wcarle / stable-diffusion-videos
Generate videos by interpolating the latent space of Stable Diffusion
Prediction
wcarle/stable-diffusion-videos:d80e8dbe5885d570087b782ff22cef888dcc1be7941a52c8ba82c7c9d884d845Input
- fps
- 15
- prompts
- a cat | a dog | a horse
- num_steps
- 50
- scheduler
- klms
- guidance_scale
- 7.5
- num_inference_steps
- 30
{ "fps": 15, "prompts": "a cat | a dog | a horse", "num_steps": 50, "scheduler": "klms", "guidance_scale": 7.5, "num_inference_steps": 30 }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 wcarle/stable-diffusion-videos using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "wcarle/stable-diffusion-videos:d80e8dbe5885d570087b782ff22cef888dcc1be7941a52c8ba82c7c9d884d845", { input: { fps: 15, prompts: "a cat | a dog | a horse", num_steps: 50, scheduler: "klms", guidance_scale: 7.5, num_inference_steps: 30 } } ); // 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 wcarle/stable-diffusion-videos using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "wcarle/stable-diffusion-videos:d80e8dbe5885d570087b782ff22cef888dcc1be7941a52c8ba82c7c9d884d845", input={ "fps": 15, "prompts": "a cat | a dog | a horse", "num_steps": 50, "scheduler": "klms", "guidance_scale": 7.5, "num_inference_steps": 30 } ) # 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 wcarle/stable-diffusion-videos 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": "wcarle/stable-diffusion-videos:d80e8dbe5885d570087b782ff22cef888dcc1be7941a52c8ba82c7c9d884d845", "input": { "fps": 15, "prompts": "a cat | a dog | a horse", "num_steps": 50, "scheduler": "klms", "guidance_scale": 7.5, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-04-02T21:30:43.209013Z", "created_at": "2023-04-02T21:12:20.569797Z", "data_removed": false, "error": null, "id": "j5ykuseosjdzpajbz5u4onuwze", "input": { "fps": 15, "prompts": "a cat | a dog | a horse", "num_steps": 50, "scheduler": "klms", "guidance_scale": 7.5, "num_inference_steps": 30 }, "logs": "Setting Random seeds.\nSeeds used for prompts are:\na cat: 36643\na dog: 56866\na horse: 38898\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:02<01:17, 2.66s/it]\n 7%|▋ | 2/30 [00:02<00:34, 1.23s/it]\n 10%|█ | 3/30 [00:03<00:21, 1.28it/s]\n 13%|█▎ | 4/30 [00:03<00:14, 1.75it/s]\n 17%|█▋ | 5/30 [00:03<00:11, 2.20it/s]\n 20%|██ | 6/30 [00:03<00:09, 2.61it/s]\n 23%|██▎ | 7/30 [00:04<00:07, 2.97it/s]\n 27%|██▋ | 8/30 [00:04<00:06, 3.24it/s]\n 30%|███ | 9/30 [00:04<00:06, 3.46it/s]\n 33%|███▎ | 10/30 [00:04<00:05, 3.65it/s]\n 37%|███▋ | 11/30 [00:05<00:05, 3.76it/s]\n 40%|████ | 12/30 [00:05<00:04, 3.84it/s]\n 43%|████▎ | 13/30 [00:05<00:04, 3.90it/s]\n 47%|████▋ | 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[00:03<00:04, 3.77it/s]\n 43%|████▎ | 13/30 [00:03<00:04, 3.77it/s]\n 47%|████▋ | 14/30 [00:03<00:04, 3.77it/s]\n 50%|█████ | 15/30 [00:03<00:03, 3.75it/s]\n 53%|█████▎ | 16/30 [00:04<00:03, 3.76it/s]\n 57%|█████▋ | 17/30 [00:04<00:03, 3.76it/s]\n 60%|██████ | 18/30 [00:04<00:03, 3.77it/s]\n 63%|██████▎ | 19/30 [00:05<00:02, 3.78it/s]\n 67%|██████▋ | 20/30 [00:05<00:02, 3.78it/s]\n 70%|███████ | 21/30 [00:05<00:02, 3.77it/s]\n 73%|███████▎ | 22/30 [00:05<00:02, 3.77it/s]\n 77%|███████▋ | 23/30 [00:06<00:01, 3.77it/s]\n 80%|████████ | 24/30 [00:06<00:01, 3.76it/s]\n 83%|████████▎ | 25/30 [00:06<00:01, 3.75it/s]\n 87%|████████▋ | 26/30 [00:06<00:01, 3.76it/s]\n 90%|█████████ | 27/30 [00:07<00:00, 3.76it/s]\n 93%|█████████▎| 28/30 [00:07<00:00, 3.76it/s]\n 97%|█████████▋| 29/30 [00:07<00:00, 3.77it/s]\n100%|██████████| 30/30 [00:07<00:00, 3.78it/s]\n100%|██████████| 30/30 [00:07<00:00, 3.76it/s]", "metrics": { "predict_time": 852.542927, "total_time": 1102.639216 }, "output": "https://replicate.delivery/pbxt/SuEOZdHqJ3ZJCNLyroDGPpCUYNCcUE6hEPHLNnAaUeTBM9WIA/out.mp4", "started_at": "2023-04-02T21:16:30.666086Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/j5ykuseosjdzpajbz5u4onuwze", "cancel": "https://api.replicate.com/v1/predictions/j5ykuseosjdzpajbz5u4onuwze/cancel" }, "version": "d80e8dbe5885d570087b782ff22cef888dcc1be7941a52c8ba82c7c9d884d845" }Generated inSetting Random seeds. Seeds used for prompts are: a cat: 36643 a dog: 56866 a horse: 38898 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:02<01:17, 2.66s/it] 7%|▋ | 2/30 [00:02<00:34, 1.23s/it] 10%|█ | 3/30 [00:03<00:21, 1.28it/s] 13%|█▎ | 4/30 [00:03<00:14, 1.75it/s] 17%|█▋ | 5/30 [00:03<00:11, 2.20it/s] 20%|██ | 6/30 [00:03<00:09, 2.61it/s] 23%|██▎ | 7/30 [00:04<00:07, 2.97it/s] 27%|██▋ | 8/30 [00:04<00:06, 3.24it/s] 30%|███ | 9/30 [00:04<00:06, 3.46it/s] 33%|███▎ | 10/30 [00:04<00:05, 3.65it/s] 37%|███▋ | 11/30 [00:05<00:05, 3.76it/s] 40%|████ | 12/30 [00:05<00:04, 3.84it/s] 43%|████▎ | 13/30 [00:05<00:04, 3.90it/s] 47%|████▋ | 14/30 [00:05<00:04, 3.96it/s] 50%|█████ | 15/30 [00:06<00:03, 3.99it/s] 53%|█████▎ | 16/30 [00:06<00:03, 4.02it/s] 57%|█████▋ | 17/30 [00:06<00:03, 4.04it/s] 60%|██████ | 18/30 [00:06<00:02, 4.06it/s] 63%|██████▎ | 19/30 [00:07<00:02, 4.06it/s] 67%|██████▋ | 20/30 [00:07<00:02, 4.07it/s] 70%|███████ | 21/30 [00:07<00:02, 4.07it/s] 73%|███████▎ | 22/30 [00:07<00:01, 4.06it/s] 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wcarle/stable-diffusion-videos:d80e8dbe5885d570087b782ff22cef888dcc1be7941a52c8ba82c7c9d884d845ID3kwtkz7movgwrjdhunapfevxpaStatusSucceededSourceWebHardware–Total durationCreatedInput
- fps
- 15
- seeds
- 42 | 1337
- prompts
- blueberry spaghetti | strawberry spaghetti
- num_steps
- 100
- scheduler
- klms
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "fps": 15, "seeds": "42 | 1337", "prompts": "blueberry spaghetti | strawberry spaghetti", "num_steps": 100, "scheduler": "klms", "guidance_scale": 7.5, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run wcarle/stable-diffusion-videos using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "wcarle/stable-diffusion-videos:d80e8dbe5885d570087b782ff22cef888dcc1be7941a52c8ba82c7c9d884d845", { input: { fps: 15, seeds: "42 | 1337", prompts: "blueberry spaghetti | strawberry spaghetti", num_steps: 100, scheduler: "klms", guidance_scale: 7.5, num_inference_steps: 50 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun wcarle/stable-diffusion-videos using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "wcarle/stable-diffusion-videos:d80e8dbe5885d570087b782ff22cef888dcc1be7941a52c8ba82c7c9d884d845", input={ "fps": 15, "seeds": "42 | 1337", "prompts": "blueberry spaghetti | strawberry spaghetti", "num_steps": 100, "scheduler": "klms", "guidance_scale": 7.5, "num_inference_steps": 50 } ) # 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 wcarle/stable-diffusion-videos 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": "wcarle/stable-diffusion-videos:d80e8dbe5885d570087b782ff22cef888dcc1be7941a52c8ba82c7c9d884d845", "input": { "fps": 15, "seeds": "42 | 1337", "prompts": "blueberry spaghetti | strawberry spaghetti", "num_steps": 100, "scheduler": "klms", "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-04-02T21:42:01.583456Z", "created_at": "2023-04-02T21:14:37.413125Z", "data_removed": false, "error": null, "id": "3kwtkz7movgwrjdhunapfevxpa", "input": { "fps": 15, "seeds": "42 | 1337", "prompts": "blueberry spaghetti | strawberry spaghetti", "num_steps": 100, "scheduler": "klms", "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Setting Random seeds.\nSeeds used for prompts are:\nblueberry spaghetti: 42\nstrawberry spaghetti: 1337\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:02<02:24, 2.94s/it]\n 4%|▍ | 2/50 [00:03<01:04, 1.34s/it]\n 6%|▌ | 3/50 [00:03<00:39, 1.19it/s]\n 8%|▊ | 4/50 [00:03<00:27, 1.66it/s]\n 10%|█ | 5/50 [00:03<00:21, 2.10it/s]\n 12%|█▏ | 6/50 [00:04<00:17, 2.52it/s]\n 14%|█▍ | 7/50 [00:04<00:14, 2.89it/s]\n 16%|█▌ | 8/50 [00:04<00:13, 3.20it/s]\n 18%|█▊ | 9/50 [00:04<00:11, 3.44it/s]\n 20%|██ | 10/50 [00:05<00:11, 3.62it/s]\n 22%|██▏ | 11/50 [00:05<00:10, 3.76it/s]\n 24%|██▍ | 12/50 [00:05<00:09, 3.85it/s]\n 26%|██▌ | 13/50 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26%|██▌ | 13/50 [00:03<00:09, 3.74it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.74it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.73it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.74it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.75it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.76it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.73it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.72it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.73it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.75it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.75it/s]\n 48%|████▊ | 24/50 [00:06<00:06, 3.73it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.74it/s]\n 52%|█████▏ | 26/50 [00:06<00:06, 3.76it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.77it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.75it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.74it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.75it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.74it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.73it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.75it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.76it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.75it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.74it/s]\n 74%|███████▍ | 37/50 [00:09<00:03, 3.75it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.75it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.74it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.75it/s]\n 82%|████████▏ | 41/50 [00:10<00:02, 3.75it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.75it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.74it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.75it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.76it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.76it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.76it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.75it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.73it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.75it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.75it/s]\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:12, 3.86it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.84it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.83it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.66it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.71it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.74it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.72it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.70it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.72it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.73it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.72it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.72it/s]\n 26%|██▌ | 13/50 [00:03<00:09, 3.74it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.74it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.73it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.75it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.76it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.77it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.76it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.75it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.75it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.73it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.75it/s]\n 48%|████▊ | 24/50 [00:06<00:06, 3.75it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.75it/s]\n 52%|█████▏ | 26/50 [00:06<00:06, 3.74it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.74it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.74it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.74it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.74it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.73it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.74it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.76it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.77it/s]\n 70%|███████ | 35/50 [00:09<00:03, 3.76it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.75it/s]\n 74%|███████▍ | 37/50 [00:09<00:03, 3.74it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.74it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.76it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.76it/s]\n 82%|████████▏ | 41/50 [00:10<00:02, 3.77it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.76it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.76it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.75it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.75it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.77it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.76it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.76it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.75it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.74it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.75it/s]", "metrics": { "predict_time": 1379.974196, "total_time": 1644.170331 }, "output": "https://replicate.delivery/pbxt/yFhpTyfPS7UfyEVlHJ1HJgODAJAgiPU0i30q8tRCex9QF1bhA/out.mp4", "started_at": "2023-04-02T21:19:01.609260Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/3kwtkz7movgwrjdhunapfevxpa", "cancel": "https://api.replicate.com/v1/predictions/3kwtkz7movgwrjdhunapfevxpa/cancel" }, "version": "d80e8dbe5885d570087b782ff22cef888dcc1be7941a52c8ba82c7c9d884d845" }Generated inSetting Random seeds. Seeds used for prompts are: blueberry spaghetti: 42 strawberry spaghetti: 1337 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:02<02:24, 2.94s/it] 4%|▍ | 2/50 [00:03<01:04, 1.34s/it] 6%|▌ | 3/50 [00:03<00:39, 1.19it/s] 8%|▊ | 4/50 [00:03<00:27, 1.66it/s] 10%|█ | 5/50 [00:03<00:21, 2.10it/s] 12%|█▏ | 6/50 [00:04<00:17, 2.52it/s] 14%|█▍ | 7/50 [00:04<00:14, 2.89it/s] 16%|█▌ | 8/50 [00:04<00:13, 3.20it/s] 18%|█▊ | 9/50 [00:04<00:11, 3.44it/s] 20%|██ | 10/50 [00:05<00:11, 3.62it/s] 22%|██▏ | 11/50 [00:05<00:10, 3.76it/s] 24%|██▍ | 12/50 [00:05<00:09, 3.85it/s] 26%|██▌ | 13/50 [00:05<00:09, 3.93it/s] 28%|██▊ | 14/50 [00:06<00:09, 3.97it/s] 30%|███ | 15/50 [00:06<00:08, 4.03it/s] 32%|███▏ | 16/50 [00:06<00:08, 4.06it/s] 34%|███▍ | 17/50 [00:06<00:08, 4.09it/s] 36%|███▌ | 18/50 [00:07<00:07, 4.10it/s] 38%|███▊ | 19/50 [00:07<00:07, 4.12it/s] 40%|████ | 20/50 [00:07<00:07, 4.12it/s] 42%|████▏ | 21/50 [00:07<00:07, 4.14it/s] 44%|████▍ | 22/50 [00:08<00:06, 4.14it/s] 46%|████▌ | 23/50 [00:08<00:06, 4.14it/s] 48%|████▊ | 24/50 [00:08<00:06, 4.13it/s] 50%|█████ | 25/50 [00:08<00:06, 4.12it/s] 52%|█████▏ | 26/50 [00:08<00:05, 4.12it/s] 54%|█████▍ | 27/50 [00:09<00:05, 4.12it/s] 56%|█████▌ | 28/50 [00:09<00:05, 4.13it/s] 58%|█████▊ | 29/50 [00:09<00:05, 4.13it/s] 60%|██████ | 30/50 [00:09<00:04, 4.14it/s] 62%|██████▏ | 31/50 [00:10<00:04, 4.15it/s] 64%|██████▍ | 32/50 [00:10<00:04, 4.16it/s] 66%|██████▌ | 33/50 [00:10<00:04, 4.16it/s] 68%|██████▊ | 34/50 [00:10<00:03, 4.16it/s] 70%|███████ | 35/50 [00:11<00:03, 4.14it/s] 72%|███████▏ | 36/50 [00:11<00:03, 4.13it/s] 74%|███████▍ | 37/50 [00:11<00:03, 4.12it/s] 76%|███████▌ | 38/50 [00:11<00:02, 4.14it/s] 78%|███████▊ | 39/50 [00:12<00:02, 4.14it/s] 80%|████████ | 40/50 [00:12<00:02, 4.15it/s] 82%|████████▏ | 41/50 [00:12<00:02, 4.16it/s] 84%|████████▍ | 42/50 [00:12<00:01, 4.16it/s] 86%|████████▌ | 43/50 [00:13<00:01, 4.15it/s] 88%|████████▊ | 44/50 [00:13<00:01, 4.15it/s] 90%|█████████ | 45/50 [00:13<00:01, 4.14it/s] 92%|█████████▏| 46/50 [00:13<00:00, 4.14it/s] 94%|█████████▍| 47/50 [00:14<00:00, 4.14it/s] 96%|█████████▌| 48/50 [00:14<00:00, 4.13it/s] 98%|█████████▊| 49/50 [00:14<00:00, 4.14it/s] 100%|██████████| 50/50 [00:14<00:00, 4.15it/s] 100%|██████████| 50/50 [00:14<00:00, 3.38it/s] 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:11, 4.17it/s] 4%|▍ | 2/50 [00:00<00:11, 4.20it/s] 6%|▌ | 3/50 [00:00<00:11, 4.20it/s] 8%|▊ | 4/50 [00:00<00:11, 4.12it/s] 10%|█ | 5/50 [00:01<00:10, 4.13it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.13it/s] 14%|█▍ | 7/50 [00:01<00:10, 4.12it/s] 16%|█▌ | 8/50 [00:01<00:10, 4.12it/s] 18%|█▊ | 9/50 [00:02<00:09, 4.14it/s] 20%|██ | 10/50 [00:02<00:09, 4.16it/s] 22%|██▏ | 11/50 [00:02<00:09, 4.16it/s] 24%|██▍ | 12/50 [00:02<00:09, 4.15it/s] 26%|██▌ | 13/50 [00:03<00:08, 4.15it/s] 28%|██▊ | 14/50 [00:03<00:08, 4.14it/s] 30%|███ | 15/50 [00:03<00:08, 4.15it/s] 32%|███▏ | 16/50 [00:03<00:08, 4.15it/s] 34%|███▍ | 17/50 [00:04<00:07, 4.16it/s] 36%|███▌ | 18/50 [00:04<00:07, 4.15it/s] 38%|███▊ | 19/50 [00:04<00:07, 4.15it/s] 40%|████ | 20/50 [00:04<00:07, 4.14it/s] 42%|████▏ | 21/50 [00:05<00:07, 4.14it/s] 44%|████▍ | 22/50 [00:05<00:06, 4.13it/s] 46%|████▌ | 23/50 [00:05<00:06, 4.12it/s] 48%|████▊ | 24/50 [00:05<00:06, 4.12it/s] 50%|█████ | 25/50 [00:06<00:06, 4.12it/s] 52%|█████▏ | 26/50 [00:06<00:05, 4.12it/s] 54%|█████▍ | 27/50 [00:06<00:05, 4.11it/s] 56%|█████▌ | 28/50 [00:06<00:05, 4.11it/s] 58%|█████▊ | 29/50 [00:07<00:05, 4.11it/s] 60%|██████ | 30/50 [00:07<00:04, 4.11it/s] 62%|██████▏ | 31/50 [00:07<00:04, 4.11it/s] 64%|██████▍ | 32/50 [00:07<00:04, 4.12it/s] 66%|██████▌ | 33/50 [00:07<00:04, 4.11it/s] 68%|██████▊ | 34/50 [00:08<00:03, 4.11it/s] 70%|███████ | 35/50 [00:08<00:03, 4.11it/s] 72%|███████▏ | 36/50 [00:08<00:03, 4.11it/s] 74%|███████▍ | 37/50 [00:08<00:03, 4.11it/s] 76%|███████▌ | 38/50 [00:09<00:02, 4.11it/s] 78%|███████▊ | 39/50 [00:09<00:02, 4.10it/s] 80%|████████ | 40/50 [00:09<00:02, 4.11it/s] 82%|████████▏ | 41/50 [00:09<00:02, 4.12it/s] 84%|████████▍ 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wcarle/stable-diffusion-videos:d80e8dbe5885d570087b782ff22cef888dcc1be7941a52c8ba82c7c9d884d845IDrush7edpbvhv5a6mcirkbl6byyStatusSucceededSourceWebHardware–Total durationCreatedInput
- fps
- 15
- seeds
- 42 | 1337
- prompts
- a cat | a rabbit
- num_steps
- 202
- scheduler
- klms
- guidance_scale
- 7.5
- num_inference_steps
- 30
{ "fps": 15, "seeds": "42 | 1337", "prompts": "a cat | a rabbit", "num_steps": 202, "scheduler": "klms", "guidance_scale": 7.5, "num_inference_steps": 30 }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 wcarle/stable-diffusion-videos using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "wcarle/stable-diffusion-videos:d80e8dbe5885d570087b782ff22cef888dcc1be7941a52c8ba82c7c9d884d845", { input: { fps: 15, seeds: "42 | 1337", prompts: "a cat | a rabbit", num_steps: 202, scheduler: "klms", guidance_scale: 7.5, num_inference_steps: 30 } } ); // 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 wcarle/stable-diffusion-videos using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "wcarle/stable-diffusion-videos:d80e8dbe5885d570087b782ff22cef888dcc1be7941a52c8ba82c7c9d884d845", input={ "fps": 15, "seeds": "42 | 1337", "prompts": "a cat | a rabbit", "num_steps": 202, "scheduler": "klms", "guidance_scale": 7.5, "num_inference_steps": 30 } ) # 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 wcarle/stable-diffusion-videos 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": "wcarle/stable-diffusion-videos:d80e8dbe5885d570087b782ff22cef888dcc1be7941a52c8ba82c7c9d884d845", "input": { "fps": 15, "seeds": "42 | 1337", "prompts": "a cat | a rabbit", "num_steps": 202, "scheduler": "klms", "guidance_scale": 7.5, "num_inference_steps": 30 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-04-02T22:03:47.260602Z", "created_at": "2023-04-02T21:50:59.419713Z", "data_removed": false, "error": null, "id": "rush7edpbvhv5a6mcirkbl6byy", "input": { "fps": 15, "seeds": "42 | 1337", "prompts": "a cat | a rabbit", "num_steps": 202, "scheduler": "klms", "guidance_scale": 7.5, "num_inference_steps": 30 }, "logs": "Setting Random seeds.\nSeeds used for prompts are:\na cat: 42\na rabbit: 1337\n 0%| | 0/30 [00:00<?, ?it/s]\n 3%|▎ | 1/30 [00:01<00:39, 1.35s/it]\n 10%|█ | 3/30 [00:01<00:11, 2.42it/s]\n 17%|█▋ | 5/30 [00:01<00:06, 4.12it/s]\n 23%|██▎ | 7/30 [00:01<00:03, 5.77it/s]\n 30%|███ | 9/30 [00:02<00:02, 7.20it/s]\n 37%|███▋ | 11/30 [00:02<00:02, 8.32it/s]\n 43%|████▎ | 13/30 [00:02<00:01, 9.30it/s]\n 50%|█████ | 15/30 [00:02<00:01, 9.91it/s]\n 57%|█████▋ | 17/30 [00:02<00:01, 10.40it/s]\n 63%|██████▎ | 19/30 [00:02<00:00, 11.08it/s]\n 70%|███████ | 21/30 [00:03<00:00, 10.93it/s]\n 77%|███████▋ | 23/30 [00:03<00:00, 11.44it/s]\n 83%|████████▎ | 25/30 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53%|█████▎ | 16/30 [00:01<00:01, 13.19it/s]\n 60%|██████ | 18/30 [00:01<00:00, 13.15it/s]\n 67%|██████▋ | 20/30 [00:01<00:00, 13.09it/s]\n 73%|███████▎ | 22/30 [00:01<00:00, 13.00it/s]\n 80%|████████ | 24/30 [00:01<00:00, 12.96it/s]\n 87%|████████▋ | 26/30 [00:01<00:00, 13.01it/s]\n 93%|█████████▎| 28/30 [00:02<00:00, 13.11it/s]\n100%|██████████| 30/30 [00:02<00:00, 13.17it/s]\n100%|██████████| 30/30 [00:02<00:00, 13.06it/s]\n 0%| | 0/30 [00:00<?, ?it/s]\n 7%|▋ | 2/30 [00:00<00:02, 13.35it/s]\n 13%|█▎ | 4/30 [00:00<00:01, 13.35it/s]\n 20%|██ | 6/30 [00:00<00:01, 13.23it/s]\n 27%|██▋ | 8/30 [00:00<00:01, 13.15it/s]\n 33%|███▎ | 10/30 [00:00<00:01, 13.07it/s]\n 40%|████ | 12/30 [00:00<00:01, 13.16it/s]\n 47%|████▋ | 14/30 [00:01<00:01, 13.00it/s]\n 53%|█████▎ | 16/30 [00:01<00:01, 13.16it/s]\n 60%|██████ | 18/30 [00:01<00:00, 13.28it/s]\n 67%|██████▋ | 20/30 [00:01<00:00, 13.31it/s]\n 73%|███████▎ | 22/30 [00:01<00:00, 13.28it/s]\n 80%|████████ | 24/30 [00:01<00:00, 13.39it/s]\n 87%|████████▋ | 26/30 [00:01<00:00, 13.37it/s]\n 93%|█████████▎| 28/30 [00:02<00:00, 13.23it/s]\n100%|██████████| 30/30 [00:02<00:00, 13.29it/s]\n100%|██████████| 30/30 [00:02<00:00, 13.24it/s]\n 0%| | 0/30 [00:00<?, ?it/s]\n 7%|▋ | 2/30 [00:00<00:02, 13.68it/s]\n 13%|█▎ | 4/30 [00:00<00:01, 13.57it/s]\n 20%|██ | 6/30 [00:00<00:01, 13.44it/s]\n 27%|██▋ | 8/30 [00:00<00:01, 13.15it/s]\n 33%|███▎ | 10/30 [00:00<00:01, 13.25it/s]\n 40%|████ | 12/30 [00:00<00:01, 13.31it/s]\n 47%|████▋ | 14/30 [00:01<00:01, 13.32it/s]\n 53%|█████▎ | 16/30 [00:01<00:01, 13.36it/s]\n 60%|██████ | 18/30 [00:01<00:00, 13.24it/s]\n 67%|██████▋ | 20/30 [00:01<00:00, 13.28it/s]\n 73%|███████▎ | 22/30 [00:01<00:00, 13.07it/s]\n 80%|████████ | 24/30 [00:01<00:00, 12.80it/s]\n 87%|████████▋ | 26/30 [00:01<00:00, 12.68it/s]\n 93%|█████████▎| 28/30 [00:02<00:00, 12.38it/s]\n100%|██████████| 30/30 [00:02<00:00, 12.24it/s]\n100%|██████████| 30/30 [00:02<00:00, 12.91it/s]", "metrics": { "predict_time": 556.558079, "total_time": 767.840889 }, "output": "https://replicate.delivery/pbxt/sD2Feqn6ahUqDKSZU3Jb1ke5uETYm2HfkLqId0bSjPyFu1bhA/out.mp4", "started_at": "2023-04-02T21:54:30.702523Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rush7edpbvhv5a6mcirkbl6byy", "cancel": "https://api.replicate.com/v1/predictions/rush7edpbvhv5a6mcirkbl6byy/cancel" }, "version": "d80e8dbe5885d570087b782ff22cef888dcc1be7941a52c8ba82c7c9d884d845" }Generated inSetting Random seeds. Seeds used for prompts are: a cat: 42 a rabbit: 1337 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:01<00:39, 1.35s/it] 10%|█ | 3/30 [00:01<00:11, 2.42it/s] 17%|█▋ | 5/30 [00:01<00:06, 4.12it/s] 23%|██▎ | 7/30 [00:01<00:03, 5.77it/s] 30%|███ | 9/30 [00:02<00:02, 7.20it/s] 37%|███▋ | 11/30 [00:02<00:02, 8.32it/s] 43%|████▎ | 13/30 [00:02<00:01, 9.30it/s] 50%|█████ | 15/30 [00:02<00:01, 9.91it/s] 57%|█████▋ | 17/30 [00:02<00:01, 10.40it/s] 63%|██████▎ | 19/30 [00:02<00:00, 11.08it/s] 70%|███████ | 21/30 [00:03<00:00, 10.93it/s] 77%|███████▋ | 23/30 [00:03<00:00, 11.44it/s] 83%|████████▎ | 25/30 [00:03<00:00, 11.82it/s] 90%|█████████ | 27/30 [00:03<00:00, 11.88it/s] 97%|█████████▋| 29/30 [00:03<00:00, 12.02it/s] 100%|██████████| 30/30 [00:03<00:00, 8.01it/s] 0%| | 0/30 [00:00<?, ?it/s] 7%|▋ | 2/30 [00:00<00:02, 12.45it/s] 13%|█▎ | 4/30 [00:00<00:02, 12.96it/s] 20%|██ | 6/30 [00:00<00:01, 13.05it/s] 27%|██▋ | 8/30 [00:00<00:01, 12.04it/s] 33%|███▎ | 10/30 [00:00<00:01, 11.72it/s] 40%|████ | 12/30 [00:00<00:01, 12.09it/s] 47%|████▋ | 14/30 [00:01<00:01, 11.96it/s] 53%|█████▎ | 16/30 [00:01<00:01, 11.71it/s] 60%|██████ | 18/30 [00:01<00:01, 11.82it/s] 67%|██████▋ | 20/30 [00:01<00:00, 11.84it/s] 73%|███████▎ | 22/30 [00:01<00:00, 12.17it/s] 80%|████████ | 24/30 [00:01<00:00, 11.94it/s] 87%|████████▋ | 26/30 [00:02<00:00, 11.74it/s] 93%|█████████▎| 28/30 [00:02<00:00, 12.02it/s] 100%|██████████| 30/30 [00:02<00:00, 12.02it/s] 100%|██████████| 30/30 [00:02<00:00, 12.02it/s] 0%| | 0/30 [00:00<?, ?it/s] 7%|▋ | 2/30 [00:00<00:02, 12.11it/s] 13%|█▎ | 4/30 [00:00<00:02, 11.02it/s] 20%|██ | 6/30 [00:00<00:02, 10.97it/s] 27%|██▋ | 8/30 [00:00<00:01, 11.21it/s] 33%|███▎ | 10/30 [00:00<00:01, 11.33it/s] 40%|████ | 12/30 [00:01<00:01, 11.87it/s] 47%|████▋ | 14/30 [00:01<00:01, 12.17it/s] 53%|█████▎ | 16/30 [00:01<00:01, 12.04it/s] 60%|██████ | 18/30 [00:01<00:01, 11.89it/s] 67%|██████▋ | 20/30 [00:01<00:00, 11.84it/s] 73%|███████▎ | 22/30 [00:01<00:00, 11.88it/s] 80%|████████ | 24/30 [00:02<00:00, 11.81it/s] 87%|████████▋ | 26/30 [00:02<00:00, 11.66it/s] 93%|█████████▎| 28/30 [00:02<00:00, 11.45it/s] 100%|██████████| 30/30 [00:02<00:00, 11.38it/s] 100%|██████████| 30/30 [00:02<00:00, 11.61it/s] 0%| | 0/30 [00:00<?, ?it/s] 7%|▋ | 2/30 [00:00<00:02, 11.38it/s] 13%|█▎ | 4/30 [00:00<00:02, 12.35it/s] 20%|██ | 6/30 [00:00<00:01, 12.71it/s] 27%|██▋ | 8/30 [00:00<00:01, 12.38it/s] 33%|███▎ | 10/30 [00:00<00:01, 12.62it/s] 40%|████ | 12/30 [00:00<00:01, 12.37it/s] 47%|████▋ | 14/30 [00:01<00:01, 12.17it/s] 53%|█████▎ | 16/30 [00:01<00:01, 12.20it/s] 60%|██████ | 18/30 [00:01<00:00, 12.01it/s] 67%|██████▋ | 20/30 [00:01<00:00, 11.75it/s] 73%|███████▎ | 22/30 [00:01<00:00, 11.85it/s] 80%|████████ | 24/30 [00:01<00:00, 11.99it/s] 87%|████████▋ | 26/30 [00:02<00:00, 11.44it/s] 93%|█████████▎| 28/30 [00:02<00:00, 11.53it/s] 100%|██████████| 30/30 [00:02<00:00, 11.69it/s] 100%|██████████| 30/30 [00:02<00:00, 11.95it/s] 0%| | 0/30 [00:00<?, ?it/s] 7%|▋ | 2/30 [00:00<00:02, 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