fofr / sdxl-ms-paint
Make MS Paint images, a lora trained by aimingfail
- Public
- 1.2K runs
-
L40S
- Paper
Prediction
fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3fIDjmeyd2z2b5rgg0cg05qbcd3y64StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- MSPaint beautiful landscape
- sampler
- Default
- scheduler
- Default
- lora_strength
- 1
- output_format
- webp
- output_quality
- 80
- number_of_images
- 1
{ "width": 1024, "height": 1024, "prompt": "MSPaint beautiful landscape", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "number_of_images": 1 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/sdxl-ms-paint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f", { input: { width: 1024, height: 1024, prompt: "MSPaint beautiful landscape", sampler: "Default", scheduler: "Default", lora_strength: 1, output_format: "webp", output_quality: 80, number_of_images: 1 } } ); // 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 replicate
Import the client:import replicate
Run fofr/sdxl-ms-paint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f", input={ "width": 1024, "height": 1024, "prompt": "MSPaint beautiful landscape", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "number_of_images": 1 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())
To learn more, take a look at the guide on getting started with Python.
Run fofr/sdxl-ms-paint 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": "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f", "input": { "width": 1024, "height": 1024, "prompt": "MSPaint beautiful landscape", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "number_of_images": 1 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-06-10T09:16:07.829931Z", "created_at": "2024-06-10T09:16:02.521000Z", "data_removed": false, "error": null, "id": "jmeyd2z2b5rgg0cg05qbcd3y64", "input": { "width": 1024, "height": 1024, "prompt": "MSPaint beautiful landscape", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "number_of_images": 1 }, "logs": "Random seed set to: 933701224\n==============================\nGeneration settings\nSampler: dpmpp_2m_sde_gpu\nScheduler: karras\nSteps: 20\nCFG: 7.0\nLORA: Using a lora. Lora strength:1.0\n==============================\nRunning workflow\ngot prompt\nExecuting node 10, title: Load LoRA, class type: LoraLoader\nExecuting node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode\nRequested to load SDXLClipModel\nLoading 1 new model\nExecuting node 7, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode\nExecuting node 3, title: KSampler, class type: KSampler\nRequested to load SDXL\nLoading 1 new model\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:03, 5.30it/s]\n 10%|█ | 2/20 [00:00<00:03, 5.24it/s]\n 15%|█▌ | 3/20 [00:00<00:03, 5.23it/s]\n 20%|██ | 4/20 [00:00<00:03, 5.23it/s]\n 25%|██▌ | 5/20 [00:00<00:02, 5.22it/s]\n 30%|███ | 6/20 [00:01<00:02, 5.22it/s]\n 35%|███▌ | 7/20 [00:01<00:02, 5.22it/s]\n 40%|████ | 8/20 [00:01<00:02, 5.22it/s]\n 45%|████▌ | 9/20 [00:01<00:02, 5.21it/s]\n 50%|█████ | 10/20 [00:01<00:01, 5.22it/s]\n 55%|█████▌ | 11/20 [00:02<00:01, 5.23it/s]\n 60%|██████ | 12/20 [00:02<00:01, 5.24it/s]\n 65%|██████▌ | 13/20 [00:02<00:01, 5.25it/s]\n 70%|███████ | 14/20 [00:02<00:01, 5.26it/s]\n 75%|███████▌ | 15/20 [00:02<00:00, 5.26it/s]\n 80%|████████ | 16/20 [00:03<00:00, 5.26it/s]\n 85%|████████▌ | 17/20 [00:03<00:00, 5.25it/s]\n 90%|█████████ | 18/20 [00:03<00:00, 5.26it/s]\n 95%|█████████▌| 19/20 [00:03<00:00, 5.29it/s]\n100%|██████████| 20/20 [00:03<00:00, 5.32it/s]\n100%|██████████| 20/20 [00:03<00:00, 5.25it/s]\nExecuting node 8, title: VAE Decode, class type: VAEDecode\nExecuting node 9, title: Save Image, class type: SaveImage\nPrompt executed in 4.59 seconds\noutputs: {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nR8__00001_.png", "metrics": { "predict_time": 5.299896, "total_time": 5.308931 }, "output": [ "https://replicate.delivery/pbxt/ED09UfWhu0QCUyW71hUB95e90YZ67U45Y1c0e695JNkuyO6lA/R8__00001_.webp" ], "started_at": "2024-06-10T09:16:02.530035Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jmeyd2z2b5rgg0cg05qbcd3y64", "cancel": "https://api.replicate.com/v1/predictions/jmeyd2z2b5rgg0cg05qbcd3y64/cancel" }, "version": "8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f" }
Generated inRandom seed set to: 933701224 ============================== Generation settings Sampler: dpmpp_2m_sde_gpu Scheduler: karras Steps: 20 CFG: 7.0 LORA: Using a lora. Lora strength:1.0 ============================== Running workflow got prompt Executing node 10, title: Load LoRA, class type: LoraLoader Executing node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode Requested to load SDXLClipModel Loading 1 new model Executing node 7, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode Executing node 3, title: KSampler, class type: KSampler Requested to load SDXL Loading 1 new model 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:03, 5.30it/s] 10%|█ | 2/20 [00:00<00:03, 5.24it/s] 15%|█▌ | 3/20 [00:00<00:03, 5.23it/s] 20%|██ | 4/20 [00:00<00:03, 5.23it/s] 25%|██▌ | 5/20 [00:00<00:02, 5.22it/s] 30%|███ | 6/20 [00:01<00:02, 5.22it/s] 35%|███▌ | 7/20 [00:01<00:02, 5.22it/s] 40%|████ | 8/20 [00:01<00:02, 5.22it/s] 45%|████▌ | 9/20 [00:01<00:02, 5.21it/s] 50%|█████ | 10/20 [00:01<00:01, 5.22it/s] 55%|█████▌ | 11/20 [00:02<00:01, 5.23it/s] 60%|██████ | 12/20 [00:02<00:01, 5.24it/s] 65%|██████▌ | 13/20 [00:02<00:01, 5.25it/s] 70%|███████ | 14/20 [00:02<00:01, 5.26it/s] 75%|███████▌ | 15/20 [00:02<00:00, 5.26it/s] 80%|████████ | 16/20 [00:03<00:00, 5.26it/s] 85%|████████▌ | 17/20 [00:03<00:00, 5.25it/s] 90%|█████████ | 18/20 [00:03<00:00, 5.26it/s] 95%|█████████▌| 19/20 [00:03<00:00, 5.29it/s] 100%|██████████| 20/20 [00:03<00:00, 5.32it/s] 100%|██████████| 20/20 [00:03<00:00, 5.25it/s] Executing node 8, title: VAE Decode, class type: VAEDecode Executing node 9, title: Save Image, class type: SaveImage Prompt executed in 4.59 seconds outputs: {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}} ==================================== R8__00001_.png
Prediction
fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3fIDqr4vfa78bnrgp0cg05qv6vbfz4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- MSPaint astronaut
- sampler
- Default
- scheduler
- Default
- lora_strength
- 1
- output_format
- webp
- output_quality
- 80
- number_of_images
- 1
{ "width": 1024, "height": 1024, "prompt": "MSPaint astronaut", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "number_of_images": 1 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/sdxl-ms-paint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f", { input: { width: 1024, height: 1024, prompt: "MSPaint astronaut", sampler: "Default", scheduler: "Default", lora_strength: 1, output_format: "webp", output_quality: 80, number_of_images: 1 } } ); // 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 replicate
Import the client:import replicate
Run fofr/sdxl-ms-paint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f", input={ "width": 1024, "height": 1024, "prompt": "MSPaint astronaut", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "number_of_images": 1 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())
To learn more, take a look at the guide on getting started with Python.
Run fofr/sdxl-ms-paint 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": "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f", "input": { "width": 1024, "height": 1024, "prompt": "MSPaint astronaut", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "number_of_images": 1 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-06-10T09:17:14.543095Z", "created_at": "2024-06-10T09:17:09.597000Z", "data_removed": false, "error": null, "id": "qr4vfa78bnrgp0cg05qv6vbfz4", "input": { "width": 1024, "height": 1024, "prompt": "MSPaint astronaut", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "number_of_images": 1 }, "logs": "Random seed set to: 305983986\n==============================\nGeneration settings\nSampler: dpmpp_2m_sde_gpu\nScheduler: karras\nSteps: 20\nCFG: 7.0\nLORA: Using a lora. Lora strength: 1.0\n==============================\nRunning workflow\ngot prompt\nExecuting node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode\nExecuting node 3, title: KSampler, class type: KSampler\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:03, 5.32it/s]\n 10%|█ | 2/20 [00:00<00:03, 5.27it/s]\n 15%|█▌ | 3/20 [00:00<00:03, 5.27it/s]\n 20%|██ | 4/20 [00:00<00:03, 5.27it/s]\n 25%|██▌ | 5/20 [00:00<00:02, 5.27it/s]\n 30%|███ | 6/20 [00:01<00:02, 5.26it/s]\n 35%|███▌ | 7/20 [00:01<00:02, 5.26it/s]\n 40%|████ | 8/20 [00:01<00:02, 5.26it/s]\n 45%|████▌ | 9/20 [00:01<00:02, 5.26it/s]\n 50%|█████ | 10/20 [00:01<00:01, 5.25it/s]\n 55%|█████▌ | 11/20 [00:02<00:01, 5.26it/s]\n 60%|██████ | 12/20 [00:02<00:01, 5.26it/s]\n 65%|██████▌ | 13/20 [00:02<00:01, 5.26it/s]\n 70%|███████ | 14/20 [00:02<00:01, 5.27it/s]\n 75%|███████▌ | 15/20 [00:02<00:00, 5.27it/s]\n 80%|████████ | 16/20 [00:03<00:00, 5.27it/s]\n 85%|████████▌ | 17/20 [00:03<00:00, 5.27it/s]\n 90%|█████████ | 18/20 [00:03<00:00, 5.25it/s]\n 95%|█████████▌| 19/20 [00:03<00:00, 5.27it/s]\n100%|██████████| 20/20 [00:03<00:00, 5.30it/s]\n100%|██████████| 20/20 [00:03<00:00, 5.27it/s]\nExecuting node 8, title: VAE Decode, class type: VAEDecode\nExecuting node 9, title: Save Image, class type: SaveImage\nPrompt executed in 4.16 seconds\noutputs: {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nR8__00001_.png", "metrics": { "predict_time": 4.937003, "total_time": 4.946095 }, "output": [ "https://replicate.delivery/pbxt/Wmxf4hRDoetdCk6YpwMpoOcWyXxwbpr7eKWFOhLKQgk00O6lA/R8__00001_.webp" ], "started_at": "2024-06-10T09:17:09.606092Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/qr4vfa78bnrgp0cg05qv6vbfz4", "cancel": "https://api.replicate.com/v1/predictions/qr4vfa78bnrgp0cg05qv6vbfz4/cancel" }, "version": "8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f" }
Generated inRandom seed set to: 305983986 ============================== Generation settings Sampler: dpmpp_2m_sde_gpu Scheduler: karras Steps: 20 CFG: 7.0 LORA: Using a lora. Lora strength: 1.0 ============================== Running workflow got prompt Executing node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode Executing node 3, title: KSampler, class type: KSampler 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:03, 5.32it/s] 10%|█ | 2/20 [00:00<00:03, 5.27it/s] 15%|█▌ | 3/20 [00:00<00:03, 5.27it/s] 20%|██ | 4/20 [00:00<00:03, 5.27it/s] 25%|██▌ | 5/20 [00:00<00:02, 5.27it/s] 30%|███ | 6/20 [00:01<00:02, 5.26it/s] 35%|███▌ | 7/20 [00:01<00:02, 5.26it/s] 40%|████ | 8/20 [00:01<00:02, 5.26it/s] 45%|████▌ | 9/20 [00:01<00:02, 5.26it/s] 50%|█████ | 10/20 [00:01<00:01, 5.25it/s] 55%|█████▌ | 11/20 [00:02<00:01, 5.26it/s] 60%|██████ | 12/20 [00:02<00:01, 5.26it/s] 65%|██████▌ | 13/20 [00:02<00:01, 5.26it/s] 70%|███████ | 14/20 [00:02<00:01, 5.27it/s] 75%|███████▌ | 15/20 [00:02<00:00, 5.27it/s] 80%|████████ | 16/20 [00:03<00:00, 5.27it/s] 85%|████████▌ | 17/20 [00:03<00:00, 5.27it/s] 90%|█████████ | 18/20 [00:03<00:00, 5.25it/s] 95%|█████████▌| 19/20 [00:03<00:00, 5.27it/s] 100%|██████████| 20/20 [00:03<00:00, 5.30it/s] 100%|██████████| 20/20 [00:03<00:00, 5.27it/s] Executing node 8, title: VAE Decode, class type: VAEDecode Executing node 9, title: Save Image, class type: SaveImage Prompt executed in 4.16 seconds outputs: {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}} ==================================== R8__00001_.png
Prediction
fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3fIDdf9mjbxpzsrgm0cg05st5bxmqcStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- MSPaint ryan gosling and margot robbie
- sampler
- Default
- scheduler
- Default
- lora_strength
- 1
- output_format
- webp
- output_quality
- 80
- number_of_images
- 1
{ "width": 1024, "height": 1024, "prompt": "MSPaint ryan gosling and margot robbie", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "number_of_images": 1 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/sdxl-ms-paint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f", { input: { width: 1024, height: 1024, prompt: "MSPaint ryan gosling and margot robbie", sampler: "Default", scheduler: "Default", lora_strength: 1, output_format: "webp", output_quality: 80, number_of_images: 1 } } ); // 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 replicate
Import the client:import replicate
Run fofr/sdxl-ms-paint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f", input={ "width": 1024, "height": 1024, "prompt": "MSPaint ryan gosling and margot robbie", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "number_of_images": 1 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())
To learn more, take a look at the guide on getting started with Python.
Run fofr/sdxl-ms-paint 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": "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f", "input": { "width": 1024, "height": 1024, "prompt": "MSPaint ryan gosling and margot robbie", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "number_of_images": 1 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-06-10T09:21:23.996216Z", "created_at": "2024-06-10T09:21:19.102000Z", "data_removed": false, "error": null, "id": "df9mjbxpzsrgm0cg05st5bxmqc", "input": { "width": 1024, "height": 1024, "prompt": "MSPaint ryan gosling and margot robbie", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "number_of_images": 1 }, "logs": "Random seed set to: 4089181157\n==============================\nGeneration settings\nSampler: dpmpp_2m_sde_gpu\nScheduler: karras\nSteps: 20\nCFG: 7.0\nLORA: Using a lora. Lora strength:1.0\n==============================\nRunning workflow\ngot prompt\nExecuting node 5, title: Empty Latent Image, class type: EmptyLatentImage\nExecuting node 3, title: KSampler, class type: KSampler\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:03, 5.28it/s]\n 10%|█ | 2/20 [00:00<00:03, 5.26it/s]\n 15%|█▌ | 3/20 [00:00<00:03, 5.25it/s]\n 20%|██ | 4/20 [00:00<00:03, 5.25it/s]\n 25%|██▌ | 5/20 [00:00<00:02, 5.27it/s]\n 30%|███ | 6/20 [00:01<00:02, 5.26it/s]\n 35%|███▌ | 7/20 [00:01<00:02, 5.26it/s]\n 40%|████ | 8/20 [00:01<00:02, 5.26it/s]\n 45%|████▌ | 9/20 [00:01<00:02, 5.26it/s]\n 50%|█████ | 10/20 [00:01<00:01, 5.25it/s]\n 55%|█████▌ | 11/20 [00:02<00:01, 5.26it/s]\n 60%|██████ | 12/20 [00:02<00:01, 5.26it/s]\n 65%|██████▌ | 13/20 [00:02<00:01, 5.26it/s]\n 70%|███████ | 14/20 [00:02<00:01, 5.27it/s]\n 75%|███████▌ | 15/20 [00:02<00:00, 5.26it/s]\n 80%|████████ | 16/20 [00:03<00:00, 5.26it/s]\n 85%|████████▌ | 17/20 [00:03<00:00, 5.26it/s]\n 90%|█████████ | 18/20 [00:03<00:00, 5.27it/s]\n 95%|█████████▌| 19/20 [00:03<00:00, 5.29it/s]\n100%|██████████| 20/20 [00:03<00:00, 5.33it/s]\n100%|██████████| 20/20 [00:03<00:00, 5.27it/s]\nExecuting node 8, title: VAE Decode, class type: VAEDecode\nExecuting node 9, title: Save Image, class type: SaveImage\nPrompt executed in 4.17 seconds\noutputs: {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nR8__00001_.png", "metrics": { "predict_time": 4.883241, "total_time": 4.894216 }, "output": [ "https://replicate.delivery/pbxt/r8reSFWMderWfov1MmoeQe01Ine27LOOoNPxiHFdGi39k3RvE/R8__00001_.webp" ], "started_at": "2024-06-10T09:21:19.112975Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/df9mjbxpzsrgm0cg05st5bxmqc", "cancel": "https://api.replicate.com/v1/predictions/df9mjbxpzsrgm0cg05st5bxmqc/cancel" }, "version": "8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f" }
Generated inRandom seed set to: 4089181157 ============================== Generation settings Sampler: dpmpp_2m_sde_gpu Scheduler: karras Steps: 20 CFG: 7.0 LORA: Using a lora. Lora strength:1.0 ============================== Running workflow got prompt Executing node 5, title: Empty Latent Image, class type: EmptyLatentImage Executing node 3, title: KSampler, class type: KSampler 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:03, 5.28it/s] 10%|█ | 2/20 [00:00<00:03, 5.26it/s] 15%|█▌ | 3/20 [00:00<00:03, 5.25it/s] 20%|██ | 4/20 [00:00<00:03, 5.25it/s] 25%|██▌ | 5/20 [00:00<00:02, 5.27it/s] 30%|███ | 6/20 [00:01<00:02, 5.26it/s] 35%|███▌ | 7/20 [00:01<00:02, 5.26it/s] 40%|████ | 8/20 [00:01<00:02, 5.26it/s] 45%|████▌ | 9/20 [00:01<00:02, 5.26it/s] 50%|█████ | 10/20 [00:01<00:01, 5.25it/s] 55%|█████▌ | 11/20 [00:02<00:01, 5.26it/s] 60%|██████ | 12/20 [00:02<00:01, 5.26it/s] 65%|██████▌ | 13/20 [00:02<00:01, 5.26it/s] 70%|███████ | 14/20 [00:02<00:01, 5.27it/s] 75%|███████▌ | 15/20 [00:02<00:00, 5.26it/s] 80%|████████ | 16/20 [00:03<00:00, 5.26it/s] 85%|████████▌ | 17/20 [00:03<00:00, 5.26it/s] 90%|█████████ | 18/20 [00:03<00:00, 5.27it/s] 95%|█████████▌| 19/20 [00:03<00:00, 5.29it/s] 100%|██████████| 20/20 [00:03<00:00, 5.33it/s] 100%|██████████| 20/20 [00:03<00:00, 5.27it/s] Executing node 8, title: VAE Decode, class type: VAEDecode Executing node 9, title: Save Image, class type: SaveImage Prompt executed in 4.17 seconds outputs: {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}} ==================================== R8__00001_.png
Prediction
fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3fIDdahcwxtb61rgp0cg05tak61hpgStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- MSPaint yoda
- sampler
- Default
- scheduler
- Default
- lora_strength
- 1
- output_format
- webp
- output_quality
- 80
- number_of_images
- 1
{ "width": 1024, "height": 1024, "prompt": "MSPaint yoda", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "number_of_images": 1 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/sdxl-ms-paint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f", { input: { width: 1024, height: 1024, prompt: "MSPaint yoda", sampler: "Default", scheduler: "Default", lora_strength: 1, output_format: "webp", output_quality: 80, number_of_images: 1 } } ); // 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 replicate
Import the client:import replicate
Run fofr/sdxl-ms-paint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f", input={ "width": 1024, "height": 1024, "prompt": "MSPaint yoda", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "number_of_images": 1 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())
To learn more, take a look at the guide on getting started with Python.
Run fofr/sdxl-ms-paint 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": "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f", "input": { "width": 1024, "height": 1024, "prompt": "MSPaint yoda", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "number_of_images": 1 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-06-10T09:22:01.890158Z", "created_at": "2024-06-10T09:21:57.040000Z", "data_removed": false, "error": null, "id": "dahcwxtb61rgp0cg05tak61hpg", "input": { "width": 1024, "height": 1024, "prompt": "MSPaint yoda", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "number_of_images": 1 }, "logs": "Random seed set to: 2666338119\n==============================\nGeneration settings\nSampler: dpmpp_2m_sde_gpu\nScheduler:karras\nSteps: 20\nCFG: 7.0\nLORA: Using a lora. Lora strength: 1.0\n==============================\nRunning workflow\ngot prompt\nExecuting node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode\nExecuting node 3, title: KSampler, class type: KSampler\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:03, 5.32it/s]\n 10%|█ | 2/20 [00:00<00:03, 5.26it/s]\n 15%|█▌ | 3/20 [00:00<00:03, 5.25it/s]\n 20%|██ | 4/20 [00:00<00:03, 5.24it/s]\n 25%|██▌ | 5/20 [00:00<00:02, 5.24it/s]\n 30%|███ | 6/20 [00:01<00:02, 5.23it/s]\n 35%|███▌ | 7/20 [00:01<00:02, 5.24it/s]\n 40%|████ | 8/20 [00:01<00:02, 5.25it/s]\n 45%|████▌ | 9/20 [00:01<00:02, 5.25it/s]\n 50%|█████ | 10/20 [00:01<00:01, 5.25it/s]\n 55%|█████▌ | 11/20 [00:02<00:01, 5.26it/s]\n 60%|██████ | 12/20 [00:02<00:01, 5.26it/s]\n 65%|██████▌ | 13/20 [00:02<00:01, 5.26it/s]\n 70%|███████ | 14/20 [00:02<00:01, 5.26it/s]\n 75%|███████▌ | 15/20 [00:02<00:00, 5.26it/s]\n 80%|████████ | 16/20 [00:03<00:00, 5.26it/s]\n 85%|████████▌ | 17/20 [00:03<00:00, 5.26it/s]\n 90%|█████████ | 18/20 [00:03<00:00, 5.27it/s]\n 95%|█████████▌| 19/20 [00:03<00:00, 5.30it/s]\n100%|██████████| 20/20 [00:03<00:00, 5.34it/s]\n100%|██████████| 20/20 [00:03<00:00, 5.27it/s]\nExecuting node 8, title: VAE Decode, class type: VAEDecode\nExecuting node 9, title: Save Image, class type: SaveImage\nPrompt executed in 4.16 seconds\noutputs: {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nR8__00001_.png", "metrics": { "predict_time": 4.839576, "total_time": 4.850158 }, "output": [ "https://replicate.delivery/pbxt/g9KZGaqtjI5rCVfeH84DU15Z2cgzvpRrSbZKfD0nqvTz9O6lA/R8__00001_.webp" ], "started_at": "2024-06-10T09:21:57.050582Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/dahcwxtb61rgp0cg05tak61hpg", "cancel": "https://api.replicate.com/v1/predictions/dahcwxtb61rgp0cg05tak61hpg/cancel" }, "version": "8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f" }
Generated inRandom seed set to: 2666338119 ============================== Generation settings Sampler: dpmpp_2m_sde_gpu Scheduler:karras Steps: 20 CFG: 7.0 LORA: Using a lora. Lora strength: 1.0 ============================== Running workflow got prompt Executing node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode Executing node 3, title: KSampler, class type: KSampler 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:03, 5.32it/s] 10%|█ | 2/20 [00:00<00:03, 5.26it/s] 15%|█▌ | 3/20 [00:00<00:03, 5.25it/s] 20%|██ | 4/20 [00:00<00:03, 5.24it/s] 25%|██▌ | 5/20 [00:00<00:02, 5.24it/s] 30%|███ | 6/20 [00:01<00:02, 5.23it/s] 35%|███▌ | 7/20 [00:01<00:02, 5.24it/s] 40%|████ | 8/20 [00:01<00:02, 5.25it/s] 45%|████▌ | 9/20 [00:01<00:02, 5.25it/s] 50%|█████ | 10/20 [00:01<00:01, 5.25it/s] 55%|█████▌ | 11/20 [00:02<00:01, 5.26it/s] 60%|██████ | 12/20 [00:02<00:01, 5.26it/s] 65%|██████▌ | 13/20 [00:02<00:01, 5.26it/s] 70%|███████ | 14/20 [00:02<00:01, 5.26it/s] 75%|███████▌ | 15/20 [00:02<00:00, 5.26it/s] 80%|████████ | 16/20 [00:03<00:00, 5.26it/s] 85%|████████▌ | 17/20 [00:03<00:00, 5.26it/s] 90%|█████████ | 18/20 [00:03<00:00, 5.27it/s] 95%|█████████▌| 19/20 [00:03<00:00, 5.30it/s] 100%|██████████| 20/20 [00:03<00:00, 5.34it/s] 100%|██████████| 20/20 [00:03<00:00, 5.27it/s] Executing node 8, title: VAE Decode, class type: VAEDecode Executing node 9, title: Save Image, class type: SaveImage Prompt executed in 4.16 seconds outputs: {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}} ==================================== R8__00001_.png
Prediction
fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3fIDdq5z1emxm1rgj0cfz7vvskbt14StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- MSPaint portrait of a dog
- sampler
- Default
- scheduler
- Default
- lora_strength
- 1
- output_format
- webp
- output_quality
- 80
- negative_prompt
- number_of_images
- 1
{ "width": 1024, "height": 1024, "prompt": "MSPaint portrait of a dog", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "negative_prompt": "", "number_of_images": 1 }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run fofr/sdxl-ms-paint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f", { input: { width: 1024, height: 1024, prompt: "MSPaint portrait of a dog", sampler: "Default", scheduler: "Default", lora_strength: 1, output_format: "webp", output_quality: 80, negative_prompt: "", number_of_images: 1 } } ); // 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 replicate
Import the client:import replicate
Run fofr/sdxl-ms-paint using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f", input={ "width": 1024, "height": 1024, "prompt": "MSPaint portrait of a dog", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "negative_prompt": "", "number_of_images": 1 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())
To learn more, take a look at the guide on getting started with Python.
Run fofr/sdxl-ms-paint 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": "fofr/sdxl-ms-paint:8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f", "input": { "width": 1024, "height": 1024, "prompt": "MSPaint portrait of a dog", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "negative_prompt": "", "number_of_images": 1 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-06-08T22:29:41.930129Z", "created_at": "2024-06-08T22:28:25.632000Z", "data_removed": false, "error": null, "id": "dq5z1emxm1rgj0cfz7vvskbt14", "input": { "width": 1024, "height": 1024, "prompt": "MSPaint portrait of a dog", "sampler": "Default", "scheduler": "Default", "lora_strength": 1, "output_format": "webp", "output_quality": 80, "negative_prompt": "", "number_of_images": 1 }, "logs": "Random seed set to: 2783082920\n==============================\nGeneration settings\nSampler: dpmpp_2m_sde_gpu\nScheduler: karras\nSteps: 20\nCFG: 7.0\nLORA: Using a lora. Lora strength: 1.0\n==============================\nRunning workflow\ngot prompt\nExecuting node 4, title: Load Checkpoint, class type: CheckpointLoaderSimple\nmodel_type EPS\nUsing pytorch attention in VAE\nUsing pytorch attention in VAE\nclip missing: ['clip_l.logit_scale', 'clip_l.transformer.text_projection.weight']\nloaded straight to GPU\nRequested to load SDXL\nLoading 1 new model\nExecuting node 10, title: Load LoRA, class type: LoraLoader\nExecuting node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode\nRequested to load SDXLClipModel\nLoading 1 new model\nExecuting node 7, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode\nExecuting node 5, title: Empty Latent Image, class type: EmptyLatentImage\nExecuting node 3, title: KSampler, class type: KSampler\nRequested to load SDXL\nLoading 1 new model\n 0%| | 0/20 [00:00<?, ?it/s]/root/.pyenv/versions/3.10.6/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=14.614643096923828 and t1=14.614643.\nwarnings.warn(f\"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.\")\n 5%|▌ | 1/20 [00:00<00:11, 1.61it/s]\n 10%|█ | 2/20 [00:00<00:06, 2.70it/s]\n 15%|█▌ | 3/20 [00:01<00:04, 3.46it/s]\n 20%|██ | 4/20 [00:01<00:04, 3.99it/s]\n 25%|██▌ | 5/20 [00:01<00:03, 4.36it/s]\n 30%|███ | 6/20 [00:01<00:03, 4.61it/s]\n 35%|███▌ | 7/20 [00:01<00:02, 4.79it/s]\n 40%|████ | 8/20 [00:01<00:02, 4.90it/s]\n 45%|████▌ | 9/20 [00:02<00:02, 4.99it/s]\n 50%|█████ | 10/20 [00:02<00:01, 5.05it/s]\n 55%|█████▌ | 11/20 [00:02<00:01, 5.10it/s]\n 60%|██████ | 12/20 [00:02<00:01, 5.14it/s]\n 65%|██████▌ | 13/20 [00:02<00:01, 5.15it/s]\n 70%|███████ | 14/20 [00:03<00:01, 5.17it/s]\n 75%|███████▌ | 15/20 [00:03<00:00, 5.17it/s]\n 80%|████████ | 16/20 [00:03<00:00, 5.18it/s]\n 85%|████████▌ | 17/20 [00:03<00:00, 5.19it/s]\n 90%|█████████ | 18/20 [00:03<00:00, 5.20it/s]\n 95%|█████████▌| 19/20 [00:04<00:00, 5.24it/s]\n100%|██████████| 20/20 [00:04<00:00, 5.26it/s]\n100%|██████████| 20/20 [00:04<00:00, 4.69it/s]\nRequested to load AutoencoderKL\nLoading 1 new model\nExecuting node 8, title: VAE Decode, class type: VAEDecode\nExecuting node 9, title: Save Image, class type: SaveImage\nPrompt executed in 8.21 seconds\noutputs: {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}}\n====================================\nR8__00001_.png", "metrics": { "predict_time": 10.870445, "total_time": 76.298129 }, "output": [ "https://replicate.delivery/pbxt/Kf3ix6vdRp0xbiR5139W1CeUtLzwJfyMAiyilmGNcrsqqR5lA/R8__00001_.webp" ], "started_at": "2024-06-08T22:29:31.059684Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/dq5z1emxm1rgj0cfz7vvskbt14", "cancel": "https://api.replicate.com/v1/predictions/dq5z1emxm1rgj0cfz7vvskbt14/cancel" }, "version": "8e36d00024aa9a55c7d8ef0f82ccd82ab4d3225d1e702149179498258177ca3f" }
Generated inRandom seed set to: 2783082920 ============================== Generation settings Sampler: dpmpp_2m_sde_gpu Scheduler: karras Steps: 20 CFG: 7.0 LORA: Using a lora. Lora strength: 1.0 ============================== Running workflow got prompt Executing node 4, title: Load Checkpoint, class type: CheckpointLoaderSimple model_type EPS Using pytorch attention in VAE Using pytorch attention in VAE clip missing: ['clip_l.logit_scale', 'clip_l.transformer.text_projection.weight'] loaded straight to GPU Requested to load SDXL Loading 1 new model Executing node 10, title: Load LoRA, class type: LoraLoader Executing node 6, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode Requested to load SDXLClipModel Loading 1 new model Executing node 7, title: CLIP Text Encode (Prompt), class type: CLIPTextEncode Executing node 5, title: Empty Latent Image, class type: EmptyLatentImage Executing node 3, title: KSampler, class type: KSampler Requested to load SDXL Loading 1 new model 0%| | 0/20 [00:00<?, ?it/s]/root/.pyenv/versions/3.10.6/lib/python3.10/site-packages/torchsde/_brownian/brownian_interval.py:608: UserWarning: Should have tb<=t1 but got tb=14.614643096923828 and t1=14.614643. warnings.warn(f"Should have {tb_name}<=t1 but got {tb_name}={tb} and t1={self._end}.") 5%|▌ | 1/20 [00:00<00:11, 1.61it/s] 10%|█ | 2/20 [00:00<00:06, 2.70it/s] 15%|█▌ | 3/20 [00:01<00:04, 3.46it/s] 20%|██ | 4/20 [00:01<00:04, 3.99it/s] 25%|██▌ | 5/20 [00:01<00:03, 4.36it/s] 30%|███ | 6/20 [00:01<00:03, 4.61it/s] 35%|███▌ | 7/20 [00:01<00:02, 4.79it/s] 40%|████ | 8/20 [00:01<00:02, 4.90it/s] 45%|████▌ | 9/20 [00:02<00:02, 4.99it/s] 50%|█████ | 10/20 [00:02<00:01, 5.05it/s] 55%|█████▌ | 11/20 [00:02<00:01, 5.10it/s] 60%|██████ | 12/20 [00:02<00:01, 5.14it/s] 65%|██████▌ | 13/20 [00:02<00:01, 5.15it/s] 70%|███████ | 14/20 [00:03<00:01, 5.17it/s] 75%|███████▌ | 15/20 [00:03<00:00, 5.17it/s] 80%|████████ | 16/20 [00:03<00:00, 5.18it/s] 85%|████████▌ | 17/20 [00:03<00:00, 5.19it/s] 90%|█████████ | 18/20 [00:03<00:00, 5.20it/s] 95%|█████████▌| 19/20 [00:04<00:00, 5.24it/s] 100%|██████████| 20/20 [00:04<00:00, 5.26it/s] 100%|██████████| 20/20 [00:04<00:00, 4.69it/s] Requested to load AutoencoderKL Loading 1 new model Executing node 8, title: VAE Decode, class type: VAEDecode Executing node 9, title: Save Image, class type: SaveImage Prompt executed in 8.21 seconds outputs: {'9': {'images': [{'filename': 'R8__00001_.png', 'subfolder': '', 'type': 'output'}]}} ==================================== R8__00001_.png
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