "logs": "0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:01<00:33, 1.40s/it]\n 8%|▊ | 2/25 [00:02<00:26, 1.16s/it]\n 12%|█▏ | 3/25 [00:03<00:23, 1.08s/it]\n 16%|█▌ | 4/25 [00:04<00:21, 1.04s/it]\n 20%|██ | 5/25 [00:05<00:20, 1.03s/it]\n 24%|██▍ | 6/25 [00:06<00:19, 1.01s/it]\n 28%|██▊ | 7/25 [00:07<00:18, 1.01s/it]\n 32%|███▏ | 8/25 [00:08<00:17, 1.00s/it]\n 36%|███▌ | 9/25 [00:09<00:15, 1.00it/s]\n 40%|████ | 10/25 [00:10<00:14, 1.00it/s]\n 44%|████▍ | 11/25 [00:11<00:13, 1.00it/s]\n 48%|████▊ | 12/25 [00:12<00:12, 1.00it/s]\n 52%|█████▏ | 13/25 [00:13<00:11, 1.00it/s]\n 56%|█████▌ | 14/25 [00:14<00:10, 1.00it/s]\n 60%|██████ | 15/25 [00:15<00:09, 1.00it/s]\n 64%|██████▍ | 16/25 [00:16<00:08, 1.01it/s]\n 68%|██████▊ | 17/25 [00:17<00:07, 1.01it/s]\n 72%|███████▏ | 18/25 [00:18<00:06, 1.01it/s]\n 76%|███████▌ | 19/25 [00:19<00:05, 1.01it/s]\n 80%|████████ | 20/25 [00:20<00:04, 1.01it/s]\n 84%|████████▍ | 21/25 [00:21<00:03, 1.01it/s]\n 88%|████████▊ | 22/25 [00:22<00:02, 1.01it/s]\n 92%|█████████▏| 23/25 [00:23<00:01, 1.01it/s]\n 96%|█████████▌| 24/25 [00:24<00:00, 1.01it/s]\n100%|██████████| 25/25 [00:25<00:00, 1.01it/s]\n100%|██████████| 25/25 [00:25<00:00, 1.01s/it]", "metrics": { "predict_time": 31.972540151, "total_time": 611.540945 }, "output": "https://replicate.delivery/pbxt/eDaGuGU7YOWfF0vXG23NY5Fm2QgzrReQ0ka3olKkqegJHXJNB/output.gif", "started_at": "2024-08-13T18:47:48.483404Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/3krc7r422drgp0ch9m4bv7m5j0", "cancel": "https://api.replicate.com/v1/predictions/3krc7r422drgp0ch9m4bv7m5j0/cancel" }, "version": "1edabe2555f533b910a57e4843b73ea16d2ff71caebcd71c19832f5d94a6c796" }Generated in0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:01<00:33, 1.40s/it] 8%|▊ | 2/25 [00:02<00:26, 1.16s/it] 12%|█▏ | 3/25 [00:03<00:23, 1.08s/it] 16%|█▌ | 4/25 [00:04<00:21, 1.04s/it] 20%|██ | 5/25 [00:05<00:20, 1.03s/it] 24%|██▍ | 6/25 [00:06<00:19, 1.01s/it] 28%|██▊ | 7/25 [00:07<00:18, 1.01s/it] 32%|███▏ | 8/25 [00:08<00:17, 1.00s/it] 36%|███▌ | 9/25 [00:09<00:15, 1.00it/s] 40%|████ | 10/25 [00:10<00:14, 1.00it/s] 44%|████▍ | 11/25 [00:11<00:13, 1.00it/s] 48%|████▊ | 12/25 [00:12<00:12, 1.00it/s] 52%|█████▏ | 13/25 [00:13<00:11, 1.00it/s] 56%|█████▌ | 14/25 [00:14<00:10, 1.00it/s] 60%|██████ | 15/25 [00:15<00:09, 1.00it/s] 64%|██████▍ | 16/25 [00:16<00:08, 1.01it/s] 68%|██████▊ | 17/25 [00:17<00:07, 1.01it/s] 72%|███████▏ | 18/25 [00:18<00:06, 1.01it/s] 76%|███████▌ | 19/25 [00:19<00:05, 1.01it/s] 80%|████████ | 20/25 [00:20<00:04, 1.01it/s] 84%|████████▍ | 21/25 [00:21<00:03, 1.01it/s] 88%|████████▊ | 22/25 [00:22<00:02, 1.01it/s] 92%|█████████▏| 23/25 [00:23<00:01, 1.01it/s] 96%|█████████▌| 24/25 [00:24<00:00, 1.01it/s] 100%|██████████| 25/25 [00:25<00:00, 1.01it/s] 100%|██████████| 25/25 [00:25<00:00, 1.01s/it]Prediction
chamuditha4/anime_diff-oolong:a4be06ddb54c1fed336bb1bd22d317d0f2ee3b8f712dbb150aad4c7053b2493eIDcrk8ecbjsdrgm0cha2ba1fsgj4StatusSucceededSourceWebHardwareA40Total durationCreatedInput
- prompt
- Astronautas tocando y cantando rock en la luna
- num_frames
- 24
- guidance_scale
- 7.5
- negative_prompt
- badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth
- num_inference_steps
- 25
{ "prompt": "Astronautas tocando y cantando rock en la luna ", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 }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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chamuditha4/anime_diff-oolong:a4be06ddb54c1fed336bb1bd22d317d0f2ee3b8f712dbb150aad4c7053b2493e", { input: { prompt: "Astronautas tocando y cantando rock en la luna ", num_frames: 24, guidance_scale: 7.5, negative_prompt: "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", num_inference_steps: 25 } } ); // 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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chamuditha4/anime_diff-oolong:a4be06ddb54c1fed336bb1bd22d317d0f2ee3b8f712dbb150aad4c7053b2493e", input={ "prompt": "Astronautas tocando y cantando rock en la luna ", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run chamuditha4/anime_diff-oolong 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": "chamuditha4/anime_diff-oolong:a4be06ddb54c1fed336bb1bd22d317d0f2ee3b8f712dbb150aad4c7053b2493e", "input": { "prompt": "Astronautas tocando y cantando rock en la luna ", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-14T11:12:34.904232Z", "created_at": "2024-08-14T11:12:02.763000Z", "data_removed": false, "error": null, "id": "crk8ecbjsdrgm0cha2ba1fsgj4", "input": { "prompt": "Astronautas tocando y cantando rock en la luna ", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 }, "logs": "Using seed: 502735675\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:01<00:30, 1.29s/it]\n 8%|▊ | 2/25 [00:02<00:25, 1.11s/it]\n 12%|█▏ | 3/25 [00:03<00:23, 1.05s/it]\n 16%|█▌ | 4/25 [00:04<00:21, 1.03s/it]\n 20%|██ | 5/25 [00:05<00:20, 1.01s/it]\n 24%|██▍ | 6/25 [00:06<00:19, 1.01s/it]\n 28%|██▊ | 7/25 [00:07<00:17, 1.00it/s]\n 32%|███▏ | 8/25 [00:08<00:16, 1.00it/s]\n 36%|███▌ | 9/25 [00:09<00:15, 1.01it/s]\n 40%|████ | 10/25 [00:10<00:14, 1.01it/s]\n 44%|████▍ | 11/25 [00:11<00:13, 1.01it/s]\n 48%|████▊ | 12/25 [00:12<00:12, 1.01it/s]\n 52%|█████▏ | 13/25 [00:13<00:11, 1.01it/s]\n 56%|█████▌ | 14/25 [00:14<00:10, 1.01it/s]\n 60%|██████ | 15/25 [00:15<00:09, 1.01it/s]\n 64%|██████▍ | 16/25 [00:16<00:08, 1.01it/s]\n 68%|██████▊ | 17/25 [00:17<00:07, 1.01it/s]\n 72%|███████▏ | 18/25 [00:18<00:06, 1.01it/s]\n 76%|███████▌ | 19/25 [00:19<00:05, 1.01it/s]\n 80%|████████ | 20/25 [00:20<00:04, 1.01it/s]\n 84%|████████▍ | 21/25 [00:21<00:03, 1.01it/s]\n 88%|████████▊ | 22/25 [00:22<00:02, 1.01it/s]\n 92%|█████████▏| 23/25 [00:23<00:01, 1.01it/s]\n 96%|█████████▌| 24/25 [00:24<00:00, 1.01it/s]\n100%|██████████| 25/25 [00:25<00:00, 1.01it/s]\n100%|██████████| 25/25 [00:25<00:00, 1.00s/it]", "metrics": { "predict_time": 32.025696419, "total_time": 32.141232 }, "output": "https://replicate.delivery/pbxt/KCcQeH53Lv0FRq2XUnUHSDW718QiqZfA66AvK8Qx8gYhMkSTA/output.gif", "started_at": "2024-08-14T11:12:02.878536Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/crk8ecbjsdrgm0cha2ba1fsgj4", "cancel": "https://api.replicate.com/v1/predictions/crk8ecbjsdrgm0cha2ba1fsgj4/cancel" }, "version": "a4be06ddb54c1fed336bb1bd22d317d0f2ee3b8f712dbb150aad4c7053b2493e" }Generated inUsing seed: 502735675 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:01<00:30, 1.29s/it] 8%|▊ | 2/25 [00:02<00:25, 1.11s/it] 12%|█▏ | 3/25 [00:03<00:23, 1.05s/it] 16%|█▌ | 4/25 [00:04<00:21, 1.03s/it] 20%|██ | 5/25 [00:05<00:20, 1.01s/it] 24%|██▍ | 6/25 [00:06<00:19, 1.01s/it] 28%|██▊ | 7/25 [00:07<00:17, 1.00it/s] 32%|███▏ | 8/25 [00:08<00:16, 1.00it/s] 36%|███▌ | 9/25 [00:09<00:15, 1.01it/s] 40%|████ | 10/25 [00:10<00:14, 1.01it/s] 44%|████▍ | 11/25 [00:11<00:13, 1.01it/s] 48%|████▊ | 12/25 [00:12<00:12, 1.01it/s] 52%|█████▏ | 13/25 [00:13<00:11, 1.01it/s] 56%|█████▌ | 14/25 [00:14<00:10, 1.01it/s] 60%|██████ | 15/25 [00:15<00:09, 1.01it/s] 64%|██████▍ | 16/25 [00:16<00:08, 1.01it/s] 68%|██████▊ | 17/25 [00:17<00:07, 1.01it/s] 72%|███████▏ | 18/25 [00:18<00:06, 1.01it/s] 76%|███████▌ | 19/25 [00:19<00:05, 1.01it/s] 80%|████████ | 20/25 [00:20<00:04, 1.01it/s] 84%|████████▍ | 21/25 [00:21<00:03, 1.01it/s] 88%|████████▊ | 22/25 [00:22<00:02, 1.01it/s] 92%|█████████▏| 23/25 [00:23<00:01, 1.01it/s] 96%|█████████▌| 24/25 [00:24<00:00, 1.01it/s] 100%|██████████| 25/25 [00:25<00:00, 1.01it/s] 100%|██████████| 25/25 [00:25<00:00, 1.00s/it]Prediction
chamuditha4/anime_diff-oolong:1edabe2555f533b910a57e4843b73ea16d2ff71caebcd71c19832f5d94a6c796IDqn3s226ahdrgg0ch9m98ygdqgmStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- prompt
- Scene: Global Cataclysm, Event 12,000 years ago that possibly destroyed ancient sites, Massive destruction, Natural disasters, Ruins of ancient civilizations, located in various ancient sites, National Geographic documentary style, captivating storytelling, immersive visuals, natural lighting, high resolution, 8K, photorealistic, hyper-detailed, vibrant colors, detailed environments, professional photography."
- num_frames
- 24
- guidance_scale
- 7.5
- negative_prompt
- bad quality, worse quality
- num_inference_steps
- 25
{ "prompt": "Scene: Global Cataclysm, Event 12,000 years ago that possibly destroyed ancient sites, Massive destruction, Natural disasters, Ruins of ancient civilizations, located in various ancient sites, National Geographic documentary style, captivating storytelling, immersive visuals, natural lighting, high resolution, 8K, photorealistic, hyper-detailed, vibrant colors, detailed environments, professional photography.\"", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "bad quality, worse quality", "num_inference_steps": 25 }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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chamuditha4/anime_diff-oolong:1edabe2555f533b910a57e4843b73ea16d2ff71caebcd71c19832f5d94a6c796", { input: { prompt: "Scene: Global Cataclysm, Event 12,000 years ago that possibly destroyed ancient sites, Massive destruction, Natural disasters, Ruins of ancient civilizations, located in various ancient sites, National Geographic documentary style, captivating storytelling, immersive visuals, natural lighting, high resolution, 8K, photorealistic, hyper-detailed, vibrant colors, detailed environments, professional photography.\"", num_frames: 24, guidance_scale: 7.5, negative_prompt: "bad quality, worse quality", num_inference_steps: 25 } } ); // 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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chamuditha4/anime_diff-oolong:1edabe2555f533b910a57e4843b73ea16d2ff71caebcd71c19832f5d94a6c796", input={ "prompt": "Scene: Global Cataclysm, Event 12,000 years ago that possibly destroyed ancient sites, Massive destruction, Natural disasters, Ruins of ancient civilizations, located in various ancient sites, National Geographic documentary style, captivating storytelling, immersive visuals, natural lighting, high resolution, 8K, photorealistic, hyper-detailed, vibrant colors, detailed environments, professional photography.\"", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "bad quality, worse quality", "num_inference_steps": 25 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run chamuditha4/anime_diff-oolong 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": "chamuditha4/anime_diff-oolong:1edabe2555f533b910a57e4843b73ea16d2ff71caebcd71c19832f5d94a6c796", "input": { "prompt": "Scene: Global Cataclysm, Event 12,000 years ago that possibly destroyed ancient sites, Massive destruction, Natural disasters, Ruins of ancient civilizations, located in various ancient sites, National Geographic documentary style, captivating storytelling, immersive visuals, natural lighting, high resolution, 8K, photorealistic, hyper-detailed, vibrant colors, detailed environments, professional photography.\\"", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "bad quality, worse quality", "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-13T18:55:01.141115Z", "created_at": "2024-08-13T18:49:22.827000Z", "data_removed": false, "error": null, "id": "qn3s226ahdrgg0ch9m98ygdqgm", "input": { "prompt": "Scene: Global Cataclysm, Event 12,000 years ago that possibly destroyed ancient sites, Massive destruction, Natural disasters, Ruins of ancient civilizations, located in various ancient sites, National Geographic documentary style, captivating storytelling, immersive visuals, natural lighting, high resolution, 8K, photorealistic, hyper-detailed, vibrant colors, detailed environments, professional photography.\"", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "bad quality, worse quality", "num_inference_steps": 25 }, "logs": "Token indices sequence length is longer than the specified maximum sequence length for this model (80 > 77). Running this sequence through the model will result in indexing errors\nThe following part of your input was truncated because CLIP can only handle sequences up to 77 tokens: ['professional photography.\"']\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:01<00:35, 1.48s/it]\n 8%|▊ | 2/25 [00:02<00:27, 1.19s/it]\n 12%|█▏ | 3/25 [00:03<00:24, 1.10s/it]\n 16%|█▌ | 4/25 [00:04<00:22, 1.06s/it]\n 20%|██ | 5/25 [00:05<00:20, 1.04s/it]\n 24%|██▍ | 6/25 [00:06<00:19, 1.02s/it]\n 28%|██▊ | 7/25 [00:07<00:18, 1.02s/it]\n 32%|███▏ | 8/25 [00:08<00:17, 1.01s/it]\n 36%|███▌ | 9/25 [00:09<00:16, 1.01s/it]\n 40%|████ | 10/25 [00:10<00:15, 1.01s/it]\n 44%|████▍ | 11/25 [00:11<00:14, 1.01s/it]\n 48%|████▊ | 12/25 [00:12<00:13, 1.00s/it]\n 52%|█████▏ | 13/25 [00:13<00:12, 1.00s/it]\n 56%|█████▌ | 14/25 [00:14<00:11, 1.00s/it]\n 60%|██████ | 15/25 [00:15<00:10, 1.01s/it]\n 64%|██████▍ | 16/25 [00:16<00:09, 1.00s/it]\n 68%|██████▊ | 17/25 [00:17<00:08, 1.00s/it]\n 72%|███████▏ | 18/25 [00:18<00:07, 1.00s/it]\n 76%|███████▌ | 19/25 [00:19<00:06, 1.00s/it]\n 80%|████████ | 20/25 [00:20<00:04, 1.00it/s]\n 84%|████████▍ | 21/25 [00:21<00:03, 1.00it/s]\n 88%|████████▊ | 22/25 [00:22<00:02, 1.00it/s]\n 92%|█████████▏| 23/25 [00:23<00:01, 1.00it/s]\n 96%|█████████▌| 24/25 [00:24<00:00, 1.00it/s]\n100%|██████████| 25/25 [00:25<00:00, 1.00it/s]\n100%|██████████| 25/25 [00:25<00:00, 1.02s/it]", "metrics": { "predict_time": 32.767462039, "total_time": 338.314115 }, "output": "https://replicate.delivery/pbxt/B7dzw6qceGRqfU0ShU4H1njQ6NejQZf2Refb7PLp8hD1AeKpJA/output.gif", "started_at": "2024-08-13T18:54:28.373653Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/qn3s226ahdrgg0ch9m98ygdqgm", "cancel": "https://api.replicate.com/v1/predictions/qn3s226ahdrgg0ch9m98ygdqgm/cancel" }, "version": "1edabe2555f533b910a57e4843b73ea16d2ff71caebcd71c19832f5d94a6c796" }Generated inToken indices sequence length is longer than the specified maximum sequence length for this model (80 > 77). Running this sequence through the model will result in indexing errors The following part of your input was truncated because CLIP can only handle sequences up to 77 tokens: ['professional photography."'] 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:01<00:35, 1.48s/it] 8%|▊ | 2/25 [00:02<00:27, 1.19s/it] 12%|█▏ | 3/25 [00:03<00:24, 1.10s/it] 16%|█▌ | 4/25 [00:04<00:22, 1.06s/it] 20%|██ | 5/25 [00:05<00:20, 1.04s/it] 24%|██▍ | 6/25 [00:06<00:19, 1.02s/it] 28%|██▊ | 7/25 [00:07<00:18, 1.02s/it] 32%|███▏ | 8/25 [00:08<00:17, 1.01s/it] 36%|███▌ | 9/25 [00:09<00:16, 1.01s/it] 40%|████ | 10/25 [00:10<00:15, 1.01s/it] 44%|████▍ | 11/25 [00:11<00:14, 1.01s/it] 48%|████▊ | 12/25 [00:12<00:13, 1.00s/it] 52%|█████▏ | 13/25 [00:13<00:12, 1.00s/it] 56%|█████▌ | 14/25 [00:14<00:11, 1.00s/it] 60%|██████ | 15/25 [00:15<00:10, 1.01s/it] 64%|██████▍ | 16/25 [00:16<00:09, 1.00s/it] 68%|██████▊ | 17/25 [00:17<00:08, 1.00s/it] 72%|███████▏ | 18/25 [00:18<00:07, 1.00s/it] 76%|███████▌ | 19/25 [00:19<00:06, 1.00s/it] 80%|████████ | 20/25 [00:20<00:04, 1.00it/s] 84%|████████▍ | 21/25 [00:21<00:03, 1.00it/s] 88%|████████▊ | 22/25 [00:22<00:02, 1.00it/s] 92%|█████████▏| 23/25 [00:23<00:01, 1.00it/s] 96%|█████████▌| 24/25 [00:24<00:00, 1.00it/s] 100%|██████████| 25/25 [00:25<00:00, 1.00it/s] 100%|██████████| 25/25 [00:25<00:00, 1.02s/it]Prediction
chamuditha4/anime_diff-oolong:a17c5f8e808c12919639046dd8a3145443c62490f531caa6234f598414184765IDja00tgjg51rgp0cha5q88qp86cStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- model
- disney
- prompt
- masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes
- num_frames
- 24
- guidance_scale
- 7.5
- negative_prompt
- badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth
- num_inference_steps
- 25
{ "model": "disney", "prompt": "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 }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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chamuditha4/anime_diff-oolong:a17c5f8e808c12919639046dd8a3145443c62490f531caa6234f598414184765", { input: { model: "disney", prompt: "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes", num_frames: 24, guidance_scale: 7.5, negative_prompt: "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", num_inference_steps: 25 } } ); // 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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chamuditha4/anime_diff-oolong:a17c5f8e808c12919639046dd8a3145443c62490f531caa6234f598414184765", input={ "model": "disney", "prompt": "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run chamuditha4/anime_diff-oolong 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": "chamuditha4/anime_diff-oolong:a17c5f8e808c12919639046dd8a3145443c62490f531caa6234f598414184765", "input": { "model": "disney", "prompt": "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-14T15:12:39.230133Z", "created_at": "2024-08-14T15:07:49.672000Z", "data_removed": false, "error": null, "id": "ja00tgjg51rgp0cha5q88qp86c", "input": { "model": "disney", "prompt": "masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 }, "logs": "/root/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/_utils.py:776: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\nreturn self.fget.__get__(instance, owner)()\nThe config attributes {'motion_activation_fn': 'geglu', 'motion_attention_bias': False, 'motion_cross_attention_dim': None} were passed to MotionAdapter, but are not expected and will be ignored. Please verify your config.json configuration file.\nLoading pipeline components...: 0%| | 0/6 [00:00<?, ?it/s]\nLoading pipeline components...: 50%|█████ | 3/6 [00:00<00:00, 9.38it/s]\nLoading pipeline components...: 67%|██████▋ | 4/6 [00:00<00:00, 7.96it/s]/root/.pyenv/versions/3.10.14/lib/python3.10/site-packages/transformers/models/clip/feature_extraction_clip.py:28: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use CLIPImageProcessor instead.\nwarnings.warn(\nLoading pipeline components...: 100%|██████████| 6/6 [00:00<00:00, 10.68it/s]\nUsing seed: 4294389149\n 0%| | 0/8 [00:00<?, ?it/s]\n 12%|█▎ | 1/8 [00:01<00:10, 1.54s/it]\n 25%|██▌ | 2/8 [00:02<00:07, 1.22s/it]\n 38%|███▊ | 3/8 [00:03<00:05, 1.11s/it]\n 50%|█████ | 4/8 [00:04<00:04, 1.07s/it]\n 62%|██████▎ | 5/8 [00:05<00:03, 1.04s/it]\n 75%|███████▌ | 6/8 [00:06<00:02, 1.02s/it]\n 88%|████████▊ | 7/8 [00:07<00:01, 1.01s/it]\n100%|██████████| 8/8 [00:08<00:00, 1.00s/it]\n100%|██████████| 8/8 [00:08<00:00, 1.06s/it]\n 0%| | 0/16 [00:00<?, ?it/s]\n 6%|▋ | 1/16 [00:00<00:14, 1.01it/s]\n 12%|█▎ | 2/16 [00:01<00:13, 1.01it/s]\n 19%|█▉ | 3/16 [00:02<00:12, 1.01it/s]\n 25%|██▌ | 4/16 [00:03<00:11, 1.01it/s]\n 31%|███▏ | 5/16 [00:04<00:10, 1.01it/s]\n 38%|███▊ | 6/16 [00:05<00:09, 1.01it/s]\n 44%|████▍ | 7/16 [00:06<00:08, 1.01it/s]\n 50%|█████ | 8/16 [00:07<00:07, 1.01it/s]\n 56%|█████▋ | 9/16 [00:08<00:06, 1.01it/s]\n 62%|██████▎ | 10/16 [00:09<00:05, 1.01it/s]\n 69%|██████▉ | 11/16 [00:10<00:04, 1.01it/s]\n 75%|███████▌ | 12/16 [00:11<00:03, 1.01it/s]\n 81%|████████▏ | 13/16 [00:12<00:02, 1.01it/s]\n 88%|████████▊ | 14/16 [00:13<00:01, 1.01it/s]\n 94%|█████████▍| 15/16 [00:14<00:00, 1.01it/s]\n100%|██████████| 16/16 [00:15<00:00, 1.01it/s]\n100%|██████████| 16/16 [00:15<00:00, 1.01it/s]\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:23, 1.01it/s]\n 8%|▊ | 2/25 [00:01<00:22, 1.01it/s]\n 12%|█▏ | 3/25 [00:02<00:21, 1.00it/s]\n 16%|█▌ | 4/25 [00:03<00:20, 1.00it/s]\n 20%|██ | 5/25 [00:04<00:19, 1.00it/s]\n 24%|██▍ | 6/25 [00:05<00:18, 1.00it/s]\n 28%|██▊ | 7/25 [00:06<00:17, 1.00it/s]\n 32%|███▏ | 8/25 [00:07<00:16, 1.00it/s]\n 36%|███▌ | 9/25 [00:08<00:15, 1.00it/s]\n 40%|████ | 10/25 [00:09<00:14, 1.00it/s]\n 44%|████▍ | 11/25 [00:10<00:13, 1.00it/s]\n 48%|████▊ | 12/25 [00:11<00:12, 1.00it/s]\n 52%|█████▏ | 13/25 [00:12<00:11, 1.00it/s]\n 56%|█████▌ | 14/25 [00:13<00:10, 1.00it/s]\n 60%|██████ | 15/25 [00:14<00:09, 1.00it/s]\n 64%|██████▍ | 16/25 [00:15<00:08, 1.00it/s]\n 68%|██████▊ | 17/25 [00:16<00:07, 1.00it/s]\n 72%|███████▏ | 18/25 [00:17<00:06, 1.00it/s]\n 76%|███████▌ | 19/25 [00:18<00:05, 1.00it/s]\n 80%|████████ | 20/25 [00:19<00:04, 1.00it/s]\n 84%|████████▍ | 21/25 [00:20<00:03, 1.00it/s]\n 88%|████████▊ | 22/25 [00:21<00:02, 1.00it/s]\n 92%|█████████▏| 23/25 [00:22<00:01, 1.00it/s]\n 96%|█████████▌| 24/25 [00:23<00:00, 1.00it/s]\n100%|██████████| 25/25 [00:24<00:00, 1.00it/s]\n100%|██████████| 25/25 [00:24<00:00, 1.00it/s]", "metrics": { "predict_time": 71.533237745, "total_time": 289.558133 }, "output": "https://replicate.delivery/pbxt/fwRqCBh4IlReDEF338jqjJe7OLLDyYsB7Ebmw3D6KHHLbPlmA/output.gif", "started_at": "2024-08-14T15:11:27.696896Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ja00tgjg51rgp0cha5q88qp86c", "cancel": "https://api.replicate.com/v1/predictions/ja00tgjg51rgp0cha5q88qp86c/cancel" }, "version": "a17c5f8e808c12919639046dd8a3145443c62490f531caa6234f598414184765" }Generated in/root/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/_utils.py:776: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() return self.fget.__get__(instance, owner)() The config attributes {'motion_activation_fn': 'geglu', 'motion_attention_bias': False, 'motion_cross_attention_dim': None} were passed to MotionAdapter, but are not expected and will be ignored. Please verify your config.json configuration file. Loading pipeline components...: 0%| | 0/6 [00:00<?, ?it/s] Loading pipeline components...: 50%|█████ | 3/6 [00:00<00:00, 9.38it/s] Loading pipeline components...: 67%|██████▋ | 4/6 [00:00<00:00, 7.96it/s]/root/.pyenv/versions/3.10.14/lib/python3.10/site-packages/transformers/models/clip/feature_extraction_clip.py:28: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use CLIPImageProcessor instead. warnings.warn( Loading pipeline components...: 100%|██████████| 6/6 [00:00<00:00, 10.68it/s] Using seed: 4294389149 0%| | 0/8 [00:00<?, ?it/s] 12%|█▎ | 1/8 [00:01<00:10, 1.54s/it] 25%|██▌ | 2/8 [00:02<00:07, 1.22s/it] 38%|███▊ | 3/8 [00:03<00:05, 1.11s/it] 50%|█████ | 4/8 [00:04<00:04, 1.07s/it] 62%|██████▎ | 5/8 [00:05<00:03, 1.04s/it] 75%|███████▌ | 6/8 [00:06<00:02, 1.02s/it] 88%|████████▊ | 7/8 [00:07<00:01, 1.01s/it] 100%|██████████| 8/8 [00:08<00:00, 1.00s/it] 100%|██████████| 8/8 [00:08<00:00, 1.06s/it] 0%| | 0/16 [00:00<?, ?it/s] 6%|▋ | 1/16 [00:00<00:14, 1.01it/s] 12%|█▎ | 2/16 [00:01<00:13, 1.01it/s] 19%|█▉ | 3/16 [00:02<00:12, 1.01it/s] 25%|██▌ | 4/16 [00:03<00:11, 1.01it/s] 31%|███▏ | 5/16 [00:04<00:10, 1.01it/s] 38%|███▊ | 6/16 [00:05<00:09, 1.01it/s] 44%|████▍ | 7/16 [00:06<00:08, 1.01it/s] 50%|█████ | 8/16 [00:07<00:07, 1.01it/s] 56%|█████▋ | 9/16 [00:08<00:06, 1.01it/s] 62%|██████▎ | 10/16 [00:09<00:05, 1.01it/s] 69%|██████▉ | 11/16 [00:10<00:04, 1.01it/s] 75%|███████▌ | 12/16 [00:11<00:03, 1.01it/s] 81%|████████▏ | 13/16 [00:12<00:02, 1.01it/s] 88%|████████▊ | 14/16 [00:13<00:01, 1.01it/s] 94%|█████████▍| 15/16 [00:14<00:00, 1.01it/s] 100%|██████████| 16/16 [00:15<00:00, 1.01it/s] 100%|██████████| 16/16 [00:15<00:00, 1.01it/s] 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:23, 1.01it/s] 8%|▊ | 2/25 [00:01<00:22, 1.01it/s] 12%|█▏ | 3/25 [00:02<00:21, 1.00it/s] 16%|█▌ | 4/25 [00:03<00:20, 1.00it/s] 20%|██ | 5/25 [00:04<00:19, 1.00it/s] 24%|██▍ | 6/25 [00:05<00:18, 1.00it/s] 28%|██▊ | 7/25 [00:06<00:17, 1.00it/s] 32%|███▏ | 8/25 [00:07<00:16, 1.00it/s] 36%|███▌ | 9/25 [00:08<00:15, 1.00it/s] 40%|████ | 10/25 [00:09<00:14, 1.00it/s] 44%|████▍ | 11/25 [00:10<00:13, 1.00it/s] 48%|████▊ | 12/25 [00:11<00:12, 1.00it/s] 52%|█████▏ | 13/25 [00:12<00:11, 1.00it/s] 56%|█████▌ | 14/25 [00:13<00:10, 1.00it/s] 60%|██████ | 15/25 [00:14<00:09, 1.00it/s] 64%|██████▍ | 16/25 [00:15<00:08, 1.00it/s] 68%|██████▊ | 17/25 [00:16<00:07, 1.00it/s] 72%|███████▏ | 18/25 [00:17<00:06, 1.00it/s] 76%|███████▌ | 19/25 [00:18<00:05, 1.00it/s] 80%|████████ | 20/25 [00:19<00:04, 1.00it/s] 84%|████████▍ | 21/25 [00:20<00:03, 1.00it/s] 88%|████████▊ | 22/25 [00:21<00:02, 1.00it/s] 92%|█████████▏| 23/25 [00:22<00:01, 1.00it/s] 96%|█████████▌| 24/25 [00:23<00:00, 1.00it/s] 100%|██████████| 25/25 [00:24<00:00, 1.00it/s] 100%|██████████| 25/25 [00:24<00:00, 1.00it/s]Prediction
chamuditha4/anime_diff-oolong:a17c5f8e808c12919639046dd8a3145443c62490f531caa6234f598414184765ID63pz9x94txrgp0cha5ssdj36ywStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- model
- disney
- prompt
- a panda playing a guitar, on a boat, in the ocean, high quality
- num_frames
- 24
- guidance_scale
- 7.5
- negative_prompt
- badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth
- num_inference_steps
- 25
{ "model": "disney", "prompt": "a panda playing a guitar, on a boat, in the ocean, high quality", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 }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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chamuditha4/anime_diff-oolong:a17c5f8e808c12919639046dd8a3145443c62490f531caa6234f598414184765", { input: { model: "disney", prompt: "a panda playing a guitar, on a boat, in the ocean, high quality", num_frames: 24, guidance_scale: 7.5, negative_prompt: "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", num_inference_steps: 25 } } ); // 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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chamuditha4/anime_diff-oolong:a17c5f8e808c12919639046dd8a3145443c62490f531caa6234f598414184765", input={ "model": "disney", "prompt": "a panda playing a guitar, on a boat, in the ocean, high quality", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run chamuditha4/anime_diff-oolong 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": "chamuditha4/anime_diff-oolong:a17c5f8e808c12919639046dd8a3145443c62490f531caa6234f598414184765", "input": { "model": "disney", "prompt": "a panda playing a guitar, on a boat, in the ocean, high quality", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-14T15:14:15.066953Z", "created_at": "2024-08-14T15:13:06.263000Z", "data_removed": false, "error": null, "id": "63pz9x94txrgp0cha5ssdj36yw", "input": { "model": "disney", "prompt": "a panda playing a guitar, on a boat, in the ocean, high quality", "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 }, "logs": "The config attributes {'motion_activation_fn': 'geglu', 'motion_attention_bias': False, 'motion_cross_attention_dim': None} were passed to MotionAdapter, but are not expected and will be ignored. Please verify your config.json configuration file.\nLoading pipeline components...: 0%| | 0/6 [00:00<?, ?it/s]\nLoading pipeline components...: 50%|█████ | 3/6 [00:00<00:00, 9.93it/s]\nLoading pipeline components...: 67%|██████▋ | 4/6 [00:00<00:00, 7.67it/s]\nLoading pipeline components...: 100%|██████████| 6/6 [00:00<00:00, 10.54it/s]\nUsing seed: 2147666731\n 0%| | 0/8 [00:00<?, ?it/s]\n 12%|█▎ | 1/8 [00:01<00:08, 1.28s/it]\n 25%|██▌ | 2/8 [00:02<00:06, 1.11s/it]\n 38%|███▊ | 3/8 [00:03<00:05, 1.06s/it]\n 50%|█████ | 4/8 [00:04<00:04, 1.03s/it]\n 62%|██████▎ | 5/8 [00:05<00:03, 1.02s/it]\n 75%|███████▌ | 6/8 [00:06<00:02, 1.01s/it]\n 88%|████████▊ | 7/8 [00:07<00:01, 1.01s/it]\n100%|██████████| 8/8 [00:08<00:00, 1.00s/it]\n100%|██████████| 8/8 [00:08<00:00, 1.03s/it]\n 0%| | 0/16 [00:00<?, ?it/s]\n 6%|▋ | 1/16 [00:00<00:14, 1.01it/s]\n 12%|█▎ | 2/16 [00:01<00:13, 1.01it/s]\n 19%|█▉ | 3/16 [00:02<00:12, 1.00it/s]\n 25%|██▌ | 4/16 [00:03<00:11, 1.00it/s]\n 31%|███▏ | 5/16 [00:04<00:10, 1.00it/s]\n 38%|███▊ | 6/16 [00:05<00:09, 1.00it/s]\n 44%|████▍ | 7/16 [00:06<00:08, 1.00it/s]\n 50%|█████ | 8/16 [00:07<00:07, 1.00it/s]\n 56%|█████▋ | 9/16 [00:08<00:06, 1.00it/s]\n 62%|██████▎ | 10/16 [00:09<00:05, 1.00it/s]\n 69%|██████▉ | 11/16 [00:10<00:04, 1.00it/s]\n 75%|███████▌ | 12/16 [00:11<00:03, 1.00it/s]\n 81%|████████▏ | 13/16 [00:12<00:02, 1.00it/s]\n 88%|████████▊ | 14/16 [00:13<00:01, 1.00it/s]\n 94%|█████████▍| 15/16 [00:14<00:00, 1.00it/s]\n100%|██████████| 16/16 [00:15<00:00, 1.00it/s]\n100%|██████████| 16/16 [00:15<00:00, 1.00it/s]\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:23, 1.01it/s]\n 8%|▊ | 2/25 [00:01<00:22, 1.00it/s]\n 12%|█▏ | 3/25 [00:02<00:21, 1.00it/s]\n 16%|█▌ | 4/25 [00:03<00:20, 1.00it/s]\n 20%|██ | 5/25 [00:04<00:19, 1.00it/s]\n 24%|██▍ | 6/25 [00:05<00:18, 1.00it/s]\n 28%|██▊ | 7/25 [00:06<00:17, 1.00it/s]\n 32%|███▏ | 8/25 [00:07<00:16, 1.00it/s]\n 36%|███▌ | 9/25 [00:08<00:15, 1.00it/s]\n 40%|████ | 10/25 [00:09<00:14, 1.00it/s]\n 44%|████▍ | 11/25 [00:10<00:13, 1.00it/s]\n 48%|████▊ | 12/25 [00:11<00:12, 1.00it/s]\n 52%|█████▏ | 13/25 [00:12<00:11, 1.00it/s]\n 56%|█████▌ | 14/25 [00:13<00:10, 1.00it/s]\n 60%|██████ | 15/25 [00:14<00:09, 1.00it/s]\n 64%|██████▍ | 16/25 [00:15<00:08, 1.00it/s]\n 68%|██████▊ | 17/25 [00:16<00:07, 1.00it/s]\n 72%|███████▏ | 18/25 [00:17<00:07, 1.00s/it]\n 76%|███████▌ | 19/25 [00:18<00:06, 1.00s/it]\n 80%|████████ | 20/25 [00:19<00:05, 1.00s/it]\n 84%|████████▍ | 21/25 [00:20<00:04, 1.00s/it]\n 88%|████████▊ | 22/25 [00:21<00:03, 1.00s/it]\n 92%|█████████▏| 23/25 [00:22<00:02, 1.00s/it]\n 96%|█████████▌| 24/25 [00:23<00:01, 1.00s/it]\n100%|██████████| 25/25 [00:24<00:00, 1.00s/it]\n100%|██████████| 25/25 [00:24<00:00, 1.00it/s]", "metrics": { "predict_time": 68.697373879, "total_time": 68.803953 }, "output": "https://replicate.delivery/pbxt/lZqLJz4EbK4lF9m5eEhOjLImVOhv0DfN7MQ3YnlGqOCFvnSTA/output.gif", "started_at": "2024-08-14T15:13:06.369579Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/63pz9x94txrgp0cha5ssdj36yw", "cancel": "https://api.replicate.com/v1/predictions/63pz9x94txrgp0cha5ssdj36yw/cancel" }, "version": "a17c5f8e808c12919639046dd8a3145443c62490f531caa6234f598414184765" }Generated inThe config attributes {'motion_activation_fn': 'geglu', 'motion_attention_bias': False, 'motion_cross_attention_dim': None} were passed to MotionAdapter, but are not expected and will be ignored. Please verify your config.json configuration file. Loading pipeline components...: 0%| | 0/6 [00:00<?, ?it/s] Loading pipeline components...: 50%|█████ | 3/6 [00:00<00:00, 9.93it/s] Loading pipeline components...: 67%|██████▋ | 4/6 [00:00<00:00, 7.67it/s] Loading pipeline components...: 100%|██████████| 6/6 [00:00<00:00, 10.54it/s] Using seed: 2147666731 0%| | 0/8 [00:00<?, ?it/s] 12%|█▎ | 1/8 [00:01<00:08, 1.28s/it] 25%|██▌ | 2/8 [00:02<00:06, 1.11s/it] 38%|███▊ | 3/8 [00:03<00:05, 1.06s/it] 50%|█████ | 4/8 [00:04<00:04, 1.03s/it] 62%|██████▎ | 5/8 [00:05<00:03, 1.02s/it] 75%|███████▌ | 6/8 [00:06<00:02, 1.01s/it] 88%|████████▊ | 7/8 [00:07<00:01, 1.01s/it] 100%|██████████| 8/8 [00:08<00:00, 1.00s/it] 100%|██████████| 8/8 [00:08<00:00, 1.03s/it] 0%| | 0/16 [00:00<?, ?it/s] 6%|▋ | 1/16 [00:00<00:14, 1.01it/s] 12%|█▎ | 2/16 [00:01<00:13, 1.01it/s] 19%|█▉ | 3/16 [00:02<00:12, 1.00it/s] 25%|██▌ | 4/16 [00:03<00:11, 1.00it/s] 31%|███▏ | 5/16 [00:04<00:10, 1.00it/s] 38%|███▊ | 6/16 [00:05<00:09, 1.00it/s] 44%|████▍ | 7/16 [00:06<00:08, 1.00it/s] 50%|█████ | 8/16 [00:07<00:07, 1.00it/s] 56%|█████▋ | 9/16 [00:08<00:06, 1.00it/s] 62%|██████▎ | 10/16 [00:09<00:05, 1.00it/s] 69%|██████▉ | 11/16 [00:10<00:04, 1.00it/s] 75%|███████▌ | 12/16 [00:11<00:03, 1.00it/s] 81%|████████▏ | 13/16 [00:12<00:02, 1.00it/s] 88%|████████▊ | 14/16 [00:13<00:01, 1.00it/s] 94%|█████████▍| 15/16 [00:14<00:00, 1.00it/s] 100%|██████████| 16/16 [00:15<00:00, 1.00it/s] 100%|██████████| 16/16 [00:15<00:00, 1.00it/s] 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:23, 1.01it/s] 8%|▊ | 2/25 [00:01<00:22, 1.00it/s] 12%|█▏ | 3/25 [00:02<00:21, 1.00it/s] 16%|█▌ | 4/25 [00:03<00:20, 1.00it/s] 20%|██ | 5/25 [00:04<00:19, 1.00it/s] 24%|██▍ | 6/25 [00:05<00:18, 1.00it/s] 28%|██▊ | 7/25 [00:06<00:17, 1.00it/s] 32%|███▏ | 8/25 [00:07<00:16, 1.00it/s] 36%|███▌ | 9/25 [00:08<00:15, 1.00it/s] 40%|████ | 10/25 [00:09<00:14, 1.00it/s] 44%|████▍ | 11/25 [00:10<00:13, 1.00it/s] 48%|████▊ | 12/25 [00:11<00:12, 1.00it/s] 52%|█████▏ | 13/25 [00:12<00:11, 1.00it/s] 56%|█████▌ | 14/25 [00:13<00:10, 1.00it/s] 60%|██████ | 15/25 [00:14<00:09, 1.00it/s] 64%|██████▍ | 16/25 [00:15<00:08, 1.00it/s] 68%|██████▊ | 17/25 [00:16<00:07, 1.00it/s] 72%|███████▏ | 18/25 [00:17<00:07, 1.00s/it] 76%|███████▌ | 19/25 [00:18<00:06, 1.00s/it] 80%|████████ | 20/25 [00:19<00:05, 1.00s/it] 84%|████████▍ | 21/25 [00:20<00:04, 1.00s/it] 88%|████████▊ | 22/25 [00:21<00:03, 1.00s/it] 92%|█████████▏| 23/25 [00:22<00:02, 1.00s/it] 96%|█████████▌| 24/25 [00:23<00:01, 1.00s/it] 100%|██████████| 25/25 [00:24<00:00, 1.00s/it] 100%|██████████| 25/25 [00:24<00:00, 1.00it/s]Prediction
chamuditha4/anime_diff-oolong:46fd4fb5b6a2323f2292003bee6726473379a8c62fcfcad845e069f6b9c438faIDf67zypm6ehrgp0chagpb0jkr94StatusSucceededSourceWebHardwareA40Total durationCreatedInput
- model
- disney
- width
- 512
- height
- 512
- prompt
- raccoon looking at the camera
- is_video
- num_frames
- 24
- guidance_scale
- 7.5
- negative_prompt
- badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth
- num_inference_steps
- 25
{ "model": "disney", "width": 512, "height": 512, "prompt": "raccoon looking at the camera", "is_video": true, "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 }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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chamuditha4/anime_diff-oolong:46fd4fb5b6a2323f2292003bee6726473379a8c62fcfcad845e069f6b9c438fa", { input: { model: "disney", width: 512, height: 512, prompt: "raccoon looking at the camera", is_video: true, num_frames: 24, guidance_scale: 7.5, negative_prompt: "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", num_inference_steps: 25 } } ); // 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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chamuditha4/anime_diff-oolong:46fd4fb5b6a2323f2292003bee6726473379a8c62fcfcad845e069f6b9c438fa", input={ "model": "disney", "width": 512, "height": 512, "prompt": "raccoon looking at the camera", "is_video": True, "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run chamuditha4/anime_diff-oolong 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": "chamuditha4/anime_diff-oolong:46fd4fb5b6a2323f2292003bee6726473379a8c62fcfcad845e069f6b9c438fa", "input": { "model": "disney", "width": 512, "height": 512, "prompt": "raccoon looking at the camera", "is_video": true, "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-15T03:59:09.692672Z", "created_at": "2024-08-15T03:54:49.844000Z", "data_removed": false, "error": null, "id": "f67zypm6ehrgp0chagpb0jkr94", "input": { "model": "disney", "width": 512, "height": 512, "prompt": "raccoon looking at the camera", "is_video": true, "num_frames": 24, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 }, "logs": "/root/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/_utils.py:776: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\nreturn self.fget.__get__(instance, owner)()\nThe config attributes {'motion_activation_fn': 'geglu', 'motion_attention_bias': False, 'motion_cross_attention_dim': None} were passed to MotionAdapter, but are not expected and will be ignored. Please verify your config.json configuration file.\nLoading pipeline components...: 0%| | 0/6 [00:00<?, ?it/s]\nLoading pipeline components...: 50%|█████ | 3/6 [00:00<00:00, 9.06it/s]/root/.pyenv/versions/3.10.14/lib/python3.10/site-packages/transformers/models/clip/feature_extraction_clip.py:28: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use CLIPImageProcessor instead.\nwarnings.warn(\nLoading pipeline components...: 100%|██████████| 6/6 [00:00<00:00, 11.40it/s]\nLoading pipeline components...: 100%|██████████| 6/6 [00:00<00:00, 10.97it/s]\nUsing seed: 2207205163\n 0%| | 0/8 [00:00<?, ?it/s]\n 12%|█▎ | 1/8 [00:02<00:16, 2.35s/it]\n 25%|██▌ | 2/8 [00:03<00:09, 1.55s/it]\n 38%|███▊ | 3/8 [00:04<00:06, 1.30s/it]\n 50%|█████ | 4/8 [00:05<00:04, 1.18s/it]\n 62%|██████▎ | 5/8 [00:06<00:03, 1.11s/it]\n 75%|███████▌ | 6/8 [00:07<00:02, 1.07s/it]\n 88%|████████▊ | 7/8 [00:08<00:01, 1.05s/it]\n100%|██████████| 8/8 [00:09<00:00, 1.03s/it]\n100%|██████████| 8/8 [00:09<00:00, 1.16s/it]\n 0%| | 0/16 [00:00<?, ?it/s]\n 6%|▋ | 1/16 [00:00<00:14, 1.01it/s]\n 12%|█▎ | 2/16 [00:01<00:13, 1.00it/s]\n 19%|█▉ | 3/16 [00:02<00:12, 1.00it/s]\n 25%|██▌ | 4/16 [00:03<00:11, 1.00it/s]\n 31%|███▏ | 5/16 [00:04<00:10, 1.00it/s]\n 38%|███▊ | 6/16 [00:05<00:09, 1.00it/s]\n 44%|████▍ | 7/16 [00:06<00:08, 1.00it/s]\n 50%|█████ | 8/16 [00:07<00:07, 1.00it/s]\n 56%|█████▋ | 9/16 [00:08<00:06, 1.00it/s]\n 62%|██████▎ | 10/16 [00:09<00:05, 1.00it/s]\n 69%|██████▉ | 11/16 [00:10<00:04, 1.00it/s]\n 75%|███████▌ | 12/16 [00:11<00:03, 1.00it/s]\n 81%|████████▏ | 13/16 [00:12<00:03, 1.00s/it]\n 88%|████████▊ | 14/16 [00:14<00:02, 1.01s/it]\n 94%|█████████▍| 15/16 [00:14<00:01, 1.00s/it]\n100%|██████████| 16/16 [00:15<00:00, 1.00s/it]\n100%|██████████| 16/16 [00:15<00:00, 1.00it/s]\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:00<00:23, 1.01it/s]\n 8%|▊ | 2/25 [00:01<00:22, 1.00it/s]\n 12%|█▏ | 3/25 [00:02<00:21, 1.00it/s]\n 16%|█▌ | 4/25 [00:03<00:20, 1.00it/s]\n 20%|██ | 5/25 [00:04<00:19, 1.00it/s]\n 24%|██▍ | 6/25 [00:05<00:18, 1.00it/s]\n 28%|██▊ | 7/25 [00:06<00:17, 1.00it/s]\n 32%|███▏ | 8/25 [00:07<00:16, 1.00it/s]\n 36%|███▌ | 9/25 [00:08<00:16, 1.00s/it]\n 40%|████ | 10/25 [00:09<00:15, 1.00s/it]\n 44%|████▍ | 11/25 [00:10<00:14, 1.00s/it]\n 48%|████▊ | 12/25 [00:12<00:13, 1.00s/it]\n 52%|█████▏ | 13/25 [00:13<00:12, 1.00s/it]\n 56%|█████▌ | 14/25 [00:14<00:11, 1.01s/it]\n 60%|██████ | 15/25 [00:15<00:10, 1.00s/it]\n 64%|██████▍ | 16/25 [00:16<00:09, 1.00s/it]\n 68%|██████▊ | 17/25 [00:17<00:08, 1.00s/it]\n 72%|███████▏ | 18/25 [00:18<00:07, 1.00s/it]\n 76%|███████▌ | 19/25 [00:19<00:06, 1.00s/it]\n 80%|████████ | 20/25 [00:20<00:05, 1.00s/it]\n 84%|████████▍ | 21/25 [00:21<00:04, 1.00s/it]\n 88%|████████▊ | 22/25 [00:22<00:03, 1.01s/it]\n 92%|█████████▏| 23/25 [00:23<00:02, 1.00s/it]\n 96%|█████████▌| 24/25 [00:24<00:01, 1.00s/it]\n100%|██████████| 25/25 [00:25<00:00, 1.01s/it]\n100%|██████████| 25/25 [00:25<00:00, 1.00s/it]", "metrics": { "predict_time": 70.045234134, "total_time": 259.848672 }, "output": "https://replicate.delivery/pbxt/xWLs9vU5foX5S6O88aeNXjd2OY5KQUFlSKp1EJZ1e3hY4llmA/output.mp4", "started_at": "2024-08-15T03:57:59.647438Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/f67zypm6ehrgp0chagpb0jkr94", "cancel": "https://api.replicate.com/v1/predictions/f67zypm6ehrgp0chagpb0jkr94/cancel" }, "version": "46fd4fb5b6a2323f2292003bee6726473379a8c62fcfcad845e069f6b9c438fa" }Generated in/root/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/_utils.py:776: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() return self.fget.__get__(instance, owner)() The config attributes {'motion_activation_fn': 'geglu', 'motion_attention_bias': False, 'motion_cross_attention_dim': None} were passed to MotionAdapter, but are not expected and will be ignored. Please verify your config.json configuration file. Loading pipeline components...: 0%| | 0/6 [00:00<?, ?it/s] Loading pipeline components...: 50%|█████ | 3/6 [00:00<00:00, 9.06it/s]/root/.pyenv/versions/3.10.14/lib/python3.10/site-packages/transformers/models/clip/feature_extraction_clip.py:28: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use CLIPImageProcessor instead. warnings.warn( Loading pipeline components...: 100%|██████████| 6/6 [00:00<00:00, 11.40it/s] Loading pipeline components...: 100%|██████████| 6/6 [00:00<00:00, 10.97it/s] Using seed: 2207205163 0%| | 0/8 [00:00<?, ?it/s] 12%|█▎ | 1/8 [00:02<00:16, 2.35s/it] 25%|██▌ | 2/8 [00:03<00:09, 1.55s/it] 38%|███▊ | 3/8 [00:04<00:06, 1.30s/it] 50%|█████ | 4/8 [00:05<00:04, 1.18s/it] 62%|██████▎ | 5/8 [00:06<00:03, 1.11s/it] 75%|███████▌ | 6/8 [00:07<00:02, 1.07s/it] 88%|████████▊ | 7/8 [00:08<00:01, 1.05s/it] 100%|██████████| 8/8 [00:09<00:00, 1.03s/it] 100%|██████████| 8/8 [00:09<00:00, 1.16s/it] 0%| | 0/16 [00:00<?, ?it/s] 6%|▋ | 1/16 [00:00<00:14, 1.01it/s] 12%|█▎ | 2/16 [00:01<00:13, 1.00it/s] 19%|█▉ | 3/16 [00:02<00:12, 1.00it/s] 25%|██▌ | 4/16 [00:03<00:11, 1.00it/s] 31%|███▏ | 5/16 [00:04<00:10, 1.00it/s] 38%|███▊ | 6/16 [00:05<00:09, 1.00it/s] 44%|████▍ | 7/16 [00:06<00:08, 1.00it/s] 50%|█████ | 8/16 [00:07<00:07, 1.00it/s] 56%|█████▋ | 9/16 [00:08<00:06, 1.00it/s] 62%|██████▎ | 10/16 [00:09<00:05, 1.00it/s] 69%|██████▉ | 11/16 [00:10<00:04, 1.00it/s] 75%|███████▌ | 12/16 [00:11<00:03, 1.00it/s] 81%|████████▏ | 13/16 [00:12<00:03, 1.00s/it] 88%|████████▊ | 14/16 [00:14<00:02, 1.01s/it] 94%|█████████▍| 15/16 [00:14<00:01, 1.00s/it] 100%|██████████| 16/16 [00:15<00:00, 1.00s/it] 100%|██████████| 16/16 [00:15<00:00, 1.00it/s] 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:00<00:23, 1.01it/s] 8%|▊ | 2/25 [00:01<00:22, 1.00it/s] 12%|█▏ | 3/25 [00:02<00:21, 1.00it/s] 16%|█▌ | 4/25 [00:03<00:20, 1.00it/s] 20%|██ | 5/25 [00:04<00:19, 1.00it/s] 24%|██▍ | 6/25 [00:05<00:18, 1.00it/s] 28%|██▊ | 7/25 [00:06<00:17, 1.00it/s] 32%|███▏ | 8/25 [00:07<00:16, 1.00it/s] 36%|███▌ | 9/25 [00:08<00:16, 1.00s/it] 40%|████ | 10/25 [00:09<00:15, 1.00s/it] 44%|████▍ | 11/25 [00:10<00:14, 1.00s/it] 48%|████▊ | 12/25 [00:12<00:13, 1.00s/it] 52%|█████▏ | 13/25 [00:13<00:12, 1.00s/it] 56%|█████▌ | 14/25 [00:14<00:11, 1.01s/it] 60%|██████ | 15/25 [00:15<00:10, 1.00s/it] 64%|██████▍ | 16/25 [00:16<00:09, 1.00s/it] 68%|██████▊ | 17/25 [00:17<00:08, 1.00s/it] 72%|███████▏ | 18/25 [00:18<00:07, 1.00s/it] 76%|███████▌ | 19/25 [00:19<00:06, 1.00s/it] 80%|████████ | 20/25 [00:20<00:05, 1.00s/it] 84%|████████▍ | 21/25 [00:21<00:04, 1.00s/it] 88%|████████▊ | 22/25 [00:22<00:03, 1.01s/it] 92%|█████████▏| 23/25 [00:23<00:02, 1.00s/it] 96%|█████████▌| 24/25 [00:24<00:01, 1.00s/it] 100%|██████████| 25/25 [00:25<00:00, 1.01s/it] 100%|██████████| 25/25 [00:25<00:00, 1.00s/it]Prediction
chamuditha4/anime_diff-oolong:b0e1db6aa779f13a9bee80dff0cd0186eeba0ae48d8438f4f649caa15d04abb6IDhte6zjy3rnrgp0chapab0kc5agStatusSucceededSourceWebHardwareA40Total durationCreatedInput
- model
- disney
- width
- 512
- height
- 512
- prompt
- 3d animation, a man in a suit standing behind a podium in a press conference, close-up shot
- is_video
- num_frames
- 32
- guidance_scale
- 7.5
- negative_prompt
- badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth
- num_inference_steps
- 25
{ "model": "disney", "width": 512, "height": 512, "prompt": "3d animation, a man in a suit standing behind a podium in a press conference, close-up shot", "is_video": true, "num_frames": 32, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 }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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "chamuditha4/anime_diff-oolong:b0e1db6aa779f13a9bee80dff0cd0186eeba0ae48d8438f4f649caa15d04abb6", { input: { model: "disney", width: 512, height: 512, prompt: "3d animation, a man in a suit standing behind a podium in a press conference, close-up shot", is_video: true, num_frames: 32, guidance_scale: 7.5, negative_prompt: "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", num_inference_steps: 25 } } ); // 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 chamuditha4/anime_diff-oolong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "chamuditha4/anime_diff-oolong:b0e1db6aa779f13a9bee80dff0cd0186eeba0ae48d8438f4f649caa15d04abb6", input={ "model": "disney", "width": 512, "height": 512, "prompt": "3d animation, a man in a suit standing behind a podium in a press conference, close-up shot", "is_video": True, "num_frames": 32, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run chamuditha4/anime_diff-oolong 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": "chamuditha4/anime_diff-oolong:b0e1db6aa779f13a9bee80dff0cd0186eeba0ae48d8438f4f649caa15d04abb6", "input": { "model": "disney", "width": 512, "height": 512, "prompt": "3d animation, a man in a suit standing behind a podium in a press conference, close-up shot", "is_video": true, "num_frames": 32, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-15T10:32:27.061430Z", "created_at": "2024-08-15T10:28:18.501000Z", "data_removed": false, "error": null, "id": "hte6zjy3rnrgp0chapab0kc5ag", "input": { "model": "disney", "width": 512, "height": 512, "prompt": "3d animation, a man in a suit standing behind a podium in a press conference, close-up shot", "is_video": true, "num_frames": 32, "guidance_scale": 7.5, "negative_prompt": "badhandv4, easynegative, ng_deepnegative_v1_75t, verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth", "num_inference_steps": 25 }, "logs": "/root/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/_utils.py:776: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\nreturn self.fget.__get__(instance, owner)()\nThe config attributes {'motion_activation_fn': 'geglu', 'motion_attention_bias': False, 'motion_cross_attention_dim': None} were passed to MotionAdapter, but are not expected and will be ignored. Please verify your config.json configuration file.\nLoading pipeline components...: 0%| | 0/6 [00:00<?, ?it/s]/root/.pyenv/versions/3.10.14/lib/python3.10/site-packages/transformers/models/clip/feature_extraction_clip.py:28: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use CLIPImageProcessor instead.\nwarnings.warn(\nLoading pipeline components...: 50%|█████ | 3/6 [00:00<00:00, 13.10it/s]\nLoading pipeline components...: 100%|██████████| 6/6 [00:00<00:00, 10.80it/s]\nLoading pipeline components...: 100%|██████████| 6/6 [00:00<00:00, 11.09it/s]\nUsing seed: 3329187324\n 0%| | 0/8 [00:00<?, ?it/s]\n 12%|█▎ | 1/8 [00:01<00:12, 1.76s/it]\n 25%|██▌ | 2/8 [00:03<00:08, 1.49s/it]\n 38%|███▊ | 3/8 [00:04<00:07, 1.40s/it]\n 50%|█████ | 4/8 [00:05<00:05, 1.36s/it]\n 62%|██████▎ | 5/8 [00:06<00:04, 1.34s/it]\n 75%|███████▌ | 6/8 [00:08<00:02, 1.33s/it]\n 88%|████████▊ | 7/8 [00:09<00:01, 1.32s/it]\n100%|██████████| 8/8 [00:10<00:00, 1.31s/it]\n100%|██████████| 8/8 [00:10<00:00, 1.36s/it]\n 0%| | 0/16 [00:00<?, ?it/s]\n 6%|▋ | 1/16 [00:01<00:19, 1.29s/it]\n 12%|█▎ | 2/16 [00:02<00:18, 1.30s/it]\n 19%|█▉ | 3/16 [00:03<00:16, 1.30s/it]\n 25%|██▌ | 4/16 [00:05<00:15, 1.30s/it]\n 31%|███▏ | 5/16 [00:06<00:14, 1.30s/it]\n 38%|███▊ | 6/16 [00:07<00:13, 1.30s/it]\n 44%|████▍ | 7/16 [00:09<00:11, 1.30s/it]\n 50%|█████ | 8/16 [00:10<00:10, 1.30s/it]\n 56%|█████▋ | 9/16 [00:11<00:09, 1.30s/it]\n 62%|██████▎ | 10/16 [00:13<00:07, 1.30s/it]\n 69%|██████▉ | 11/16 [00:14<00:06, 1.30s/it]\n 75%|███████▌ | 12/16 [00:15<00:05, 1.31s/it]\n 81%|████████▏ | 13/16 [00:16<00:03, 1.31s/it]\n 88%|████████▊ | 14/16 [00:18<00:02, 1.31s/it]\n 94%|█████████▍| 15/16 [00:19<00:01, 1.31s/it]\n100%|██████████| 16/16 [00:20<00:00, 1.31s/it]\n100%|██████████| 16/16 [00:20<00:00, 1.30s/it]\n 0%| | 0/25 [00:00<?, ?it/s]\n 4%|▍ | 1/25 [00:01<00:31, 1.31s/it]\n 8%|▊ | 2/25 [00:02<00:30, 1.31s/it]\n 12%|█▏ | 3/25 [00:03<00:28, 1.31s/it]\n 16%|█▌ | 4/25 [00:05<00:27, 1.31s/it]\n 20%|██ | 5/25 [00:06<00:26, 1.31s/it]\n 24%|██▍ | 6/25 [00:07<00:24, 1.31s/it]\n 28%|██▊ | 7/25 [00:09<00:23, 1.31s/it]\n 32%|███▏ | 8/25 [00:10<00:22, 1.31s/it]\n 36%|███▌ | 9/25 [00:11<00:20, 1.31s/it]\n 40%|████ | 10/25 [00:13<00:19, 1.31s/it]\n 44%|████▍ | 11/25 [00:14<00:18, 1.31s/it]\n 48%|████▊ | 12/25 [00:15<00:17, 1.31s/it]\n 52%|█████▏ | 13/25 [00:17<00:15, 1.31s/it]\n 56%|█████▌ | 14/25 [00:18<00:14, 1.31s/it]\n 60%|██████ | 15/25 [00:19<00:13, 1.31s/it]\n 64%|██████▍ | 16/25 [00:20<00:11, 1.31s/it]\n 68%|██████▊ | 17/25 [00:22<00:10, 1.31s/it]\n 72%|███████▏ | 18/25 [00:23<00:09, 1.31s/it]\n 76%|███████▌ | 19/25 [00:24<00:07, 1.31s/it]\n 80%|████████ | 20/25 [00:26<00:06, 1.31s/it]\n 84%|████████▍ | 21/25 [00:27<00:05, 1.31s/it]\n 88%|████████▊ | 22/25 [00:28<00:03, 1.31s/it]\n 92%|█████████▏| 23/25 [00:30<00:02, 1.31s/it]\n 96%|█████████▌| 24/25 [00:31<00:01, 1.31s/it]\n100%|██████████| 25/25 [00:32<00:00, 1.31s/it]\n100%|██████████| 25/25 [00:32<00:00, 1.31s/it]", "metrics": { "predict_time": 85.771519318, "total_time": 248.56043 }, "output": "https://replicate.delivery/pbxt/hEfZMVfmIGtGckd4afe6o57QWkPeBaUmw1yDx0filAWnOLu0E/output.mp4", "started_at": "2024-08-15T10:31:01.289910Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/hte6zjy3rnrgp0chapab0kc5ag", "cancel": "https://api.replicate.com/v1/predictions/hte6zjy3rnrgp0chapab0kc5ag/cancel" }, "version": "b0e1db6aa779f13a9bee80dff0cd0186eeba0ae48d8438f4f649caa15d04abb6" }Generated in/root/.pyenv/versions/3.10.14/lib/python3.10/site-packages/torch/_utils.py:776: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly. To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage() return self.fget.__get__(instance, owner)() The config attributes {'motion_activation_fn': 'geglu', 'motion_attention_bias': False, 'motion_cross_attention_dim': None} were passed to MotionAdapter, but are not expected and will be ignored. Please verify your config.json configuration file. Loading pipeline components...: 0%| | 0/6 [00:00<?, ?it/s]/root/.pyenv/versions/3.10.14/lib/python3.10/site-packages/transformers/models/clip/feature_extraction_clip.py:28: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use CLIPImageProcessor instead. warnings.warn( Loading pipeline components...: 50%|█████ | 3/6 [00:00<00:00, 13.10it/s] Loading pipeline components...: 100%|██████████| 6/6 [00:00<00:00, 10.80it/s] Loading pipeline components...: 100%|██████████| 6/6 [00:00<00:00, 11.09it/s] Using seed: 3329187324 0%| | 0/8 [00:00<?, ?it/s] 12%|█▎ | 1/8 [00:01<00:12, 1.76s/it] 25%|██▌ | 2/8 [00:03<00:08, 1.49s/it] 38%|███▊ | 3/8 [00:04<00:07, 1.40s/it] 50%|█████ | 4/8 [00:05<00:05, 1.36s/it] 62%|██████▎ | 5/8 [00:06<00:04, 1.34s/it] 75%|███████▌ | 6/8 [00:08<00:02, 1.33s/it] 88%|████████▊ | 7/8 [00:09<00:01, 1.32s/it] 100%|██████████| 8/8 [00:10<00:00, 1.31s/it] 100%|██████████| 8/8 [00:10<00:00, 1.36s/it] 0%| | 0/16 [00:00<?, ?it/s] 6%|▋ | 1/16 [00:01<00:19, 1.29s/it] 12%|█▎ | 2/16 [00:02<00:18, 1.30s/it] 19%|█▉ | 3/16 [00:03<00:16, 1.30s/it] 25%|██▌ | 4/16 [00:05<00:15, 1.30s/it] 31%|███▏ | 5/16 [00:06<00:14, 1.30s/it] 38%|███▊ | 6/16 [00:07<00:13, 1.30s/it] 44%|████▍ | 7/16 [00:09<00:11, 1.30s/it] 50%|█████ | 8/16 [00:10<00:10, 1.30s/it] 56%|█████▋ | 9/16 [00:11<00:09, 1.30s/it] 62%|██████▎ | 10/16 [00:13<00:07, 1.30s/it] 69%|██████▉ | 11/16 [00:14<00:06, 1.30s/it] 75%|███████▌ | 12/16 [00:15<00:05, 1.31s/it] 81%|████████▏ | 13/16 [00:16<00:03, 1.31s/it] 88%|████████▊ | 14/16 [00:18<00:02, 1.31s/it] 94%|█████████▍| 15/16 [00:19<00:01, 1.31s/it] 100%|██████████| 16/16 [00:20<00:00, 1.31s/it] 100%|██████████| 16/16 [00:20<00:00, 1.30s/it] 0%| | 0/25 [00:00<?, ?it/s] 4%|▍ | 1/25 [00:01<00:31, 1.31s/it] 8%|▊ | 2/25 [00:02<00:30, 1.31s/it] 12%|█▏ | 3/25 [00:03<00:28, 1.31s/it] 16%|█▌ | 4/25 [00:05<00:27, 1.31s/it] 20%|██ | 5/25 [00:06<00:26, 1.31s/it] 24%|██▍ | 6/25 [00:07<00:24, 1.31s/it] 28%|██▊ | 7/25 [00:09<00:23, 1.31s/it] 32%|███▏ | 8/25 [00:10<00:22, 1.31s/it] 36%|███▌ | 9/25 [00:11<00:20, 1.31s/it] 40%|████ | 10/25 [00:13<00:19, 1.31s/it] 44%|████▍ | 11/25 [00:14<00:18, 1.31s/it] 48%|████▊ | 12/25 [00:15<00:17, 1.31s/it] 52%|█████▏ | 13/25 [00:17<00:15, 1.31s/it] 56%|█████▌ | 14/25 [00:18<00:14, 1.31s/it] 60%|██████ | 15/25 [00:19<00:13, 1.31s/it] 64%|██████▍ | 16/25 [00:20<00:11, 1.31s/it] 68%|██████▊ | 17/25 [00:22<00:10, 1.31s/it] 72%|███████▏ | 18/25 [00:23<00:09, 1.31s/it] 76%|███████▌ | 19/25 [00:24<00:07, 1.31s/it] 80%|████████ | 20/25 [00:26<00:06, 1.31s/it] 84%|████████▍ | 21/25 [00:27<00:05, 1.31s/it] 88%|████████▊ | 22/25 [00:28<00:03, 1.31s/it] 92%|█████████▏| 23/25 [00:30<00:02, 1.31s/it] 96%|█████████▌| 24/25 [00:31<00:01, 1.31s/it] 100%|██████████| 25/25 [00:32<00:00, 1.31s/it] 100%|██████████| 25/25 [00:32<00:00, 1.31s/it]
Want to make some of these yourself?
Run this model