fofr
/
flux-y2k
Flux lora, use "Y2K" to trigger image generation
- Public
- 915 runs
-
H100
- Paper
Prediction
fofr/flux-y2k:a9f9a7abIDg6ncs5j5t1rm40chdeha27re4cStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- Y2K consumer tech, retro aesthetic, transparent plastic tech, portrait photo of a cyberpunk with wearables
- lora_scale
- 0.75
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "Y2K consumer tech, retro aesthetic, transparent plastic tech, portrait photo of a cyberpunk with wearables", "lora_scale": 0.75, "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 replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/flux-y2k using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-y2k:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204", { input: { model: "dev", prompt: "Y2K consumer tech, retro aesthetic, transparent plastic tech, portrait photo of a cyberpunk with wearables", lora_scale: 0.75, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 28 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fofr/flux-y2k using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-y2k:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204", input={ "model": "dev", "prompt": "Y2K consumer tech, retro aesthetic, transparent plastic tech, portrait photo of a cyberpunk with wearables", "lora_scale": 0.75, "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.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/flux-y2k 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": "a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204", "input": { "model": "dev", "prompt": "Y2K consumer tech, retro aesthetic, transparent plastic tech, portrait photo of a cyberpunk with wearables", "lora_scale": 0.75, "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/fofr/flux-y2k@sha256:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204 \ -i 'model="dev"' \ -i 'prompt="Y2K consumer tech, retro aesthetic, transparent plastic tech, portrait photo of a cyberpunk with wearables"' \ -i 'lora_scale=0.75' \ -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/fofr/flux-y2k@sha256:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "model": "dev", "prompt": "Y2K consumer tech, retro aesthetic, transparent plastic tech, portrait photo of a cyberpunk with wearables", "lora_scale": 0.75, "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": "2024-08-19T17:14:50.914728Z", "created_at": "2024-08-19T17:14:42.512000Z", "data_removed": false, "error": null, "id": "g6ncs5j5t1rm40chdeha27re4c", "input": { "model": "dev", "prompt": "Y2K consumer tech, retro aesthetic, transparent plastic tech, portrait photo of a cyberpunk with wearables", "lora_scale": 0.75, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 1650\nPrompt: Y2K consumer tech, retro aesthetic, transparent plastic tech, portrait photo of a cyberpunk with wearables\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nweights already loaded!\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.53it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.95it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.76it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.66it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.63it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.60it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.57it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.57it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.56it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.54it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.56it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.55it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.55it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.54it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.53it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.55it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.55it/s]\n 64%|██████▍ | 18/28 [00:05<00:02, 3.55it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.54it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.54it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.52it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.52it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.54it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.55it/s]\n 89%|████████▉ | 25/28 [00:07<00:00, 3.55it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.55it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.53it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.52it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.56it/s]", "metrics": { "predict_time": 8.365481625, "total_time": 8.402728 }, "output": [ "https://replicate.delivery/yhqm/glkl3veqOtyNPyEh34X1dysNGsemakjLIf0n4L0rBVfq4LRNB/out-0.webp" ], "started_at": "2024-08-19T17:14:42.549246Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/g6ncs5j5t1rm40chdeha27re4c", "cancel": "https://api.replicate.com/v1/predictions/g6ncs5j5t1rm40chdeha27re4c/cancel" }, "version": "a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204" }Generated inUsing seed: 1650 Prompt: Y2K consumer tech, retro aesthetic, transparent plastic tech, portrait photo of a cyberpunk with wearables txt2img mode Using dev model Loading LoRA weights weights already loaded! 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.53it/s] 7%|▋ | 2/28 [00:00<00:06, 3.95it/s] 11%|█ | 3/28 [00:00<00:06, 3.76it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.66it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.63it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.60it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.57it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.57it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.56it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.54it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.56it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.55it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.55it/s] 50%|█████ | 14/28 [00:03<00:03, 3.54it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.53it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.55it/s] 61%|██████ | 17/28 [00:04<00:03, 3.55it/s] 64%|██████▍ | 18/28 [00:05<00:02, 3.55it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.54it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.54it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.52it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.52it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.54it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.55it/s] 89%|████████▉ | 25/28 [00:07<00:00, 3.55it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.55it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.53it/s] 100%|██████████| 28/28 [00:07<00:00, 3.52it/s] 100%|██████████| 28/28 [00:07<00:00, 3.56it/s]Prediction
fofr/flux-y2k:a9f9a7abIDwnhn8qcys9rm40chdhq8p9ypnrStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a Y2K poster with 90s CGI and the title "Replicate" in the style of Y2K, cyberpunk portrait
- lora_scale
- 0.75
- num_outputs
- 1
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "a Y2K poster with 90s CGI and the title \"Replicate\" in the style of Y2K, cyberpunk portrait", "lora_scale": 0.75, "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 replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/flux-y2k using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-y2k:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204", { input: { model: "dev", prompt: "a Y2K poster with 90s CGI and the title \"Replicate\" in the style of Y2K, cyberpunk portrait", lora_scale: 0.75, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 28 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fofr/flux-y2k using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-y2k:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204", input={ "model": "dev", "prompt": "a Y2K poster with 90s CGI and the title \"Replicate\" in the style of Y2K, cyberpunk portrait", "lora_scale": 0.75, "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.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/flux-y2k 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": "a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204", "input": { "model": "dev", "prompt": "a Y2K poster with 90s CGI and the title \\"Replicate\\" in the style of Y2K, cyberpunk portrait", "lora_scale": 0.75, "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/fofr/flux-y2k@sha256:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204 \ -i 'model="dev"' \ -i $'prompt="a Y2K poster with 90s CGI and the title \\"Replicate\\" in the style of Y2K, cyberpunk portrait"' \ -i 'lora_scale=0.75' \ -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/fofr/flux-y2k@sha256:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "model": "dev", "prompt": "a Y2K poster with 90s CGI and the title \\"Replicate\\" in the style of Y2K, cyberpunk portrait", "lora_scale": 0.75, "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": "2024-08-19T20:59:22.200099Z", "created_at": "2024-08-19T20:57:54.634000Z", "data_removed": false, "error": null, "id": "wnhn8qcys9rm40chdhq8p9ypnr", "input": { "model": "dev", "prompt": "a Y2K poster with 90s CGI and the title \"Replicate\" in the style of Y2K, cyberpunk portrait", "lora_scale": 0.75, "num_outputs": 1, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 47562\nPrompt: a Y2K poster with 90s CGI and the title \"Replicate\" in the style of Y2K, cyberpunk portrait\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, 3.54it/s]\n 7%|▋ | 2/28 [00:00<00:06, 3.99it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.79it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.71it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.66it/s]\n 21%|██▏ | 6/28 [00:01<00:06, 3.63it/s]\n 25%|██▌ | 7/28 [00:01<00:05, 3.62it/s]\n 29%|██▊ | 8/28 [00:02<00:05, 3.60it/s]\n 32%|███▏ | 9/28 [00:02<00:05, 3.59it/s]\n 36%|███▌ | 10/28 [00:02<00:05, 3.59it/s]\n 39%|███▉ | 11/28 [00:03<00:04, 3.59it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.59it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.58it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.58it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.58it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.58it/s]\n 61%|██████ | 17/28 [00:04<00:03, 3.57it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.58it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.58it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.58it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.58it/s]\n 79%|███████▊ | 22/28 [00:06<00:01, 3.58it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.58it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.58it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.58it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.58it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.58it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.58it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.60it/s]", "metrics": { "predict_time": 46.186952124, "total_time": 87.566099 }, "output": [ "https://replicate.delivery/yhqm/l4ekCbleK7mcUEHmP24ifBASfrlCdOEaQBgblLXScLXrCZRNB/out-0.webp" ], "started_at": "2024-08-19T20:58:36.013147Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/wnhn8qcys9rm40chdhq8p9ypnr", "cancel": "https://api.replicate.com/v1/predictions/wnhn8qcys9rm40chdhq8p9ypnr/cancel" }, "version": "a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204" }Generated inUsing seed: 47562 Prompt: a Y2K poster with 90s CGI and the title "Replicate" in the style of Y2K, cyberpunk portrait 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, 3.54it/s] 7%|▋ | 2/28 [00:00<00:06, 3.99it/s] 11%|█ | 3/28 [00:00<00:06, 3.79it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.71it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.66it/s] 21%|██▏ | 6/28 [00:01<00:06, 3.63it/s] 25%|██▌ | 7/28 [00:01<00:05, 3.62it/s] 29%|██▊ | 8/28 [00:02<00:05, 3.60it/s] 32%|███▏ | 9/28 [00:02<00:05, 3.59it/s] 36%|███▌ | 10/28 [00:02<00:05, 3.59it/s] 39%|███▉ | 11/28 [00:03<00:04, 3.59it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.59it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.58it/s] 50%|█████ | 14/28 [00:03<00:03, 3.58it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.58it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.58it/s] 61%|██████ | 17/28 [00:04<00:03, 3.57it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.58it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.58it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.58it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.58it/s] 79%|███████▊ | 22/28 [00:06<00:01, 3.58it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.58it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.58it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.58it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.58it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.58it/s] 100%|██████████| 28/28 [00:07<00:00, 3.58it/s] 100%|██████████| 28/28 [00:07<00:00, 3.60it/s]Prediction
fofr/flux-y2k:a9f9a7abID0d57dgvmq1rm60chdbdb4cq06rStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- consumer electronics in the style of Y2K, blue transparent plastic with the text "FOFR PLAYER" around the edge
- 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": "consumer electronics in the style of Y2K, blue transparent plastic with the text \"FOFR PLAYER\" around the edge", "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 replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/flux-y2k using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-y2k:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204", { input: { model: "dev", prompt: "consumer electronics in the style of Y2K, blue transparent plastic with the text \"FOFR PLAYER\" around the edge", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 28 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fofr/flux-y2k using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-y2k:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204", input={ "model": "dev", "prompt": "consumer electronics in the style of Y2K, blue transparent plastic with the text \"FOFR PLAYER\" around the edge", "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.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/flux-y2k 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": "a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204", "input": { "model": "dev", "prompt": "consumer electronics in the style of Y2K, blue transparent plastic with the text \\"FOFR PLAYER\\" around the edge", "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/fofr/flux-y2k@sha256:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204 \ -i 'model="dev"' \ -i $'prompt="consumer electronics in the style of Y2K, blue transparent plastic with the text \\"FOFR PLAYER\\" around the edge"' \ -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/fofr/flux-y2k@sha256:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "model": "dev", "prompt": "consumer electronics in the style of Y2K, blue transparent plastic with the text \\"FOFR PLAYER\\" around the edge", "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": "2024-08-19T13:36:35.429894Z", "created_at": "2024-08-19T13:36:27.320000Z", "data_removed": false, "error": null, "id": "0d57dgvmq1rm60chdbdb4cq06r", "input": { "model": "dev", "prompt": "consumer electronics in the style of Y2K, blue transparent plastic with the text \"FOFR PLAYER\" around the edge", "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: 47600\nPrompt: consumer electronics in the style of Y2K, blue transparent plastic with the text \"FOFR PLAYER\" around the edge\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nweights already loaded!\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.67it/s]\n 7%|▋ | 2/28 [00:00<00:06, 4.22it/s]\n 11%|█ | 3/28 [00:00<00:06, 3.96it/s]\n 14%|█▍ | 4/28 [00:01<00:06, 3.84it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.78it/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, 3.66it/s]\n 43%|████▎ | 12/28 [00:03<00:04, 3.67it/s]\n 46%|████▋ | 13/28 [00:03<00:04, 3.67it/s]\n 50%|█████ | 14/28 [00:03<00:03, 3.67it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.67it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.67it/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, 3.67it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.67it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.67it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.67it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.67it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.68it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.70it/s]", "metrics": { "predict_time": 8.072312197, "total_time": 8.109894 }, "output": [ "https://replicate.delivery/yhqm/9SyiEMMefGmEA0aZqEksrLfG79UGtAqM7ftmnf4hc2ndMeD1E/out-0.webp" ], "started_at": "2024-08-19T13:36:27.357582Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/0d57dgvmq1rm60chdbdb4cq06r", "cancel": "https://api.replicate.com/v1/predictions/0d57dgvmq1rm60chdbdb4cq06r/cancel" }, "version": "db656b8337fb4663b2445e93deeefc9e9b584a960b36dfd04f2b0f819a211ec2" }Generated inUsing seed: 47600 Prompt: consumer electronics in the style of Y2K, blue transparent plastic with the text "FOFR PLAYER" around the edge txt2img mode Using dev model Loading LoRA weights weights already loaded! 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.67it/s] 7%|▋ | 2/28 [00:00<00:06, 4.22it/s] 11%|█ | 3/28 [00:00<00:06, 3.96it/s] 14%|█▍ | 4/28 [00:01<00:06, 3.84it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.78it/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, 3.66it/s] 43%|████▎ | 12/28 [00:03<00:04, 3.67it/s] 46%|████▋ | 13/28 [00:03<00:04, 3.67it/s] 50%|█████ | 14/28 [00:03<00:03, 3.67it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.67it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.67it/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, 3.67it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.67it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.67it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.67it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.67it/s] 100%|██████████| 28/28 [00:07<00:00, 3.68it/s] 100%|██████████| 28/28 [00:07<00:00, 3.70it/s]Prediction
fofr/flux-y2k:a9f9a7abID3jjqgrs1e5rm00chdbe8t922ecStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- a photo in the style of Y2K
- 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": "a photo in the style of Y2K", "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 replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/flux-y2k using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-y2k:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204", { input: { model: "dev", prompt: "a photo in the style of Y2K", lora_scale: 1, num_outputs: 1, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 28 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fofr/flux-y2k using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-y2k:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204", input={ "model": "dev", "prompt": "a photo in the style of Y2K", "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.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/flux-y2k 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": "a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204", "input": { "model": "dev", "prompt": "a photo in the style of Y2K", "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/fofr/flux-y2k@sha256:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204 \ -i 'model="dev"' \ -i 'prompt="a photo in the style of Y2K"' \ -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/fofr/flux-y2k@sha256:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "model": "dev", "prompt": "a photo in the style of Y2K", "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": "2024-08-19T13:39:03.315913Z", "created_at": "2024-08-19T13:38:17.073000Z", "data_removed": false, "error": null, "id": "3jjqgrs1e5rm00chdbe8t922ec", "input": { "model": "dev", "prompt": "a photo in the style of Y2K", "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: 21516\nPrompt: a photo in the style of Y2K\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9747794665472\nDownloading weights\n2024-08-19T13:38:42Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/7035ee8159d756fd url=https://replicate.delivery/yhqm/fKeBZZgZA2m1kU0bQnTJzb31EFkPws8e6tBtXfVL791lB8QNB/trained_model.tar\n2024-08-19T13:38:46Z | INFO | [ Complete ] dest=/src/weights-cache/7035ee8159d756fd size=\"172 MB\" total_elapsed=3.234s url=https://replicate.delivery/yhqm/fKeBZZgZA2m1kU0bQnTJzb31EFkPws8e6tBtXfVL791lB8QNB/trained_model.tar\nb''\nDownloaded weights in 3.2620677947998047 seconds\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:07, 3.68it/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, 3.84it/s]\n 18%|█▊ | 5/28 [00:01<00:06, 3.78it/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, 3.69it/s]\n 36%|███▌ | 10/28 [00:02<00:04, 3.69it/s]\n 39%|███▉ | 11/28 [00:02<00:04, 3.68it/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, 3.67it/s]\n 54%|█████▎ | 15/28 [00:04<00:03, 3.67it/s]\n 57%|█████▋ | 16/28 [00:04<00:03, 3.67it/s]\n 61%|██████ | 17/28 [00:04<00:02, 3.67it/s]\n 64%|██████▍ | 18/28 [00:04<00:02, 3.67it/s]\n 68%|██████▊ | 19/28 [00:05<00:02, 3.67it/s]\n 71%|███████▏ | 20/28 [00:05<00:02, 3.67it/s]\n 75%|███████▌ | 21/28 [00:05<00:01, 3.67it/s]\n 79%|███████▊ | 22/28 [00:05<00:01, 3.67it/s]\n 82%|████████▏ | 23/28 [00:06<00:01, 3.67it/s]\n 86%|████████▌ | 24/28 [00:06<00:01, 3.67it/s]\n 89%|████████▉ | 25/28 [00:06<00:00, 3.67it/s]\n 93%|█████████▎| 26/28 [00:07<00:00, 3.67it/s]\n 96%|█████████▋| 27/28 [00:07<00:00, 3.67it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.67it/s]\n100%|██████████| 28/28 [00:07<00:00, 3.70it/s]", "metrics": { "predict_time": 20.43542929, "total_time": 46.242913 }, "output": [ "https://replicate.delivery/yhqm/lpRGJFKsSPbrKNM4nHlwxN5tQhsn2OepRtNzfhpKrmG3zPUTA/out-0.webp" ], "started_at": "2024-08-19T13:38:42.880483Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/3jjqgrs1e5rm00chdbe8t922ec", "cancel": "https://api.replicate.com/v1/predictions/3jjqgrs1e5rm00chdbe8t922ec/cancel" }, "version": "db656b8337fb4663b2445e93deeefc9e9b584a960b36dfd04f2b0f819a211ec2" }Generated inUsing seed: 21516 Prompt: a photo in the style of Y2K txt2img mode Using dev model Loading LoRA weights Ensuring enough disk space... Free disk space: 9747794665472 Downloading weights 2024-08-19T13:38:42Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/7035ee8159d756fd url=https://replicate.delivery/yhqm/fKeBZZgZA2m1kU0bQnTJzb31EFkPws8e6tBtXfVL791lB8QNB/trained_model.tar 2024-08-19T13:38:46Z | INFO | [ Complete ] dest=/src/weights-cache/7035ee8159d756fd size="172 MB" total_elapsed=3.234s url=https://replicate.delivery/yhqm/fKeBZZgZA2m1kU0bQnTJzb31EFkPws8e6tBtXfVL791lB8QNB/trained_model.tar b'' Downloaded weights in 3.2620677947998047 seconds LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:07, 3.68it/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, 3.84it/s] 18%|█▊ | 5/28 [00:01<00:06, 3.78it/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, 3.69it/s] 36%|███▌ | 10/28 [00:02<00:04, 3.69it/s] 39%|███▉ | 11/28 [00:02<00:04, 3.68it/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, 3.67it/s] 54%|█████▎ | 15/28 [00:04<00:03, 3.67it/s] 57%|█████▋ | 16/28 [00:04<00:03, 3.67it/s] 61%|██████ | 17/28 [00:04<00:02, 3.67it/s] 64%|██████▍ | 18/28 [00:04<00:02, 3.67it/s] 68%|██████▊ | 19/28 [00:05<00:02, 3.67it/s] 71%|███████▏ | 20/28 [00:05<00:02, 3.67it/s] 75%|███████▌ | 21/28 [00:05<00:01, 3.67it/s] 79%|███████▊ | 22/28 [00:05<00:01, 3.67it/s] 82%|████████▏ | 23/28 [00:06<00:01, 3.67it/s] 86%|████████▌ | 24/28 [00:06<00:01, 3.67it/s] 89%|████████▉ | 25/28 [00:06<00:00, 3.67it/s] 93%|█████████▎| 26/28 [00:07<00:00, 3.67it/s] 96%|█████████▋| 27/28 [00:07<00:00, 3.67it/s] 100%|██████████| 28/28 [00:07<00:00, 3.67it/s] 100%|██████████| 28/28 [00:07<00:00, 3.70it/s]Prediction
fofr/flux-y2k:a9f9a7abID972g88arb5rm00chdbmrway9zcStatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A Y2K product photo of a handheld gaming device, translucent blue plastic, retro tech aesthetic
- lora_scale
- 1
- num_outputs
- 4
- aspect_ratio
- 1:1
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "A Y2K product photo of a handheld gaming device, translucent blue plastic, retro tech aesthetic", "lora_scale": 1, "num_outputs": 4, "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 replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/flux-y2k using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-y2k:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204", { input: { model: "dev", prompt: "A Y2K product photo of a handheld gaming device, translucent blue plastic, retro tech aesthetic", lora_scale: 1, num_outputs: 4, aspect_ratio: "1:1", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 28 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fofr/flux-y2k using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-y2k:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204", input={ "model": "dev", "prompt": "A Y2K product photo of a handheld gaming device, translucent blue plastic, retro tech aesthetic", "lora_scale": 1, "num_outputs": 4, "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.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/flux-y2k 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": "a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204", "input": { "model": "dev", "prompt": "A Y2K product photo of a handheld gaming device, translucent blue plastic, retro tech aesthetic", "lora_scale": 1, "num_outputs": 4, "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/fofr/flux-y2k@sha256:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204 \ -i 'model="dev"' \ -i 'prompt="A Y2K product photo of a handheld gaming device, translucent blue plastic, retro tech aesthetic"' \ -i 'lora_scale=1' \ -i 'num_outputs=4' \ -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/fofr/flux-y2k@sha256:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "model": "dev", "prompt": "A Y2K product photo of a handheld gaming device, translucent blue plastic, retro tech aesthetic", "lora_scale": 1, "num_outputs": 4, "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": "2024-08-19T13:53:29.780551Z", "created_at": "2024-08-19T13:52:43.097000Z", "data_removed": false, "error": null, "id": "972g88arb5rm00chdbmrway9zc", "input": { "model": "dev", "prompt": "A Y2K product photo of a handheld gaming device, translucent blue plastic, retro tech aesthetic", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "1:1", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 56977\nPrompt: A Y2K product photo of a handheld gaming device, translucent blue plastic, retro tech aesthetic\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nweights already loaded!\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:26, 1.00it/s]\n 7%|▋ | 2/28 [00:01<00:22, 1.14it/s]\n 11%|█ | 3/28 [00:02<00:23, 1.07it/s]\n 14%|█▍ | 4/28 [00:03<00:23, 1.04it/s]\n 18%|█▊ | 5/28 [00:04<00:22, 1.02it/s]\n 21%|██▏ | 6/28 [00:05<00:21, 1.01it/s]\n 25%|██▌ | 7/28 [00:06<00:20, 1.01it/s]\n 29%|██▊ | 8/28 [00:07<00:19, 1.00it/s]\n 32%|███▏ | 9/28 [00:08<00:19, 1.00s/it]\n 36%|███▌ | 10/28 [00:09<00:18, 1.00s/it]\n 39%|███▉ | 11/28 [00:10<00:17, 1.00s/it]\n 43%|████▎ | 12/28 [00:11<00:16, 1.01s/it]\n 46%|████▋ | 13/28 [00:12<00:15, 1.00s/it]\n 50%|█████ | 14/28 [00:13<00:14, 1.01s/it]\n 54%|█████▎ | 15/28 [00:14<00:13, 1.01s/it]\n 57%|█████▋ | 16/28 [00:15<00:12, 1.00s/it]\n 61%|██████ | 17/28 [00:16<00:11, 1.00s/it]\n 64%|██████▍ | 18/28 [00:17<00:10, 1.00s/it]\n 68%|██████▊ | 19/28 [00:18<00:09, 1.01s/it]\n 71%|███████▏ | 20/28 [00:19<00:08, 1.01s/it]\n 75%|███████▌ | 21/28 [00:20<00:07, 1.01s/it]\n 79%|███████▊ | 22/28 [00:21<00:06, 1.01s/it]\n 82%|████████▏ | 23/28 [00:22<00:05, 1.01s/it]\n 86%|████████▌ | 24/28 [00:23<00:04, 1.01s/it]\n 89%|████████▉ | 25/28 [00:24<00:03, 1.01s/it]\n 93%|█████████▎| 26/28 [00:25<00:02, 1.01s/it]\n 96%|█████████▋| 27/28 [00:26<00:01, 1.01s/it]\n100%|██████████| 28/28 [00:27<00:00, 1.01s/it]\n100%|██████████| 28/28 [00:27<00:00, 1.00it/s]", "metrics": { "predict_time": 29.545199807, "total_time": 46.683551 }, "output": [ "https://replicate.delivery/yhqm/eGK0U4iG261LWCYJkzLgtp5TdiDQG7dDcEleyGIwf3WyCgomA/out-0.webp", "https://replicate.delivery/yhqm/FdZDkjfPp71ZISTyi8taUiWGedfdVmieel7hWqGflJWbWAE1E/out-1.webp", "https://replicate.delivery/yhqm/WhN1LqSCfT3nZaNhFyCONdNcwY3ICYV0IpzYdofwPB3ZBQUTA/out-2.webp", "https://replicate.delivery/yhqm/k56gE0EheGWjDqmbJC4vne95aoSN4qzJJu78jJxswTxZBQUTA/out-3.webp" ], "started_at": "2024-08-19T13:53:00.235351Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/972g88arb5rm00chdbmrway9zc", "cancel": "https://api.replicate.com/v1/predictions/972g88arb5rm00chdbmrway9zc/cancel" }, "version": "db656b8337fb4663b2445e93deeefc9e9b584a960b36dfd04f2b0f819a211ec2" }Generated inUsing seed: 56977 Prompt: A Y2K product photo of a handheld gaming device, translucent blue plastic, retro tech aesthetic txt2img mode Using dev model Loading LoRA weights weights already loaded! 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:26, 1.00it/s] 7%|▋ | 2/28 [00:01<00:22, 1.14it/s] 11%|█ | 3/28 [00:02<00:23, 1.07it/s] 14%|█▍ | 4/28 [00:03<00:23, 1.04it/s] 18%|█▊ | 5/28 [00:04<00:22, 1.02it/s] 21%|██▏ | 6/28 [00:05<00:21, 1.01it/s] 25%|██▌ | 7/28 [00:06<00:20, 1.01it/s] 29%|██▊ | 8/28 [00:07<00:19, 1.00it/s] 32%|███▏ | 9/28 [00:08<00:19, 1.00s/it] 36%|███▌ | 10/28 [00:09<00:18, 1.00s/it] 39%|███▉ | 11/28 [00:10<00:17, 1.00s/it] 43%|████▎ | 12/28 [00:11<00:16, 1.01s/it] 46%|████▋ | 13/28 [00:12<00:15, 1.00s/it] 50%|█████ | 14/28 [00:13<00:14, 1.01s/it] 54%|█████▎ | 15/28 [00:14<00:13, 1.01s/it] 57%|█████▋ | 16/28 [00:15<00:12, 1.00s/it] 61%|██████ | 17/28 [00:16<00:11, 1.00s/it] 64%|██████▍ | 18/28 [00:17<00:10, 1.00s/it] 68%|██████▊ | 19/28 [00:18<00:09, 1.01s/it] 71%|███████▏ | 20/28 [00:19<00:08, 1.01s/it] 75%|███████▌ | 21/28 [00:20<00:07, 1.01s/it] 79%|███████▊ | 22/28 [00:21<00:06, 1.01s/it] 82%|████████▏ | 23/28 [00:22<00:05, 1.01s/it] 86%|████████▌ | 24/28 [00:23<00:04, 1.01s/it] 89%|████████▉ | 25/28 [00:24<00:03, 1.01s/it] 93%|█████████▎| 26/28 [00:25<00:02, 1.01s/it] 96%|█████████▋| 27/28 [00:26<00:01, 1.01s/it] 100%|██████████| 28/28 [00:27<00:00, 1.01s/it] 100%|██████████| 28/28 [00:27<00:00, 1.00it/s]Prediction
fofr/flux-y2k:a9f9a7abIDkzjyhr6aw1rm60chdbnr47q434StatusSucceededSourceWebHardwareH100Total durationCreatedInput
- model
- dev
- prompt
- A Y2K product photo of a handheld gaming device with the text "FOFR" on a dot matrix display, translucent green plastic, retro tech aesthetic, a monstera plant in the background
- lora_scale
- 1
- num_outputs
- 4
- aspect_ratio
- 2:3
- output_format
- webp
- guidance_scale
- 3.5
- output_quality
- 80
- num_inference_steps
- 28
{ "model": "dev", "prompt": "A Y2K product photo of a handheld gaming device with the text \"FOFR\" on a dot matrix display, translucent green plastic, retro tech aesthetic, a monstera plant in the background", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "2:3", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run fofr/flux-y2k using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/flux-y2k:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204", { input: { model: "dev", prompt: "A Y2K product photo of a handheld gaming device with the text \"FOFR\" on a dot matrix display, translucent green plastic, retro tech aesthetic, a monstera plant in the background", lora_scale: 1, num_outputs: 4, aspect_ratio: "2:3", output_format: "webp", guidance_scale: 3.5, output_quality: 80, num_inference_steps: 28 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun fofr/flux-y2k using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/flux-y2k:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204", input={ "model": "dev", "prompt": "A Y2K product photo of a handheld gaming device with the text \"FOFR\" on a dot matrix display, translucent green plastic, retro tech aesthetic, a monstera plant in the background", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "2:3", "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.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run fofr/flux-y2k 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": "a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204", "input": { "model": "dev", "prompt": "A Y2K product photo of a handheld gaming device with the text \\"FOFR\\" on a dot matrix display, translucent green plastic, retro tech aesthetic, a monstera plant in the background", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "2:3", "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/fofr/flux-y2k@sha256:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204 \ -i 'model="dev"' \ -i $'prompt="A Y2K product photo of a handheld gaming device with the text \\"FOFR\\" on a dot matrix display, translucent green plastic, retro tech aesthetic, a monstera plant in the background"' \ -i 'lora_scale=1' \ -i 'num_outputs=4' \ -i 'aspect_ratio="2:3"' \ -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/fofr/flux-y2k@sha256:a9f9a7ab88250ad3a1071865324b5de5be601c0fc610b263002ab490b9697204curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "model": "dev", "prompt": "A Y2K product photo of a handheld gaming device with the text \\"FOFR\\" on a dot matrix display, translucent green plastic, retro tech aesthetic, a monstera plant in the background", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "2:3", "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": "2024-08-19T13:56:37.681019Z", "created_at": "2024-08-19T13:55:23.488000Z", "data_removed": false, "error": null, "id": "kzjyhr6aw1rm60chdbnr47q434", "input": { "model": "dev", "prompt": "A Y2K product photo of a handheld gaming device with the text \"FOFR\" on a dot matrix display, translucent green plastic, retro tech aesthetic, a monstera plant in the background", "lora_scale": 1, "num_outputs": 4, "aspect_ratio": "2:3", "output_format": "webp", "guidance_scale": 3.5, "output_quality": 80, "num_inference_steps": 28 }, "logs": "Using seed: 60319\nPrompt: A Y2K product photo of a handheld gaming device with the text \"FOFR\" on a dot matrix display, translucent green plastic, retro tech aesthetic, a monstera plant in the background\ntxt2img mode\nUsing dev model\nLoading LoRA weights\nEnsuring enough disk space...\nFree disk space: 9695253413888\nDownloading weights\n2024-08-19T13:55:59Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/7035ee8159d756fd url=https://replicate.delivery/yhqm/fKeBZZgZA2m1kU0bQnTJzb31EFkPws8e6tBtXfVL791lB8QNB/trained_model.tar\n2024-08-19T13:56:00Z | INFO | [ Complete ] dest=/src/weights-cache/7035ee8159d756fd size=\"172 MB\" total_elapsed=1.275s url=https://replicate.delivery/yhqm/fKeBZZgZA2m1kU0bQnTJzb31EFkPws8e6tBtXfVL791lB8QNB/trained_model.tar\nb''\nDownloaded weights in 1.3021209239959717 seconds\nLoRA weights loaded successfully\n 0%| | 0/28 [00:00<?, ?it/s]\n 4%|▎ | 1/28 [00:00<00:26, 1.04it/s]\n 7%|▋ | 2/28 [00:01<00:21, 1.18it/s]\n 11%|█ | 3/28 [00:02<00:22, 1.11it/s]\n 14%|█▍ | 4/28 [00:03<00:22, 1.07it/s]\n 18%|█▊ | 5/28 [00:04<00:21, 1.06it/s]\n 21%|██▏ | 6/28 [00:05<00:21, 1.05it/s]\n 25%|██▌ | 7/28 [00:06<00:20, 1.04it/s]\n 29%|██▊ | 8/28 [00:07<00:19, 1.04it/s]\n 32%|███▏ | 9/28 [00:08<00:18, 1.03it/s]\n 36%|███▌ | 10/28 [00:09<00:17, 1.03it/s]\n 39%|███▉ | 11/28 [00:10<00:16, 1.03it/s]\n 43%|████▎ | 12/28 [00:11<00:15, 1.03it/s]\n 46%|████▋ | 13/28 [00:12<00:14, 1.03it/s]\n 50%|█████ | 14/28 [00:13<00:13, 1.03it/s]\n 54%|█████▎ | 15/28 [00:14<00:12, 1.03it/s]\n 57%|█████▋ | 16/28 [00:15<00:11, 1.03it/s]\n 61%|██████ | 17/28 [00:16<00:10, 1.03it/s]\n 64%|██████▍ | 18/28 [00:17<00:09, 1.03it/s]\n 68%|██████▊ | 19/28 [00:18<00:08, 1.03it/s]\n 71%|███████▏ | 20/28 [00:19<00:07, 1.03it/s]\n 75%|███████▌ | 21/28 [00:20<00:06, 1.03it/s]\n 79%|███████▊ | 22/28 [00:21<00:05, 1.03it/s]\n 82%|████████▏ | 23/28 [00:22<00:04, 1.03it/s]\n 86%|████████▌ | 24/28 [00:23<00:03, 1.03it/s]\n 89%|████████▉ | 25/28 [00:24<00:02, 1.03it/s]\n 93%|█████████▎| 26/28 [00:25<00:01, 1.02it/s]\n 96%|█████████▋| 27/28 [00:26<00:00, 1.03it/s]\n100%|██████████| 28/28 [00:27<00:00, 1.03it/s]\n100%|██████████| 28/28 [00:27<00:00, 1.04it/s]", "metrics": { "predict_time": 38.332474363, "total_time": 74.193019 }, "output": [ "https://replicate.delivery/yhqm/O6Nv5MBuuuJfbCwYfDmuuBw5RwmNMKeNiDnfLApHVrvXRARNB/out-0.webp", "https://replicate.delivery/yhqm/nkMQU2wLzsp4DlF0ZbBvs7lgfLYSlmmcqR1SN1pSSarKCIqJA/out-1.webp", "https://replicate.delivery/yhqm/1YHebJwwvU2XOyOJfSisYm0KBFhyozaq8x1HuQXID85VEQUTA/out-2.webp", "https://replicate.delivery/yhqm/enenbSyw2UixoEDoVTfoio99IK3gnv6w7OOdfxzI2VwVRARNB/out-3.webp" ], "started_at": "2024-08-19T13:55:59.348545Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kzjyhr6aw1rm60chdbnr47q434", "cancel": "https://api.replicate.com/v1/predictions/kzjyhr6aw1rm60chdbnr47q434/cancel" }, "version": "db656b8337fb4663b2445e93deeefc9e9b584a960b36dfd04f2b0f819a211ec2" }Generated inUsing seed: 60319 Prompt: A Y2K product photo of a handheld gaming device with the text "FOFR" on a dot matrix display, translucent green plastic, retro tech aesthetic, a monstera plant in the background txt2img mode Using dev model Loading LoRA weights Ensuring enough disk space... Free disk space: 9695253413888 Downloading weights 2024-08-19T13:55:59Z | INFO | [ Initiating ] chunk_size=150M dest=/src/weights-cache/7035ee8159d756fd url=https://replicate.delivery/yhqm/fKeBZZgZA2m1kU0bQnTJzb31EFkPws8e6tBtXfVL791lB8QNB/trained_model.tar 2024-08-19T13:56:00Z | INFO | [ Complete ] dest=/src/weights-cache/7035ee8159d756fd size="172 MB" total_elapsed=1.275s url=https://replicate.delivery/yhqm/fKeBZZgZA2m1kU0bQnTJzb31EFkPws8e6tBtXfVL791lB8QNB/trained_model.tar b'' Downloaded weights in 1.3021209239959717 seconds LoRA weights loaded successfully 0%| | 0/28 [00:00<?, ?it/s] 4%|▎ | 1/28 [00:00<00:26, 1.04it/s] 7%|▋ | 2/28 [00:01<00:21, 1.18it/s] 11%|█ | 3/28 [00:02<00:22, 1.11it/s] 14%|█▍ | 4/28 [00:03<00:22, 1.07it/s] 18%|█▊ | 5/28 [00:04<00:21, 1.06it/s] 21%|██▏ | 6/28 [00:05<00:21, 1.05it/s] 25%|██▌ | 7/28 [00:06<00:20, 1.04it/s] 29%|██▊ | 8/28 [00:07<00:19, 1.04it/s] 32%|███▏ | 9/28 [00:08<00:18, 1.03it/s] 36%|███▌ | 10/28 [00:09<00:17, 1.03it/s] 39%|███▉ | 11/28 [00:10<00:16, 1.03it/s] 43%|████▎ | 12/28 [00:11<00:15, 1.03it/s] 46%|████▋ | 13/28 [00:12<00:14, 1.03it/s] 50%|█████ | 14/28 [00:13<00:13, 1.03it/s] 54%|█████▎ | 15/28 [00:14<00:12, 1.03it/s] 57%|█████▋ | 16/28 [00:15<00:11, 1.03it/s] 61%|██████ | 17/28 [00:16<00:10, 1.03it/s] 64%|██████▍ | 18/28 [00:17<00:09, 1.03it/s] 68%|██████▊ | 19/28 [00:18<00:08, 1.03it/s] 71%|███████▏ | 20/28 [00:19<00:07, 1.03it/s] 75%|███████▌ | 21/28 [00:20<00:06, 1.03it/s] 79%|███████▊ | 22/28 [00:21<00:05, 1.03it/s] 82%|████████▏ | 23/28 [00:22<00:04, 1.03it/s] 86%|████████▌ | 24/28 [00:23<00:03, 1.03it/s] 89%|████████▉ | 25/28 [00:24<00:02, 1.03it/s] 93%|█████████▎| 26/28 [00:25<00:01, 1.02it/s] 96%|█████████▋| 27/28 [00:26<00:00, 1.03it/s] 100%|██████████| 28/28 [00:27<00:00, 1.03it/s] 100%|██████████| 28/28 [00:27<00:00, 1.04it/s]
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