wcarle/stable-diffusion-videos-openjourney
stable-diffusion-videos-openjourney
Generate videos by interpolating the latent space of Stable Diffusion using the Openjourney Model
Prediction
wcarle/stable-diffusion-videos-openjourney:bd5fd429Input
- fps
- 15
- seeds
- 42 | 1337
- prompts
- full body cyborg full-length portrait detailed face symmetric steampunk cyberpunk cyborg intricate detailed to scale hyperrealistic cinematic lighting digital art concept art mdjrny-v4 style | full body cyborg full-length portrait detailed face symmetric steampunk cyberpunk cyborg intricate detailed to scale hyperrealistic cinematic lighting digital art concept art mdjrny-v4 style
- num_steps
- 100
- scheduler
- klms
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "fps": 15, "seeds": "42 | 1337", "prompts": "full body cyborg full-length portrait detailed face symmetric steampunk cyberpunk cyborg intricate detailed to scale hyperrealistic cinematic lighting digital art concept art mdjrny-v4 style | full body cyborg full-length portrait detailed face symmetric steampunk cyberpunk cyborg intricate detailed to scale hyperrealistic cinematic lighting digital art concept art mdjrny-v4 style", "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-openjourney 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-openjourney:bd5fd4290fc2ab4b6931c90aee17581a62047470422737e035f34badb8af4132", { input: { fps: 15, seeds: "42 | 1337", prompts: "full body cyborg full-length portrait detailed face symmetric steampunk cyberpunk cyborg intricate detailed to scale hyperrealistic cinematic lighting digital art concept art mdjrny-v4 style | full body cyborg full-length portrait detailed face symmetric steampunk cyberpunk cyborg intricate detailed to scale hyperrealistic cinematic lighting digital art concept art mdjrny-v4 style", 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-openjourney using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "wcarle/stable-diffusion-videos-openjourney:bd5fd4290fc2ab4b6931c90aee17581a62047470422737e035f34badb8af4132", input={ "fps": 15, "seeds": "42 | 1337", "prompts": "full body cyborg full-length portrait detailed face symmetric steampunk cyberpunk cyborg intricate detailed to scale hyperrealistic cinematic lighting digital art concept art mdjrny-v4 style | full body cyborg full-length portrait detailed face symmetric steampunk cyberpunk cyborg intricate detailed to scale hyperrealistic cinematic lighting digital art concept art mdjrny-v4 style", "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-openjourney 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-openjourney:bd5fd4290fc2ab4b6931c90aee17581a62047470422737e035f34badb8af4132", "input": { "fps": 15, "seeds": "42 | 1337", "prompts": "full body cyborg full-length portrait detailed face symmetric steampunk cyberpunk cyborg intricate detailed to scale hyperrealistic cinematic lighting digital art concept art mdjrny-v4 style | full body cyborg full-length portrait detailed face symmetric steampunk cyberpunk cyborg intricate detailed to scale hyperrealistic cinematic lighting digital art concept art mdjrny-v4 style", "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": "2023-04-02T22:25:41.634762Z", "created_at": "2023-04-02T22:12:30.305167Z", "data_removed": false, "error": null, "id": "zuhb43jfizhnjo3tf7cgjpqpam", "input": { "fps": 15, "seeds": "42 | 1337", "prompts": "full body cyborg full-length portrait detailed face symmetric steampunk cyberpunk cyborg intricate detailed to scale hyperrealistic cinematic lighting digital art concept art mdjrny-v4 style | full body cyborg full-length portrait detailed face symmetric steampunk cyberpunk cyborg intricate detailed to scale hyperrealistic cinematic lighting digital art concept art mdjrny-v4 style", "num_steps": 100, "scheduler": "klms", "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Setting Random seeds.\nSeeds used for prompts are:\nfull body cyborg full-length portrait detailed face symmetric steampunk cyberpunk cyborg intricate detailed to scale hyperrealistic cinematic lighting digital art concept art mdjrny-v4 style: 42\nfull body cyborg full-length portrait 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[00:00<00:03, 12.74it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 12.37it/s]\n 16%|█▌ | 8/50 [00:00<00:03, 12.16it/s]\n 20%|██ | 10/50 [00:00<00:03, 12.07it/s]\n 24%|██▍ | 12/50 [00:00<00:03, 11.78it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 11.59it/s]\n 32%|███▏ | 16/50 [00:01<00:02, 12.11it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 12.07it/s]\n 40%|████ | 20/50 [00:01<00:02, 12.07it/s]\n 44%|████▍ | 22/50 [00:01<00:02, 12.42it/s]\n 48%|████▊ | 24/50 [00:01<00:02, 12.23it/s]\n 52%|█████▏ | 26/50 [00:02<00:01, 12.00it/s]\n 56%|█████▌ | 28/50 [00:02<00:01, 11.96it/s]\n 60%|██████ | 30/50 [00:02<00:01, 12.00it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 11.95it/s]\n 68%|██████▊ | 34/50 [00:02<00:01, 12.10it/s]\n 72%|███████▏ | 36/50 [00:02<00:01, 12.07it/s]\n 76%|███████▌ | 38/50 [00:03<00:01, 11.61it/s]\n 80%|████████ | 40/50 [00:03<00:00, 11.37it/s]\n 84%|████████▍ | 42/50 [00:03<00:00, 11.67it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 11.31it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 10.89it/s]\n 96%|█████████▌| 48/50 [00:04<00:00, 10.88it/s]\n100%|██████████| 50/50 [00:04<00:00, 11.32it/s]\n100%|██████████| 50/50 [00:04<00:00, 11.78it/s]\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:03, 12.09it/s]\n 8%|▊ | 4/50 [00:00<00:03, 12.13it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 12.19it/s]\n 16%|█▌ | 8/50 [00:00<00:03, 11.98it/s]\n 20%|██ | 10/50 [00:00<00:03, 12.13it/s]\n 24%|██▍ | 12/50 [00:00<00:03, 12.41it/s]\n 28%|██▊ | 14/50 [00:01<00:02, 12.67it/s]\n 32%|███▏ | 16/50 [00:01<00:02, 12.15it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 11.93it/s]\n 40%|████ | 20/50 [00:01<00:02, 11.96it/s]\n 44%|████▍ | 22/50 [00:01<00:02, 11.87it/s]\n 48%|████▊ | 24/50 [00:01<00:02, 11.71it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 11.91it/s]\n 56%|█████▌ | 28/50 [00:02<00:01, 11.72it/s]\n 60%|██████ | 30/50 [00:02<00:01, 11.63it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 11.78it/s]\n 68%|██████▊ | 34/50 [00:02<00:01, 12.16it/s]\n 72%|███████▏ | 36/50 [00:02<00:01, 12.26it/s]\n 76%|███████▌ | 38/50 [00:03<00:00, 12.55it/s]\n 80%|████████ | 40/50 [00:03<00:00, 12.73it/s]\n 84%|████████▍ | 42/50 [00:03<00:00, 12.64it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 12.24it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 12.48it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 12.71it/s]\n100%|██████████| 50/50 [00:04<00:00, 12.85it/s]\n100%|██████████| 50/50 [00:04<00:00, 12.25it/s]", "metrics": { "predict_time": 465.409085, "total_time": 791.329595 }, "output": "https://replicate.delivery/pbxt/LgHnEUPwkoInKluO8PPW3JAwf7PfSs00fCVvfxewssnlcZvFC/out.mp4", "started_at": "2023-04-02T22:17:56.225677Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zuhb43jfizhnjo3tf7cgjpqpam", "cancel": "https://api.replicate.com/v1/predictions/zuhb43jfizhnjo3tf7cgjpqpam/cancel" }, "version": "bd5fd4290fc2ab4b6931c90aee17581a62047470422737e035f34badb8af4132" }Generated inSetting Random seeds. Seeds used for prompts are: full body cyborg full-length portrait detailed face symmetric steampunk cyberpunk cyborg intricate detailed to scale hyperrealistic cinematic lighting digital art concept art mdjrny-v4 style: 42 full body cyborg full-length portrait detailed face symmetric steampunk cyberpunk cyborg intricate detailed to scale hyperrealistic cinematic lighting digital art concept art mdjrny-v4 style: 1337 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<01:19, 1.63s/it] 6%|▌ | 3/50 [00:01<00:22, 2.08it/s] 10%|█ | 5/50 [00:01<00:12, 3.56it/s] 14%|█▍ | 7/50 [00:02<00:08, 5.08it/s] 18%|█▊ | 9/50 [00:02<00:06, 6.49it/s] 22%|██▏ | 11/50 [00:02<00:05, 7.65it/s] 26%|██▌ | 13/50 [00:02<00:04, 8.59it/s] 30%|███ | 15/50 [00:02<00:03, 9.42it/s] 34%|███▍ | 17/50 [00:02<00:03, 10.05it/s] 38%|███▊ | 19/50 [00:03<00:02, 10.58it/s] 42%|████▏ | 21/50 [00:03<00:02, 11.13it/s] 46%|████▌ | 23/50 [00:03<00:02, 11.10it/s] 50%|█████ | 25/50 [00:03<00:02, 10.87it/s] 54%|█████▍ | 27/50 [00:03<00:02, 11.03it/s] 58%|█████▊ | 29/50 [00:04<00:01, 10.80it/s] 62%|██████▏ | 31/50 [00:04<00:01, 11.04it/s] 66%|██████▌ | 33/50 [00:04<00:01, 11.54it/s] 70%|███████ | 35/50 [00:04<00:01, 11.84it/s] 74%|███████▍ | 37/50 [00:04<00:01, 11.89it/s] 78%|███████▊ | 39/50 [00:04<00:00, 12.03it/s] 82%|████████▏ | 41/50 [00:05<00:00, 11.92it/s] 86%|████████▌ | 43/50 [00:05<00:00, 11.61it/s] 90%|█████████ | 45/50 [00:05<00:00, 11.55it/s] 94%|█████████▍| 47/50 [00:05<00:00, 12.02it/s] 98%|█████████▊| 49/50 [00:05<00:00, 11.80it/s] 100%|██████████| 50/50 [00:05<00:00, 8.60it/s] 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:04, 11.83it/s] 8%|▊ | 4/50 [00:00<00:03, 12.78it/s] 12%|█▏ | 6/50 [00:00<00:03, 12.70it/s] 16%|█▌ | 8/50 [00:00<00:03, 12.55it/s] 20%|██ | 10/50 [00:00<00:03, 12.42it/s] 24%|██▍ | 12/50 [00:00<00:03, 12.14it/s] 28%|██▊ | 14/50 [00:01<00:02, 12.28it/s] 32%|███▏ | 16/50 [00:01<00:02, 12.30it/s] 36%|███▌ | 18/50 [00:01<00:02, 11.79it/s] 40%|████ | 20/50 [00:01<00:02, 11.43it/s] 44%|████▍ | 22/50 [00:01<00:02, 11.16it/s] 48%|████▊ | 24/50 [00:02<00:02, 11.65it/s] 52%|█████▏ | 26/50 [00:02<00:01, 12.06it/s] 56%|█████▌ | 28/50 [00:02<00:01, 12.38it/s] 60%|██████ | 30/50 [00:02<00:01, 12.54it/s] 64%|██████▍ | 32/50 [00:02<00:01, 12.68it/s] 68%|██████▊ | 34/50 [00:02<00:01, 11.92it/s] 72%|███████▏ | 36/50 [00:02<00:01, 11.78it/s] 76%|███████▌ | 38/50 [00:03<00:01, 11.74it/s] 80%|████████ | 40/50 [00:03<00:00, 11.92it/s] 84%|████████▍ | 42/50 [00:03<00:00, 11.87it/s] 88%|████████▊ | 44/50 [00:03<00:00, 12.26it/s] 92%|█████████▏| 46/50 [00:03<00:00, 12.53it/s] 96%|█████████▌| 48/50 [00:03<00:00, 11.94it/s] 100%|██████████| 50/50 [00:04<00:00, 11.86it/s] 100%|██████████| 50/50 [00:04<00:00, 12.04it/s] 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 12.00it/s] 8%|▊ | 4/50 [00:00<00:03, 12.03it/s] 12%|█▏ | 6/50 [00:00<00:03, 11.56it/s] 16%|█▌ | 8/50 [00:00<00:03, 11.55it/s] 20%|██ | 10/50 [00:00<00:03, 11.57it/s] 24%|██▍ | 12/50 [00:01<00:03, 11.54it/s] 28%|██▊ | 14/50 [00:01<00:03, 11.54it/s] 32%|███▏ | 16/50 [00:01<00:02, 11.34it/s] 36%|███▌ | 18/50 [00:01<00:02, 11.27it/s] 40%|████ | 20/50 [00:01<00:02, 11.39it/s] 44%|████▍ | 22/50 [00:01<00:02, 11.30it/s] 48%|████▊ | 24/50 [00:02<00:02, 11.11it/s] 52%|█████▏ | 26/50 [00:02<00:02, 11.21it/s] 56%|█████▌ | 28/50 [00:02<00:01, 11.47it/s] 60%|██████ | 30/50 [00:02<00:01, 11.60it/s] 64%|██████▍ | 32/50 [00:02<00:01, 11.92it/s] 68%|██████▊ | 34/50 [00:02<00:01, 12.01it/s] 72%|███████▏ | 36/50 [00:03<00:01, 11.75it/s] 76%|███████▌ | 38/50 [00:03<00:01, 11.51it/s] 80%|████████ | 40/50 [00:03<00:00, 11.38it/s] 84%|████████▍ | 42/50 [00:03<00:00, 11.33it/s] 88%|████████▊ | 44/50 [00:03<00:00, 11.68it/s] 92%|█████████▏| 46/50 [00:03<00:00, 11.81it/s] 96%|█████████▌| 48/50 [00:04<00:00, 11.43it/s] 100%|██████████| 50/50 [00:04<00:00, 11.89it/s] 100%|██████████| 50/50 [00:04<00:00, 11.57it/s] 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:04, 11.94it/s] 8%|▊ | 4/50 [00:00<00:03, 11.63it/s] 12%|█▏ | 6/50 [00:00<00:03, 11.38it/s] 16%|█▌ | 8/50 [00:00<00:03, 11.32it/s] 20%|██ | 10/50 [00:00<00:03, 11.27it/s] 24%|██▍ | 12/50 [00:01<00:03, 10.73it/s] 28%|██▊ | 14/50 [00:01<00:03, 10.68it/s] 32%|███▏ | 16/50 [00:01<00:03, 10.52it/s] 36%|███▌ | 18/50 [00:01<00:02, 11.02it/s] 40%|████ | 20/50 [00:01<00:02, 11.37it/s] 44%|████▍ | 22/50 [00:01<00:02, 11.66it/s] 48%|████▊ | 24/50 [00:02<00:02, 11.79it/s] 52%|█████▏ | 26/50 [00:02<00:01, 12.14it/s] 56%|█████▌ | 28/50 [00:02<00:01, 12.37it/s] 60%|██████ | 30/50 [00:02<00:01, 12.45it/s] 64%|██████▍ | 32/50 [00:02<00:01, 12.26it/s] 68%|██████▊ | 34/50 [00:02<00:01, 11.76it/s] 72%|███████▏ | 36/50 [00:03<00:01, 11.22it/s] 76%|███████▌ | 38/50 [00:03<00:01, 11.62it/s] 80%|████████ | 40/50 [00:03<00:00, 11.97it/s] 84%|████████▍ | 42/50 [00:03<00:00, 11.31it/s] 88%|████████▊ | 44/50 [00:03<00:00, 11.64it/s] 92%|█████████▏| 46/50 [00:04<00:00, 11.31it/s] 96%|█████████▌| 48/50 [00:04<00:00, 11.46it/s] 100%|██████████| 50/50 [00:04<00:00, 11.16it/s] 100%|██████████| 50/50 [00:04<00:00, 11.46it/s] 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 12.71it/s] 8%|▊ | 4/50 [00:00<00:03, 12.41it/s] 12%|█▏ | 6/50 [00:00<00:03, 11.63it/s] 16%|█▌ | 8/50 [00:00<00:03, 11.40it/s] 20%|██ | 10/50 [00:00<00:03, 11.28it/s] 24%|██▍ | 12/50 [00:01<00:03, 11.75it/s] 28%|██▊ | 14/50 [00:01<00:03, 11.77it/s] 32%|███▏ | 16/50 [00:01<00:02, 11.51it/s] 36%|███▌ | 18/50 [00:01<00:02, 11.29it/s] 40%|████ | 20/50 [00:01<00:02, 11.35it/s] 44%|████▍ | 22/50 [00:01<00:02, 11.13it/s] 48%|████▊ | 24/50 [00:02<00:02, 11.68it/s] 52%|█████▏ | 26/50 [00:02<00:02, 11.77it/s] 56%|█████▌ | 28/50 [00:02<00:01, 11.40it/s] 60%|██████ | 30/50 [00:02<00:01, 11.34it/s] 64%|██████▍ | 32/50 [00:02<00:01, 11.14it/s] 68%|██████▊ | 34/50 [00:02<00:01, 11.18it/s] 72%|███████▏ | 36/50 [00:03<00:01, 11.66it/s] 76%|███████▌ | 38/50 [00:03<00:01, 11.90it/s] 80%|████████ | 40/50 [00:03<00:00, 11.97it/s] 84%|████████▍ | 42/50 [00:03<00:00, 11.62it/s] 88%|████████▊ | 44/50 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wcarle/stable-diffusion-videos-openjourney:bd5fd429IDilfc27v6p5bqzir7hwuxgdibriStatusSucceededSourceWebHardware–Total durationCreatedInput
- fps
- 15
- prompts
- A dark haired woman sitting on a dock on a lake serenely watching the sunset | a willow tree growing from the bank of a lake with hundreds of stars in the sky
- num_steps
- 201
- scheduler
- klms
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "fps": 15, "prompts": "A dark haired woman sitting on a dock on a lake serenely watching the sunset | a willow tree growing from the bank of a lake with hundreds of stars in the sky", "num_steps": 201, "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-openjourney 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-openjourney:bd5fd4290fc2ab4b6931c90aee17581a62047470422737e035f34badb8af4132", { input: { fps: 15, prompts: "A dark haired woman sitting on a dock on a lake serenely watching the sunset | a willow tree growing from the bank of a lake with hundreds of stars in the sky", num_steps: 201, 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-openjourney using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "wcarle/stable-diffusion-videos-openjourney:bd5fd4290fc2ab4b6931c90aee17581a62047470422737e035f34badb8af4132", input={ "fps": 15, "prompts": "A dark haired woman sitting on a dock on a lake serenely watching the sunset | a willow tree growing from the bank of a lake with hundreds of stars in the sky", "num_steps": 201, "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-openjourney 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-openjourney:bd5fd4290fc2ab4b6931c90aee17581a62047470422737e035f34badb8af4132", "input": { "fps": 15, "prompts": "A dark haired woman sitting on a dock on a lake serenely watching the sunset | a willow tree growing from the bank of a lake with hundreds of stars in the sky", "num_steps": 201, "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": "2023-04-02T22:33:56.061113Z", "created_at": "2023-04-02T22:13:32.809632Z", "data_removed": false, "error": null, "id": "ilfc27v6p5bqzir7hwuxgdibri", "input": { "fps": 15, "prompts": "A dark haired woman sitting on a dock on a lake serenely watching the sunset | a willow tree growing from the bank of a lake with hundreds of stars in the sky", "num_steps": 201, "scheduler": "klms", "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Setting Random seeds.\nSeeds used for prompts are:\nA dark haired woman sitting on a dock on a lake serenely watching the sunset: 15764\na willow tree growing from the bank of a lake with hundreds of stars in the sky: 23266\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<00:58, 1.19s/it]\n 6%|▌ | 3/50 [00:01<00:17, 2.64it/s]\n 10%|█ | 5/50 [00:01<00:10, 4.37it/s]\n 14%|█▍ | 7/50 [00:01<00:07, 6.04it/s]\n 18%|█▊ | 9/50 [00:01<00:05, 7.54it/s]\n 22%|██▏ | 11/50 [00:02<00:04, 8.86it/s]\n 26%|██▌ | 13/50 [00:02<00:03, 9.93it/s]\n 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11.98it/s]\n 52%|█████▏ | 26/50 [00:02<00:01, 12.20it/s]\n 56%|█████▌ | 28/50 [00:02<00:01, 12.19it/s]\n 60%|██████ | 30/50 [00:02<00:01, 12.12it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 12.10it/s]\n 68%|██████▊ | 34/50 [00:02<00:01, 12.32it/s]\n 72%|███████▏ | 36/50 [00:02<00:01, 12.24it/s]\n 76%|███████▌ | 38/50 [00:03<00:00, 12.28it/s]\n 80%|████████ | 40/50 [00:03<00:00, 12.12it/s]\n 84%|████████▍ | 42/50 [00:03<00:00, 12.39it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 12.23it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 11.88it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 11.66it/s]\n100%|██████████| 50/50 [00:04<00:00, 11.66it/s]\n100%|██████████| 50/50 [00:04<00:00, 12.12it/s]\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:03, 12.74it/s]\n 8%|▊ | 4/50 [00:00<00:03, 12.21it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 11.32it/s]\n 16%|█▌ | 8/50 [00:00<00:03, 11.33it/s]\n 20%|██ | 10/50 [00:00<00:03, 11.01it/s]\n 24%|██▍ | 12/50 [00:01<00:03, 11.34it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 11.56it/s]\n 32%|███▏ | 16/50 [00:01<00:02, 11.65it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 11.78it/s]\n 40%|████ | 20/50 [00:01<00:02, 11.73it/s]\n 44%|████▍ | 22/50 [00:01<00:02, 11.99it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 12.23it/s]\n 52%|█████▏ | 26/50 [00:02<00:01, 12.43it/s]\n 56%|█████▌ | 28/50 [00:02<00:01, 12.52it/s]\n 60%|██████ | 30/50 [00:02<00:01, 12.31it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 12.13it/s]\n 68%|██████▊ | 34/50 [00:02<00:01, 12.41it/s]\n 72%|███████▏ | 36/50 [00:03<00:01, 12.18it/s]\n 76%|███████▌ | 38/50 [00:03<00:01, 11.75it/s]\n 80%|████████ | 40/50 [00:03<00:00, 11.57it/s]\n 84%|████████▍ | 42/50 [00:03<00:00, 11.87it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 11.64it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 11.75it/s]\n 96%|█████████▌| 48/50 [00:04<00:00, 11.96it/s]\n100%|██████████| 50/50 [00:04<00:00, 12.16it/s]\n100%|██████████| 50/50 [00:04<00:00, 11.91it/s]\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:03, 13.18it/s]\n 8%|▊ | 4/50 [00:00<00:03, 11.60it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 11.87it/s]\n 16%|█▌ | 8/50 [00:00<00:03, 11.88it/s]\n 20%|██ | 10/50 [00:00<00:03, 11.52it/s]\n 24%|██▍ | 12/50 [00:01<00:03, 11.74it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 11.98it/s]\n 32%|███▏ | 16/50 [00:01<00:02, 12.15it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 12.40it/s]\n 40%|████ | 20/50 [00:01<00:02, 12.36it/s]\n 44%|████▍ | 22/50 [00:01<00:02, 12.40it/s]\n 48%|████▊ | 24/50 [00:01<00:02, 12.16it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 11.78it/s]\n 56%|█████▌ | 28/50 [00:02<00:01, 11.76it/s]\n 60%|██████ | 30/50 [00:02<00:01, 11.87it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 11.80it/s]\n 68%|██████▊ | 34/50 [00:02<00:01, 12.03it/s]\n 72%|███████▏ | 36/50 [00:03<00:01, 11.85it/s]\n 76%|███████▌ | 38/50 [00:03<00:00, 12.00it/s]\n 80%|████████ | 40/50 [00:03<00:00, 12.30it/s]\n 84%|████████▍ | 42/50 [00:03<00:00, 12.55it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 12.65it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 12.65it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 12.66it/s]\n100%|██████████| 50/50 [00:04<00:00, 12.64it/s]\n100%|██████████| 50/50 [00:04<00:00, 12.18it/s]", "metrics": { "predict_time": 918.563608, "total_time": 1223.251481 }, "output": "https://replicate.delivery/pbxt/hNjOmoKcfVzqbiSqVHKAnlQffsadf6xl8fh5jFdeScwy00eWIA/out.mp4", "started_at": "2023-04-02T22:18:37.497505Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ilfc27v6p5bqzir7hwuxgdibri", "cancel": "https://api.replicate.com/v1/predictions/ilfc27v6p5bqzir7hwuxgdibri/cancel" }, "version": "bd5fd4290fc2ab4b6931c90aee17581a62047470422737e035f34badb8af4132" }Generated inSetting Random seeds. Seeds used for prompts are: A dark haired woman sitting on a dock on a lake serenely watching the sunset: 15764 a willow tree growing from the bank of a lake with hundreds of stars in the sky: 23266 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<00:58, 1.19s/it] 6%|▌ | 3/50 [00:01<00:17, 2.64it/s] 10%|█ | 5/50 [00:01<00:10, 4.37it/s] 14%|█▍ | 7/50 [00:01<00:07, 6.04it/s] 18%|█▊ | 9/50 [00:01<00:05, 7.54it/s] 22%|██▏ | 11/50 [00:02<00:04, 8.86it/s] 26%|██▌ | 13/50 [00:02<00:03, 9.93it/s] 30%|███ | 15/50 [00:02<00:03, 10.52it/s] 34%|███▍ | 17/50 [00:02<00:02, 11.20it/s] 38%|███▊ | 19/50 [00:02<00:02, 11.64it/s] 42%|████▏ | 21/50 [00:02<00:02, 11.93it/s] 46%|████▌ | 23/50 [00:02<00:02, 12.26it/s] 50%|█████ | 25/50 [00:03<00:01, 12.51it/s] 54%|█████▍ | 27/50 [00:03<00:01, 12.77it/s] 58%|█████▊ | 29/50 [00:03<00:01, 12.74it/s] 62%|██████▏ | 31/50 [00:03<00:01, 12.89it/s] 66%|██████▌ | 33/50 [00:03<00:01, 12.92it/s] 70%|███████ | 35/50 [00:03<00:01, 12.97it/s] 74%|███████▍ | 37/50 [00:04<00:00, 13.02it/s] 78%|███████▊ | 39/50 [00:04<00:00, 12.97it/s] 82%|████████▏ | 41/50 [00:04<00:00, 12.95it/s] 86%|████████▌ | 43/50 [00:04<00:00, 12.69it/s] 90%|█████████ | 45/50 [00:04<00:00, 12.81it/s] 94%|█████████▍| 47/50 [00:04<00:00, 12.84it/s] 98%|█████████▊| 49/50 [00:04<00:00, 12.91it/s] 100%|██████████| 50/50 [00:05<00:00, 9.92it/s] 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 12.07it/s] 8%|▊ | 4/50 [00:00<00:03, 12.63it/s] 12%|█▏ | 6/50 [00:00<00:03, 12.65it/s] 16%|█▌ | 8/50 [00:00<00:03, 12.44it/s] 20%|██ | 10/50 [00:00<00:03, 12.27it/s] 24%|██▍ | 12/50 [00:00<00:03, 12.22it/s] 28%|██▊ | 14/50 [00:01<00:02, 12.22it/s] 32%|███▏ | 16/50 [00:01<00:02, 12.47it/s] 36%|███▌ | 18/50 [00:01<00:02, 12.62it/s] 40%|████ | 20/50 [00:01<00:02, 12.73it/s] 44%|████▍ | 22/50 [00:01<00:02, 12.76it/s] 48%|████▊ | 24/50 [00:01<00:02, 12.90it/s] 52%|█████▏ | 26/50 [00:02<00:01, 12.99it/s] 56%|█████▌ | 28/50 [00:02<00:01, 12.89it/s] 60%|██████ | 30/50 [00:02<00:01, 12.98it/s] 64%|██████▍ | 32/50 [00:02<00:01, 13.01it/s] 68%|██████▊ | 34/50 [00:02<00:01, 13.13it/s] 72%|███████▏ | 36/50 [00:02<00:01, 13.22it/s] 76%|███████▌ | 38/50 [00:02<00:00, 13.28it/s] 80%|████████ | 40/50 [00:03<00:00, 13.32it/s] 84%|████████▍ | 42/50 [00:03<00:00, 13.22it/s] 88%|████████▊ | 44/50 [00:03<00:00, 13.24it/s] 92%|█████████▏| 46/50 [00:03<00:00, 13.26it/s] 96%|█████████▌| 48/50 [00:03<00:00, 13.30it/s] 100%|██████████| 50/50 [00:03<00:00, 13.24it/s] 100%|██████████| 50/50 [00:03<00:00, 12.91it/s] 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 12.55it/s] 8%|▊ | 4/50 [00:00<00:03, 13.00it/s] 12%|█▏ | 6/50 [00:00<00:03, 13.03it/s] 16%|█▌ | 8/50 [00:00<00:03, 13.10it/s] 20%|██ | 10/50 [00:00<00:03, 13.14it/s] 24%|██▍ | 12/50 [00:00<00:02, 13.17it/s] 28%|██▊ | 14/50 [00:01<00:02, 13.18it/s] 32%|███▏ | 16/50 [00:01<00:02, 12.73it/s] 36%|███▌ | 18/50 [00:01<00:02, 12.79it/s] 40%|████ | 20/50 [00:01<00:02, 12.95it/s] 44%|████▍ | 22/50 [00:01<00:02, 13.05it/s] 48%|████▊ | 24/50 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[00:01<00:02, 12.71it/s] 36%|███▌ | 18/50 [00:01<00:02, 12.78it/s] 40%|████ | 20/50 [00:01<00:02, 12.71it/s] 44%|████▍ | 22/50 [00:01<00:02, 12.80it/s] 48%|████▊ | 24/50 [00:01<00:02, 12.89it/s] 52%|█████▏ | 26/50 [00:02<00:01, 12.84it/s] 56%|█████▌ | 28/50 [00:02<00:01, 12.82it/s] 60%|██████ | 30/50 [00:02<00:01, 12.63it/s] 64%|██████▍ | 32/50 [00:02<00:01, 12.78it/s] 68%|██████▊ | 34/50 [00:02<00:01, 12.78it/s] 72%|███████▏ | 36/50 [00:02<00:01, 12.83it/s] 76%|███████▌ | 38/50 [00:02<00:00, 12.84it/s] 80%|████████ | 40/50 [00:03<00:00, 12.88it/s] 84%|████████▍ | 42/50 [00:03<00:00, 12.69it/s] 88%|████████▊ | 44/50 [00:03<00:00, 12.81it/s] 92%|█████████▏| 46/50 [00:03<00:00, 12.96it/s] 96%|█████████▌| 48/50 [00:03<00:00, 13.05it/s] 100%|██████████| 50/50 [00:03<00:00, 12.96it/s] 100%|██████████| 50/50 [00:03<00:00, 12.86it/s] 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 13.62it/s] 8%|▊ | 4/50 [00:00<00:03, 12.59it/s] 12%|█▏ | 6/50 [00:00<00:03, 12.63it/s] 16%|█▌ | 8/50 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wcarle/stable-diffusion-videos-openjourney:bd5fd429ID7bnsledvd5dpjnpyqn7gzhj52mStatusSucceededSourceWebHardware–Total durationCreatedInput
- fps
- 15
- seeds
- 256 | 128
- prompts
- an areal photo of a modern city with skyscrapers | an areal photo of a dense jungle with large trees
- num_steps
- 200
- scheduler
- klms
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "fps": 15, "seeds": "256 | 128", "prompts": "an areal photo of a modern city with skyscrapers | an areal photo of a dense jungle with large trees", "num_steps": 200, "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-openjourney 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-openjourney:bd5fd4290fc2ab4b6931c90aee17581a62047470422737e035f34badb8af4132", { input: { fps: 15, seeds: "256 | 128", prompts: "an areal photo of a modern city with skyscrapers | an areal photo of a dense jungle with large trees", num_steps: 200, 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-openjourney using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "wcarle/stable-diffusion-videos-openjourney:bd5fd4290fc2ab4b6931c90aee17581a62047470422737e035f34badb8af4132", input={ "fps": 15, "seeds": "256 | 128", "prompts": "an areal photo of a modern city with skyscrapers | an areal photo of a dense jungle with large trees", "num_steps": 200, "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-openjourney 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-openjourney:bd5fd4290fc2ab4b6931c90aee17581a62047470422737e035f34badb8af4132", "input": { "fps": 15, "seeds": "256 | 128", "prompts": "an areal photo of a modern city with skyscrapers | an areal photo of a dense jungle with large trees", "num_steps": 200, "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": "2023-04-02T22:53:52.719821Z", "created_at": "2023-04-02T22:39:02.910619Z", "data_removed": false, "error": null, "id": "7bnsledvd5dpjnpyqn7gzhj52m", "input": { "fps": 15, "seeds": "256 | 128", "prompts": "an areal photo of a modern city with skyscrapers | an areal photo of a dense jungle with large trees", "num_steps": 200, "scheduler": "klms", "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Setting Random seeds.\nSeeds used for prompts are:\nan areal photo of a modern city with skyscrapers: 256\nan areal photo of a dense jungle with large trees: 128\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:05, 9.77it/s]\n 6%|▌ | 3/50 [00:00<00:04, 11.44it/s]\n 10%|█ | 5/50 [00:00<00:03, 11.51it/s]\n 14%|█▍ | 7/50 [00:00<00:03, 11.50it/s]\n 18%|█▊ | 9/50 [00:00<00:03, 10.75it/s]\n 22%|██▏ | 11/50 [00:00<00:03, 11.30it/s]\n 26%|██▌ | 13/50 [00:01<00:03, 11.43it/s]\n 30%|███ | 15/50 [00:01<00:03, 11.61it/s]\n 34%|███▍ | 17/50 [00:01<00:02, 11.78it/s]\n 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10/50 [00:00<00:03, 11.55it/s]\n 24%|██▍ | 12/50 [00:01<00:03, 11.69it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 11.71it/s]\n 32%|███▏ | 16/50 [00:01<00:02, 11.75it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 11.63it/s]\n 40%|████ | 20/50 [00:01<00:02, 11.77it/s]\n 44%|████▍ | 22/50 [00:01<00:02, 11.75it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 11.80it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 11.69it/s]\n 56%|█████▌ | 28/50 [00:02<00:01, 11.47it/s]\n 60%|██████ | 30/50 [00:02<00:01, 11.50it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 11.72it/s]\n 68%|██████▊ | 34/50 [00:02<00:01, 12.01it/s]\n 72%|███████▏ | 36/50 [00:03<00:01, 12.06it/s]\n 76%|███████▌ | 38/50 [00:03<00:00, 12.08it/s]\n 80%|████████ | 40/50 [00:03<00:00, 12.28it/s]\n 84%|████████▍ | 42/50 [00:03<00:00, 12.08it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 12.10it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 12.20it/s]\n 96%|█████████▌| 48/50 [00:04<00:00, 12.14it/s]\n100%|██████████| 50/50 [00:04<00:00, 12.19it/s]\n100%|██████████| 50/50 [00:04<00:00, 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88%|████████▊ | 44/50 [00:03<00:00, 11.84it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 11.81it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 11.99it/s]\n100%|██████████| 50/50 [00:04<00:00, 12.27it/s]\n100%|██████████| 50/50 [00:04<00:00, 12.07it/s]\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:03, 12.88it/s]\n 8%|▊ | 4/50 [00:00<00:03, 12.68it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 12.52it/s]\n 16%|█▌ | 8/50 [00:00<00:03, 12.35it/s]\n 20%|██ | 10/50 [00:00<00:03, 12.50it/s]\n 24%|██▍ | 12/50 [00:00<00:03, 11.92it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 11.88it/s]\n 32%|███▏ | 16/50 [00:01<00:02, 12.14it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 12.49it/s]\n 40%|████ | 20/50 [00:01<00:02, 12.10it/s]\n 44%|████▍ | 22/50 [00:01<00:02, 11.56it/s]\n 48%|████▊ | 24/50 [00:01<00:02, 11.63it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 11.62it/s]\n 56%|█████▌ | 28/50 [00:02<00:01, 11.37it/s]\n 60%|██████ | 30/50 [00:02<00:01, 11.56it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 12.06it/s]\n 68%|██████▊ | 34/50 [00:02<00:01, 12.35it/s]\n 72%|███████▏ | 36/50 [00:02<00:01, 12.28it/s]\n 76%|███████▌ | 38/50 [00:03<00:00, 12.26it/s]\n 80%|████████ | 40/50 [00:03<00:00, 12.61it/s]\n 84%|████████▍ | 42/50 [00:03<00:00, 12.84it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 12.78it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 12.24it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 12.40it/s]\n100%|██████████| 50/50 [00:04<00:00, 11.71it/s]\n100%|██████████| 50/50 [00:04<00:00, 12.09it/s]", "metrics": { "predict_time": 889.708972, "total_time": 889.809202 }, "output": "https://replicate.delivery/pbxt/bh62asyG06IRNxD1F6APrCKMXhucwj6fctes0nljJK7fL3bhA/out.mp4", "started_at": "2023-04-02T22:39:03.010849Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/7bnsledvd5dpjnpyqn7gzhj52m", "cancel": "https://api.replicate.com/v1/predictions/7bnsledvd5dpjnpyqn7gzhj52m/cancel" }, "version": "bd5fd4290fc2ab4b6931c90aee17581a62047470422737e035f34badb8af4132" }Generated inSetting Random seeds. Seeds used for prompts are: an areal photo of a modern city with skyscrapers: 256 an areal photo of a dense jungle with large trees: 128 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:05, 9.77it/s] 6%|▌ | 3/50 [00:00<00:04, 11.44it/s] 10%|█ | 5/50 [00:00<00:03, 11.51it/s] 14%|█▍ | 7/50 [00:00<00:03, 11.50it/s] 18%|█▊ | 9/50 [00:00<00:03, 10.75it/s] 22%|██▏ | 11/50 [00:00<00:03, 11.30it/s] 26%|██▌ | 13/50 [00:01<00:03, 11.43it/s] 30%|███ | 15/50 [00:01<00:03, 11.61it/s] 34%|███▍ | 17/50 [00:01<00:02, 11.78it/s] 38%|███▊ | 19/50 [00:01<00:02, 11.74it/s] 42%|████▏ | 21/50 [00:01<00:02, 11.89it/s] 46%|████▌ | 23/50 [00:01<00:02, 11.87it/s] 50%|█████ | 25/50 [00:02<00:02, 11.71it/s] 54%|█████▍ | 27/50 [00:02<00:01, 11.66it/s] 58%|█████▊ | 29/50 [00:02<00:01, 11.86it/s] 62%|██████▏ | 31/50 [00:02<00:01, 11.83it/s] 66%|██████▌ | 33/50 [00:02<00:01, 11.92it/s] 70%|███████ | 35/50 [00:03<00:01, 11.68it/s] 74%|███████▍ | 37/50 [00:03<00:01, 11.58it/s] 78%|███████▊ | 39/50 [00:03<00:00, 11.55it/s] 82%|████████▏ | 41/50 [00:03<00:00, 11.50it/s] 86%|████████▌ | 43/50 [00:03<00:00, 11.68it/s] 90%|█████████ | 45/50 [00:03<00:00, 11.82it/s] 94%|█████████▍| 47/50 [00:04<00:00, 11.99it/s] 98%|█████████▊| 49/50 [00:04<00:00, 12.19it/s] 100%|██████████| 50/50 [00:04<00:00, 11.72it/s] 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 12.06it/s] 8%|▊ | 4/50 [00:00<00:03, 11.71it/s] 12%|█▏ | 6/50 [00:00<00:03, 11.64it/s] 16%|█▌ | 8/50 [00:00<00:03, 11.69it/s] 20%|██ | 10/50 [00:00<00:03, 11.55it/s] 24%|██▍ | 12/50 [00:01<00:03, 11.69it/s] 28%|██▊ | 14/50 [00:01<00:03, 11.71it/s] 32%|███▏ | 16/50 [00:01<00:02, 11.75it/s] 36%|███▌ | 18/50 [00:01<00:02, 11.63it/s] 40%|████ | 20/50 [00:01<00:02, 11.77it/s] 44%|████▍ | 22/50 [00:01<00:02, 11.75it/s] 48%|████▊ | 24/50 [00:02<00:02, 11.80it/s] 52%|█████▏ | 26/50 [00:02<00:02, 11.69it/s] 56%|█████▌ | 28/50 [00:02<00:01, 11.47it/s] 60%|██████ | 30/50 [00:02<00:01, 11.50it/s] 64%|██████▍ | 32/50 [00:02<00:01, 11.72it/s] 68%|██████▊ | 34/50 [00:02<00:01, 12.01it/s] 72%|███████▏ | 36/50 [00:03<00:01, 12.06it/s] 76%|███████▌ | 38/50 [00:03<00:00, 12.08it/s] 80%|████████ | 40/50 [00:03<00:00, 12.28it/s] 84%|████████▍ | 42/50 [00:03<00:00, 12.08it/s] 88%|████████▊ | 44/50 [00:03<00:00, 12.10it/s] 92%|█████████▏| 46/50 [00:03<00:00, 12.20it/s] 96%|█████████▌| 48/50 [00:04<00:00, 12.14it/s] 100%|██████████| 50/50 [00:04<00:00, 12.19it/s] 100%|██████████| 50/50 [00:04<00:00, 11.88it/s] 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:04, 11.95it/s] 8%|▊ | 4/50 [00:00<00:03, 12.03it/s] 12%|█▏ | 6/50 [00:00<00:03, 12.04it/s] 16%|█▌ | 8/50 [00:00<00:03, 11.62it/s] 20%|██ | 10/50 [00:00<00:03, 11.63it/s] 24%|██▍ | 12/50 [00:01<00:03, 11.65it/s] 28%|██▊ | 14/50 [00:01<00:03, 11.72it/s] 32%|███▏ | 16/50 [00:01<00:02, 11.65it/s] 36%|███▌ | 18/50 [00:01<00:02, 11.70it/s] 40%|████ | 20/50 [00:01<00:02, 11.58it/s] 44%|████▍ | 22/50 [00:01<00:02, 11.58it/s] 48%|████▊ | 24/50 [00:02<00:02, 11.72it/s] 52%|█████▏ | 26/50 [00:02<00:02, 11.56it/s] 56%|█████▌ | 28/50 [00:02<00:01, 11.72it/s] 60%|██████ | 30/50 [00:02<00:01, 11.85it/s] 64%|██████▍ | 32/50 [00:02<00:01, 11.80it/s] 68%|██████▊ | 34/50 [00:02<00:01, 11.54it/s] 72%|███████▏ | 36/50 [00:03<00:01, 11.51it/s] 76%|███████▌ | 38/50 [00:03<00:01, 11.96it/s] 80%|████████ | 40/50 [00:03<00:00, 12.06it/s] 84%|████████▍ | 42/50 [00:03<00:00, 12.09it/s] 88%|████████▊ | 44/50 [00:03<00:00, 12.15it/s] 92%|█████████▏| 46/50 [00:03<00:00, 12.16it/s] 96%|█████████▌| 48/50 [00:04<00:00, 11.95it/s] 100%|██████████| 50/50 [00:04<00:00, 12.05it/s] 100%|██████████| 50/50 [00:04<00:00, 11.82it/s] 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 13.28it/s] 8%|▊ | 4/50 [00:00<00:03, 12.77it/s] 12%|█▏ | 6/50 [00:00<00:03, 12.67it/s] 16%|█▌ | 8/50 [00:00<00:03, 12.39it/s] 20%|██ | 10/50 [00:00<00:03, 12.23it/s] 24%|██▍ | 12/50 [00:00<00:03, 12.36it/s] 28%|██▊ | 14/50 [00:01<00:02, 12.00it/s] 32%|███▏ | 16/50 [00:01<00:02, 11.64it/s] 36%|███▌ | 18/50 [00:01<00:02, 11.67it/s] 40%|████ | 20/50 [00:01<00:02, 11.85it/s] 44%|████▍ | 22/50 [00:01<00:02, 11.95it/s] 48%|████▊ | 24/50 [00:02<00:02, 11.61it/s] 52%|█████▏ | 26/50 [00:02<00:02, 11.56it/s] 56%|█████▌ | 28/50 [00:02<00:01, 11.64it/s] 60%|██████ | 30/50 [00:02<00:01, 11.79it/s] 64%|██████▍ | 32/50 [00:02<00:01, 11.70it/s] 68%|██████▊ | 34/50 [00:02<00:01, 11.80it/s] 72%|███████▏ | 36/50 [00:03<00:01, 11.96it/s] 76%|███████▌ | 38/50 [00:03<00:01, 11.92it/s] 80%|████████ | 40/50 [00:03<00:00, 11.67it/s] 84%|████████▍ | 42/50 [00:03<00:00, 11.44it/s] 88%|████████▊ | 44/50 [00:03<00:00, 11.30it/s] 92%|█████████▏| 46/50 [00:03<00:00, 11.16it/s] 96%|█████████▌| 48/50 [00:04<00:00, 11.05it/s] 100%|██████████| 50/50 [00:04<00:00, 11.48it/s] 100%|██████████| 50/50 [00:04<00:00, 11.75it/s] 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 13.05it/s] 8%|▊ | 4/50 [00:00<00:03, 12.24it/s] 12%|█▏ | 6/50 [00:00<00:03, 12.60it/s] 16%|█▌ | 8/50 [00:00<00:03, 12.45it/s] 20%|██ | 10/50 [00:00<00:03, 12.44it/s] 24%|██▍ | 12/50 [00:00<00:03, 12.41it/s] 28%|██▊ | 14/50 [00:01<00:02, 12.28it/s] 32%|███▏ | 16/50 [00:01<00:02, 12.06it/s] 36%|███▌ | 18/50 [00:01<00:02, 12.32it/s] 40%|████ | 20/50 [00:01<00:02, 12.34it/s] 44%|████▍ | 22/50 [00:01<00:02, 12.54it/s] 48%|████▊ | 24/50 [00:01<00:02, 11.94it/s] 52%|█████▏ | 26/50 [00:02<00:02, 12.00it/s] 56%|█████▌ | 28/50 [00:02<00:01, 11.95it/s] 60%|██████ | 30/50 [00:02<00:01, 12.15it/s] 64%|██████▍ | 32/50 [00:02<00:01, 12.25it/s] 68%|██████▊ | 34/50 [00:02<00:01, 12.32it/s] 72%|███████▏ | 36/50 [00:02<00:01, 12.44it/s] 76%|███████▌ | 38/50 [00:03<00:00, 12.33it/s] 80%|████████ | 40/50 [00:03<00:00, 12.36it/s] 84%|████████▍ | 42/50 [00:03<00:00, 12.44it/s] 88%|████████▊ | 44/50 [00:03<00:00, 12.08it/s] 92%|█████████▏| 46/50 [00:03<00:00, 11.95it/s] 96%|█████████▌| 48/50 [00:03<00:00, 12.20it/s] 100%|██████████| 50/50 [00:04<00:00, 12.19it/s] 100%|██████████| 50/50 [00:04<00:00, 12.25it/s] 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:04, 11.35it/s] 8%|▊ | 4/50 [00:00<00:04, 11.47it/s] 12%|█▏ | 6/50 [00:00<00:03, 11.63it/s] 16%|█▌ | 8/50 [00:00<00:03, 12.17it/s] 20%|██ | 10/50 [00:00<00:03, 12.33it/s] 24%|██▍ | 12/50 [00:00<00:03, 12.26it/s] 28%|██▊ | 14/50 [00:01<00:02, 12.27it/s] 32%|███▏ | 16/50 [00:01<00:02, 12.37it/s] 36%|███▌ | 18/50 [00:01<00:02, 12.38it/s] 40%|████ | 20/50 [00:01<00:02, 12.21it/s] 44%|████▍ | 22/50 [00:01<00:02, 12.48it/s] 48%|████▊ | 24/50 [00:01<00:02, 12.57it/s] 52%|█████▏ | 26/50 [00:02<00:01, 12.21it/s] 56%|█████▌ | 28/50 [00:02<00:01, 12.32it/s] 60%|██████ | 30/50 [00:02<00:01, 12.06it/s] 64%|██████▍ | 32/50 [00:02<00:01, 11.85it/s] 68%|██████▊ | 34/50 [00:02<00:01, 11.86it/s] 72%|███████▏ | 36/50 [00:02<00:01, 11.64it/s] 76%|███████▌ | 38/50 [00:03<00:01, 11.89it/s] 80%|████████ | 40/50 [00:03<00:00, 12.15it/s] 84%|████████▍ | 42/50 [00:03<00:00, 12.48it/s] 88%|████████▊ | 44/50 [00:03<00:00, 12.69it/s] 92%|█████████▏| 46/50 [00:03<00:00, 12.85it/s] 96%|█████████▌| 48/50 [00:03<00:00, 12.95it/s] 100%|██████████| 50/50 [00:04<00:00, 12.87it/s] 100%|██████████| 50/50 [00:04<00:00, 12.30it/s] 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 12.48it/s] 8%|▊ | 4/50 [00:00<00:03, 11.76it/s] 12%|█▏ | 6/50 [00:00<00:03, 11.58it/s] 16%|█▌ | 8/50 [00:00<00:03, 11.84it/s] 20%|██ | 10/50 [00:00<00:03, 11.75it/s] 24%|██▍ | 12/50 [00:01<00:03, 12.19it/s] 28%|██▊ | 14/50 [00:01<00:02, 12.55it/s] 32%|███▏ | 16/50 [00:01<00:02, 12.77it/s] 36%|███▌ | 18/50 [00:01<00:02, 12.38it/s] 40%|████ | 20/50 [00:01<00:02, 12.16it/s] 44%|████▍ | 22/50 [00:01<00:02, 11.93it/s] 48%|████▊ | 24/50 [00:01<00:02, 11.84it/s] 52%|█████▏ | 26/50 [00:02<00:02, 11.92it/s] 56%|█████▌ | 28/50 [00:02<00:01, 11.78it/s] 60%|██████ | 30/50 [00:02<00:01, 11.79it/s] 64%|██████▍ | 32/50 [00:02<00:01, 11.85it/s] 68%|██████▊ | 34/50 [00:02<00:01, 11.84it/s] 72%|███████▏ | 36/50 [00:03<00:01, 11.56it/s] 76%|███████▌ | 38/50 [00:03<00:01, 11.46it/s] 80%|████████ | 40/50 [00:03<00:00, 11.38it/s] 84%|████████▍ | 42/50 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