georgedavila / sdxl-beethoven-spectrograms-lora
SDXL LoRA finetuned on spectrograms of Beethoven songs
- Public
- 17 runs
-
L40S
- GitHub
Prediction
georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5IDykwc1qbarnrgp0cfrkms10k5pgStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- myprompt
- A SPECTROGRAM image
- outWidth
- 1024
- outHeight
- 1024
- lora_scale
- 0.6
- num_outputs
- 1
- guidanceScale
- 7.5
- promptAddendum
- high_noise_frac
- 0.8
- negative_prompt
- fuzzy, lone pixels
- num_inference_steps
- 50
{ "myprompt": "A SPECTROGRAM image", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "fuzzy, lone pixels", "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run georgedavila/sdxl-beethoven-spectrograms-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", { input: { myprompt: "A SPECTROGRAM image", outWidth: 1024, outHeight: 1024, lora_scale: 0.6, num_outputs: 1, guidanceScale: 7.5, promptAddendum: "", high_noise_frac: 0.8, negative_prompt: "fuzzy, lone pixels", num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 georgedavila/sdxl-beethoven-spectrograms-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", input={ "myprompt": "A SPECTROGRAM image", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "fuzzy, lone pixels", "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run georgedavila/sdxl-beethoven-spectrograms-lora 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": "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", "input": { "myprompt": "A SPECTROGRAM image", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "fuzzy, lone pixels", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-29T15:37:23.499320Z", "created_at": "2024-05-29T15:13:02.661000Z", "data_removed": false, "error": null, "id": "ykwc1qbarnrgp0cfrkms10k5pg", "input": { "myprompt": "A SPECTROGRAM image", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "fuzzy, lone pixels", "num_inference_steps": 50 }, "logs": "Using seed: 11262\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:24, 2.03it/s]\n 4%|▍ | 2/50 [00:00<00:16, 2.95it/s]\n 6%|▌ | 3/50 [00:00<00:13, 3.45it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.75it/s]\n 10%|█ | 5/50 [00:01<00:11, 3.93it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.05it/s]\n 14%|█▍ | 7/50 [00:01<00:10, 4.13it/s]\n 16%|█▌ | 8/50 [00:02<00:10, 4.18it/s]\n 18%|█▊ | 9/50 [00:02<00:09, 4.22it/s]\n 20%|██ | 10/50 [00:02<00:09, 4.24it/s]\n 22%|██▏ | 11/50 [00:02<00:09, 4.26it/s]\n 24%|██▍ | 12/50 [00:03<00:08, 4.27it/s]\n 26%|██▌ | 13/50 [00:03<00:08, 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36/50 [00:08<00:03, 4.27it/s]\n 74%|███████▍ | 37/50 [00:08<00:03, 4.27it/s]\n 76%|███████▌ | 38/50 [00:09<00:02, 4.27it/s]\n 78%|███████▊ | 39/50 [00:09<00:02, 4.27it/s]\n 80%|████████ | 40/50 [00:09<00:02, 4.27it/s]\n 82%|████████▏ | 41/50 [00:09<00:02, 4.27it/s]\n 84%|████████▍ | 42/50 [00:10<00:01, 4.27it/s]\n 86%|████████▌ | 43/50 [00:10<00:01, 4.27it/s]\n 88%|████████▊ | 44/50 [00:10<00:01, 4.27it/s]\n 90%|█████████ | 45/50 [00:10<00:01, 4.27it/s]\n 92%|█████████▏| 46/50 [00:10<00:00, 4.27it/s]\n 94%|█████████▍| 47/50 [00:11<00:00, 4.27it/s]\n 96%|█████████▌| 48/50 [00:11<00:00, 4.27it/s]\n 98%|█████████▊| 49/50 [00:11<00:00, 4.27it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.27it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.19it/s]", "metrics": { "predict_time": 20.954389, "total_time": 1460.83832 }, "output": [ "https://replicate.delivery/pbxt/oOCw5hxV2IZWDFHHoYRJPrWCoXd7Ng5OIOFqifY6xRiV7ncJA/out-0.png" ], "started_at": "2024-05-29T15:37:02.544931Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ykwc1qbarnrgp0cfrkms10k5pg", "cancel": "https://api.replicate.com/v1/predictions/ykwc1qbarnrgp0cfrkms10k5pg/cancel" }, "version": "193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5" }Generated inUsing seed: 11262 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:24, 2.03it/s] 4%|▍ | 2/50 [00:00<00:16, 2.95it/s] 6%|▌ | 3/50 [00:00<00:13, 3.45it/s] 8%|▊ | 4/50 [00:01<00:12, 3.75it/s] 10%|█ | 5/50 [00:01<00:11, 3.93it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.05it/s] 14%|█▍ | 7/50 [00:01<00:10, 4.13it/s] 16%|█▌ | 8/50 [00:02<00:10, 4.18it/s] 18%|█▊ | 9/50 [00:02<00:09, 4.22it/s] 20%|██ | 10/50 [00:02<00:09, 4.24it/s] 22%|██▏ | 11/50 [00:02<00:09, 4.26it/s] 24%|██▍ | 12/50 [00:03<00:08, 4.27it/s] 26%|██▌ | 13/50 [00:03<00:08, 4.28it/s] 28%|██▊ | 14/50 [00:03<00:08, 4.28it/s] 30%|███ | 15/50 [00:03<00:08, 4.29it/s] 32%|███▏ | 16/50 [00:03<00:07, 4.29it/s] 34%|███▍ | 17/50 [00:04<00:07, 4.29it/s] 36%|███▌ | 18/50 [00:04<00:07, 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4.27it/s] 84%|████████▍ | 42/50 [00:10<00:01, 4.27it/s] 86%|████████▌ | 43/50 [00:10<00:01, 4.27it/s] 88%|████████▊ | 44/50 [00:10<00:01, 4.27it/s] 90%|█████████ | 45/50 [00:10<00:01, 4.27it/s] 92%|█████████▏| 46/50 [00:10<00:00, 4.27it/s] 94%|█████████▍| 47/50 [00:11<00:00, 4.27it/s] 96%|█████████▌| 48/50 [00:11<00:00, 4.27it/s] 98%|█████████▊| 49/50 [00:11<00:00, 4.27it/s] 100%|██████████| 50/50 [00:11<00:00, 4.27it/s] 100%|██████████| 50/50 [00:11<00:00, 4.19it/s]Prediction
georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5IDq38mneyzm1rgm0cfrmfva99c7cStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- myprompt
- A SPECTROGRAM image
- outWidth
- 1024
- outHeight
- 1024
- lora_scale
- 0.6
- num_outputs
- 1
- guidanceScale
- 7.5
- promptAddendum
- high_noise_frac
- 0.8
- num_inference_steps
- 50
{ "myprompt": "A SPECTROGRAM image", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run georgedavila/sdxl-beethoven-spectrograms-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", { input: { myprompt: "A SPECTROGRAM image", outWidth: 1024, outHeight: 1024, lora_scale: 0.6, num_outputs: 1, guidanceScale: 7.5, promptAddendum: "", high_noise_frac: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 georgedavila/sdxl-beethoven-spectrograms-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", input={ "myprompt": "A SPECTROGRAM image", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run georgedavila/sdxl-beethoven-spectrograms-lora 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": "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", "input": { "myprompt": "A SPECTROGRAM image", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-29T16:14:09.349554Z", "created_at": "2024-05-29T16:12:31.520000Z", "data_removed": false, "error": null, "id": "q38mneyzm1rgm0cfrmfva99c7c", "input": { "myprompt": "A SPECTROGRAM image", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 9964\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:16, 2.93it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.60it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.87it/s]\n 8%|▊ | 4/50 [00:01<00:11, 4.02it/s]\n 10%|█ | 5/50 [00:01<00:10, 4.10it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.15it/s]\n 14%|█▍ | 7/50 [00:01<00:10, 4.18it/s]\n 16%|█▌ | 8/50 [00:01<00:09, 4.20it/s]\n 18%|█▊ | 9/50 [00:02<00:09, 4.22it/s]\n 20%|██ | 10/50 [00:02<00:09, 4.22it/s]\n 22%|██▏ | 11/50 [00:02<00:09, 4.23it/s]\n 24%|██▍ | 12/50 [00:02<00:08, 4.23it/s]\n 26%|██▌ | 13/50 [00:03<00:08, 4.24it/s]\n 28%|██▊ | 14/50 [00:03<00:08, 4.24it/s]\n 30%|███ | 15/50 [00:03<00:08, 4.24it/s]\n 32%|███▏ | 16/50 [00:03<00:08, 4.24it/s]\n 34%|███▍ | 17/50 [00:04<00:07, 4.24it/s]\n 36%|███▌ | 18/50 [00:04<00:07, 4.24it/s]\n 38%|███▊ | 19/50 [00:04<00:07, 4.24it/s]\n 40%|████ | 20/50 [00:04<00:07, 4.24it/s]\n 42%|████▏ | 21/50 [00:05<00:06, 4.24it/s]\n 44%|████▍ | 22/50 [00:05<00:06, 4.24it/s]\n 46%|████▌ | 23/50 [00:05<00:06, 4.24it/s]\n 48%|████▊ | 24/50 [00:05<00:06, 4.24it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.24it/s]\n 52%|█████▏ | 26/50 [00:06<00:05, 4.25it/s]\n 54%|█████▍ | 27/50 [00:06<00:05, 4.26it/s]\n 56%|█████▌ | 28/50 [00:06<00:05, 4.26it/s]\n 58%|█████▊ | 29/50 [00:06<00:04, 4.26it/s]\n 60%|██████ | 30/50 [00:07<00:04, 4.27it/s]\n 62%|██████▏ | 31/50 [00:07<00:04, 4.27it/s]\n 64%|██████▍ | 32/50 [00:07<00:04, 4.27it/s]\n 66%|██████▌ | 33/50 [00:07<00:03, 4.27it/s]\n 68%|██████▊ | 34/50 [00:08<00:03, 4.26it/s]\n 70%|███████ | 35/50 [00:08<00:03, 4.26it/s]\n 72%|███████▏ | 36/50 [00:08<00:03, 4.26it/s]\n 74%|███████▍ | 37/50 [00:08<00:03, 4.26it/s]\n 76%|███████▌ | 38/50 [00:09<00:02, 4.26it/s]\n 78%|███████▊ | 39/50 [00:09<00:02, 4.26it/s]\n 80%|████████ | 40/50 [00:09<00:02, 4.26it/s]\n 82%|████████▏ | 41/50 [00:09<00:02, 4.26it/s]\n 84%|████████▍ | 42/50 [00:09<00:01, 4.26it/s]\n 86%|████████▌ | 43/50 [00:10<00:01, 4.26it/s]\n 88%|████████▊ | 44/50 [00:10<00:01, 4.25it/s]\n 90%|█████████ | 45/50 [00:10<00:01, 4.25it/s]\n 92%|█████████▏| 46/50 [00:10<00:00, 4.25it/s]\n 94%|█████████▍| 47/50 [00:11<00:00, 4.25it/s]\n 96%|█████████▌| 48/50 [00:11<00:00, 4.25it/s]\n 98%|█████████▊| 49/50 [00:11<00:00, 4.25it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.26it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.22it/s]", "metrics": { "predict_time": 14.871608, "total_time": 97.829554 }, "output": [ "https://replicate.delivery/pbxt/Y9Owe7ENpI12VKYDUNnwWLJXaem0mNHVhflfrqJbjoF9kBlLB/out-0.png" ], "started_at": "2024-05-29T16:13:54.477946Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/q38mneyzm1rgm0cfrmfva99c7c", "cancel": "https://api.replicate.com/v1/predictions/q38mneyzm1rgm0cfrmfva99c7c/cancel" }, "version": "193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5" }Generated inUsing seed: 9964 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:16, 2.93it/s] 4%|▍ | 2/50 [00:00<00:13, 3.60it/s] 6%|▌ | 3/50 [00:00<00:12, 3.87it/s] 8%|▊ | 4/50 [00:01<00:11, 4.02it/s] 10%|█ | 5/50 [00:01<00:10, 4.10it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.15it/s] 14%|█▍ | 7/50 [00:01<00:10, 4.18it/s] 16%|█▌ | 8/50 [00:01<00:09, 4.20it/s] 18%|█▊ | 9/50 [00:02<00:09, 4.22it/s] 20%|██ | 10/50 [00:02<00:09, 4.22it/s] 22%|██▏ | 11/50 [00:02<00:09, 4.23it/s] 24%|██▍ | 12/50 [00:02<00:08, 4.23it/s] 26%|██▌ | 13/50 [00:03<00:08, 4.24it/s] 28%|██▊ | 14/50 [00:03<00:08, 4.24it/s] 30%|███ | 15/50 [00:03<00:08, 4.24it/s] 32%|███▏ | 16/50 [00:03<00:08, 4.24it/s] 34%|███▍ | 17/50 [00:04<00:07, 4.24it/s] 36%|███▌ | 18/50 [00:04<00:07, 4.24it/s] 38%|███▊ 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84%|████████▍ | 42/50 [00:09<00:01, 4.26it/s] 86%|████████▌ | 43/50 [00:10<00:01, 4.26it/s] 88%|████████▊ | 44/50 [00:10<00:01, 4.25it/s] 90%|█████████ | 45/50 [00:10<00:01, 4.25it/s] 92%|█████████▏| 46/50 [00:10<00:00, 4.25it/s] 94%|█████████▍| 47/50 [00:11<00:00, 4.25it/s] 96%|█████████▌| 48/50 [00:11<00:00, 4.25it/s] 98%|█████████▊| 49/50 [00:11<00:00, 4.25it/s] 100%|██████████| 50/50 [00:11<00:00, 4.26it/s] 100%|██████████| 50/50 [00:11<00:00, 4.22it/s]Prediction
georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5IDq38mneyzm1rgm0cfrmfva99c7cStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- myprompt
- A SPECTROGRAM image
- outWidth
- 1024
- outHeight
- 1024
- lora_scale
- 0.6
- num_outputs
- 1
- guidanceScale
- 7.5
- promptAddendum
- high_noise_frac
- 0.8
- num_inference_steps
- 50
{ "myprompt": "A SPECTROGRAM image", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run georgedavila/sdxl-beethoven-spectrograms-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", { input: { myprompt: "A SPECTROGRAM image", outWidth: 1024, outHeight: 1024, lora_scale: 0.6, num_outputs: 1, guidanceScale: 7.5, promptAddendum: "", high_noise_frac: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 georgedavila/sdxl-beethoven-spectrograms-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", input={ "myprompt": "A SPECTROGRAM image", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run georgedavila/sdxl-beethoven-spectrograms-lora 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": "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", "input": { "myprompt": "A SPECTROGRAM image", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-29T16:14:09.349554Z", "created_at": "2024-05-29T16:12:31.520000Z", "data_removed": false, "error": null, "id": "q38mneyzm1rgm0cfrmfva99c7c", "input": { "myprompt": "A SPECTROGRAM image", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 9964\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:16, 2.93it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.60it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.87it/s]\n 8%|▊ | 4/50 [00:01<00:11, 4.02it/s]\n 10%|█ | 5/50 [00:01<00:10, 4.10it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.15it/s]\n 14%|█▍ | 7/50 [00:01<00:10, 4.18it/s]\n 16%|█▌ | 8/50 [00:01<00:09, 4.20it/s]\n 18%|█▊ | 9/50 [00:02<00:09, 4.22it/s]\n 20%|██ | 10/50 [00:02<00:09, 4.22it/s]\n 22%|██▏ | 11/50 [00:02<00:09, 4.23it/s]\n 24%|██▍ | 12/50 [00:02<00:08, 4.23it/s]\n 26%|██▌ | 13/50 [00:03<00:08, 4.24it/s]\n 28%|██▊ | 14/50 [00:03<00:08, 4.24it/s]\n 30%|███ | 15/50 [00:03<00:08, 4.24it/s]\n 32%|███▏ | 16/50 [00:03<00:08, 4.24it/s]\n 34%|███▍ | 17/50 [00:04<00:07, 4.24it/s]\n 36%|███▌ | 18/50 [00:04<00:07, 4.24it/s]\n 38%|███▊ | 19/50 [00:04<00:07, 4.24it/s]\n 40%|████ | 20/50 [00:04<00:07, 4.24it/s]\n 42%|████▏ | 21/50 [00:05<00:06, 4.24it/s]\n 44%|████▍ | 22/50 [00:05<00:06, 4.24it/s]\n 46%|████▌ | 23/50 [00:05<00:06, 4.24it/s]\n 48%|████▊ | 24/50 [00:05<00:06, 4.24it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.24it/s]\n 52%|█████▏ | 26/50 [00:06<00:05, 4.25it/s]\n 54%|█████▍ | 27/50 [00:06<00:05, 4.26it/s]\n 56%|█████▌ | 28/50 [00:06<00:05, 4.26it/s]\n 58%|█████▊ | 29/50 [00:06<00:04, 4.26it/s]\n 60%|██████ | 30/50 [00:07<00:04, 4.27it/s]\n 62%|██████▏ | 31/50 [00:07<00:04, 4.27it/s]\n 64%|██████▍ | 32/50 [00:07<00:04, 4.27it/s]\n 66%|██████▌ | 33/50 [00:07<00:03, 4.27it/s]\n 68%|██████▊ | 34/50 [00:08<00:03, 4.26it/s]\n 70%|███████ | 35/50 [00:08<00:03, 4.26it/s]\n 72%|███████▏ | 36/50 [00:08<00:03, 4.26it/s]\n 74%|███████▍ | 37/50 [00:08<00:03, 4.26it/s]\n 76%|███████▌ | 38/50 [00:09<00:02, 4.26it/s]\n 78%|███████▊ | 39/50 [00:09<00:02, 4.26it/s]\n 80%|████████ | 40/50 [00:09<00:02, 4.26it/s]\n 82%|████████▏ | 41/50 [00:09<00:02, 4.26it/s]\n 84%|████████▍ | 42/50 [00:09<00:01, 4.26it/s]\n 86%|████████▌ | 43/50 [00:10<00:01, 4.26it/s]\n 88%|████████▊ | 44/50 [00:10<00:01, 4.25it/s]\n 90%|█████████ | 45/50 [00:10<00:01, 4.25it/s]\n 92%|█████████▏| 46/50 [00:10<00:00, 4.25it/s]\n 94%|█████████▍| 47/50 [00:11<00:00, 4.25it/s]\n 96%|█████████▌| 48/50 [00:11<00:00, 4.25it/s]\n 98%|█████████▊| 49/50 [00:11<00:00, 4.25it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.26it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.22it/s]", "metrics": { "predict_time": 14.871608, "total_time": 97.829554 }, "output": [ "https://replicate.delivery/pbxt/Y9Owe7ENpI12VKYDUNnwWLJXaem0mNHVhflfrqJbjoF9kBlLB/out-0.png" ], "started_at": "2024-05-29T16:13:54.477946Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/q38mneyzm1rgm0cfrmfva99c7c", "cancel": "https://api.replicate.com/v1/predictions/q38mneyzm1rgm0cfrmfva99c7c/cancel" }, "version": "193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5" }Generated inUsing seed: 9964 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:16, 2.93it/s] 4%|▍ | 2/50 [00:00<00:13, 3.60it/s] 6%|▌ | 3/50 [00:00<00:12, 3.87it/s] 8%|▊ | 4/50 [00:01<00:11, 4.02it/s] 10%|█ | 5/50 [00:01<00:10, 4.10it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.15it/s] 14%|█▍ | 7/50 [00:01<00:10, 4.18it/s] 16%|█▌ | 8/50 [00:01<00:09, 4.20it/s] 18%|█▊ | 9/50 [00:02<00:09, 4.22it/s] 20%|██ | 10/50 [00:02<00:09, 4.22it/s] 22%|██▏ | 11/50 [00:02<00:09, 4.23it/s] 24%|██▍ | 12/50 [00:02<00:08, 4.23it/s] 26%|██▌ | 13/50 [00:03<00:08, 4.24it/s] 28%|██▊ | 14/50 [00:03<00:08, 4.24it/s] 30%|███ | 15/50 [00:03<00:08, 4.24it/s] 32%|███▏ | 16/50 [00:03<00:08, 4.24it/s] 34%|███▍ | 17/50 [00:04<00:07, 4.24it/s] 36%|███▌ | 18/50 [00:04<00:07, 4.24it/s] 38%|███▊ 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84%|████████▍ | 42/50 [00:09<00:01, 4.26it/s] 86%|████████▌ | 43/50 [00:10<00:01, 4.26it/s] 88%|████████▊ | 44/50 [00:10<00:01, 4.25it/s] 90%|█████████ | 45/50 [00:10<00:01, 4.25it/s] 92%|█████████▏| 46/50 [00:10<00:00, 4.25it/s] 94%|█████████▍| 47/50 [00:11<00:00, 4.25it/s] 96%|█████████▌| 48/50 [00:11<00:00, 4.25it/s] 98%|█████████▊| 49/50 [00:11<00:00, 4.25it/s] 100%|██████████| 50/50 [00:11<00:00, 4.26it/s] 100%|██████████| 50/50 [00:11<00:00, 4.22it/s]Prediction
georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5IDc94qm3f74srgg0cfrngre1q1z8StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- myprompt
- A SPECTROGRAM image
- outWidth
- 640
- outHeight
- 480
- lora_scale
- 0.6
- num_outputs
- 1
- guidanceScale
- 7.5
- promptAddendum
- high_noise_frac
- 0.8
- num_inference_steps
- 50
{ "myprompt": "A SPECTROGRAM image", "outWidth": 640, "outHeight": 480, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run georgedavila/sdxl-beethoven-spectrograms-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", { input: { myprompt: "A SPECTROGRAM image", outWidth: 640, outHeight: 480, lora_scale: 0.6, num_outputs: 1, guidanceScale: 7.5, promptAddendum: "", high_noise_frac: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 georgedavila/sdxl-beethoven-spectrograms-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", input={ "myprompt": "A SPECTROGRAM image", "outWidth": 640, "outHeight": 480, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run georgedavila/sdxl-beethoven-spectrograms-lora 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": "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", "input": { "myprompt": "A SPECTROGRAM image", "outWidth": 640, "outHeight": 480, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-29T17:26:21.628299Z", "created_at": "2024-05-29T17:24:38.822000Z", "data_removed": false, "error": null, "id": "c94qm3f74srgg0cfrngre1q1z8", "input": { "myprompt": "A SPECTROGRAM image", "outWidth": 640, "outHeight": 480, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 18199\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:15, 3.14it/s]\n 4%|▍ | 2/50 [00:00<00:09, 4.94it/s]\n 6%|▌ | 3/50 [00:00<00:07, 6.09it/s]\n 8%|▊ | 4/50 [00:00<00:06, 6.80it/s]\n 10%|█ | 5/50 [00:00<00:06, 7.37it/s]\n 12%|█▏ | 6/50 [00:00<00:05, 7.69it/s]\n 14%|█▍ | 7/50 [00:01<00:05, 7.88it/s]\n 16%|█▌ | 8/50 [00:01<00:05, 8.02it/s]\n 18%|█▊ | 9/50 [00:01<00:05, 8.18it/s]\n 20%|██ | 10/50 [00:01<00:04, 8.28it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 8.38it/s]\n 24%|██▍ | 12/50 [00:01<00:04, 8.45it/s]\n 26%|██▌ | 13/50 [00:01<00:04, 8.37it/s]\n 28%|██▊ | 14/50 [00:01<00:04, 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74%|███████▍ | 37/50 [00:04<00:01, 8.52it/s]\n 76%|███████▌ | 38/50 [00:04<00:01, 8.61it/s]\n 78%|███████▊ | 39/50 [00:04<00:01, 8.68it/s]\n 80%|████████ | 40/50 [00:04<00:01, 8.75it/s]\n 82%|████████▏ | 41/50 [00:05<00:01, 8.62it/s]\n 84%|████████▍ | 42/50 [00:05<00:00, 8.59it/s]\n 86%|████████▌ | 43/50 [00:05<00:00, 8.30it/s]\n 88%|████████▊ | 44/50 [00:05<00:00, 8.40it/s]\n 90%|█████████ | 45/50 [00:05<00:00, 8.36it/s]\n 92%|█████████▏| 46/50 [00:05<00:00, 8.50it/s]\n 94%|█████████▍| 47/50 [00:05<00:00, 8.57it/s]\n 96%|█████████▌| 48/50 [00:05<00:00, 8.67it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 8.68it/s]\n100%|██████████| 50/50 [00:06<00:00, 8.51it/s]\n100%|██████████| 50/50 [00:06<00:00, 8.20it/s]", "metrics": { "predict_time": 8.101818, "total_time": 102.806299 }, "output": [ "https://replicate.delivery/pbxt/ONVrTY2rODpGLhzX1j5d9Ls98MFJVmueynpeY7Agdym8cR5SA/out-0.png" ], "started_at": "2024-05-29T17:26:13.526481Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/c94qm3f74srgg0cfrngre1q1z8", "cancel": "https://api.replicate.com/v1/predictions/c94qm3f74srgg0cfrngre1q1z8/cancel" }, "version": "193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5" }Generated inUsing seed: 18199 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:15, 3.14it/s] 4%|▍ | 2/50 [00:00<00:09, 4.94it/s] 6%|▌ | 3/50 [00:00<00:07, 6.09it/s] 8%|▊ | 4/50 [00:00<00:06, 6.80it/s] 10%|█ | 5/50 [00:00<00:06, 7.37it/s] 12%|█▏ | 6/50 [00:00<00:05, 7.69it/s] 14%|█▍ | 7/50 [00:01<00:05, 7.88it/s] 16%|█▌ | 8/50 [00:01<00:05, 8.02it/s] 18%|█▊ | 9/50 [00:01<00:05, 8.18it/s] 20%|██ | 10/50 [00:01<00:04, 8.28it/s] 22%|██▏ | 11/50 [00:01<00:04, 8.38it/s] 24%|██▍ | 12/50 [00:01<00:04, 8.45it/s] 26%|██▌ | 13/50 [00:01<00:04, 8.37it/s] 28%|██▊ | 14/50 [00:01<00:04, 8.17it/s] 30%|███ | 15/50 [00:02<00:04, 7.94it/s] 32%|███▏ | 16/50 [00:02<00:04, 8.10it/s] 34%|███▍ | 17/50 [00:02<00:04, 8.24it/s] 36%|███▌ | 18/50 [00:02<00:03, 8.36it/s] 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84%|████████▍ | 42/50 [00:05<00:00, 8.59it/s] 86%|████████▌ | 43/50 [00:05<00:00, 8.30it/s] 88%|████████▊ | 44/50 [00:05<00:00, 8.40it/s] 90%|█████████ | 45/50 [00:05<00:00, 8.36it/s] 92%|█████████▏| 46/50 [00:05<00:00, 8.50it/s] 94%|█████████▍| 47/50 [00:05<00:00, 8.57it/s] 96%|█████████▌| 48/50 [00:05<00:00, 8.67it/s] 98%|█████████▊| 49/50 [00:05<00:00, 8.68it/s] 100%|██████████| 50/50 [00:06<00:00, 8.51it/s] 100%|██████████| 50/50 [00:06<00:00, 8.20it/s]Prediction
georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5IDvgb734pgthrgm0cfrnhtgfrdy0StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- myprompt
- A SPECTROGRAM image
- outWidth
- 640
- outHeight
- 480
- lora_scale
- 0.6
- num_outputs
- 1
- guidanceScale
- 7.5
- promptAddendum
- high_noise_frac
- 0.8
- negative_prompt
- noisy
- num_inference_steps
- 50
{ "myprompt": "A SPECTROGRAM image", "outWidth": 640, "outHeight": 480, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "noisy", "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run georgedavila/sdxl-beethoven-spectrograms-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", { input: { myprompt: "A SPECTROGRAM image", outWidth: 640, outHeight: 480, lora_scale: 0.6, num_outputs: 1, guidanceScale: 7.5, promptAddendum: "", high_noise_frac: 0.8, negative_prompt: "noisy", num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 georgedavila/sdxl-beethoven-spectrograms-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", input={ "myprompt": "A SPECTROGRAM image", "outWidth": 640, "outHeight": 480, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "noisy", "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run georgedavila/sdxl-beethoven-spectrograms-lora 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": "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", "input": { "myprompt": "A SPECTROGRAM image", "outWidth": 640, "outHeight": 480, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "noisy", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-29T17:26:52.221181Z", "created_at": "2024-05-29T17:26:44.180000Z", "data_removed": false, "error": null, "id": "vgb734pgthrgm0cfrnhtgfrdy0", "input": { "myprompt": "A SPECTROGRAM image", "outWidth": 640, "outHeight": 480, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "noisy", "num_inference_steps": 50 }, "logs": "Using seed: 55418\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:05, 8.20it/s]\n 4%|▍ | 2/50 [00:00<00:05, 8.27it/s]\n 6%|▌ | 3/50 [00:00<00:05, 8.41it/s]\n 8%|▊ | 4/50 [00:00<00:05, 8.49it/s]\n 10%|█ | 5/50 [00:00<00:05, 8.58it/s]\n 12%|█▏ | 6/50 [00:00<00:05, 8.63it/s]\n 14%|█▍ | 7/50 [00:00<00:04, 8.67it/s]\n 16%|█▌ | 8/50 [00:00<00:04, 8.59it/s]\n 18%|█▊ | 9/50 [00:01<00:04, 8.56it/s]\n 20%|██ | 10/50 [00:01<00:04, 8.44it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 8.21it/s]\n 24%|██▍ | 12/50 [00:01<00:04, 8.19it/s]\n 26%|██▌ | 13/50 [00:01<00:04, 8.13it/s]\n 28%|██▊ | 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8.12it/s]\n 74%|███████▍ | 37/50 [00:04<00:01, 8.13it/s]\n 76%|███████▌ | 38/50 [00:04<00:01, 7.98it/s]\n 78%|███████▊ | 39/50 [00:04<00:01, 7.96it/s]\n 80%|████████ | 40/50 [00:04<00:01, 7.87it/s]\n 82%|████████▏ | 41/50 [00:05<00:01, 7.79it/s]\n 84%|████████▍ | 42/50 [00:05<00:01, 7.90it/s]\n 86%|████████▌ | 43/50 [00:05<00:00, 7.93it/s]\n 88%|████████▊ | 44/50 [00:05<00:00, 7.89it/s]\n 90%|█████████ | 45/50 [00:05<00:00, 7.72it/s]\n 92%|█████████▏| 46/50 [00:05<00:00, 7.54it/s]\n 94%|█████████▍| 47/50 [00:05<00:00, 7.35it/s]\n 96%|█████████▌| 48/50 [00:06<00:00, 7.14it/s]\n 98%|█████████▊| 49/50 [00:06<00:00, 7.05it/s]\n100%|██████████| 50/50 [00:06<00:00, 7.03it/s]\n100%|██████████| 50/50 [00:06<00:00, 7.94it/s]", "metrics": { "predict_time": 8.000885, "total_time": 8.041181 }, "output": [ "https://replicate.delivery/pbxt/R6e9Ju3g4rxHCK8LolnwY0l03xafzUYRBXk2RomOQptbdR5SA/out-0.png" ], "started_at": "2024-05-29T17:26:44.220296Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/vgb734pgthrgm0cfrnhtgfrdy0", "cancel": "https://api.replicate.com/v1/predictions/vgb734pgthrgm0cfrnhtgfrdy0/cancel" }, "version": "193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5" }Generated inUsing seed: 55418 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:05, 8.20it/s] 4%|▍ | 2/50 [00:00<00:05, 8.27it/s] 6%|▌ | 3/50 [00:00<00:05, 8.41it/s] 8%|▊ | 4/50 [00:00<00:05, 8.49it/s] 10%|█ | 5/50 [00:00<00:05, 8.58it/s] 12%|█▏ | 6/50 [00:00<00:05, 8.63it/s] 14%|█▍ | 7/50 [00:00<00:04, 8.67it/s] 16%|█▌ | 8/50 [00:00<00:04, 8.59it/s] 18%|█▊ | 9/50 [00:01<00:04, 8.56it/s] 20%|██ | 10/50 [00:01<00:04, 8.44it/s] 22%|██▏ | 11/50 [00:01<00:04, 8.21it/s] 24%|██▍ | 12/50 [00:01<00:04, 8.19it/s] 26%|██▌ | 13/50 [00:01<00:04, 8.13it/s] 28%|██▊ | 14/50 [00:01<00:04, 7.97it/s] 30%|███ | 15/50 [00:01<00:04, 7.84it/s] 32%|███▏ | 16/50 [00:01<00:04, 7.78it/s] 34%|███▍ | 17/50 [00:02<00:04, 7.62it/s] 36%|███▌ | 18/50 [00:02<00:04, 7.56it/s] 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84%|████████▍ | 42/50 [00:05<00:01, 7.90it/s] 86%|████████▌ | 43/50 [00:05<00:00, 7.93it/s] 88%|████████▊ | 44/50 [00:05<00:00, 7.89it/s] 90%|█████████ | 45/50 [00:05<00:00, 7.72it/s] 92%|█████████▏| 46/50 [00:05<00:00, 7.54it/s] 94%|█████████▍| 47/50 [00:05<00:00, 7.35it/s] 96%|█████████▌| 48/50 [00:06<00:00, 7.14it/s] 98%|█████████▊| 49/50 [00:06<00:00, 7.05it/s] 100%|██████████| 50/50 [00:06<00:00, 7.03it/s] 100%|██████████| 50/50 [00:06<00:00, 7.94it/s]Prediction
georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5ID7wsj0yy2qhrgp0cfrnjrqk0gg8StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- myprompt
- A SPECTROGRAM image in the shape of a dog
- outWidth
- 640
- outHeight
- 480
- lora_scale
- 0.6
- num_outputs
- 1
- guidanceScale
- 7.5
- promptAddendum
- high_noise_frac
- 0.8
- num_inference_steps
- 50
{ "myprompt": "A SPECTROGRAM image in the shape of a dog", "outWidth": 640, "outHeight": 480, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run georgedavila/sdxl-beethoven-spectrograms-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", { input: { myprompt: "A SPECTROGRAM image in the shape of a dog", outWidth: 640, outHeight: 480, lora_scale: 0.6, num_outputs: 1, guidanceScale: 7.5, promptAddendum: "", high_noise_frac: 0.8, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 georgedavila/sdxl-beethoven-spectrograms-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", input={ "myprompt": "A SPECTROGRAM image in the shape of a dog", "outWidth": 640, "outHeight": 480, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run georgedavila/sdxl-beethoven-spectrograms-lora 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": "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", "input": { "myprompt": "A SPECTROGRAM image in the shape of a dog", "outWidth": 640, "outHeight": 480, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-29T17:28:59.144922Z", "created_at": "2024-05-29T17:28:51.644000Z", "data_removed": false, "error": null, "id": "7wsj0yy2qhrgp0cfrnjrqk0gg8", "input": { "myprompt": "A SPECTROGRAM image in the shape of a dog", "outWidth": 640, "outHeight": 480, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 21628\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 8.09it/s]\n 4%|▍ | 2/50 [00:00<00:05, 8.07it/s]\n 6%|▌ | 3/50 [00:00<00:05, 8.15it/s]\n 8%|▊ | 4/50 [00:00<00:05, 8.17it/s]\n 10%|█ | 5/50 [00:00<00:05, 8.31it/s]\n 12%|█▏ | 6/50 [00:00<00:05, 8.44it/s]\n 14%|█▍ | 7/50 [00:00<00:05, 8.44it/s]\n 16%|█▌ | 8/50 [00:00<00:04, 8.46it/s]\n 18%|█▊ | 9/50 [00:01<00:04, 8.56it/s]\n 20%|██ | 10/50 [00:01<00:04, 8.63it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 8.70it/s]\n 24%|██▍ | 12/50 [00:01<00:04, 8.76it/s]\n 26%|██▌ | 13/50 [00:01<00:04, 8.82it/s]\n 28%|██▊ | 14/50 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8.51it/s]\n 74%|███████▍ | 37/50 [00:04<00:01, 8.38it/s]\n 76%|███████▌ | 38/50 [00:04<00:01, 8.33it/s]\n 78%|███████▊ | 39/50 [00:04<00:01, 8.40it/s]\n 80%|████████ | 40/50 [00:04<00:01, 8.47it/s]\n 82%|████████▏ | 41/50 [00:04<00:01, 8.56it/s]\n 84%|████████▍ | 42/50 [00:04<00:00, 8.61it/s]\n 86%|████████▌ | 43/50 [00:05<00:00, 8.65it/s]\n 88%|████████▊ | 44/50 [00:05<00:00, 8.31it/s]\n 90%|█████████ | 45/50 [00:05<00:00, 8.33it/s]\n 92%|█████████▏| 46/50 [00:05<00:00, 8.35it/s]\n 94%|█████████▍| 47/50 [00:05<00:00, 8.25it/s]\n 96%|█████████▌| 48/50 [00:05<00:00, 8.21it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 8.33it/s]\n100%|██████████| 50/50 [00:05<00:00, 8.41it/s]\n100%|██████████| 50/50 [00:05<00:00, 8.52it/s]", "metrics": { "predict_time": 7.455723, "total_time": 7.500922 }, "output": [ "https://replicate.delivery/pbxt/ITCaj8VgPab6AxniB5WVkVJzv5NYlPntazzO11jeoxAtvocJA/out-0.png" ], "started_at": "2024-05-29T17:28:51.689199Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/7wsj0yy2qhrgp0cfrnjrqk0gg8", "cancel": "https://api.replicate.com/v1/predictions/7wsj0yy2qhrgp0cfrnjrqk0gg8/cancel" }, "version": "193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5" }Generated inUsing seed: 21628 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:06, 8.09it/s] 4%|▍ | 2/50 [00:00<00:05, 8.07it/s] 6%|▌ | 3/50 [00:00<00:05, 8.15it/s] 8%|▊ | 4/50 [00:00<00:05, 8.17it/s] 10%|█ | 5/50 [00:00<00:05, 8.31it/s] 12%|█▏ | 6/50 [00:00<00:05, 8.44it/s] 14%|█▍ | 7/50 [00:00<00:05, 8.44it/s] 16%|█▌ | 8/50 [00:00<00:04, 8.46it/s] 18%|█▊ | 9/50 [00:01<00:04, 8.56it/s] 20%|██ | 10/50 [00:01<00:04, 8.63it/s] 22%|██▏ | 11/50 [00:01<00:04, 8.70it/s] 24%|██▍ | 12/50 [00:01<00:04, 8.76it/s] 26%|██▌ | 13/50 [00:01<00:04, 8.82it/s] 28%|██▊ | 14/50 [00:01<00:04, 8.85it/s] 30%|███ | 15/50 [00:01<00:03, 8.89it/s] 32%|███▏ | 16/50 [00:01<00:03, 8.79it/s] 34%|███▍ | 17/50 [00:01<00:03, 8.70it/s] 36%|███▌ | 18/50 [00:02<00:03, 8.49it/s] 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84%|████████▍ | 42/50 [00:04<00:00, 8.61it/s] 86%|████████▌ | 43/50 [00:05<00:00, 8.65it/s] 88%|████████▊ | 44/50 [00:05<00:00, 8.31it/s] 90%|█████████ | 45/50 [00:05<00:00, 8.33it/s] 92%|█████████▏| 46/50 [00:05<00:00, 8.35it/s] 94%|█████████▍| 47/50 [00:05<00:00, 8.25it/s] 96%|█████████▌| 48/50 [00:05<00:00, 8.21it/s] 98%|█████████▊| 49/50 [00:05<00:00, 8.33it/s] 100%|██████████| 50/50 [00:05<00:00, 8.41it/s] 100%|██████████| 50/50 [00:05<00:00, 8.52it/s]Prediction
georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5ID3q12svp5ahrgj0cfrnqscsqdyrStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- myprompt
- A photo of a dog in the style of SPECTROGRAM
- outWidth
- 1024
- outHeight
- 1024
- lora_scale
- 0.6
- num_outputs
- 1
- guidanceScale
- 7.5
- promptAddendum
- high_noise_frac
- 0.8
- negative_prompt
- noisy
- num_inference_steps
- 50
{ "myprompt": "A photo of a dog in the style of SPECTROGRAM", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "noisy", "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run georgedavila/sdxl-beethoven-spectrograms-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", { input: { myprompt: "A photo of a dog in the style of SPECTROGRAM", outWidth: 1024, outHeight: 1024, lora_scale: 0.6, num_outputs: 1, guidanceScale: 7.5, promptAddendum: "", high_noise_frac: 0.8, negative_prompt: "noisy", num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 georgedavila/sdxl-beethoven-spectrograms-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", input={ "myprompt": "A photo of a dog in the style of SPECTROGRAM", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "noisy", "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run georgedavila/sdxl-beethoven-spectrograms-lora 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": "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", "input": { "myprompt": "A photo of a dog in the style of SPECTROGRAM", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "noisy", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-29T17:40:02.149910Z", "created_at": "2024-05-29T17:39:47.668000Z", "data_removed": false, "error": null, "id": "3q12svp5ahrgj0cfrnqscsqdyr", "input": { "myprompt": "A photo of a dog in the style of SPECTROGRAM", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "noisy", "num_inference_steps": 50 }, "logs": "Using seed: 50271\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:11, 4.26it/s]\n 4%|▍ | 2/50 [00:00<00:11, 4.25it/s]\n 6%|▌ | 3/50 [00:00<00:11, 4.26it/s]\n 8%|▊ | 4/50 [00:00<00:10, 4.26it/s]\n 10%|█ | 5/50 [00:01<00:10, 4.26it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.26it/s]\n 14%|█▍ | 7/50 [00:01<00:10, 4.26it/s]\n 16%|█▌ | 8/50 [00:01<00:09, 4.26it/s]\n 18%|█▊ | 9/50 [00:02<00:09, 4.25it/s]\n 20%|██ | 10/50 [00:02<00:09, 4.26it/s]\n 22%|██▏ | 11/50 [00:02<00:09, 4.25it/s]\n 24%|██▍ | 12/50 [00:02<00:08, 4.25it/s]\n 26%|██▌ | 13/50 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72%|███████▏ | 36/50 [00:08<00:03, 4.23it/s]\n 74%|███████▍ | 37/50 [00:08<00:03, 4.23it/s]\n 76%|███████▌ | 38/50 [00:08<00:02, 4.23it/s]\n 78%|███████▊ | 39/50 [00:09<00:02, 4.23it/s]\n 80%|████████ | 40/50 [00:09<00:02, 4.23it/s]\n 82%|████████▏ | 41/50 [00:09<00:02, 4.23it/s]\n 84%|████████▍ | 42/50 [00:09<00:01, 4.23it/s]\n 86%|████████▌ | 43/50 [00:10<00:01, 4.23it/s]\n 88%|████████▊ | 44/50 [00:10<00:01, 4.23it/s]\n 90%|█████████ | 45/50 [00:10<00:01, 4.23it/s]\n 92%|█████████▏| 46/50 [00:10<00:00, 4.23it/s]\n 94%|█████████▍| 47/50 [00:11<00:00, 4.23it/s]\n 96%|█████████▌| 48/50 [00:11<00:00, 4.22it/s]\n 98%|█████████▊| 49/50 [00:11<00:00, 4.22it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.22it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.24it/s]", "metrics": { "predict_time": 14.441356, "total_time": 14.48191 }, "output": [ "https://replicate.delivery/pbxt/lB08kDvBIc5EPt8IPPwflOxxyif8bqzz3HucUmJpVsMwpR5SA/out-0.png" ], "started_at": "2024-05-29T17:39:47.708554Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/3q12svp5ahrgj0cfrnqscsqdyr", "cancel": "https://api.replicate.com/v1/predictions/3q12svp5ahrgj0cfrnqscsqdyr/cancel" }, "version": "193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5" }Generated inUsing seed: 50271 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:11, 4.26it/s] 4%|▍ | 2/50 [00:00<00:11, 4.25it/s] 6%|▌ | 3/50 [00:00<00:11, 4.26it/s] 8%|▊ | 4/50 [00:00<00:10, 4.26it/s] 10%|█ | 5/50 [00:01<00:10, 4.26it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.26it/s] 14%|█▍ | 7/50 [00:01<00:10, 4.26it/s] 16%|█▌ | 8/50 [00:01<00:09, 4.26it/s] 18%|█▊ | 9/50 [00:02<00:09, 4.25it/s] 20%|██ | 10/50 [00:02<00:09, 4.26it/s] 22%|██▏ | 11/50 [00:02<00:09, 4.25it/s] 24%|██▍ | 12/50 [00:02<00:08, 4.25it/s] 26%|██▌ | 13/50 [00:03<00:08, 4.25it/s] 28%|██▊ | 14/50 [00:03<00:08, 4.25it/s] 30%|███ | 15/50 [00:03<00:08, 4.25it/s] 32%|███▏ | 16/50 [00:03<00:08, 4.25it/s] 34%|███▍ | 17/50 [00:03<00:07, 4.25it/s] 36%|███▌ | 18/50 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[00:09<00:02, 4.23it/s] 84%|████████▍ | 42/50 [00:09<00:01, 4.23it/s] 86%|████████▌ | 43/50 [00:10<00:01, 4.23it/s] 88%|████████▊ | 44/50 [00:10<00:01, 4.23it/s] 90%|█████████ | 45/50 [00:10<00:01, 4.23it/s] 92%|█████████▏| 46/50 [00:10<00:00, 4.23it/s] 94%|█████████▍| 47/50 [00:11<00:00, 4.23it/s] 96%|█████████▌| 48/50 [00:11<00:00, 4.22it/s] 98%|█████████▊| 49/50 [00:11<00:00, 4.22it/s] 100%|██████████| 50/50 [00:11<00:00, 4.22it/s] 100%|██████████| 50/50 [00:11<00:00, 4.24it/s]Prediction
georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5IDw0vm7jxbt5rgp0cfrnntgy2f48StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- myprompt
- A SPECTROGRAM image
- outWidth
- 1024
- outHeight
- 1024
- lora_scale
- 0.6
- num_outputs
- 1
- guidanceScale
- 7.5
- promptAddendum
- high_noise_frac
- 0.8
- negative_prompt
- noisy
- num_inference_steps
- 50
{ "myprompt": "A SPECTROGRAM image", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "noisy", "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run georgedavila/sdxl-beethoven-spectrograms-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", { input: { myprompt: "A SPECTROGRAM image", outWidth: 1024, outHeight: 1024, lora_scale: 0.6, num_outputs: 1, guidanceScale: 7.5, promptAddendum: "", high_noise_frac: 0.8, negative_prompt: "noisy", num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 georgedavila/sdxl-beethoven-spectrograms-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", input={ "myprompt": "A SPECTROGRAM image", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "noisy", "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run georgedavila/sdxl-beethoven-spectrograms-lora 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": "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", "input": { "myprompt": "A SPECTROGRAM image", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "noisy", "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-29T17:38:35.134171Z", "created_at": "2024-05-29T17:35:18.993000Z", "data_removed": false, "error": null, "id": "w0vm7jxbt5rgp0cfrnntgy2f48", "input": { "myprompt": "A SPECTROGRAM image", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 7.5, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "noisy", "num_inference_steps": 50 }, "logs": "Using seed: 20905\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:18, 2.58it/s]\n 4%|▍ | 2/50 [00:00<00:14, 3.37it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.72it/s]\n 8%|▊ | 4/50 [00:01<00:11, 3.92it/s]\n 10%|█ | 5/50 [00:01<00:11, 4.04it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.11it/s]\n 14%|█▍ | 7/50 [00:01<00:10, 4.16it/s]\n 16%|█▌ | 8/50 [00:02<00:10, 4.19it/s]\n 18%|█▊ | 9/50 [00:02<00:09, 4.21it/s]\n 20%|██ | 10/50 [00:02<00:09, 4.22it/s]\n 22%|██▏ | 11/50 [00:02<00:09, 4.23it/s]\n 24%|██▍ | 12/50 [00:02<00:08, 4.24it/s]\n 26%|██▌ | 13/50 [00:03<00:08, 4.24it/s]\n 28%|██▊ | 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4.26it/s]\n 74%|███████▍ | 37/50 [00:08<00:03, 4.27it/s]\n 76%|███████▌ | 38/50 [00:09<00:02, 4.26it/s]\n 78%|███████▊ | 39/50 [00:09<00:02, 4.27it/s]\n 80%|████████ | 40/50 [00:09<00:02, 4.26it/s]\n 82%|████████▏ | 41/50 [00:09<00:02, 4.27it/s]\n 84%|████████▍ | 42/50 [00:10<00:01, 4.27it/s]\n 86%|████████▌ | 43/50 [00:10<00:01, 4.27it/s]\n 88%|████████▊ | 44/50 [00:10<00:01, 4.27it/s]\n 90%|█████████ | 45/50 [00:10<00:01, 4.26it/s]\n 92%|█████████▏| 46/50 [00:10<00:00, 4.26it/s]\n 94%|█████████▍| 47/50 [00:11<00:00, 4.27it/s]\n 96%|█████████▌| 48/50 [00:11<00:00, 4.27it/s]\n 98%|█████████▊| 49/50 [00:11<00:00, 4.27it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.26it/s]\n100%|██████████| 50/50 [00:11<00:00, 4.21it/s]", "metrics": { "predict_time": 14.517835, "total_time": 196.141171 }, "output": [ "https://replicate.delivery/pbxt/qIq2DQwUXaIVJVJKe6PvewDb8NB7sWSsw06bYBMk1sFZoR5SA/out-0.png" ], "started_at": "2024-05-29T17:38:20.616336Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/w0vm7jxbt5rgp0cfrnntgy2f48", "cancel": "https://api.replicate.com/v1/predictions/w0vm7jxbt5rgp0cfrnntgy2f48/cancel" }, "version": "193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5" }Generated inUsing seed: 20905 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:18, 2.58it/s] 4%|▍ | 2/50 [00:00<00:14, 3.37it/s] 6%|▌ | 3/50 [00:00<00:12, 3.72it/s] 8%|▊ | 4/50 [00:01<00:11, 3.92it/s] 10%|█ | 5/50 [00:01<00:11, 4.04it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.11it/s] 14%|█▍ | 7/50 [00:01<00:10, 4.16it/s] 16%|█▌ | 8/50 [00:02<00:10, 4.19it/s] 18%|█▊ | 9/50 [00:02<00:09, 4.21it/s] 20%|██ | 10/50 [00:02<00:09, 4.22it/s] 22%|██▏ | 11/50 [00:02<00:09, 4.23it/s] 24%|██▍ | 12/50 [00:02<00:08, 4.24it/s] 26%|██▌ | 13/50 [00:03<00:08, 4.24it/s] 28%|██▊ | 14/50 [00:03<00:08, 4.25it/s] 30%|███ | 15/50 [00:03<00:08, 4.25it/s] 32%|███▏ | 16/50 [00:03<00:07, 4.25it/s] 34%|███▍ | 17/50 [00:04<00:07, 4.25it/s] 36%|███▌ | 18/50 [00:04<00:07, 4.25it/s] 38%|███▊ | 19/50 [00:04<00:07, 4.25it/s] 40%|████ | 20/50 [00:04<00:07, 4.25it/s] 42%|████▏ | 21/50 [00:05<00:06, 4.24it/s] 44%|████▍ | 22/50 [00:05<00:06, 4.25it/s] 46%|████▌ | 23/50 [00:05<00:06, 4.25it/s] 48%|████▊ | 24/50 [00:05<00:06, 4.25it/s] 50%|█████ | 25/50 [00:06<00:05, 4.26it/s] 52%|█████▏ | 26/50 [00:06<00:05, 4.26it/s] 54%|█████▍ | 27/50 [00:06<00:05, 4.27it/s] 56%|█████▌ | 28/50 [00:06<00:05, 4.27it/s] 58%|█████▊ | 29/50 [00:06<00:04, 4.27it/s] 60%|██████ | 30/50 [00:07<00:04, 4.27it/s] 62%|██████▏ | 31/50 [00:07<00:04, 4.27it/s] 64%|██████▍ | 32/50 [00:07<00:04, 4.27it/s] 66%|██████▌ | 33/50 [00:07<00:03, 4.27it/s] 68%|██████▊ | 34/50 [00:08<00:03, 4.27it/s] 70%|███████ | 35/50 [00:08<00:03, 4.27it/s] 72%|███████▏ | 36/50 [00:08<00:03, 4.26it/s] 74%|███████▍ | 37/50 [00:08<00:03, 4.27it/s] 76%|███████▌ | 38/50 [00:09<00:02, 4.26it/s] 78%|███████▊ | 39/50 [00:09<00:02, 4.27it/s] 80%|████████ | 40/50 [00:09<00:02, 4.26it/s] 82%|████████▏ | 41/50 [00:09<00:02, 4.27it/s] 84%|████████▍ | 42/50 [00:10<00:01, 4.27it/s] 86%|████████▌ | 43/50 [00:10<00:01, 4.27it/s] 88%|████████▊ | 44/50 [00:10<00:01, 4.27it/s] 90%|█████████ | 45/50 [00:10<00:01, 4.26it/s] 92%|█████████▏| 46/50 [00:10<00:00, 4.26it/s] 94%|█████████▍| 47/50 [00:11<00:00, 4.27it/s] 96%|█████████▌| 48/50 [00:11<00:00, 4.27it/s] 98%|█████████▊| 49/50 [00:11<00:00, 4.27it/s] 100%|██████████| 50/50 [00:11<00:00, 4.26it/s] 100%|██████████| 50/50 [00:11<00:00, 4.21it/s]Prediction
georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5IDnvwq0249txrgm0cfrnt85zjr7rStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- myprompt
- A photo of a dog in the style of SPECTROGRAM
- outWidth
- 1024
- outHeight
- 1024
- lora_scale
- 0.6
- num_outputs
- 1
- guidanceScale
- 40
- promptAddendum
- high_noise_frac
- 0.8
- negative_prompt
- noisy
- num_inference_steps
- 100
{ "myprompt": "A photo of a dog in the style of SPECTROGRAM", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 40, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "noisy", "num_inference_steps": 100 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run georgedavila/sdxl-beethoven-spectrograms-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", { input: { myprompt: "A photo of a dog in the style of SPECTROGRAM", outWidth: 1024, outHeight: 1024, lora_scale: 0.6, num_outputs: 1, guidanceScale: 40, promptAddendum: "", high_noise_frac: 0.8, negative_prompt: "noisy", num_inference_steps: 100 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);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 georgedavila/sdxl-beethoven-spectrograms-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", input={ "myprompt": "A photo of a dog in the style of SPECTROGRAM", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 40, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "noisy", "num_inference_steps": 100 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run georgedavila/sdxl-beethoven-spectrograms-lora 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": "georgedavila/sdxl-beethoven-spectrograms-lora:193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5", "input": { "myprompt": "A photo of a dog in the style of SPECTROGRAM", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 40, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "noisy", "num_inference_steps": 100 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-05-29T17:47:22.163727Z", "created_at": "2024-05-29T17:45:00.119000Z", "data_removed": false, "error": null, "id": "nvwq0249txrgm0cfrnt85zjr7r", "input": { "myprompt": "A photo of a dog in the style of SPECTROGRAM", "outWidth": 1024, "outHeight": 1024, "lora_scale": 0.6, "num_outputs": 1, "guidanceScale": 40, "promptAddendum": "", "high_noise_frac": 0.8, "negative_prompt": "noisy", "num_inference_steps": 100 }, "logs": "Using seed: 49736\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:33, 2.93it/s]\n 2%|▏ | 2/100 [00:00<00:26, 3.64it/s]\n 3%|▎ | 3/100 [00:00<00:24, 3.93it/s]\n 4%|▍ | 4/100 [00:01<00:23, 4.08it/s]\n 5%|▌ | 5/100 [00:01<00:22, 4.17it/s]\n 6%|▌ | 6/100 [00:01<00:22, 4.22it/s]\n 7%|▋ | 7/100 [00:01<00:21, 4.26it/s]\n 8%|▊ | 8/100 [00:01<00:21, 4.28it/s]\n 9%|▉ | 9/100 [00:02<00:21, 4.30it/s]\n 10%|█ | 10/100 [00:02<00:20, 4.30it/s]\n 11%|█ | 11/100 [00:02<00:20, 4.31it/s]\n 12%|█▏ | 12/100 [00:02<00:20, 4.31it/s]\n 13%|█▎ | 13/100 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4.31it/s]\n 80%|████████ | 80/100 [00:18<00:04, 4.31it/s]\n 81%|████████ | 81/100 [00:18<00:04, 4.31it/s]\n 82%|████████▏ | 82/100 [00:19<00:04, 4.31it/s]\n 83%|████████▎ | 83/100 [00:19<00:03, 4.31it/s]\n 84%|████████▍ | 84/100 [00:19<00:03, 4.32it/s]\n 85%|████████▌ | 85/100 [00:19<00:03, 4.31it/s]\n 86%|████████▌ | 86/100 [00:20<00:03, 4.31it/s]\n 87%|████████▋ | 87/100 [00:20<00:03, 4.31it/s]\n 88%|████████▊ | 88/100 [00:20<00:02, 4.31it/s]\n 89%|████████▉ | 89/100 [00:20<00:02, 4.31it/s]\n 90%|█████████ | 90/100 [00:20<00:02, 4.31it/s]\n 91%|█████████ | 91/100 [00:21<00:02, 4.31it/s]\n 92%|█████████▏| 92/100 [00:21<00:01, 4.31it/s]\n 93%|█████████▎| 93/100 [00:21<00:01, 4.31it/s]\n 94%|█████████▍| 94/100 [00:21<00:01, 4.31it/s]\n 95%|█████████▌| 95/100 [00:22<00:01, 4.31it/s]\n 96%|█████████▌| 96/100 [00:22<00:00, 4.31it/s]\n 97%|█████████▋| 97/100 [00:22<00:00, 4.31it/s]\n 98%|█████████▊| 98/100 [00:22<00:00, 4.30it/s]\n 99%|█████████▉| 99/100 [00:23<00:00, 4.30it/s]\n100%|██████████| 100/100 [00:23<00:00, 4.30it/s]\n100%|██████████| 100/100 [00:23<00:00, 4.29it/s]", "metrics": { "predict_time": 25.93508, "total_time": 142.044727 }, "output": [ "https://replicate.delivery/pbxt/7evSXbejSdsi10GLQbtz6bzDI6RBRyY0YgAHlwvnkJWowR5SA/out-0.png" ], "started_at": "2024-05-29T17:46:56.228647Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/nvwq0249txrgm0cfrnt85zjr7r", "cancel": "https://api.replicate.com/v1/predictions/nvwq0249txrgm0cfrnt85zjr7r/cancel" }, "version": "193c41eaa2f90912bb886b75950af5d433eb9e6007c17507e5da11e527ad38e5" }Generated inUsing seed: 49736 0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:33, 2.93it/s] 2%|▏ | 2/100 [00:00<00:26, 3.64it/s] 3%|▎ | 3/100 [00:00<00:24, 3.93it/s] 4%|▍ | 4/100 [00:01<00:23, 4.08it/s] 5%|▌ | 5/100 [00:01<00:22, 4.17it/s] 6%|▌ | 6/100 [00:01<00:22, 4.22it/s] 7%|▋ | 7/100 [00:01<00:21, 4.26it/s] 8%|▊ | 8/100 [00:01<00:21, 4.28it/s] 9%|▉ | 9/100 [00:02<00:21, 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