jimmywong974
/
yoshi
- Public
- 17 runs
-
H100
Prediction
jimmywong974/yoshi:ab17c148487da4a627f30c82d80c1261a60b37d0b084ff7b0e57cd5e72ee609cIDp5f7rea23drm20chn9y9j5tsymStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- schnell
- prompt
- MCATYOSHI and MCATKIRBY leaping through a colorful array of fallen leaves in a sun-dappled park, dynamic action shot, shallow depth of field, rich saturated hues.
- extra_lora
- jimmywong974/kirby
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- png
- guidance_scale
- 3.5
- output_quality
- 80
- extra_lora_scale
- 1
- num_inference_steps
- 28
{ "model": "schnell", "prompt": "MCATYOSHI and MCATKIRBY leaping through a colorful array of fallen leaves in a sun-dappled park, dynamic action shot, shallow depth of field, rich saturated hues.", "extra_lora": "jimmywong974/kirby", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 1, "num_inference_steps": 28 }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 jimmywong974/yoshi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "jimmywong974/yoshi:ab17c148487da4a627f30c82d80c1261a60b37d0b084ff7b0e57cd5e72ee609c", { input: { model: "schnell", prompt: "MCATYOSHI and MCATKIRBY leaping through a colorful array of fallen leaves in a sun-dappled park, dynamic action shot, shallow depth of field, rich saturated hues.", extra_lora: "jimmywong974/kirby", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "png", guidance_scale: 3.5, output_quality: 80, extra_lora_scale: 1, num_inference_steps: 28 } } ); // 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 jimmywong974/yoshi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "jimmywong974/yoshi:ab17c148487da4a627f30c82d80c1261a60b37d0b084ff7b0e57cd5e72ee609c", input={ "model": "schnell", "prompt": "MCATYOSHI and MCATKIRBY leaping through a colorful array of fallen leaves in a sun-dappled park, dynamic action shot, shallow depth of field, rich saturated hues.", "extra_lora": "jimmywong974/kirby", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 1, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run jimmywong974/yoshi 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": "ab17c148487da4a627f30c82d80c1261a60b37d0b084ff7b0e57cd5e72ee609c", "input": { "model": "schnell", "prompt": "MCATYOSHI and MCATKIRBY leaping through a colorful array of fallen leaves in a sun-dappled park, dynamic action shot, shallow depth of field, rich saturated hues.", "extra_lora": "jimmywong974/kirby", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 1, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-08-31T22:09:51.341098Z", "created_at": "2024-08-31T22:09:15.803000Z", "data_removed": false, "error": null, "id": "p5f7rea23drm20chn9y9j5tsym", "input": { "model": "schnell", "prompt": "MCATYOSHI and MCATKIRBY leaping through a colorful array of fallen leaves in a sun-dappled park, dynamic action shot, shallow depth of field, rich saturated hues.", "extra_lora": "jimmywong974/kirby", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "png", "guidance_scale": 3.5, "output_quality": 80, "extra_lora_scale": 1, "num_inference_steps": 28 }, "logs": "Using seed: 45679\nPrompt: MCATYOSHI and MCATKIRBY leaping through a colorful array of fallen leaves in a sun-dappled park, dynamic action shot, shallow depth of field, rich saturated hues.\ntxt2img mode\nUsing schnell model\nLoading extra LoRA weights from: jimmywong974/kirby\nfree=9535395139584\nDownloading weights\n2024-08-31T22:09:17Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmph5nf4pev/weights url=https://replicate.delivery/yhqm/e2RgVNJxGxXETqC0AGAhxkdUZ2VBelX4X3jCSjexynle3PhNB/trained_model.tar\n2024-08-31T22:09:19Z | INFO | [ Complete ] dest=/tmp/tmph5nf4pev/weights size=\"172 MB\" total_elapsed=2.372s url=https://replicate.delivery/yhqm/e2RgVNJxGxXETqC0AGAhxkdUZ2VBelX4X3jCSjexynle3PhNB/trained_model.tar\nDownloaded weights in 2.40s\nfree=9535224532992\nDownloading weights\n2024-08-31T22:09:28Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpu9zk9p1b/weights url=https://replicate.com/jimmywong974/kirby/_weights\n2024-08-31T22:09:29Z | INFO | [ Redirect ] redirect_url=https://replicate.delivery/yhqm/I7i7YBMnWlJnOpIModq9PeB3RduEeITwgtPlheydlrNYBowmA/trained_model.tar url=https://replicate.com/jimmywong974/kirby/_weights\n2024-08-31T22:09:31Z | INFO | [ Complete ] dest=/tmp/tmpu9zk9p1b/weights size=\"172 MB\" total_elapsed=2.054s url=https://replicate.com/jimmywong974/kirby/_weights\nDownloaded weights in 2.08s\nLoaded LoRAs in 25.13s\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:08, 3.34it/s]\n 7%|▋ | 2/28 [00:00<00:07, 3.67it/s]\n 11%|█ | 3/28 [00:00<00:07, 3.50it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.43it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.39it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.37it/s]\n 25%|██▌ | 7/28 [00:02<00:06, 3.36it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.35it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.35it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.34it/s]\n 39%|███▉ | 11/28 [00:03<00:05, 3.34it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.34it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.34it/s]\n 50%|█████ | 14/28 [00:04<00:04, 3.34it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.33it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.33it/s]\n 61%|██████ | 17/28 [00:05<00:03, 3.34it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.34it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.34it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.33it/s]\n 75%|███████▌ | 21/28 [00:06<00:02, 3.33it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.33it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.34it/s]\n 86%|████████▌ | 24/28 [00:07<00:01, 3.34it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.33it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.33it/s]\n 96%|█████████▋| 27/28 [00:08<00:00, 3.33it/s]\n100%|██████████| 28/28 [00:08<00:00, 3.33it/s]\n100%|██████████| 28/28 [00:08<00:00, 3.35it/s]", "metrics": { "predict_time": 34.358293812, "total_time": 35.538098 }, "output": [ "https://replicate.delivery/yhqm/JmeYhmeXLGk99k6HrQLXEtZffRoNXybW0eDEf0iNlMzzrGF2E/out-0.png" ], "started_at": "2024-08-31T22:09:16.982804Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/p5f7rea23drm20chn9y9j5tsym", "cancel": "https://api.replicate.com/v1/predictions/p5f7rea23drm20chn9y9j5tsym/cancel" }, "version": "ab17c148487da4a627f30c82d80c1261a60b37d0b084ff7b0e57cd5e72ee609c" }Generated inUsing seed: 45679 Prompt: MCATYOSHI and MCATKIRBY leaping through a colorful array of fallen leaves in a sun-dappled park, dynamic action shot, shallow depth of field, rich saturated hues. txt2img mode Using schnell model Loading extra LoRA weights from: jimmywong974/kirby free=9535395139584 Downloading weights 2024-08-31T22:09:17Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmph5nf4pev/weights url=https://replicate.delivery/yhqm/e2RgVNJxGxXETqC0AGAhxkdUZ2VBelX4X3jCSjexynle3PhNB/trained_model.tar 2024-08-31T22:09:19Z | INFO | [ Complete ] dest=/tmp/tmph5nf4pev/weights size="172 MB" total_elapsed=2.372s url=https://replicate.delivery/yhqm/e2RgVNJxGxXETqC0AGAhxkdUZ2VBelX4X3jCSjexynle3PhNB/trained_model.tar Downloaded weights in 2.40s free=9535224532992 Downloading weights 2024-08-31T22:09:28Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmpu9zk9p1b/weights url=https://replicate.com/jimmywong974/kirby/_weights 2024-08-31T22:09:29Z | INFO | [ Redirect ] redirect_url=https://replicate.delivery/yhqm/I7i7YBMnWlJnOpIModq9PeB3RduEeITwgtPlheydlrNYBowmA/trained_model.tar url=https://replicate.com/jimmywong974/kirby/_weights 2024-08-31T22:09:31Z | INFO | [ Complete ] dest=/tmp/tmpu9zk9p1b/weights size="172 MB" total_elapsed=2.054s url=https://replicate.com/jimmywong974/kirby/_weights Downloaded weights in 2.08s Loaded LoRAs in 25.13s 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:08, 3.34it/s] 7%|▋ | 2/28 [00:00<00:07, 3.67it/s] 11%|█ | 3/28 [00:00<00:07, 3.50it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.43it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.39it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.37it/s] 25%|██▌ | 7/28 [00:02<00:06, 3.36it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.35it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.35it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.34it/s] 39%|███▉ | 11/28 [00:03<00:05, 3.34it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.34it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.34it/s] 50%|█████ | 14/28 [00:04<00:04, 3.34it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.33it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.33it/s] 61%|██████ | 17/28 [00:05<00:03, 3.34it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.34it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.34it/s] 71%|███████▏ | 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