bendedkneeface / vanitas
Vanitas style paintings
- Public
- 104 runs
-
L40S
- SDXL fine-tune
Prediction
bendedkneeface/vanitas:38ff900a27856a1182c44cd2bce84890e048b34e27ef8226fbe0d42a4474a951IDhoq5dalbzrrze3rkqvs575y3taStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A painting of a lobster in the VNT style
- refine
- expert_ensemble_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- Blurry, under exposed, pixelated
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A painting of a lobster in the VNT style", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "Blurry, under exposed, pixelated", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 bendedkneeface/vanitas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "bendedkneeface/vanitas:38ff900a27856a1182c44cd2bce84890e048b34e27ef8226fbe0d42a4474a951", { input: { width: 1024, height: 1024, prompt: "A painting of a lobster in the VNT style", refine: "expert_ensemble_refiner", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "Blurry, under exposed, pixelated", 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 replicate
Import the client:import replicate
Run bendedkneeface/vanitas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "bendedkneeface/vanitas:38ff900a27856a1182c44cd2bce84890e048b34e27ef8226fbe0d42a4474a951", input={ "width": 1024, "height": 1024, "prompt": "A painting of a lobster in the VNT style", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "Blurry, under exposed, pixelated", "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 bendedkneeface/vanitas 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": "bendedkneeface/vanitas:38ff900a27856a1182c44cd2bce84890e048b34e27ef8226fbe0d42a4474a951", "input": { "width": 1024, "height": 1024, "prompt": "A painting of a lobster in the VNT style", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "Blurry, under exposed, pixelated", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-04-01T04:01:37.412269Z", "created_at": "2024-04-01T04:00:55.291208Z", "data_removed": false, "error": null, "id": "hoq5dalbzrrze3rkqvs575y3ta", "input": { "width": 1024, "height": 1024, "prompt": "A painting of a lobster in the VNT style", "refine": "expert_ensemble_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "Blurry, under exposed, pixelated", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 49493\nEnsuring enough disk space...\nFree disk space: 1570436567040\nDownloading weights: https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar\n2024-04-01T04:01:21Z | INFO | [ Initiating ] dest=/src/weights-cache/fb46d00f92950def minimum_chunk_size=150M url=https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar\n2024-04-01T04:01:24Z | INFO | [ Complete ] dest=/src/weights-cache/fb46d00f92950def size=\"186 MB\" total_elapsed=2.690s url=https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar\nb''\nDownloaded weights in 2.8463382720947266 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A painting of a lobster in the <s0><s1> style\ntxt2img mode\n 0%| | 0/32 [00:00<?, ?it/s]\n 3%|▎ | 1/32 [00:00<00:08, 3.72it/s]\n 6%|▋ | 2/32 [00:00<00:08, 3.70it/s]\n 9%|▉ | 3/32 [00:00<00:07, 3.70it/s]\n 12%|█▎ | 4/32 [00:01<00:07, 3.69it/s]\n 16%|█▌ | 5/32 [00:01<00:07, 3.69it/s]\n 19%|█▉ | 6/32 [00:01<00:07, 3.68it/s]\n 22%|██▏ | 7/32 [00:01<00:06, 3.68it/s]\n 25%|██▌ | 8/32 [00:02<00:06, 3.68it/s]\n 28%|██▊ | 9/32 [00:02<00:06, 3.68it/s]\n 31%|███▏ | 10/32 [00:02<00:05, 3.68it/s]\n 34%|███▍ | 11/32 [00:02<00:05, 3.68it/s]\n 38%|███▊ | 12/32 [00:03<00:05, 3.68it/s]\n 41%|████ | 13/32 [00:03<00:05, 3.68it/s]\n 44%|████▍ | 14/32 [00:03<00:04, 3.68it/s]\n 47%|████▋ | 15/32 [00:04<00:04, 3.68it/s]\n 50%|█████ | 16/32 [00:04<00:04, 3.68it/s]\n 53%|█████▎ | 17/32 [00:04<00:04, 3.68it/s]\n 56%|█████▋ | 18/32 [00:04<00:03, 3.68it/s]\n 59%|█████▉ | 19/32 [00:05<00:03, 3.68it/s]\n 62%|██████▎ | 20/32 [00:05<00:03, 3.68it/s]\n 66%|██████▌ | 21/32 [00:05<00:02, 3.68it/s]\n 69%|██████▉ | 22/32 [00:05<00:02, 3.68it/s]\n 72%|███████▏ | 23/32 [00:06<00:02, 3.68it/s]\n 75%|███████▌ | 24/32 [00:06<00:02, 3.68it/s]\n 78%|███████▊ | 25/32 [00:06<00:01, 3.68it/s]\n 81%|████████▏ | 26/32 [00:07<00:01, 3.68it/s]\n 84%|████████▍ | 27/32 [00:07<00:01, 3.68it/s]\n 88%|████████▊ | 28/32 [00:07<00:01, 3.69it/s]\n 91%|█████████ | 29/32 [00:07<00:00, 3.69it/s]\n 94%|█████████▍| 30/32 [00:08<00:00, 3.68it/s]\n 97%|█████████▋| 31/32 [00:08<00:00, 3.68it/s]\n100%|██████████| 32/32 [00:08<00:00, 3.68it/s]\n100%|██████████| 32/32 [00:08<00:00, 3.68it/s]\n 0%| | 0/10 [00:00<?, ?it/s]\n 10%|█ | 1/10 [00:00<00:02, 4.28it/s]\n 20%|██ | 2/10 [00:00<00:01, 4.25it/s]\n 30%|███ | 3/10 [00:00<00:01, 4.24it/s]\n 40%|████ | 4/10 [00:00<00:01, 4.23it/s]\n 50%|█████ | 5/10 [00:01<00:01, 4.23it/s]\n 60%|██████ | 6/10 [00:01<00:00, 4.22it/s]\n 70%|███████ | 7/10 [00:01<00:00, 4.22it/s]\n 80%|████████ | 8/10 [00:01<00:00, 4.21it/s]\n 90%|█████████ | 9/10 [00:02<00:00, 4.22it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.21it/s]\n100%|██████████| 10/10 [00:02<00:00, 4.22it/s]", "metrics": { "predict_time": 15.702941, "total_time": 42.121061 }, "output": [ "https://replicate.delivery/pbxt/YSqO9XEbvp7pLV0UGjjYJiZhqE1kKdfV8wtE2BoS6JvQHflSA/out-0.png" ], "started_at": "2024-04-01T04:01:21.709328Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/hoq5dalbzrrze3rkqvs575y3ta", "cancel": "https://api.replicate.com/v1/predictions/hoq5dalbzrrze3rkqvs575y3ta/cancel" }, "version": "38ff900a27856a1182c44cd2bce84890e048b34e27ef8226fbe0d42a4474a951" }
Generated inUsing seed: 49493 Ensuring enough disk space... Free disk space: 1570436567040 Downloading weights: https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar 2024-04-01T04:01:21Z | INFO | [ Initiating ] dest=/src/weights-cache/fb46d00f92950def minimum_chunk_size=150M url=https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar 2024-04-01T04:01:24Z | INFO | [ Complete ] dest=/src/weights-cache/fb46d00f92950def size="186 MB" total_elapsed=2.690s url=https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar b'' Downloaded weights in 2.8463382720947266 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A painting of a lobster in the <s0><s1> style txt2img mode 0%| | 0/32 [00:00<?, ?it/s] 3%|▎ | 1/32 [00:00<00:08, 3.72it/s] 6%|▋ | 2/32 [00:00<00:08, 3.70it/s] 9%|▉ | 3/32 [00:00<00:07, 3.70it/s] 12%|█▎ | 4/32 [00:01<00:07, 3.69it/s] 16%|█▌ | 5/32 [00:01<00:07, 3.69it/s] 19%|█▉ | 6/32 [00:01<00:07, 3.68it/s] 22%|██▏ | 7/32 [00:01<00:06, 3.68it/s] 25%|██▌ | 8/32 [00:02<00:06, 3.68it/s] 28%|██▊ | 9/32 [00:02<00:06, 3.68it/s] 31%|███▏ | 10/32 [00:02<00:05, 3.68it/s] 34%|███▍ | 11/32 [00:02<00:05, 3.68it/s] 38%|███▊ | 12/32 [00:03<00:05, 3.68it/s] 41%|████ | 13/32 [00:03<00:05, 3.68it/s] 44%|████▍ | 14/32 [00:03<00:04, 3.68it/s] 47%|████▋ | 15/32 [00:04<00:04, 3.68it/s] 50%|█████ | 16/32 [00:04<00:04, 3.68it/s] 53%|█████▎ | 17/32 [00:04<00:04, 3.68it/s] 56%|█████▋ | 18/32 [00:04<00:03, 3.68it/s] 59%|█████▉ | 19/32 [00:05<00:03, 3.68it/s] 62%|██████▎ | 20/32 [00:05<00:03, 3.68it/s] 66%|██████▌ | 21/32 [00:05<00:02, 3.68it/s] 69%|██████▉ | 22/32 [00:05<00:02, 3.68it/s] 72%|███████▏ | 23/32 [00:06<00:02, 3.68it/s] 75%|███████▌ | 24/32 [00:06<00:02, 3.68it/s] 78%|███████▊ | 25/32 [00:06<00:01, 3.68it/s] 81%|████████▏ | 26/32 [00:07<00:01, 3.68it/s] 84%|████████▍ | 27/32 [00:07<00:01, 3.68it/s] 88%|████████▊ | 28/32 [00:07<00:01, 3.69it/s] 91%|█████████ | 29/32 [00:07<00:00, 3.69it/s] 94%|█████████▍| 30/32 [00:08<00:00, 3.68it/s] 97%|█████████▋| 31/32 [00:08<00:00, 3.68it/s] 100%|██████████| 32/32 [00:08<00:00, 3.68it/s] 100%|██████████| 32/32 [00:08<00:00, 3.68it/s] 0%| | 0/10 [00:00<?, ?it/s] 10%|█ | 1/10 [00:00<00:02, 4.28it/s] 20%|██ | 2/10 [00:00<00:01, 4.25it/s] 30%|███ | 3/10 [00:00<00:01, 4.24it/s] 40%|████ | 4/10 [00:00<00:01, 4.23it/s] 50%|█████ | 5/10 [00:01<00:01, 4.23it/s] 60%|██████ | 6/10 [00:01<00:00, 4.22it/s] 70%|███████ | 7/10 [00:01<00:00, 4.22it/s] 80%|████████ | 8/10 [00:01<00:00, 4.21it/s] 90%|█████████ | 9/10 [00:02<00:00, 4.22it/s] 100%|██████████| 10/10 [00:02<00:00, 4.21it/s] 100%|██████████| 10/10 [00:02<00:00, 4.22it/s]
Prediction
bendedkneeface/vanitas:38ff900a27856a1182c44cd2bce84890e048b34e27ef8226fbe0d42a4474a951IDzgnlvgtbpr4csrx2miw5smoebaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A vanitas painting of a lobster in the VNT style
- refine
- base_image_refiner
- scheduler
- DDIM
- lora_scale
- 0.99
- num_outputs
- 1
- refine_steps
- 10
- guidance_scale
- 3.73
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- Blurry, under exposed, pixelated, simple, abnormal biology
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "width": 1024, "height": 1024, "prompt": "A vanitas painting of a lobster in the VNT style", "refine": "base_image_refiner", "scheduler": "DDIM", "lora_scale": 0.99, "num_outputs": 1, "refine_steps": 10, "guidance_scale": 3.73, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "Blurry, under exposed, pixelated, simple, abnormal biology", "prompt_strength": 0.8, "num_inference_steps": 50 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 bendedkneeface/vanitas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "bendedkneeface/vanitas:38ff900a27856a1182c44cd2bce84890e048b34e27ef8226fbe0d42a4474a951", { input: { width: 1024, height: 1024, prompt: "A vanitas painting of a lobster in the VNT style", refine: "base_image_refiner", scheduler: "DDIM", lora_scale: 0.99, num_outputs: 1, refine_steps: 10, guidance_scale: 3.73, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "Blurry, under exposed, pixelated, simple, abnormal biology", 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 replicate
Import the client:import replicate
Run bendedkneeface/vanitas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "bendedkneeface/vanitas:38ff900a27856a1182c44cd2bce84890e048b34e27ef8226fbe0d42a4474a951", input={ "width": 1024, "height": 1024, "prompt": "A vanitas painting of a lobster in the VNT style", "refine": "base_image_refiner", "scheduler": "DDIM", "lora_scale": 0.99, "num_outputs": 1, "refine_steps": 10, "guidance_scale": 3.73, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "Blurry, under exposed, pixelated, simple, abnormal biology", "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 bendedkneeface/vanitas 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": "bendedkneeface/vanitas:38ff900a27856a1182c44cd2bce84890e048b34e27ef8226fbe0d42a4474a951", "input": { "width": 1024, "height": 1024, "prompt": "A vanitas painting of a lobster in the VNT style", "refine": "base_image_refiner", "scheduler": "DDIM", "lora_scale": 0.99, "num_outputs": 1, "refine_steps": 10, "guidance_scale": 3.73, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "Blurry, under exposed, pixelated, simple, abnormal biology", "prompt_strength": 0.8, "num_inference_steps": 50 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-04-01T05:04:32.766478Z", "created_at": "2024-04-01T05:04:06.915930Z", "data_removed": false, "error": null, "id": "zgnlvgtbpr4csrx2miw5smoeba", "input": { "width": 1024, "height": 1024, "prompt": "A vanitas painting of a lobster in the VNT style", "refine": "base_image_refiner", "scheduler": "DDIM", "lora_scale": 0.99, "num_outputs": 1, "refine_steps": 10, "guidance_scale": 3.73, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "Blurry, under exposed, pixelated, simple, abnormal biology", "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 11920\nEnsuring enough disk space...\nFree disk space: 1555755638784\nDownloading weights: https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar\n2024-04-01T05:04:16Z | INFO | [ Initiating ] dest=/src/weights-cache/fb46d00f92950def minimum_chunk_size=150M url=https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar\n2024-04-01T05:04:16Z | INFO | [ Complete ] dest=/src/weights-cache/fb46d00f92950def size=\"186 MB\" total_elapsed=0.413s url=https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar\nb''\nDownloaded weights in 0.5079295635223389 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A vanitas painting of a lobster in the <s0><s1> style\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, 3.67it/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, 3.68it/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, 3.67it/s]\n 32%|███▏ | 16/50 [00:04<00:09, 3.67it/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 [00:05<00:07, 3.66it/s]\n 46%|████▌ | 23/50 [00:06<00:07, 3.66it/s]\n 48%|████▊ | 24/50 [00:06<00:07, 3.65it/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, 3.64it/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, 3.65it/s]\n 98%|█████████▊| 49/50 [00:13<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.64it/s]\n100%|██████████| 50/50 [00:13<00:00, 3.66it/s]\n 0%| | 0/3 [00:00<?, ?it/s]\n 33%|███▎ | 1/3 [00:00<00:00, 3.93it/s]\n 67%|██████▋ | 2/3 [00:00<00:00, 4.11it/s]\n100%|██████████| 3/3 [00:00<00:00, 4.16it/s]\n100%|██████████| 3/3 [00:00<00:00, 4.13it/s]", "metrics": { "predict_time": 16.595402, "total_time": 25.850548 }, "output": [ "https://replicate.delivery/pbxt/f0Oj5cs31UzGf0QoV6HjAlZg2GB3JIeDni8o6D2WSiTBTeXKB/out-0.png" ], "started_at": "2024-04-01T05:04:16.171076Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/zgnlvgtbpr4csrx2miw5smoeba", "cancel": "https://api.replicate.com/v1/predictions/zgnlvgtbpr4csrx2miw5smoeba/cancel" }, "version": "38ff900a27856a1182c44cd2bce84890e048b34e27ef8226fbe0d42a4474a951" }
Generated inUsing seed: 11920 Ensuring enough disk space... Free disk space: 1555755638784 Downloading weights: https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar 2024-04-01T05:04:16Z | INFO | [ Initiating ] dest=/src/weights-cache/fb46d00f92950def minimum_chunk_size=150M url=https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar 2024-04-01T05:04:16Z | INFO | [ Complete ] dest=/src/weights-cache/fb46d00f92950def size="186 MB" total_elapsed=0.413s url=https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar b'' Downloaded weights in 0.5079295635223389 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A vanitas painting of a lobster in the <s0><s1> style 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, 3.67it/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, 3.68it/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 [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<00:09, 3.66it/s] 36%|███▌ | 18/50 [00:04<00:08, 3.66it/s] 38%|███▊ | 19/50 [00:05<00:08, 3.66it/s] 40%|████ | 20/50 [00:05<00:08, 3.66it/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, 3.65it/s] 50%|█████ | 25/50 [00:06<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, 3.65it/s] 58%|█████▊ | 29/50 [00:07<00:05, 3.65it/s] 60%|██████ | 30/50 [00:08<00:05, 3.65it/s] 62%|██████▏ | 31/50 [00:08<00:05, 3.65it/s] 64%|██████▍ | 32/50 [00:08<00:04, 3.65it/s] 66%|██████▌ | 33/50 [00:09<00:04, 3.65it/s] 68%|██████▊ | 34/50 [00:09<00:04, 3.65it/s] 70%|███████ | 35/50 [00:09<00:04, 3.65it/s] 72%|███████▏ | 36/50 [00:09<00:03, 3.65it/s] 74%|███████▍ | 37/50 [00:10<00:03, 3.65it/s] 76%|███████▌ | 38/50 [00:10<00:03, 3.65it/s] 78%|███████▊ | 39/50 [00:10<00:03, 3.65it/s] 80%|████████ | 40/50 [00:10<00:02, 3.65it/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, 3.64it/s] 92%|█████████▏| 46/50 [00:12<00:01, 3.64it/s] 94%|█████████▍| 47/50 [00:12<00:00, 3.64it/s] 96%|█████████▌| 48/50 [00:13<00:00, 3.65it/s] 98%|█████████▊| 49/50 [00:13<00:00, 3.64it/s] 100%|██████████| 50/50 [00:13<00:00, 3.64it/s] 100%|██████████| 50/50 [00:13<00:00, 3.66it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 3.93it/s] 67%|██████▋ | 2/3 [00:00<00:00, 4.11it/s] 100%|██████████| 3/3 [00:00<00:00, 4.16it/s] 100%|██████████| 3/3 [00:00<00:00, 4.13it/s]
Prediction
bendedkneeface/vanitas:38ff900a27856a1182c44cd2bce84890e048b34e27ef8226fbe0d42a4474a951IDznq7le3ba5fg3vrlpjugtnx5xiStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A vanitas painting of a lobster in the VNT style
- refine
- no_refiner
- scheduler
- K_EULER
- lora_scale
- 0.8
- num_outputs
- 1
- guidance_scale
- 7.5
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- Blurry, under exposed, pixelated, abstract
- prompt_strength
- 0.8
- num_inference_steps
- 100
{ "width": 1024, "height": 1024, "prompt": "A vanitas painting of a lobster in the VNT style", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "Blurry, under exposed, pixelated, abstract ", "prompt_strength": 0.8, "num_inference_steps": 100 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 bendedkneeface/vanitas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "bendedkneeface/vanitas:38ff900a27856a1182c44cd2bce84890e048b34e27ef8226fbe0d42a4474a951", { input: { width: 1024, height: 1024, prompt: "A vanitas painting of a lobster in the VNT style", refine: "no_refiner", scheduler: "K_EULER", lora_scale: 0.8, num_outputs: 1, guidance_scale: 7.5, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "Blurry, under exposed, pixelated, abstract ", prompt_strength: 0.8, num_inference_steps: 100 } } ); // 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 replicate
Import the client:import replicate
Run bendedkneeface/vanitas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "bendedkneeface/vanitas:38ff900a27856a1182c44cd2bce84890e048b34e27ef8226fbe0d42a4474a951", input={ "width": 1024, "height": 1024, "prompt": "A vanitas painting of a lobster in the VNT style", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "Blurry, under exposed, pixelated, abstract ", "prompt_strength": 0.8, "num_inference_steps": 100 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run bendedkneeface/vanitas 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": "bendedkneeface/vanitas:38ff900a27856a1182c44cd2bce84890e048b34e27ef8226fbe0d42a4474a951", "input": { "width": 1024, "height": 1024, "prompt": "A vanitas painting of a lobster in the VNT style", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "Blurry, under exposed, pixelated, abstract ", "prompt_strength": 0.8, "num_inference_steps": 100 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-04-01T04:19:42.486604Z", "created_at": "2024-04-01T04:19:09.730885Z", "data_removed": false, "error": null, "id": "znq7le3ba5fg3vrlpjugtnx5xi", "input": { "width": 1024, "height": 1024, "prompt": "A vanitas painting of a lobster in the VNT style", "refine": "no_refiner", "scheduler": "K_EULER", "lora_scale": 0.8, "num_outputs": 1, "guidance_scale": 7.5, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "Blurry, under exposed, pixelated, abstract ", "prompt_strength": 0.8, "num_inference_steps": 100 }, "logs": "Using seed: 64199\nEnsuring enough disk space...\nFree disk space: 1340739112960\nDownloading weights: https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar\n2024-04-01T04:19:12Z | INFO | [ Initiating ] dest=/src/weights-cache/fb46d00f92950def minimum_chunk_size=150M url=https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar\n2024-04-01T04:19:13Z | INFO | [ Complete ] dest=/src/weights-cache/fb46d00f92950def size=\"186 MB\" total_elapsed=0.573s url=https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar\nb''\nDownloaded weights in 0.6822450160980225 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A vanitas painting of a lobster in the <s0><s1> style\ntxt2img mode\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<00:26, 3.71it/s]\n 2%|▏ | 2/100 [00:00<00:26, 3.70it/s]\n 3%|▎ | 3/100 [00:00<00:26, 3.69it/s]\n 4%|▍ | 4/100 [00:01<00:26, 3.68it/s]\n 5%|▌ | 5/100 [00:01<00:25, 3.67it/s]\n 6%|▌ | 6/100 [00:01<00:25, 3.67it/s]\n 7%|▋ | 7/100 [00:01<00:25, 3.67it/s]\n 8%|▊ | 8/100 [00:02<00:25, 3.67it/s]\n 9%|▉ | 9/100 [00:02<00:24, 3.66it/s]\n 10%|█ | 10/100 [00:02<00:24, 3.67it/s]\n 11%|█ | 11/100 [00:02<00:24, 3.67it/s]\n 12%|█▏ | 12/100 [00:03<00:24, 3.66it/s]\n 13%|█▎ | 13/100 [00:03<00:23, 3.66it/s]\n 14%|█▍ | 14/100 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3.65it/s]\n100%|██████████| 100/100 [00:27<00:00, 3.67it/s]", "metrics": { "predict_time": 29.821949, "total_time": 32.755719 }, "output": [ "https://replicate.delivery/pbxt/Z4R5GMkIbZ6KNt3eeeWGqjff3gGpvrdeJ0trcGRL986d3nfSJA/out-0.png" ], "started_at": "2024-04-01T04:19:12.664655Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/znq7le3ba5fg3vrlpjugtnx5xi", "cancel": "https://api.replicate.com/v1/predictions/znq7le3ba5fg3vrlpjugtnx5xi/cancel" }, "version": "38ff900a27856a1182c44cd2bce84890e048b34e27ef8226fbe0d42a4474a951" }
Generated inUsing seed: 64199 Ensuring enough disk space... Free disk space: 1340739112960 Downloading weights: https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar 2024-04-01T04:19:12Z | INFO | [ Initiating ] dest=/src/weights-cache/fb46d00f92950def minimum_chunk_size=150M url=https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar 2024-04-01T04:19:13Z | INFO | [ Complete ] dest=/src/weights-cache/fb46d00f92950def size="186 MB" total_elapsed=0.573s url=https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar b'' Downloaded weights in 0.6822450160980225 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A vanitas painting of a lobster in the <s0><s1> style txt2img mode 0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<00:26, 3.71it/s] 2%|▏ | 2/100 [00:00<00:26, 3.70it/s] 3%|▎ | 3/100 [00:00<00:26, 3.69it/s] 4%|▍ | 4/100 [00:01<00:26, 3.68it/s] 5%|▌ | 5/100 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Prediction
bendedkneeface/vanitas:38ff900a27856a1182c44cd2bce84890e048b34e27ef8226fbe0d42a4474a951IDqab64z3b6put67s2zlnymgtwmmStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- width
- 1024
- height
- 1024
- prompt
- A vanitas painting of a lobster in the VNT style
- refine
- expert_ensemble_refiner
- scheduler
- DDIM
- lora_scale
- 0.99
- num_outputs
- 1
- refine_steps
- 10
- guidance_scale
- 3.73
- apply_watermark
- high_noise_frac
- 0.8
- negative_prompt
- Blurry, under exposed, pixelated, simple, abnormal biology
- prompt_strength
- 0.8
- num_inference_steps
- 100
{ "image": "https://replicate.delivery/pbxt/KfZWl4IsaiuJrQGaBIa3awyR1bt9xVkTOBHZjvnFnGyN9YQZ/replicate-prediction-zgnlvgtbpr4csrx2miw5smoeba.png", "width": 1024, "height": 1024, "prompt": "A vanitas painting of a lobster in the VNT style", "refine": "expert_ensemble_refiner", "scheduler": "DDIM", "lora_scale": 0.99, "num_outputs": 1, "refine_steps": 10, "guidance_scale": 3.73, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "Blurry, under exposed, pixelated, simple, abnormal biology", "prompt_strength": 0.8, "num_inference_steps": 100 }
Install Replicate’s Node.js client library:npm install replicate
Import 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 bendedkneeface/vanitas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "bendedkneeface/vanitas:38ff900a27856a1182c44cd2bce84890e048b34e27ef8226fbe0d42a4474a951", { input: { image: "https://replicate.delivery/pbxt/KfZWl4IsaiuJrQGaBIa3awyR1bt9xVkTOBHZjvnFnGyN9YQZ/replicate-prediction-zgnlvgtbpr4csrx2miw5smoeba.png", width: 1024, height: 1024, prompt: "A vanitas painting of a lobster in the VNT style", refine: "expert_ensemble_refiner", scheduler: "DDIM", lora_scale: 0.99, num_outputs: 1, refine_steps: 10, guidance_scale: 3.73, apply_watermark: false, high_noise_frac: 0.8, negative_prompt: "Blurry, under exposed, pixelated, simple, abnormal biology", prompt_strength: 0.8, num_inference_steps: 100 } } ); // 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 replicate
Import the client:import replicate
Run bendedkneeface/vanitas using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "bendedkneeface/vanitas:38ff900a27856a1182c44cd2bce84890e048b34e27ef8226fbe0d42a4474a951", input={ "image": "https://replicate.delivery/pbxt/KfZWl4IsaiuJrQGaBIa3awyR1bt9xVkTOBHZjvnFnGyN9YQZ/replicate-prediction-zgnlvgtbpr4csrx2miw5smoeba.png", "width": 1024, "height": 1024, "prompt": "A vanitas painting of a lobster in the VNT style", "refine": "expert_ensemble_refiner", "scheduler": "DDIM", "lora_scale": 0.99, "num_outputs": 1, "refine_steps": 10, "guidance_scale": 3.73, "apply_watermark": False, "high_noise_frac": 0.8, "negative_prompt": "Blurry, under exposed, pixelated, simple, abnormal biology", "prompt_strength": 0.8, "num_inference_steps": 100 } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run bendedkneeface/vanitas 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": "bendedkneeface/vanitas:38ff900a27856a1182c44cd2bce84890e048b34e27ef8226fbe0d42a4474a951", "input": { "image": "https://replicate.delivery/pbxt/KfZWl4IsaiuJrQGaBIa3awyR1bt9xVkTOBHZjvnFnGyN9YQZ/replicate-prediction-zgnlvgtbpr4csrx2miw5smoeba.png", "width": 1024, "height": 1024, "prompt": "A vanitas painting of a lobster in the VNT style", "refine": "expert_ensemble_refiner", "scheduler": "DDIM", "lora_scale": 0.99, "num_outputs": 1, "refine_steps": 10, "guidance_scale": 3.73, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "Blurry, under exposed, pixelated, simple, abnormal biology", "prompt_strength": 0.8, "num_inference_steps": 100 } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2024-04-01T05:07:04.796970Z", "created_at": "2024-04-01T05:06:31.025651Z", "data_removed": false, "error": null, "id": "qab64z3b6put67s2zlnymgtwmm", "input": { "image": "https://replicate.delivery/pbxt/KfZWl4IsaiuJrQGaBIa3awyR1bt9xVkTOBHZjvnFnGyN9YQZ/replicate-prediction-zgnlvgtbpr4csrx2miw5smoeba.png", "width": 1024, "height": 1024, "prompt": "A vanitas painting of a lobster in the VNT style", "refine": "expert_ensemble_refiner", "scheduler": "DDIM", "lora_scale": 0.99, "num_outputs": 1, "refine_steps": 10, "guidance_scale": 3.73, "apply_watermark": false, "high_noise_frac": 0.8, "negative_prompt": "Blurry, under exposed, pixelated, simple, abnormal biology", "prompt_strength": 0.8, "num_inference_steps": 100 }, "logs": "Using seed: 46775\nEnsuring enough disk space...\nFree disk space: 1702765846528\nDownloading weights: https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar\n2024-04-01T05:06:33Z | INFO | [ Initiating ] dest=/src/weights-cache/fb46d00f92950def minimum_chunk_size=150M url=https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar\n2024-04-01T05:06:40Z | INFO | [ Complete ] dest=/src/weights-cache/fb46d00f92950def size=\"186 MB\" total_elapsed=6.365s url=https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar\nb''\nDownloaded weights in 6.503854274749756 seconds\nLoading fine-tuned model\nDoes not have Unet. assume we are using LoRA\nLoading Unet LoRA\nPrompt: A vanitas painting of a lobster in the <s0><s1> style\nimg2img mode\n 0%| | 0/60 [00:00<?, ?it/s]\n 2%|▏ | 1/60 [00:00<00:16, 3.66it/s]\n 3%|▎ | 2/60 [00:00<00:15, 3.65it/s]\n 5%|▌ | 3/60 [00:00<00:15, 3.64it/s]\n 7%|▋ | 4/60 [00:01<00:15, 3.64it/s]\n 8%|▊ | 5/60 [00:01<00:15, 3.64it/s]\n 10%|█ | 6/60 [00:01<00:14, 3.63it/s]\n 12%|█▏ | 7/60 [00:01<00:14, 3.63it/s]\n 13%|█▎ | 8/60 [00:02<00:14, 3.63it/s]\n 15%|█▌ | 9/60 [00:02<00:14, 3.63it/s]\n 17%|█▋ | 10/60 [00:02<00:13, 3.63it/s]\n 18%|█▊ | 11/60 [00:03<00:13, 3.63it/s]\n 20%|██ | 12/60 [00:03<00:13, 3.63it/s]\n 22%|██▏ | 13/60 [00:03<00:12, 3.63it/s]\n 23%|██▎ | 14/60 [00:03<00:12, 3.63it/s]\n 25%|██▌ | 15/60 [00:04<00:12, 3.63it/s]\n 27%|██▋ | 16/60 [00:04<00:12, 3.63it/s]\n 28%|██▊ | 17/60 [00:04<00:11, 3.64it/s]\n 30%|███ | 18/60 [00:04<00:11, 3.64it/s]\n 32%|███▏ | 19/60 [00:05<00:11, 3.64it/s]\n 33%|███▎ | 20/60 [00:05<00:10, 3.64it/s]\n 35%|███▌ | 21/60 [00:05<00:10, 3.65it/s]\n 37%|███▋ | 22/60 [00:06<00:10, 3.65it/s]\n 38%|███▊ | 23/60 [00:06<00:10, 3.65it/s]\n 40%|████ | 24/60 [00:06<00:09, 3.65it/s]\n 42%|████▏ | 25/60 [00:06<00:09, 3.65it/s]\n 43%|████▎ | 26/60 [00:07<00:09, 3.65it/s]\n 45%|████▌ | 27/60 [00:07<00:09, 3.65it/s]\n 47%|████▋ | 28/60 [00:07<00:08, 3.65it/s]\n 48%|████▊ | 29/60 [00:07<00:08, 3.65it/s]\n 50%|█████ | 30/60 [00:08<00:08, 3.65it/s]\n 52%|█████▏ | 31/60 [00:08<00:07, 3.65it/s]\n 53%|█████▎ | 32/60 [00:08<00:07, 3.65it/s]\n 55%|█████▌ | 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3.64it/s]\n 92%|█████████▏| 55/60 [00:15<00:01, 3.64it/s]\n 93%|█████████▎| 56/60 [00:15<00:01, 3.63it/s]\n 95%|█████████▌| 57/60 [00:15<00:00, 3.63it/s]\n 97%|█████████▋| 58/60 [00:15<00:00, 3.64it/s]\n 98%|█████████▊| 59/60 [00:16<00:00, 3.64it/s]\n100%|██████████| 60/60 [00:16<00:00, 3.64it/s]\n100%|██████████| 60/60 [00:16<00:00, 3.64it/s]\n 0%| | 0/20 [00:00<?, ?it/s]\n 5%|▌ | 1/20 [00:00<00:04, 3.88it/s]\n 10%|█ | 2/20 [00:00<00:04, 4.05it/s]\n 15%|█▌ | 3/20 [00:00<00:04, 4.11it/s]\n 20%|██ | 4/20 [00:00<00:03, 4.13it/s]\n 25%|██▌ | 5/20 [00:01<00:03, 4.16it/s]\n 30%|███ | 6/20 [00:01<00:03, 4.17it/s]\n 35%|███▌ | 7/20 [00:01<00:03, 4.18it/s]\n 40%|████ | 8/20 [00:01<00:02, 4.18it/s]\n 45%|████▌ | 9/20 [00:02<00:02, 4.18it/s]\n 50%|█████ | 10/20 [00:02<00:02, 4.18it/s]\n 55%|█████▌ | 11/20 [00:02<00:02, 4.18it/s]\n 60%|██████ | 12/20 [00:02<00:01, 4.18it/s]\n 65%|██████▌ | 13/20 [00:03<00:01, 4.18it/s]\n 70%|███████ | 14/20 [00:03<00:01, 4.18it/s]\n 75%|███████▌ | 15/20 [00:03<00:01, 4.18it/s]\n 80%|████████ | 16/20 [00:03<00:00, 4.17it/s]\n 85%|████████▌ | 17/20 [00:04<00:00, 4.18it/s]\n 90%|█████████ | 18/20 [00:04<00:00, 4.18it/s]\n 95%|█████████▌| 19/20 [00:04<00:00, 4.18it/s]\n100%|██████████| 20/20 [00:04<00:00, 4.18it/s]\n100%|██████████| 20/20 [00:04<00:00, 4.17it/s]", "metrics": { "predict_time": 31.81283, "total_time": 33.771319 }, "output": [ "https://replicate.delivery/pbxt/Yzb8JvgUunYnDpmL4o5uLkxwaJC8D1KneZC3VeZoDRo3LfLlA/out-0.png" ], "started_at": "2024-04-01T05:06:32.984140Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/qab64z3b6put67s2zlnymgtwmm", "cancel": "https://api.replicate.com/v1/predictions/qab64z3b6put67s2zlnymgtwmm/cancel" }, "version": "38ff900a27856a1182c44cd2bce84890e048b34e27ef8226fbe0d42a4474a951" }
Generated inUsing seed: 46775 Ensuring enough disk space... Free disk space: 1702765846528 Downloading weights: https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar 2024-04-01T05:06:33Z | INFO | [ Initiating ] dest=/src/weights-cache/fb46d00f92950def minimum_chunk_size=150M url=https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar 2024-04-01T05:06:40Z | INFO | [ Complete ] dest=/src/weights-cache/fb46d00f92950def size="186 MB" total_elapsed=6.365s url=https://replicate.delivery/pbxt/r2tBnQn2ySLjCxw421W5TCqTqcB6E3dkMpI1aZFQk9oXgfSJA/trained_model.tar b'' Downloaded weights in 6.503854274749756 seconds Loading fine-tuned model Does not have Unet. assume we are using LoRA Loading Unet LoRA Prompt: A vanitas painting of a lobster in the <s0><s1> style img2img mode 0%| | 0/60 [00:00<?, ?it/s] 2%|▏ | 1/60 [00:00<00:16, 3.66it/s] 3%|▎ | 2/60 [00:00<00:15, 3.65it/s] 5%|▌ | 3/60 [00:00<00:15, 3.64it/s] 7%|▋ | 4/60 [00:01<00:15, 3.64it/s] 8%|▊ | 5/60 [00:01<00:15, 3.64it/s] 10%|█ | 6/60 [00:01<00:14, 3.63it/s] 12%|█▏ | 7/60 [00:01<00:14, 3.63it/s] 13%|█▎ | 8/60 [00:02<00:14, 3.63it/s] 15%|█▌ | 9/60 [00:02<00:14, 3.63it/s] 17%|█▋ | 10/60 [00:02<00:13, 3.63it/s] 18%|█▊ | 11/60 [00:03<00:13, 3.63it/s] 20%|██ | 12/60 [00:03<00:13, 3.63it/s] 22%|██▏ | 13/60 [00:03<00:12, 3.63it/s] 23%|██▎ | 14/60 [00:03<00:12, 3.63it/s] 25%|██▌ | 15/60 [00:04<00:12, 3.63it/s] 27%|██▋ | 16/60 [00:04<00:12, 3.63it/s] 28%|██▊ | 17/60 [00:04<00:11, 3.64it/s] 30%|███ | 18/60 [00:04<00:11, 3.64it/s] 32%|███▏ | 19/60 [00:05<00:11, 3.64it/s] 33%|███▎ | 20/60 [00:05<00:10, 3.64it/s] 35%|███▌ | 21/60 [00:05<00:10, 3.65it/s] 37%|███▋ | 22/60 [00:06<00:10, 3.65it/s] 38%|███▊ | 23/60 [00:06<00:10, 3.65it/s] 40%|████ | 24/60 [00:06<00:09, 3.65it/s] 42%|████▏ | 25/60 [00:06<00:09, 3.65it/s] 43%|████▎ | 26/60 [00:07<00:09, 3.65it/s] 45%|████▌ | 27/60 [00:07<00:09, 3.65it/s] 47%|████▋ | 28/60 [00:07<00:08, 3.65it/s] 48%|████▊ | 29/60 [00:07<00:08, 3.65it/s] 50%|█████ | 30/60 [00:08<00:08, 3.65it/s] 52%|█████▏ | 31/60 [00:08<00:07, 3.65it/s] 53%|█████▎ | 32/60 [00:08<00:07, 3.65it/s] 55%|█████▌ | 33/60 [00:09<00:07, 3.65it/s] 57%|█████▋ | 34/60 [00:09<00:07, 3.65it/s] 58%|█████▊ | 35/60 [00:09<00:06, 3.64it/s] 60%|██████ | 36/60 [00:09<00:06, 3.64it/s] 62%|██████▏ | 37/60 [00:10<00:06, 3.64it/s] 63%|██████▎ | 38/60 [00:10<00:06, 3.64it/s] 65%|██████▌ | 39/60 [00:10<00:05, 3.64it/s] 67%|██████▋ | 40/60 [00:10<00:05, 3.65it/s] 68%|██████▊ | 41/60 [00:11<00:05, 3.65it/s] 70%|███████ | 42/60 [00:11<00:04, 3.64it/s] 72%|███████▏ | 43/60 [00:11<00:04, 3.64it/s] 73%|███████▎ | 44/60 [00:12<00:04, 3.64it/s] 75%|███████▌ | 45/60 [00:12<00:04, 3.63it/s] 77%|███████▋ | 46/60 [00:12<00:03, 3.63it/s] 78%|███████▊ | 47/60 [00:12<00:03, 3.64it/s] 80%|████████ | 48/60 [00:13<00:03, 3.64it/s] 82%|████████▏ | 49/60 [00:13<00:03, 3.64it/s] 83%|████████▎ | 50/60 [00:13<00:02, 3.64it/s] 85%|████████▌ | 51/60 [00:14<00:02, 3.64it/s] 87%|████████▋ | 52/60 [00:14<00:02, 3.64it/s] 88%|████████▊ | 53/60 [00:14<00:01, 3.64it/s] 90%|█████████ | 54/60 [00:14<00:01, 3.64it/s] 92%|█████████▏| 55/60 [00:15<00:01, 3.64it/s] 93%|█████████▎| 56/60 [00:15<00:01, 3.63it/s] 95%|█████████▌| 57/60 [00:15<00:00, 3.63it/s] 97%|█████████▋| 58/60 [00:15<00:00, 3.64it/s] 98%|█████████▊| 59/60 [00:16<00:00, 3.64it/s] 100%|██████████| 60/60 [00:16<00:00, 3.64it/s] 100%|██████████| 60/60 [00:16<00:00, 3.64it/s] 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:04, 3.88it/s] 10%|█ | 2/20 [00:00<00:04, 4.05it/s] 15%|█▌ | 3/20 [00:00<00:04, 4.11it/s] 20%|██ | 4/20 [00:00<00:03, 4.13it/s] 25%|██▌ | 5/20 [00:01<00:03, 4.16it/s] 30%|███ | 6/20 [00:01<00:03, 4.17it/s] 35%|███▌ | 7/20 [00:01<00:03, 4.18it/s] 40%|████ | 8/20 [00:01<00:02, 4.18it/s] 45%|████▌ | 9/20 [00:02<00:02, 4.18it/s] 50%|█████ | 10/20 [00:02<00:02, 4.18it/s] 55%|█████▌ | 11/20 [00:02<00:02, 4.18it/s] 60%|██████ | 12/20 [00:02<00:01, 4.18it/s] 65%|██████▌ | 13/20 [00:03<00:01, 4.18it/s] 70%|███████ | 14/20 [00:03<00:01, 4.18it/s] 75%|███████▌ | 15/20 [00:03<00:01, 4.18it/s] 80%|████████ | 16/20 [00:03<00:00, 4.17it/s] 85%|████████▌ | 17/20 [00:04<00:00, 4.18it/s] 90%|█████████ | 18/20 [00:04<00:00, 4.18it/s] 95%|█████████▌| 19/20 [00:04<00:00, 4.18it/s] 100%|██████████| 20/20 [00:04<00:00, 4.18it/s] 100%|██████████| 20/20 [00:04<00:00, 4.17it/s]
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