oshtz
/
flux-celpast
flux.1 'celestial pastel' lora
- Public
- 427 runs
-
H100
- License
Prediction
oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436IDhyktbrs44srm40chfc181v3fw4StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- celpast style illustration, spiderman sitting atop a pagoda in a japanese village
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- jpg
- guidance_scale
- 3.5
- output_quality
- 100
- num_inference_steps
- 28
{ "model": "dev", "prompt": "celpast style illustration, spiderman sitting atop a pagoda in a japanese village", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "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 oshtz/flux-celpast using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436", { input: { model: "dev", prompt: "celpast style illustration, spiderman sitting atop a pagoda in a japanese village", lora_scale: 1, num_outputs: 1, aspect_ratio: "16:9", output_format: "jpg", guidance_scale: 3.5, output_quality: 100, 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 oshtz/flux-celpast using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436", input={ "model": "dev", "prompt": "celpast style illustration, spiderman sitting atop a pagoda in a japanese village", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run oshtz/flux-celpast 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": "oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436", "input": { "model": "dev", "prompt": "celpast style illustration, spiderman sitting atop a pagoda in a japanese village", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "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/oshtz/flux-celpast@sha256:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436 \ -i 'model="dev"' \ -i 'prompt="celpast style illustration, spiderman sitting atop a pagoda in a japanese village"' \ -i 'lora_scale=1' \ -i 'num_outputs=1' \ -i 'aspect_ratio="16:9"' \ -i 'output_format="jpg"' \ -i 'guidance_scale=3.5' \ -i 'output_quality=100' \ -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/oshtz/flux-celpast@sha256:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "model": "dev", "prompt": "celpast style illustration, spiderman sitting atop a pagoda in a japanese village", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "num_inference_steps": 28 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2024-08-22T16:54:19.030271Z", "created_at": "2024-08-22T16:53:43.590000Z", "data_removed": false, "error": null, "id": "hyktbrs44srm40chfc181v3fw4", "input": { "model": "dev", "prompt": "celpast style illustration, spiderman sitting atop a pagoda in a japanese village", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 3.5, "output_quality": 100, "num_inference_steps": 28 }, "logs": "Using seed: 29969\nPrompt: celpast style illustration, spiderman sitting atop a pagoda in a japanese village\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9790329958400\nDownloading weights\n2024-08-22T16:54:01Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/97a6c84e893efd63 url=https://replicate.delivery/yhqm/7dTkAjcOUEKuDZx1mNBO3UnzM0u6WrTUuoHANtDHfs0yJfUTA/trained_model.tar\n2024-08-22T16:54:03Z | INFO | [ Complete ] dest=/src/weights-cache/97a6c84e893efd63 size=\"172 MB\" total_elapsed=2.071s url=https://replicate.delivery/yhqm/7dTkAjcOUEKuDZx1mNBO3UnzM0u6WrTUuoHANtDHfs0yJfUTA/trained_model.tar\nb''\nDownloaded weights in 2.1036641597747803 seconds\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.71it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.27it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.99it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.87it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.81it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.77it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.75it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.74it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.72it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.72it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.71it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.71it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.71it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.70it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.70it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.70it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.70it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.70it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.70it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.70it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.70it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.70it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.70it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.70it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.70it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.70it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.70it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.70it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.73it/s]", "metrics": { "predict_time": 17.781688757, "total_time": 35.440271 }, "output": [ "https://replicate.delivery/yhqm/7fpzow2oUm29fUOMFFfByUuNU7Xg28zj303YBqv6Vmh05jqmA/out-0.jpg" ], "started_at": "2024-08-22T16:54:01.248582Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/hyktbrs44srm40chfc181v3fw4", "cancel": "https://api.replicate.com/v1/predictions/hyktbrs44srm40chfc181v3fw4/cancel" }, "version": "663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436" }Generated inUsing seed: 29969 Prompt: celpast style illustration, spiderman sitting atop a pagoda in a japanese village txt2img mode Using dev model Loading LoRA weights Ensuring enough disk space... Free disk space: 9790329958400 Downloading weights 2024-08-22T16:54:01Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/97a6c84e893efd63 url=https://replicate.delivery/yhqm/7dTkAjcOUEKuDZx1mNBO3UnzM0u6WrTUuoHANtDHfs0yJfUTA/trained_model.tar 2024-08-22T16:54:03Z | INFO | [ Complete ] dest=/src/weights-cache/97a6c84e893efd63 size="172 MB" total_elapsed=2.071s url=https://replicate.delivery/yhqm/7dTkAjcOUEKuDZx1mNBO3UnzM0u6WrTUuoHANtDHfs0yJfUTA/trained_model.tar b'' Downloaded weights in 2.1036641597747803 seconds LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.71it/s] 7%|▋ | 2/28 [00:00<00:06, 4.27it/s] 11%|█ | 3/28 [00:00<00:06, 3.99it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.87it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.81it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.77it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.75it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.74it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.72it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.72it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.71it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.71it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.71it/s] 50%|█████ | 14/28 [00:03<00:03, 3.70it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.70it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.70it/s] 61%|██████ | 17/28 [00:04<00:02, 3.70it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.70it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.70it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.70it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.70it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.70it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.70it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.70it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.70it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.70it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.70it/s] 100%|██████████| 28/28 [00:07<00:00, 3.70it/s] 100%|██████████| 28/28 [00:07<00:00, 3.73it/s]Prediction
oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436IDdxbwnx0mfnrm40cherzre1z93rStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- seed
- 123
- model
- dev
- prompt
- celpast style illustration, a samurai sits on a pagoda in a serene village
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- jpg
- guidance_scale
- 4
- output_quality
- 100
- num_inference_steps
- 28
{ "seed": 123, "model": "dev", "prompt": "celpast style illustration, a samurai sits on a pagoda in a serene village", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "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 oshtz/flux-celpast using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436", { input: { seed: 123, model: "dev", prompt: "celpast style illustration, a samurai sits on a pagoda in a serene village", lora_scale: 1, num_outputs: 1, aspect_ratio: "16:9", output_format: "jpg", guidance_scale: 4, output_quality: 100, 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 oshtz/flux-celpast using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436", input={ "seed": 123, "model": "dev", "prompt": "celpast style illustration, a samurai sits on a pagoda in a serene village", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run oshtz/flux-celpast 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": "oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436", "input": { "seed": 123, "model": "dev", "prompt": "celpast style illustration, a samurai sits on a pagoda in a serene village", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "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/oshtz/flux-celpast@sha256:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436 \ -i 'seed=123' \ -i 'model="dev"' \ -i 'prompt="celpast style illustration, a samurai sits on a pagoda in a serene village"' \ -i 'lora_scale=1' \ -i 'num_outputs=1' \ -i 'aspect_ratio="16:9"' \ -i 'output_format="jpg"' \ -i 'guidance_scale=4' \ -i 'output_quality=100' \ -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/oshtz/flux-celpast@sha256:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 123, "model": "dev", "prompt": "celpast style illustration, a samurai sits on a pagoda in a serene village", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "num_inference_steps": 28 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2024-08-21T18:42:30.075508Z", "created_at": "2024-08-21T18:42:11.197000Z", "data_removed": false, "error": null, "id": "dxbwnx0mfnrm40cherzre1z93r", "input": { "seed": 123, "model": "dev", "prompt": "celpast style illustration, a samurai sits on a pagoda in a serene village", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "num_inference_steps": 28 }, "logs": "Using seed: 123\nPrompt: celpast style illustration, a samurai sits on a pagoda in a serene village\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9862386491392\nDownloading weights\n2024-08-21T18:42:11Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/97a6c84e893efd63 url=https://replicate.delivery/yhqm/7dTkAjcOUEKuDZx1mNBO3UnzM0u6WrTUuoHANtDHfs0yJfUTA/trained_model.tar\n2024-08-21T18:42:13Z | INFO | [ Complete ] dest=/src/weights-cache/97a6c84e893efd63 size=\"172 MB\" total_elapsed=1.830s url=https://replicate.delivery/yhqm/7dTkAjcOUEKuDZx1mNBO3UnzM0u6WrTUuoHANtDHfs0yJfUTA/trained_model.tar\nb''\nDownloaded weights in 1.860318899154663 seconds\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.73it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.29it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.01it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.89it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.83it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.80it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.77it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.75it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.74it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.74it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.73it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.73it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.73it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.73it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.73it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.72it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.72it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.72it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.72it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.72it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.72it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.72it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.72it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.72it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.72it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.72it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.72it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.72it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.75it/s]", "metrics": { "predict_time": 18.797193482, "total_time": 18.878508 }, "output": [ "https://replicate.delivery/yhqm/jKHXsXuS03qlNJrS1SlFP8hhWzfgYQwaRdM7k9YElVgKOfUTA/out-0.jpg" ], "started_at": "2024-08-21T18:42:11.278315Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/dxbwnx0mfnrm40cherzre1z93r", "cancel": "https://api.replicate.com/v1/predictions/dxbwnx0mfnrm40cherzre1z93r/cancel" }, "version": "663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436" }Generated inUsing seed: 123 Prompt: celpast style illustration, a samurai sits on a pagoda in a serene village txt2img mode Using dev model Loading LoRA weights Ensuring enough disk space... Free disk space: 9862386491392 Downloading weights 2024-08-21T18:42:11Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/97a6c84e893efd63 url=https://replicate.delivery/yhqm/7dTkAjcOUEKuDZx1mNBO3UnzM0u6WrTUuoHANtDHfs0yJfUTA/trained_model.tar 2024-08-21T18:42:13Z | INFO | [ Complete ] dest=/src/weights-cache/97a6c84e893efd63 size="172 MB" total_elapsed=1.830s url=https://replicate.delivery/yhqm/7dTkAjcOUEKuDZx1mNBO3UnzM0u6WrTUuoHANtDHfs0yJfUTA/trained_model.tar b'' Downloaded weights in 1.860318899154663 seconds LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.73it/s] 7%|▋ | 2/28 [00:00<00:06, 4.29it/s] 11%|█ | 3/28 [00:00<00:06, 4.01it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.89it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.83it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.80it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.77it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.75it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.74it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.74it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.73it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.73it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.73it/s] 50%|█████ | 14/28 [00:03<00:03, 3.73it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.73it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.72it/s] 61%|██████ | 17/28 [00:04<00:02, 3.72it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.72it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.72it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.72it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.72it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.72it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.72it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.72it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.72it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.72it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.72it/s] 100%|██████████| 28/28 [00:07<00:00, 3.72it/s] 100%|██████████| 28/28 [00:07<00:00, 3.75it/s]Prediction
oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436IDzt6amt0twhrm60ches0rej3np0StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- seed
- 123
- model
- dev
- prompt
- celpast style illustration, pikachu in the forest
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- jpg
- guidance_scale
- 4
- output_quality
- 100
- num_inference_steps
- 28
{ "seed": 123, "model": "dev", "prompt": "celpast style illustration, pikachu in the forest", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "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 oshtz/flux-celpast using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436", { input: { seed: 123, model: "dev", prompt: "celpast style illustration, pikachu in the forest", lora_scale: 1, num_outputs: 1, aspect_ratio: "16:9", output_format: "jpg", guidance_scale: 4, output_quality: 100, 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 oshtz/flux-celpast using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436", input={ "seed": 123, "model": "dev", "prompt": "celpast style illustration, pikachu in the forest", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run oshtz/flux-celpast 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": "oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436", "input": { "seed": 123, "model": "dev", "prompt": "celpast style illustration, pikachu in the forest", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "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/oshtz/flux-celpast@sha256:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436 \ -i 'seed=123' \ -i 'model="dev"' \ -i 'prompt="celpast style illustration, pikachu in the forest"' \ -i 'lora_scale=1' \ -i 'num_outputs=1' \ -i 'aspect_ratio="16:9"' \ -i 'output_format="jpg"' \ -i 'guidance_scale=4' \ -i 'output_quality=100' \ -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/oshtz/flux-celpast@sha256:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 123, "model": "dev", "prompt": "celpast style illustration, pikachu in the forest", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "num_inference_steps": 28 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2024-08-21T18:44:45.405074Z", "created_at": "2024-08-21T18:44:23.908000Z", "data_removed": false, "error": null, "id": "zt6amt0twhrm60ches0rej3np0", "input": { "seed": 123, "model": "dev", "prompt": "celpast style illustration, pikachu in the forest", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "num_inference_steps": 28 }, "logs": "Using seed: 123\nPrompt: celpast style illustration, pikachu in the forest\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9828552228864\nDownloading weights\n2024-08-21T18:44:24Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/97a6c84e893efd63 url=https://replicate.delivery/yhqm/7dTkAjcOUEKuDZx1mNBO3UnzM0u6WrTUuoHANtDHfs0yJfUTA/trained_model.tar\n2024-08-21T18:44:28Z | INFO | [ Complete ] dest=/src/weights-cache/97a6c84e893efd63 size=\"172 MB\" total_elapsed=4.058s url=https://replicate.delivery/yhqm/7dTkAjcOUEKuDZx1mNBO3UnzM0u6WrTUuoHANtDHfs0yJfUTA/trained_model.tar\nb''\nDownloaded weights in 4.157642841339111 seconds\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.70it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.25it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.97it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.86it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.80it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.76it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.74it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.72it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.71it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.70it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.70it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.70it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.70it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.70it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.69it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.69it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.69it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.69it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.69it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.69it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.70it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.69it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.69it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.69it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.69it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.69it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.69it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.72it/s]", "metrics": { "predict_time": 21.450919591999998, "total_time": 21.497074 }, "output": [ "https://replicate.delivery/yhqm/zzXbye3fVjvipUqifTDfchqdMSocxaZrq1KeNsXGhfccnnP1E/out-0.jpg" ], "started_at": "2024-08-21T18:44:23.954155Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zt6amt0twhrm60ches0rej3np0", "cancel": "https://api.replicate.com/v1/predictions/zt6amt0twhrm60ches0rej3np0/cancel" }, "version": "663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436" }Generated inUsing seed: 123 Prompt: celpast style illustration, pikachu in the forest txt2img mode Using dev model Loading LoRA weights Ensuring enough disk space... Free disk space: 9828552228864 Downloading weights 2024-08-21T18:44:24Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/97a6c84e893efd63 url=https://replicate.delivery/yhqm/7dTkAjcOUEKuDZx1mNBO3UnzM0u6WrTUuoHANtDHfs0yJfUTA/trained_model.tar 2024-08-21T18:44:28Z | INFO | [ Complete ] dest=/src/weights-cache/97a6c84e893efd63 size="172 MB" total_elapsed=4.058s url=https://replicate.delivery/yhqm/7dTkAjcOUEKuDZx1mNBO3UnzM0u6WrTUuoHANtDHfs0yJfUTA/trained_model.tar b'' Downloaded weights in 4.157642841339111 seconds LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.70it/s] 7%|▋ | 2/28 [00:00<00:06, 4.25it/s] 11%|█ | 3/28 [00:00<00:06, 3.97it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.86it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.80it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.76it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.74it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.72it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.71it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.70it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.70it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.70it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.70it/s] 50%|█████ | 14/28 [00:03<00:03, 3.70it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.69it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.69it/s] 61%|██████ | 17/28 [00:04<00:02, 3.69it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.69it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.69it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.69it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.70it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.69it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.69it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.69it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.69it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.69it/s] 100%|██████████| 28/28 [00:07<00:00, 3.69it/s] 100%|██████████| 28/28 [00:07<00:00, 3.72it/s]Prediction
oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436ID97vj2ftaa1rm60chesf8qj0v84StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- celpast style illustration, donald trump speaking at a rally
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- jpg
- guidance_scale
- 4
- output_quality
- 100
- num_inference_steps
- 28
{ "model": "dev", "prompt": "celpast style illustration, donald trump speaking at a rally", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "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 oshtz/flux-celpast using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436", { input: { model: "dev", prompt: "celpast style illustration, donald trump speaking at a rally", lora_scale: 1, num_outputs: 1, aspect_ratio: "16:9", output_format: "jpg", guidance_scale: 4, output_quality: 100, 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 oshtz/flux-celpast using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436", input={ "model": "dev", "prompt": "celpast style illustration, donald trump speaking at a rally", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run oshtz/flux-celpast 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": "oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436", "input": { "model": "dev", "prompt": "celpast style illustration, donald trump speaking at a rally", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "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/oshtz/flux-celpast@sha256:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436 \ -i 'model="dev"' \ -i 'prompt="celpast style illustration, donald trump speaking at a rally"' \ -i 'lora_scale=1' \ -i 'num_outputs=1' \ -i 'aspect_ratio="16:9"' \ -i 'output_format="jpg"' \ -i 'guidance_scale=4' \ -i 'output_quality=100' \ -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/oshtz/flux-celpast@sha256:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "model": "dev", "prompt": "celpast style illustration, donald trump speaking at a rally", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "num_inference_steps": 28 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2024-08-21T19:16:44.282516Z", "created_at": "2024-08-21T19:16:16.592000Z", "data_removed": false, "error": null, "id": "97vj2ftaa1rm60chesf8qj0v84", "input": { "model": "dev", "prompt": "celpast style illustration, donald trump speaking at a rally", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "num_inference_steps": 28 }, "logs": "Using seed: 3636\nPrompt: celpast style illustration, donald trump speaking at a rally\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.72it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.28it/s]\n 11%|█ | 3/28 [00:00<00:06, 4.01it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.88it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.82it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.79it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.77it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.75it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.74it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.74it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.73it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.72it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.73it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.73it/s]\n 54%|█████▎ | 15/28 [00:03<00:03, 3.72it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.72it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.72it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.72it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.72it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.72it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.72it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.73it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.72it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.72it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.73it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.73it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.72it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.72it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.75it/s]", "metrics": { "predict_time": 17.47247393, "total_time": 27.690516 }, "output": [ "https://replicate.delivery/yhqm/fsd5WmsCA3QvGytEb4jryXdAxBCgLQmwmPCenT4KODQc8epmA/out-0.jpg" ], "started_at": "2024-08-21T19:16:26.810043Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/97vj2ftaa1rm60chesf8qj0v84", "cancel": "https://api.replicate.com/v1/predictions/97vj2ftaa1rm60chesf8qj0v84/cancel" }, "version": "663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436" }Generated inUsing seed: 3636 Prompt: celpast style illustration, donald trump speaking at a rally txt2img mode Using dev model Loading LoRA weights LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.72it/s] 7%|▋ | 2/28 [00:00<00:06, 4.28it/s] 11%|█ | 3/28 [00:00<00:06, 4.01it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.88it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.82it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.79it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.77it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.75it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.74it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.74it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.73it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.72it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.73it/s] 50%|█████ | 14/28 [00:03<00:03, 3.73it/s] 54%|█████▎ | 15/28 [00:03<00:03, 3.72it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.72it/s] 61%|██████ | 17/28 [00:04<00:02, 3.72it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.72it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.72it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.72it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.72it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.73it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.72it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.72it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.73it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.73it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.72it/s] 100%|██████████| 28/28 [00:07<00:00, 3.72it/s] 100%|██████████| 28/28 [00:07<00:00, 3.75it/s]Prediction
oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436ID8wk8kdf9edrm40chesft656ejgStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- celpast style illustration, high priestess in a forest at night
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- jpg
- guidance_scale
- 4
- output_quality
- 100
- num_inference_steps
- 28
{ "model": "dev", "prompt": "celpast style illustration, high priestess in a forest at night", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "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 oshtz/flux-celpast using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436", { input: { model: "dev", prompt: "celpast style illustration, high priestess in a forest at night", lora_scale: 1, num_outputs: 1, aspect_ratio: "16:9", output_format: "jpg", guidance_scale: 4, output_quality: 100, 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 oshtz/flux-celpast using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436", input={ "model": "dev", "prompt": "celpast style illustration, high priestess in a forest at night", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run oshtz/flux-celpast 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": "oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436", "input": { "model": "dev", "prompt": "celpast style illustration, high priestess in a forest at night", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "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/oshtz/flux-celpast@sha256:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436 \ -i 'model="dev"' \ -i 'prompt="celpast style illustration, high priestess in a forest at night"' \ -i 'lora_scale=1' \ -i 'num_outputs=1' \ -i 'aspect_ratio="16:9"' \ -i 'output_format="jpg"' \ -i 'guidance_scale=4' \ -i 'output_quality=100' \ -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/oshtz/flux-celpast@sha256:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "model": "dev", "prompt": "celpast style illustration, high priestess in a forest at night", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "num_inference_steps": 28 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2024-08-21T19:18:23.380659Z", "created_at": "2024-08-21T19:18:02.867000Z", "data_removed": false, "error": null, "id": "8wk8kdf9edrm40chesft656ejg", "input": { "model": "dev", "prompt": "celpast style illustration, high priestess in a forest at night", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "num_inference_steps": 28 }, "logs": "Using seed: 27789\nPrompt: celpast style illustration, high priestess in a forest at night\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9774795223040\nDownloading weights\n2024-08-21T19:18:02Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/97a6c84e893efd63 url=https://replicate.delivery/yhqm/7dTkAjcOUEKuDZx1mNBO3UnzM0u6WrTUuoHANtDHfs0yJfUTA/trained_model.tar\n2024-08-21T19:18:06Z | INFO | [ Complete ] dest=/src/weights-cache/97a6c84e893efd63 size=\"172 MB\" total_elapsed=3.804s url=https://replicate.delivery/yhqm/7dTkAjcOUEKuDZx1mNBO3UnzM0u6WrTUuoHANtDHfs0yJfUTA/trained_model.tar\nb''\nDownloaded weights in 3.8355283737182617 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, 4.24it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.97it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.86it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.79it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.76it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.74it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.73it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.71it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.70it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.70it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.70it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.70it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.69it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.69it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.69it/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, 3.69it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.69it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.69it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.69it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.69it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.68it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.69it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.68it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.72it/s]", "metrics": { "predict_time": 20.500674109, "total_time": 20.513659 }, "output": [ "https://replicate.delivery/yhqm/wbfjWeERsdifKJUM1ehmgEGApdK3VfiI5g5BDUSaDvp4v3naC/out-0.jpg" ], "started_at": "2024-08-21T19:18:02.879985Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/8wk8kdf9edrm40chesft656ejg", "cancel": "https://api.replicate.com/v1/predictions/8wk8kdf9edrm40chesft656ejg/cancel" }, "version": "663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436" }Generated inUsing seed: 27789 Prompt: celpast style illustration, high priestess in a forest at night txt2img mode Using dev model Loading LoRA weights Ensuring enough disk space... Free disk space: 9774795223040 Downloading weights 2024-08-21T19:18:02Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/97a6c84e893efd63 url=https://replicate.delivery/yhqm/7dTkAjcOUEKuDZx1mNBO3UnzM0u6WrTUuoHANtDHfs0yJfUTA/trained_model.tar 2024-08-21T19:18:06Z | INFO | [ Complete ] dest=/src/weights-cache/97a6c84e893efd63 size="172 MB" total_elapsed=3.804s url=https://replicate.delivery/yhqm/7dTkAjcOUEKuDZx1mNBO3UnzM0u6WrTUuoHANtDHfs0yJfUTA/trained_model.tar b'' Downloaded weights in 3.8355283737182617 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, 4.24it/s] 11%|█ | 3/28 [00:00<00:06, 3.97it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.86it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.79it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.76it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.74it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.73it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.71it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.70it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.70it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.70it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.70it/s] 50%|█████ | 14/28 [00:03<00:03, 3.69it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.69it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.69it/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, 3.69it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.69it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.69it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.69it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.69it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.68it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.69it/s] 100%|██████████| 28/28 [00:07<00:00, 3.68it/s] 100%|██████████| 28/28 [00:07<00:00, 3.72it/s]Prediction
oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436IDapc3getx21rm40chfc4ad4j3r0StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- celpast style illustration, monk in a zen garden at night
- lora_scale
- 1
- num_outputs
- 1
- aspect_ratio
- 16:9
- output_format
- jpg
- guidance_scale
- 4
- output_quality
- 100
- num_inference_steps
- 28
{ "model": "dev", "prompt": "celpast style illustration, monk in a zen garden at night", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "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 oshtz/flux-celpast using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436", { input: { model: "dev", prompt: "celpast style illustration, monk in a zen garden at night", lora_scale: 1, num_outputs: 1, aspect_ratio: "16:9", output_format: "jpg", guidance_scale: 4, output_quality: 100, 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 oshtz/flux-celpast using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436", input={ "model": "dev", "prompt": "celpast style illustration, monk in a zen garden at night", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "num_inference_steps": 28 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run oshtz/flux-celpast 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": "oshtz/flux-celpast:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436", "input": { "model": "dev", "prompt": "celpast style illustration, monk in a zen garden at night", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "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/oshtz/flux-celpast@sha256:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436 \ -i 'model="dev"' \ -i 'prompt="celpast style illustration, monk in a zen garden at night"' \ -i 'lora_scale=1' \ -i 'num_outputs=1' \ -i 'aspect_ratio="16:9"' \ -i 'output_format="jpg"' \ -i 'guidance_scale=4' \ -i 'output_quality=100' \ -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/oshtz/flux-celpast@sha256:663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "model": "dev", "prompt": "celpast style illustration, monk in a zen garden at night", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "num_inference_steps": 28 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2024-08-22T17:00:47.021384Z", "created_at": "2024-08-22T17:00:31.376000Z", "data_removed": false, "error": null, "id": "apc3getx21rm40chfc4ad4j3r0", "input": { "model": "dev", "prompt": "celpast style illustration, monk in a zen garden at night", "lora_scale": 1, "num_outputs": 1, "aspect_ratio": "16:9", "output_format": "jpg", "guidance_scale": 4, "output_quality": 100, "num_inference_steps": 28 }, "logs": "Using seed: 42648\nPrompt: celpast style illustration, monk in a zen garden at night\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.71it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.27it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.99it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.86it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.79it/s]\n 21%|██▏ | 6/28 [00:01<00:05, 3.76it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.74it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.71it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.71it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.70it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.70it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.70it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.69it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.70it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.69it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.69it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.68it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.69it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.69it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.68it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.69it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.69it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.69it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.70it/s]\n 93%|█████████▎| 26/28 [00:06<00:00, 3.69it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.69it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.69it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.72it/s]", "metrics": { "predict_time": 15.636556074, "total_time": 15.645384 }, "output": [ "https://replicate.delivery/yhqm/XCAO4O5pn1pqIF2accraVYNOsde85f9Tr0nBgVqXh7ReFkqmA/out-0.jpg" ], "started_at": "2024-08-22T17:00:31.384828Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/apc3getx21rm40chfc4ad4j3r0", "cancel": "https://api.replicate.com/v1/predictions/apc3getx21rm40chfc4ad4j3r0/cancel" }, "version": "663b31be7ea29088c07905e1fa833d3a2ee53d0dbd81681c92282a037bf72436" }Generated inUsing seed: 42648 Prompt: celpast style illustration, monk in a zen garden at night txt2img mode Using dev model Loading LoRA weights LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.71it/s] 7%|▋ | 2/28 [00:00<00:06, 4.27it/s] 11%|█ | 3/28 [00:00<00:06, 3.99it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.86it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.79it/s] 21%|██▏ | 6/28 [00:01<00:05, 3.76it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.74it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.71it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.71it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.70it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.70it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.70it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.69it/s] 50%|█████ | 14/28 [00:03<00:03, 3.70it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.69it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.69it/s] 61%|██████ | 17/28 [00:04<00:02, 3.68it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.69it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.69it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.68it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.69it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.69it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.69it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.69it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.70it/s] 93%|█████████▎| 26/28 [00:06<00:00, 3.69it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.69it/s] 100%|██████████| 28/28 [00:07<00:00, 3.69it/s] 100%|██████████| 28/28 [00:07<00:00, 3.72it/s]
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