mdzor/game-assets
game-assets
Creates voxels like game asset
Prediction
mdzor/game-assets:9e6883cdIDw3rrr9wny5rgj0cf185a6pedp0StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- TOK, a prince wearing golden armor
- 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": "TOK, a prince wearing golden armor", "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 mdzor/game-assets using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mdzor/game-assets:9e6883cda77d4c71a71277c984bd5bffe82500b265b20c9c4d3c144ff5dc8271", { input: { width: 1024, height: 1024, prompt: "TOK, a prince wearing golden armor", 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 mdzor/game-assets using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mdzor/game-assets:9e6883cda77d4c71a71277c984bd5bffe82500b265b20c9c4d3c144ff5dc8271", input={ "width": 1024, "height": 1024, "prompt": "TOK, a prince wearing golden armor", "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: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run mdzor/game-assets 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": "mdzor/game-assets:9e6883cda77d4c71a71277c984bd5bffe82500b265b20c9c4d3c144ff5dc8271", "input": { "width": 1024, "height": 1024, "prompt": "TOK, a prince wearing golden armor", "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.
Output
{ "completed_at": "2024-04-23T08:22:00.004952Z", "created_at": "2024-04-23T08:20:17.009000Z", "data_removed": false, "error": null, "id": "w3rrr9wny5rgj0cf185a6pedp0", "input": { "width": 1024, "height": 1024, "prompt": "TOK, a prince wearing golden armor", "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: 21770\nEnsuring enough disk space...\nFree disk space: 1596964429824\nDownloading weights: https://replicate.delivery/pbxt/joTL3fsV6IXUciYuaGVRS1RZpV7Dru2U0SNZnpzQyfXlkRtSA/trained_model.tar\n2024-04-23T08:21:42Z | INFO | [ Initiating ] dest=/src/weights-cache/1de1469304b3a9ac minimum_chunk_size=150M url=https://replicate.delivery/pbxt/joTL3fsV6IXUciYuaGVRS1RZpV7Dru2U0SNZnpzQyfXlkRtSA/trained_model.tar\n2024-04-23T08:21:44Z | INFO | [ Complete ] dest=/src/weights-cache/1de1469304b3a9ac size=\"186 MB\" total_elapsed=1.727s url=https://replicate.delivery/pbxt/joTL3fsV6IXUciYuaGVRS1RZpV7Dru2U0SNZnpzQyfXlkRtSA/trained_model.tar\nb''\nDownloaded weights in 1.7760472297668457 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1>, a prince wearing golden armor\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:14, 3.32it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.49it/s]\n 6%|▌ | 3/50 [00:00<00:13, 3.54it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.57it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.58it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.58it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.59it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.59it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.59it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.59it/s]\n 22%|██▏ | 11/50 [00:03<00:10, 3.59it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.59it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.59it/s]\n 28%|██▊ | 14/50 [00:03<00:10, 3.59it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.59it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.59it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.59it/s]\n 36%|███▌ | 18/50 [00:05<00:08, 3.59it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.59it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.58it/s]\n 42%|████▏ | 21/50 [00:05<00:08, 3.59it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.58it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.59it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.59it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.58it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.58it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.58it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.58it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.58it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.58it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.58it/s]\n 64%|██████▍ | 32/50 [00:08<00:05, 3.58it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.58it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.58it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.57it/s]\n 72%|███████▏ | 36/50 [00:10<00:03, 3.57it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.57it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.57it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.57it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.57it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.57it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.57it/s]\n 86%|████████▌ | 43/50 [00:12<00:01, 3.57it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.57it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.57it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.57it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.57it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.57it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.57it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.56it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.58it/s]", "metrics": { "predict_time": 17.673156, "total_time": 102.995952 }, "output": [ "https://replicate.delivery/pbxt/zj3Zotjsk17kK5V7ePxNAFERj8xEPVEKWmab0eW2iJ9nGStSA/out-0.png" ], "started_at": "2024-04-23T08:21:42.331796Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/w3rrr9wny5rgj0cf185a6pedp0", "cancel": "https://api.replicate.com/v1/predictions/w3rrr9wny5rgj0cf185a6pedp0/cancel" }, "version": "9e6883cda77d4c71a71277c984bd5bffe82500b265b20c9c4d3c144ff5dc8271" }Generated inUsing seed: 21770 Ensuring enough disk space... Free disk space: 1596964429824 Downloading weights: https://replicate.delivery/pbxt/joTL3fsV6IXUciYuaGVRS1RZpV7Dru2U0SNZnpzQyfXlkRtSA/trained_model.tar 2024-04-23T08:21:42Z | INFO | [ Initiating ] dest=/src/weights-cache/1de1469304b3a9ac minimum_chunk_size=150M url=https://replicate.delivery/pbxt/joTL3fsV6IXUciYuaGVRS1RZpV7Dru2U0SNZnpzQyfXlkRtSA/trained_model.tar 2024-04-23T08:21:44Z | INFO | [ Complete ] dest=/src/weights-cache/1de1469304b3a9ac size="186 MB" total_elapsed=1.727s url=https://replicate.delivery/pbxt/joTL3fsV6IXUciYuaGVRS1RZpV7Dru2U0SNZnpzQyfXlkRtSA/trained_model.tar b'' Downloaded weights in 1.7760472297668457 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: <s0><s1>, a prince wearing golden armor txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:14, 3.32it/s] 4%|▍ | 2/50 [00:00<00:13, 3.49it/s] 6%|▌ | 3/50 [00:00<00:13, 3.54it/s] 8%|▊ | 4/50 [00:01<00:12, 3.57it/s] 10%|█ | 5/50 [00:01<00:12, 3.58it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.58it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.59it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.59it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.59it/s] 20%|██ | 10/50 [00:02<00:11, 3.59it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.59it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.59it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.59it/s] 28%|██▊ | 14/50 [00:03<00:10, 3.59it/s] 30%|███ | 15/50 [00:04<00:09, 3.59it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.59it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.59it/s] 36%|███▌ | 18/50 [00:05<00:08, 3.59it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.59it/s] 40%|████ | 20/50 [00:05<00:08, 3.58it/s] 42%|████▏ | 21/50 [00:05<00:08, 3.59it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.58it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.59it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.59it/s] 50%|█████ | 25/50 [00:06<00:06, 3.58it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.58it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.58it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.58it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.58it/s] 60%|██████ | 30/50 [00:08<00:05, 3.58it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.58it/s] 64%|██████▍ | 32/50 [00:08<00:05, 3.58it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.58it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.58it/s] 70%|███████ | 35/50 [00:09<00:04, 3.57it/s] 72%|███████▏ | 36/50 [00:10<00:03, 3.57it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.57it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.57it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.57it/s] 80%|████████ | 40/50 [00:11<00:02, 3.57it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.57it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.57it/s] 86%|████████▌ | 43/50 [00:12<00:01, 3.57it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.57it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.57it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.57it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.57it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.57it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.57it/s] 100%|██████████| 50/50 [00:13<00:00, 3.56it/s] 100%|██████████| 50/50 [00:13<00:00, 3.58it/s]Prediction
mdzor/game-assets:9e6883cdIDj71pkpfqmdrgm0cf186bpqspymStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- TOK, an orc holding an axe
- 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": "TOK, an orc holding an axe", "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 mdzor/game-assets using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mdzor/game-assets:9e6883cda77d4c71a71277c984bd5bffe82500b265b20c9c4d3c144ff5dc8271", { input: { width: 1024, height: 1024, prompt: "TOK, an orc holding an axe", 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 mdzor/game-assets using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mdzor/game-assets:9e6883cda77d4c71a71277c984bd5bffe82500b265b20c9c4d3c144ff5dc8271", input={ "width": 1024, "height": 1024, "prompt": "TOK, an orc holding an axe", "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: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run mdzor/game-assets 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": "mdzor/game-assets:9e6883cda77d4c71a71277c984bd5bffe82500b265b20c9c4d3c144ff5dc8271", "input": { "width": 1024, "height": 1024, "prompt": "TOK, an orc holding an axe", "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.
Output
{ "completed_at": "2024-04-23T08:24:07.775086Z", "created_at": "2024-04-23T08:22:53.091000Z", "data_removed": false, "error": null, "id": "j71pkpfqmdrgm0cf186bpqspym", "input": { "width": 1024, "height": 1024, "prompt": "TOK, an orc holding an axe", "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: 25145\nEnsuring enough disk space...\nFree disk space: 1975058984960\nDownloading weights: https://replicate.delivery/pbxt/joTL3fsV6IXUciYuaGVRS1RZpV7Dru2U0SNZnpzQyfXlkRtSA/trained_model.tar\n2024-04-23T08:23:50Z | INFO | [ Initiating ] dest=/src/weights-cache/1de1469304b3a9ac minimum_chunk_size=150M url=https://replicate.delivery/pbxt/joTL3fsV6IXUciYuaGVRS1RZpV7Dru2U0SNZnpzQyfXlkRtSA/trained_model.tar\n2024-04-23T08:23:51Z | INFO | [ Complete ] dest=/src/weights-cache/1de1469304b3a9ac size=\"186 MB\" total_elapsed=0.536s url=https://replicate.delivery/pbxt/joTL3fsV6IXUciYuaGVRS1RZpV7Dru2U0SNZnpzQyfXlkRtSA/trained_model.tar\nb''\nDownloaded weights in 0.6397325992584229 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1>, an orc holding an axe\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<00:21, 2.25it/s]\n 4%|▍ | 2/50 [00:00<00:16, 2.92it/s]\n 6%|▌ | 3/50 [00:00<00:14, 3.22it/s]\n 8%|▊ | 4/50 [00:01<00:13, 3.38it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.48it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.54it/s]\n 14%|█▍ | 7/50 [00:02<00:12, 3.58it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.60it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.62it/s]\n 20%|██ | 10/50 [00:02<00:11, 3.63it/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, 3.65it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.65it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.65it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s]\n 36%|███▌ | 18/50 [00:05<00:08, 3.65it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.64it/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 [00:07<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, 3.64it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.64it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.64it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.63it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.63it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.63it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.63it/s]\n 72%|███████▏ | 36/50 [00:10<00:03, 3.63it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.63it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.63it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.63it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.64it/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, 3.65it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.65it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.60it/s]", "metrics": { "predict_time": 17.075111, "total_time": 74.684086 }, "output": [ "https://replicate.delivery/pbxt/ZqsemOn4bGxxYyuc6xEKe7jMKR7N1vkSKyGozbBAnLBnIStSA/out-0.png" ], "started_at": "2024-04-23T08:23:50.699975Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/j71pkpfqmdrgm0cf186bpqspym", "cancel": "https://api.replicate.com/v1/predictions/j71pkpfqmdrgm0cf186bpqspym/cancel" }, "version": "9e6883cda77d4c71a71277c984bd5bffe82500b265b20c9c4d3c144ff5dc8271" }Generated inUsing seed: 25145 Ensuring enough disk space... Free disk space: 1975058984960 Downloading weights: https://replicate.delivery/pbxt/joTL3fsV6IXUciYuaGVRS1RZpV7Dru2U0SNZnpzQyfXlkRtSA/trained_model.tar 2024-04-23T08:23:50Z | INFO | [ Initiating ] dest=/src/weights-cache/1de1469304b3a9ac minimum_chunk_size=150M url=https://replicate.delivery/pbxt/joTL3fsV6IXUciYuaGVRS1RZpV7Dru2U0SNZnpzQyfXlkRtSA/trained_model.tar 2024-04-23T08:23:51Z | INFO | [ Complete ] dest=/src/weights-cache/1de1469304b3a9ac size="186 MB" total_elapsed=0.536s url=https://replicate.delivery/pbxt/joTL3fsV6IXUciYuaGVRS1RZpV7Dru2U0SNZnpzQyfXlkRtSA/trained_model.tar b'' Downloaded weights in 0.6397325992584229 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: <s0><s1>, an orc holding an axe 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<00:21, 2.25it/s] 4%|▍ | 2/50 [00:00<00:16, 2.92it/s] 6%|▌ | 3/50 [00:00<00:14, 3.22it/s] 8%|▊ | 4/50 [00:01<00:13, 3.38it/s] 10%|█ | 5/50 [00:01<00:12, 3.48it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.54it/s] 14%|█▍ | 7/50 [00:02<00:12, 3.58it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.60it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.62it/s] 20%|██ | 10/50 [00:02<00:11, 3.63it/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, 3.65it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.65it/s] 30%|███ | 15/50 [00:04<00:09, 3.65it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.64it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.64it/s] 36%|███▌ | 18/50 [00:05<00:08, 3.65it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s] 40%|████ | 20/50 [00:05<00:08, 3.65it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.65it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.65it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.65it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.65it/s] 50%|█████ | 25/50 [00:07<00:06, 3.65it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.65it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.65it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.64it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.64it/s] 60%|██████ | 30/50 [00:08<00:05, 3.64it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.63it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.63it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.63it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.63it/s] 70%|███████ | 35/50 [00:09<00:04, 3.63it/s] 72%|███████▏ | 36/50 [00:10<00:03, 3.63it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.63it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.63it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s] 80%|████████ | 40/50 [00:11<00:02, 3.63it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.63it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.64it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.64it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.64it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.65it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.65it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.66it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s] 100%|██████████| 50/50 [00:13<00:00, 3.60it/s]Prediction
mdzor/game-assets:9e6883cdIDbt215xfsn5rgm0cf1879zgj0f4StatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- TOK, an evil vampire
- 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": "TOK, an evil vampire", "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 mdzor/game-assets using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "mdzor/game-assets:9e6883cda77d4c71a71277c984bd5bffe82500b265b20c9c4d3c144ff5dc8271", { input: { width: 1024, height: 1024, prompt: "TOK, an evil vampire", 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 mdzor/game-assets using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "mdzor/game-assets:9e6883cda77d4c71a71277c984bd5bffe82500b265b20c9c4d3c144ff5dc8271", input={ "width": 1024, "height": 1024, "prompt": "TOK, an evil vampire", "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: print(output[0].url()) #=> "http://example.com" # To write the file to disk: with open("my-image.png", "wb") as file: file.write(output[0].read())To learn more, take a look at the guide on getting started with Python.
Run mdzor/game-assets 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": "mdzor/game-assets:9e6883cda77d4c71a71277c984bd5bffe82500b265b20c9c4d3c144ff5dc8271", "input": { "width": 1024, "height": 1024, "prompt": "TOK, an evil vampire", "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.
Output
{ "completed_at": "2024-04-23T08:25:23.071866Z", "created_at": "2024-04-23T08:25:04.681000Z", "data_removed": false, "error": null, "id": "bt215xfsn5rgm0cf1879zgj0f4", "input": { "width": 1024, "height": 1024, "prompt": "TOK, an evil vampire", "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: 1841\nEnsuring enough disk space...\nFree disk space: 2817258405888\nDownloading weights: https://replicate.delivery/pbxt/joTL3fsV6IXUciYuaGVRS1RZpV7Dru2U0SNZnpzQyfXlkRtSA/trained_model.tar\n2024-04-23T08:25:07Z | INFO | [ Initiating ] dest=/src/weights-cache/1de1469304b3a9ac minimum_chunk_size=150M url=https://replicate.delivery/pbxt/joTL3fsV6IXUciYuaGVRS1RZpV7Dru2U0SNZnpzQyfXlkRtSA/trained_model.tar\n2024-04-23T08:25:07Z | INFO | [ Complete ] dest=/src/weights-cache/1de1469304b3a9ac size=\"186 MB\" total_elapsed=0.364s url=https://replicate.delivery/pbxt/joTL3fsV6IXUciYuaGVRS1RZpV7Dru2U0SNZnpzQyfXlkRtSA/trained_model.tar\nb''\nDownloaded weights in 0.39786672592163086 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: <s0><s1>, an evil vampire\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.62it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.62it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.62it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.63it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.63it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.63it/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, 3.62it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.62it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.62it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.62it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.62it/s]\n 42%|████▏ | 21/50 [00:05<00:08, 3.62it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.62it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.62it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.62it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.62it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.61it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.61it/s]\n 56%|█████▌ | 28/50 [00:07<00:06, 3.61it/s]\n 58%|█████▊ | 29/50 [00:08<00:05, 3.61it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.61it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.61it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.61it/s]\n 66%|██████▌ | 33/50 [00:09<00:04, 3.61it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.61it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.61it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.61it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.61it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.61it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.61it/s]\n 80%|████████ | 40/50 [00:11<00:02, 3.61it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.60it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.60it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.60it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.60it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.60it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.60it/s]\n 94%|█████████▍| 47/50 [00:13<00:00, 3.61it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.60it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.60it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.60it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.61it/s]", "metrics": { "predict_time": 15.924438, "total_time": 18.390866 }, "output": [ "https://replicate.delivery/pbxt/pcAkldXw5BqUDxSjeaoWbgE8OYKF7fSkWfyq8rutLbClTkalA/out-0.png" ], "started_at": "2024-04-23T08:25:07.147428Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/bt215xfsn5rgm0cf1879zgj0f4", "cancel": "https://api.replicate.com/v1/predictions/bt215xfsn5rgm0cf1879zgj0f4/cancel" }, "version": "9e6883cda77d4c71a71277c984bd5bffe82500b265b20c9c4d3c144ff5dc8271" }Generated inUsing seed: 1841 Ensuring enough disk space... Free disk space: 2817258405888 Downloading weights: https://replicate.delivery/pbxt/joTL3fsV6IXUciYuaGVRS1RZpV7Dru2U0SNZnpzQyfXlkRtSA/trained_model.tar 2024-04-23T08:25:07Z | INFO | [ Initiating ] dest=/src/weights-cache/1de1469304b3a9ac minimum_chunk_size=150M url=https://replicate.delivery/pbxt/joTL3fsV6IXUciYuaGVRS1RZpV7Dru2U0SNZnpzQyfXlkRtSA/trained_model.tar 2024-04-23T08:25:07Z | INFO | [ Complete ] dest=/src/weights-cache/1de1469304b3a9ac size="186 MB" total_elapsed=0.364s url=https://replicate.delivery/pbxt/joTL3fsV6IXUciYuaGVRS1RZpV7Dru2U0SNZnpzQyfXlkRtSA/trained_model.tar b'' Downloaded weights in 0.39786672592163086 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: <s0><s1>, an evil vampire txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.62it/s] 4%|▍ | 2/50 [00:00<00:13, 3.62it/s] 6%|▌ | 3/50 [00:00<00:12, 3.62it/s] 8%|▊ | 4/50 [00:01<00:12, 3.63it/s] 10%|█ | 5/50 [00:01<00:12, 3.63it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.63it/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, 3.62it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.62it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.62it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.62it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.62it/s] 40%|████ | 20/50 [00:05<00:08, 3.62it/s] 42%|████▏ | 21/50 [00:05<00:08, 3.62it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.62it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.62it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.62it/s] 50%|█████ | 25/50 [00:06<00:06, 3.62it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.61it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.61it/s] 56%|█████▌ | 28/50 [00:07<00:06, 3.61it/s] 58%|█████▊ | 29/50 [00:08<00:05, 3.61it/s] 60%|██████ | 30/50 [00:08<00:05, 3.61it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.61it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.61it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.61it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.61it/s] 70%|███████ | 35/50 [00:09<00:04, 3.61it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.61it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.61it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.61it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.61it/s] 80%|████████ | 40/50 [00:11<00:02, 3.61it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.60it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.60it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.60it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.60it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.60it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.60it/s] 94%|█████████▍| 47/50 [00:13<00:00, 3.61it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.60it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.60it/s] 100%|██████████| 50/50 [00:13<00:00, 3.60it/s] 100%|██████████| 50/50 [00:13<00:00, 3.61it/s]
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