codingfu
/
bayc
- Public
- 486 runs
-
H100
Prediction
codingfu/bayc:d111b63dd1444d142fe8a0c0812ce6796e270f05b791d48d2a5c6296627da82aIDby3t857kt9rm00chfdfagf0v4rStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- black rooster in the style of TOK
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "black rooster in the style of TOK", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "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 codingfu/bayc using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "codingfu/bayc:d111b63dd1444d142fe8a0c0812ce6796e270f05b791d48d2a5c6296627da82a", { input: { model: "dev", prompt: "black rooster in the style of TOK", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, 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 codingfu/bayc using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "codingfu/bayc:d111b63dd1444d142fe8a0c0812ce6796e270f05b791d48d2a5c6296627da82a", input={ "model": "dev", "prompt": "black rooster in the style of TOK", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run codingfu/bayc 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": "d111b63dd1444d142fe8a0c0812ce6796e270f05b791d48d2a5c6296627da82a", "input": { "model": "dev", "prompt": "black rooster in the style of TOK", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/codingfu/bayc@sha256:d111b63dd1444d142fe8a0c0812ce6796e270f05b791d48d2a5c6296627da82a \ -i 'model="dev"' \ -i 'prompt="black rooster in the style of TOK"' \ -i 'lora_scale=1' \ -i 'num_outputs=1' \ -i 'aspect_ratio="1:1"' \ -i 'output_format="webp"' \ -i 'guidance_scale=3.5' \ -i 'output_quality=80' \ -i 'num_inference_steps=28'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/codingfu/bayc@sha256:d111b63dd1444d142fe8a0c0812ce6796e270f05b791d48d2a5c6296627da82acurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "model": "dev", "prompt": "black rooster in the style of TOK", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2024-08-22T18:35:53.880687Z", "created_at": "2024-08-22T18:35:06.066000Z", "data_removed": false, "error": null, "id": "by3t857kt9rm00chfdfagf0v4r", "input": { "model": "dev", "prompt": "black rooster in the style of TOK", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 51857\nPrompt: black rooster in the style of TOK\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9781506404352\nDownloading weights\n2024-08-22T18:35:36Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/6421da47ded8c72a url=https://replicate.delivery/yhqm/0jDnFOgloXY9IpHvsM82mSKLWxkt59uYkc3CketKvQ7dtpqJA/trained_model.tar\n2024-08-22T18:35:37Z | INFO | [ Complete ] dest=/src/weights-cache/6421da47ded8c72a size=\"172 MB\" total_elapsed=1.590s url=https://replicate.delivery/yhqm/0jDnFOgloXY9IpHvsM82mSKLWxkt59uYkc3CketKvQ7dtpqJA/trained_model.tar\nb''\nDownloaded weights in 1.6112327575683594 seconds\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.69it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.23it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.96it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.85it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.79it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.75it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.72it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.71it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.70it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.69it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.69it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.68it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.68it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.68it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.68it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.68it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.68it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.68it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.68it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.68it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.68it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.68it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.68it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.68it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.68it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.68it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.68it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.68it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.71it/s]", "metrics": { "predict_time": 17.844050377, "total_time": 47.814687 }, "output": [ "https://replicate.delivery/yhqm/f4be162dhUlzMEXKUSUnaqOw2OYue0g9efxLjGitpJjLhbqaC/out-0.webp" ], "started_at": "2024-08-22T18:35:36.036636Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/by3t857kt9rm00chfdfagf0v4r", "cancel": "https://api.replicate.com/v1/predictions/by3t857kt9rm00chfdfagf0v4r/cancel" }, "version": "d111b63dd1444d142fe8a0c0812ce6796e270f05b791d48d2a5c6296627da82a" }Generated inUsing seed: 51857 Prompt: black rooster in the style of TOK txt2img mode Using dev model Loading LoRA weights Ensuring enough disk space... Free disk space: 9781506404352 Downloading weights 2024-08-22T18:35:36Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/6421da47ded8c72a url=https://replicate.delivery/yhqm/0jDnFOgloXY9IpHvsM82mSKLWxkt59uYkc3CketKvQ7dtpqJA/trained_model.tar 2024-08-22T18:35:37Z | INFO | [ Complete ] dest=/src/weights-cache/6421da47ded8c72a size="172 MB" total_elapsed=1.590s url=https://replicate.delivery/yhqm/0jDnFOgloXY9IpHvsM82mSKLWxkt59uYkc3CketKvQ7dtpqJA/trained_model.tar b'' Downloaded weights in 1.6112327575683594 seconds LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.69it/s] 7%|▋ | 2/28 [00:00<00:06, 4.23it/s] 11%|█ | 3/28 [00:00<00:06, 3.96it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.85it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.79it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.75it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.72it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.71it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.70it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.69it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.69it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.68it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.68it/s] 50%|█████ | 14/28 [00:03<00:03, 3.68it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.68it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.68it/s] 61%|██████ | 17/28 [00:04<00:02, 3.68it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.68it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.68it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.68it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.68it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.68it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.68it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.68it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.68it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.68it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.68it/s] 100%|██████████| 28/28 [00:07<00:00, 3.68it/s] 100%|██████████| 28/28 [00:07<00:00, 3.71it/s]
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