stability-ai / stable-diffusion-img2img
Generate a new image from an input image with Stable Diffusion
Prediction
stability-ai/stable-diffusion-img2img:ddd4eb440853a42c055203289a3da0c8886b0b9492fe619b1c1dbd34be160ce7Input
- image
- width
- 512
- height
- 512
- prompt
- A fantasy landscape, trending on artstation
- scheduler
- DPMSolverMultistep
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- "25"
{ "image": "https://replicate.delivery/pbxt/HtKMvJSvuGWDn2B35mM396QGzcrgCNkcgSko8JxtXux4aX9H/sketch-mountains-input.jpeg", "width": 512, "height": 512, "prompt": "A fantasy landscape, trending on artstation", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "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 stability-ai/stable-diffusion-img2img using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "stability-ai/stable-diffusion-img2img:ddd4eb440853a42c055203289a3da0c8886b0b9492fe619b1c1dbd34be160ce7", { input: { image: "https://replicate.delivery/pbxt/HtKMvJSvuGWDn2B35mM396QGzcrgCNkcgSko8JxtXux4aX9H/sketch-mountains-input.jpeg", width: 512, height: 512, prompt: "A fantasy landscape, trending on artstation", scheduler: "DPMSolverMultistep", num_outputs: 1, guidance_scale: 7.5, prompt_strength: 0.8, num_inference_steps: "25" } } ); // 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 stability-ai/stable-diffusion-img2img using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "stability-ai/stable-diffusion-img2img:ddd4eb440853a42c055203289a3da0c8886b0b9492fe619b1c1dbd34be160ce7", input={ "image": "https://replicate.delivery/pbxt/HtKMvJSvuGWDn2B35mM396QGzcrgCNkcgSko8JxtXux4aX9H/sketch-mountains-input.jpeg", "width": 512, "height": 512, "prompt": "A fantasy landscape, trending on artstation", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "25" } ) # 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 stability-ai/stable-diffusion-img2img 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": "stability-ai/stable-diffusion-img2img:ddd4eb440853a42c055203289a3da0c8886b0b9492fe619b1c1dbd34be160ce7", "input": { "image": "https://replicate.delivery/pbxt/HtKMvJSvuGWDn2B35mM396QGzcrgCNkcgSko8JxtXux4aX9H/sketch-mountains-input.jpeg", "width": 512, "height": 512, "prompt": "A fantasy landscape, trending on artstation", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "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/stability-ai/stable-diffusion-img2img@sha256:ddd4eb440853a42c055203289a3da0c8886b0b9492fe619b1c1dbd34be160ce7 \ -i 'image="https://replicate.delivery/pbxt/HtKMvJSvuGWDn2B35mM396QGzcrgCNkcgSko8JxtXux4aX9H/sketch-mountains-input.jpeg"' \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="A fantasy landscape, trending on artstation"' \ -i 'scheduler="DPMSolverMultistep"' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'prompt_strength=0.8' \ -i 'num_inference_steps="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/stability-ai/stable-diffusion-img2img@sha256:ddd4eb440853a42c055203289a3da0c8886b0b9492fe619b1c1dbd34be160ce7curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/pbxt/HtKMvJSvuGWDn2B35mM396QGzcrgCNkcgSko8JxtXux4aX9H/sketch-mountains-input.jpeg", "width": 512, "height": 512, "prompt": "A fantasy landscape, trending on artstation", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "25" } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-12-02T22:58:41.057001Z", "created_at": "2022-12-02T22:58:37.527127Z", "data_removed": false, "error": null, "id": "m2yxuhgatfe73fvwyhcijgjera", "input": { "image": "https://replicate.delivery/pbxt/HtKMvJSvuGWDn2B35mM396QGzcrgCNkcgSko8JxtXux4aX9H/sketch-mountains-input.jpeg", "width": 512, "height": 512, "prompt": "A fantasy landscape, trending on artstation", "scheduler": "DPMSolverMultistep", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "25" }, "logs": "Using seed: 53915\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:02, 8.32it/s]\n 10%|█ | 2/20 [00:00<00:01, 9.21it/s]\n 20%|██ | 4/20 [00:00<00:01, 10.05it/s]\n 30%|███ | 6/20 [00:00<00:01, 10.31it/s]\n 40%|████ | 8/20 [00:00<00:01, 10.43it/s]\n 50%|█████ | 10/20 [00:00<00:00, 10.50it/s]\n 60%|██████ | 12/20 [00:01<00:00, 10.53it/s]\n 70%|███████ | 14/20 [00:01<00:00, 10.56it/s]\n 80%|████████ | 16/20 [00:01<00:00, 10.58it/s]\n 90%|█████████ | 18/20 [00:01<00:00, 10.59it/s]\n100%|██████████| 20/20 [00:01<00:00, 10.59it/s]\n100%|██████████| 20/20 [00:01<00:00, 10.43it/s]", "metrics": { "predict_time": 3.495285, "total_time": 3.529874 }, "output": [ "https://replicate.delivery/pbxt/ql3ndbIRkiZrChA31NxWt7kHMw9GM61lTrrqTIQcgtBI1gBE/out-0.png" ], "started_at": "2022-12-02T22:58:37.561716Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/m2yxuhgatfe73fvwyhcijgjera", "cancel": "https://api.replicate.com/v1/predictions/m2yxuhgatfe73fvwyhcijgjera/cancel" }, "version": "ddd4eb440853a42c055203289a3da0c8886b0b9492fe619b1c1dbd34be160ce7" }Generated inUsing seed: 53915 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:02, 8.32it/s] 10%|█ | 2/20 [00:00<00:01, 9.21it/s] 20%|██ | 4/20 [00:00<00:01, 10.05it/s] 30%|███ | 6/20 [00:00<00:01, 10.31it/s] 40%|████ | 8/20 [00:00<00:01, 10.43it/s] 50%|█████ | 10/20 [00:00<00:00, 10.50it/s] 60%|██████ | 12/20 [00:01<00:00, 10.53it/s] 70%|███████ | 14/20 [00:01<00:00, 10.56it/s] 80%|████████ | 16/20 [00:01<00:00, 10.58it/s] 90%|█████████ | 18/20 [00:01<00:00, 10.59it/s] 100%|██████████| 20/20 [00:01<00:00, 10.59it/s] 100%|██████████| 20/20 [00:01<00:00, 10.43it/s]Prediction
stability-ai/stable-diffusion-img2img:9a9b6aa5ac2793993aaaff48fd0e05fc5be213bc85a0bafd24e578d3bb81e628Input
- image
- width
- 512
- height
- 512
- prompt
- A fantasy landscape, trending on artstation
- scheduler
- K_EULER_ANCESTRAL
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- "25"
{ "image": "https://replicate.delivery/pbxt/HtKMvJSvuGWDn2B35mM396QGzcrgCNkcgSko8JxtXux4aX9H/sketch-mountains-input.jpeg", "width": 512, "height": 512, "prompt": "A fantasy landscape, trending on artstation", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "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 stability-ai/stable-diffusion-img2img using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "stability-ai/stable-diffusion-img2img:9a9b6aa5ac2793993aaaff48fd0e05fc5be213bc85a0bafd24e578d3bb81e628", { input: { image: "https://replicate.delivery/pbxt/HtKMvJSvuGWDn2B35mM396QGzcrgCNkcgSko8JxtXux4aX9H/sketch-mountains-input.jpeg", width: 512, height: 512, prompt: "A fantasy landscape, trending on artstation", scheduler: "K_EULER_ANCESTRAL", num_outputs: 1, guidance_scale: 7.5, prompt_strength: 0.8, num_inference_steps: "25" } } ); // 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 stability-ai/stable-diffusion-img2img using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "stability-ai/stable-diffusion-img2img:9a9b6aa5ac2793993aaaff48fd0e05fc5be213bc85a0bafd24e578d3bb81e628", input={ "image": "https://replicate.delivery/pbxt/HtKMvJSvuGWDn2B35mM396QGzcrgCNkcgSko8JxtXux4aX9H/sketch-mountains-input.jpeg", "width": 512, "height": 512, "prompt": "A fantasy landscape, trending on artstation", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "25" } ) # 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 stability-ai/stable-diffusion-img2img 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": "stability-ai/stable-diffusion-img2img:9a9b6aa5ac2793993aaaff48fd0e05fc5be213bc85a0bafd24e578d3bb81e628", "input": { "image": "https://replicate.delivery/pbxt/HtKMvJSvuGWDn2B35mM396QGzcrgCNkcgSko8JxtXux4aX9H/sketch-mountains-input.jpeg", "width": 512, "height": 512, "prompt": "A fantasy landscape, trending on artstation", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "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/stability-ai/stable-diffusion-img2img@sha256:9a9b6aa5ac2793993aaaff48fd0e05fc5be213bc85a0bafd24e578d3bb81e628 \ -i 'image="https://replicate.delivery/pbxt/HtKMvJSvuGWDn2B35mM396QGzcrgCNkcgSko8JxtXux4aX9H/sketch-mountains-input.jpeg"' \ -i 'width=512' \ -i 'height=512' \ -i 'prompt="A fantasy landscape, trending on artstation"' \ -i 'scheduler="K_EULER_ANCESTRAL"' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'prompt_strength=0.8' \ -i 'num_inference_steps="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/stability-ai/stable-diffusion-img2img@sha256:9a9b6aa5ac2793993aaaff48fd0e05fc5be213bc85a0bafd24e578d3bb81e628curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/pbxt/HtKMvJSvuGWDn2B35mM396QGzcrgCNkcgSko8JxtXux4aX9H/sketch-mountains-input.jpeg", "width": 512, "height": 512, "prompt": "A fantasy landscape, trending on artstation", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "25" } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2022-12-03T00:14:26.914917Z", "created_at": "2022-12-03T00:08:54.911903Z", "data_removed": false, "error": null, "id": "qddjw5nx4jg27ocmusjrxtejs4", "input": { "image": "https://replicate.delivery/pbxt/HtKMvJSvuGWDn2B35mM396QGzcrgCNkcgSko8JxtXux4aX9H/sketch-mountains-input.jpeg", "width": 512, "height": 512, "prompt": "A fantasy landscape, trending on artstation", "scheduler": "K_EULER_ANCESTRAL", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "25" }, "logs": "Using seed: 9445\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:03, 5.53it/s]\n 10%|█ | 2/20 [00:00<00:02, 6.84it/s]\n 15%|█▌ | 3/20 [00:00<00:02, 7.40it/s]\n 20%|██ | 4/20 [00:00<00:02, 7.70it/s]\n 25%|██▌ | 5/20 [00:00<00:01, 7.88it/s]\n 30%|███ | 6/20 [00:00<00:01, 7.99it/s]\n 35%|███▌ | 7/20 [00:00<00:01, 8.07it/s]\n 40%|████ | 8/20 [00:01<00:01, 8.12it/s]\n 45%|████▌ | 9/20 [00:01<00:01, 8.16it/s]\n 50%|█████ | 10/20 [00:01<00:01, 8.18it/s]\n 55%|█████▌ | 11/20 [00:01<00:01, 8.19it/s]\n 60%|██████ | 12/20 [00:01<00:00, 8.20it/s]\n 65%|██████▌ | 13/20 [00:01<00:00, 8.21it/s]\n 70%|███████ | 14/20 [00:01<00:00, 8.19it/s]\n 75%|███████▌ | 15/20 [00:01<00:00, 8.20it/s]\n 80%|████████ | 16/20 [00:02<00:00, 8.20it/s]\n 85%|████████▌ | 17/20 [00:02<00:00, 8.20it/s]\n 90%|█████████ | 18/20 [00:02<00:00, 8.20it/s]\n 95%|█████████▌| 19/20 [00:02<00:00, 8.20it/s]\n100%|██████████| 20/20 [00:02<00:00, 8.20it/s]\n100%|██████████| 20/20 [00:02<00:00, 8.02it/s]", "metrics": { "predict_time": 5.559297, "total_time": 332.003014 }, "output": [ "https://replicate.delivery/pbxt/4vMrIQLDhRLnOt8PMiAPdKuePh8KZ6RmLjW8FyR6feeLuRYAB/out-0.png" ], "started_at": "2022-12-03T00:14:21.355620Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/qddjw5nx4jg27ocmusjrxtejs4", "cancel": "https://api.replicate.com/v1/predictions/qddjw5nx4jg27ocmusjrxtejs4/cancel" }, "version": "9a9b6aa5ac2793993aaaff48fd0e05fc5be213bc85a0bafd24e578d3bb81e628" }Generated inUsing seed: 9445 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:03, 5.53it/s] 10%|█ | 2/20 [00:00<00:02, 6.84it/s] 15%|█▌ | 3/20 [00:00<00:02, 7.40it/s] 20%|██ | 4/20 [00:00<00:02, 7.70it/s] 25%|██▌ | 5/20 [00:00<00:01, 7.88it/s] 30%|███ | 6/20 [00:00<00:01, 7.99it/s] 35%|███▌ | 7/20 [00:00<00:01, 8.07it/s] 40%|████ | 8/20 [00:01<00:01, 8.12it/s] 45%|████▌ | 9/20 [00:01<00:01, 8.16it/s] 50%|█████ | 10/20 [00:01<00:01, 8.18it/s] 55%|█████▌ | 11/20 [00:01<00:01, 8.19it/s] 60%|██████ | 12/20 [00:01<00:00, 8.20it/s] 65%|██████▌ | 13/20 [00:01<00:00, 8.21it/s] 70%|███████ | 14/20 [00:01<00:00, 8.19it/s] 75%|███████▌ | 15/20 [00:01<00:00, 8.20it/s] 80%|████████ | 16/20 [00:02<00:00, 8.20it/s] 85%|████████▌ | 17/20 [00:02<00:00, 8.20it/s] 90%|█████████ | 18/20 [00:02<00:00, 8.20it/s] 95%|█████████▌| 19/20 [00:02<00:00, 8.20it/s] 100%|██████████| 20/20 [00:02<00:00, 8.20it/s] 100%|██████████| 20/20 [00:02<00:00, 8.02it/s]
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