fermatresearch / high-resolution-controlnet-tile
UPDATE: new upscaling algorithm for a much improved image quality. Fermat.app open-source implementation of an efficient ControlNet 1.1 tile for high-quality upscales. Increase the creativity to encourage hallucination.
- Public
- 634.2K runs
-
L40S
Prediction
fermatresearch/high-resolution-controlnet-tile:4af11083a13ebb9bf97a88d7906ef21cf79d1f2e5fa9d87b70739ce6b8113d29IDkw2n3bdbdcd5upayz2ogtyitkuStatusSucceededSourceWebHardwareA100 (80GB)Total durationCreatedInput
- hdr
- 1
- image
- steps
- 20
- prompt
- a very dark image, close up of a girl in a dark room, some neon lights in the background, cyberpunk vibes, it's so dark you can barely see anything
- scheduler
- DDIM
- creativity
- 0.15
- guess_mode
- resolution
- 2048
- resemblance
- 0.99
- guidance_scale
- 5
- negative_prompt
- Teeth, tooth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant
{ "hdr": 1, "image": "https://replicate.delivery/pbxt/K5vHl2fdS6hSShLXd1Z9ODQ7RUYCFFLZB1ZZkcX47bqUUWh3/out-0.png", "steps": 20, "prompt": "a very dark image, close up of a girl in a dark room, some neon lights in the background, cyberpunk vibes, it's so dark you can barely see anything", "scheduler": "DDIM", "creativity": 0.15, "guess_mode": false, "resolution": 2048, "resemblance": 0.99, "guidance_scale": 5, "negative_prompt": "Teeth, tooth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant" }Install Replicate’s Node.js client library:npm install replicateImport 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 fermatresearch/high-resolution-controlnet-tile using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fermatresearch/high-resolution-controlnet-tile:4af11083a13ebb9bf97a88d7906ef21cf79d1f2e5fa9d87b70739ce6b8113d29", { input: { hdr: 1, image: "https://replicate.delivery/pbxt/K5vHl2fdS6hSShLXd1Z9ODQ7RUYCFFLZB1ZZkcX47bqUUWh3/out-0.png", steps: 20, prompt: "a very dark image, close up of a girl in a dark room, some neon lights in the background, cyberpunk vibes, it's so dark you can barely see anything", scheduler: "DDIM", creativity: 0.15, guess_mode: false, resolution: 2048, resemblance: 0.99, guidance_scale: 5, negative_prompt: "Teeth, tooth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun fermatresearch/high-resolution-controlnet-tile using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fermatresearch/high-resolution-controlnet-tile:4af11083a13ebb9bf97a88d7906ef21cf79d1f2e5fa9d87b70739ce6b8113d29", input={ "hdr": 1, "image": "https://replicate.delivery/pbxt/K5vHl2fdS6hSShLXd1Z9ODQ7RUYCFFLZB1ZZkcX47bqUUWh3/out-0.png", "steps": 20, "prompt": "a very dark image, close up of a girl in a dark room, some neon lights in the background, cyberpunk vibes, it's so dark you can barely see anything", "scheduler": "DDIM", "creativity": 0.15, "guess_mode": False, "resolution": 2048, "resemblance": 0.99, "guidance_scale": 5, "negative_prompt": "Teeth, tooth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant" } ) # 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 fermatresearch/high-resolution-controlnet-tile 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": "fermatresearch/high-resolution-controlnet-tile:4af11083a13ebb9bf97a88d7906ef21cf79d1f2e5fa9d87b70739ce6b8113d29", "input": { "hdr": 1, "image": "https://replicate.delivery/pbxt/K5vHl2fdS6hSShLXd1Z9ODQ7RUYCFFLZB1ZZkcX47bqUUWh3/out-0.png", "steps": 20, "prompt": "a very dark image, close up of a girl in a dark room, some neon lights in the background, cyberpunk vibes, it\'s so dark you can barely see anything", "scheduler": "DDIM", "creativity": 0.15, "guess_mode": false, "resolution": 2048, "resemblance": 0.99, "guidance_scale": 5, "negative_prompt": "Teeth, tooth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant" } }' \ 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/fermatresearch/high-resolution-controlnet-tile@sha256:4af11083a13ebb9bf97a88d7906ef21cf79d1f2e5fa9d87b70739ce6b8113d29 \ -i 'hdr=1' \ -i 'image="https://replicate.delivery/pbxt/K5vHl2fdS6hSShLXd1Z9ODQ7RUYCFFLZB1ZZkcX47bqUUWh3/out-0.png"' \ -i 'steps=20' \ -i $'prompt="a very dark image, close up of a girl in a dark room, some neon lights in the background, cyberpunk vibes, it\'s so dark you can barely see anything"' \ -i 'scheduler="DDIM"' \ -i 'creativity=0.15' \ -i 'guess_mode=false' \ -i 'resolution=2048' \ -i 'resemblance=0.99' \ -i 'guidance_scale=5' \ -i 'negative_prompt="Teeth, tooth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant"'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/fermatresearch/high-resolution-controlnet-tile@sha256:4af11083a13ebb9bf97a88d7906ef21cf79d1f2e5fa9d87b70739ce6b8113d29curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "hdr": 1, "image": "https://replicate.delivery/pbxt/K5vHl2fdS6hSShLXd1Z9ODQ7RUYCFFLZB1ZZkcX47bqUUWh3/out-0.png", "steps": 20, "prompt": "a very dark image, close up of a girl in a dark room, some neon lights in the background, cyberpunk vibes, it\'s so dark you can barely see anything", "scheduler": "DDIM", "creativity": 0.15, "guess_mode": false, "resolution": 2048, "resemblance": 0.99, "guidance_scale": 5, "negative_prompt": "Teeth, tooth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant" } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
We were unable to load these images. Please make sure the URLs are valid.
{ "input": "https://replicate.delivery/pbxt/K5vHl2fdS6hSShLXd1Z9ODQ7RUYCFFLZB1ZZkcX47bqUUWh3/out-0.png", "outut": "https://replicate.delivery/pbxt/etT436Z2RrWAOajwhQm6YLBHiT5Y1Oix2aZnDLnIkfM7u4ESA/out-0.png" }{ "completed_at": "2023-12-22T18:47:23.844959Z", "created_at": "2023-12-22T18:47:14.502953Z", "data_removed": false, "error": null, "id": "kw2n3bdbdcd5upayz2ogtyitku", "input": { "hdr": 1, "image": "https://replicate.delivery/pbxt/K5vHl2fdS6hSShLXd1Z9ODQ7RUYCFFLZB1ZZkcX47bqUUWh3/out-0.png", "steps": 20, "prompt": "a very dark image, close up of a girl in a dark room, some neon lights in the background, cyberpunk vibes, it's so dark you can barely see anything", "scheduler": "DDIM", "creativity": 0.15, "guess_mode": false, "resolution": 2048, "resemblance": 0.99, "guidance_scale": 5, "negative_prompt": "Teeth, tooth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant" }, "logs": "Using seed: 18629\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:01<00:02, 1.06s/it]\n 67%|██████▋ | 2/3 [00:02<00:01, 1.06s/it]\n100%|██████████| 3/3 [00:03<00:00, 1.06s/it]\n100%|██████████| 3/3 [00:03<00:00, 1.06s/it]", "metrics": { "predict_time": 9.332866, "total_time": 9.342006 }, "output": [ "https://replicate.delivery/pbxt/etT436Z2RrWAOajwhQm6YLBHiT5Y1Oix2aZnDLnIkfM7u4ESA/out-0.png" ], "started_at": "2023-12-22T18:47:14.512093Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/kw2n3bdbdcd5upayz2ogtyitku", "cancel": "https://api.replicate.com/v1/predictions/kw2n3bdbdcd5upayz2ogtyitku/cancel" }, "version": "4af11083a13ebb9bf97a88d7906ef21cf79d1f2e5fa9d87b70739ce6b8113d29" }Prediction
fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76IDf02rccks2drgp0cgxr5b1pwe6cStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- hdr
- 0
- image
- steps
- 8
- format
- jpg
- prompt
- Professional portrait, a beautiful woman in a field of wheat, 4k UHD, fashion magazine cover, award winning photography, sunset light
- scheduler
- DDIM
- tile_size
- 512
- creativity
- 0.35
- guess_mode
- resolution
- 2048
- resemblance
- 0.85
- guidance_scale
- 0
- negative_prompt
- Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant
- lora_details_strength
- 0.75
- lora_sharpness_strength
- 1.25
{ "hdr": 0, "image": "https://replicate.delivery/pbxt/LCeXdKMrL42FTur24eINAaLiJqIWByMkt2rLEe1Avi9g8J4o/fermat_app_realistic_portrait_of_a_girl_with_beautiful_backgrou_62e1eacd-1a76-483d-b495-9e8946347ca4.png", "steps": 8, "format": "jpg", "prompt": "Professional portrait, a beautiful woman in a field of wheat, 4k UHD, fashion magazine cover, award winning photography, sunset light", "scheduler": "DDIM", "tile_size": 512, "creativity": 0.35, "guess_mode": false, "resolution": 2048, "resemblance": 0.85, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 0.75, "lora_sharpness_strength": 1.25 }Install Replicate’s Node.js client library:npm install replicateImport 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 fermatresearch/high-resolution-controlnet-tile using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76", { input: { hdr: 0, image: "https://replicate.delivery/pbxt/LCeXdKMrL42FTur24eINAaLiJqIWByMkt2rLEe1Avi9g8J4o/fermat_app_realistic_portrait_of_a_girl_with_beautiful_backgrou_62e1eacd-1a76-483d-b495-9e8946347ca4.png", steps: 8, format: "jpg", prompt: "Professional portrait, a beautiful woman in a field of wheat, 4k UHD, fashion magazine cover, award winning photography, sunset light", scheduler: "DDIM", tile_size: 512, creativity: 0.35, guess_mode: false, resolution: 2048, resemblance: 0.85, guidance_scale: 0, negative_prompt: "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", lora_details_strength: 0.75, lora_sharpness_strength: 1.25 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun fermatresearch/high-resolution-controlnet-tile using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76", input={ "hdr": 0, "image": "https://replicate.delivery/pbxt/LCeXdKMrL42FTur24eINAaLiJqIWByMkt2rLEe1Avi9g8J4o/fermat_app_realistic_portrait_of_a_girl_with_beautiful_backgrou_62e1eacd-1a76-483d-b495-9e8946347ca4.png", "steps": 8, "format": "jpg", "prompt": "Professional portrait, a beautiful woman in a field of wheat, 4k UHD, fashion magazine cover, award winning photography, sunset light", "scheduler": "DDIM", "tile_size": 512, "creativity": 0.35, "guess_mode": False, "resolution": 2048, "resemblance": 0.85, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 0.75, "lora_sharpness_strength": 1.25 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run fermatresearch/high-resolution-controlnet-tile 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": "fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76", "input": { "hdr": 0, "image": "https://replicate.delivery/pbxt/LCeXdKMrL42FTur24eINAaLiJqIWByMkt2rLEe1Avi9g8J4o/fermat_app_realistic_portrait_of_a_girl_with_beautiful_backgrou_62e1eacd-1a76-483d-b495-9e8946347ca4.png", "steps": 8, "format": "jpg", "prompt": "Professional portrait, a beautiful woman in a field of wheat, 4k UHD, fashion magazine cover, award winning photography, sunset light", "scheduler": "DDIM", "tile_size": 512, "creativity": 0.35, "guess_mode": false, "resolution": 2048, "resemblance": 0.85, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 0.75, "lora_sharpness_strength": 1.25 } }' \ 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/fermatresearch/high-resolution-controlnet-tile@sha256:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76 \ -i 'hdr=0' \ -i 'image="https://replicate.delivery/pbxt/LCeXdKMrL42FTur24eINAaLiJqIWByMkt2rLEe1Avi9g8J4o/fermat_app_realistic_portrait_of_a_girl_with_beautiful_backgrou_62e1eacd-1a76-483d-b495-9e8946347ca4.png"' \ -i 'steps=8' \ -i 'format="jpg"' \ -i 'prompt="Professional portrait, a beautiful woman in a field of wheat, 4k UHD, fashion magazine cover, award winning photography, sunset light"' \ -i 'scheduler="DDIM"' \ -i 'tile_size=512' \ -i 'creativity=0.35' \ -i 'guess_mode=false' \ -i 'resolution=2048' \ -i 'resemblance=0.85' \ -i 'guidance_scale=0' \ -i 'negative_prompt="Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant"' \ -i 'lora_details_strength=0.75' \ -i 'lora_sharpness_strength=1.25'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/fermatresearch/high-resolution-controlnet-tile@sha256:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "hdr": 0, "image": "https://replicate.delivery/pbxt/LCeXdKMrL42FTur24eINAaLiJqIWByMkt2rLEe1Avi9g8J4o/fermat_app_realistic_portrait_of_a_girl_with_beautiful_backgrou_62e1eacd-1a76-483d-b495-9e8946347ca4.png", "steps": 8, "format": "jpg", "prompt": "Professional portrait, a beautiful woman in a field of wheat, 4k UHD, fashion magazine cover, award winning photography, sunset light", "scheduler": "DDIM", "tile_size": 512, "creativity": 0.35, "guess_mode": false, "resolution": 2048, "resemblance": 0.85, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 0.75, "lora_sharpness_strength": 1.25 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
We were unable to load these images. Please make sure the URLs are valid.
{ "input": "https://replicate.delivery/pbxt/LCeXdKMrL42FTur24eINAaLiJqIWByMkt2rLEe1Avi9g8J4o/fermat_app_realistic_portrait_of_a_girl_with_beautiful_backgrou_62e1eacd-1a76-483d-b495-9e8946347ca4.png", "outut": "https://replicate.delivery/pbxt/1rbKAbFss7ZUGNxKnFGmOEHBaeEZ7cI7Sx61eiOo9AyGjQMTA/output.jpg" }{ "completed_at": "2024-07-26T07:56:55.964538Z", "created_at": "2024-07-26T07:56:22.163000Z", "data_removed": false, "error": null, "id": "f02rccks2drgp0cgxr5b1pwe6c", "input": { "hdr": 0, "image": "https://replicate.delivery/pbxt/LCeXdKMrL42FTur24eINAaLiJqIWByMkt2rLEe1Avi9g8J4o/fermat_app_realistic_portrait_of_a_girl_with_beautiful_backgrou_62e1eacd-1a76-483d-b495-9e8946347ca4.png", "steps": 8, "format": "jpg", "prompt": "Professional portrait, a beautiful woman in a field of wheat, 4k UHD, fashion magazine cover, award winning photography, sunset light", "scheduler": "DDIM", "tile_size": 512, "creativity": 0.35, "guess_mode": false, "resolution": 2048, "resemblance": 0.85, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 0.75, "lora_sharpness_strength": 1.25 }, "logs": "Using seed: 36505\nThe config attributes {'solver_order': 2, 'algorithm_type': 'deis', 'solver_type': 'logrho', 'lower_order_final': True, 'use_karras_sigmas': False} were passed to LCMScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file.\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:00<00:00, 6.42it/s]\n100%|██████████| 2/2 [00:00<00:00, 8.80it/s]\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:00<00:00, 4.83it/s]\n100%|██████████| 2/2 [00:00<00:00, 6.71it/s]\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:00<00:00, 3.31it/s]\n100%|██████████| 2/2 [00:00<00:00, 4.93it/s]\n100%|██████████| 2/2 [00:00<00:00, 4.59it/s]\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:00<00:00, 4.74it/s]\n100%|██████████| 2/2 [00:00<00:00, 6.59it/s]\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:00<00:00, 4.74it/s]\n100%|██████████| 2/2 [00:00<00:00, 6.59it/s]\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:00<00:00, 3.31it/s]\n100%|██████████| 2/2 [00:00<00:00, 4.92it/s]\n100%|██████████| 2/2 [00:00<00:00, 4.59it/s]\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:00<00:00, 4.80it/s]\n100%|██████████| 2/2 [00:00<00:00, 6.67it/s]\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:00<00:00, 6.44it/s]\n100%|██████████| 2/2 [00:00<00:00, 8.81it/s]\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:00<00:00, 4.80it/s]\n100%|██████████| 2/2 [00:00<00:00, 6.68it/s]\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:00<00:00, 6.41it/s]\n100%|██████████| 2/2 [00:00<00:00, 8.80it/s]\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:00<00:00, 4.74it/s]\n100%|██████████| 2/2 [00:00<00:00, 6.58it/s]\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:00<00:00, 3.31it/s]\n100%|██████████| 2/2 [00:00<00:00, 4.91it/s]\n100%|██████████| 2/2 [00:00<00:00, 4.57it/s]\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:00<00:00, 3.30it/s]\n100%|██████████| 2/2 [00:00<00:00, 4.91it/s]\n100%|██████████| 2/2 [00:00<00:00, 4.57it/s]\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:00<00:00, 4.72it/s]\n100%|██████████| 2/2 [00:00<00:00, 6.56it/s]\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:00<00:00, 6.42it/s]\n100%|██████████| 2/2 [00:00<00:00, 8.81it/s]\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:00<00:00, 4.79it/s]\n100%|██████████| 2/2 [00:00<00:00, 6.66it/s]\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:01<00:01, 1.94s/it]\n100%|██████████| 2/2 [00:02<00:00, 1.39s/it]\n100%|██████████| 2/2 [00:02<00:00, 1.47s/it]", "metrics": { "predict_time": 33.650878339, "total_time": 33.801538 }, "output": "https://replicate.delivery/pbxt/1rbKAbFss7ZUGNxKnFGmOEHBaeEZ7cI7Sx61eiOo9AyGjQMTA/output.jpg", "started_at": "2024-07-26T07:56:22.313659Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/f02rccks2drgp0cgxr5b1pwe6c", "cancel": "https://api.replicate.com/v1/predictions/f02rccks2drgp0cgxr5b1pwe6c/cancel" }, "version": "13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76" }Generated inUsing seed: 36505 The config attributes {'solver_order': 2, 'algorithm_type': 'deis', 'solver_type': 'logrho', 'lower_order_final': True, 'use_karras_sigmas': False} were passed to LCMScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file. 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:00<00:00, 6.42it/s] 100%|██████████| 2/2 [00:00<00:00, 8.80it/s] 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:00<00:00, 4.83it/s] 100%|██████████| 2/2 [00:00<00:00, 6.71it/s] 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:00<00:00, 3.31it/s] 100%|██████████| 2/2 [00:00<00:00, 4.93it/s] 100%|██████████| 2/2 [00:00<00:00, 4.59it/s] 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:00<00:00, 4.74it/s] 100%|██████████| 2/2 [00:00<00:00, 6.59it/s] 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:00<00:00, 4.74it/s] 100%|██████████| 2/2 [00:00<00:00, 6.59it/s] 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:00<00:00, 3.31it/s] 100%|██████████| 2/2 [00:00<00:00, 4.92it/s] 100%|██████████| 2/2 [00:00<00:00, 4.59it/s] 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:00<00:00, 4.80it/s] 100%|██████████| 2/2 [00:00<00:00, 6.67it/s] 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:00<00:00, 6.44it/s] 100%|██████████| 2/2 [00:00<00:00, 8.81it/s] 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:00<00:00, 4.80it/s] 100%|██████████| 2/2 [00:00<00:00, 6.68it/s] 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:00<00:00, 6.41it/s] 100%|██████████| 2/2 [00:00<00:00, 8.80it/s] 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:00<00:00, 4.74it/s] 100%|██████████| 2/2 [00:00<00:00, 6.58it/s] 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:00<00:00, 3.31it/s] 100%|██████████| 2/2 [00:00<00:00, 4.91it/s] 100%|██████████| 2/2 [00:00<00:00, 4.57it/s] 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:00<00:00, 3.30it/s] 100%|██████████| 2/2 [00:00<00:00, 4.91it/s] 100%|██████████| 2/2 [00:00<00:00, 4.57it/s] 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:00<00:00, 4.72it/s] 100%|██████████| 2/2 [00:00<00:00, 6.56it/s] 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:00<00:00, 6.42it/s] 100%|██████████| 2/2 [00:00<00:00, 8.81it/s] 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:00<00:00, 4.79it/s] 100%|██████████| 2/2 [00:00<00:00, 6.66it/s] 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:01<00:01, 1.94s/it] 100%|██████████| 2/2 [00:02<00:00, 1.39s/it] 100%|██████████| 2/2 [00:02<00:00, 1.47s/it]Prediction
fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76ID4xg26j3bc9rgm0cgxs28xehaagStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- hdr
- 0
- image
- steps
- 8
- format
- jpg
- prompt
- a nordic livingroom, 4k interior photography, uhd
- scheduler
- DDIM
- tile_size
- 768
- creativity
- 0.4
- guess_mode
- resolution
- 2560
- resemblance
- 0.85
- guidance_scale
- 0
- negative_prompt
- Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant
- lora_details_strength
- -0.25
- lora_sharpness_strength
- 0.75
{ "hdr": 0, "image": "https://replicate.delivery/pbxt/LKnw8rSgafZf4IlAVyPhzpX1TpTVcyfRa1saoaoiSfUYZLiL/fermat_app_a_living_room_modern_and_minimalistic_39b5a58a-e05b-4281-ac24-e87435256333-1.webp", "steps": 8, "format": "jpg", "prompt": "a nordic livingroom, 4k interior photography, uhd", "scheduler": "DDIM", "tile_size": 768, "creativity": 0.4, "guess_mode": false, "resolution": 2560, "resemblance": 0.85, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": -0.25, "lora_sharpness_strength": 0.75 }Install Replicate’s Node.js client library:npm install replicateImport 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 fermatresearch/high-resolution-controlnet-tile using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76", { input: { hdr: 0, image: "https://replicate.delivery/pbxt/LKnw8rSgafZf4IlAVyPhzpX1TpTVcyfRa1saoaoiSfUYZLiL/fermat_app_a_living_room_modern_and_minimalistic_39b5a58a-e05b-4281-ac24-e87435256333-1.webp", steps: 8, format: "jpg", prompt: "a nordic livingroom, 4k interior photography, uhd", scheduler: "DDIM", tile_size: 768, creativity: 0.4, guess_mode: false, resolution: 2560, resemblance: 0.85, guidance_scale: 0, negative_prompt: "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", lora_details_strength: -0.25, lora_sharpness_strength: 0.75 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun fermatresearch/high-resolution-controlnet-tile using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76", input={ "hdr": 0, "image": "https://replicate.delivery/pbxt/LKnw8rSgafZf4IlAVyPhzpX1TpTVcyfRa1saoaoiSfUYZLiL/fermat_app_a_living_room_modern_and_minimalistic_39b5a58a-e05b-4281-ac24-e87435256333-1.webp", "steps": 8, "format": "jpg", "prompt": "a nordic livingroom, 4k interior photography, uhd", "scheduler": "DDIM", "tile_size": 768, "creativity": 0.4, "guess_mode": False, "resolution": 2560, "resemblance": 0.85, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": -0.25, "lora_sharpness_strength": 0.75 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run fermatresearch/high-resolution-controlnet-tile 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": "fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76", "input": { "hdr": 0, "image": "https://replicate.delivery/pbxt/LKnw8rSgafZf4IlAVyPhzpX1TpTVcyfRa1saoaoiSfUYZLiL/fermat_app_a_living_room_modern_and_minimalistic_39b5a58a-e05b-4281-ac24-e87435256333-1.webp", "steps": 8, "format": "jpg", "prompt": "a nordic livingroom, 4k interior photography, uhd", "scheduler": "DDIM", "tile_size": 768, "creativity": 0.4, "guess_mode": false, "resolution": 2560, "resemblance": 0.85, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": -0.25, "lora_sharpness_strength": 0.75 } }' \ 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/fermatresearch/high-resolution-controlnet-tile@sha256:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76 \ -i 'hdr=0' \ -i 'image="https://replicate.delivery/pbxt/LKnw8rSgafZf4IlAVyPhzpX1TpTVcyfRa1saoaoiSfUYZLiL/fermat_app_a_living_room_modern_and_minimalistic_39b5a58a-e05b-4281-ac24-e87435256333-1.webp"' \ -i 'steps=8' \ -i 'format="jpg"' \ -i 'prompt="a nordic livingroom, 4k interior photography, uhd"' \ -i 'scheduler="DDIM"' \ -i 'tile_size=768' \ -i 'creativity=0.4' \ -i 'guess_mode=false' \ -i 'resolution=2560' \ -i 'resemblance=0.85' \ -i 'guidance_scale=0' \ -i 'negative_prompt="Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant"' \ -i 'lora_details_strength=-0.25' \ -i 'lora_sharpness_strength=0.75'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/fermatresearch/high-resolution-controlnet-tile@sha256:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "hdr": 0, "image": "https://replicate.delivery/pbxt/LKnw8rSgafZf4IlAVyPhzpX1TpTVcyfRa1saoaoiSfUYZLiL/fermat_app_a_living_room_modern_and_minimalistic_39b5a58a-e05b-4281-ac24-e87435256333-1.webp", "steps": 8, "format": "jpg", "prompt": "a nordic livingroom, 4k interior photography, uhd", "scheduler": "DDIM", "tile_size": 768, "creativity": 0.4, "guess_mode": false, "resolution": 2560, "resemblance": 0.85, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": -0.25, "lora_sharpness_strength": 0.75 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
We were unable to load these images. Please make sure the URLs are valid.
{ "input": "https://replicate.delivery/pbxt/LKnw8rSgafZf4IlAVyPhzpX1TpTVcyfRa1saoaoiSfUYZLiL/fermat_app_a_living_room_modern_and_minimalistic_39b5a58a-e05b-4281-ac24-e87435256333-1.webp", "outut": "https://replicate.delivery/pbxt/YFlNVWt9oEpTMVEKg69wXDRGtjHSHKV02ESZeChKj69ffiYmA/output.jpg" }{ "completed_at": "2024-07-26T09:01:53.303407Z", "created_at": "2024-07-26T08:59:39.746000Z", "data_removed": false, "error": null, "id": "4xg26j3bc9rgm0cgxs28xehaag", "input": { "hdr": 0, "image": "https://replicate.delivery/pbxt/LKnw8rSgafZf4IlAVyPhzpX1TpTVcyfRa1saoaoiSfUYZLiL/fermat_app_a_living_room_modern_and_minimalistic_39b5a58a-e05b-4281-ac24-e87435256333-1.webp", "steps": 8, "format": "jpg", "prompt": "a nordic livingroom, 4k interior photography, uhd", "scheduler": "DDIM", "tile_size": 768, "creativity": 0.4, "guess_mode": false, "resolution": 2560, "resemblance": 0.85, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": -0.25, "lora_sharpness_strength": 0.75 }, "logs": "Using seed: 20941\nThe config attributes {'solver_order': 2, 'algorithm_type': 'deis', 'solver_type': 'logrho', 'lower_order_final': True, 'use_karras_sigmas': False} were passed to LCMScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file.\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 2.53it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 3.74it/s]\n100%|██████████| 3/3 [00:00<00:00, 4.42it/s]\n100%|██████████| 3/3 [00:00<00:00, 4.00it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:01, 1.95it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 2.87it/s]\n100%|██████████| 3/3 [00:00<00:00, 3.39it/s]\n100%|██████████| 3/3 [00:00<00:00, 3.07it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:01, 1.19it/s]\n 67%|██████▋ | 2/3 [00:01<00:00, 1.72it/s]\n100%|██████████| 3/3 [00:01<00:00, 2.01it/s]\n100%|██████████| 3/3 [00:01<00:00, 1.83it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 3.68it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 5.52it/s]\n100%|██████████| 3/3 [00:00<00:00, 6.58it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.92it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:01, 1.96it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 2.89it/s]\n100%|██████████| 3/3 [00:00<00:00, 3.41it/s]\n100%|██████████| 3/3 [00:00<00:00, 3.09it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:01, 1.52it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 2.22it/s]\n100%|██████████| 3/3 [00:01<00:00, 2.60it/s]\n100%|██████████| 3/3 [00:01<00:00, 2.36it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 3.60it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 5.34it/s]\n100%|██████████| 3/3 [00:00<00:00, 6.30it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.70it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 9.58it/s]\n100%|██████████| 3/3 [00:00<00:00, 14.86it/s]\n100%|██████████| 3/3 [00:00<00:00, 14.07it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:01, 1.76it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 2.59it/s]\n100%|██████████| 3/3 [00:01<00:00, 3.05it/s]\n100%|██████████| 3/3 [00:01<00:00, 2.76it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 5.07it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.61it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.05it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:01, 1.76it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 2.61it/s]\n100%|██████████| 3/3 [00:01<00:00, 3.08it/s]\n100%|██████████| 3/3 [00:01<00:00, 2.78it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:01, 1.34it/s]\n 67%|██████▋ | 2/3 [00:01<00:00, 1.95it/s]\n100%|██████████| 3/3 [00:01<00:00, 2.28it/s]\n100%|██████████| 3/3 [00:01<00:00, 2.08it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:01, 1.34it/s]\n 67%|██████▋ | 2/3 [00:01<00:00, 1.95it/s]\n100%|██████████| 3/3 [00:01<00:00, 2.27it/s]\n100%|██████████| 3/3 [00:01<00:00, 2.07it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.06it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 6.06it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.20it/s]\n100%|██████████| 3/3 [00:00<00:00, 6.49it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.88it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.16it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.65it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 3.97it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 5.86it/s]\n100%|██████████| 3/3 [00:00<00:00, 6.92it/s]\n100%|██████████| 3/3 [00:00<00:00, 6.26it/s]\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:03<00:03, 3.98s/it]\n100%|██████████| 2/2 [00:06<00:00, 2.94s/it]\n100%|██████████| 2/2 [00:06<00:00, 3.10s/it]", "metrics": { "predict_time": 59.897140822, "total_time": 133.557407 }, "output": "https://replicate.delivery/pbxt/YFlNVWt9oEpTMVEKg69wXDRGtjHSHKV02ESZeChKj69ffiYmA/output.jpg", "started_at": "2024-07-26T09:00:53.406266Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4xg26j3bc9rgm0cgxs28xehaag", "cancel": "https://api.replicate.com/v1/predictions/4xg26j3bc9rgm0cgxs28xehaag/cancel" }, "version": "13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76" }Generated inUsing seed: 20941 The config attributes {'solver_order': 2, 'algorithm_type': 'deis', 'solver_type': 'logrho', 'lower_order_final': True, 'use_karras_sigmas': False} were passed to LCMScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file. 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 2.53it/s] 67%|██████▋ | 2/3 [00:00<00:00, 3.74it/s] 100%|██████████| 3/3 [00:00<00:00, 4.42it/s] 100%|██████████| 3/3 [00:00<00:00, 4.00it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:01, 1.95it/s] 67%|██████▋ | 2/3 [00:00<00:00, 2.87it/s] 100%|██████████| 3/3 [00:00<00:00, 3.39it/s] 100%|██████████| 3/3 [00:00<00:00, 3.07it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:01, 1.19it/s] 67%|██████▋ | 2/3 [00:01<00:00, 1.72it/s] 100%|██████████| 3/3 [00:01<00:00, 2.01it/s] 100%|██████████| 3/3 [00:01<00:00, 1.83it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 3.68it/s] 67%|██████▋ | 2/3 [00:00<00:00, 5.52it/s] 100%|██████████| 3/3 [00:00<00:00, 6.58it/s] 100%|██████████| 3/3 [00:00<00:00, 5.92it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:01, 1.96it/s] 67%|██████▋ | 2/3 [00:00<00:00, 2.89it/s] 100%|██████████| 3/3 [00:00<00:00, 3.41it/s] 100%|██████████| 3/3 [00:00<00:00, 3.09it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:01, 1.52it/s] 67%|██████▋ | 2/3 [00:00<00:00, 2.22it/s] 100%|██████████| 3/3 [00:01<00:00, 2.60it/s] 100%|██████████| 3/3 [00:01<00:00, 2.36it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 3.60it/s] 67%|██████▋ | 2/3 [00:00<00:00, 5.34it/s] 100%|██████████| 3/3 [00:00<00:00, 6.30it/s] 100%|██████████| 3/3 [00:00<00:00, 5.70it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 9.58it/s] 100%|██████████| 3/3 [00:00<00:00, 14.86it/s] 100%|██████████| 3/3 [00:00<00:00, 14.07it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:01, 1.76it/s] 67%|██████▋ | 2/3 [00:00<00:00, 2.59it/s] 100%|██████████| 3/3 [00:01<00:00, 3.05it/s] 100%|██████████| 3/3 [00:01<00:00, 2.76it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 5.07it/s] 100%|██████████| 3/3 [00:00<00:00, 8.61it/s] 100%|██████████| 3/3 [00:00<00:00, 8.05it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:01, 1.76it/s] 67%|██████▋ | 2/3 [00:00<00:00, 2.61it/s] 100%|██████████| 3/3 [00:01<00:00, 3.08it/s] 100%|██████████| 3/3 [00:01<00:00, 2.78it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:01, 1.34it/s] 67%|██████▋ | 2/3 [00:01<00:00, 1.95it/s] 100%|██████████| 3/3 [00:01<00:00, 2.28it/s] 100%|██████████| 3/3 [00:01<00:00, 2.08it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:01, 1.34it/s] 67%|██████▋ | 2/3 [00:01<00:00, 1.95it/s] 100%|██████████| 3/3 [00:01<00:00, 2.27it/s] 100%|██████████| 3/3 [00:01<00:00, 2.07it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.06it/s] 67%|██████▋ | 2/3 [00:00<00:00, 6.06it/s] 100%|██████████| 3/3 [00:00<00:00, 7.20it/s] 100%|██████████| 3/3 [00:00<00:00, 6.49it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.88it/s] 100%|██████████| 3/3 [00:00<00:00, 8.16it/s] 100%|██████████| 3/3 [00:00<00:00, 7.65it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 3.97it/s] 67%|██████▋ | 2/3 [00:00<00:00, 5.86it/s] 100%|██████████| 3/3 [00:00<00:00, 6.92it/s] 100%|██████████| 3/3 [00:00<00:00, 6.26it/s] 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:03<00:03, 3.98s/it] 100%|██████████| 2/2 [00:06<00:00, 2.94s/it] 100%|██████████| 2/2 [00:06<00:00, 3.10s/it]Prediction
fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76IDpqmgq2cmxxrgm0cgx9hv41drkwStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- hdr
- 0
- image
- steps
- 8
- format
- jpg
- prompt
- professional photography of Barcelona, futuristic city, light photography, daylight, flying cars, mediterranean, catalan modernism monuments, UHD, 4k intricate details, award-winning photography
- scheduler
- DDIM
- tile_size
- 512
- creativity
- 0.4
- guess_mode
- resolution
- 2048
- resemblance
- 0.75
- guidance_scale
- 0
- negative_prompt
- Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant
- lora_details_strength
- 0.75
- lora_sharpness_strength
- 1.25
{ "hdr": 0, "image": "https://replicate.delivery/pbxt/LKXMbSIso1TA6L9llaUo9gaYvk3zm00fVxU7zVWCaw6THWgm/relight2-1.webp", "steps": 8, "format": "jpg", "prompt": "professional photography of Barcelona, futuristic city, light photography, daylight, flying cars, mediterranean, catalan modernism monuments, UHD, 4k intricate details, award-winning photography", "scheduler": "DDIM", "tile_size": 512, "creativity": 0.4, "guess_mode": false, "resolution": 2048, "resemblance": 0.75, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 0.75, "lora_sharpness_strength": 1.25 }Install Replicate’s Node.js client library:npm install replicateImport 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 fermatresearch/high-resolution-controlnet-tile using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76", { input: { hdr: 0, image: "https://replicate.delivery/pbxt/LKXMbSIso1TA6L9llaUo9gaYvk3zm00fVxU7zVWCaw6THWgm/relight2-1.webp", steps: 8, format: "jpg", prompt: "professional photography of Barcelona, futuristic city, light photography, daylight, flying cars, mediterranean, catalan modernism monuments, UHD, 4k intricate details, award-winning photography", scheduler: "DDIM", tile_size: 512, creativity: 0.4, guess_mode: false, resolution: 2048, resemblance: 0.75, guidance_scale: 0, negative_prompt: "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", lora_details_strength: 0.75, lora_sharpness_strength: 1.25 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun fermatresearch/high-resolution-controlnet-tile using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76", input={ "hdr": 0, "image": "https://replicate.delivery/pbxt/LKXMbSIso1TA6L9llaUo9gaYvk3zm00fVxU7zVWCaw6THWgm/relight2-1.webp", "steps": 8, "format": "jpg", "prompt": "professional photography of Barcelona, futuristic city, light photography, daylight, flying cars, mediterranean, catalan modernism monuments, UHD, 4k intricate details, award-winning photography", "scheduler": "DDIM", "tile_size": 512, "creativity": 0.4, "guess_mode": False, "resolution": 2048, "resemblance": 0.75, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 0.75, "lora_sharpness_strength": 1.25 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run fermatresearch/high-resolution-controlnet-tile 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": "fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76", "input": { "hdr": 0, "image": "https://replicate.delivery/pbxt/LKXMbSIso1TA6L9llaUo9gaYvk3zm00fVxU7zVWCaw6THWgm/relight2-1.webp", "steps": 8, "format": "jpg", "prompt": "professional photography of Barcelona, futuristic city, light photography, daylight, flying cars, mediterranean, catalan modernism monuments, UHD, 4k intricate details, award-winning photography", "scheduler": "DDIM", "tile_size": 512, "creativity": 0.4, "guess_mode": false, "resolution": 2048, "resemblance": 0.75, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 0.75, "lora_sharpness_strength": 1.25 } }' \ 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/fermatresearch/high-resolution-controlnet-tile@sha256:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76 \ -i 'hdr=0' \ -i 'image="https://replicate.delivery/pbxt/LKXMbSIso1TA6L9llaUo9gaYvk3zm00fVxU7zVWCaw6THWgm/relight2-1.webp"' \ -i 'steps=8' \ -i 'format="jpg"' \ -i 'prompt="professional photography of Barcelona, futuristic city, light photography, daylight, flying cars, mediterranean, catalan modernism monuments, UHD, 4k intricate details, award-winning photography"' \ -i 'scheduler="DDIM"' \ -i 'tile_size=512' \ -i 'creativity=0.4' \ -i 'guess_mode=false' \ -i 'resolution=2048' \ -i 'resemblance=0.75' \ -i 'guidance_scale=0' \ -i 'negative_prompt="Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant"' \ -i 'lora_details_strength=0.75' \ -i 'lora_sharpness_strength=1.25'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/fermatresearch/high-resolution-controlnet-tile@sha256:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "hdr": 0, "image": "https://replicate.delivery/pbxt/LKXMbSIso1TA6L9llaUo9gaYvk3zm00fVxU7zVWCaw6THWgm/relight2-1.webp", "steps": 8, "format": "jpg", "prompt": "professional photography of Barcelona, futuristic city, light photography, daylight, flying cars, mediterranean, catalan modernism monuments, UHD, 4k intricate details, award-winning photography", "scheduler": "DDIM", "tile_size": 512, "creativity": 0.4, "guess_mode": false, "resolution": 2048, "resemblance": 0.75, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 0.75, "lora_sharpness_strength": 1.25 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
We were unable to load these images. Please make sure the URLs are valid.
{ "input": "https://replicate.delivery/pbxt/LKXMbSIso1TA6L9llaUo9gaYvk3zm00fVxU7zVWCaw6THWgm/relight2-1.webp", "outut": "https://replicate.delivery/pbxt/AYgqNyqYGdbROFgaHFX8Czh7BH0H1YvXuwVLmqi7DOPyZAzE/output.jpg" }{ "completed_at": "2024-07-25T14:57:12.384119Z", "created_at": "2024-07-25T14:55:13.135000Z", "data_removed": false, "error": null, "id": "pqmgq2cmxxrgm0cgx9hv41drkw", "input": { "hdr": 0, "image": "https://replicate.delivery/pbxt/LKXMbSIso1TA6L9llaUo9gaYvk3zm00fVxU7zVWCaw6THWgm/relight2-1.webp", "steps": 8, "format": "jpg", "prompt": "professional photography of Barcelona, futuristic city, light photography, daylight, flying cars, mediterranean, catalan modernism monuments, UHD, 4k intricate details, award-winning photography", "scheduler": "DDIM", "tile_size": 512, "creativity": 0.4, "guess_mode": false, "resolution": 2048, "resemblance": 0.75, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 0.75, "lora_sharpness_strength": 1.25 }, "logs": "Using seed: 33461\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:01, 1.74it/s]\n100%|██████████| 3/3 [00:00<00:00, 4.95it/s]\n100%|██████████| 3/3 [00:00<00:00, 4.18it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 3.96it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.52it/s]\n100%|██████████| 3/3 [00:00<00:00, 6.89it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 3.31it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 4.91it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.81it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.25it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.75it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.10it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.56it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.75it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.09it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.55it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 3.30it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 4.89it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.78it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.22it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.83it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.20it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.66it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 6.46it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.72it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.04it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.84it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.22it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.68it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 6.43it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.69it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.02it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.74it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.06it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.53it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 3.30it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 4.89it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.78it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.22it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 3.29it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 4.87it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.77it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.21it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.74it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.05it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.52it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 6.42it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.69it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.01it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.82it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.19it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.65it/s]\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:01<00:01, 1.93s/it]\n100%|██████████| 2/2 [00:02<00:00, 1.37s/it]\n100%|██████████| 2/2 [00:02<00:00, 1.46s/it]", "metrics": { "predict_time": 37.649093636, "total_time": 119.249119 }, "output": "https://replicate.delivery/pbxt/AYgqNyqYGdbROFgaHFX8Czh7BH0H1YvXuwVLmqi7DOPyZAzE/output.jpg", "started_at": "2024-07-25T14:56:34.735025Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/pqmgq2cmxxrgm0cgx9hv41drkw", "cancel": "https://api.replicate.com/v1/predictions/pqmgq2cmxxrgm0cgx9hv41drkw/cancel" }, "version": "13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76" }Generated inUsing seed: 33461 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:01, 1.74it/s] 100%|██████████| 3/3 [00:00<00:00, 4.95it/s] 100%|██████████| 3/3 [00:00<00:00, 4.18it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 3.96it/s] 100%|██████████| 3/3 [00:00<00:00, 7.52it/s] 100%|██████████| 3/3 [00:00<00:00, 6.89it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 3.31it/s] 67%|██████▋ | 2/3 [00:00<00:00, 4.91it/s] 100%|██████████| 3/3 [00:00<00:00, 5.81it/s] 100%|██████████| 3/3 [00:00<00:00, 5.25it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.75it/s] 100%|██████████| 3/3 [00:00<00:00, 8.10it/s] 100%|██████████| 3/3 [00:00<00:00, 7.56it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.75it/s] 100%|██████████| 3/3 [00:00<00:00, 8.09it/s] 100%|██████████| 3/3 [00:00<00:00, 7.55it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 3.30it/s] 67%|██████▋ | 2/3 [00:00<00:00, 4.89it/s] 100%|██████████| 3/3 [00:00<00:00, 5.78it/s] 100%|██████████| 3/3 [00:00<00:00, 5.22it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.83it/s] 100%|██████████| 3/3 [00:00<00:00, 8.20it/s] 100%|██████████| 3/3 [00:00<00:00, 7.66it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 6.46it/s] 100%|██████████| 3/3 [00:00<00:00, 10.72it/s] 100%|██████████| 3/3 [00:00<00:00, 10.04it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.84it/s] 100%|██████████| 3/3 [00:00<00:00, 8.22it/s] 100%|██████████| 3/3 [00:00<00:00, 7.68it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 6.43it/s] 100%|██████████| 3/3 [00:00<00:00, 10.69it/s] 100%|██████████| 3/3 [00:00<00:00, 10.02it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.74it/s] 100%|██████████| 3/3 [00:00<00:00, 8.06it/s] 100%|██████████| 3/3 [00:00<00:00, 7.53it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 3.30it/s] 67%|██████▋ | 2/3 [00:00<00:00, 4.89it/s] 100%|██████████| 3/3 [00:00<00:00, 5.78it/s] 100%|██████████| 3/3 [00:00<00:00, 5.22it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 3.29it/s] 67%|██████▋ | 2/3 [00:00<00:00, 4.87it/s] 100%|██████████| 3/3 [00:00<00:00, 5.77it/s] 100%|██████████| 3/3 [00:00<00:00, 5.21it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.74it/s] 100%|██████████| 3/3 [00:00<00:00, 8.05it/s] 100%|██████████| 3/3 [00:00<00:00, 7.52it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 6.42it/s] 100%|██████████| 3/3 [00:00<00:00, 10.69it/s] 100%|██████████| 3/3 [00:00<00:00, 10.01it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.82it/s] 100%|██████████| 3/3 [00:00<00:00, 8.19it/s] 100%|██████████| 3/3 [00:00<00:00, 7.65it/s] 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:01<00:01, 1.93s/it] 100%|██████████| 2/2 [00:02<00:00, 1.37s/it] 100%|██████████| 2/2 [00:02<00:00, 1.46s/it]Prediction
fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76ID4nt04qw3exrgm0cgx9stm0nek8StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- hdr
- 0
- image
- steps
- 8
- format
- jpg
- prompt
- a woman wearing a colorful suit
- scheduler
- DDIM
- tile_size
- 512
- creativity
- 0.4
- guess_mode
- resolution
- 2048
- resemblance
- 0.85
- guidance_scale
- 0
- negative_prompt
- Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant
- lora_details_strength
- 0.75
- lora_sharpness_strength
- 1
{ "hdr": 0, "image": "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg", "steps": 8, "format": "jpg", "prompt": "a woman wearing a colorful suit", "scheduler": "DDIM", "tile_size": 512, "creativity": 0.4, "guess_mode": false, "resolution": 2048, "resemblance": 0.85, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 0.75, "lora_sharpness_strength": 1 }Install Replicate’s Node.js client library:npm install replicateImport 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 fermatresearch/high-resolution-controlnet-tile using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76", { input: { hdr: 0, image: "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg", steps: 8, format: "jpg", prompt: "a woman wearing a colorful suit", scheduler: "DDIM", tile_size: 512, creativity: 0.4, guess_mode: false, resolution: 2048, resemblance: 0.85, guidance_scale: 0, negative_prompt: "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", lora_details_strength: 0.75, lora_sharpness_strength: 1 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun fermatresearch/high-resolution-controlnet-tile using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76", input={ "hdr": 0, "image": "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg", "steps": 8, "format": "jpg", "prompt": "a woman wearing a colorful suit", "scheduler": "DDIM", "tile_size": 512, "creativity": 0.4, "guess_mode": False, "resolution": 2048, "resemblance": 0.85, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 0.75, "lora_sharpness_strength": 1 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run fermatresearch/high-resolution-controlnet-tile 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": "fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76", "input": { "hdr": 0, "image": "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg", "steps": 8, "format": "jpg", "prompt": "a woman wearing a colorful suit", "scheduler": "DDIM", "tile_size": 512, "creativity": 0.4, "guess_mode": false, "resolution": 2048, "resemblance": 0.85, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 0.75, "lora_sharpness_strength": 1 } }' \ 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/fermatresearch/high-resolution-controlnet-tile@sha256:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76 \ -i 'hdr=0' \ -i 'image="https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg"' \ -i 'steps=8' \ -i 'format="jpg"' \ -i 'prompt="a woman wearing a colorful suit"' \ -i 'scheduler="DDIM"' \ -i 'tile_size=512' \ -i 'creativity=0.4' \ -i 'guess_mode=false' \ -i 'resolution=2048' \ -i 'resemblance=0.85' \ -i 'guidance_scale=0' \ -i 'negative_prompt="Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant"' \ -i 'lora_details_strength=0.75' \ -i 'lora_sharpness_strength=1'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/fermatresearch/high-resolution-controlnet-tile@sha256:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "hdr": 0, "image": "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg", "steps": 8, "format": "jpg", "prompt": "a woman wearing a colorful suit", "scheduler": "DDIM", "tile_size": 512, "creativity": 0.4, "guess_mode": false, "resolution": 2048, "resemblance": 0.85, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 0.75, "lora_sharpness_strength": 1 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
We were unable to load these images. Please make sure the URLs are valid.
{ "input": "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg", "outut": "https://replicate.delivery/pbxt/uVYXPohfE3U5WC67kSr4TahCjwuu1hJJZz3f633PfMwQtDYmA/output.jpg" }{ "completed_at": "2024-07-25T15:13:45.687896Z", "created_at": "2024-07-25T15:12:37.239000Z", "data_removed": false, "error": null, "id": "4nt04qw3exrgm0cgx9stm0nek8", "input": { "hdr": 0, "image": "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg", "steps": 8, "format": "jpg", "prompt": "a woman wearing a colorful suit", "scheduler": "DDIM", "tile_size": 512, "creativity": 0.4, "guess_mode": false, "resolution": 2048, "resemblance": 0.85, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 0.75, "lora_sharpness_strength": 1 }, "logs": "Using seed: 33864\nThe config attributes {'solver_order': 2, 'algorithm_type': 'deis', 'solver_type': 'logrho', 'lower_order_final': True, 'use_karras_sigmas': False} were passed to LCMScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file.\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 6.35it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.64it/s]\n100%|██████████| 3/3 [00:00<00:00, 9.96it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.80it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.21it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.66it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 3.28it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 4.88it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.80it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.23it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.72it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.08it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.54it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.72it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.08it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.54it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 3.28it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 4.87it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.78it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.21it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.77it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.16it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.61it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 6.38it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.61it/s]\n100%|██████████| 3/3 [00:00<00:00, 9.94it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.78it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.15it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.61it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 6.34it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.58it/s]\n100%|██████████| 3/3 [00:00<00:00, 9.91it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.71it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.04it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.51it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 3.28it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 4.86it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.76it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.20it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 3.27it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 4.86it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.75it/s]\n100%|██████████| 3/3 [00:00<00:00, 5.19it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.70it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.04it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.50it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 6.35it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.59it/s]\n100%|██████████| 3/3 [00:00<00:00, 9.92it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 4.77it/s]\n100%|██████████| 3/3 [00:00<00:00, 8.14it/s]\n100%|██████████| 3/3 [00:00<00:00, 7.60it/s]\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:01<00:01, 1.95s/it]\n100%|██████████| 2/2 [00:02<00:00, 1.39s/it]\n100%|██████████| 2/2 [00:02<00:00, 1.47s/it]", "metrics": { "predict_time": 34.368119126, "total_time": 68.448896 }, "output": "https://replicate.delivery/pbxt/uVYXPohfE3U5WC67kSr4TahCjwuu1hJJZz3f633PfMwQtDYmA/output.jpg", "started_at": "2024-07-25T15:13:11.319777Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4nt04qw3exrgm0cgx9stm0nek8", "cancel": "https://api.replicate.com/v1/predictions/4nt04qw3exrgm0cgx9stm0nek8/cancel" }, "version": "13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76" }Generated inUsing seed: 33864 The config attributes {'solver_order': 2, 'algorithm_type': 'deis', 'solver_type': 'logrho', 'lower_order_final': True, 'use_karras_sigmas': False} were passed to LCMScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file. 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 6.35it/s] 100%|██████████| 3/3 [00:00<00:00, 10.64it/s] 100%|██████████| 3/3 [00:00<00:00, 9.96it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.80it/s] 100%|██████████| 3/3 [00:00<00:00, 8.21it/s] 100%|██████████| 3/3 [00:00<00:00, 7.66it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 3.28it/s] 67%|██████▋ | 2/3 [00:00<00:00, 4.88it/s] 100%|██████████| 3/3 [00:00<00:00, 5.80it/s] 100%|██████████| 3/3 [00:00<00:00, 5.23it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.72it/s] 100%|██████████| 3/3 [00:00<00:00, 8.08it/s] 100%|██████████| 3/3 [00:00<00:00, 7.54it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.72it/s] 100%|██████████| 3/3 [00:00<00:00, 8.08it/s] 100%|██████████| 3/3 [00:00<00:00, 7.54it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 3.28it/s] 67%|██████▋ | 2/3 [00:00<00:00, 4.87it/s] 100%|██████████| 3/3 [00:00<00:00, 5.78it/s] 100%|██████████| 3/3 [00:00<00:00, 5.21it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.77it/s] 100%|██████████| 3/3 [00:00<00:00, 8.16it/s] 100%|██████████| 3/3 [00:00<00:00, 7.61it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 6.38it/s] 100%|██████████| 3/3 [00:00<00:00, 10.61it/s] 100%|██████████| 3/3 [00:00<00:00, 9.94it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.78it/s] 100%|██████████| 3/3 [00:00<00:00, 8.15it/s] 100%|██████████| 3/3 [00:00<00:00, 7.61it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 6.34it/s] 100%|██████████| 3/3 [00:00<00:00, 10.58it/s] 100%|██████████| 3/3 [00:00<00:00, 9.91it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.71it/s] 100%|██████████| 3/3 [00:00<00:00, 8.04it/s] 100%|██████████| 3/3 [00:00<00:00, 7.51it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 3.28it/s] 67%|██████▋ | 2/3 [00:00<00:00, 4.86it/s] 100%|██████████| 3/3 [00:00<00:00, 5.76it/s] 100%|██████████| 3/3 [00:00<00:00, 5.20it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 3.27it/s] 67%|██████▋ | 2/3 [00:00<00:00, 4.86it/s] 100%|██████████| 3/3 [00:00<00:00, 5.75it/s] 100%|██████████| 3/3 [00:00<00:00, 5.19it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.70it/s] 100%|██████████| 3/3 [00:00<00:00, 8.04it/s] 100%|██████████| 3/3 [00:00<00:00, 7.50it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 6.35it/s] 100%|██████████| 3/3 [00:00<00:00, 10.59it/s] 100%|██████████| 3/3 [00:00<00:00, 9.92it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.77it/s] 100%|██████████| 3/3 [00:00<00:00, 8.14it/s] 100%|██████████| 3/3 [00:00<00:00, 7.60it/s] 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:01<00:01, 1.95s/it] 100%|██████████| 2/2 [00:02<00:00, 1.39s/it] 100%|██████████| 2/2 [00:02<00:00, 1.47s/it]Prediction
fermatresearch/high-resolution-controlnet-tile:674a90f4adea8d0887a9cfcdbd7cc36c14d61f1dae041f823f97c306e68216caIDa02cajpw15rgg0cggawtr3q170StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- hdr
- 0
- seed
- 12345
- image
- steps
- 20
- prompt
- UHD 4k, professional outdoors photography, sunny small beach with palm trees, beatiful water and blue sky
- scheduler
- DDIM
- tile_size
- 768
- creativity
- 0.72
- guess_mode
- resolution
- 2048
- resemblance
- 0.9
- guidance_scale
- 7
- negative_prompt
- Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant
- lora_details_strength
- 0
- lora_sharpness_strength
- 0
{ "hdr": 0, "seed": 12345, "image": "https://replicate.delivery/pbxt/LDON3SO62ZGAMT36B6VQGVOFSJyRytPjDagqCGht0bwX1Gql/fermat_app_realistic_image_of_a_paradisiac_beach_with_daylight_1c7638bb-a50f-47f9-b398-9f161701e7e4.webp", "steps": 20, "prompt": "UHD 4k, professional outdoors photography, sunny small beach with palm trees, beatiful water and blue sky", "scheduler": "DDIM", "tile_size": 768, "creativity": 0.72, "guess_mode": false, "resolution": 2048, "resemblance": 0.9, "guidance_scale": 7, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 0, "lora_sharpness_strength": 0 }Install Replicate’s Node.js client library:npm install replicateImport 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 fermatresearch/high-resolution-controlnet-tile using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fermatresearch/high-resolution-controlnet-tile:674a90f4adea8d0887a9cfcdbd7cc36c14d61f1dae041f823f97c306e68216ca", { input: { hdr: 0, seed: 12345, image: "https://replicate.delivery/pbxt/LDON3SO62ZGAMT36B6VQGVOFSJyRytPjDagqCGht0bwX1Gql/fermat_app_realistic_image_of_a_paradisiac_beach_with_daylight_1c7638bb-a50f-47f9-b398-9f161701e7e4.webp", steps: 20, prompt: "UHD 4k, professional outdoors photography, sunny small beach with palm trees, beatiful water and blue sky", scheduler: "DDIM", tile_size: 768, creativity: 0.72, guess_mode: false, resolution: 2048, resemblance: 0.9, guidance_scale: 7, negative_prompt: "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", lora_details_strength: 0, lora_sharpness_strength: 0 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun fermatresearch/high-resolution-controlnet-tile using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fermatresearch/high-resolution-controlnet-tile:674a90f4adea8d0887a9cfcdbd7cc36c14d61f1dae041f823f97c306e68216ca", input={ "hdr": 0, "seed": 12345, "image": "https://replicate.delivery/pbxt/LDON3SO62ZGAMT36B6VQGVOFSJyRytPjDagqCGht0bwX1Gql/fermat_app_realistic_image_of_a_paradisiac_beach_with_daylight_1c7638bb-a50f-47f9-b398-9f161701e7e4.webp", "steps": 20, "prompt": "UHD 4k, professional outdoors photography, sunny small beach with palm trees, beatiful water and blue sky", "scheduler": "DDIM", "tile_size": 768, "creativity": 0.72, "guess_mode": False, "resolution": 2048, "resemblance": 0.9, "guidance_scale": 7, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 0, "lora_sharpness_strength": 0 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run fermatresearch/high-resolution-controlnet-tile 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": "fermatresearch/high-resolution-controlnet-tile:674a90f4adea8d0887a9cfcdbd7cc36c14d61f1dae041f823f97c306e68216ca", "input": { "hdr": 0, "seed": 12345, "image": "https://replicate.delivery/pbxt/LDON3SO62ZGAMT36B6VQGVOFSJyRytPjDagqCGht0bwX1Gql/fermat_app_realistic_image_of_a_paradisiac_beach_with_daylight_1c7638bb-a50f-47f9-b398-9f161701e7e4.webp", "steps": 20, "prompt": "UHD 4k, professional outdoors photography, sunny small beach with palm trees, beatiful water and blue sky", "scheduler": "DDIM", "tile_size": 768, "creativity": 0.72, "guess_mode": false, "resolution": 2048, "resemblance": 0.9, "guidance_scale": 7, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 0, "lora_sharpness_strength": 0 } }' \ 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/fermatresearch/high-resolution-controlnet-tile@sha256:674a90f4adea8d0887a9cfcdbd7cc36c14d61f1dae041f823f97c306e68216ca \ -i 'hdr=0' \ -i 'seed=12345' \ -i 'image="https://replicate.delivery/pbxt/LDON3SO62ZGAMT36B6VQGVOFSJyRytPjDagqCGht0bwX1Gql/fermat_app_realistic_image_of_a_paradisiac_beach_with_daylight_1c7638bb-a50f-47f9-b398-9f161701e7e4.webp"' \ -i 'steps=20' \ -i 'prompt="UHD 4k, professional outdoors photography, sunny small beach with palm trees, beatiful water and blue sky"' \ -i 'scheduler="DDIM"' \ -i 'tile_size=768' \ -i 'creativity=0.72' \ -i 'guess_mode=false' \ -i 'resolution=2048' \ -i 'resemblance=0.9' \ -i 'guidance_scale=7' \ -i 'negative_prompt="Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant"' \ -i 'lora_details_strength=0' \ -i 'lora_sharpness_strength=0'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/fermatresearch/high-resolution-controlnet-tile@sha256:674a90f4adea8d0887a9cfcdbd7cc36c14d61f1dae041f823f97c306e68216cacurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "hdr": 0, "seed": 12345, "image": "https://replicate.delivery/pbxt/LDON3SO62ZGAMT36B6VQGVOFSJyRytPjDagqCGht0bwX1Gql/fermat_app_realistic_image_of_a_paradisiac_beach_with_daylight_1c7638bb-a50f-47f9-b398-9f161701e7e4.webp", "steps": 20, "prompt": "UHD 4k, professional outdoors photography, sunny small beach with palm trees, beatiful water and blue sky", "scheduler": "DDIM", "tile_size": 768, "creativity": 0.72, "guess_mode": false, "resolution": 2048, "resemblance": 0.9, "guidance_scale": 7, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 0, "lora_sharpness_strength": 0 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
We were unable to load these images. Please make sure the URLs are valid.
{ "input": "https://replicate.delivery/pbxt/LDON3SO62ZGAMT36B6VQGVOFSJyRytPjDagqCGht0bwX1Gql/fermat_app_realistic_image_of_a_paradisiac_beach_with_daylight_1c7638bb-a50f-47f9-b398-9f161701e7e4.webp", "outut": "https://replicate.delivery/pbxt/WIUx5e35XyR4aSJAgAqD9f1uwvoNsjietkh3bLxUe5r59jVMB/output.jpg" }{ "completed_at": "2024-07-05T11:49:50.558202Z", "created_at": "2024-07-05T11:48:56.969000Z", "data_removed": false, "error": null, "id": "a02cajpw15rgg0cggawtr3q170", "input": { "hdr": 0, "seed": 12345, "image": "https://replicate.delivery/pbxt/LDON3SO62ZGAMT36B6VQGVOFSJyRytPjDagqCGht0bwX1Gql/fermat_app_realistic_image_of_a_paradisiac_beach_with_daylight_1c7638bb-a50f-47f9-b398-9f161701e7e4.webp", "steps": 20, "prompt": "UHD 4k, professional outdoors photography, sunny small beach with palm trees, beatiful water and blue sky", "scheduler": "DDIM", "tile_size": 768, "creativity": 0.72, "guess_mode": false, "resolution": 2048, "resemblance": 0.9, "guidance_scale": 7, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 0, "lora_sharpness_strength": 0 }, "logs": "Using seed: 12345\n 0%| | 0/14 [00:00<?, ?it/s]\n 7%|▋ | 1/14 [00:00<00:09, 1.36it/s]\n 14%|█▍ | 2/14 [00:01<00:06, 1.78it/s]\n 21%|██▏ | 3/14 [00:01<00:05, 1.98it/s]\n 29%|██▊ | 4/14 [00:02<00:04, 2.09it/s]\n 36%|███▌ | 5/14 [00:02<00:04, 2.16it/s]\n 43%|████▎ | 6/14 [00:02<00:03, 2.20it/s]\n 50%|█████ | 7/14 [00:03<00:03, 2.23it/s]\n 57%|█████▋ | 8/14 [00:03<00:02, 2.25it/s]\n 64%|██████▍ | 9/14 [00:04<00:02, 2.26it/s]\n 71%|███████▏ | 10/14 [00:04<00:01, 2.27it/s]\n 79%|███████▊ | 11/14 [00:05<00:01, 2.27it/s]\n 86%|████████▌ | 12/14 [00:05<00:00, 2.28it/s]\n 93%|█████████▎| 13/14 [00:05<00:00, 2.28it/s]\n100%|██████████| 14/14 [00:06<00:00, 2.28it/s]\n100%|██████████| 14/14 [00:06<00:00, 2.18it/s]\n 0%| | 0/14 [00:00<?, ?it/s]\n 7%|▋ | 1/14 [00:00<00:06, 2.07it/s]\n 14%|█▍ | 2/14 [00:00<00:04, 2.59it/s]\n 21%|██▏ | 3/14 [00:01<00:03, 2.81it/s]\n 29%|██▊ | 4/14 [00:01<00:03, 2.93it/s]\n 36%|███▌ | 5/14 [00:01<00:03, 3.00it/s]\n 43%|████▎ | 6/14 [00:02<00:02, 3.04it/s]\n 50%|█████ | 7/14 [00:02<00:02, 3.06it/s]\n 57%|█████▋ | 8/14 [00:02<00:01, 3.08it/s]\n 64%|██████▍ | 9/14 [00:03<00:01, 3.09it/s]\n 71%|███████▏ | 10/14 [00:03<00:01, 3.10it/s]\n 79%|███████▊ | 11/14 [00:03<00:00, 3.11it/s]\n 86%|████████▌ | 12/14 [00:04<00:00, 3.11it/s]\n 93%|█████████▎| 13/14 [00:04<00:00, 3.12it/s]\n100%|██████████| 14/14 [00:04<00:00, 3.12it/s]\n100%|██████████| 14/14 [00:04<00:00, 3.01it/s]\n 0%| | 0/14 [00:00<?, ?it/s]\n 7%|▋ | 1/14 [00:01<00:17, 1.37s/it]\n 14%|█▍ | 2/14 [00:02<00:13, 1.11s/it]\n 21%|██▏ | 3/14 [00:03<00:11, 1.03s/it]\n 29%|██▊ | 4/14 [00:04<00:09, 1.01it/s]\n 36%|███▌ | 5/14 [00:05<00:08, 1.03it/s]\n 43%|████▎ | 6/14 [00:06<00:07, 1.05it/s]\n 50%|█████ | 7/14 [00:06<00:06, 1.06it/s]\n 57%|█████▋ | 8/14 [00:07<00:05, 1.06it/s]\n 64%|██████▍ | 9/14 [00:08<00:04, 1.07it/s]\n 71%|███████▏ | 10/14 [00:09<00:03, 1.07it/s]\n 79%|███████▊ | 11/14 [00:10<00:02, 1.07it/s]\n 86%|████████▌ | 12/14 [00:11<00:01, 1.07it/s]\n 93%|█████████▎| 13/14 [00:12<00:00, 1.07it/s]\n100%|██████████| 14/14 [00:13<00:00, 1.07it/s]\n100%|██████████| 14/14 [00:13<00:00, 1.04it/s]\n 0%| | 0/14 [00:00<?, ?it/s]\n 7%|▋ | 1/14 [00:00<00:06, 2.06it/s]\n 14%|█▍ | 2/14 [00:00<00:04, 2.57it/s]\n 21%|██▏ | 3/14 [00:01<00:03, 2.79it/s]\n 29%|██▊ | 4/14 [00:01<00:03, 2.91it/s]\n 36%|███▌ | 5/14 [00:01<00:03, 2.97it/s]\n 43%|████▎ | 6/14 [00:02<00:02, 3.02it/s]\n 50%|█████ | 7/14 [00:02<00:02, 3.04it/s]\n 57%|█████▋ | 8/14 [00:02<00:01, 3.06it/s]\n 64%|██████▍ | 9/14 [00:03<00:01, 3.07it/s]\n 71%|███████▏ | 10/14 [00:03<00:01, 3.08it/s]\n 79%|███████▊ | 11/14 [00:03<00:00, 3.08it/s]\n 86%|████████▌ | 12/14 [00:04<00:00, 3.09it/s]\n 93%|█████████▎| 13/14 [00:04<00:00, 3.09it/s]\n100%|██████████| 14/14 [00:04<00:00, 3.10it/s]\n100%|██████████| 14/14 [00:04<00:00, 2.99it/s]\n 0%| | 0/14 [00:00<?, ?it/s]\n 7%|▋ | 1/14 [00:00<00:04, 2.74it/s]\n 14%|█▍ | 2/14 [00:00<00:03, 3.39it/s]\n 21%|██▏ | 3/14 [00:00<00:02, 3.68it/s]\n 29%|██▊ | 4/14 [00:01<00:02, 3.83it/s]\n 36%|███▌ | 5/14 [00:01<00:02, 3.92it/s]\n 43%|████▎ | 6/14 [00:01<00:02, 3.97it/s]\n 50%|█████ | 7/14 [00:01<00:01, 4.01it/s]\n 57%|█████▋ | 8/14 [00:02<00:01, 4.03it/s]\n 64%|██████▍ | 9/14 [00:02<00:01, 4.04it/s]\n 71%|███████▏ | 10/14 [00:02<00:00, 4.05it/s]\n 79%|███████▊ | 11/14 [00:02<00:00, 4.06it/s]\n 86%|████████▌ | 12/14 [00:03<00:00, 4.07it/s]\n 93%|█████████▎| 13/14 [00:03<00:00, 4.07it/s]\n100%|██████████| 14/14 [00:03<00:00, 4.07it/s]\n100%|██████████| 14/14 [00:03<00:00, 3.94it/s]\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:03<00:09, 3.14s/it]\n 50%|█████ | 2/4 [00:05<00:05, 2.58s/it]\n 75%|███████▌ | 3/4 [00:07<00:02, 2.40s/it]\n100%|██████████| 4/4 [00:09<00:00, 2.32s/it]\n100%|██████████| 4/4 [00:09<00:00, 2.43s/it]", "metrics": { "predict_time": 53.563921462, "total_time": 53.589202 }, "output": "https://replicate.delivery/pbxt/WIUx5e35XyR4aSJAgAqD9f1uwvoNsjietkh3bLxUe5r59jVMB/output.jpg", "started_at": "2024-07-05T11:48:56.994281Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/a02cajpw15rgg0cggawtr3q170", "cancel": "https://api.replicate.com/v1/predictions/a02cajpw15rgg0cggawtr3q170/cancel" }, "version": "674a90f4adea8d0887a9cfcdbd7cc36c14d61f1dae041f823f97c306e68216ca" }Generated inUsing seed: 12345 0%| | 0/14 [00:00<?, ?it/s] 7%|▋ | 1/14 [00:00<00:09, 1.36it/s] 14%|█▍ | 2/14 [00:01<00:06, 1.78it/s] 21%|██▏ | 3/14 [00:01<00:05, 1.98it/s] 29%|██▊ | 4/14 [00:02<00:04, 2.09it/s] 36%|███▌ | 5/14 [00:02<00:04, 2.16it/s] 43%|████▎ | 6/14 [00:02<00:03, 2.20it/s] 50%|█████ | 7/14 [00:03<00:03, 2.23it/s] 57%|█████▋ | 8/14 [00:03<00:02, 2.25it/s] 64%|██████▍ | 9/14 [00:04<00:02, 2.26it/s] 71%|███████▏ | 10/14 [00:04<00:01, 2.27it/s] 79%|███████▊ | 11/14 [00:05<00:01, 2.27it/s] 86%|████████▌ | 12/14 [00:05<00:00, 2.28it/s] 93%|█████████▎| 13/14 [00:05<00:00, 2.28it/s] 100%|██████████| 14/14 [00:06<00:00, 2.28it/s] 100%|██████████| 14/14 [00:06<00:00, 2.18it/s] 0%| | 0/14 [00:00<?, ?it/s] 7%|▋ | 1/14 [00:00<00:06, 2.07it/s] 14%|█▍ | 2/14 [00:00<00:04, 2.59it/s] 21%|██▏ | 3/14 [00:01<00:03, 2.81it/s] 29%|██▊ | 4/14 [00:01<00:03, 2.93it/s] 36%|███▌ | 5/14 [00:01<00:03, 3.00it/s] 43%|████▎ | 6/14 [00:02<00:02, 3.04it/s] 50%|█████ | 7/14 [00:02<00:02, 3.06it/s] 57%|█████▋ | 8/14 [00:02<00:01, 3.08it/s] 64%|██████▍ | 9/14 [00:03<00:01, 3.09it/s] 71%|███████▏ | 10/14 [00:03<00:01, 3.10it/s] 79%|███████▊ | 11/14 [00:03<00:00, 3.11it/s] 86%|████████▌ | 12/14 [00:04<00:00, 3.11it/s] 93%|█████████▎| 13/14 [00:04<00:00, 3.12it/s] 100%|██████████| 14/14 [00:04<00:00, 3.12it/s] 100%|██████████| 14/14 [00:04<00:00, 3.01it/s] 0%| | 0/14 [00:00<?, ?it/s] 7%|▋ | 1/14 [00:01<00:17, 1.37s/it] 14%|█▍ | 2/14 [00:02<00:13, 1.11s/it] 21%|██▏ | 3/14 [00:03<00:11, 1.03s/it] 29%|██▊ | 4/14 [00:04<00:09, 1.01it/s] 36%|███▌ | 5/14 [00:05<00:08, 1.03it/s] 43%|████▎ | 6/14 [00:06<00:07, 1.05it/s] 50%|█████ | 7/14 [00:06<00:06, 1.06it/s] 57%|█████▋ | 8/14 [00:07<00:05, 1.06it/s] 64%|██████▍ | 9/14 [00:08<00:04, 1.07it/s] 71%|███████▏ | 10/14 [00:09<00:03, 1.07it/s] 79%|███████▊ | 11/14 [00:10<00:02, 1.07it/s] 86%|████████▌ | 12/14 [00:11<00:01, 1.07it/s] 93%|█████████▎| 13/14 [00:12<00:00, 1.07it/s] 100%|██████████| 14/14 [00:13<00:00, 1.07it/s] 100%|██████████| 14/14 [00:13<00:00, 1.04it/s] 0%| | 0/14 [00:00<?, ?it/s] 7%|▋ | 1/14 [00:00<00:06, 2.06it/s] 14%|█▍ | 2/14 [00:00<00:04, 2.57it/s] 21%|██▏ | 3/14 [00:01<00:03, 2.79it/s] 29%|██▊ | 4/14 [00:01<00:03, 2.91it/s] 36%|███▌ | 5/14 [00:01<00:03, 2.97it/s] 43%|████▎ | 6/14 [00:02<00:02, 3.02it/s] 50%|█████ | 7/14 [00:02<00:02, 3.04it/s] 57%|█████▋ | 8/14 [00:02<00:01, 3.06it/s] 64%|██████▍ | 9/14 [00:03<00:01, 3.07it/s] 71%|███████▏ | 10/14 [00:03<00:01, 3.08it/s] 79%|███████▊ | 11/14 [00:03<00:00, 3.08it/s] 86%|████████▌ | 12/14 [00:04<00:00, 3.09it/s] 93%|█████████▎| 13/14 [00:04<00:00, 3.09it/s] 100%|██████████| 14/14 [00:04<00:00, 3.10it/s] 100%|██████████| 14/14 [00:04<00:00, 2.99it/s] 0%| | 0/14 [00:00<?, ?it/s] 7%|▋ | 1/14 [00:00<00:04, 2.74it/s] 14%|█▍ | 2/14 [00:00<00:03, 3.39it/s] 21%|██▏ | 3/14 [00:00<00:02, 3.68it/s] 29%|██▊ | 4/14 [00:01<00:02, 3.83it/s] 36%|███▌ | 5/14 [00:01<00:02, 3.92it/s] 43%|████▎ | 6/14 [00:01<00:02, 3.97it/s] 50%|█████ | 7/14 [00:01<00:01, 4.01it/s] 57%|█████▋ | 8/14 [00:02<00:01, 4.03it/s] 64%|██████▍ | 9/14 [00:02<00:01, 4.04it/s] 71%|███████▏ | 10/14 [00:02<00:00, 4.05it/s] 79%|███████▊ | 11/14 [00:02<00:00, 4.06it/s] 86%|████████▌ | 12/14 [00:03<00:00, 4.07it/s] 93%|█████████▎| 13/14 [00:03<00:00, 4.07it/s] 100%|██████████| 14/14 [00:03<00:00, 4.07it/s] 100%|██████████| 14/14 [00:03<00:00, 3.94it/s] 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:03<00:09, 3.14s/it] 50%|█████ | 2/4 [00:05<00:05, 2.58s/it] 75%|███████▌ | 3/4 [00:07<00:02, 2.40s/it] 100%|██████████| 4/4 [00:09<00:00, 2.32s/it] 100%|██████████| 4/4 [00:09<00:00, 2.43s/it]Prediction
fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76IDw1h4nzjrpdrgm0cgxsc947ys9rStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- hdr
- 0
- image
- steps
- 8
- format
- jpg
- prompt
- UHD 4k vogue, a woman wearing a colorful organic hat
- scheduler
- DDIM
- tile_size
- 512
- creativity
- 0.5
- guess_mode
- resolution
- 2048
- resemblance
- 0.85
- guidance_scale
- 0
- negative_prompt
- Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant
- lora_details_strength
- 1
- lora_sharpness_strength
- 1
{ "hdr": 0, "image": "https://replicate.delivery/pbxt/K2gjWl0c5hGCKrNBj2xyJpt7QhOpwNpYXfJ4pnJS56RoN1KK/4904b1be-61dc-4ef0-916b-2f33b2ca953a.webp", "steps": 8, "format": "jpg", "prompt": "UHD 4k vogue, a woman wearing a colorful organic hat", "scheduler": "DDIM", "tile_size": 512, "creativity": 0.5, "guess_mode": false, "resolution": 2048, "resemblance": 0.85, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 1, "lora_sharpness_strength": 1 }Install Replicate’s Node.js client library:npm install replicateImport 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 fermatresearch/high-resolution-controlnet-tile using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76", { input: { hdr: 0, image: "https://replicate.delivery/pbxt/K2gjWl0c5hGCKrNBj2xyJpt7QhOpwNpYXfJ4pnJS56RoN1KK/4904b1be-61dc-4ef0-916b-2f33b2ca953a.webp", steps: 8, format: "jpg", prompt: "UHD 4k vogue, a woman wearing a colorful organic hat", scheduler: "DDIM", tile_size: 512, creativity: 0.5, guess_mode: false, resolution: 2048, resemblance: 0.85, guidance_scale: 0, negative_prompt: "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", lora_details_strength: 1, lora_sharpness_strength: 1 } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun fermatresearch/high-resolution-controlnet-tile using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76", input={ "hdr": 0, "image": "https://replicate.delivery/pbxt/K2gjWl0c5hGCKrNBj2xyJpt7QhOpwNpYXfJ4pnJS56RoN1KK/4904b1be-61dc-4ef0-916b-2f33b2ca953a.webp", "steps": 8, "format": "jpg", "prompt": "UHD 4k vogue, a woman wearing a colorful organic hat", "scheduler": "DDIM", "tile_size": 512, "creativity": 0.5, "guess_mode": False, "resolution": 2048, "resemblance": 0.85, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 1, "lora_sharpness_strength": 1 } ) # To access the file URL: print(output.url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output.read())To learn more, take a look at the guide on getting started with Python.
Run fermatresearch/high-resolution-controlnet-tile 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": "fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76", "input": { "hdr": 0, "image": "https://replicate.delivery/pbxt/K2gjWl0c5hGCKrNBj2xyJpt7QhOpwNpYXfJ4pnJS56RoN1KK/4904b1be-61dc-4ef0-916b-2f33b2ca953a.webp", "steps": 8, "format": "jpg", "prompt": "UHD 4k vogue, a woman wearing a colorful organic hat", "scheduler": "DDIM", "tile_size": 512, "creativity": 0.5, "guess_mode": false, "resolution": 2048, "resemblance": 0.85, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 1, "lora_sharpness_strength": 1 } }' \ 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/fermatresearch/high-resolution-controlnet-tile@sha256:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76 \ -i 'hdr=0' \ -i 'image="https://replicate.delivery/pbxt/K2gjWl0c5hGCKrNBj2xyJpt7QhOpwNpYXfJ4pnJS56RoN1KK/4904b1be-61dc-4ef0-916b-2f33b2ca953a.webp"' \ -i 'steps=8' \ -i 'format="jpg"' \ -i 'prompt="UHD 4k vogue, a woman wearing a colorful organic hat"' \ -i 'scheduler="DDIM"' \ -i 'tile_size=512' \ -i 'creativity=0.5' \ -i 'guess_mode=false' \ -i 'resolution=2048' \ -i 'resemblance=0.85' \ -i 'guidance_scale=0' \ -i 'negative_prompt="Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant"' \ -i 'lora_details_strength=1' \ -i 'lora_sharpness_strength=1'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/fermatresearch/high-resolution-controlnet-tile@sha256:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "hdr": 0, "image": "https://replicate.delivery/pbxt/K2gjWl0c5hGCKrNBj2xyJpt7QhOpwNpYXfJ4pnJS56RoN1KK/4904b1be-61dc-4ef0-916b-2f33b2ca953a.webp", "steps": 8, "format": "jpg", "prompt": "UHD 4k vogue, a woman wearing a colorful organic hat", "scheduler": "DDIM", "tile_size": 512, "creativity": 0.5, "guess_mode": false, "resolution": 2048, "resemblance": 0.85, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 1, "lora_sharpness_strength": 1 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
We were unable to load these images. Please make sure the URLs are valid.
{ "input": "https://replicate.delivery/pbxt/K2gjWl0c5hGCKrNBj2xyJpt7QhOpwNpYXfJ4pnJS56RoN1KK/4904b1be-61dc-4ef0-916b-2f33b2ca953a.webp", "outut": "https://replicate.delivery/pbxt/7eGrkbFfBKnULU3ubOrjfqCFeyqhdQCr9VEiKAS2159fhOiZC/output.jpg" }{ "completed_at": "2024-07-26T09:23:28.920947Z", "created_at": "2024-07-26T09:21:25.683000Z", "data_removed": false, "error": null, "id": "w1h4nzjrpdrgm0cgxsc947ys9r", "input": { "hdr": 0, "image": "https://replicate.delivery/pbxt/K2gjWl0c5hGCKrNBj2xyJpt7QhOpwNpYXfJ4pnJS56RoN1KK/4904b1be-61dc-4ef0-916b-2f33b2ca953a.webp", "steps": 8, "format": "jpg", "prompt": "UHD 4k vogue, a woman wearing a colorful organic hat", "scheduler": "DDIM", "tile_size": 512, "creativity": 0.5, "guess_mode": false, "resolution": 2048, "resemblance": 0.85, "guidance_scale": 0, "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant", "lora_details_strength": 1, "lora_sharpness_strength": 1 }, "logs": "Using seed: 42670\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:01, 2.71it/s]\n 75%|███████▌ | 3/4 [00:00<00:00, 6.44it/s]\n100%|██████████| 4/4 [00:00<00:00, 6.55it/s]\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:00, 4.04it/s]\n 50%|█████ | 2/4 [00:00<00:00, 6.06it/s]\n 75%|███████▌ | 3/4 [00:00<00:00, 7.21it/s]\n100%|██████████| 4/4 [00:00<00:00, 7.93it/s]\n100%|██████████| 4/4 [00:00<00:00, 7.03it/s]\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:01, 2.94it/s]\n 50%|█████ | 2/4 [00:00<00:00, 4.37it/s]\n 75%|███████▌ | 3/4 [00:00<00:00, 5.18it/s]\n100%|██████████| 4/4 [00:00<00:00, 5.67it/s]\n100%|██████████| 4/4 [00:00<00:00, 5.05it/s]\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:00, 4.11it/s]\n 50%|█████ | 2/4 [00:00<00:00, 6.13it/s]\n 75%|███████▌ | 3/4 [00:00<00:00, 7.29it/s]\n100%|██████████| 4/4 [00:00<00:00, 8.00it/s]\n100%|██████████| 4/4 [00:00<00:00, 7.10it/s]\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:00, 4.12it/s]\n 50%|█████ | 2/4 [00:00<00:00, 6.15it/s]\n 75%|███████▌ | 3/4 [00:00<00:00, 7.31it/s]\n100%|██████████| 4/4 [00:00<00:00, 8.02it/s]\n100%|██████████| 4/4 [00:00<00:00, 7.12it/s]\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:01, 2.94it/s]\n 50%|█████ | 2/4 [00:00<00:00, 4.36it/s]\n 75%|███████▌ | 3/4 [00:00<00:00, 5.17it/s]\n100%|██████████| 4/4 [00:00<00:00, 5.65it/s]\n100%|██████████| 4/4 [00:00<00:00, 5.03it/s]\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:00, 4.03it/s]\n 50%|█████ | 2/4 [00:00<00:00, 6.05it/s]\n 75%|███████▌ | 3/4 [00:00<00:00, 7.19it/s]\n100%|██████████| 4/4 [00:00<00:00, 7.90it/s]\n100%|██████████| 4/4 [00:00<00:00, 7.01it/s]\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:00, 5.52it/s]\n 75%|███████▌ | 3/4 [00:00<00:00, 9.34it/s]\n100%|██████████| 4/4 [00:00<00:00, 9.43it/s]\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:00, 4.03it/s]\n 50%|█████ | 2/4 [00:00<00:00, 6.03it/s]\n 75%|███████▌ | 3/4 [00:00<00:00, 7.17it/s]\n100%|██████████| 4/4 [00:00<00:00, 7.87it/s]\n100%|██████████| 4/4 [00:00<00:00, 6.99it/s]\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:00, 5.51it/s]\n 75%|███████▌ | 3/4 [00:00<00:00, 9.29it/s]\n100%|██████████| 4/4 [00:00<00:00, 9.38it/s]\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:00, 4.09it/s]\n 50%|█████ | 2/4 [00:00<00:00, 6.12it/s]\n 75%|███████▌ | 3/4 [00:00<00:00, 7.25it/s]\n100%|██████████| 4/4 [00:00<00:00, 7.96it/s]\n100%|██████████| 4/4 [00:00<00:00, 7.07it/s]\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:01, 2.93it/s]\n 50%|█████ | 2/4 [00:00<00:00, 4.36it/s]\n 75%|███████▌ | 3/4 [00:00<00:00, 5.16it/s]\n100%|██████████| 4/4 [00:00<00:00, 5.65it/s]\n100%|██████████| 4/4 [00:00<00:00, 5.03it/s]\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:01, 2.93it/s]\n 50%|█████ | 2/4 [00:00<00:00, 4.35it/s]\n 75%|███████▌ | 3/4 [00:00<00:00, 5.15it/s]\n100%|██████████| 4/4 [00:00<00:00, 5.65it/s]\n100%|██████████| 4/4 [00:00<00:00, 5.03it/s]\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:00, 4.09it/s]\n 50%|█████ | 2/4 [00:00<00:00, 6.10it/s]\n 75%|███████▌ | 3/4 [00:00<00:00, 7.25it/s]\n100%|██████████| 4/4 [00:00<00:00, 7.95it/s]\n100%|██████████| 4/4 [00:00<00:00, 7.07it/s]\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:00, 5.49it/s]\n 75%|███████▌ | 3/4 [00:00<00:00, 9.29it/s]\n100%|██████████| 4/4 [00:00<00:00, 9.38it/s]\n 0%| | 0/4 [00:00<?, ?it/s]\n 25%|██▌ | 1/4 [00:00<00:00, 4.02it/s]\n 50%|█████ | 2/4 [00:00<00:00, 6.02it/s]\n 75%|███████▌ | 3/4 [00:00<00:00, 7.16it/s]\n100%|██████████| 4/4 [00:00<00:00, 7.86it/s]\n100%|██████████| 4/4 [00:00<00:00, 6.98it/s]\n 0%| | 0/2 [00:00<?, ?it/s]\n 50%|█████ | 1/2 [00:02<00:02, 2.72s/it]\n100%|██████████| 2/2 [00:04<00:00, 1.97s/it]\n100%|██████████| 2/2 [00:04<00:00, 2.09s/it]", "metrics": { "predict_time": 44.639884306, "total_time": 123.237947 }, "output": "https://replicate.delivery/pbxt/7eGrkbFfBKnULU3ubOrjfqCFeyqhdQCr9VEiKAS2159fhOiZC/output.jpg", "started_at": "2024-07-26T09:22:44.281063Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/w1h4nzjrpdrgm0cgxsc947ys9r", "cancel": "https://api.replicate.com/v1/predictions/w1h4nzjrpdrgm0cgxsc947ys9r/cancel" }, "version": "13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76" }Generated inUsing seed: 42670 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:01, 2.71it/s] 75%|███████▌ | 3/4 [00:00<00:00, 6.44it/s] 100%|██████████| 4/4 [00:00<00:00, 6.55it/s] 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:00, 4.04it/s] 50%|█████ | 2/4 [00:00<00:00, 6.06it/s] 75%|███████▌ | 3/4 [00:00<00:00, 7.21it/s] 100%|██████████| 4/4 [00:00<00:00, 7.93it/s] 100%|██████████| 4/4 [00:00<00:00, 7.03it/s] 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:01, 2.94it/s] 50%|█████ | 2/4 [00:00<00:00, 4.37it/s] 75%|███████▌ | 3/4 [00:00<00:00, 5.18it/s] 100%|██████████| 4/4 [00:00<00:00, 5.67it/s] 100%|██████████| 4/4 [00:00<00:00, 5.05it/s] 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:00, 4.11it/s] 50%|█████ | 2/4 [00:00<00:00, 6.13it/s] 75%|███████▌ | 3/4 [00:00<00:00, 7.29it/s] 100%|██████████| 4/4 [00:00<00:00, 8.00it/s] 100%|██████████| 4/4 [00:00<00:00, 7.10it/s] 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:00, 4.12it/s] 50%|█████ | 2/4 [00:00<00:00, 6.15it/s] 75%|███████▌ | 3/4 [00:00<00:00, 7.31it/s] 100%|██████████| 4/4 [00:00<00:00, 8.02it/s] 100%|██████████| 4/4 [00:00<00:00, 7.12it/s] 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:01, 2.94it/s] 50%|█████ | 2/4 [00:00<00:00, 4.36it/s] 75%|███████▌ | 3/4 [00:00<00:00, 5.17it/s] 100%|██████████| 4/4 [00:00<00:00, 5.65it/s] 100%|██████████| 4/4 [00:00<00:00, 5.03it/s] 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:00, 4.03it/s] 50%|█████ | 2/4 [00:00<00:00, 6.05it/s] 75%|███████▌ | 3/4 [00:00<00:00, 7.19it/s] 100%|██████████| 4/4 [00:00<00:00, 7.90it/s] 100%|██████████| 4/4 [00:00<00:00, 7.01it/s] 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:00, 5.52it/s] 75%|███████▌ | 3/4 [00:00<00:00, 9.34it/s] 100%|██████████| 4/4 [00:00<00:00, 9.43it/s] 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:00, 4.03it/s] 50%|█████ | 2/4 [00:00<00:00, 6.03it/s] 75%|███████▌ | 3/4 [00:00<00:00, 7.17it/s] 100%|██████████| 4/4 [00:00<00:00, 7.87it/s] 100%|██████████| 4/4 [00:00<00:00, 6.99it/s] 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:00, 5.51it/s] 75%|███████▌ | 3/4 [00:00<00:00, 9.29it/s] 100%|██████████| 4/4 [00:00<00:00, 9.38it/s] 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:00, 4.09it/s] 50%|█████ | 2/4 [00:00<00:00, 6.12it/s] 75%|███████▌ | 3/4 [00:00<00:00, 7.25it/s] 100%|██████████| 4/4 [00:00<00:00, 7.96it/s] 100%|██████████| 4/4 [00:00<00:00, 7.07it/s] 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:01, 2.93it/s] 50%|█████ | 2/4 [00:00<00:00, 4.36it/s] 75%|███████▌ | 3/4 [00:00<00:00, 5.16it/s] 100%|██████████| 4/4 [00:00<00:00, 5.65it/s] 100%|██████████| 4/4 [00:00<00:00, 5.03it/s] 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:01, 2.93it/s] 50%|█████ | 2/4 [00:00<00:00, 4.35it/s] 75%|███████▌ | 3/4 [00:00<00:00, 5.15it/s] 100%|██████████| 4/4 [00:00<00:00, 5.65it/s] 100%|██████████| 4/4 [00:00<00:00, 5.03it/s] 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:00, 4.09it/s] 50%|█████ | 2/4 [00:00<00:00, 6.10it/s] 75%|███████▌ | 3/4 [00:00<00:00, 7.25it/s] 100%|██████████| 4/4 [00:00<00:00, 7.95it/s] 100%|██████████| 4/4 [00:00<00:00, 7.07it/s] 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:00, 5.49it/s] 75%|███████▌ | 3/4 [00:00<00:00, 9.29it/s] 100%|██████████| 4/4 [00:00<00:00, 9.38it/s] 0%| | 0/4 [00:00<?, ?it/s] 25%|██▌ | 1/4 [00:00<00:00, 4.02it/s] 50%|█████ | 2/4 [00:00<00:00, 6.02it/s] 75%|███████▌ | 3/4 [00:00<00:00, 7.16it/s] 100%|██████████| 4/4 [00:00<00:00, 7.86it/s] 100%|██████████| 4/4 [00:00<00:00, 6.98it/s] 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:02<00:02, 2.72s/it] 100%|██████████| 2/2 [00:04<00:00, 1.97s/it] 100%|██████████| 2/2 [00:04<00:00, 2.09s/it]
Want to make some of these yourself?
Run this model
