shapestudio / markline
Line illustration
- Public
- 164 runs
-
L40S
- SDXL fine-tune
Prediction
shapestudio/markline:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6dIDclphp7tbezu5fpnlj75yoa4diaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of a TOK, line art, pizza slice, on t-shirt
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.64
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo of a TOK, line art, pizza slice, on t-shirt", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.64, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run shapestudio/markline using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "shapestudio/markline:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6d", { input: { width: 1024, height: 1024, prompt: "A photo of a TOK, line art, pizza slice, on t-shirt", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.64, num_outputs: 4, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, 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 shapestudio/markline using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "shapestudio/markline:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6d", input={ "width": 1024, "height": 1024, "prompt": "A photo of a TOK, line art, pizza slice, on t-shirt", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.64, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run shapestudio/markline 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": "shapestudio/markline:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6d", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK, line art, pizza slice, on t-shirt", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.64, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "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/shapestudio/markline@sha256:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6d \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="A photo of a TOK, line art, pizza slice, on t-shirt"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.64' \ -i 'num_outputs=4' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.8' \ -i 'negative_prompt=""' \ -i 'prompt_strength=0.8' \ -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/shapestudio/markline@sha256:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6dcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK, line art, pizza slice, on t-shirt", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.64, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-12-14T13:20:22.940917Z", "created_at": "2023-12-14T13:19:12.292023Z", "data_removed": false, "error": null, "id": "clphp7tbezu5fpnlj75yoa4dia", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK, line art, pizza slice, on t-shirt", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.64, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 28329\nEnsuring enough disk space...\nFree disk space: 1898366201856\nDownloading weights: https://replicate.delivery/pbxt/3hry604uErIPLt8FDeqf6owKjAMiHbCTgRql1iRWNJRGsU4RA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.249s (746 MB/s)\\nExtracted 186 MB in 0.061s (3.1 GB/s)\\n'\nDownloaded weights in 0.6226294040679932 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of a <s0><s1>, line art, pizza slice, on t-shirt\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<00:51, 1.06s/it]\n 4%|▍ | 2/50 [00:02<00:50, 1.06s/it]\n 6%|▌ | 3/50 [00:03<00:49, 1.06s/it]\n 8%|▊ | 4/50 [00:04<00:48, 1.06s/it]\n 10%|█ | 5/50 [00:05<00:47, 1.06s/it]\n 12%|█▏ | 6/50 [00:06<00:46, 1.06s/it]\n 14%|█▍ | 7/50 [00:07<00:45, 1.06s/it]\n 16%|█▌ | 8/50 [00:08<00:44, 1.06s/it]\n 18%|█▊ | 9/50 [00:09<00:43, 1.06s/it]\n 20%|██ | 10/50 [00:10<00:42, 1.06s/it]\n 22%|██▏ | 11/50 [00:11<00:41, 1.06s/it]\n 24%|██▍ | 12/50 [00:12<00:40, 1.06s/it]\n 26%|██▌ | 13/50 [00:13<00:39, 1.06s/it]\n 28%|██▊ | 14/50 [00:14<00:38, 1.06s/it]\n 30%|███ | 15/50 [00:15<00:37, 1.06s/it]\n 32%|███▏ | 16/50 [00:16<00:36, 1.06s/it]\n 34%|███▍ | 17/50 [00:18<00:35, 1.06s/it]\n 36%|███▌ | 18/50 [00:19<00:33, 1.06s/it]\n 38%|███▊ | 19/50 [00:20<00:32, 1.06s/it]\n 40%|████ | 20/50 [00:21<00:31, 1.06s/it]\n 42%|████▏ | 21/50 [00:22<00:30, 1.06s/it]\n 44%|████▍ | 22/50 [00:23<00:29, 1.06s/it]\n 46%|████▌ | 23/50 [00:24<00:28, 1.06s/it]\n 48%|████▊ | 24/50 [00:25<00:27, 1.06s/it]\n 50%|█████ | 25/50 [00:26<00:26, 1.06s/it]\n 52%|█████▏ | 26/50 [00:27<00:25, 1.06s/it]\n 54%|█████▍ | 27/50 [00:28<00:24, 1.06s/it]\n 56%|█████▌ | 28/50 [00:29<00:23, 1.06s/it]\n 58%|█████▊ | 29/50 [00:30<00:22, 1.06s/it]\n 60%|██████ | 30/50 [00:31<00:21, 1.06s/it]\n 62%|██████▏ | 31/50 [00:32<00:20, 1.06s/it]\n 64%|██████▍ | 32/50 [00:33<00:19, 1.06s/it]\n 66%|██████▌ | 33/50 [00:35<00:18, 1.06s/it]\n 68%|██████▊ | 34/50 [00:36<00:17, 1.06s/it]\n 70%|███████ | 35/50 [00:37<00:15, 1.06s/it]\n 72%|███████▏ | 36/50 [00:38<00:14, 1.06s/it]\n 74%|███████▍ | 37/50 [00:39<00:13, 1.06s/it]\n 76%|███████▌ | 38/50 [00:40<00:12, 1.06s/it]\n 78%|███████▊ | 39/50 [00:41<00:11, 1.06s/it]\n 80%|████████ | 40/50 [00:42<00:10, 1.06s/it]\n 82%|████████▏ | 41/50 [00:43<00:09, 1.06s/it]\n 84%|████████▍ | 42/50 [00:44<00:08, 1.06s/it]\n 86%|████████▌ | 43/50 [00:45<00:07, 1.06s/it]\n 88%|████████▊ | 44/50 [00:46<00:06, 1.06s/it]\n 90%|█████████ | 45/50 [00:47<00:05, 1.06s/it]\n 92%|█████████▏| 46/50 [00:48<00:04, 1.06s/it]\n 94%|█████████▍| 47/50 [00:49<00:03, 1.06s/it]\n 96%|█████████▌| 48/50 [00:50<00:02, 1.06s/it]\n 98%|█████████▊| 49/50 [00:52<00:01, 1.06s/it]\n100%|██████████| 50/50 [00:53<00:00, 1.06s/it]\n100%|██████████| 50/50 [00:53<00:00, 1.06s/it]\nPotential NSFW content was detected in one or more images. A black image will be returned instead. Try again with a different prompt and/or seed.\nNSFW content detected in image 3", "metrics": { "predict_time": 59.221708, "total_time": 70.648894 }, "output": [ "https://replicate.delivery/pbxt/pmCU8JhzRga8NVb9CqHMADg4XHoSOWuhJqNMXcCPRvbFzigE/out-0.png", "https://replicate.delivery/pbxt/hnwPqd8vT9YIBBW9iu2IN0BXDH0LiugxRX56e4CkwtBLmFBJA/out-1.png", "https://replicate.delivery/pbxt/xeUvO8hbFpxpBCbRdHqbwujIMRO7GfGyk6tfersdrz3YxsIIB/out-2.png" ], "started_at": "2023-12-14T13:19:23.719209Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/clphp7tbezu5fpnlj75yoa4dia", "cancel": "https://api.replicate.com/v1/predictions/clphp7tbezu5fpnlj75yoa4dia/cancel" }, "version": "860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6d" }Generated inUsing seed: 28329 Ensuring enough disk space... Free disk space: 1898366201856 Downloading weights: https://replicate.delivery/pbxt/3hry604uErIPLt8FDeqf6owKjAMiHbCTgRql1iRWNJRGsU4RA/trained_model.tar b'Downloaded 186 MB bytes in 0.249s (746 MB/s)\nExtracted 186 MB in 0.061s (3.1 GB/s)\n' Downloaded weights in 0.6226294040679932 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A photo of a <s0><s1>, line art, pizza slice, on t-shirt txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<00:51, 1.06s/it] 4%|▍ | 2/50 [00:02<00:50, 1.06s/it] 6%|▌ | 3/50 [00:03<00:49, 1.06s/it] 8%|▊ | 4/50 [00:04<00:48, 1.06s/it] 10%|█ | 5/50 [00:05<00:47, 1.06s/it] 12%|█▏ | 6/50 [00:06<00:46, 1.06s/it] 14%|█▍ | 7/50 [00:07<00:45, 1.06s/it] 16%|█▌ | 8/50 [00:08<00:44, 1.06s/it] 18%|█▊ | 9/50 [00:09<00:43, 1.06s/it] 20%|██ | 10/50 [00:10<00:42, 1.06s/it] 22%|██▏ | 11/50 [00:11<00:41, 1.06s/it] 24%|██▍ | 12/50 [00:12<00:40, 1.06s/it] 26%|██▌ | 13/50 [00:13<00:39, 1.06s/it] 28%|██▊ | 14/50 [00:14<00:38, 1.06s/it] 30%|███ | 15/50 [00:15<00:37, 1.06s/it] 32%|███▏ | 16/50 [00:16<00:36, 1.06s/it] 34%|███▍ | 17/50 [00:18<00:35, 1.06s/it] 36%|███▌ | 18/50 [00:19<00:33, 1.06s/it] 38%|███▊ | 19/50 [00:20<00:32, 1.06s/it] 40%|████ | 20/50 [00:21<00:31, 1.06s/it] 42%|████▏ | 21/50 [00:22<00:30, 1.06s/it] 44%|████▍ | 22/50 [00:23<00:29, 1.06s/it] 46%|████▌ | 23/50 [00:24<00:28, 1.06s/it] 48%|████▊ | 24/50 [00:25<00:27, 1.06s/it] 50%|█████ | 25/50 [00:26<00:26, 1.06s/it] 52%|█████▏ | 26/50 [00:27<00:25, 1.06s/it] 54%|█████▍ | 27/50 [00:28<00:24, 1.06s/it] 56%|█████▌ | 28/50 [00:29<00:23, 1.06s/it] 58%|█████▊ | 29/50 [00:30<00:22, 1.06s/it] 60%|██████ | 30/50 [00:31<00:21, 1.06s/it] 62%|██████▏ | 31/50 [00:32<00:20, 1.06s/it] 64%|██████▍ | 32/50 [00:33<00:19, 1.06s/it] 66%|██████▌ | 33/50 [00:35<00:18, 1.06s/it] 68%|██████▊ | 34/50 [00:36<00:17, 1.06s/it] 70%|███████ | 35/50 [00:37<00:15, 1.06s/it] 72%|███████▏ | 36/50 [00:38<00:14, 1.06s/it] 74%|███████▍ | 37/50 [00:39<00:13, 1.06s/it] 76%|███████▌ | 38/50 [00:40<00:12, 1.06s/it] 78%|███████▊ | 39/50 [00:41<00:11, 1.06s/it] 80%|████████ | 40/50 [00:42<00:10, 1.06s/it] 82%|████████▏ | 41/50 [00:43<00:09, 1.06s/it] 84%|████████▍ | 42/50 [00:44<00:08, 1.06s/it] 86%|████████▌ | 43/50 [00:45<00:07, 1.06s/it] 88%|████████▊ | 44/50 [00:46<00:06, 1.06s/it] 90%|█████████ | 45/50 [00:47<00:05, 1.06s/it] 92%|█████████▏| 46/50 [00:48<00:04, 1.06s/it] 94%|█████████▍| 47/50 [00:49<00:03, 1.06s/it] 96%|█████████▌| 48/50 [00:50<00:02, 1.06s/it] 98%|█████████▊| 49/50 [00:52<00:01, 1.06s/it] 100%|██████████| 50/50 [00:53<00:00, 1.06s/it] 100%|██████████| 50/50 [00:53<00:00, 1.06s/it] Potential NSFW content was detected in one or more images. A black image will be returned instead. Try again with a different prompt and/or seed. NSFW content detected in image 3Prediction
shapestudio/markline:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6dIDmuba2htbfeugse2pxdbxmiry6yStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of a TOK, line art, shrimp on the plate , on a t-shirt
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.64
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo of a TOK, line art, shrimp on the plate , on a t-shirt", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.64, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run shapestudio/markline using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "shapestudio/markline:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6d", { input: { width: 1024, height: 1024, prompt: "A photo of a TOK, line art, shrimp on the plate , on a t-shirt", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.64, num_outputs: 4, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, 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 shapestudio/markline using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "shapestudio/markline:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6d", input={ "width": 1024, "height": 1024, "prompt": "A photo of a TOK, line art, shrimp on the plate , on a t-shirt", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.64, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run shapestudio/markline 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": "shapestudio/markline:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6d", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK, line art, shrimp on the plate , on a t-shirt", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.64, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "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/shapestudio/markline@sha256:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6d \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="A photo of a TOK, line art, shrimp on the plate , on a t-shirt"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.64' \ -i 'num_outputs=4' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.8' \ -i 'negative_prompt=""' \ -i 'prompt_strength=0.8' \ -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/shapestudio/markline@sha256:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6dcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK, line art, shrimp on the plate , on a t-shirt", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.64, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-12-15T12:00:26.657107Z", "created_at": "2023-12-15T11:59:19.357176Z", "data_removed": false, "error": null, "id": "muba2htbfeugse2pxdbxmiry6y", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK, line art, shrimp on the plate , on a t-shirt", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.64, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 43221\nEnsuring enough disk space...\nFree disk space: 1691100737536\nDownloading weights: https://replicate.delivery/pbxt/3hry604uErIPLt8FDeqf6owKjAMiHbCTgRql1iRWNJRGsU4RA/trained_model.tar\nb'Downloaded 186 MB bytes in 4.453s (42 MB/s)\\nExtracted 186 MB in 0.058s (3.2 GB/s)\\n'\nDownloaded weights in 5.092494249343872 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of a <s0><s1>, line art, shrimp on the plate , on a t-shirt\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<00:51, 1.05s/it]\n 4%|▍ | 2/50 [00:02<00:50, 1.05s/it]\n 6%|▌ | 3/50 [00:03<00:49, 1.05s/it]\n 8%|▊ | 4/50 [00:04<00:48, 1.05s/it]\n 10%|█ | 5/50 [00:05<00:47, 1.05s/it]\n 12%|█▏ | 6/50 [00:06<00:46, 1.05s/it]\n 14%|█▍ | 7/50 [00:07<00:45, 1.06s/it]\n 16%|█▌ | 8/50 [00:08<00:44, 1.06s/it]\n 18%|█▊ | 9/50 [00:09<00:43, 1.06s/it]\n 20%|██ | 10/50 [00:10<00:42, 1.06s/it]\n 22%|██▏ | 11/50 [00:11<00:41, 1.06s/it]\n 24%|██▍ | 12/50 [00:12<00:40, 1.06s/it]\n 26%|██▌ | 13/50 [00:13<00:39, 1.06s/it]\n 28%|██▊ | 14/50 [00:14<00:38, 1.06s/it]\n 30%|███ | 15/50 [00:15<00:37, 1.06s/it]\n 32%|███▏ | 16/50 [00:16<00:35, 1.06s/it]\n 34%|███▍ | 17/50 [00:17<00:34, 1.06s/it]\n 36%|███▌ | 18/50 [00:19<00:33, 1.06s/it]\n 38%|███▊ | 19/50 [00:20<00:32, 1.06s/it]\n 40%|████ | 20/50 [00:21<00:31, 1.06s/it]\n 42%|████▏ | 21/50 [00:22<00:30, 1.06s/it]\n 44%|████▍ | 22/50 [00:23<00:29, 1.06s/it]\n 46%|████▌ | 23/50 [00:24<00:28, 1.06s/it]\n 48%|████▊ | 24/50 [00:25<00:27, 1.06s/it]\n 50%|█████ | 25/50 [00:26<00:26, 1.06s/it]\n 52%|█████▏ | 26/50 [00:27<00:25, 1.06s/it]\n 54%|█████▍ | 27/50 [00:28<00:24, 1.06s/it]\n 56%|█████▌ | 28/50 [00:29<00:23, 1.06s/it]\n 58%|█████▊ | 29/50 [00:30<00:22, 1.06s/it]\n 60%|██████ | 30/50 [00:31<00:21, 1.06s/it]\n 62%|██████▏ | 31/50 [00:32<00:20, 1.06s/it]\n 64%|██████▍ | 32/50 [00:33<00:19, 1.06s/it]\n 66%|██████▌ | 33/50 [00:34<00:18, 1.06s/it]\n 68%|██████▊ | 34/50 [00:35<00:16, 1.06s/it]\n 70%|███████ | 35/50 [00:37<00:15, 1.06s/it]\n 72%|███████▏ | 36/50 [00:38<00:14, 1.06s/it]\n 74%|███████▍ | 37/50 [00:39<00:13, 1.06s/it]\n 76%|███████▌ | 38/50 [00:40<00:12, 1.06s/it]\n 78%|███████▊ | 39/50 [00:41<00:11, 1.06s/it]\n 80%|████████ | 40/50 [00:42<00:10, 1.06s/it]\n 82%|████████▏ | 41/50 [00:43<00:09, 1.06s/it]\n 84%|████████▍ | 42/50 [00:44<00:08, 1.06s/it]\n 86%|████████▌ | 43/50 [00:45<00:07, 1.06s/it]\n 88%|████████▊ | 44/50 [00:46<00:06, 1.06s/it]\n 90%|█████████ | 45/50 [00:47<00:05, 1.06s/it]\n 92%|█████████▏| 46/50 [00:48<00:04, 1.06s/it]\n 94%|█████████▍| 47/50 [00:49<00:03, 1.06s/it]\n 96%|█████████▌| 48/50 [00:50<00:02, 1.06s/it]\n 98%|█████████▊| 49/50 [00:51<00:01, 1.06s/it]\n100%|██████████| 50/50 [00:52<00:00, 1.06s/it]\n100%|██████████| 50/50 [00:52<00:00, 1.06s/it]", "metrics": { "predict_time": 65.85482, "total_time": 67.299931 }, "output": [ "https://replicate.delivery/pbxt/JfmExafPD7migkFOSGDO4lemN9XCNGgSPYmjdf28HHZgd8JIB/out-0.png", "https://replicate.delivery/pbxt/vurQemYB7euNeJz7sCa272rvdOgY3RhwWnayCmsTzG1zOeJIB/out-1.png", "https://replicate.delivery/pbxt/deWKYeORf5TRnopXebTV7Sy8tEfXggf5BRBeOGHmvvpxsjPBJA/out-2.png", "https://replicate.delivery/pbxt/rhLzG2PDfWzeHE1CE0daUyiPRp5II4KP0PR3dd2iBbdaHfEkA/out-3.png" ], "started_at": "2023-12-15T11:59:20.802287Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/muba2htbfeugse2pxdbxmiry6y", "cancel": "https://api.replicate.com/v1/predictions/muba2htbfeugse2pxdbxmiry6y/cancel" }, "version": "860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6d" }Generated inUsing seed: 43221 Ensuring enough disk space... Free disk space: 1691100737536 Downloading weights: https://replicate.delivery/pbxt/3hry604uErIPLt8FDeqf6owKjAMiHbCTgRql1iRWNJRGsU4RA/trained_model.tar b'Downloaded 186 MB bytes in 4.453s (42 MB/s)\nExtracted 186 MB in 0.058s (3.2 GB/s)\n' Downloaded weights in 5.092494249343872 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A photo of a <s0><s1>, line art, shrimp on the plate , on a t-shirt txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<00:51, 1.05s/it] 4%|▍ | 2/50 [00:02<00:50, 1.05s/it] 6%|▌ | 3/50 [00:03<00:49, 1.05s/it] 8%|▊ | 4/50 [00:04<00:48, 1.05s/it] 10%|█ | 5/50 [00:05<00:47, 1.05s/it] 12%|█▏ | 6/50 [00:06<00:46, 1.05s/it] 14%|█▍ | 7/50 [00:07<00:45, 1.06s/it] 16%|█▌ | 8/50 [00:08<00:44, 1.06s/it] 18%|█▊ | 9/50 [00:09<00:43, 1.06s/it] 20%|██ | 10/50 [00:10<00:42, 1.06s/it] 22%|██▏ | 11/50 [00:11<00:41, 1.06s/it] 24%|██▍ | 12/50 [00:12<00:40, 1.06s/it] 26%|██▌ | 13/50 [00:13<00:39, 1.06s/it] 28%|██▊ | 14/50 [00:14<00:38, 1.06s/it] 30%|███ | 15/50 [00:15<00:37, 1.06s/it] 32%|███▏ | 16/50 [00:16<00:35, 1.06s/it] 34%|███▍ | 17/50 [00:17<00:34, 1.06s/it] 36%|███▌ | 18/50 [00:19<00:33, 1.06s/it] 38%|███▊ | 19/50 [00:20<00:32, 1.06s/it] 40%|████ | 20/50 [00:21<00:31, 1.06s/it] 42%|████▏ | 21/50 [00:22<00:30, 1.06s/it] 44%|████▍ | 22/50 [00:23<00:29, 1.06s/it] 46%|████▌ | 23/50 [00:24<00:28, 1.06s/it] 48%|████▊ | 24/50 [00:25<00:27, 1.06s/it] 50%|█████ | 25/50 [00:26<00:26, 1.06s/it] 52%|█████▏ | 26/50 [00:27<00:25, 1.06s/it] 54%|█████▍ | 27/50 [00:28<00:24, 1.06s/it] 56%|█████▌ | 28/50 [00:29<00:23, 1.06s/it] 58%|█████▊ | 29/50 [00:30<00:22, 1.06s/it] 60%|██████ | 30/50 [00:31<00:21, 1.06s/it] 62%|██████▏ | 31/50 [00:32<00:20, 1.06s/it] 64%|██████▍ | 32/50 [00:33<00:19, 1.06s/it] 66%|██████▌ | 33/50 [00:34<00:18, 1.06s/it] 68%|██████▊ | 34/50 [00:35<00:16, 1.06s/it] 70%|███████ | 35/50 [00:37<00:15, 1.06s/it] 72%|███████▏ | 36/50 [00:38<00:14, 1.06s/it] 74%|███████▍ | 37/50 [00:39<00:13, 1.06s/it] 76%|███████▌ | 38/50 [00:40<00:12, 1.06s/it] 78%|███████▊ | 39/50 [00:41<00:11, 1.06s/it] 80%|████████ | 40/50 [00:42<00:10, 1.06s/it] 82%|████████▏ | 41/50 [00:43<00:09, 1.06s/it] 84%|████████▍ | 42/50 [00:44<00:08, 1.06s/it] 86%|████████▌ | 43/50 [00:45<00:07, 1.06s/it] 88%|████████▊ | 44/50 [00:46<00:06, 1.06s/it] 90%|█████████ | 45/50 [00:47<00:05, 1.06s/it] 92%|█████████▏| 46/50 [00:48<00:04, 1.06s/it] 94%|█████████▍| 47/50 [00:49<00:03, 1.06s/it] 96%|█████████▌| 48/50 [00:50<00:02, 1.06s/it] 98%|█████████▊| 49/50 [00:51<00:01, 1.06s/it] 100%|██████████| 50/50 [00:52<00:00, 1.06s/it] 100%|██████████| 50/50 [00:52<00:00, 1.06s/it]Prediction
shapestudio/markline:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6dID4gxxk23b5egptpjasp53gcjglmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of a TOK, line art, food chicken wrap, on t-shirt
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.64
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo of a TOK, line art, food chicken wrap, on t-shirt", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.64, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run shapestudio/markline using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "shapestudio/markline:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6d", { input: { width: 1024, height: 1024, prompt: "A photo of a TOK, line art, food chicken wrap, on t-shirt", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.64, num_outputs: 4, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, 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 shapestudio/markline using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "shapestudio/markline:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6d", input={ "width": 1024, "height": 1024, "prompt": "A photo of a TOK, line art, food chicken wrap, on t-shirt", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.64, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run shapestudio/markline 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": "shapestudio/markline:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6d", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK, line art, food chicken wrap, on t-shirt", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.64, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "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/shapestudio/markline@sha256:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6d \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="A photo of a TOK, line art, food chicken wrap, on t-shirt"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.64' \ -i 'num_outputs=4' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.8' \ -i 'negative_prompt=""' \ -i 'prompt_strength=0.8' \ -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/shapestudio/markline@sha256:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6dcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK, line art, food chicken wrap, on t-shirt", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.64, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-12-14T13:47:20.967908Z", "created_at": "2023-12-14T13:46:19.237640Z", "data_removed": false, "error": null, "id": "4gxxk23b5egptpjasp53gcjglm", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK, line art, food chicken wrap, on t-shirt", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.64, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 21384\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of a <s0><s1>, line art, food chicken wrap, on t-shirt\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<00:51, 1.05s/it]\n 4%|▍ | 2/50 [00:02<00:50, 1.05s/it]\n 6%|▌ | 3/50 [00:03<00:49, 1.05s/it]\n 8%|▊ | 4/50 [00:04<00:48, 1.05s/it]\n 10%|█ | 5/50 [00:05<00:47, 1.05s/it]\n 12%|█▏ | 6/50 [00:06<00:46, 1.05s/it]\n 14%|█▍ | 7/50 [00:07<00:45, 1.05s/it]\n 16%|█▌ | 8/50 [00:08<00:44, 1.05s/it]\n 18%|█▊ | 9/50 [00:09<00:43, 1.05s/it]\n 20%|██ | 10/50 [00:10<00:42, 1.05s/it]\n 22%|██▏ | 11/50 [00:11<00:41, 1.05s/it]\n 24%|██▍ | 12/50 [00:12<00:40, 1.06s/it]\n 26%|██▌ | 13/50 [00:13<00:39, 1.06s/it]\n 28%|██▊ | 14/50 [00:14<00:38, 1.06s/it]\n 30%|███ | 15/50 [00:15<00:36, 1.06s/it]\n 32%|███▏ | 16/50 [00:16<00:35, 1.06s/it]\n 34%|███▍ | 17/50 [00:17<00:34, 1.06s/it]\n 36%|███▌ | 18/50 [00:18<00:33, 1.06s/it]\n 38%|███▊ | 19/50 [00:20<00:32, 1.06s/it]\n 40%|████ | 20/50 [00:21<00:31, 1.06s/it]\n 42%|████▏ | 21/50 [00:22<00:30, 1.06s/it]\n 44%|████▍ | 22/50 [00:23<00:29, 1.06s/it]\n 46%|████▌ | 23/50 [00:24<00:28, 1.06s/it]\n 48%|████▊ | 24/50 [00:25<00:27, 1.06s/it]\n 50%|█████ | 25/50 [00:26<00:26, 1.06s/it]\n 52%|█████▏ | 26/50 [00:27<00:25, 1.06s/it]\n 54%|█████▍ | 27/50 [00:28<00:24, 1.06s/it]\n 56%|█████▌ | 28/50 [00:29<00:23, 1.06s/it]\n 58%|█████▊ | 29/50 [00:30<00:22, 1.06s/it]\n 60%|██████ | 30/50 [00:31<00:21, 1.06s/it]\n 62%|██████▏ | 31/50 [00:32<00:20, 1.06s/it]\n 64%|██████▍ | 32/50 [00:33<00:19, 1.06s/it]\n 66%|██████▌ | 33/50 [00:34<00:18, 1.06s/it]\n 68%|██████▊ | 34/50 [00:35<00:16, 1.06s/it]\n 70%|███████ | 35/50 [00:36<00:15, 1.06s/it]\n 72%|███████▏ | 36/50 [00:38<00:14, 1.06s/it]\n 74%|███████▍ | 37/50 [00:39<00:13, 1.06s/it]\n 76%|███████▌ | 38/50 [00:40<00:12, 1.06s/it]\n 78%|███████▊ | 39/50 [00:41<00:11, 1.06s/it]\n 80%|████████ | 40/50 [00:42<00:10, 1.06s/it]\n 82%|████████▏ | 41/50 [00:43<00:09, 1.06s/it]\n 84%|████████▍ | 42/50 [00:44<00:08, 1.06s/it]\n 86%|████████▌ | 43/50 [00:45<00:07, 1.06s/it]\n 88%|████████▊ | 44/50 [00:46<00:06, 1.06s/it]\n 90%|█████████ | 45/50 [00:47<00:05, 1.06s/it]\n 92%|█████████▏| 46/50 [00:48<00:04, 1.06s/it]\n 94%|█████████▍| 47/50 [00:49<00:03, 1.06s/it]\n 96%|█████████▌| 48/50 [00:50<00:02, 1.06s/it]\n 98%|█████████▊| 49/50 [00:51<00:01, 1.06s/it]\n100%|██████████| 50/50 [00:52<00:00, 1.06s/it]\n100%|██████████| 50/50 [00:52<00:00, 1.06s/it]", "metrics": { "predict_time": 60.365849, "total_time": 61.730268 }, "output": [ "https://replicate.delivery/pbxt/YZ6D93U3FZodEZZ1VyUXE9ZvjK78ezHoJqPFqWHyL0ZzyFBJA/out-0.png", "https://replicate.delivery/pbxt/3Z9Xz6ZHje1FbCu2ST6x5jH89g7wruweRUXULOGOtQhnlLCSA/out-1.png", "https://replicate.delivery/pbxt/AxMHppjdY87FBFN96l7wgdg61rJyBcq4R229M9FTJWLa5igE/out-2.png", "https://replicate.delivery/pbxt/1vbKwXyN2mpJKV6VLqQZFIxHyyBHeG4fSDC5fRQhWmmRLXEkA/out-3.png" ], "started_at": "2023-12-14T13:46:20.602059Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4gxxk23b5egptpjasp53gcjglm", "cancel": "https://api.replicate.com/v1/predictions/4gxxk23b5egptpjasp53gcjglm/cancel" }, "version": "860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6d" }Generated inUsing seed: 21384 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A photo of a <s0><s1>, line art, food chicken wrap, on t-shirt txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<00:51, 1.05s/it] 4%|▍ | 2/50 [00:02<00:50, 1.05s/it] 6%|▌ | 3/50 [00:03<00:49, 1.05s/it] 8%|▊ | 4/50 [00:04<00:48, 1.05s/it] 10%|█ | 5/50 [00:05<00:47, 1.05s/it] 12%|█▏ | 6/50 [00:06<00:46, 1.05s/it] 14%|█▍ | 7/50 [00:07<00:45, 1.05s/it] 16%|█▌ | 8/50 [00:08<00:44, 1.05s/it] 18%|█▊ | 9/50 [00:09<00:43, 1.05s/it] 20%|██ | 10/50 [00:10<00:42, 1.05s/it] 22%|██▏ | 11/50 [00:11<00:41, 1.05s/it] 24%|██▍ | 12/50 [00:12<00:40, 1.06s/it] 26%|██▌ | 13/50 [00:13<00:39, 1.06s/it] 28%|██▊ | 14/50 [00:14<00:38, 1.06s/it] 30%|███ | 15/50 [00:15<00:36, 1.06s/it] 32%|███▏ | 16/50 [00:16<00:35, 1.06s/it] 34%|███▍ | 17/50 [00:17<00:34, 1.06s/it] 36%|███▌ | 18/50 [00:18<00:33, 1.06s/it] 38%|███▊ | 19/50 [00:20<00:32, 1.06s/it] 40%|████ | 20/50 [00:21<00:31, 1.06s/it] 42%|████▏ | 21/50 [00:22<00:30, 1.06s/it] 44%|████▍ | 22/50 [00:23<00:29, 1.06s/it] 46%|████▌ | 23/50 [00:24<00:28, 1.06s/it] 48%|████▊ | 24/50 [00:25<00:27, 1.06s/it] 50%|█████ | 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shapestudio/markline:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6dIDptzrlk3bljfzibdys72vyrbvu4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A photo of a TOK, line art, plate of soup salmon pieces floating, on t-shirt
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.64
- num_outputs
- 4
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A photo of a TOK, line art, plate of soup salmon pieces floating, on t-shirt", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.64, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run shapestudio/markline using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "shapestudio/markline:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6d", { input: { width: 1024, height: 1024, prompt: "A photo of a TOK, line art, plate of soup salmon pieces floating, on t-shirt", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.64, num_outputs: 4, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "", prompt_strength: 0.8, 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 shapestudio/markline using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "shapestudio/markline:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6d", input={ "width": 1024, "height": 1024, "prompt": "A photo of a TOK, line art, plate of soup salmon pieces floating, on t-shirt", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.64, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run shapestudio/markline 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": "shapestudio/markline:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6d", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK, line art, plate of soup salmon pieces floating, on t-shirt", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.64, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "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/shapestudio/markline@sha256:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6d \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="A photo of a TOK, line art, plate of soup salmon pieces floating, on t-shirt"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.64' \ -i 'num_outputs=4' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.8' \ -i 'negative_prompt=""' \ -i 'prompt_strength=0.8' \ -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/shapestudio/markline@sha256:860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6dcurl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK, line art, plate of soup salmon pieces floating, on t-shirt", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.64, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-11-14T17:08:10.886454Z", "created_at": "2023-11-14T17:06:53.514843Z", "data_removed": false, "error": null, "id": "ptzrlk3bljfzibdys72vyrbvu4", "input": { "width": 1024, "height": 1024, "prompt": "A photo of a TOK, line art, plate of soup salmon pieces floating, on t-shirt", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.64, "num_outputs": 4, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 15044\nEnsuring enough disk space...\nFree disk space: 3604306325504\nDownloading weights: https://replicate.delivery/pbxt/3hry604uErIPLt8FDeqf6owKjAMiHbCTgRql1iRWNJRGsU4RA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.242s (768 MB/s)\\nExtracted 186 MB in 0.047s (3.9 GB/s)\\n'\nDownloaded weights in 0.38319921493530273 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A photo of a <s0><s1>, line art, plate of soup salmon pieces floating, on t-shirt\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:01<00:51, 1.05s/it]\n 4%|▍ | 2/50 [00:02<00:50, 1.05s/it]\n 6%|▌ | 3/50 [00:03<00:49, 1.05s/it]\n 8%|▊ | 4/50 [00:04<00:48, 1.05s/it]\n 10%|█ | 5/50 [00:05<00:47, 1.05s/it]\n 12%|█▏ | 6/50 [00:06<00:46, 1.05s/it]\n 14%|█▍ | 7/50 [00:07<00:45, 1.06s/it]\n 16%|█▌ | 8/50 [00:08<00:44, 1.06s/it]\n 18%|█▊ | 9/50 [00:09<00:43, 1.06s/it]\n 20%|██ | 10/50 [00:10<00:42, 1.06s/it]\n 22%|██▏ | 11/50 [00:11<00:41, 1.05s/it]\n 24%|██▍ | 12/50 [00:12<00:40, 1.06s/it]\n 26%|██▌ | 13/50 [00:13<00:39, 1.06s/it]\n 28%|██▊ | 14/50 [00:14<00:38, 1.06s/it]\n 30%|███ | 15/50 [00:15<00:36, 1.06s/it]\n 32%|███▏ | 16/50 [00:16<00:35, 1.05s/it]\n 34%|███▍ | 17/50 [00:17<00:34, 1.06s/it]\n 36%|███▌ | 18/50 [00:19<00:33, 1.06s/it]\n 38%|███▊ | 19/50 [00:20<00:32, 1.06s/it]\n 40%|████ | 20/50 [00:21<00:31, 1.06s/it]\n 42%|████▏ | 21/50 [00:22<00:30, 1.06s/it]\n 44%|████▍ | 22/50 [00:23<00:29, 1.06s/it]\n 46%|████▌ | 23/50 [00:24<00:28, 1.06s/it]\n 48%|████▊ | 24/50 [00:25<00:27, 1.06s/it]\n 50%|█████ | 25/50 [00:26<00:26, 1.06s/it]\n 52%|█████▏ | 26/50 [00:27<00:25, 1.06s/it]\n 54%|█████▍ | 27/50 [00:28<00:24, 1.06s/it]\n 56%|█████▌ | 28/50 [00:29<00:23, 1.06s/it]\n 58%|█████▊ | 29/50 [00:30<00:22, 1.06s/it]\n 60%|██████ | 30/50 [00:31<00:21, 1.06s/it]\n 62%|██████▏ | 31/50 [00:32<00:20, 1.06s/it]\n 64%|██████▍ | 32/50 [00:33<00:19, 1.06s/it]\n 66%|██████▌ | 33/50 [00:34<00:18, 1.06s/it]\n 68%|██████▊ | 34/50 [00:35<00:16, 1.06s/it]\n 70%|███████ | 35/50 [00:37<00:15, 1.06s/it]\n 72%|███████▏ | 36/50 [00:38<00:14, 1.06s/it]\n 74%|███████▍ | 37/50 [00:39<00:13, 1.06s/it]\n 76%|███████▌ | 38/50 [00:40<00:12, 1.06s/it]\n 78%|███████▊ | 39/50 [00:41<00:11, 1.06s/it]\n 80%|████████ | 40/50 [00:42<00:10, 1.06s/it]\n 82%|████████▏ | 41/50 [00:43<00:09, 1.06s/it]\n 84%|████████▍ | 42/50 [00:44<00:08, 1.06s/it]\n 86%|████████▌ | 43/50 [00:45<00:07, 1.06s/it]\n 88%|████████▊ | 44/50 [00:46<00:06, 1.06s/it]\n 90%|█████████ | 45/50 [00:47<00:05, 1.06s/it]\n 92%|█████████▏| 46/50 [00:48<00:04, 1.06s/it]\n 94%|█████████▍| 47/50 [00:49<00:03, 1.06s/it]\n 96%|█████████▌| 48/50 [00:50<00:02, 1.06s/it]\n 98%|█████████▊| 49/50 [00:51<00:01, 1.06s/it]\n100%|██████████| 50/50 [00:52<00:00, 1.06s/it]\n100%|██████████| 50/50 [00:52<00:00, 1.06s/it]", "metrics": { "predict_time": 71.131317, "total_time": 77.371611 }, "output": [ "https://replicate.delivery/pbxt/Bs158VNqxEb6OF7H4MfEy86eSpGxdAblpb7ceUr1rrf52WhHB/out-0.png", "https://replicate.delivery/pbxt/ayN4sddJpZ5CBl3Nnjs6fCZvdpfRGqzSk3D4rUoj2RWvtV4RA/out-1.png", "https://replicate.delivery/pbxt/Wl8EyhjVY5qFMdeSSwbJbwSW7OTVXgu11NhOyA7wYDE92K8IA/out-2.png", "https://replicate.delivery/pbxt/ac5RMP3tQabMCZUhF6luBnN7n9bfixphnSM0laxbE2O92K8IA/out-3.png" ], "started_at": "2023-11-14T17:06:59.755137Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ptzrlk3bljfzibdys72vyrbvu4", "cancel": "https://api.replicate.com/v1/predictions/ptzrlk3bljfzibdys72vyrbvu4/cancel" }, "version": "860613e17b1335102f5385f1e2158462af852f0de2e5d834cfc01f9794b29b6d" }Generated inUsing seed: 15044 Ensuring enough disk space... Free disk space: 3604306325504 Downloading weights: https://replicate.delivery/pbxt/3hry604uErIPLt8FDeqf6owKjAMiHbCTgRql1iRWNJRGsU4RA/trained_model.tar b'Downloaded 186 MB bytes in 0.242s (768 MB/s)\nExtracted 186 MB in 0.047s (3.9 GB/s)\n' Downloaded weights in 0.38319921493530273 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A photo of a <s0><s1>, line art, plate of soup salmon pieces floating, on t-shirt txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:01<00:51, 1.05s/it] 4%|▍ | 2/50 [00:02<00:50, 1.05s/it] 6%|▌ | 3/50 [00:03<00:49, 1.05s/it] 8%|▊ | 4/50 [00:04<00:48, 1.05s/it] 10%|█ | 5/50 [00:05<00:47, 1.05s/it] 12%|█▏ | 6/50 [00:06<00:46, 1.05s/it] 14%|█▍ | 7/50 [00:07<00:45, 1.06s/it] 16%|█▌ | 8/50 [00:08<00:44, 1.06s/it] 18%|█▊ | 9/50 [00:09<00:43, 1.06s/it] 20%|██ | 10/50 [00:10<00:42, 1.06s/it] 22%|██▏ | 11/50 [00:11<00:41, 1.05s/it] 24%|██▍ | 12/50 [00:12<00:40, 1.06s/it] 26%|██▌ | 13/50 [00:13<00:39, 1.06s/it] 28%|██▊ | 14/50 [00:14<00:38, 1.06s/it] 30%|███ | 15/50 [00:15<00:36, 1.06s/it] 32%|███▏ | 16/50 [00:16<00:35, 1.05s/it] 34%|███▍ | 17/50 [00:17<00:34, 1.06s/it] 36%|███▌ | 18/50 [00:19<00:33, 1.06s/it] 38%|███▊ | 19/50 [00:20<00:32, 1.06s/it] 40%|████ | 20/50 [00:21<00:31, 1.06s/it] 42%|████▏ | 21/50 [00:22<00:30, 1.06s/it] 44%|████▍ | 22/50 [00:23<00:29, 1.06s/it] 46%|████▌ | 23/50 [00:24<00:28, 1.06s/it] 48%|████▊ | 24/50 [00:25<00:27, 1.06s/it] 50%|█████ | 25/50 [00:26<00:26, 1.06s/it] 52%|█████▏ | 26/50 [00:27<00:25, 1.06s/it] 54%|█████▍ | 27/50 [00:28<00:24, 1.06s/it] 56%|█████▌ | 28/50 [00:29<00:23, 1.06s/it] 58%|█████▊ | 29/50 [00:30<00:22, 1.06s/it] 60%|██████ | 30/50 [00:31<00:21, 1.06s/it] 62%|██████▏ | 31/50 [00:32<00:20, 1.06s/it] 64%|██████▍ | 32/50 [00:33<00:19, 1.06s/it] 66%|██████▌ | 33/50 [00:34<00:18, 1.06s/it] 68%|██████▊ | 34/50 [00:35<00:16, 1.06s/it] 70%|███████ | 35/50 [00:37<00:15, 1.06s/it] 72%|███████▏ | 36/50 [00:38<00:14, 1.06s/it] 74%|███████▍ | 37/50 [00:39<00:13, 1.06s/it] 76%|███████▌ | 38/50 [00:40<00:12, 1.06s/it] 78%|███████▊ | 39/50 [00:41<00:11, 1.06s/it] 80%|████████ | 40/50 [00:42<00:10, 1.06s/it] 82%|████████▏ | 41/50 [00:43<00:09, 1.06s/it] 84%|████████▍ | 42/50 [00:44<00:08, 1.06s/it] 86%|████████▌ | 43/50 [00:45<00:07, 1.06s/it] 88%|████████▊ | 44/50 [00:46<00:06, 1.06s/it] 90%|█████████ | 45/50 [00:47<00:05, 1.06s/it] 92%|█████████▏| 46/50 [00:48<00:04, 1.06s/it] 94%|█████████▍| 47/50 [00:49<00:03, 1.06s/it] 96%|█████████▌| 48/50 [00:50<00:02, 1.06s/it] 98%|█████████▊| 49/50 [00:51<00:01, 1.06s/it] 100%|██████████| 50/50 [00:52<00:00, 1.06s/it] 100%|██████████| 50/50 [00:52<00:00, 1.06s/it]
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