fofr
/
sdxl-wrong
An SDXL fine-tune on bad 2048x2048 images
- Public
- 648 runs
-
L40S
- SDXL fine-tune
Prediction
fofr/sdxl-wrong:164e357bIDqrxqc53bqhtcjsonjgr4bqcg4qStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @fofrInput
- width
- 1024
- height
- 1024
- prompt
- A TOK image
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A TOK image", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }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/sdxl-wrong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-wrong:164e357b3d844b3023b7467f84b9da9534c58a43f2588b60bb77eb0d51f41ec0", { input: { width: 1024, height: 1024, prompt: "A TOK image", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); 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/sdxl-wrong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-wrong:164e357b3d844b3023b7467f84b9da9534c58a43f2588b60bb77eb0d51f41ec0", input={ "width": 1024, "height": 1024, "prompt": "A TOK image", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) 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/sdxl-wrong 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": "164e357b3d844b3023b7467f84b9da9534c58a43f2588b60bb77eb0d51f41ec0", "input": { "width": 1024, "height": 1024, "prompt": "A TOK image", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ 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/sdxl-wrong@sha256:164e357b3d844b3023b7467f84b9da9534c58a43f2588b60bb77eb0d51f41ec0 \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="A TOK image"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.6' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.8' \ -i 'negative_prompt=""' \ -i 'prompt_strength=0.8' \ -i 'num_inference_steps=50'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/sdxl-wrong@sha256:164e357b3d844b3023b7467f84b9da9534c58a43f2588b60bb77eb0d51f41ec0curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "A TOK image", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-09-26T14:29:00.441482Z", "created_at": "2023-09-26T14:28:42.168581Z", "data_removed": false, "error": null, "id": "qrxqc53bqhtcjsonjgr4bqcg4q", "input": { "width": 1024, "height": 1024, "prompt": "A TOK image", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": null, "metrics": { "predict_time": 16.469505, "total_time": 18.272901 }, "output": [ "https://pbxt.replicate.delivery/fc5IBfqQyGhfeSfUfI0zTdaf1gBpmb00k1O94vlojIp0V5E0IA/out-0.png" ], "started_at": "2023-09-26T14:28:43.971977Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/qrxqc53bqhtcjsonjgr4bqcg4q", "cancel": "https://api.replicate.com/v1/predictions/qrxqc53bqhtcjsonjgr4bqcg4q/cancel" }, "version": "164e357b3d844b3023b7467f84b9da9534c58a43f2588b60bb77eb0d51f41ec0" }Generated inPrediction
fofr/sdxl-wrong:164e357bID34kfuclbvxe2reajmlsl2inlkyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A TOK photo of a living room, IKEA catalogue image
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A TOK photo of a living room, IKEA catalogue image", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }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/sdxl-wrong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-wrong:164e357b3d844b3023b7467f84b9da9534c58a43f2588b60bb77eb0d51f41ec0", { input: { width: 1024, height: 1024, prompt: "A TOK photo of a living room, IKEA catalogue image", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); 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/sdxl-wrong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-wrong:164e357b3d844b3023b7467f84b9da9534c58a43f2588b60bb77eb0d51f41ec0", input={ "width": 1024, "height": 1024, "prompt": "A TOK photo of a living room, IKEA catalogue image", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) 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/sdxl-wrong 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": "164e357b3d844b3023b7467f84b9da9534c58a43f2588b60bb77eb0d51f41ec0", "input": { "width": 1024, "height": 1024, "prompt": "A TOK photo of a living room, IKEA catalogue image", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ 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/sdxl-wrong@sha256:164e357b3d844b3023b7467f84b9da9534c58a43f2588b60bb77eb0d51f41ec0 \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="A TOK photo of a living room, IKEA catalogue image"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.6' \ -i 'num_outputs=4' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.8' \ -i 'negative_prompt=""' \ -i 'prompt_strength=0.8' \ -i 'num_inference_steps=50'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/sdxl-wrong@sha256:164e357b3d844b3023b7467f84b9da9534c58a43f2588b60bb77eb0d51f41ec0curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "A TOK photo of a living room, IKEA catalogue image", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-09-26T14:44:21.290056Z", "created_at": "2023-09-26T14:43:20.246342Z", "data_removed": false, "error": null, "id": "34kfuclbvxe2reajmlsl2inlky", "input": { "width": 1024, "height": 1024, "prompt": "A TOK photo of a living room, IKEA catalogue image", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": null, "metrics": { "predict_time": 59.795052, "total_time": 61.043714 }, "output": [ "https://pbxt.replicate.delivery/tssAwj9CK1IHHJBjInkgmGcPS9lqKOkw3dZxIDmEA1ehAF0IA/out-0.png", "https://pbxt.replicate.delivery/BgvVqnf6afmaZkH60khzBZZty5aKXiN4QpInTZva83cEBKoRA/out-1.png", "https://pbxt.replicate.delivery/dhq4fckNaKTVYC7PIOUPDhfcRoGTeF00wid3ATwaoOAICUQjA/out-2.png", "https://pbxt.replicate.delivery/w6ieP6NiPl2aYiY8ewQKCNAXJGQc3y0hvyNSWfvebD6WEogGB/out-3.png" ], "started_at": "2023-09-26T14:43:21.495004Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/34kfuclbvxe2reajmlsl2inlky", "cancel": "https://api.replicate.com/v1/predictions/34kfuclbvxe2reajmlsl2inlky/cancel" }, "version": "164e357b3d844b3023b7467f84b9da9534c58a43f2588b60bb77eb0d51f41ec0" }Generated inPrediction
fofr/sdxl-wrong:164e357bID6qkfog3b6xrwt7zwkbjxso34iiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A TOK studio portrait photo of an anthropomorphic robot dog
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A TOK studio portrait photo of an anthropomorphic robot dog", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }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/sdxl-wrong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-wrong:164e357b3d844b3023b7467f84b9da9534c58a43f2588b60bb77eb0d51f41ec0", { input: { width: 1024, height: 1024, prompt: "A TOK studio portrait photo of an anthropomorphic robot dog", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 4, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, num_inference_steps: 50 } } ); 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/sdxl-wrong using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-wrong:164e357b3d844b3023b7467f84b9da9534c58a43f2588b60bb77eb0d51f41ec0", input={ "width": 1024, "height": 1024, "prompt": "A TOK studio portrait photo of an anthropomorphic robot dog", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) 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/sdxl-wrong 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": "164e357b3d844b3023b7467f84b9da9534c58a43f2588b60bb77eb0d51f41ec0", "input": { "width": 1024, "height": 1024, "prompt": "A TOK studio portrait photo of an anthropomorphic robot dog", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ 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/sdxl-wrong@sha256:164e357b3d844b3023b7467f84b9da9534c58a43f2588b60bb77eb0d51f41ec0 \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="A TOK studio portrait photo of an anthropomorphic robot dog"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.6' \ -i 'num_outputs=4' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.8' \ -i 'negative_prompt=""' \ -i 'prompt_strength=0.8' \ -i 'num_inference_steps=50'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/sdxl-wrong@sha256:164e357b3d844b3023b7467f84b9da9534c58a43f2588b60bb77eb0d51f41ec0curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "A TOK studio portrait photo of an anthropomorphic robot dog", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-09-26T14:57:04.302158Z", "created_at": "2023-09-26T14:56:04.375297Z", "data_removed": false, "error": null, "id": "6qkfog3b6xrwt7zwkbjxso34ii", "input": { "width": 1024, "height": 1024, "prompt": "A TOK studio portrait photo of an anthropomorphic robot dog", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": null, "metrics": { "predict_time": 59.147908, "total_time": 59.926861 }, "output": [ "https://pbxt.replicate.delivery/KeJesn2w3Fq4tUpEylbhVCDGnugpNG3uUBc75FPSLAAeZUQjA/out-0.png", "https://pbxt.replicate.delivery/g6ce8CzgRft8K00F6BT061NVetCfnVroKdcaO2DRzlYenRBNC/out-1.png", "https://pbxt.replicate.delivery/fg48JXtNtfk3LkYLh1Ro0Km5eujfQi6bG2ygMRHbIOOenRBNC/out-2.png", "https://pbxt.replicate.delivery/yCfLVttVkVXPHSBPxVkBYuRR3ICJtJet3f7oUWcJZ33BaUQjA/out-3.png" ], "started_at": "2023-09-26T14:56:05.154250Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6qkfog3b6xrwt7zwkbjxso34ii", "cancel": "https://api.replicate.com/v1/predictions/6qkfog3b6xrwt7zwkbjxso34ii/cancel" }, "version": "164e357b3d844b3023b7467f84b9da9534c58a43f2588b60bb77eb0d51f41ec0" }Generated in
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