cjwbw/stable-diffusion-2-1-unclip
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stable-diffusion-2-1-unclip
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cjwbw/stable-diffusion-2-1-unclip
Stable Diffusion v2-1-unclip Model
cjwbw/clip-guided-diffusion
Clip-Guided Diffusion Model for Image Generation
cjwbw/clip-guided-diffusion-pokemon
Generates pokemon sprites from prompt
cjwbw/rudalle-sr
Real-ESRGAN super-resolution model from ruDALL-E
cjwbw/face-align-cog
face alignment using stylegan-encoding
cjwbw/diffae
Image Manipulatinon with Diffusion Autoencoders
cjwbw/vqfr
Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder
cjwbw/global_tracking_transformers
Global Tracking Transformers
cjwbw/bigcolor
Colorization using a Generative Color Prior for Natural Images
cjwbw/clip-gen
Language-Free Training of a Text-to-Image Generator with CLIP
cjwbw/compositional-vsual-generation-with-composable-diffusion-models-pytorch
Composable Diffusion
cjwbw/micromotion-stylegan
Decoding Micromotion in Low-dimensional Latent Spaces from StyleGAN
cjwbw/vq-diffusion
VQ-Diffusion for Text-to-Image Synthesis
cjwbw/mindall-e
text-to-image generation
cjwbw/openpsg
Panoptic Scene Graph Generation
cjwbw/latent-diffusion-text2img
text-to-image with latent diffusion
cjwbw/night-enhancement
Unsupervised Night Image Enhancement
cjwbw/repaint
Inpainting using Denoising Diffusion Probabilistic Models
cjwbw/stable-diffusion
stable-diffusion with negative prompts, more scheduler
cjwbw/style-your-hair
Pose-Invariant Hairstyle Transfer
cjwbw/docentr
End-to-End Document Image Enhancement Transformer
Prediction
cjwbw/stable-diffusion-2-1-unclip:3a8d2f4f25198c92beb3c633ed94f8013399509c7321cd09c9036c2e33c67617IDlyn4luh3w5bvdg3ptvaun5oiaqStatusSucceededSourceWebHardware–Total durationCreatedInput
- image
- scheduler
- DPMSolverMultistep
- num_outputs
- "2"
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "image": "https://replicate.delivery/pbxt/IXIWb2CGjBD2vKuzrpKxHVertX5srMqH2xw0LwDUIm0Afby4/tarsila_do_amaral.png", "scheduler": "DPMSolverMultistep", "num_outputs": "2", "guidance_scale": 7.5, "num_inference_steps": 50 }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 cjwbw/stable-diffusion-2-1-unclip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/stable-diffusion-2-1-unclip:3a8d2f4f25198c92beb3c633ed94f8013399509c7321cd09c9036c2e33c67617", { input: { image: "https://replicate.delivery/pbxt/IXIWb2CGjBD2vKuzrpKxHVertX5srMqH2xw0LwDUIm0Afby4/tarsila_do_amaral.png", scheduler: "DPMSolverMultistep", num_outputs: "2", guidance_scale: 7.5, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cjwbw/stable-diffusion-2-1-unclip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/stable-diffusion-2-1-unclip:3a8d2f4f25198c92beb3c633ed94f8013399509c7321cd09c9036c2e33c67617", input={ "image": "https://replicate.delivery/pbxt/IXIWb2CGjBD2vKuzrpKxHVertX5srMqH2xw0LwDUIm0Afby4/tarsila_do_amaral.png", "scheduler": "DPMSolverMultistep", "num_outputs": "2", "guidance_scale": 7.5, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run cjwbw/stable-diffusion-2-1-unclip 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/stable-diffusion-2-1-unclip:3a8d2f4f25198c92beb3c633ed94f8013399509c7321cd09c9036c2e33c67617", "input": { "image": "https://replicate.delivery/pbxt/IXIWb2CGjBD2vKuzrpKxHVertX5srMqH2xw0LwDUIm0Afby4/tarsila_do_amaral.png", "scheduler": "DPMSolverMultistep", "num_outputs": "2", "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ 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/chenxwh/stable-diffusion-2-1-unclip@sha256:3a8d2f4f25198c92beb3c633ed94f8013399509c7321cd09c9036c2e33c67617 \ -i 'image="https://replicate.delivery/pbxt/IXIWb2CGjBD2vKuzrpKxHVertX5srMqH2xw0LwDUIm0Afby4/tarsila_do_amaral.png"' \ -i 'scheduler="DPMSolverMultistep"' \ -i 'num_outputs="2"' \ -i 'guidance_scale=7.5' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/chenxwh/stable-diffusion-2-1-unclip@sha256:3a8d2f4f25198c92beb3c633ed94f8013399509c7321cd09c9036c2e33c67617curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/pbxt/IXIWb2CGjBD2vKuzrpKxHVertX5srMqH2xw0LwDUIm0Afby4/tarsila_do_amaral.png", "scheduler": "DPMSolverMultistep", "num_outputs": "2", "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-03-25T13:42:29.945924Z", "created_at": "2023-03-25T13:39:28.532178Z", "data_removed": false, "error": null, "id": "lyn4luh3w5bvdg3ptvaun5oiaq", "input": { "image": "https://replicate.delivery/pbxt/IXIWb2CGjBD2vKuzrpKxHVertX5srMqH2xw0LwDUIm0Afby4/tarsila_do_amaral.png", "scheduler": "DPMSolverMultistep", "num_outputs": "2", "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Using seed: 40855\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:17, 2.79it/s]\n 4%|▍ | 2/50 [00:00<00:14, 3.21it/s]\n 6%|▌ | 3/50 [00:00<00:13, 3.38it/s]\n 8%|▊ | 4/50 [00:01<00:13, 3.46it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.51it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.54it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.55it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.56it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.57it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.57it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.57it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.58it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.58it/s]\n 28%|██▊ | 14/50 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3.58it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.58it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.58it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.58it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.59it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.59it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.59it/s]\n 86%|████████▌ | 43/50 [00:12<00:01, 3.59it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.59it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.59it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.59it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.60it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.60it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.60it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.60it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.56it/s]", "metrics": { "predict_time": 18.661192, "total_time": 181.413746 }, "output": [ "https://replicate.delivery/pbxt/j1d9Lx2KUZYLH5eETw1lxMDmjJ2DkHircxohQgJ6BtSiYlVIA/out-0.png", "https://replicate.delivery/pbxt/3OLJDjemaKyYH62TfGe8eRTFCWdUEIsdAp3nikynkfTrIWZFC/out-1.png" ], "started_at": "2023-03-25T13:42:11.284732Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/lyn4luh3w5bvdg3ptvaun5oiaq", "cancel": "https://api.replicate.com/v1/predictions/lyn4luh3w5bvdg3ptvaun5oiaq/cancel" }, "version": "3a8d2f4f25198c92beb3c633ed94f8013399509c7321cd09c9036c2e33c67617" }Generated inUsing seed: 40855 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:17, 2.79it/s] 4%|▍ | 2/50 [00:00<00:14, 3.21it/s] 6%|▌ | 3/50 [00:00<00:13, 3.38it/s] 8%|▊ | 4/50 [00:01<00:13, 3.46it/s] 10%|█ | 5/50 [00:01<00:12, 3.51it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.54it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.55it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.56it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.57it/s] 20%|██ | 10/50 [00:02<00:11, 3.57it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.57it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.58it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.58it/s] 28%|██▊ | 14/50 [00:03<00:10, 3.58it/s] 30%|███ | 15/50 [00:04<00:09, 3.58it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.58it/s] 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80%|████████ | 40/50 [00:11<00:02, 3.59it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.59it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.59it/s] 86%|████████▌ | 43/50 [00:12<00:01, 3.59it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.59it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.59it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.59it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.60it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.60it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.60it/s] 100%|██████████| 50/50 [00:14<00:00, 3.60it/s] 100%|██████████| 50/50 [00:14<00:00, 3.56it/s]Prediction
cjwbw/stable-diffusion-2-1-unclip:3a8d2f4f25198c92beb3c633ed94f8013399509c7321cd09c9036c2e33c67617Input
- image
- scheduler
- DPMSolverMultistep
- num_outputs
- "1"
- guidance_scale
- 7.5
- num_inference_steps
- 50
{ "image": "https://replicate.delivery/pbxt/IXIWb2CGjBD2vKuzrpKxHVertX5srMqH2xw0LwDUIm0Afby4/tarsila_do_amaral.png", "scheduler": "DPMSolverMultistep", "num_outputs": "1", "guidance_scale": 7.5, "num_inference_steps": 50 }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 cjwbw/stable-diffusion-2-1-unclip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cjwbw/stable-diffusion-2-1-unclip:3a8d2f4f25198c92beb3c633ed94f8013399509c7321cd09c9036c2e33c67617", { input: { image: "https://replicate.delivery/pbxt/IXIWb2CGjBD2vKuzrpKxHVertX5srMqH2xw0LwDUIm0Afby4/tarsila_do_amaral.png", scheduler: "DPMSolverMultistep", num_outputs: "1", guidance_scale: 7.5, num_inference_steps: 50 } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun cjwbw/stable-diffusion-2-1-unclip using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cjwbw/stable-diffusion-2-1-unclip:3a8d2f4f25198c92beb3c633ed94f8013399509c7321cd09c9036c2e33c67617", input={ "image": "https://replicate.delivery/pbxt/IXIWb2CGjBD2vKuzrpKxHVertX5srMqH2xw0LwDUIm0Afby4/tarsila_do_amaral.png", "scheduler": "DPMSolverMultistep", "num_outputs": "1", "guidance_scale": 7.5, "num_inference_steps": 50 } ) # To access the file URL: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run cjwbw/stable-diffusion-2-1-unclip 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/stable-diffusion-2-1-unclip:3a8d2f4f25198c92beb3c633ed94f8013399509c7321cd09c9036c2e33c67617", "input": { "image": "https://replicate.delivery/pbxt/IXIWb2CGjBD2vKuzrpKxHVertX5srMqH2xw0LwDUIm0Afby4/tarsila_do_amaral.png", "scheduler": "DPMSolverMultistep", "num_outputs": "1", "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ 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/chenxwh/stable-diffusion-2-1-unclip@sha256:3a8d2f4f25198c92beb3c633ed94f8013399509c7321cd09c9036c2e33c67617 \ -i 'image="https://replicate.delivery/pbxt/IXIWb2CGjBD2vKuzrpKxHVertX5srMqH2xw0LwDUIm0Afby4/tarsila_do_amaral.png"' \ -i 'scheduler="DPMSolverMultistep"' \ -i 'num_outputs="1"' \ -i 'guidance_scale=7.5' \ -i 'num_inference_steps=50'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 --gpus=all r8.im/chenxwh/stable-diffusion-2-1-unclip@sha256:3a8d2f4f25198c92beb3c633ed94f8013399509c7321cd09c9036c2e33c67617curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "image": "https://replicate.delivery/pbxt/IXIWb2CGjBD2vKuzrpKxHVertX5srMqH2xw0LwDUIm0Afby4/tarsila_do_amaral.png", "scheduler": "DPMSolverMultistep", "num_outputs": "1", "guidance_scale": 7.5, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-03-25T13:42:51.547790Z", "created_at": "2023-03-25T13:42:42.494900Z", "data_removed": false, "error": null, "id": "e6ikufsv3bcmtged2mbhzx653a", "input": { "image": "https://replicate.delivery/pbxt/IXIWb2CGjBD2vKuzrpKxHVertX5srMqH2xw0LwDUIm0Afby4/tarsila_do_amaral.png", "scheduler": "DPMSolverMultistep", "num_outputs": "1", "guidance_scale": 7.5, "num_inference_steps": 50 }, "logs": "Using seed: 42077\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:08, 5.85it/s]\n 4%|▍ | 2/50 [00:00<00:07, 6.06it/s]\n 6%|▌ | 3/50 [00:00<00:07, 6.37it/s]\n 8%|▊ | 4/50 [00:00<00:07, 6.53it/s]\n 10%|█ | 5/50 [00:00<00:06, 6.61it/s]\n 12%|█▏ | 6/50 [00:00<00:06, 6.66it/s]\n 14%|█▍ | 7/50 [00:01<00:06, 6.70it/s]\n 16%|█▌ | 8/50 [00:01<00:06, 6.73it/s]\n 18%|█▊ | 9/50 [00:01<00:06, 6.74it/s]\n 20%|██ | 10/50 [00:01<00:05, 6.74it/s]\n 22%|██▏ | 11/50 [00:01<00:05, 6.74it/s]\n 24%|██▍ | 12/50 [00:01<00:05, 6.74it/s]\n 26%|██▌ | 13/50 [00:01<00:05, 6.75it/s]\n 28%|██▊ | 14/50 [00:02<00:05, 6.75it/s]\n 30%|███ | 15/50 [00:02<00:05, 6.75it/s]\n 32%|███▏ | 16/50 [00:02<00:05, 6.75it/s]\n 34%|███▍ | 17/50 [00:02<00:04, 6.75it/s]\n 36%|███▌ | 18/50 [00:02<00:04, 6.74it/s]\n 38%|███▊ | 19/50 [00:02<00:04, 6.75it/s]\n 40%|████ | 20/50 [00:02<00:04, 6.75it/s]\n 42%|████▏ | 21/50 [00:03<00:04, 6.75it/s]\n 44%|████▍ | 22/50 [00:03<00:04, 6.76it/s]\n 46%|████▌ | 23/50 [00:03<00:03, 6.75it/s]\n 48%|████▊ | 24/50 [00:03<00:03, 6.75it/s]\n 50%|█████ | 25/50 [00:03<00:03, 6.75it/s]\n 52%|█████▏ | 26/50 [00:03<00:03, 6.75it/s]\n 54%|█████▍ | 27/50 [00:04<00:03, 6.74it/s]\n 56%|█████▌ | 28/50 [00:04<00:03, 6.73it/s]\n 58%|█████▊ | 29/50 [00:04<00:03, 6.74it/s]\n 60%|██████ | 30/50 [00:04<00:02, 6.75it/s]\n 62%|██████▏ | 31/50 [00:04<00:02, 6.76it/s]\n 64%|██████▍ | 32/50 [00:04<00:02, 6.75it/s]\n 66%|██████▌ | 33/50 [00:04<00:02, 6.74it/s]\n 68%|██████▊ | 34/50 [00:05<00:02, 6.75it/s]\n 70%|███████ | 35/50 [00:05<00:02, 6.76it/s]\n 72%|███████▏ | 36/50 [00:05<00:02, 6.75it/s]\n 74%|███████▍ | 37/50 [00:05<00:01, 6.76it/s]\n 76%|███████▌ | 38/50 [00:05<00:01, 6.76it/s]\n 78%|███████▊ | 39/50 [00:05<00:01, 6.77it/s]\n 80%|████████ | 40/50 [00:05<00:01, 6.77it/s]\n 82%|████████▏ | 41/50 [00:06<00:01, 6.77it/s]\n 84%|████████▍ | 42/50 [00:06<00:01, 6.77it/s]\n 86%|████████▌ | 43/50 [00:06<00:01, 6.78it/s]\n 88%|████████▊ | 44/50 [00:06<00:00, 6.78it/s]\n 90%|█████████ | 45/50 [00:06<00:00, 6.79it/s]\n 92%|█████████▏| 46/50 [00:06<00:00, 6.78it/s]\n 94%|█████████▍| 47/50 [00:06<00:00, 6.79it/s]\n 96%|█████████▌| 48/50 [00:07<00:00, 6.79it/s]\n 98%|█████████▊| 49/50 [00:07<00:00, 6.79it/s]\n100%|██████████| 50/50 [00:07<00:00, 6.80it/s]\n100%|██████████| 50/50 [00:07<00:00, 6.73it/s]", "metrics": { "predict_time": 8.976115, "total_time": 9.05289 }, "output": [ "https://replicate.delivery/pbxt/ixZq1VlmAtpcB5fGVIb8DV3rmw4RYvHO7zmfDx2QEeO0iVWhA/out-0.png" ], "started_at": "2023-03-25T13:42:42.571675Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/e6ikufsv3bcmtged2mbhzx653a", "cancel": "https://api.replicate.com/v1/predictions/e6ikufsv3bcmtged2mbhzx653a/cancel" }, "version": "3a8d2f4f25198c92beb3c633ed94f8013399509c7321cd09c9036c2e33c67617" }Generated inUsing seed: 42077 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:08, 5.85it/s] 4%|▍ | 2/50 [00:00<00:07, 6.06it/s] 6%|▌ | 3/50 [00:00<00:07, 6.37it/s] 8%|▊ | 4/50 [00:00<00:07, 6.53it/s] 10%|█ | 5/50 [00:00<00:06, 6.61it/s] 12%|█▏ | 6/50 [00:00<00:06, 6.66it/s] 14%|█▍ | 7/50 [00:01<00:06, 6.70it/s] 16%|█▌ | 8/50 [00:01<00:06, 6.73it/s] 18%|█▊ | 9/50 [00:01<00:06, 6.74it/s] 20%|██ | 10/50 [00:01<00:05, 6.74it/s] 22%|██▏ | 11/50 [00:01<00:05, 6.74it/s] 24%|██▍ | 12/50 [00:01<00:05, 6.74it/s] 26%|██▌ | 13/50 [00:01<00:05, 6.75it/s] 28%|██▊ | 14/50 [00:02<00:05, 6.75it/s] 30%|███ | 15/50 [00:02<00:05, 6.75it/s] 32%|███▏ | 16/50 [00:02<00:05, 6.75it/s] 34%|███▍ | 17/50 [00:02<00:04, 6.75it/s] 36%|███▌ | 18/50 [00:02<00:04, 6.74it/s] 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