adirik/leditsplusplus

LEdits++ for image editing

Text-Guided Image Generation and Manipulation

PyTorch version of Lightweight OpenPose as introduced in "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose"

Modify images using line art

Modify images using canny edges

Modify images using sketches

Modify images using human pose

Modify images using depth maps

Inst-Inpaint: Instructing to Remove Objects with Diffusion Models

Zero-shot / open vocabulary object detection

Generate videos from text prompts with Kandinsky-2.2

Detect everything with language!

Generates 3D assets from images

Generate 3D assets using text descriptions

Detects objects in an image

Performs speaker identity verification

Generates speech from text

Generate texture for your mesh with text prompts

Kosmos-G: Generating Images in Context with Multimodal Large Language Models

Edit real or generated images

Edit real or generated images
Prediction
adirik/leditsplusplus:18916a9500f503aa4aa92ec0b2dbf3cecfa1995ee2280b2033e80d50973af9f2IDhw6si63bv7m3amueh2qhvfcsouStatusSucceededSourceWebHardwareA100 (80GB)Total durationCreatedInput
- skip
- 0.2
- source_prompt
- edit_threshold
- 0.9, 0.85
- editing_prompts
- tennis ball, tomato
- edit_warmup_steps
- 0
- edit_guidance_scale
- 5.0, 10.0
- num_inversion_steps
- 50
- source_guidance_scale
- 3.5
- reverse_editing_directions
- True, False
{ "skip": 0.2, "image": "https://replicate.delivery/pbxt/Kdrl0kuNYX3VCwJtdSfIoN8rzHBkcVuAhD9FLLzEI82ZywHT/tennis.jpg", "source_prompt": "", "edit_threshold": "0.9, 0.85", "editing_prompts": "tennis ball, tomato", "edit_warmup_steps": 0, "edit_guidance_scale": "5.0, 10.0", "num_inversion_steps": 50, "source_guidance_scale": 3.5, "reverse_editing_directions": "True, False" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run adirik/leditsplusplus using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/leditsplusplus:18916a9500f503aa4aa92ec0b2dbf3cecfa1995ee2280b2033e80d50973af9f2", { input: { skip: 0.2, image: "https://replicate.delivery/pbxt/Kdrl0kuNYX3VCwJtdSfIoN8rzHBkcVuAhD9FLLzEI82ZywHT/tennis.jpg", source_prompt: "", edit_threshold: "0.9, 0.85", editing_prompts: "tennis ball, tomato", edit_warmup_steps: 0, edit_guidance_scale: "5.0, 10.0", num_inversion_steps: 50, source_guidance_scale: 3.5, reverse_editing_directions: "True, False" } } ); // 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 replicate
Import the client:import replicate
Run adirik/leditsplusplus using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/leditsplusplus:18916a9500f503aa4aa92ec0b2dbf3cecfa1995ee2280b2033e80d50973af9f2", input={ "skip": 0.2, "image": "https://replicate.delivery/pbxt/Kdrl0kuNYX3VCwJtdSfIoN8rzHBkcVuAhD9FLLzEI82ZywHT/tennis.jpg", "source_prompt": "", "edit_threshold": "0.9, 0.85", "editing_prompts": "tennis ball, tomato", "edit_warmup_steps": 0, "edit_guidance_scale": "5.0, 10.0", "num_inversion_steps": 50, "source_guidance_scale": 3.5, "reverse_editing_directions": "True, False" } ) # 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 adirik/leditsplusplus 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": "adirik/leditsplusplus:18916a9500f503aa4aa92ec0b2dbf3cecfa1995ee2280b2033e80d50973af9f2", "input": { "skip": 0.2, "image": "https://replicate.delivery/pbxt/Kdrl0kuNYX3VCwJtdSfIoN8rzHBkcVuAhD9FLLzEI82ZywHT/tennis.jpg", "source_prompt": "", "edit_threshold": "0.9, 0.85", "editing_prompts": "tennis ball, tomato", "edit_warmup_steps": 0, "edit_guidance_scale": "5.0, 10.0", "num_inversion_steps": 50, "source_guidance_scale": 3.5, "reverse_editing_directions": "True, False" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/adirik/leditsplusplus@sha256:18916a9500f503aa4aa92ec0b2dbf3cecfa1995ee2280b2033e80d50973af9f2 \ -i 'skip=0.2' \ -i 'image="https://replicate.delivery/pbxt/Kdrl0kuNYX3VCwJtdSfIoN8rzHBkcVuAhD9FLLzEI82ZywHT/tennis.jpg"' \ -i 'source_prompt=""' \ -i 'edit_threshold="0.9, 0.85"' \ -i 'editing_prompts="tennis ball, tomato"' \ -i 'edit_warmup_steps=0' \ -i 'edit_guidance_scale="5.0, 10.0"' \ -i 'num_inversion_steps=50' \ -i 'source_guidance_scale=3.5' \ -i 'reverse_editing_directions="True, False"'
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/adirik/leditsplusplus@sha256:18916a9500f503aa4aa92ec0b2dbf3cecfa1995ee2280b2033e80d50973af9f2
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "skip": 0.2, "image": "https://replicate.delivery/pbxt/Kdrl0kuNYX3VCwJtdSfIoN8rzHBkcVuAhD9FLLzEI82ZywHT/tennis.jpg", "source_prompt": "", "edit_threshold": "0.9, 0.85", "editing_prompts": "tennis ball, tomato", "edit_warmup_steps": 0, "edit_guidance_scale": "5.0, 10.0", "num_inversion_steps": 50, "source_guidance_scale": 3.5, "reverse_editing_directions": "True, False" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2024-03-27T09:47:28.583402Z", "created_at": "2024-03-27T09:46:08.567289Z", "data_removed": false, "error": null, "id": "hw6si63bv7m3amueh2qhvfcsou", "input": { "skip": 0.2, "image": "https://replicate.delivery/pbxt/Kdrl0kuNYX3VCwJtdSfIoN8rzHBkcVuAhD9FLLzEI82ZywHT/tennis.jpg", "source_prompt": "", "edit_threshold": "0.9, 0.85", "editing_prompts": "tennis ball, tomato", "edit_warmup_steps": 0, "edit_guidance_scale": "5.0, 10.0", "num_inversion_steps": 50, "source_guidance_scale": 3.5, "reverse_editing_directions": "True, False" }, "logs": "Your input images far exceed the default resolution of the underlying diffusion model. The output images may contain severe artifacts! Consider down-sampling the input using the `height` and `width` parameters\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:08, 5.82it/s]\n 6%|▌ | 3/50 [00:00<00:04, 9.75it/s]\n 10%|█ | 5/50 [00:00<00:04, 10.71it/s]\n 14%|█▍ | 7/50 [00:00<00:03, 11.19it/s]\n 18%|█▊ | 9/50 [00:00<00:03, 11.51it/s]\n 22%|██▏ | 11/50 [00:00<00:03, 11.67it/s]\n 26%|██▌ | 13/50 [00:01<00:03, 11.75it/s]\n 30%|███ | 15/50 [00:01<00:02, 11.73it/s]\n 34%|███▍ | 17/50 [00:01<00:02, 11.86it/s]\n 38%|███▊ | 19/50 [00:01<00:02, 11.91it/s]\n 42%|████▏ | 21/50 [00:01<00:02, 11.90it/s]\n 46%|████▌ | 23/50 [00:02<00:02, 11.92it/s]\n 50%|█████ | 25/50 [00:02<00:02, 11.94it/s]\n 54%|█████▍ | 27/50 [00:02<00:01, 11.95it/s]\n 58%|█████▊ | 29/50 [00:02<00:01, 11.89it/s]\n 62%|██████▏ | 31/50 [00:02<00:01, 11.91it/s]\n 66%|██████▌ | 33/50 [00:02<00:01, 11.94it/s]\n 70%|███████ | 35/50 [00:03<00:01, 11.96it/s]\n 74%|███████▍ | 37/50 [00:03<00:01, 11.96it/s]\n 78%|███████▊ | 39/50 [00:03<00:00, 11.96it/s]\n 82%|████████▏ | 41/50 [00:03<00:00, 11.95it/s]\n 86%|████████▌ | 43/50 [00:03<00:00, 11.93it/s]\n 90%|█████████ | 45/50 [00:03<00:00, 11.92it/s]\n 94%|█████████▍| 47/50 [00:04<00:00, 11.95it/s]\n 98%|█████████▊| 49/50 [00:04<00:00, 11.96it/s]\n100%|██████████| 50/50 [00:04<00:00, 11.68it/s]\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.65it/s]\n 4%|▍ | 2/50 [00:00<00:11, 4.24it/s]\n 6%|▌ | 3/50 [00:00<00:10, 4.47it/s]\n 8%|▊ | 4/50 [00:00<00:10, 4.58it/s]\n 10%|█ | 5/50 [00:01<00:09, 4.65it/s]\n 12%|█▏ | 6/50 [00:01<00:09, 4.69it/s]\n 14%|█▍ | 7/50 [00:01<00:09, 4.71it/s]\n 16%|█▌ | 8/50 [00:01<00:08, 4.73it/s]\n 18%|█▊ | 9/50 [00:01<00:08, 4.74it/s]\n 20%|██ | 10/50 [00:02<00:08, 4.74it/s]\n 22%|██▏ | 11/50 [00:02<00:08, 4.74it/s]\n 24%|██▍ | 12/50 [00:02<00:08, 4.75it/s]\n 26%|██▌ | 13/50 [00:02<00:07, 4.75it/s]\n 28%|██▊ | 14/50 [00:03<00:07, 4.75it/s]\n 30%|███ | 15/50 [00:03<00:07, 4.75it/s]\n 32%|███▏ | 16/50 [00:03<00:07, 4.75it/s]\n 34%|███▍ | 17/50 [00:03<00:06, 4.76it/s]\n 36%|███▌ | 18/50 [00:03<00:06, 4.76it/s]\n 38%|███▊ | 19/50 [00:04<00:06, 4.76it/s]\n 40%|████ | 20/50 [00:04<00:06, 4.76it/s]\n 42%|████▏ | 21/50 [00:04<00:06, 4.75it/s]\n 44%|████▍ | 22/50 [00:04<00:05, 4.76it/s]\n 46%|████▌ | 23/50 [00:04<00:05, 4.76it/s]\n 48%|████▊ | 24/50 [00:05<00:05, 4.76it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.75it/s]\n 52%|█████▏ | 26/50 [00:05<00:05, 4.75it/s]\n 54%|█████▍ | 27/50 [00:05<00:04, 4.75it/s]\n 56%|█████▌ | 28/50 [00:05<00:04, 4.75it/s]\n 58%|█████▊ | 29/50 [00:06<00:04, 4.75it/s]\n 60%|██████ | 30/50 [00:06<00:04, 4.75it/s]\n 62%|██████▏ | 31/50 [00:06<00:03, 4.75it/s]\n 64%|██████▍ | 32/50 [00:06<00:03, 4.75it/s]\n 66%|██████▌ | 33/50 [00:07<00:03, 4.75it/s]\n 68%|██████▊ | 34/50 [00:07<00:03, 4.75it/s]\n 70%|███████ | 35/50 [00:07<00:03, 4.69it/s]\n 72%|███████▏ | 36/50 [00:07<00:02, 4.71it/s]\n 74%|███████▍ | 37/50 [00:07<00:02, 4.72it/s]\n 76%|███████▌ | 38/50 [00:08<00:02, 4.73it/s]\n 78%|███████▊ | 39/50 [00:08<00:02, 4.73it/s]\n 80%|████████ | 40/50 [00:08<00:02, 4.74it/s]\n 82%|████████▏ | 41/50 [00:08<00:01, 4.74it/s]\n 84%|████████▍ | 42/50 [00:08<00:01, 4.71it/s]\n 86%|████████▌ | 43/50 [00:09<00:01, 4.72it/s]\n 88%|████████▊ | 44/50 [00:09<00:01, 4.72it/s]\n 90%|█████████ | 45/50 [00:09<00:01, 4.73it/s]\n 92%|█████████▏| 46/50 [00:09<00:00, 4.73it/s]\n 94%|█████████▍| 47/50 [00:09<00:00, 4.73it/s]\n 96%|█████████▌| 48/50 [00:10<00:00, 4.73it/s]\n 98%|█████████▊| 49/50 [00:10<00:00, 4.73it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.73it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.72it/s]", "metrics": { "predict_time": 19.052099, "total_time": 80.016113 }, "output": "https://replicate.delivery/pbxt/zwHbyMZQby5TDhyewKnOcIoVg6ZisivUxvJiL6DlMXrX6MSJA/output.png", "started_at": "2024-03-27T09:47:09.531303Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/hw6si63bv7m3amueh2qhvfcsou", "cancel": "https://api.replicate.com/v1/predictions/hw6si63bv7m3amueh2qhvfcsou/cancel" }, "version": "18916a9500f503aa4aa92ec0b2dbf3cecfa1995ee2280b2033e80d50973af9f2" }
Generated inYour input images far exceed the default resolution of the underlying diffusion model. The output images may contain severe artifacts! Consider down-sampling the input using the `height` and `width` parameters 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:08, 5.82it/s] 6%|▌ | 3/50 [00:00<00:04, 9.75it/s] 10%|█ | 5/50 [00:00<00:04, 10.71it/s] 14%|█▍ | 7/50 [00:00<00:03, 11.19it/s] 18%|█▊ | 9/50 [00:00<00:03, 11.51it/s] 22%|██▏ | 11/50 [00:00<00:03, 11.67it/s] 26%|██▌ | 13/50 [00:01<00:03, 11.75it/s] 30%|███ | 15/50 [00:01<00:02, 11.73it/s] 34%|███▍ | 17/50 [00:01<00:02, 11.86it/s] 38%|███▊ | 19/50 [00:01<00:02, 11.91it/s] 42%|████▏ | 21/50 [00:01<00:02, 11.90it/s] 46%|████▌ | 23/50 [00:02<00:02, 11.92it/s] 50%|█████ | 25/50 [00:02<00:02, 11.94it/s] 54%|█████▍ | 27/50 [00:02<00:01, 11.95it/s] 58%|█████▊ | 29/50 [00:02<00:01, 11.89it/s] 62%|██████▏ | 31/50 [00:02<00:01, 11.91it/s] 66%|██████▌ | 33/50 [00:02<00:01, 11.94it/s] 70%|███████ | 35/50 [00:03<00:01, 11.96it/s] 74%|███████▍ | 37/50 [00:03<00:01, 11.96it/s] 78%|███████▊ | 39/50 [00:03<00:00, 11.96it/s] 82%|████████▏ | 41/50 [00:03<00:00, 11.95it/s] 86%|████████▌ | 43/50 [00:03<00:00, 11.93it/s] 90%|█████████ | 45/50 [00:03<00:00, 11.92it/s] 94%|█████████▍| 47/50 [00:04<00:00, 11.95it/s] 98%|█████████▊| 49/50 [00:04<00:00, 11.96it/s] 100%|██████████| 50/50 [00:04<00:00, 11.68it/s] 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.65it/s] 4%|▍ | 2/50 [00:00<00:11, 4.24it/s] 6%|▌ | 3/50 [00:00<00:10, 4.47it/s] 8%|▊ | 4/50 [00:00<00:10, 4.58it/s] 10%|█ | 5/50 [00:01<00:09, 4.65it/s] 12%|█▏ | 6/50 [00:01<00:09, 4.69it/s] 14%|█▍ | 7/50 [00:01<00:09, 4.71it/s] 16%|█▌ | 8/50 [00:01<00:08, 4.73it/s] 18%|█▊ | 9/50 [00:01<00:08, 4.74it/s] 20%|██ | 10/50 [00:02<00:08, 4.74it/s] 22%|██▏ | 11/50 [00:02<00:08, 4.74it/s] 24%|██▍ | 12/50 [00:02<00:08, 4.75it/s] 26%|██▌ | 13/50 [00:02<00:07, 4.75it/s] 28%|██▊ | 14/50 [00:03<00:07, 4.75it/s] 30%|███ | 15/50 [00:03<00:07, 4.75it/s] 32%|███▏ | 16/50 [00:03<00:07, 4.75it/s] 34%|███▍ | 17/50 [00:03<00:06, 4.76it/s] 36%|███▌ | 18/50 [00:03<00:06, 4.76it/s] 38%|███▊ | 19/50 [00:04<00:06, 4.76it/s] 40%|████ | 20/50 [00:04<00:06, 4.76it/s] 42%|████▏ | 21/50 [00:04<00:06, 4.75it/s] 44%|████▍ | 22/50 [00:04<00:05, 4.76it/s] 46%|████▌ | 23/50 [00:04<00:05, 4.76it/s] 48%|████▊ | 24/50 [00:05<00:05, 4.76it/s] 50%|█████ | 25/50 [00:05<00:05, 4.75it/s] 52%|█████▏ | 26/50 [00:05<00:05, 4.75it/s] 54%|█████▍ | 27/50 [00:05<00:04, 4.75it/s] 56%|█████▌ | 28/50 [00:05<00:04, 4.75it/s] 58%|█████▊ | 29/50 [00:06<00:04, 4.75it/s] 60%|██████ | 30/50 [00:06<00:04, 4.75it/s] 62%|██████▏ | 31/50 [00:06<00:03, 4.75it/s] 64%|██████▍ | 32/50 [00:06<00:03, 4.75it/s] 66%|██████▌ | 33/50 [00:07<00:03, 4.75it/s] 68%|██████▊ | 34/50 [00:07<00:03, 4.75it/s] 70%|███████ | 35/50 [00:07<00:03, 4.69it/s] 72%|███████▏ | 36/50 [00:07<00:02, 4.71it/s] 74%|███████▍ | 37/50 [00:07<00:02, 4.72it/s] 76%|███████▌ | 38/50 [00:08<00:02, 4.73it/s] 78%|███████▊ | 39/50 [00:08<00:02, 4.73it/s] 80%|████████ | 40/50 [00:08<00:02, 4.74it/s] 82%|████████▏ | 41/50 [00:08<00:01, 4.74it/s] 84%|████████▍ | 42/50 [00:08<00:01, 4.71it/s] 86%|████████▌ | 43/50 [00:09<00:01, 4.72it/s] 88%|████████▊ | 44/50 [00:09<00:01, 4.72it/s] 90%|█████████ | 45/50 [00:09<00:01, 4.73it/s] 92%|█████████▏| 46/50 [00:09<00:00, 4.73it/s] 94%|█████████▍| 47/50 [00:09<00:00, 4.73it/s] 96%|█████████▌| 48/50 [00:10<00:00, 4.73it/s] 98%|█████████▊| 49/50 [00:10<00:00, 4.73it/s] 100%|██████████| 50/50 [00:10<00:00, 4.73it/s] 100%|██████████| 50/50 [00:10<00:00, 4.72it/s]
Prediction
adirik/leditsplusplus:18916a9500f503aa4aa92ec0b2dbf3cecfa1995ee2280b2033e80d50973af9f2IDiots6ptb7k45tak4fwusfgatlaStatusSucceededSourceWebHardwareA100 (80GB)Total durationCreatedInput
- skip
- 0.3
- edit_threshold
- 0.75
- editing_prompts
- glasses
- edit_warmup_steps
- 8
- edit_guidance_scale
- 3.0
- num_inversion_steps
- 50
- source_guidance_scale
- 3.5
- reverse_editing_directions
- False
{ "skip": 0.3, "image": "https://replicate.delivery/pbxt/Kdrtkd4IdmtW53B6l9tG1upqxL6xFMhXobvcQ27qayMQFAIA/girl_with_a_pearl_earring.jpeg", "edit_threshold": "0.75", "editing_prompts": "glasses", "edit_warmup_steps": 8, "edit_guidance_scale": "3.0", "num_inversion_steps": 50, "source_guidance_scale": 3.5, "reverse_editing_directions": "False" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run adirik/leditsplusplus using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "adirik/leditsplusplus:18916a9500f503aa4aa92ec0b2dbf3cecfa1995ee2280b2033e80d50973af9f2", { input: { skip: 0.3, image: "https://replicate.delivery/pbxt/Kdrtkd4IdmtW53B6l9tG1upqxL6xFMhXobvcQ27qayMQFAIA/girl_with_a_pearl_earring.jpeg", edit_threshold: "0.75", editing_prompts: "glasses", edit_warmup_steps: 8, edit_guidance_scale: "3.0", num_inversion_steps: 50, source_guidance_scale: 3.5, reverse_editing_directions: "False" } } ); // 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 replicate
Import the client:import replicate
Run adirik/leditsplusplus using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "adirik/leditsplusplus:18916a9500f503aa4aa92ec0b2dbf3cecfa1995ee2280b2033e80d50973af9f2", input={ "skip": 0.3, "image": "https://replicate.delivery/pbxt/Kdrtkd4IdmtW53B6l9tG1upqxL6xFMhXobvcQ27qayMQFAIA/girl_with_a_pearl_earring.jpeg", "edit_threshold": "0.75", "editing_prompts": "glasses", "edit_warmup_steps": 8, "edit_guidance_scale": "3.0", "num_inversion_steps": 50, "source_guidance_scale": 3.5, "reverse_editing_directions": "False" } ) # 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 adirik/leditsplusplus 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": "adirik/leditsplusplus:18916a9500f503aa4aa92ec0b2dbf3cecfa1995ee2280b2033e80d50973af9f2", "input": { "skip": 0.3, "image": "https://replicate.delivery/pbxt/Kdrtkd4IdmtW53B6l9tG1upqxL6xFMhXobvcQ27qayMQFAIA/girl_with_a_pearl_earring.jpeg", "edit_threshold": "0.75", "editing_prompts": "glasses", "edit_warmup_steps": 8, "edit_guidance_scale": "3.0", "num_inversion_steps": 50, "source_guidance_scale": 3.5, "reverse_editing_directions": "False" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
You can run this model locally using Cog. First, install Cog:brew install cog
If you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/adirik/leditsplusplus@sha256:18916a9500f503aa4aa92ec0b2dbf3cecfa1995ee2280b2033e80d50973af9f2 \ -i 'skip=0.3' \ -i 'image="https://replicate.delivery/pbxt/Kdrtkd4IdmtW53B6l9tG1upqxL6xFMhXobvcQ27qayMQFAIA/girl_with_a_pearl_earring.jpeg"' \ -i 'edit_threshold="0.75"' \ -i 'editing_prompts="glasses"' \ -i 'edit_warmup_steps=8' \ -i 'edit_guidance_scale="3.0"' \ -i 'num_inversion_steps=50' \ -i 'source_guidance_scale=3.5' \ -i 'reverse_editing_directions="False"'
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/adirik/leditsplusplus@sha256:18916a9500f503aa4aa92ec0b2dbf3cecfa1995ee2280b2033e80d50973af9f2
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "skip": 0.3, "image": "https://replicate.delivery/pbxt/Kdrtkd4IdmtW53B6l9tG1upqxL6xFMhXobvcQ27qayMQFAIA/girl_with_a_pearl_earring.jpeg", "edit_threshold": "0.75", "editing_prompts": "glasses", "edit_warmup_steps": 8, "edit_guidance_scale": "3.0", "num_inversion_steps": 50, "source_guidance_scale": 3.5, "reverse_editing_directions": "False" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2024-03-27T09:58:44.144141Z", "created_at": "2024-03-27T09:58:32.337830Z", "data_removed": false, "error": null, "id": "iots6ptb7k45tak4fwusfgatla", "input": { "skip": 0.3, "image": "https://replicate.delivery/pbxt/Kdrtkd4IdmtW53B6l9tG1upqxL6xFMhXobvcQ27qayMQFAIA/girl_with_a_pearl_earring.jpeg", "edit_threshold": "0.75", "editing_prompts": "glasses", "edit_warmup_steps": 8, "edit_guidance_scale": "3.0", "num_inversion_steps": 50, "source_guidance_scale": 3.5, "reverse_editing_directions": "False" }, "logs": "Your input images far exceed the default resolution of the underlying diffusion model. The output images may contain severe artifacts! Consider down-sampling the input using the `height` and `width` parameters\n 0%| | 0/50 [00:00<?, ?it/s]\n 4%|▍ | 2/50 [00:00<00:03, 13.15it/s]\n 8%|▊ | 4/50 [00:00<00:03, 12.97it/s]\n 12%|█▏ | 6/50 [00:00<00:03, 12.77it/s]\n 16%|█▌ | 8/50 [00:00<00:03, 12.73it/s]\n 20%|██ | 10/50 [00:00<00:03, 12.76it/s]\n 24%|██▍ | 12/50 [00:00<00:02, 12.75it/s]\n 28%|██▊ | 14/50 [00:01<00:02, 12.78it/s]\n 32%|███▏ | 16/50 [00:01<00:02, 12.85it/s]\n 36%|███▌ | 18/50 [00:01<00:02, 12.82it/s]\n 40%|████ | 20/50 [00:01<00:02, 12.79it/s]\n 44%|████▍ | 22/50 [00:01<00:02, 12.79it/s]\n 48%|████▊ | 24/50 [00:01<00:02, 12.70it/s]\n 52%|█████▏ | 26/50 [00:02<00:01, 12.67it/s]\n 56%|█████▌ | 28/50 [00:02<00:01, 12.70it/s]\n 60%|██████ | 30/50 [00:02<00:01, 12.70it/s]\n 64%|██████▍ | 32/50 [00:02<00:01, 12.63it/s]\n 68%|██████▊ | 34/50 [00:02<00:01, 12.60it/s]\n 72%|███████▏ | 36/50 [00:02<00:01, 12.62it/s]\n 76%|███████▌ | 38/50 [00:02<00:00, 12.60it/s]\n 80%|████████ | 40/50 [00:03<00:00, 12.63it/s]\n 84%|████████▍ | 42/50 [00:03<00:00, 12.67it/s]\n 88%|████████▊ | 44/50 [00:03<00:00, 12.63it/s]\n 92%|█████████▏| 46/50 [00:03<00:00, 12.60it/s]\n 96%|█████████▌| 48/50 [00:03<00:00, 12.56it/s]\n100%|██████████| 50/50 [00:03<00:00, 12.36it/s]\n100%|██████████| 50/50 [00:03<00:00, 12.66it/s]\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:06, 7.98it/s]\n 4%|▍ | 2/50 [00:00<00:06, 7.96it/s]\n 6%|▌ | 3/50 [00:00<00:05, 7.95it/s]\n 8%|▊ | 4/50 [00:00<00:05, 7.95it/s]\n 10%|█ | 5/50 [00:00<00:05, 7.95it/s]\n 12%|█▏ | 6/50 [00:00<00:05, 7.93it/s]\n 14%|█▍ | 7/50 [00:00<00:05, 7.94it/s]\n 16%|█▌ | 8/50 [00:01<00:05, 7.94it/s]\n 18%|█▊ | 9/50 [00:01<00:05, 7.93it/s]\n 20%|██ | 10/50 [00:01<00:05, 7.93it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 7.93it/s]\n 24%|██▍ | 12/50 [00:01<00:04, 7.93it/s]\n 26%|██▌ | 13/50 [00:01<00:04, 7.93it/s]\n 28%|██▊ | 14/50 [00:01<00:04, 7.74it/s]\n 30%|███ | 15/50 [00:01<00:04, 7.80it/s]\n 32%|███▏ | 16/50 [00:02<00:04, 7.84it/s]\n 34%|███▍ | 17/50 [00:02<00:04, 7.87it/s]\n 36%|███▌ | 18/50 [00:02<00:04, 7.89it/s]\n 38%|███▊ | 19/50 [00:02<00:03, 7.90it/s]\n 40%|████ | 20/50 [00:02<00:03, 7.91it/s]\n 42%|████▏ | 21/50 [00:02<00:03, 7.92it/s]\n 44%|████▍ | 22/50 [00:02<00:03, 7.92it/s]\n 46%|████▌ | 23/50 [00:02<00:03, 7.92it/s]\n 48%|████▊ | 24/50 [00:03<00:03, 7.93it/s]\n 50%|█████ | 25/50 [00:03<00:03, 7.93it/s]\n 52%|█████▏ | 26/50 [00:03<00:03, 7.93it/s]\n 54%|█████▍ | 27/50 [00:03<00:02, 7.92it/s]\n 56%|█████▌ | 28/50 [00:03<00:02, 7.93it/s]\n 58%|█████▊ | 29/50 [00:03<00:02, 7.91it/s]\n 60%|██████ | 30/50 [00:03<00:02, 7.91it/s]\n 62%|██████▏ | 31/50 [00:03<00:02, 7.91it/s]\n 64%|██████▍ | 32/50 [00:04<00:02, 7.92it/s]\n 66%|██████▌ | 33/50 [00:04<00:02, 7.92it/s]\n 68%|██████▊ | 34/50 [00:04<00:02, 7.92it/s]\n 70%|███████ | 35/50 [00:04<00:01, 7.92it/s]\n 72%|███████▏ | 36/50 [00:04<00:01, 7.92it/s]\n 74%|███████▍ | 37/50 [00:04<00:01, 7.76it/s]\n 76%|███████▌ | 38/50 [00:04<00:01, 7.80it/s]\n 78%|███████▊ | 39/50 [00:04<00:01, 7.84it/s]\n 80%|████████ | 40/50 [00:05<00:01, 7.87it/s]\n 82%|████████▏ | 41/50 [00:05<00:01, 7.87it/s]\n 84%|████████▍ | 42/50 [00:05<00:01, 7.89it/s]\n 86%|████████▌ | 43/50 [00:05<00:00, 7.90it/s]\n 88%|████████▊ | 44/50 [00:05<00:00, 7.92it/s]\n 90%|█████████ | 45/50 [00:05<00:00, 7.92it/s]\n 92%|█████████▏| 46/50 [00:05<00:00, 7.92it/s]\n 94%|█████████▍| 47/50 [00:05<00:00, 7.92it/s]\n 96%|█████████▌| 48/50 [00:06<00:00, 7.92it/s]\n 98%|█████████▊| 49/50 [00:06<00:00, 7.93it/s]\n100%|██████████| 50/50 [00:06<00:00, 7.93it/s]\n100%|██████████| 50/50 [00:06<00:00, 7.90it/s]", "metrics": { "predict_time": 11.797109, "total_time": 11.806311 }, "output": "https://replicate.delivery/pbxt/cyvUwrQY1KLzMJEODz13q4fcg0NpAcqAbeRGrPStsvPTfzIlA/output.png", "started_at": "2024-03-27T09:58:32.347032Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/iots6ptb7k45tak4fwusfgatla", "cancel": "https://api.replicate.com/v1/predictions/iots6ptb7k45tak4fwusfgatla/cancel" }, "version": "18916a9500f503aa4aa92ec0b2dbf3cecfa1995ee2280b2033e80d50973af9f2" }
Generated inYour input images far exceed the default resolution of the underlying diffusion model. The output images may contain severe artifacts! Consider down-sampling the input using the `height` and `width` parameters 0%| | 0/50 [00:00<?, ?it/s] 4%|▍ | 2/50 [00:00<00:03, 13.15it/s] 8%|▊ | 4/50 [00:00<00:03, 12.97it/s] 12%|█▏ | 6/50 [00:00<00:03, 12.77it/s] 16%|█▌ | 8/50 [00:00<00:03, 12.73it/s] 20%|██ | 10/50 [00:00<00:03, 12.76it/s] 24%|██▍ | 12/50 [00:00<00:02, 12.75it/s] 28%|██▊ | 14/50 [00:01<00:02, 12.78it/s] 32%|███▏ | 16/50 [00:01<00:02, 12.85it/s] 36%|███▌ | 18/50 [00:01<00:02, 12.82it/s] 40%|████ | 20/50 [00:01<00:02, 12.79it/s] 44%|████▍ | 22/50 [00:01<00:02, 12.79it/s] 48%|████▊ | 24/50 [00:01<00:02, 12.70it/s] 52%|█████▏ | 26/50 [00:02<00:01, 12.67it/s] 56%|█████▌ | 28/50 [00:02<00:01, 12.70it/s] 60%|██████ | 30/50 [00:02<00:01, 12.70it/s] 64%|██████▍ | 32/50 [00:02<00:01, 12.63it/s] 68%|██████▊ | 34/50 [00:02<00:01, 12.60it/s] 72%|███████▏ | 36/50 [00:02<00:01, 12.62it/s] 76%|███████▌ | 38/50 [00:02<00:00, 12.60it/s] 80%|████████ | 40/50 [00:03<00:00, 12.63it/s] 84%|████████▍ | 42/50 [00:03<00:00, 12.67it/s] 88%|████████▊ | 44/50 [00:03<00:00, 12.63it/s] 92%|█████████▏| 46/50 [00:03<00:00, 12.60it/s] 96%|█████████▌| 48/50 [00:03<00:00, 12.56it/s] 100%|██████████| 50/50 [00:03<00:00, 12.36it/s] 100%|██████████| 50/50 [00:03<00:00, 12.66it/s] 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:06, 7.98it/s] 4%|▍ | 2/50 [00:00<00:06, 7.96it/s] 6%|▌ | 3/50 [00:00<00:05, 7.95it/s] 8%|▊ | 4/50 [00:00<00:05, 7.95it/s] 10%|█ | 5/50 [00:00<00:05, 7.95it/s] 12%|█▏ | 6/50 [00:00<00:05, 7.93it/s] 14%|█▍ | 7/50 [00:00<00:05, 7.94it/s] 16%|█▌ | 8/50 [00:01<00:05, 7.94it/s] 18%|█▊ | 9/50 [00:01<00:05, 7.93it/s] 20%|██ | 10/50 [00:01<00:05, 7.93it/s] 22%|██▏ | 11/50 [00:01<00:04, 7.93it/s] 24%|██▍ | 12/50 [00:01<00:04, 7.93it/s] 26%|██▌ | 13/50 [00:01<00:04, 7.93it/s] 28%|██▊ | 14/50 [00:01<00:04, 7.74it/s] 30%|███ | 15/50 [00:01<00:04, 7.80it/s] 32%|███▏ | 16/50 [00:02<00:04, 7.84it/s] 34%|███▍ | 17/50 [00:02<00:04, 7.87it/s] 36%|███▌ | 18/50 [00:02<00:04, 7.89it/s] 38%|███▊ | 19/50 [00:02<00:03, 7.90it/s] 40%|████ | 20/50 [00:02<00:03, 7.91it/s] 42%|████▏ | 21/50 [00:02<00:03, 7.92it/s] 44%|████▍ | 22/50 [00:02<00:03, 7.92it/s] 46%|████▌ | 23/50 [00:02<00:03, 7.92it/s] 48%|████▊ | 24/50 [00:03<00:03, 7.93it/s] 50%|█████ | 25/50 [00:03<00:03, 7.93it/s] 52%|█████▏ | 26/50 [00:03<00:03, 7.93it/s] 54%|█████▍ | 27/50 [00:03<00:02, 7.92it/s] 56%|█████▌ | 28/50 [00:03<00:02, 7.93it/s] 58%|█████▊ | 29/50 [00:03<00:02, 7.91it/s] 60%|██████ | 30/50 [00:03<00:02, 7.91it/s] 62%|██████▏ | 31/50 [00:03<00:02, 7.91it/s] 64%|██████▍ | 32/50 [00:04<00:02, 7.92it/s] 66%|██████▌ | 33/50 [00:04<00:02, 7.92it/s] 68%|██████▊ | 34/50 [00:04<00:02, 7.92it/s] 70%|███████ | 35/50 [00:04<00:01, 7.92it/s] 72%|███████▏ | 36/50 [00:04<00:01, 7.92it/s] 74%|███████▍ | 37/50 [00:04<00:01, 7.76it/s] 76%|███████▌ | 38/50 [00:04<00:01, 7.80it/s] 78%|███████▊ | 39/50 [00:04<00:01, 7.84it/s] 80%|████████ | 40/50 [00:05<00:01, 7.87it/s] 82%|████████▏ | 41/50 [00:05<00:01, 7.87it/s] 84%|████████▍ | 42/50 [00:05<00:01, 7.89it/s] 86%|████████▌ | 43/50 [00:05<00:00, 7.90it/s] 88%|████████▊ | 44/50 [00:05<00:00, 7.92it/s] 90%|█████████ | 45/50 [00:05<00:00, 7.92it/s] 92%|█████████▏| 46/50 [00:05<00:00, 7.92it/s] 94%|█████████▍| 47/50 [00:05<00:00, 7.92it/s] 96%|█████████▌| 48/50 [00:06<00:00, 7.92it/s] 98%|█████████▊| 49/50 [00:06<00:00, 7.93it/s] 100%|██████████| 50/50 [00:06<00:00, 7.93it/s] 100%|██████████| 50/50 [00:06<00:00, 7.90it/s]
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