pwntus / material-diffusion-sdxl
Tileable Stable Diffusion XL
Prediction
pwntus/material-diffusion-sdxl:ce888cbe17a7c04d4b9c4cbd2b576715d480c55b2ba8f9f3d33f2ad70a26cd99IDryowfydbtgzz3bxsqnpvfjuhliStatusSucceededSourceWebHardwareA100 (80GB)Total durationCreatedby @pwntusInput
- width
- 768
- height
- 768
- prompt
- Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k
- refine
- expert_ensemble_refiner
- scheduler
- DDIM
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- num_inference_steps
- 50
{ "width": 768, "height": 768, "prompt": "Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k", "refine": "expert_ensemble_refiner", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "num_inference_steps": 50 }
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 pwntus/material-diffusion-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "pwntus/material-diffusion-sdxl:ce888cbe17a7c04d4b9c4cbd2b576715d480c55b2ba8f9f3d33f2ad70a26cd99", { input: { width: 768, height: 768, prompt: "Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k", refine: "expert_ensemble_refiner", scheduler: "DDIM", num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, num_inference_steps: 50 } } ); // 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 pwntus/material-diffusion-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "pwntus/material-diffusion-sdxl:ce888cbe17a7c04d4b9c4cbd2b576715d480c55b2ba8f9f3d33f2ad70a26cd99", input={ "width": 768, "height": 768, "prompt": "Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k", "refine": "expert_ensemble_refiner", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "num_inference_steps": 50 } ) # 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 pwntus/material-diffusion-sdxl 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": "pwntus/material-diffusion-sdxl:ce888cbe17a7c04d4b9c4cbd2b576715d480c55b2ba8f9f3d33f2ad70a26cd99", "input": { "width": 768, "height": 768, "prompt": "Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k", "refine": "expert_ensemble_refiner", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To 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 cog
If 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/pwntus/material-diffusion-sdxl@sha256:ce888cbe17a7c04d4b9c4cbd2b576715d480c55b2ba8f9f3d33f2ad70a26cd99 \ -i 'width=768' \ -i 'height=768' \ -i 'prompt="Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k"' \ -i 'refine="expert_ensemble_refiner"' \ -i 'scheduler="DDIM"' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=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/pwntus/material-diffusion-sdxl@sha256:ce888cbe17a7c04d4b9c4cbd2b576715d480c55b2ba8f9f3d33f2ad70a26cd99
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 768, "height": 768, "prompt": "Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k", "refine": "expert_ensemble_refiner", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-08-07T18:13:07.429490Z", "created_at": "2023-08-07T18:13:02.314225Z", "data_removed": false, "error": null, "id": "ryowfydbtgzz3bxsqnpvfjuhli", "input": { "width": 768, "height": 768, "prompt": "Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k", "refine": "expert_ensemble_refiner", "scheduler": "DDIM", "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 21776\nPrompt: Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k\ntxt2img mode\n 0%| | 0/40 [00:00<?, ?it/s]\n 5%|▌ | 2/40 [00:00<00:02, 14.41it/s]\n 10%|█ | 4/40 [00:00<00:02, 14.42it/s]\n 15%|█▌ | 6/40 [00:00<00:02, 14.43it/s]\n 20%|██ | 8/40 [00:00<00:02, 14.42it/s]\n 25%|██▌ | 10/40 [00:00<00:02, 14.41it/s]\n 30%|███ | 12/40 [00:00<00:01, 14.42it/s]\n 35%|███▌ | 14/40 [00:00<00:01, 14.42it/s]\n 40%|████ | 16/40 [00:01<00:01, 14.43it/s]\n 45%|████▌ | 18/40 [00:01<00:01, 14.43it/s]\n 50%|█████ | 20/40 [00:01<00:01, 14.42it/s]\n 55%|█████▌ | 22/40 [00:01<00:01, 14.42it/s]\n 60%|██████ | 24/40 [00:01<00:01, 14.43it/s]\n 65%|██████▌ | 26/40 [00:01<00:00, 14.42it/s]\n 70%|███████ | 28/40 [00:01<00:00, 14.41it/s]\n 75%|███████▌ | 30/40 [00:02<00:00, 14.42it/s]\n 80%|████████ | 32/40 [00:02<00:00, 14.43it/s]\n 85%|████████▌ | 34/40 [00:02<00:00, 14.43it/s]\n 90%|█████████ | 36/40 [00:02<00:00, 14.44it/s]\n 95%|█████████▌| 38/40 [00:02<00:00, 14.43it/s]\n100%|██████████| 40/40 [00:02<00:00, 14.43it/s]\n100%|██████████| 40/40 [00:02<00:00, 14.42it/s]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:01, 8.16it/s]\n 30%|███ | 3/10 [00:00<00:00, 12.29it/s]\n 50%|█████ | 5/10 [00:00<00:00, 13.45it/s]\n 70%|███████ | 7/10 [00:00<00:00, 13.99it/s]\n 90%|█████████ | 9/10 [00:00<00:00, 14.28it/s]\n100%|██████████| 10/10 [00:00<00:00, 13.70it/s]", "metrics": { "predict_time": 5.136958, "total_time": 5.115265 }, "output": [ "https://replicate.delivery/pbxt/7gJ7y5PLGEbed6g44CD624HjmMdXDjK0MUloxlT9fKcyYuXRA/out-0.png" ], "started_at": "2023-08-07T18:13:02.292532Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ryowfydbtgzz3bxsqnpvfjuhli", "cancel": "https://api.replicate.com/v1/predictions/ryowfydbtgzz3bxsqnpvfjuhli/cancel" }, "version": "ce888cbe17a7c04d4b9c4cbd2b576715d480c55b2ba8f9f3d33f2ad70a26cd99" }
Generated inUsing seed: 21776 Prompt: Mossy Runic Bricks seamless texture, trending on artstation, stone, moss, base color, albedo, 4k txt2img mode 0%| | 0/40 [00:00<?, ?it/s] 5%|▌ | 2/40 [00:00<00:02, 14.41it/s] 10%|█ | 4/40 [00:00<00:02, 14.42it/s] 15%|█▌ | 6/40 [00:00<00:02, 14.43it/s] 20%|██ | 8/40 [00:00<00:02, 14.42it/s] 25%|██▌ | 10/40 [00:00<00:02, 14.41it/s] 30%|███ | 12/40 [00:00<00:01, 14.42it/s] 35%|███▌ | 14/40 [00:00<00:01, 14.42it/s] 40%|████ | 16/40 [00:01<00:01, 14.43it/s] 45%|████▌ | 18/40 [00:01<00:01, 14.43it/s] 50%|█████ | 20/40 [00:01<00:01, 14.42it/s] 55%|█████▌ | 22/40 [00:01<00:01, 14.42it/s] 60%|██████ | 24/40 [00:01<00:01, 14.43it/s] 65%|██████▌ | 26/40 [00:01<00:00, 14.42it/s] 70%|███████ | 28/40 [00:01<00:00, 14.41it/s] 75%|███████▌ | 30/40 [00:02<00:00, 14.42it/s] 80%|████████ | 32/40 [00:02<00:00, 14.43it/s] 85%|████████▌ | 34/40 [00:02<00:00, 14.43it/s] 90%|█████████ | 36/40 [00:02<00:00, 14.44it/s] 95%|█████████▌| 38/40 [00:02<00:00, 14.43it/s] 100%|██████████| 40/40 [00:02<00:00, 14.43it/s] 100%|██████████| 40/40 [00:02<00:00, 14.42it/s] 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:01, 8.16it/s] 30%|███ | 3/10 [00:00<00:00, 12.29it/s] 50%|█████ | 5/10 [00:00<00:00, 13.45it/s] 70%|███████ | 7/10 [00:00<00:00, 13.99it/s] 90%|█████████ | 9/10 [00:00<00:00, 14.28it/s] 100%|██████████| 10/10 [00:00<00:00, 13.70it/s]
Prediction
pwntus/material-diffusion-sdxl:ce888cbe17a7c04d4b9c4cbd2b576715d480c55b2ba8f9f3d33f2ad70a26cd99IDdwrnqdtbmcyniliaitgemnswsiStatusSucceededSourceWebHardwareA100 (80GB)Total durationCreatedInput
- width
- "768"
- height
- "768"
- prompt
- poppies seamless texture, trending on artstation, base color, albedo, 4k
- refine
- no_refiner
- scheduler
- K_EULER_ANCESTRAL
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- num_inference_steps
- 50
{ "width": "768", "height": "768", "prompt": "poppies seamless texture, trending on artstation, base color, albedo, 4k", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "num_inference_steps": 50 }
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 pwntus/material-diffusion-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "pwntus/material-diffusion-sdxl:ce888cbe17a7c04d4b9c4cbd2b576715d480c55b2ba8f9f3d33f2ad70a26cd99", { input: { width: "768", height: "768", prompt: "poppies seamless texture, trending on artstation, base color, albedo, 4k", refine: "no_refiner", scheduler: "K_EULER_ANCESTRAL", num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, num_inference_steps: 50 } } ); // 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 pwntus/material-diffusion-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "pwntus/material-diffusion-sdxl:ce888cbe17a7c04d4b9c4cbd2b576715d480c55b2ba8f9f3d33f2ad70a26cd99", input={ "width": "768", "height": "768", "prompt": "poppies seamless texture, trending on artstation, base color, albedo, 4k", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "num_inference_steps": 50 } ) # 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 pwntus/material-diffusion-sdxl 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": "pwntus/material-diffusion-sdxl:ce888cbe17a7c04d4b9c4cbd2b576715d480c55b2ba8f9f3d33f2ad70a26cd99", "input": { "width": "768", "height": "768", "prompt": "poppies seamless texture, trending on artstation, base color, albedo, 4k", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To 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 cog
If 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/pwntus/material-diffusion-sdxl@sha256:ce888cbe17a7c04d4b9c4cbd2b576715d480c55b2ba8f9f3d33f2ad70a26cd99 \ -i 'width="768"' \ -i 'height="768"' \ -i 'prompt="poppies seamless texture, trending on artstation, base color, albedo, 4k"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER_ANCESTRAL"' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=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/pwntus/material-diffusion-sdxl@sha256:ce888cbe17a7c04d4b9c4cbd2b576715d480c55b2ba8f9f3d33f2ad70a26cd99
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": "768", "height": "768", "prompt": "poppies seamless texture, trending on artstation, base color, albedo, 4k", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-08-07T18:17:30.806229Z", "created_at": "2023-08-07T18:17:25.735139Z", "data_removed": false, "error": null, "id": "dwrnqdtbmcyniliaitgemnswsi", "input": { "width": "768", "height": "768", "prompt": "poppies seamless texture, trending on artstation, base color, albedo, 4k", "refine": "no_refiner", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 19247\nPrompt: poppies seamless texture, trending on artstation, base color, albedo, 4k\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:03, 14.38it/s]\n 8%|▊ | 4/50 [00:00<00:03, 14.36it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 14.36it/s]\n 16%|█▌ | 8/50 [00:00<00:02, 14.37it/s]\n 20%|██ | 10/50 [00:00<00:02, 14.38it/s]\n 24%|██▍ | 12/50 [00:00<00:02, 14.39it/s]\n 28%|██▊ | 14/50 [00:00<00:02, 14.39it/s]\n 32%|███▏ | 16/50 [00:01<00:02, 14.39it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 14.39it/s]\n 40%|████ | 20/50 [00:01<00:02, 14.38it/s]\n 44%|████▍ | 22/50 [00:01<00:01, 14.38it/s]\n 48%|████▊ | 24/50 [00:01<00:01, 14.38it/s]\n 52%|█████▏ | 26/50 [00:01<00:01, 14.37it/s]\n 56%|█████▌ | 28/50 [00:01<00:01, 14.37it/s]\n 60%|██████ | 30/50 [00:02<00:01, 14.38it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 14.38it/s]\n 68%|██████▊ | 34/50 [00:02<00:01, 14.38it/s]\n 72%|███████▏ | 36/50 [00:02<00:00, 14.38it/s]\n 76%|███████▌ | 38/50 [00:02<00:00, 14.37it/s]\n 80%|████████ | 40/50 [00:02<00:00, 14.37it/s]\n 84%|████████▍ | 42/50 [00:02<00:00, 14.23it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 14.28it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 14.31it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 14.32it/s]\n100%|██████████| 50/50 [00:03<00:00, 14.32it/s]\n100%|██████████| 50/50 [00:03<00:00, 14.36it/s]", "metrics": { "predict_time": 5.087462, "total_time": 5.07109 }, "output": [ "https://replicate.delivery/pbxt/GwHaNtNn6waNLRigLUWCL4gWfSFOWqqUzdvoqMjdmI3cO3rIA/out-0.png" ], "started_at": "2023-08-07T18:17:25.718767Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/dwrnqdtbmcyniliaitgemnswsi", "cancel": "https://api.replicate.com/v1/predictions/dwrnqdtbmcyniliaitgemnswsi/cancel" }, "version": "ce888cbe17a7c04d4b9c4cbd2b576715d480c55b2ba8f9f3d33f2ad70a26cd99" }
Generated inUsing seed: 19247 Prompt: poppies seamless texture, trending on artstation, base color, albedo, 4k txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 14.38it/s] 8%|▊ | 4/50 [00:00<00:03, 14.36it/s] 12%|█▏ | 6/50 [00:00<00:03, 14.36it/s] 16%|█▌ | 8/50 [00:00<00:02, 14.37it/s] 20%|██ | 10/50 [00:00<00:02, 14.38it/s] 24%|██▍ | 12/50 [00:00<00:02, 14.39it/s] 28%|██▊ | 14/50 [00:00<00:02, 14.39it/s] 32%|███▏ | 16/50 [00:01<00:02, 14.39it/s] 36%|███▌ | 18/50 [00:01<00:02, 14.39it/s] 40%|████ | 20/50 [00:01<00:02, 14.38it/s] 44%|████▍ | 22/50 [00:01<00:01, 14.38it/s] 48%|████▊ | 24/50 [00:01<00:01, 14.38it/s] 52%|█████▏ | 26/50 [00:01<00:01, 14.37it/s] 56%|█████▌ | 28/50 [00:01<00:01, 14.37it/s] 60%|██████ | 30/50 [00:02<00:01, 14.38it/s] 64%|██████▍ | 32/50 [00:02<00:01, 14.38it/s] 68%|██████▊ | 34/50 [00:02<00:01, 14.38it/s] 72%|███████▏ | 36/50 [00:02<00:00, 14.38it/s] 76%|███████▌ | 38/50 [00:02<00:00, 14.37it/s] 80%|████████ | 40/50 [00:02<00:00, 14.37it/s] 84%|████████▍ | 42/50 [00:02<00:00, 14.23it/s] 88%|████████▊ | 44/50 [00:03<00:00, 14.28it/s] 92%|█████████▏| 46/50 [00:03<00:00, 14.31it/s] 96%|█████████▌| 48/50 [00:03<00:00, 14.32it/s] 100%|██████████| 50/50 [00:03<00:00, 14.32it/s] 100%|██████████| 50/50 [00:03<00:00, 14.36it/s]
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