cjwbw/bigcolor

Colorization using a Generative Color Prior for Natural Images

Clip-Guided Diffusion Model for Image Generation

Generates pokemon sprites from prompt

Real-ESRGAN super-resolution model from ruDALL-E

face alignment using stylegan-encoding

Image Manipulatinon with Diffusion Autoencoders

Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder
Global Tracking Transformers

Language-Free Training of a Text-to-Image Generator with CLIP

Composable Diffusion

Decoding Micromotion in Low-dimensional Latent Spaces from StyleGAN

VQ-Diffusion for Text-to-Image Synthesis

text-to-image generation

Panoptic Scene Graph Generation

text-to-image with latent diffusion

Unsupervised Night Image Enhancement

Inpainting using Denoising Diffusion Probabilistic Models

stable-diffusion with negative prompts, more scheduler

Pose-Invariant Hairstyle Transfer

End-to-End Document Image Enhancement Transformer

Detailed, higher-resolution images from Stable Diffusion
Prediction
cjwbw/bigcolor:9451bfbf652b21a9bccc741e5c7046540faa5586cfa3aa45abc7dbb46151a4f7Input
{ "mode": "Real Gray Colorization", "image": "https://replicate.delivery/mgxm/b0ca2e9b-7818-442a-9db4-30d79111790c/chaplin_1.jpeg" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run cjwbw/bigcolor using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/bigcolor:9451bfbf652b21a9bccc741e5c7046540faa5586cfa3aa45abc7dbb46151a4f7", { input: { mode: "Real Gray Colorization", image: "https://replicate.delivery/mgxm/b0ca2e9b-7818-442a-9db4-30d79111790c/chaplin_1.jpeg" } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run cjwbw/bigcolor using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/bigcolor:9451bfbf652b21a9bccc741e5c7046540faa5586cfa3aa45abc7dbb46151a4f7", input={ "mode": "Real Gray Colorization", "image": "https://replicate.delivery/mgxm/b0ca2e9b-7818-442a-9db4-30d79111790c/chaplin_1.jpeg" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run cjwbw/bigcolor 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": "cjwbw/bigcolor:9451bfbf652b21a9bccc741e5c7046540faa5586cfa3aa45abc7dbb46151a4f7", "input": { "mode": "Real Gray Colorization", "image": "https://replicate.delivery/mgxm/b0ca2e9b-7818-442a-9db4-30d79111790c/chaplin_1.jpeg" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-03T21:38:43.388871Z", "created_at": "2022-08-03T21:36:33.610557Z", "data_removed": false, "error": null, "id": "jb2gkeh3arbpfb46gkzrfvsrly", "input": { "mode": "Real Gray Colorization", "image": "https://replicate.delivery/mgxm/b0ca2e9b-7818-442a-9db4-30d79111790c/chaplin_1.jpeg" }, "logs": null, "metrics": { "predict_time": 4.591618, "total_time": 129.778314 }, "output": [ { "image": "https://replicate.delivery/mgxm/66b33d3b-d0c1-41f0-9345-080422857759/output.png" }, { "image": "https://replicate.delivery/mgxm/ee452d46-da53-43e6-815a-4a41dc4502cd/output.png" }, { "image": "https://replicate.delivery/mgxm/491fa2e9-d9b9-4efc-a02e-e246b3b2107d/output.png" }, { "image": "https://replicate.delivery/mgxm/3b8ba864-0eb0-4f43-b8b9-9e57adb0dd79/output.png" }, { "image": "https://replicate.delivery/mgxm/7c716bfc-2b84-4192-9127-0d58e187011c/output.png" } ], "started_at": "2022-08-03T21:38:38.797253Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jb2gkeh3arbpfb46gkzrfvsrly", "cancel": "https://api.replicate.com/v1/predictions/jb2gkeh3arbpfb46gkzrfvsrly/cancel" }, "version": "9451bfbf652b21a9bccc741e5c7046540faa5586cfa3aa45abc7dbb46151a4f7" }
Prediction
cjwbw/bigcolor:9451bfbf652b21a9bccc741e5c7046540faa5586cfa3aa45abc7dbb46151a4f7Input
{ "mode": "Real Gray Colorization", "image": "https://replicate.delivery/mgxm/8d0ff427-ef73-4da9-88fd-bfb27d965c24/marilyn_monroe_1.jpeg" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run cjwbw/bigcolor using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/bigcolor:9451bfbf652b21a9bccc741e5c7046540faa5586cfa3aa45abc7dbb46151a4f7", { input: { mode: "Real Gray Colorization", image: "https://replicate.delivery/mgxm/8d0ff427-ef73-4da9-88fd-bfb27d965c24/marilyn_monroe_1.jpeg" } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run cjwbw/bigcolor using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/bigcolor:9451bfbf652b21a9bccc741e5c7046540faa5586cfa3aa45abc7dbb46151a4f7", input={ "mode": "Real Gray Colorization", "image": "https://replicate.delivery/mgxm/8d0ff427-ef73-4da9-88fd-bfb27d965c24/marilyn_monroe_1.jpeg" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run cjwbw/bigcolor 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": "cjwbw/bigcolor:9451bfbf652b21a9bccc741e5c7046540faa5586cfa3aa45abc7dbb46151a4f7", "input": { "mode": "Real Gray Colorization", "image": "https://replicate.delivery/mgxm/8d0ff427-ef73-4da9-88fd-bfb27d965c24/marilyn_monroe_1.jpeg" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-03T21:39:12.699518Z", "created_at": "2022-08-03T21:39:08.271536Z", "data_removed": false, "error": null, "id": "5qc432zdgbdcjh65oyccqkg4o4", "input": { "mode": "Real Gray Colorization", "image": "https://replicate.delivery/mgxm/8d0ff427-ef73-4da9-88fd-bfb27d965c24/marilyn_monroe_1.jpeg" }, "logs": null, "metrics": { "predict_time": 4.27763, "total_time": 4.427982 }, "output": [ { "image": "https://replicate.delivery/mgxm/c0231294-b755-48f9-8db0-bda4d2e0cf60/output.png" }, { "image": "https://replicate.delivery/mgxm/f1e74faf-0488-49c3-bb8b-4edeff66d98a/output.png" }, { "image": "https://replicate.delivery/mgxm/0fb9aab5-8f07-4f7f-b9ed-0c1ee4cff037/output.png" }, { "image": "https://replicate.delivery/mgxm/d0813623-a32f-4320-b283-ea6c667559cf/output.png" }, { "image": "https://replicate.delivery/mgxm/a466cf5d-b648-4c66-9109-2c52e547fd9b/output.png" } ], "started_at": "2022-08-03T21:39:08.421888Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/5qc432zdgbdcjh65oyccqkg4o4", "cancel": "https://api.replicate.com/v1/predictions/5qc432zdgbdcjh65oyccqkg4o4/cancel" }, "version": "9451bfbf652b21a9bccc741e5c7046540faa5586cfa3aa45abc7dbb46151a4f7" }
Prediction
cjwbw/bigcolor:9451bfbf652b21a9bccc741e5c7046540faa5586cfa3aa45abc7dbb46151a4f7Input
{ "mode": "Real Gray Colorization", "image": "https://replicate.delivery/mgxm/f80aa79d-1242-4fd0-8e97-d491489b0042/einstein_3.jpeg" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run cjwbw/bigcolor using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/bigcolor:9451bfbf652b21a9bccc741e5c7046540faa5586cfa3aa45abc7dbb46151a4f7", { input: { mode: "Real Gray Colorization", image: "https://replicate.delivery/mgxm/f80aa79d-1242-4fd0-8e97-d491489b0042/einstein_3.jpeg" } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run cjwbw/bigcolor using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/bigcolor:9451bfbf652b21a9bccc741e5c7046540faa5586cfa3aa45abc7dbb46151a4f7", input={ "mode": "Real Gray Colorization", "image": "https://replicate.delivery/mgxm/f80aa79d-1242-4fd0-8e97-d491489b0042/einstein_3.jpeg" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run cjwbw/bigcolor 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": "cjwbw/bigcolor:9451bfbf652b21a9bccc741e5c7046540faa5586cfa3aa45abc7dbb46151a4f7", "input": { "mode": "Real Gray Colorization", "image": "https://replicate.delivery/mgxm/f80aa79d-1242-4fd0-8e97-d491489b0042/einstein_3.jpeg" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-03T21:39:29.304021Z", "created_at": "2022-08-03T21:39:24.600033Z", "data_removed": false, "error": null, "id": "agl5opnp2nc6rocgnrzi7g2d2q", "input": { "mode": "Real Gray Colorization", "image": "https://replicate.delivery/mgxm/f80aa79d-1242-4fd0-8e97-d491489b0042/einstein_3.jpeg" }, "logs": null, "metrics": { "predict_time": 4.556251, "total_time": 4.703988 }, "output": [ { "image": "https://replicate.delivery/mgxm/661efeb2-767f-441b-8578-a9a13a7c54c8/output.png" }, { "image": "https://replicate.delivery/mgxm/a0359238-0af1-4a54-bea3-c817cfb3a7ad/output.png" }, { "image": "https://replicate.delivery/mgxm/84f62e40-a5cc-40c9-b004-35dfc16428f8/output.png" }, { "image": "https://replicate.delivery/mgxm/51f40787-ddbc-40cb-9402-b93f8de79be4/output.png" }, { "image": "https://replicate.delivery/mgxm/a8f771ce-74ca-4441-9a0b-381941304bb6/output.png" } ], "started_at": "2022-08-03T21:39:24.747770Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/agl5opnp2nc6rocgnrzi7g2d2q", "cancel": "https://api.replicate.com/v1/predictions/agl5opnp2nc6rocgnrzi7g2d2q/cancel" }, "version": "9451bfbf652b21a9bccc741e5c7046540faa5586cfa3aa45abc7dbb46151a4f7" }
Prediction
cjwbw/bigcolor:9451bfbf652b21a9bccc741e5c7046540faa5586cfa3aa45abc7dbb46151a4f7Input
{ "mode": "Multi-modal class vector c", "image": "https://replicate.delivery/mgxm/e5b27baf-20fe-4bcf-9b9a-6231f02ca860/ILSVRC2012_val_00002071.jpeg", "classes": "40 88 200" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run cjwbw/bigcolor using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/bigcolor:9451bfbf652b21a9bccc741e5c7046540faa5586cfa3aa45abc7dbb46151a4f7", { input: { mode: "Multi-modal class vector c", image: "https://replicate.delivery/mgxm/e5b27baf-20fe-4bcf-9b9a-6231f02ca860/ILSVRC2012_val_00002071.jpeg", classes: "40 88 200" } } ); console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicate
Import the client:import replicate
Run cjwbw/bigcolor using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/bigcolor:9451bfbf652b21a9bccc741e5c7046540faa5586cfa3aa45abc7dbb46151a4f7", input={ "mode": "Multi-modal class vector c", "image": "https://replicate.delivery/mgxm/e5b27baf-20fe-4bcf-9b9a-6231f02ca860/ILSVRC2012_val_00002071.jpeg", "classes": "40 88 200" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run cjwbw/bigcolor 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": "cjwbw/bigcolor:9451bfbf652b21a9bccc741e5c7046540faa5586cfa3aa45abc7dbb46151a4f7", "input": { "mode": "Multi-modal class vector c", "image": "https://replicate.delivery/mgxm/e5b27baf-20fe-4bcf-9b9a-6231f02ca860/ILSVRC2012_val_00002071.jpeg", "classes": "40 88 200" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-08-03T21:40:23.354018Z", "created_at": "2022-08-03T21:40:20.382888Z", "data_removed": false, "error": null, "id": "cf4bbjf53fe7hdfh64exazdd6i", "input": { "mode": "Multi-modal class vector c", "image": "https://replicate.delivery/mgxm/e5b27baf-20fe-4bcf-9b9a-6231f02ca860/ILSVRC2012_val_00002071.jpeg", "classes": "40 88 200" }, "logs": "/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/skimage/_shared/utils.py:394: UserWarning: Color data out of range: Z < 0 in 135 pixels\n return func(*args, **kwargs)", "metrics": { "predict_time": 2.773501, "total_time": 2.97113 }, "output": [ { "image": "https://replicate.delivery/mgxm/1ccdfad8-4137-457c-8237-e5947da7ef42/output_0.png" }, { "image": "https://replicate.delivery/mgxm/d1d45de4-5d37-470e-9a5b-eb284340e284/output_1.png" }, { "image": "https://replicate.delivery/mgxm/1adef735-25f0-466b-b9d3-11f9b630cf07/output_2.png" } ], "started_at": "2022-08-03T21:40:20.580517Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/cf4bbjf53fe7hdfh64exazdd6i", "cancel": "https://api.replicate.com/v1/predictions/cf4bbjf53fe7hdfh64exazdd6i/cancel" }, "version": "9451bfbf652b21a9bccc741e5c7046540faa5586cfa3aa45abc7dbb46151a4f7" }
Want to make some of these yourself?
Run this model