semonxue / makspacesuit
A Ma.K Maschinen Krieger Suit model
- Public
- 206 runs
-
L40S
- SDXL fine-tune
Prediction
semonxue/makspacesuit:3a865c593a480656b72a0539e99635e98fbc435f59549eeeb9cf4c7d262dcc88IDemv4swlb7wjgych7nrwpejo67uStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a man in a makspacesuit,at spaceship wreckage ,war
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- 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 man in a makspacesuit,at spaceship wreckage ,war", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "semonxue/makspacesuit:3a865c593a480656b72a0539e99635e98fbc435f59549eeeb9cf4c7d262dcc88", { input: { width: 1024, height: 1024, prompt: "a man in a makspacesuit,at spaceship wreckage ,war", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, 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 semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "semonxue/makspacesuit:3a865c593a480656b72a0539e99635e98fbc435f59549eeeb9cf4c7d262dcc88", input={ "width": 1024, "height": 1024, "prompt": "a man in a makspacesuit,at spaceship wreckage ,war", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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 semonxue/makspacesuit 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": "semonxue/makspacesuit:3a865c593a480656b72a0539e99635e98fbc435f59549eeeb9cf4c7d262dcc88", "input": { "width": 1024, "height": 1024, "prompt": "a man in a makspacesuit,at spaceship wreckage ,war", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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/semonxue/makspacesuit@sha256:3a865c593a480656b72a0539e99635e98fbc435f59549eeeb9cf4c7d262dcc88 \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="a man in a makspacesuit,at spaceship wreckage ,war"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.6' \ -i 'num_outputs=1' \ -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/semonxue/makspacesuit@sha256:3a865c593a480656b72a0539e99635e98fbc435f59549eeeb9cf4c7d262dcc88curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "a man in a makspacesuit,at spaceship wreckage ,war", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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": "2023-11-06T06:31:18.514521Z", "created_at": "2023-11-06T06:31:02.212736Z", "data_removed": false, "error": null, "id": "emv4swlb7wjgych7nrwpejo67u", "input": { "width": 1024, "height": 1024, "prompt": "a man in a makspacesuit,at spaceship wreckage ,war", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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: 27378\nskipping loading .. weights already loaded\nPrompt: a man in a <s0><s1>,at spaceship wreckage ,war\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.68it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.68it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.68it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.68it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.68it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.67it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.66it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.66it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.66it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.66it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.66it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.66it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.66it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.66it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.65it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.65it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.65it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s]\n 64%|██████▍ 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}, "output": [ "https://replicate.delivery/pbxt/mvo5mkb3UWLiE5qVyQN2PdsMiuFRVYj0uxsIIWFeZBna0x6IA/out-0.png" ], "started_at": "2023-11-06T06:31:02.190615Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/emv4swlb7wjgych7nrwpejo67u", "cancel": "https://api.replicate.com/v1/predictions/emv4swlb7wjgych7nrwpejo67u/cancel" }, "version": "3a865c593a480656b72a0539e99635e98fbc435f59549eeeb9cf4c7d262dcc88" }Generated inUsing seed: 27378 skipping loading .. weights already loaded Prompt: a man in a <s0><s1>,at spaceship wreckage ,war txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.68it/s] 4%|▍ | 2/50 [00:00<00:13, 3.68it/s] 6%|▌ | 3/50 [00:00<00:12, 3.68it/s] 8%|▊ | 4/50 [00:01<00:12, 3.68it/s] 10%|█ | 5/50 [00:01<00:12, 3.68it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.67it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.67it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.67it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s] 20%|██ | 10/50 [00:02<00:10, 3.67it/s] 22%|██▏ | 11/50 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semonxue/makspacesuit:3a865c593a480656b72a0539e99635e98fbc435f59549eeeb9cf4c7d262dcc88IDpo6jmkdbwnsj5lpmnujmkz5vyeStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a makspacesuit in crazy mode
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- 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 makspacesuit in crazy mode", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "semonxue/makspacesuit:3a865c593a480656b72a0539e99635e98fbc435f59549eeeb9cf4c7d262dcc88", { input: { width: 1024, height: 1024, prompt: "a makspacesuit in crazy mode", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, 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 semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "semonxue/makspacesuit:3a865c593a480656b72a0539e99635e98fbc435f59549eeeb9cf4c7d262dcc88", input={ "width": 1024, "height": 1024, "prompt": "a makspacesuit in crazy mode", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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 semonxue/makspacesuit 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": "semonxue/makspacesuit:3a865c593a480656b72a0539e99635e98fbc435f59549eeeb9cf4c7d262dcc88", "input": { "width": 1024, "height": 1024, "prompt": "a makspacesuit in crazy mode", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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/semonxue/makspacesuit@sha256:3a865c593a480656b72a0539e99635e98fbc435f59549eeeb9cf4c7d262dcc88 \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="a makspacesuit in crazy mode"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.6' \ -i 'num_outputs=1' \ -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/semonxue/makspacesuit@sha256:3a865c593a480656b72a0539e99635e98fbc435f59549eeeb9cf4c7d262dcc88curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "a makspacesuit in crazy mode", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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": "2023-11-06T06:33:29.059756Z", "created_at": "2023-11-06T06:33:12.011455Z", "data_removed": false, "error": null, "id": "po6jmkdbwnsj5lpmnujmkz5vye", "input": { "width": 1024, "height": 1024, "prompt": "a makspacesuit in crazy mode", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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: 8084\nEnsuring enough disk space...\nFree disk space: 1462349279232\nDownloading weights: https://replicate.delivery/pbxt/0gkcgFoGnYLnNh7WMJ5aw4tBadhGA6QSuexm62GV0koiux6IA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.266s (700 MB/s)\\nExtracted 186 MB in 0.055s (3.4 GB/s)\\n'\nDownloaded weights in 0.44139719009399414 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a <s0><s1> in crazy mode\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.71it/s]\n 4%|▍ | 2/50 [00:00<00:12, 3.70it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.70it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.70it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.69it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.69it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.68it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.68it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.68it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.68it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.68it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.67it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.67it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.67it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.68it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.67it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.67it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.67it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.67it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.68it/s]", "metrics": { "predict_time": 15.977285, "total_time": 17.048301 }, "output": [ "https://replicate.delivery/pbxt/sPNG9rlmSf3HQK5M9V9fMHSmTenOrKKRzX57pPJ81roxVHrjA/out-0.png" ], "started_at": "2023-11-06T06:33:13.082471Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/po6jmkdbwnsj5lpmnujmkz5vye", "cancel": "https://api.replicate.com/v1/predictions/po6jmkdbwnsj5lpmnujmkz5vye/cancel" }, "version": "3a865c593a480656b72a0539e99635e98fbc435f59549eeeb9cf4c7d262dcc88" }Generated inUsing seed: 8084 Ensuring enough disk space... Free disk space: 1462349279232 Downloading weights: https://replicate.delivery/pbxt/0gkcgFoGnYLnNh7WMJ5aw4tBadhGA6QSuexm62GV0koiux6IA/trained_model.tar b'Downloaded 186 MB bytes in 0.266s (700 MB/s)\nExtracted 186 MB in 0.055s (3.4 GB/s)\n' Downloaded weights in 0.44139719009399414 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a <s0><s1> in crazy mode txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.71it/s] 4%|▍ | 2/50 [00:00<00:12, 3.70it/s] 6%|▌ | 3/50 [00:00<00:12, 3.70it/s] 8%|▊ | 4/50 [00:01<00:12, 3.70it/s] 10%|█ | 5/50 [00:01<00:12, 3.69it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.69it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.69it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.69it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.69it/s] 20%|██ | 10/50 [00:02<00:10, 3.69it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.69it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.68it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.68it/s] 30%|███ | 15/50 [00:04<00:09, 3.68it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.68it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.68it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s] 40%|████ | 20/50 [00:05<00:08, 3.67it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.67it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.67it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s] 50%|█████ | 25/50 [00:06<00:06, 3.67it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.67it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.67it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.67it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.68it/s] 60%|██████ | 30/50 [00:08<00:05, 3.68it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.67it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.67it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.67it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s] 70%|███████ | 35/50 [00:09<00:04, 3.67it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.67it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.67it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.67it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.67it/s] 80%|████████ | 40/50 [00:10<00:02, 3.67it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.67it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.67it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.67it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.67it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.67it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.67it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.67it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.67it/s] 100%|██████████| 50/50 [00:13<00:00, 3.68it/s]Prediction
semonxue/makspacesuit:3a865c593a480656b72a0539e99635e98fbc435f59549eeeb9cf4c7d262dcc88IDq335lddbu4ylgddd47e3qwffuuStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a makspacesuit at Tokey street
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- 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 makspacesuit at Tokey street", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "semonxue/makspacesuit:3a865c593a480656b72a0539e99635e98fbc435f59549eeeb9cf4c7d262dcc88", { input: { width: 1024, height: 1024, prompt: " a makspacesuit at Tokey street", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, 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 semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "semonxue/makspacesuit:3a865c593a480656b72a0539e99635e98fbc435f59549eeeb9cf4c7d262dcc88", input={ "width": 1024, "height": 1024, "prompt": " a makspacesuit at Tokey street", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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 semonxue/makspacesuit 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": "semonxue/makspacesuit:3a865c593a480656b72a0539e99635e98fbc435f59549eeeb9cf4c7d262dcc88", "input": { "width": 1024, "height": 1024, "prompt": " a makspacesuit at Tokey street", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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/semonxue/makspacesuit@sha256:3a865c593a480656b72a0539e99635e98fbc435f59549eeeb9cf4c7d262dcc88 \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt=" a makspacesuit at Tokey street"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.6' \ -i 'num_outputs=1' \ -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/semonxue/makspacesuit@sha256:3a865c593a480656b72a0539e99635e98fbc435f59549eeeb9cf4c7d262dcc88curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": " a makspacesuit at Tokey street", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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": "2023-11-06T06:30:52.726112Z", "created_at": "2023-11-06T06:30:29.522438Z", "data_removed": false, "error": null, "id": "q335lddbu4ylgddd47e3qwffuu", "input": { "width": 1024, "height": 1024, "prompt": " a makspacesuit at Tokey street", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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: 64142\nEnsuring enough disk space...\nFree disk space: 1641151311872\nDownloading weights: https://replicate.delivery/pbxt/0gkcgFoGnYLnNh7WMJ5aw4tBadhGA6QSuexm62GV0koiux6IA/trained_model.tar\nb'Downloaded 186 MB bytes in 3.698s (50 MB/s)\\nExtracted 186 MB in 0.057s (3.3 GB/s)\\n'\nDownloaded weights in 4.113420248031616 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a <s0><s1> at Tokey street\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.68it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.67it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.68it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.68it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.68it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.68it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.67it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.67it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.67it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.66it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.66it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.66it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.66it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.66it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.66it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.66it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.66it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.66it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.65it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.66it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.66it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.66it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]", "metrics": { "predict_time": 20.908435, "total_time": 23.203674 }, "output": [ "https://replicate.delivery/pbxt/gkFoEaLO9tb8GtAQ6qsv0FyW6O0BdtUwfyTH7BpGWWoN0x6IA/out-0.png" ], "started_at": "2023-11-06T06:30:31.817677Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/q335lddbu4ylgddd47e3qwffuu", "cancel": "https://api.replicate.com/v1/predictions/q335lddbu4ylgddd47e3qwffuu/cancel" }, "version": "3a865c593a480656b72a0539e99635e98fbc435f59549eeeb9cf4c7d262dcc88" }Generated inUsing seed: 64142 Ensuring enough disk space... Free disk space: 1641151311872 Downloading weights: https://replicate.delivery/pbxt/0gkcgFoGnYLnNh7WMJ5aw4tBadhGA6QSuexm62GV0koiux6IA/trained_model.tar b'Downloaded 186 MB bytes in 3.698s (50 MB/s)\nExtracted 186 MB in 0.057s (3.3 GB/s)\n' Downloaded weights in 4.113420248031616 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a <s0><s1> at Tokey street txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.68it/s] 4%|▍ | 2/50 [00:00<00:13, 3.67it/s] 6%|▌ | 3/50 [00:00<00:12, 3.68it/s] 8%|▊ | 4/50 [00:01<00:12, 3.68it/s] 10%|█ | 5/50 [00:01<00:12, 3.68it/s] 12%|█▏ | 6/50 [00:01<00:11, 3.68it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.68it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.68it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.67it/s] 20%|██ | 10/50 [00:02<00:10, 3.67it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.67it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.67it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.67it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.67it/s] 30%|███ | 15/50 [00:04<00:09, 3.67it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.67it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.67it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.67it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.67it/s] 40%|████ | 20/50 [00:05<00:08, 3.67it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.66it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.66it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.66it/s] 50%|█████ | 25/50 [00:06<00:06, 3.66it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.66it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.66it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.66it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.66it/s] 60%|██████ | 30/50 [00:08<00:05, 3.66it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.66it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.66it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.65it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.66it/s] 70%|███████ | 35/50 [00:09<00:04, 3.66it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.66it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.66it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.66it/s] 80%|████████ | 40/50 [00:10<00:02, 3.66it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.65it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s]Prediction
semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889IDcxop5ptbesgkzxz7j42624wejyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a astronaut in a makspacesuit, in london , raining night
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- 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 astronaut in a makspacesuit, in london , raining night", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", { input: { width: 1024, height: 1024, prompt: "a astronaut in a makspacesuit, in london , raining night", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, 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 semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", input={ "width": 1024, "height": 1024, "prompt": "a astronaut in a makspacesuit, in london , raining night", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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 semonxue/makspacesuit 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": "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", "input": { "width": 1024, "height": 1024, "prompt": "a astronaut in a makspacesuit, in london , raining night", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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/semonxue/makspacesuit@sha256:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889 \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="a astronaut in a makspacesuit, in london , raining night"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.6' \ -i 'num_outputs=1' \ -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/semonxue/makspacesuit@sha256:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "a astronaut in a makspacesuit, in london , raining night", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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": "2023-11-05T08:21:42.099454Z", "created_at": "2023-11-05T08:21:22.827236Z", "data_removed": false, "error": null, "id": "cxop5ptbesgkzxz7j42624wejy", "input": { "width": 1024, "height": 1024, "prompt": "a astronaut in a makspacesuit, in london , raining night", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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: 54565\nEnsuring enough disk space...\nFree disk space: 1636405907456\nDownloading weights: https://replicate.delivery/pbxt/bmf4yfWXzatxcU3hGAQeXfKmfeE7dHK4fv1ueieSvoUAHQgqjA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.234s (795 MB/s)\\nExtracted 186 MB in 0.069s (2.7 GB/s)\\n'\nDownloaded weights in 0.7665541172027588 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a astronaut in a <s0><s1>, in london , raining night\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:14, 3.47it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.48it/s]\n 6%|▌ | 3/50 [00:00<00:13, 3.48it/s]\n 8%|▊ | 4/50 [00:01<00:13, 3.47it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.47it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.47it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.46it/s]\n 16%|█▌ | 8/50 [00:02<00:12, 3.46it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.47it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.47it/s]\n 22%|██▏ | 11/50 [00:03<00:11, 3.46it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.47it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.47it/s]\n 28%|██▊ | 14/50 [00:04<00:10, 3.47it/s]\n 30%|███ | 15/50 [00:04<00:10, 3.46it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.46it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.46it/s]\n 36%|███▌ | 18/50 [00:05<00:09, 3.46it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.46it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.46it/s]\n 42%|████▏ | 21/50 [00:06<00:08, 3.46it/s]\n 44%|████▍ | 22/50 [00:06<00:08, 3.46it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.46it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.46it/s]\n 50%|█████ | 25/50 [00:07<00:07, 3.46it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.45it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.46it/s]\n 56%|█████▌ | 28/50 [00:08<00:06, 3.46it/s]\n 58%|█████▊ | 29/50 [00:08<00:06, 3.46it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.45it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.46it/s]\n 64%|██████▍ | 32/50 [00:09<00:05, 3.45it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.45it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.45it/s]\n 70%|███████ | 35/50 [00:10<00:04, 3.45it/s]\n 72%|███████▏ | 36/50 [00:10<00:04, 3.45it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.45it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.45it/s]\n 78%|███████▊ | 39/50 [00:11<00:03, 3.45it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.45it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.45it/s]\n 84%|████████▍ | 42/50 [00:12<00:02, 3.45it/s]\n 86%|████████▌ | 43/50 [00:12<00:02, 3.45it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.45it/s]\n 90%|█████████ | 45/50 [00:13<00:01, 3.45it/s]\n 92%|█████████▏| 46/50 [00:13<00:01, 3.45it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.45it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.45it/s]\n 98%|█████████▊| 49/50 [00:14<00:00, 3.45it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.45it/s]\n100%|██████████| 50/50 [00:14<00:00, 3.46it/s]", "metrics": { "predict_time": 17.51333, "total_time": 19.272218 }, "output": [ "https://replicate.delivery/pbxt/t66o8cUGfLx7Myerl0Xe5svz38hbeuSsDCDafo6eGtf2KFo6IA/out-0.png" ], "started_at": "2023-11-05T08:21:24.586124Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/cxop5ptbesgkzxz7j42624wejy", "cancel": "https://api.replicate.com/v1/predictions/cxop5ptbesgkzxz7j42624wejy/cancel" }, "version": "e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889" }Generated inUsing seed: 54565 Ensuring enough disk space... Free disk space: 1636405907456 Downloading weights: https://replicate.delivery/pbxt/bmf4yfWXzatxcU3hGAQeXfKmfeE7dHK4fv1ueieSvoUAHQgqjA/trained_model.tar b'Downloaded 186 MB bytes in 0.234s (795 MB/s)\nExtracted 186 MB in 0.069s (2.7 GB/s)\n' Downloaded weights in 0.7665541172027588 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a astronaut in a <s0><s1>, in london , raining night txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:14, 3.47it/s] 4%|▍ | 2/50 [00:00<00:13, 3.48it/s] 6%|▌ | 3/50 [00:00<00:13, 3.48it/s] 8%|▊ | 4/50 [00:01<00:13, 3.47it/s] 10%|█ | 5/50 [00:01<00:12, 3.47it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.47it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.46it/s] 16%|█▌ | 8/50 [00:02<00:12, 3.46it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.47it/s] 20%|██ | 10/50 [00:02<00:11, 3.47it/s] 22%|██▏ | 11/50 [00:03<00:11, 3.46it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.47it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.47it/s] 28%|██▊ | 14/50 [00:04<00:10, 3.47it/s] 30%|███ | 15/50 [00:04<00:10, 3.46it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.46it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.46it/s] 36%|███▌ | 18/50 [00:05<00:09, 3.46it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.46it/s] 40%|████ | 20/50 [00:05<00:08, 3.46it/s] 42%|████▏ | 21/50 [00:06<00:08, 3.46it/s] 44%|████▍ | 22/50 [00:06<00:08, 3.46it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.46it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.46it/s] 50%|█████ | 25/50 [00:07<00:07, 3.46it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.45it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.46it/s] 56%|█████▌ | 28/50 [00:08<00:06, 3.46it/s] 58%|█████▊ | 29/50 [00:08<00:06, 3.46it/s] 60%|██████ | 30/50 [00:08<00:05, 3.45it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.46it/s] 64%|██████▍ | 32/50 [00:09<00:05, 3.45it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.45it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.45it/s] 70%|███████ | 35/50 [00:10<00:04, 3.45it/s] 72%|███████▏ | 36/50 [00:10<00:04, 3.45it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.45it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.45it/s] 78%|███████▊ | 39/50 [00:11<00:03, 3.45it/s] 80%|████████ | 40/50 [00:11<00:02, 3.45it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.45it/s] 84%|████████▍ | 42/50 [00:12<00:02, 3.45it/s] 86%|████████▌ | 43/50 [00:12<00:02, 3.45it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.45it/s] 90%|█████████ | 45/50 [00:13<00:01, 3.45it/s] 92%|█████████▏| 46/50 [00:13<00:01, 3.45it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.45it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.45it/s] 98%|█████████▊| 49/50 [00:14<00:00, 3.45it/s] 100%|██████████| 50/50 [00:14<00:00, 3.45it/s] 100%|██████████| 50/50 [00:14<00:00, 3.46it/s]Prediction
semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889ID7zhqtntb4o42dkk3i2pp4ttmweStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a astronaut in a makspacesuit,side view, in london , raining night
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- 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 astronaut in a makspacesuit,side view, in london , raining night", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", { input: { width: 1024, height: 1024, prompt: "a astronaut in a makspacesuit,side view, in london , raining night", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, 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 semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", input={ "width": 1024, "height": 1024, "prompt": "a astronaut in a makspacesuit,side view, in london , raining night", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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 semonxue/makspacesuit 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": "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", "input": { "width": 1024, "height": 1024, "prompt": "a astronaut in a makspacesuit,side view, in london , raining night", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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/semonxue/makspacesuit@sha256:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889 \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="a astronaut in a makspacesuit,side view, in london , raining night"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.6' \ -i 'num_outputs=1' \ -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/semonxue/makspacesuit@sha256:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "a astronaut in a makspacesuit,side view, in london , raining night", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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": "2023-11-05T08:22:22.796841Z", "created_at": "2023-11-05T08:22:04.657646Z", "data_removed": false, "error": null, "id": "7zhqtntb4o42dkk3i2pp4ttmwe", "input": { "width": 1024, "height": 1024, "prompt": "a astronaut in a makspacesuit,side view, in london , raining night", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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: 19178\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a astronaut in a <s0><s1>,side view, in london , raining night\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.67it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.66it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.66it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.65it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.65it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.64it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.64it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.64it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.64it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.64it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.64it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.63it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.63it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.63it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.63it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.63it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.63it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.63it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.63it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.63it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.63it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.63it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.63it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.63it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.63it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.64it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.64it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.64it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.65it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.64it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.64it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.65it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.64it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.64it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.64it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.64it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.64it/s]", "metrics": { "predict_time": 16.026689, "total_time": 18.139195 }, "output": [ "https://replicate.delivery/pbxt/WKmf1Z4U6g1oGKFqpwEtRL21Gxe4pAnUTvNHF2v0s3ueVgqjA/out-0.png" ], "started_at": "2023-11-05T08:22:06.770152Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/7zhqtntb4o42dkk3i2pp4ttmwe", "cancel": "https://api.replicate.com/v1/predictions/7zhqtntb4o42dkk3i2pp4ttmwe/cancel" }, "version": "e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889" }Generated inUsing seed: 19178 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a astronaut in a <s0><s1>,side view, in london , raining night txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.67it/s] 4%|▍ | 2/50 [00:00<00:13, 3.66it/s] 6%|▌ | 3/50 [00:00<00:12, 3.66it/s] 8%|▊ | 4/50 [00:01<00:12, 3.65it/s] 10%|█ | 5/50 [00:01<00:12, 3.65it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.64it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.64it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.64it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.64it/s] 20%|██ | 10/50 [00:02<00:10, 3.64it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.64it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.64it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.64it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.64it/s] 30%|███ | 15/50 [00:04<00:09, 3.64it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.63it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.63it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.63it/s] 40%|████ | 20/50 [00:05<00:08, 3.63it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.63it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.63it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.63it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.63it/s] 50%|█████ | 25/50 [00:06<00:06, 3.63it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.63it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.63it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.63it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.63it/s] 60%|██████ | 30/50 [00:08<00:05, 3.63it/s] 62%|██████▏ | 31/50 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semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889ID4mzjs2tbvit5zzf4tkh7w2aosiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 768
- height
- 1024
- prompt
- a astronaut in a makspacesuit,at spaceship wreckage , in forest with cat and birds
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,at spaceship wreckage , in forest with cat and birds", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", { input: { width: 768, height: 1024, prompt: "a astronaut in a makspacesuit,at spaceship wreckage , in forest with cat and birds", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, 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 semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", input={ "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,at spaceship wreckage , in forest with cat and birds", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "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 semonxue/makspacesuit 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": "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", "input": { "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,at spaceship wreckage , in forest with cat and birds", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "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/semonxue/makspacesuit@sha256:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889 \ -i 'width=768' \ -i 'height=1024' \ -i 'prompt="a astronaut in a makspacesuit,at spaceship wreckage , in forest with cat and birds"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.6' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.8' \ -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/semonxue/makspacesuit@sha256:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,at spaceship wreckage , in forest with cat and birds", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-11-05T08:27:26.086439Z", "created_at": "2023-11-05T08:27:12.132436Z", "data_removed": false, "error": null, "id": "4mzjs2tbvit5zzf4tkh7w2aosi", "input": { "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,at spaceship wreckage , in forest with cat and birds", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 3427\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a astronaut in a <s0><s1>,at spaceship wreckage , in forest with cat and birds\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:10, 4.53it/s]\n 4%|▍ | 2/50 [00:00<00:10, 4.72it/s]\n 6%|▌ | 3/50 [00:00<00:09, 4.78it/s]\n 8%|▊ | 4/50 [00:00<00:09, 4.81it/s]\n 10%|█ | 5/50 [00:01<00:09, 4.83it/s]\n 12%|█▏ | 6/50 [00:01<00:09, 4.85it/s]\n 14%|█▍ | 7/50 [00:01<00:08, 4.86it/s]\n 16%|█▌ | 8/50 [00:01<00:08, 4.86it/s]\n 18%|█▊ | 9/50 [00:01<00:08, 4.86it/s]\n 20%|██ | 10/50 [00:02<00:08, 4.86it/s]\n 22%|██▏ | 11/50 [00:02<00:08, 4.87it/s]\n 24%|██▍ | 12/50 [00:02<00:07, 4.87it/s]\n 26%|██▌ | 13/50 [00:02<00:07, 4.87it/s]\n 28%|██▊ | 14/50 [00:02<00:07, 4.87it/s]\n 30%|███ | 15/50 [00:03<00:07, 4.87it/s]\n 32%|███▏ | 16/50 [00:03<00:06, 4.87it/s]\n 34%|███▍ | 17/50 [00:03<00:06, 4.87it/s]\n 36%|███▌ | 18/50 [00:03<00:06, 4.87it/s]\n 38%|███▊ | 19/50 [00:03<00:06, 4.87it/s]\n 40%|████ | 20/50 [00:04<00:06, 4.88it/s]\n 42%|████▏ | 21/50 [00:04<00:05, 4.87it/s]\n 44%|████▍ | 22/50 [00:04<00:05, 4.87it/s]\n 46%|████▌ | 23/50 [00:04<00:05, 4.88it/s]\n 48%|████▊ | 24/50 [00:04<00:05, 4.87it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.87it/s]\n 52%|█████▏ | 26/50 [00:05<00:04, 4.87it/s]\n 54%|█████▍ | 27/50 [00:05<00:04, 4.87it/s]\n 56%|█████▌ | 28/50 [00:05<00:04, 4.87it/s]\n 58%|█████▊ | 29/50 [00:05<00:04, 4.87it/s]\n 60%|██████ | 30/50 [00:06<00:04, 4.87it/s]\n 62%|██████▏ | 31/50 [00:06<00:03, 4.87it/s]\n 64%|██████▍ | 32/50 [00:06<00:03, 4.86it/s]\n 66%|██████▌ | 33/50 [00:06<00:03, 4.86it/s]\n 68%|██████▊ | 34/50 [00:06<00:03, 4.86it/s]\n 70%|███████ | 35/50 [00:07<00:03, 4.86it/s]\n 72%|███████▏ | 36/50 [00:07<00:02, 4.86it/s]\n 74%|███████▍ | 37/50 [00:07<00:02, 4.86it/s]\n 76%|███████▌ | 38/50 [00:07<00:02, 4.86it/s]\n 78%|███████▊ | 39/50 [00:08<00:02, 4.86it/s]\n 80%|████████ | 40/50 [00:08<00:02, 4.86it/s]\n 82%|████████▏ | 41/50 [00:08<00:01, 4.85it/s]\n 84%|████████▍ | 42/50 [00:08<00:01, 4.85it/s]\n 86%|████████▌ | 43/50 [00:08<00:01, 4.85it/s]\n 88%|████████▊ | 44/50 [00:09<00:01, 4.84it/s]\n 90%|█████████ | 45/50 [00:09<00:01, 4.84it/s]\n 92%|█████████▏| 46/50 [00:09<00:00, 4.84it/s]\n 94%|█████████▍| 47/50 [00:09<00:00, 4.85it/s]\n 96%|█████████▌| 48/50 [00:09<00:00, 4.85it/s]\n 98%|█████████▊| 49/50 [00:10<00:00, 4.85it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.85it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.85it/s]", "metrics": { "predict_time": 11.985229, "total_time": 13.954003 }, "output": [ "https://replicate.delivery/pbxt/maWrULSxGtZdCBxjfwJHJTNTlPVwfMytfW70jeMvhjJ1eBqOC/out-0.png" ], "started_at": "2023-11-05T08:27:14.101210Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/4mzjs2tbvit5zzf4tkh7w2aosi", "cancel": "https://api.replicate.com/v1/predictions/4mzjs2tbvit5zzf4tkh7w2aosi/cancel" }, "version": "e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889" }Generated inUsing seed: 3427 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a astronaut in a <s0><s1>,at spaceship wreckage , in forest with cat and birds txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:10, 4.53it/s] 4%|▍ | 2/50 [00:00<00:10, 4.72it/s] 6%|▌ | 3/50 [00:00<00:09, 4.78it/s] 8%|▊ | 4/50 [00:00<00:09, 4.81it/s] 10%|█ | 5/50 [00:01<00:09, 4.83it/s] 12%|█▏ | 6/50 [00:01<00:09, 4.85it/s] 14%|█▍ | 7/50 [00:01<00:08, 4.86it/s] 16%|█▌ | 8/50 [00:01<00:08, 4.86it/s] 18%|█▊ | 9/50 [00:01<00:08, 4.86it/s] 20%|██ | 10/50 [00:02<00:08, 4.86it/s] 22%|██▏ | 11/50 [00:02<00:08, 4.87it/s] 24%|██▍ | 12/50 [00:02<00:07, 4.87it/s] 26%|██▌ | 13/50 [00:02<00:07, 4.87it/s] 28%|██▊ | 14/50 [00:02<00:07, 4.87it/s] 30%|███ | 15/50 [00:03<00:07, 4.87it/s] 32%|███▏ | 16/50 [00:03<00:06, 4.87it/s] 34%|███▍ | 17/50 [00:03<00:06, 4.87it/s] 36%|███▌ | 18/50 [00:03<00:06, 4.87it/s] 38%|███▊ | 19/50 [00:03<00:06, 4.87it/s] 40%|████ | 20/50 [00:04<00:06, 4.88it/s] 42%|████▏ | 21/50 [00:04<00:05, 4.87it/s] 44%|████▍ | 22/50 [00:04<00:05, 4.87it/s] 46%|████▌ | 23/50 [00:04<00:05, 4.88it/s] 48%|████▊ | 24/50 [00:04<00:05, 4.87it/s] 50%|█████ | 25/50 [00:05<00:05, 4.87it/s] 52%|█████▏ | 26/50 [00:05<00:04, 4.87it/s] 54%|█████▍ | 27/50 [00:05<00:04, 4.87it/s] 56%|█████▌ | 28/50 [00:05<00:04, 4.87it/s] 58%|█████▊ | 29/50 [00:05<00:04, 4.87it/s] 60%|██████ | 30/50 [00:06<00:04, 4.87it/s] 62%|██████▏ | 31/50 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semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889ID2f43cylbnb55ygyhj5b5hufm2uStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 768
- height
- 1024
- prompt
- a astronaut in a makspacesuit,at spaceship wreckage ,war
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- painting
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,at spaceship wreckage ,war", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "painting", "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", { input: { width: 768, height: 1024, prompt: "a astronaut in a makspacesuit,at spaceship wreckage ,war", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "painting", 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 semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", input={ "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,at spaceship wreckage ,war", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "painting", "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 semonxue/makspacesuit 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": "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", "input": { "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,at spaceship wreckage ,war", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "painting", "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/semonxue/makspacesuit@sha256:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889 \ -i 'width=768' \ -i 'height=1024' \ -i 'prompt="a astronaut in a makspacesuit,at spaceship wreckage ,war"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.6' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.8' \ -i 'negative_prompt="painting"' \ -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/semonxue/makspacesuit@sha256:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,at spaceship wreckage ,war", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "painting", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-11-05T08:26:30.672976Z", "created_at": "2023-11-05T08:26:15.635710Z", "data_removed": false, "error": null, "id": "2f43cylbnb55ygyhj5b5hufm2u", "input": { "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,at spaceship wreckage ,war", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "painting", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 29511\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a astronaut in a <s0><s1>,at spaceship wreckage ,war\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:11, 4.34it/s]\n 4%|▍ | 2/50 [00:00<00:10, 4.53it/s]\n 6%|▌ | 3/50 [00:00<00:10, 4.60it/s]\n 8%|▊ | 4/50 [00:00<00:09, 4.63it/s]\n 10%|█ | 5/50 [00:01<00:09, 4.64it/s]\n 12%|█▏ | 6/50 [00:01<00:09, 4.66it/s]\n 14%|█▍ | 7/50 [00:01<00:09, 4.67it/s]\n 16%|█▌ | 8/50 [00:01<00:08, 4.67it/s]\n 18%|█▊ | 9/50 [00:01<00:08, 4.67it/s]\n 20%|██ | 10/50 [00:02<00:08, 4.67it/s]\n 22%|██▏ | 11/50 [00:02<00:08, 4.68it/s]\n 24%|██▍ | 12/50 [00:02<00:08, 4.67it/s]\n 26%|██▌ | 13/50 [00:02<00:07, 4.67it/s]\n 28%|██▊ | 14/50 [00:03<00:07, 4.67it/s]\n 30%|███ | 15/50 [00:03<00:07, 4.67it/s]\n 32%|███▏ | 16/50 [00:03<00:07, 4.67it/s]\n 34%|███▍ | 17/50 [00:03<00:07, 4.67it/s]\n 36%|███▌ | 18/50 [00:03<00:06, 4.68it/s]\n 38%|███▊ | 19/50 [00:04<00:06, 4.67it/s]\n 40%|████ | 20/50 [00:04<00:06, 4.67it/s]\n 42%|████▏ | 21/50 [00:04<00:06, 4.67it/s]\n 44%|████▍ | 22/50 [00:04<00:05, 4.67it/s]\n 46%|████▌ | 23/50 [00:04<00:05, 4.67it/s]\n 48%|████▊ | 24/50 [00:05<00:05, 4.67it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.67it/s]\n 52%|█████▏ | 26/50 [00:05<00:05, 4.67it/s]\n 54%|█████▍ | 27/50 [00:05<00:04, 4.67it/s]\n 56%|█████▌ | 28/50 [00:06<00:04, 4.66it/s]\n 58%|█████▊ | 29/50 [00:06<00:04, 4.66it/s]\n 60%|██████ | 30/50 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[00:10<00:00, 4.66it/s]", "metrics": { "predict_time": 12.556942, "total_time": 15.037266 }, "output": [ "https://replicate.delivery/pbxt/NpLh330IN7qcD9Vxf7eQSy5LBUg4NyYoW2gV4kIO9tU2OQ1RA/out-0.png" ], "started_at": "2023-11-05T08:26:18.116034Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/2f43cylbnb55ygyhj5b5hufm2u", "cancel": "https://api.replicate.com/v1/predictions/2f43cylbnb55ygyhj5b5hufm2u/cancel" }, "version": "e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889" }Generated inUsing seed: 29511 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a astronaut in a <s0><s1>,at spaceship wreckage ,war txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:11, 4.34it/s] 4%|▍ | 2/50 [00:00<00:10, 4.53it/s] 6%|▌ | 3/50 [00:00<00:10, 4.60it/s] 8%|▊ | 4/50 [00:00<00:09, 4.63it/s] 10%|█ | 5/50 [00:01<00:09, 4.64it/s] 12%|█▏ | 6/50 [00:01<00:09, 4.66it/s] 14%|█▍ | 7/50 [00:01<00:09, 4.67it/s] 16%|█▌ | 8/50 [00:01<00:08, 4.67it/s] 18%|█▊ | 9/50 [00:01<00:08, 4.67it/s] 20%|██ | 10/50 [00:02<00:08, 4.67it/s] 22%|██▏ | 11/50 [00:02<00:08, 4.68it/s] 24%|██▍ | 12/50 [00:02<00:08, 4.67it/s] 26%|██▌ | 13/50 [00:02<00:07, 4.67it/s] 28%|██▊ | 14/50 [00:03<00:07, 4.67it/s] 30%|███ | 15/50 [00:03<00:07, 4.67it/s] 32%|███▏ | 16/50 [00:03<00:07, 4.67it/s] 34%|███▍ | 17/50 [00:03<00:07, 4.67it/s] 36%|███▌ | 18/50 [00:03<00:06, 4.68it/s] 38%|███▊ | 19/50 [00:04<00:06, 4.67it/s] 40%|████ | 20/50 [00:04<00:06, 4.67it/s] 42%|████▏ | 21/50 [00:04<00:06, 4.67it/s] 44%|████▍ | 22/50 [00:04<00:05, 4.67it/s] 46%|████▌ | 23/50 [00:04<00:05, 4.67it/s] 48%|████▊ | 24/50 [00:05<00:05, 4.67it/s] 50%|█████ | 25/50 [00:05<00:05, 4.67it/s] 52%|█████▏ | 26/50 [00:05<00:05, 4.67it/s] 54%|█████▍ | 27/50 [00:05<00:04, 4.67it/s] 56%|█████▌ | 28/50 [00:06<00:04, 4.66it/s] 58%|█████▊ | 29/50 [00:06<00:04, 4.66it/s] 60%|██████ | 30/50 [00:06<00:04, 4.66it/s] 62%|██████▏ | 31/50 [00:06<00:04, 4.66it/s] 64%|██████▍ | 32/50 [00:06<00:03, 4.66it/s] 66%|██████▌ | 33/50 [00:07<00:03, 4.65it/s] 68%|██████▊ | 34/50 [00:07<00:03, 4.65it/s] 70%|███████ | 35/50 [00:07<00:03, 4.66it/s] 72%|███████▏ | 36/50 [00:07<00:03, 4.65it/s] 74%|███████▍ | 37/50 [00:07<00:02, 4.65it/s] 76%|███████▌ | 38/50 [00:08<00:02, 4.65it/s] 78%|███████▊ | 39/50 [00:08<00:02, 4.65it/s] 80%|████████ | 40/50 [00:08<00:02, 4.65it/s] 82%|████████▏ | 41/50 [00:08<00:01, 4.65it/s] 84%|████████▍ | 42/50 [00:09<00:01, 4.65it/s] 86%|████████▌ | 43/50 [00:09<00:01, 4.65it/s] 88%|████████▊ | 44/50 [00:09<00:01, 4.65it/s] 90%|█████████ | 45/50 [00:09<00:01, 4.65it/s] 92%|█████████▏| 46/50 [00:09<00:00, 4.65it/s] 94%|█████████▍| 47/50 [00:10<00:00, 4.65it/s] 96%|█████████▌| 48/50 [00:10<00:00, 4.65it/s] 98%|█████████▊| 49/50 [00:10<00:00, 4.64it/s] 100%|██████████| 50/50 [00:10<00:00, 4.65it/s] 100%|██████████| 50/50 [00:10<00:00, 4.66it/s]Prediction
semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889IDjzjb2ndbutglfvctjn4dmmov2yStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 768
- height
- 1024
- prompt
- a astronaut in a makspacesuit,at spaceship wreckage , in forest with a cat near foot and a bird in sky
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,at spaceship wreckage , in forest with a cat near foot and a bird in sky", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", { input: { width: 768, height: 1024, prompt: "a astronaut in a makspacesuit,at spaceship wreckage , in forest with a cat near foot and a bird in sky", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, 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 semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", input={ "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,at spaceship wreckage , in forest with a cat near foot and a bird in sky", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "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 semonxue/makspacesuit 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": "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", "input": { "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,at spaceship wreckage , in forest with a cat near foot and a bird in sky", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "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/semonxue/makspacesuit@sha256:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889 \ -i 'width=768' \ -i 'height=1024' \ -i 'prompt="a astronaut in a makspacesuit,at spaceship wreckage , in forest with a cat near foot and a bird in sky"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.6' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.8' \ -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/semonxue/makspacesuit@sha256:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,at spaceship wreckage , in forest with a cat near foot and a bird in sky", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-11-05T08:28:38.346439Z", "created_at": "2023-11-05T08:28:21.948875Z", "data_removed": false, "error": null, "id": "jzjb2ndbutglfvctjn4dmmov2y", "input": { "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,at spaceship wreckage , in forest with a cat near foot and a bird in sky", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 45624\nEnsuring enough disk space...\nFree disk space: 1595674034176\nDownloading weights: https://replicate.delivery/pbxt/bmf4yfWXzatxcU3hGAQeXfKmfeE7dHK4fv1ueieSvoUAHQgqjA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.069s (2.7 GB/s)\\nExtracted 186 MB in 0.068s (2.7 GB/s)\\n'\nDownloaded weights in 0.25943756103515625 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a astronaut in a <s0><s1>,at spaceship wreckage , in forest with a cat near foot and a bird in sky\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)\nreturn F.conv2d(input, weight, bias, self.stride,\n 2%|▏ | 1/50 [00:00<00:43, 1.13it/s]\n 4%|▍ | 2/50 [00:01<00:23, 2.08it/s]\n 6%|▌ | 3/50 [00:01<00:16, 2.82it/s]\n 8%|▊ | 4/50 [00:01<00:13, 3.40it/s]\n 10%|█ | 5/50 [00:01<00:11, 3.83it/s]\n 12%|█▏ | 6/50 [00:01<00:10, 4.14it/s]\n 14%|█▍ | 7/50 [00:02<00:09, 4.37it/s]\n 16%|█▌ | 8/50 [00:02<00:09, 4.54it/s]\n 18%|█▊ | 9/50 [00:02<00:08, 4.65it/s]\n 20%|██ | 10/50 [00:02<00:08, 4.74it/s]\n 22%|██▏ | 11/50 [00:02<00:08, 4.79it/s]\n 24%|██▍ | 12/50 [00:03<00:07, 4.83it/s]\n 26%|██▌ | 13/50 [00:03<00:07, 4.86it/s]\n 28%|██▊ | 14/50 [00:03<00:07, 4.87it/s]\n 30%|███ | 15/50 [00:03<00:07, 4.89it/s]\n 32%|███▏ | 16/50 [00:03<00:06, 4.90it/s]\n 34%|███▍ | 17/50 [00:04<00:06, 4.90it/s]\n 36%|███▌ | 18/50 [00:04<00:06, 4.91it/s]\n 38%|███▊ | 19/50 [00:04<00:06, 4.91it/s]\n 40%|████ | 20/50 [00:04<00:06, 4.91it/s]\n 42%|████▏ | 21/50 [00:04<00:05, 4.92it/s]\n 44%|████▍ | 22/50 [00:05<00:05, 4.91it/s]\n 46%|████▌ | 23/50 [00:05<00:05, 4.91it/s]\n 48%|████▊ | 24/50 [00:05<00:05, 4.91it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.92it/s]\n 52%|█████▏ | 26/50 [00:05<00:04, 4.92it/s]\n 54%|█████▍ | 27/50 [00:06<00:04, 4.92it/s]\n 56%|█████▌ | 28/50 [00:06<00:04, 4.91it/s]\n 58%|█████▊ | 29/50 [00:06<00:04, 4.91it/s]\n 60%|██████ | 30/50 [00:06<00:04, 4.91it/s]\n 62%|██████▏ | 31/50 [00:06<00:03, 4.91it/s]\n 64%|██████▍ | 32/50 [00:07<00:03, 4.90it/s]\n 66%|██████▌ | 33/50 [00:07<00:03, 4.90it/s]\n 68%|██████▊ | 34/50 [00:07<00:03, 4.90it/s]\n 70%|███████ | 35/50 [00:07<00:03, 4.90it/s]\n 72%|███████▏ | 36/50 [00:07<00:02, 4.90it/s]\n 74%|███████▍ | 37/50 [00:08<00:02, 4.90it/s]\n 76%|███████▌ | 38/50 [00:08<00:02, 4.90it/s]\n 78%|███████▊ | 39/50 [00:08<00:02, 4.90it/s]\n 80%|████████ | 40/50 [00:08<00:02, 4.90it/s]\n 82%|████████▏ | 41/50 [00:09<00:01, 4.90it/s]\n 84%|████████▍ | 42/50 [00:09<00:01, 4.90it/s]\n 86%|████████▌ | 43/50 [00:09<00:01, 4.90it/s]\n 88%|████████▊ | 44/50 [00:09<00:01, 4.90it/s]\n 90%|█████████ | 45/50 [00:09<00:01, 4.90it/s]\n 92%|█████████▏| 46/50 [00:10<00:00, 4.90it/s]\n 94%|█████████▍| 47/50 [00:10<00:00, 4.90it/s]\n 96%|█████████▌| 48/50 [00:10<00:00, 4.90it/s]\n 98%|█████████▊| 49/50 [00:10<00:00, 4.90it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.90it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.61it/s]", "metrics": { "predict_time": 14.27529, "total_time": 16.397564 }, "output": [ "https://replicate.delivery/pbxt/sBAld167n457MxRBryseGUsTFLgeuMFZfrJaD5fDESevGCqOC/out-0.png" ], "started_at": "2023-11-05T08:28:24.071149Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/jzjb2ndbutglfvctjn4dmmov2y", "cancel": "https://api.replicate.com/v1/predictions/jzjb2ndbutglfvctjn4dmmov2y/cancel" }, "version": "e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889" }Generated inUsing seed: 45624 Ensuring enough disk space... Free disk space: 1595674034176 Downloading weights: https://replicate.delivery/pbxt/bmf4yfWXzatxcU3hGAQeXfKmfeE7dHK4fv1ueieSvoUAHQgqjA/trained_model.tar b'Downloaded 186 MB bytes in 0.069s (2.7 GB/s)\nExtracted 186 MB in 0.068s (2.7 GB/s)\n' Downloaded weights in 0.25943756103515625 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a astronaut in a <s0><s1>,at spaceship wreckage , in forest with a cat near foot and a bird in sky txt2img mode 0%| | 0/50 [00:00<?, ?it/s]/usr/local/lib/python3.9/site-packages/torch/nn/modules/conv.py:459: UserWarning: Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:80.) return F.conv2d(input, weight, bias, self.stride, 2%|▏ | 1/50 [00:00<00:43, 1.13it/s] 4%|▍ | 2/50 [00:01<00:23, 2.08it/s] 6%|▌ | 3/50 [00:01<00:16, 2.82it/s] 8%|▊ | 4/50 [00:01<00:13, 3.40it/s] 10%|█ | 5/50 [00:01<00:11, 3.83it/s] 12%|█▏ | 6/50 [00:01<00:10, 4.14it/s] 14%|█▍ | 7/50 [00:02<00:09, 4.37it/s] 16%|█▌ | 8/50 [00:02<00:09, 4.54it/s] 18%|█▊ | 9/50 [00:02<00:08, 4.65it/s] 20%|██ | 10/50 [00:02<00:08, 4.74it/s] 22%|██▏ | 11/50 [00:02<00:08, 4.79it/s] 24%|██▍ | 12/50 [00:03<00:07, 4.83it/s] 26%|██▌ | 13/50 [00:03<00:07, 4.86it/s] 28%|██▊ | 14/50 [00:03<00:07, 4.87it/s] 30%|███ | 15/50 [00:03<00:07, 4.89it/s] 32%|███▏ | 16/50 [00:03<00:06, 4.90it/s] 34%|███▍ | 17/50 [00:04<00:06, 4.90it/s] 36%|███▌ | 18/50 [00:04<00:06, 4.91it/s] 38%|███▊ | 19/50 [00:04<00:06, 4.91it/s] 40%|████ | 20/50 [00:04<00:06, 4.91it/s] 42%|████▏ | 21/50 [00:04<00:05, 4.92it/s] 44%|████▍ | 22/50 [00:05<00:05, 4.91it/s] 46%|████▌ | 23/50 [00:05<00:05, 4.91it/s] 48%|████▊ | 24/50 [00:05<00:05, 4.91it/s] 50%|█████ | 25/50 [00:05<00:05, 4.92it/s] 52%|█████▏ | 26/50 [00:05<00:04, 4.92it/s] 54%|█████▍ | 27/50 [00:06<00:04, 4.92it/s] 56%|█████▌ | 28/50 [00:06<00:04, 4.91it/s] 58%|█████▊ | 29/50 [00:06<00:04, 4.91it/s] 60%|██████ | 30/50 [00:06<00:04, 4.91it/s] 62%|██████▏ | 31/50 [00:06<00:03, 4.91it/s] 64%|██████▍ | 32/50 [00:07<00:03, 4.90it/s] 66%|██████▌ | 33/50 [00:07<00:03, 4.90it/s] 68%|██████▊ | 34/50 [00:07<00:03, 4.90it/s] 70%|███████ | 35/50 [00:07<00:03, 4.90it/s] 72%|███████▏ | 36/50 [00:07<00:02, 4.90it/s] 74%|███████▍ | 37/50 [00:08<00:02, 4.90it/s] 76%|███████▌ | 38/50 [00:08<00:02, 4.90it/s] 78%|███████▊ | 39/50 [00:08<00:02, 4.90it/s] 80%|████████ | 40/50 [00:08<00:02, 4.90it/s] 82%|████████▏ | 41/50 [00:09<00:01, 4.90it/s] 84%|████████▍ | 42/50 [00:09<00:01, 4.90it/s] 86%|████████▌ | 43/50 [00:09<00:01, 4.90it/s] 88%|████████▊ | 44/50 [00:09<00:01, 4.90it/s] 90%|█████████ | 45/50 [00:09<00:01, 4.90it/s] 92%|█████████▏| 46/50 [00:10<00:00, 4.90it/s] 94%|█████████▍| 47/50 [00:10<00:00, 4.90it/s] 96%|█████████▌| 48/50 [00:10<00:00, 4.90it/s] 98%|█████████▊| 49/50 [00:10<00:00, 4.90it/s] 100%|██████████| 50/50 [00:10<00:00, 4.90it/s] 100%|██████████| 50/50 [00:10<00:00, 4.61it/s]Prediction
semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889IDesjfl7lbfzgcsn7abjczzkt5c4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 768
- height
- 1024
- prompt
- a astronaut in a makspacesuit,red pattern , snow field , realistic
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- painting, pipes
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,red pattern , snow field , realistic ", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "painting, pipes ", "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", { input: { width: 768, height: 1024, prompt: "a astronaut in a makspacesuit,red pattern , snow field , realistic ", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "painting, pipes ", 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 semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", input={ "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,red pattern , snow field , realistic ", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "painting, pipes ", "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 semonxue/makspacesuit 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": "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", "input": { "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,red pattern , snow field , realistic ", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "painting, pipes ", "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/semonxue/makspacesuit@sha256:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889 \ -i 'width=768' \ -i 'height=1024' \ -i 'prompt="a astronaut in a makspacesuit,red pattern , snow field , realistic "' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.6' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.8' \ -i 'negative_prompt="painting, pipes "' \ -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/semonxue/makspacesuit@sha256:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,red pattern , snow field , realistic ", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "painting, pipes ", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-11-05T08:31:47.435919Z", "created_at": "2023-11-05T08:31:36.129884Z", "data_removed": false, "error": null, "id": "esjfl7lbfzgcsn7abjczzkt5c4", "input": { "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,red pattern , snow field , realistic ", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "painting, pipes ", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 7637\nskipping loading .. weights already loaded\nPrompt: a astronaut in a <s0><s1>,red pattern , snow field , realistic\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:09, 4.95it/s]\n 4%|▍ | 2/50 [00:00<00:09, 4.93it/s]\n 6%|▌ | 3/50 [00:00<00:09, 4.92it/s]\n 8%|▊ | 4/50 [00:00<00:09, 4.92it/s]\n 10%|█ | 5/50 [00:01<00:09, 4.92it/s]\n 12%|█▏ | 6/50 [00:01<00:08, 4.92it/s]\n 14%|█▍ | 7/50 [00:01<00:08, 4.92it/s]\n 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"metrics": { "predict_time": 11.404222, "total_time": 11.306035 }, "output": [ "https://replicate.delivery/pbxt/MUxrQLTVEBJUId3zSbL4dPeJKPisKVKYbENkNvsRfJ1zTQ1RA/out-0.png" ], "started_at": "2023-11-05T08:31:36.031697Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/esjfl7lbfzgcsn7abjczzkt5c4", "cancel": "https://api.replicate.com/v1/predictions/esjfl7lbfzgcsn7abjczzkt5c4/cancel" }, "version": "e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889" }Generated inUsing seed: 7637 skipping loading .. weights already loaded Prompt: a astronaut in a <s0><s1>,red pattern , snow field , realistic txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:09, 4.95it/s] 4%|▍ | 2/50 [00:00<00:09, 4.93it/s] 6%|▌ | 3/50 [00:00<00:09, 4.92it/s] 8%|▊ | 4/50 [00:00<00:09, 4.92it/s] 10%|█ | 5/50 [00:01<00:09, 4.92it/s] 12%|█▏ | 6/50 [00:01<00:08, 4.92it/s] 14%|█▍ | 7/50 [00:01<00:08, 4.92it/s] 16%|█▌ | 8/50 [00:01<00:08, 4.92it/s] 18%|█▊ | 9/50 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semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889IDfsmafkdb2ixeox3nkv2ptyupvyStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 768
- height
- 1024
- prompt
- a astronaut in a makspacesuit,cyberpunk city
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- , pipes
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,cyberpunk city", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": ", pipes ", "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", { input: { width: 768, height: 1024, prompt: "a astronaut in a makspacesuit,cyberpunk city", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: ", pipes ", 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 semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", input={ "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,cyberpunk city", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": ", pipes ", "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 semonxue/makspacesuit 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": "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", "input": { "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,cyberpunk city", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": ", pipes ", "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/semonxue/makspacesuit@sha256:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889 \ -i 'width=768' \ -i 'height=1024' \ -i 'prompt="a astronaut in a makspacesuit,cyberpunk city"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.6' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.8' \ -i 'negative_prompt=", pipes "' \ -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/semonxue/makspacesuit@sha256:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,cyberpunk city", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": ", pipes ", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-11-05T08:35:38.804069Z", "created_at": "2023-11-05T08:35:25.535538Z", "data_removed": false, "error": null, "id": "fsmafkdb2ixeox3nkv2ptyupvy", "input": { "width": 768, "height": 1024, "prompt": "a astronaut in a makspacesuit,cyberpunk city", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": ", pipes ", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 4571\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: a astronaut in a <s0><s1>,cyberpunk city\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:10, 4.70it/s]\n 4%|▍ | 2/50 [00:00<00:10, 4.68it/s]\n 6%|▌ | 3/50 [00:00<00:10, 4.67it/s]\n 8%|▊ | 4/50 [00:00<00:09, 4.67it/s]\n 10%|█ | 5/50 [00:01<00:09, 4.66it/s]\n 12%|█▏ | 6/50 [00:01<00:09, 4.65it/s]\n 14%|█▍ | 7/50 [00:01<00:09, 4.65it/s]\n 16%|█▌ | 8/50 [00:01<00:09, 4.65it/s]\n 18%|█▊ | 9/50 [00:01<00:08, 4.65it/s]\n 20%|██ | 10/50 [00:02<00:08, 4.65it/s]\n 22%|██▏ | 11/50 [00:02<00:08, 4.65it/s]\n 24%|██▍ | 12/50 [00:02<00:08, 4.65it/s]\n 26%|██▌ | 13/50 [00:02<00:07, 4.66it/s]\n 28%|██▊ | 14/50 [00:03<00:07, 4.66it/s]\n 30%|███ | 15/50 [00:03<00:07, 4.66it/s]\n 32%|███▏ | 16/50 [00:03<00:07, 4.67it/s]\n 34%|███▍ | 17/50 [00:03<00:07, 4.67it/s]\n 36%|███▌ | 18/50 [00:03<00:06, 4.67it/s]\n 38%|███▊ | 19/50 [00:04<00:06, 4.67it/s]\n 40%|████ | 20/50 [00:04<00:06, 4.67it/s]\n 42%|████▏ | 21/50 [00:04<00:06, 4.67it/s]\n 44%|████▍ | 22/50 [00:04<00:05, 4.67it/s]\n 46%|████▌ | 23/50 [00:04<00:05, 4.67it/s]\n 48%|████▊ | 24/50 [00:05<00:05, 4.67it/s]\n 50%|█████ | 25/50 [00:05<00:05, 4.67it/s]\n 52%|█████▏ | 26/50 [00:05<00:05, 4.67it/s]\n 54%|█████▍ | 27/50 [00:05<00:04, 4.67it/s]\n 56%|█████▌ | 28/50 [00:06<00:04, 4.67it/s]\n 58%|█████▊ | 29/50 [00:06<00:04, 4.67it/s]\n 60%|██████ | 30/50 [00:06<00:04, 4.67it/s]\n 62%|██████▏ | 31/50 [00:06<00:04, 4.67it/s]\n 64%|██████▍ | 32/50 [00:06<00:03, 4.67it/s]\n 66%|██████▌ | 33/50 [00:07<00:03, 4.67it/s]\n 68%|██████▊ | 34/50 [00:07<00:03, 4.67it/s]\n 70%|███████ | 35/50 [00:07<00:03, 4.67it/s]\n 72%|███████▏ | 36/50 [00:07<00:02, 4.67it/s]\n 74%|███████▍ | 37/50 [00:07<00:02, 4.66it/s]\n 76%|███████▌ | 38/50 [00:08<00:02, 4.67it/s]\n 78%|███████▊ | 39/50 [00:08<00:02, 4.66it/s]\n 80%|████████ | 40/50 [00:08<00:02, 4.66it/s]\n 82%|████████▏ | 41/50 [00:08<00:01, 4.66it/s]\n 84%|████████▍ | 42/50 [00:09<00:01, 4.66it/s]\n 86%|████████▌ | 43/50 [00:09<00:01, 4.66it/s]\n 88%|████████▊ | 44/50 [00:09<00:01, 4.66it/s]\n 90%|█████████ | 45/50 [00:09<00:01, 4.66it/s]\n 92%|█████████▏| 46/50 [00:09<00:00, 4.66it/s]\n 94%|█████████▍| 47/50 [00:10<00:00, 4.66it/s]\n 96%|█████████▌| 48/50 [00:10<00:00, 4.66it/s]\n 98%|█████████▊| 49/50 [00:10<00:00, 4.66it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.66it/s]\n100%|██████████| 50/50 [00:10<00:00, 4.66it/s]", "metrics": { "predict_time": 12.534602, "total_time": 13.268531 }, "output": [ "https://replicate.delivery/pbxt/9DCh815UrQ48EpOr5ZEldtNYfkBF4t4xefSREQJCDBl1ugqjA/out-0.png" ], "started_at": "2023-11-05T08:35:26.269467Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fsmafkdb2ixeox3nkv2ptyupvy", "cancel": "https://api.replicate.com/v1/predictions/fsmafkdb2ixeox3nkv2ptyupvy/cancel" }, "version": "e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889" }Generated inUsing seed: 4571 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: a astronaut in a <s0><s1>,cyberpunk city txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:10, 4.70it/s] 4%|▍ | 2/50 [00:00<00:10, 4.68it/s] 6%|▌ | 3/50 [00:00<00:10, 4.67it/s] 8%|▊ | 4/50 [00:00<00:09, 4.67it/s] 10%|█ | 5/50 [00:01<00:09, 4.66it/s] 12%|█▏ | 6/50 [00:01<00:09, 4.65it/s] 14%|█▍ | 7/50 [00:01<00:09, 4.65it/s] 16%|█▌ | 8/50 [00:01<00:09, 4.65it/s] 18%|█▊ | 9/50 [00:01<00:08, 4.65it/s] 20%|██ | 10/50 [00:02<00:08, 4.65it/s] 22%|██▏ | 11/50 [00:02<00:08, 4.65it/s] 24%|██▍ | 12/50 [00:02<00:08, 4.65it/s] 26%|██▌ | 13/50 [00:02<00:07, 4.66it/s] 28%|██▊ | 14/50 [00:03<00:07, 4.66it/s] 30%|███ | 15/50 [00:03<00:07, 4.66it/s] 32%|███▏ | 16/50 [00:03<00:07, 4.67it/s] 34%|███▍ | 17/50 [00:03<00:07, 4.67it/s] 36%|███▌ | 18/50 [00:03<00:06, 4.67it/s] 38%|███▊ | 19/50 [00:04<00:06, 4.67it/s] 40%|████ | 20/50 [00:04<00:06, 4.67it/s] 42%|████▏ | 21/50 [00:04<00:06, 4.67it/s] 44%|████▍ | 22/50 [00:04<00:05, 4.67it/s] 46%|████▌ | 23/50 [00:04<00:05, 4.67it/s] 48%|████▊ | 24/50 [00:05<00:05, 4.67it/s] 50%|█████ | 25/50 [00:05<00:05, 4.67it/s] 52%|█████▏ | 26/50 [00:05<00:05, 4.67it/s] 54%|█████▍ | 27/50 [00:05<00:04, 4.67it/s] 56%|█████▌ | 28/50 [00:06<00:04, 4.67it/s] 58%|█████▊ | 29/50 [00:06<00:04, 4.67it/s] 60%|██████ | 30/50 [00:06<00:04, 4.67it/s] 62%|██████▏ | 31/50 [00:06<00:04, 4.67it/s] 64%|██████▍ | 32/50 [00:06<00:03, 4.67it/s] 66%|██████▌ | 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semonxue/makspacesuit:a8fed5c62f75099b6fd49d1174a15047d1ae2cfa26d50bc23d13f6163a6a60f0IDxa3yum3b6ni5y2clhuw2ronwtmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedby @semonxueInput
- width
- 1024
- height
- 1024
- prompt
- in a makspacesuit, a man floating in space, broken ships
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- 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": "in a makspacesuit, a man floating in space, broken ships", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "semonxue/makspacesuit:a8fed5c62f75099b6fd49d1174a15047d1ae2cfa26d50bc23d13f6163a6a60f0", { input: { width: 1024, height: 1024, prompt: "in a makspacesuit, a man floating in space, broken ships", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, 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 semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "semonxue/makspacesuit:a8fed5c62f75099b6fd49d1174a15047d1ae2cfa26d50bc23d13f6163a6a60f0", input={ "width": 1024, "height": 1024, "prompt": "in a makspacesuit, a man floating in space, broken ships", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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 semonxue/makspacesuit 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": "semonxue/makspacesuit:a8fed5c62f75099b6fd49d1174a15047d1ae2cfa26d50bc23d13f6163a6a60f0", "input": { "width": 1024, "height": 1024, "prompt": "in a makspacesuit, a man floating in space, broken ships", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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/semonxue/makspacesuit@sha256:a8fed5c62f75099b6fd49d1174a15047d1ae2cfa26d50bc23d13f6163a6a60f0 \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="in a makspacesuit, a man floating in space, broken ships"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.6' \ -i 'num_outputs=1' \ -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/semonxue/makspacesuit@sha256:a8fed5c62f75099b6fd49d1174a15047d1ae2cfa26d50bc23d13f6163a6a60f0curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "in a makspacesuit, a man floating in space, broken ships", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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": "2023-11-05T10:05:07.491236Z", "created_at": "2023-11-05T10:04:48.561405Z", "data_removed": false, "error": null, "id": "xa3yum3b6ni5y2clhuw2ronwtm", "input": { "width": 1024, "height": 1024, "prompt": "in a makspacesuit, a man floating in space, broken ships", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "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: 14951\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: in a <s0><s1>, a man floating in space, broken ships\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.64it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.64it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.63it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.64it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.64it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.64it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.63it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.63it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.63it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.63it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.63it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.63it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.63it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.63it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.63it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.63it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.63it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.63it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.63it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.63it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.63it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.63it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.62it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.63it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.62it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.62it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.62it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.62it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.62it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.62it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.61it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.62it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.62it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.62it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.62it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.61it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.61it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.61it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.61it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.61it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.61it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.61it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.61it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.61it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.61it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.61it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.62it/s]", "metrics": { "predict_time": 16.619898, "total_time": 18.929831 }, "output": [ "https://replicate.delivery/pbxt/C5Je4iHofRs7mERDSblCVeTllIc6LMvF17tKoePQbJhItGVHB/out-0.png" ], "started_at": "2023-11-05T10:04:50.871338Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/xa3yum3b6ni5y2clhuw2ronwtm", "cancel": "https://api.replicate.com/v1/predictions/xa3yum3b6ni5y2clhuw2ronwtm/cancel" }, "version": "a8fed5c62f75099b6fd49d1174a15047d1ae2cfa26d50bc23d13f6163a6a60f0" }Generated inUsing seed: 14951 Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: in a <s0><s1>, a man floating in space, broken ships txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.64it/s] 4%|▍ | 2/50 [00:00<00:13, 3.64it/s] 6%|▌ | 3/50 [00:00<00:12, 3.63it/s] 8%|▊ | 4/50 [00:01<00:12, 3.64it/s] 10%|█ | 5/50 [00:01<00:12, 3.64it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.64it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.63it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.63it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.63it/s] 20%|██ | 10/50 [00:02<00:11, 3.63it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.63it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.63it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.63it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.63it/s] 30%|███ | 15/50 [00:04<00:09, 3.63it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.63it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.63it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.63it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.63it/s] 40%|████ | 20/50 [00:05<00:08, 3.63it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.63it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.63it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.63it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.63it/s] 50%|█████ | 25/50 [00:06<00:06, 3.63it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.63it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.62it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.63it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.62it/s] 60%|██████ | 30/50 [00:08<00:05, 3.62it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.62it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.62it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.62it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.62it/s] 70%|███████ | 35/50 [00:09<00:04, 3.61it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.62it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.62it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.62it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.62it/s] 80%|████████ | 40/50 [00:11<00:02, 3.61it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.61it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.61it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.61it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.61it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.61it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.61it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.61it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.61it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.61it/s] 100%|██████████| 50/50 [00:13<00:00, 3.61it/s] 100%|██████████| 50/50 [00:13<00:00, 3.62it/s]Prediction
semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889IDyivgu4tb73nlnbq46ed6pqvtueStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- a astronaut in a makspacesuit,at spaceship wreckage ,war
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.6
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- painting
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "a astronaut in a makspacesuit,at spaceship wreckage ,war", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "painting", "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"; import fs from "node:fs"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", { input: { width: 1024, height: 1024, prompt: "a astronaut in a makspacesuit,at spaceship wreckage ,war", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.6, num_outputs: 1, guidance_scale: 7.5, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "painting", 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 semonxue/makspacesuit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", input={ "width": 1024, "height": 1024, "prompt": "a astronaut in a makspacesuit,at spaceship wreckage ,war", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "painting", "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 semonxue/makspacesuit 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": "semonxue/makspacesuit:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889", "input": { "width": 1024, "height": 1024, "prompt": "a astronaut in a makspacesuit,at spaceship wreckage ,war", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "painting", "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/semonxue/makspacesuit@sha256:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889 \ -i 'width=1024' \ -i 'height=1024' \ -i 'prompt="a astronaut in a makspacesuit,at spaceship wreckage ,war"' \ -i 'refine="no_refiner"' \ -i 'scheduler="K_EULER"' \ -i 'lora_scale=0.6' \ -i 'num_outputs=1' \ -i 'guidance_scale=7.5' \ -i 'apply_watermark=true' \ -i 'high_noise_frac=0.8' \ -i 'negative_prompt="painting"' \ -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/semonxue/makspacesuit@sha256:e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 1024, "height": 1024, "prompt": "a astronaut in a makspacesuit,at spaceship wreckage ,war", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "painting", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ http://localhost:5000/predictionsTo learn more, take a look at the Cog documentation.
Output
{ "completed_at": "2023-11-05T16:29:10.752507Z", "created_at": "2023-11-05T16:28:54.311623Z", "data_removed": false, "error": null, "id": "yivgu4tb73nlnbq46ed6pqvtue", "input": { "width": 1024, "height": 1024, "prompt": "a astronaut in a makspacesuit,at spaceship wreckage ,war", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.6, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "painting", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 8267\nskipping loading .. weights already loaded\nPrompt: a astronaut in a <s0><s1>,at spaceship wreckage ,war\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.70it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.68it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.68it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.67it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.67it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.66it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.66it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.65it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.65it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.66it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.66it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.65it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.65it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.65it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.65it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.65it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.65it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.65it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.65it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.65it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.65it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.65it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.65it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.65it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.65it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.65it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.65it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.65it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.65it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.65it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.65it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.65it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.65it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.65it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.65it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.65it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.65it/s]", "metrics": { "predict_time": 16.488499, "total_time": 16.440884 }, "output": [ "https://replicate.delivery/pbxt/I7oryS5W7Dp8MBDffbVd1L9Eby3EI09ixZvFzC0xzHjVTX1RA/out-0.png" ], "started_at": "2023-11-05T16:28:54.264008Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/yivgu4tb73nlnbq46ed6pqvtue", "cancel": "https://api.replicate.com/v1/predictions/yivgu4tb73nlnbq46ed6pqvtue/cancel" }, "version": "e474e316aafe13e23ba25b32e33561e5f28d89fffc2934714ad2beb277f13889" }Generated inUsing seed: 8267 skipping loading .. weights already loaded Prompt: a astronaut in a <s0><s1>,at spaceship wreckage ,war txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.70it/s] 4%|▍ | 2/50 [00:00<00:13, 3.68it/s] 6%|▌ | 3/50 [00:00<00:12, 3.68it/s] 8%|▊ | 4/50 [00:01<00:12, 3.67it/s] 10%|█ | 5/50 [00:01<00:12, 3.67it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.66it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.66it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.66it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.66it/s] 20%|██ | 10/50 [00:02<00:10, 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