dylanhecht / nikki_training_test
- Public
- 336 runs
-
L40S
- SDXL fine-tune
Prediction
dylanhecht/nikki_training_test:1a24fa5226b4c5ff7885c0eef08e43505edd91b9773b4c2b804b8b5081c418c6IDa3yb2btb4cuazwdj7yolwmb63uStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- Adorable 3D Character, TOK dog, 3D render, adorable character, 3D art
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 1
- num_outputs
- 1
- refine_steps
- 20
- guidance_scale
- 13
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "Adorable 3D Character, TOK dog, 3D render, adorable character, 3D art", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 13, "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 dylanhecht/nikki_training_test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "dylanhecht/nikki_training_test:1a24fa5226b4c5ff7885c0eef08e43505edd91b9773b4c2b804b8b5081c418c6", { input: { width: 1024, height: 1024, prompt: "Adorable 3D Character, TOK dog, 3D render, adorable character, 3D art", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 1, num_outputs: 1, refine_steps: 20, guidance_scale: 13, 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 dylanhecht/nikki_training_test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "dylanhecht/nikki_training_test:1a24fa5226b4c5ff7885c0eef08e43505edd91b9773b4c2b804b8b5081c418c6", input={ "width": 1024, "height": 1024, "prompt": "Adorable 3D Character, TOK dog, 3D render, adorable character, 3D art", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 13, "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 dylanhecht/nikki_training_test 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": "dylanhecht/nikki_training_test:1a24fa5226b4c5ff7885c0eef08e43505edd91b9773b4c2b804b8b5081c418c6", "input": { "width": 1024, "height": 1024, "prompt": "Adorable 3D Character, TOK dog, 3D render, adorable character, 3D art", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 13, "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": "2023-11-15T03:22:56.727122Z", "created_at": "2023-11-15T03:22:29.267412Z", "data_removed": false, "error": null, "id": "a3yb2btb4cuazwdj7yolwmb63u", "input": { "width": 1024, "height": 1024, "prompt": "Adorable 3D Character, TOK dog, 3D render, adorable character, 3D art", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 13, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 552\nEnsuring enough disk space...\nFree disk space: 1485384228864\nDownloading weights: https://pbxt.replicate.delivery/w0Ok5Ff3UzzrBaYVIthA4IHi0ZLSdPEgEkKtXd2z0QNJKCxIA/trained_model.tar\nb'Downloaded 186 MB bytes in 3.476s (54 MB/s)\\nExtracted 186 MB in 0.053s (3.5 GB/s)\\n'\nDownloaded weights in 3.9553568363189697 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: Adorable 3D Character, <s0><s1> dog, 3D render, adorable character, 3D art\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.66it/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, 3.66it/s]\n 10%|█ | 5/50 [00:01<00:12, 3.66it/s]\n 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s]\n 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s]\n 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s]\n 18%|█▊ | 9/50 [00:02<00:11, 3.65it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.65it/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, 3.65it/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, 3.65it/s]\n 34%|███▍ | 17/50 [00:04<00:09, 3.65it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.64it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.64it/s]\n 44%|████▍ | 22/50 [00:06<00:07, 3.64it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.64it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.64it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.64it/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, 3.64it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/s]\n 60%|██████ | 30/50 [00:08<00:05, 3.63it/s]\n 62%|██████▏ | 31/50 [00:08<00:05, 3.63it/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, 3.63it/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, 3.64it/s]\n 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.63it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.64it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.63it/s]\n 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.64it/s]\n 92%|█████████▏| 46/50 [00:12<00:01, 3.63it/s]\n 94%|█████████▍| 47/50 [00:12<00:00, 3.63it/s]\n 96%|█████████▌| 48/50 [00:13<00:00, 3.63it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.63it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.63it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.64it/s]", "metrics": { "predict_time": 20.262053, "total_time": 27.45971 }, "output": [ "https://replicate.delivery/pbxt/M1ke6kmiGkT1YKahZYqNVZWCt98mY3jvZfDAHqEJ7CuPuewjA/out-0.png" ], "started_at": "2023-11-15T03:22:36.465069Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/a3yb2btb4cuazwdj7yolwmb63u", "cancel": "https://api.replicate.com/v1/predictions/a3yb2btb4cuazwdj7yolwmb63u/cancel" }, "version": "1a24fa5226b4c5ff7885c0eef08e43505edd91b9773b4c2b804b8b5081c418c6" }Generated inUsing seed: 552 Ensuring enough disk space... Free disk space: 1485384228864 Downloading weights: https://pbxt.replicate.delivery/w0Ok5Ff3UzzrBaYVIthA4IHi0ZLSdPEgEkKtXd2z0QNJKCxIA/trained_model.tar b'Downloaded 186 MB bytes in 3.476s (54 MB/s)\nExtracted 186 MB in 0.053s (3.5 GB/s)\n' Downloaded weights in 3.9553568363189697 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: Adorable 3D Character, <s0><s1> dog, 3D render, adorable character, 3D art txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.66it/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, 3.66it/s] 10%|█ | 5/50 [00:01<00:12, 3.66it/s] 12%|█▏ | 6/50 [00:01<00:12, 3.65it/s] 14%|█▍ | 7/50 [00:01<00:11, 3.65it/s] 16%|█▌ | 8/50 [00:02<00:11, 3.65it/s] 18%|█▊ | 9/50 [00:02<00:11, 3.65it/s] 20%|██ | 10/50 [00:02<00:10, 3.65it/s] 22%|██▏ | 11/50 [00:03<00:10, 3.65it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.65it/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, 3.65it/s] 34%|███▍ | 17/50 [00:04<00:09, 3.65it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.64it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.64it/s] 40%|████ | 20/50 [00:05<00:08, 3.64it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.64it/s] 44%|████▍ | 22/50 [00:06<00:07, 3.64it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.64it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.64it/s] 50%|█████ | 25/50 [00:06<00:06, 3.64it/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, 3.64it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.64it/s] 60%|██████ | 30/50 [00:08<00:05, 3.63it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.63it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.64it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.64it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.64it/s] 70%|███████ | 35/50 [00:09<00:04, 3.63it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.64it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.64it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.64it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.63it/s] 80%|████████ | 40/50 [00:10<00:02, 3.63it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.64it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.63it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.63it/s] 88%|████████▊ | 44/50 [00:12<00:01, 3.63it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.64it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.63it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.63it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.63it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.63it/s] 100%|██████████| 50/50 [00:13<00:00, 3.63it/s] 100%|██████████| 50/50 [00:13<00:00, 3.64it/s]Prediction
dylanhecht/nikki_training_test:1a24fa5226b4c5ff7885c0eef08e43505edd91b9773b4c2b804b8b5081c418c6IDcycaz3tbtyfctgkdnlpxhtwtgaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 420
- width
- 1024
- height
- 1024
- prompt
- Adorable 3D Character, TOK dog, 3D render, adorable character, 3D art
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 1
- num_outputs
- 1
- refine_steps
- 20
- guidance_scale
- 8
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- ugly, deformed, noisy, blurry, low contrast, grunge, sloppy, unkempt, photograph, photo, realistic
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "seed": 420, "width": 1024, "height": 1024, "prompt": "Adorable 3D Character, TOK dog, 3D render, adorable character, 3D art\n", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 8, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, grunge, sloppy, unkempt, photograph, photo, realistic", "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 dylanhecht/nikki_training_test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "dylanhecht/nikki_training_test:1a24fa5226b4c5ff7885c0eef08e43505edd91b9773b4c2b804b8b5081c418c6", { input: { seed: 420, width: 1024, height: 1024, prompt: "Adorable 3D Character, TOK dog, 3D render, adorable character, 3D art\n", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 1, num_outputs: 1, refine_steps: 20, guidance_scale: 8, apply_watermark: true, high_noise_frac: 0.8, negative_prompt: "ugly, deformed, noisy, blurry, low contrast, grunge, sloppy, unkempt, photograph, photo, realistic", 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 dylanhecht/nikki_training_test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "dylanhecht/nikki_training_test:1a24fa5226b4c5ff7885c0eef08e43505edd91b9773b4c2b804b8b5081c418c6", input={ "seed": 420, "width": 1024, "height": 1024, "prompt": "Adorable 3D Character, TOK dog, 3D render, adorable character, 3D art\n", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 8, "apply_watermark": True, "high_noise_frac": 0.8, "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, grunge, sloppy, unkempt, photograph, photo, realistic", "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 dylanhecht/nikki_training_test 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": "dylanhecht/nikki_training_test:1a24fa5226b4c5ff7885c0eef08e43505edd91b9773b4c2b804b8b5081c418c6", "input": { "seed": 420, "width": 1024, "height": 1024, "prompt": "Adorable 3D Character, TOK dog, 3D render, adorable character, 3D art\\n", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 8, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, grunge, sloppy, unkempt, photograph, photo, realistic", "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": "2023-11-15T03:39:29.025602Z", "created_at": "2023-11-15T03:39:07.340182Z", "data_removed": false, "error": null, "id": "cycaz3tbtyfctgkdnlpxhtwtga", "input": { "seed": 420, "width": 1024, "height": 1024, "prompt": "Adorable 3D Character, TOK dog, 3D render, adorable character, 3D art\n", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 1, "num_outputs": 1, "refine_steps": 20, "guidance_scale": 8, "apply_watermark": true, "high_noise_frac": 0.8, "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, grunge, sloppy, unkempt, photograph, photo, realistic", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 420\nEnsuring enough disk space...\nFree disk space: 2002320121856\nDownloading weights: https://pbxt.replicate.delivery/w0Ok5Ff3UzzrBaYVIthA4IHi0ZLSdPEgEkKtXd2z0QNJKCxIA/trained_model.tar\nb'Downloaded 186 MB bytes in 0.083s (2.2 GB/s)\\nExtracted 186 MB in 0.069s (2.7 GB/s)\\n'\nDownloaded weights in 0.3087611198425293 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: Adorable 3D Character, <s0><s1> dog, 3D render, adorable character, 3D art\ntxt2img mode\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:13, 3.69it/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<00:11, 3.67it/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, 3.68it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.68it/s]\n 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s]\n 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s]\n 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.69it/s]\n 30%|███ | 15/50 [00:04<00:09, 3.69it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.69it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.69it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.69it/s]\n 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s]\n 40%|████ | 20/50 [00:05<00:08, 3.68it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.68it/s]\n 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s]\n 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.68it/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, 3.68it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s]\n 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s]\n 70%|███████ | 35/50 [00:09<00:04, 3.68it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s]\n 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s]\n 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s]\n 78%|███████▊ | 39/50 [00:10<00:02, 3.68it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.68it/s]\n 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s]\n 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s]\n 86%|████████▌ | 43/50 [00:11<00:01, 3.68it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s]\n 90%|█████████ | 45/50 [00:12<00:01, 3.68it/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": 15.824766, "total_time": 21.68542 }, "output": [ "https://replicate.delivery/pbxt/G8zMoCdVpU7lAlEokj8IJvDUHTiIB6J3dlaL0yIpVAJcvHeIA/out-0.png" ], "started_at": "2023-11-15T03:39:13.200836Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/cycaz3tbtyfctgkdnlpxhtwtga", "cancel": "https://api.replicate.com/v1/predictions/cycaz3tbtyfctgkdnlpxhtwtga/cancel" }, "version": "1a24fa5226b4c5ff7885c0eef08e43505edd91b9773b4c2b804b8b5081c418c6" }Generated inUsing seed: 420 Ensuring enough disk space... Free disk space: 2002320121856 Downloading weights: https://pbxt.replicate.delivery/w0Ok5Ff3UzzrBaYVIthA4IHi0ZLSdPEgEkKtXd2z0QNJKCxIA/trained_model.tar b'Downloaded 186 MB bytes in 0.083s (2.2 GB/s)\nExtracted 186 MB in 0.069s (2.7 GB/s)\n' Downloaded weights in 0.3087611198425293 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: Adorable 3D Character, <s0><s1> dog, 3D render, adorable character, 3D art txt2img mode 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:13, 3.69it/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<00:11, 3.67it/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, 3.68it/s] 20%|██ | 10/50 [00:02<00:10, 3.68it/s] 22%|██▏ | 11/50 [00:02<00:10, 3.68it/s] 24%|██▍ | 12/50 [00:03<00:10, 3.69it/s] 26%|██▌ | 13/50 [00:03<00:10, 3.69it/s] 28%|██▊ | 14/50 [00:03<00:09, 3.69it/s] 30%|███ | 15/50 [00:04<00:09, 3.69it/s] 32%|███▏ | 16/50 [00:04<00:09, 3.69it/s] 34%|███▍ | 17/50 [00:04<00:08, 3.69it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.69it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.68it/s] 40%|████ | 20/50 [00:05<00:08, 3.68it/s] 42%|████▏ | 21/50 [00:05<00:07, 3.68it/s] 44%|████▍ | 22/50 [00:05<00:07, 3.68it/s] 46%|████▌ | 23/50 [00:06<00:07, 3.68it/s] 48%|████▊ | 24/50 [00:06<00:07, 3.68it/s] 50%|█████ | 25/50 [00:06<00:06, 3.68it/s] 52%|█████▏ | 26/50 [00:07<00:06, 3.68it/s] 54%|█████▍ | 27/50 [00:07<00:06, 3.68it/s] 56%|█████▌ | 28/50 [00:07<00:05, 3.68it/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, 3.68it/s] 66%|██████▌ | 33/50 [00:08<00:04, 3.68it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.67it/s] 70%|███████ | 35/50 [00:09<00:04, 3.68it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.68it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.68it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.68it/s] 78%|███████▊ | 39/50 [00:10<00:02, 3.68it/s] 80%|████████ | 40/50 [00:10<00:02, 3.68it/s] 82%|████████▏ | 41/50 [00:11<00:02, 3.68it/s] 84%|████████▍ | 42/50 [00:11<00:02, 3.68it/s] 86%|████████▌ | 43/50 [00:11<00:01, 3.68it/s] 88%|████████▊ | 44/50 [00:11<00:01, 3.67it/s] 90%|█████████ | 45/50 [00:12<00:01, 3.68it/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]
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