tstramer / elden-ring-diffusion
- Public
- 63K runs
-
A100 (80GB)
Prediction
tstramer/elden-ring-diffusion:983bec81934c199eac56178cd545e007cf931b6dc90e4c4a6f06378eedca9c1eIDgfuk2d4mczgahph6sy2ihtalzyStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- seed
- 5
- width
- 512
- height
- 512
- prompt
- a magical princess with golden hair, elden ring style
- scheduler
- K-LMS
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- 50
{ "seed": 5, "width": 512, "height": 512, "prompt": "a magical princess with golden hair, elden ring style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "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 tstramer/elden-ring-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "tstramer/elden-ring-diffusion:983bec81934c199eac56178cd545e007cf931b6dc90e4c4a6f06378eedca9c1e", { input: { seed: 5, width: 512, height: 512, prompt: "a magical princess with golden hair, elden ring style", scheduler: "K-LMS", num_outputs: 1, guidance_scale: 7.5, 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 tstramer/elden-ring-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "tstramer/elden-ring-diffusion:983bec81934c199eac56178cd545e007cf931b6dc90e4c4a6f06378eedca9c1e", input={ "seed": 5, "width": 512, "height": 512, "prompt": "a magical princess with golden hair, elden ring style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "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 tstramer/elden-ring-diffusion 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": "tstramer/elden-ring-diffusion:983bec81934c199eac56178cd545e007cf931b6dc90e4c4a6f06378eedca9c1e", "input": { "seed": 5, "width": 512, "height": 512, "prompt": "a magical princess with golden hair, elden ring style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "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": "2022-11-07T22:53:33.642624Z", "created_at": "2022-11-07T22:51:03.158432Z", "data_removed": false, "error": null, "id": "gfuk2d4mczgahph6sy2ihtalzy", "input": { "seed": 5, "width": 512, "height": 512, "prompt": "a magical princess with golden hair, elden ring style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": 50 }, "logs": "Using seed: 5\n\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:04<03:45, 4.61s/it]\n 6%|▌ | 3/50 [00:04<00:58, 1.25s/it]\n 10%|█ | 5/50 [00:04<00:28, 1.55it/s]\n 14%|█▍ | 7/50 [00:05<00:17, 2.49it/s]\n 18%|█▊ | 9/50 [00:05<00:11, 3.61it/s]\n 22%|██▏ | 11/50 [00:05<00:08, 4.86it/s]\n 26%|██▌ | 13/50 [00:05<00:05, 6.17it/s]\n 30%|███ | 15/50 [00:05<00:04, 7.54it/s]\n 34%|███▍ | 17/50 [00:05<00:03, 8.84it/s]\n 38%|███▊ | 19/50 [00:05<00:03, 9.99it/s]\n 42%|████▏ | 21/50 [00:06<00:02, 10.96it/s]\n 46%|████▌ | 23/50 [00:06<00:02, 11.76it/s]\n 50%|█████ | 25/50 [00:06<00:02, 12.39it/s]\n 54%|█████▍ | 27/50 [00:06<00:01, 12.91it/s]\n 58%|█████▊ | 29/50 [00:06<00:01, 13.19it/s]\n 62%|██████▏ | 31/50 [00:06<00:01, 13.50it/s]\n 66%|██████▌ | 33/50 [00:06<00:01, 13.73it/s]\n 70%|███████ | 35/50 [00:07<00:01, 13.89it/s]\n 74%|███████▍ | 37/50 [00:07<00:00, 13.97it/s]\n 78%|███████▊ | 39/50 [00:07<00:00, 13.91it/s]\n 82%|████████▏ | 41/50 [00:07<00:00, 13.94it/s]\n 86%|████████▌ | 43/50 [00:07<00:00, 13.76it/s]\n 90%|█████████ | 45/50 [00:07<00:00, 13.88it/s]\n 94%|█████████▍| 47/50 [00:07<00:00, 13.82it/s]\n 98%|█████████▊| 49/50 [00:08<00:00, 13.38it/s]\n100%|██████████| 50/50 [00:08<00:00, 6.15it/s]", "metrics": { "predict_time": 11.271878, "total_time": 150.484192 }, "output": [ "https://replicate.delivery/pbxt/a0tV11EQg0JHHdxXr8GDmELyQSwazdBfGiv8lPYdFJp285ePA/out-0.png" ], "started_at": "2022-11-07T22:53:22.370746Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/gfuk2d4mczgahph6sy2ihtalzy", "cancel": "https://api.replicate.com/v1/predictions/gfuk2d4mczgahph6sy2ihtalzy/cancel" }, "version": "983bec81934c199eac56178cd545e007cf931b6dc90e4c4a6f06378eedca9c1e" }
Generated inUsing seed: 5 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:04<03:45, 4.61s/it] 6%|▌ | 3/50 [00:04<00:58, 1.25s/it] 10%|█ | 5/50 [00:04<00:28, 1.55it/s] 14%|█▍ | 7/50 [00:05<00:17, 2.49it/s] 18%|█▊ | 9/50 [00:05<00:11, 3.61it/s] 22%|██▏ | 11/50 [00:05<00:08, 4.86it/s] 26%|██▌ | 13/50 [00:05<00:05, 6.17it/s] 30%|███ | 15/50 [00:05<00:04, 7.54it/s] 34%|███▍ | 17/50 [00:05<00:03, 8.84it/s] 38%|███▊ | 19/50 [00:05<00:03, 9.99it/s] 42%|████▏ | 21/50 [00:06<00:02, 10.96it/s] 46%|████▌ | 23/50 [00:06<00:02, 11.76it/s] 50%|█████ | 25/50 [00:06<00:02, 12.39it/s] 54%|█████▍ | 27/50 [00:06<00:01, 12.91it/s] 58%|█████▊ | 29/50 [00:06<00:01, 13.19it/s] 62%|██████▏ | 31/50 [00:06<00:01, 13.50it/s] 66%|██████▌ | 33/50 [00:06<00:01, 13.73it/s] 70%|███████ | 35/50 [00:07<00:01, 13.89it/s] 74%|███████▍ | 37/50 [00:07<00:00, 13.97it/s] 78%|███████▊ | 39/50 [00:07<00:00, 13.91it/s] 82%|████████▏ | 41/50 [00:07<00:00, 13.94it/s] 86%|████████▌ | 43/50 [00:07<00:00, 13.76it/s] 90%|█████████ | 45/50 [00:07<00:00, 13.88it/s] 94%|█████████▍| 47/50 [00:07<00:00, 13.82it/s] 98%|█████████▊| 49/50 [00:08<00:00, 13.38it/s] 100%|██████████| 50/50 [00:08<00:00, 6.15it/s]
Prediction
tstramer/elden-ring-diffusion:983bec81934c199eac56178cd545e007cf931b6dc90e4c4a6f06378eedca9c1eID7voijl6gtfg2fo52qrh5nnubiyStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- seed
- 5
- width
- 512
- height
- 512
- prompt
- a magical prince with golden hair, elden ring style
- scheduler
- K-LMS
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- "163"
{ "seed": 5, "width": 512, "height": 512, "prompt": "a magical prince with golden hair, elden ring style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "163" }
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 tstramer/elden-ring-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "tstramer/elden-ring-diffusion:983bec81934c199eac56178cd545e007cf931b6dc90e4c4a6f06378eedca9c1e", { input: { seed: 5, width: 512, height: 512, prompt: "a magical prince with golden hair, elden ring style", scheduler: "K-LMS", num_outputs: 1, guidance_scale: 7.5, prompt_strength: 0.8, num_inference_steps: "163" } } ); // 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 tstramer/elden-ring-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "tstramer/elden-ring-diffusion:983bec81934c199eac56178cd545e007cf931b6dc90e4c4a6f06378eedca9c1e", input={ "seed": 5, "width": 512, "height": 512, "prompt": "a magical prince with golden hair, elden ring style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "163" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run tstramer/elden-ring-diffusion 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": "tstramer/elden-ring-diffusion:983bec81934c199eac56178cd545e007cf931b6dc90e4c4a6f06378eedca9c1e", "input": { "seed": 5, "width": 512, "height": 512, "prompt": "a magical prince with golden hair, elden ring style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "163" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-11-08T01:56:30.303813Z", "created_at": "2022-11-08T01:53:47.210718Z", "data_removed": false, "error": null, "id": "7voijl6gtfg2fo52qrh5nnubiy", "input": { "seed": 5, "width": 512, "height": 512, "prompt": "a magical prince with golden hair, elden ring style", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "163" }, "logs": "Using seed: 5\n\n 0%| | 0/163 [00:00<?, ?it/s]\n 1%| | 1/163 [00:02<06:10, 2.29s/it]\n 2%|▏ | 3/163 [00:02<01:44, 1.53it/s]\n 3%|▎ | 5/163 [00:02<00:56, 2.81it/s]\n 4%|▍ | 7/163 [00:02<00:37, 4.20it/s]\n 6%|▌ | 9/163 [00:02<00:27, 5.68it/s]\n 7%|▋ | 11/163 [00:03<00:21, 7.15it/s]\n 8%|▊ | 13/163 [00:03<00:17, 8.50it/s]\n 9%|▉ | 15/163 [00:03<00:15, 9.65it/s]\n 10%|█ | 17/163 [00:03<00:13, 10.63it/s]\n 12%|█▏ | 19/163 [00:03<00:12, 11.41it/s]\n 13%|█▎ | 21/163 [00:03<00:11, 11.94it/s]\n 14%|█▍ | 23/163 [00:03<00:11, 12.31it/s]\n 15%|█▌ | 25/163 [00:04<00:10, 12.67it/s]\n 17%|█▋ | 27/163 [00:04<00:10, 12.87it/s]\n 18%|█▊ | 29/163 [00:04<00:10, 13.10it/s]\n 19%|█▉ | 31/163 [00:04<00:09, 13.21it/s]\n 20%|██ | 33/163 [00:04<00:09, 13.22it/s]\n 21%|██▏ | 35/163 [00:04<00:09, 13.21it/s]\n 23%|██▎ | 37/163 [00:04<00:09, 13.22it/s]\n 24%|██▍ | 39/163 [00:05<00:09, 13.31it/s]\n 25%|██▌ | 41/163 [00:05<00:09, 13.42it/s]\n 26%|██▋ | 43/163 [00:05<00:08, 13.50it/s]\n 28%|██▊ | 45/163 [00:05<00:08, 13.60it/s]\n 29%|██▉ | 47/163 [00:05<00:08, 13.69it/s]\n 30%|███ | 49/163 [00:05<00:08, 13.60it/s]\n 31%|███▏ | 51/163 [00:06<00:08, 13.67it/s]\n 33%|███▎ | 53/163 [00:06<00:08, 13.73it/s]\n 34%|███▎ | 55/163 [00:06<00:07, 13.66it/s]\n 35%|███▍ | 57/163 [00:06<00:07, 13.66it/s]\n 36%|███▌ | 59/163 [00:06<00:07, 13.67it/s]\n 37%|███▋ | 61/163 [00:06<00:07, 13.56it/s]\n 39%|███▊ | 63/163 [00:06<00:07, 13.38it/s]\n 40%|███▉ | 65/163 [00:07<00:07, 13.48it/s]\n 41%|████ | 67/163 [00:07<00:07, 13.59it/s]\n 42%|████▏ | 69/163 [00:07<00:06, 13.69it/s]\n 44%|████▎ | 71/163 [00:07<00:06, 13.43it/s]\n 45%|████▍ | 73/163 [00:07<00:06, 13.28it/s]\n 46%|████▌ | 75/163 [00:07<00:06, 13.47it/s]\n 47%|████▋ | 77/163 [00:07<00:06, 13.48it/s]\n 48%|████▊ | 79/163 [00:08<00:06, 13.45it/s]\n 50%|████▉ | 81/163 [00:08<00:06, 13.48it/s]\n 51%|█████ | 83/163 [00:08<00:05, 13.40it/s]\n 52%|█████▏ | 85/163 [00:08<00:05, 13.47it/s]\n 53%|█████▎ | 87/163 [00:08<00:05, 13.47it/s]\n 55%|█████▍ | 89/163 [00:08<00:05, 13.36it/s]\n 56%|█████▌ | 91/163 [00:08<00:05, 13.25it/s]\n 57%|█████▋ | 93/163 [00:09<00:05, 13.38it/s]\n 58%|█████▊ | 95/163 [00:09<00:05, 13.42it/s]\n 60%|█████▉ | 97/163 [00:09<00:04, 13.49it/s]\n 61%|██████ | 99/163 [00:09<00:04, 13.52it/s]\n 62%|██████▏ | 101/163 [00:09<00:04, 13.55it/s]\n 63%|██████▎ | 103/163 [00:09<00:04, 13.47it/s]\n 64%|██████▍ | 105/163 [00:10<00:04, 13.31it/s]\n 66%|██████▌ | 107/163 [00:10<00:04, 13.42it/s]\n 67%|██████▋ | 109/163 [00:10<00:03, 13.51it/s]\n 68%|██████▊ | 111/163 [00:10<00:03, 13.52it/s]\n 69%|██████▉ | 113/163 [00:10<00:03, 13.58it/s]\n 71%|███████ | 115/163 [00:10<00:03, 13.66it/s]\n 72%|███████▏ | 117/163 [00:10<00:03, 13.68it/s]\n 73%|███████▎ | 119/163 [00:11<00:03, 13.36it/s]\n 74%|███████▍ | 121/163 [00:11<00:03, 12.99it/s]\n 75%|███████▌ | 123/163 [00:11<00:03, 13.21it/s]\n 77%|███████▋ | 125/163 [00:11<00:02, 13.39it/s]\n 78%|███████▊ | 127/163 [00:11<00:02, 13.49it/s]\n 79%|███████▉ | 129/163 [00:11<00:02, 13.58it/s]\n 80%|████████ | 131/163 [00:11<00:02, 13.62it/s]\n 82%|████████▏ | 133/163 [00:12<00:02, 13.42it/s]\n 83%|████████▎ | 135/163 [00:12<00:02, 13.54it/s]\n 84%|████████▍ | 137/163 [00:12<00:01, 13.67it/s]\n 85%|████████▌ | 139/163 [00:12<00:01, 13.66it/s]\n 87%|████████▋ | 141/163 [00:12<00:01, 13.70it/s]\n 88%|████████▊ | 143/163 [00:12<00:01, 13.71it/s]\n 89%|████████▉ | 145/163 [00:12<00:01, 13.70it/s]\n 90%|█████████ | 147/163 [00:13<00:01, 13.47it/s]\n 91%|█████████▏| 149/163 [00:13<00:01, 13.55it/s]\n 93%|█████████▎| 151/163 [00:13<00:00, 13.64it/s]\n 94%|█████████▍| 153/163 [00:13<00:00, 13.73it/s]\n 95%|█████████▌| 155/163 [00:13<00:00, 13.79it/s]\n 96%|█████████▋| 157/163 [00:13<00:00, 13.85it/s]\n 98%|█████████▊| 159/163 [00:13<00:00, 13.91it/s]\n 99%|█████████▉| 161/163 [00:14<00:00, 13.69it/s]\n100%|██████████| 163/163 [00:14<00:00, 13.62it/s]\n100%|██████████| 163/163 [00:14<00:00, 11.41it/s]", "metrics": { "predict_time": 17.48902, "total_time": 163.093095 }, "output": [ "https://replicate.delivery/pbxt/GK0j47G1uopoEFvHEKiuo7NCfjKmt8G13fIbhY417XecKt7fA/out-0.png" ], "started_at": "2022-11-08T01:56:12.814793Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/7voijl6gtfg2fo52qrh5nnubiy", "cancel": "https://api.replicate.com/v1/predictions/7voijl6gtfg2fo52qrh5nnubiy/cancel" }, "version": "983bec81934c199eac56178cd545e007cf931b6dc90e4c4a6f06378eedca9c1e" }
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Prediction
tstramer/elden-ring-diffusion:983bec81934c199eac56178cd545e007cf931b6dc90e4c4a6f06378eedca9c1eID2pzyvzljubhhfg5sxytodcwbemStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- seed
- 5
- width
- 512
- height
- 512
- prompt
- elden ring style, a magical prince with golden hair, intricate highly detailed digital painting artstation concept art smooth sharp focus illustration Unreal Engine 5 8K
- scheduler
- K-LMS
- num_outputs
- 1
- guidance_scale
- 7.5
- prompt_strength
- 0.8
- num_inference_steps
- "163"
{ "seed": 5, "width": 512, "height": 512, "prompt": "elden ring style, a magical prince with golden hair, intricate highly detailed digital painting artstation concept art smooth sharp focus illustration Unreal Engine 5 8K", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "163" }
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 tstramer/elden-ring-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "tstramer/elden-ring-diffusion:983bec81934c199eac56178cd545e007cf931b6dc90e4c4a6f06378eedca9c1e", { input: { seed: 5, width: 512, height: 512, prompt: "elden ring style, a magical prince with golden hair, intricate highly detailed digital painting artstation concept art smooth sharp focus illustration Unreal Engine 5 8K", scheduler: "K-LMS", num_outputs: 1, guidance_scale: 7.5, prompt_strength: 0.8, num_inference_steps: "163" } } ); // 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 tstramer/elden-ring-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "tstramer/elden-ring-diffusion:983bec81934c199eac56178cd545e007cf931b6dc90e4c4a6f06378eedca9c1e", input={ "seed": 5, "width": 512, "height": 512, "prompt": "elden ring style, a magical prince with golden hair, intricate highly detailed digital painting artstation concept art smooth sharp focus illustration Unreal Engine 5 8K", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "163" } ) print(output)
To learn more, take a look at the guide on getting started with Python.
Run tstramer/elden-ring-diffusion 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": "tstramer/elden-ring-diffusion:983bec81934c199eac56178cd545e007cf931b6dc90e4c4a6f06378eedca9c1e", "input": { "seed": 5, "width": 512, "height": 512, "prompt": "elden ring style, a magical prince with golden hair, intricate highly detailed digital painting artstation concept art smooth sharp focus illustration Unreal Engine 5 8K", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "163" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-11-08T02:07:08.070027Z", "created_at": "2022-11-08T02:04:35.421011Z", "data_removed": false, "error": null, "id": "2pzyvzljubhhfg5sxytodcwbem", "input": { "seed": 5, "width": 512, "height": 512, "prompt": "elden ring style, a magical prince with golden hair, intricate highly detailed digital painting artstation concept art smooth sharp focus illustration Unreal Engine 5 8K", "scheduler": "K-LMS", "num_outputs": 1, "guidance_scale": 7.5, "prompt_strength": 0.8, "num_inference_steps": "163" }, "logs": "Using seed: 5\n\n 0%| | 0/163 [00:00<?, ?it/s]\n 1%| | 1/163 [00:02<05:57, 2.21s/it]\n 2%|▏ | 3/163 [00:02<01:40, 1.60it/s]\n 3%|▎ | 5/163 [00:02<00:54, 2.92it/s]\n 4%|▍ | 7/163 [00:02<00:35, 4.38it/s]\n 6%|▌ | 9/163 [00:02<00:26, 5.91it/s]\n 7%|▋ | 11/163 [00:02<00:20, 7.36it/s]\n 8%|▊ | 13/163 [00:03<00:17, 8.70it/s]\n 9%|▉ | 15/163 [00:03<00:15, 9.63it/s]\n 10%|█ | 17/163 [00:03<00:13, 10.52it/s]\n 12%|█▏ | 19/163 [00:03<00:12, 11.22it/s]\n 13%|█▎ | 21/163 [00:03<00:11, 11.91it/s]\n 14%|█▍ | 23/163 [00:03<00:11, 12.25it/s]\n 15%|█▌ | 25/163 [00:03<00:10, 12.57it/s]\n 17%|█▋ | 27/163 [00:04<00:10, 12.94it/s]\n 18%|█▊ | 29/163 [00:04<00:10, 13.03it/s]\n 19%|█▉ | 31/163 [00:04<00:10, 13.12it/s]\n 20%|██ | 33/163 [00:04<00:09, 13.29it/s]\n 21%|██▏ | 35/163 [00:04<00:09, 13.38it/s]\n 23%|██▎ | 37/163 [00:04<00:09, 13.30it/s]\n 24%|██▍ | 39/163 [00:05<00:09, 13.45it/s]\n 25%|██▌ | 41/163 [00:05<00:09, 13.53it/s]\n 26%|██▋ | 43/163 [00:05<00:08, 13.39it/s]\n 28%|██▊ | 45/163 [00:05<00:08, 13.42it/s]\n 29%|██▉ | 47/163 [00:05<00:08, 13.59it/s]\n 30%|███ | 49/163 [00:05<00:08, 13.43it/s]\n 31%|███▏ | 51/163 [00:05<00:08, 13.55it/s]\n 33%|███▎ | 53/163 [00:06<00:08, 13.65it/s]\n 34%|███▎ | 55/163 [00:06<00:07, 13.53it/s]\n 35%|███▍ | 57/163 [00:06<00:07, 13.63it/s]\n 36%|███▌ | 59/163 [00:06<00:07, 13.59it/s]\n 37%|███▋ | 61/163 [00:06<00:07, 13.52it/s]\n 39%|███▊ | 63/163 [00:06<00:07, 13.44it/s]\n 40%|███▉ | 65/163 [00:06<00:07, 13.51it/s]\n 41%|████ | 67/163 [00:07<00:07, 13.60it/s]\n 42%|████▏ | 69/163 [00:07<00:07, 13.40it/s]\n 44%|████▎ | 71/163 [00:07<00:06, 13.51it/s]\n 45%|████▍ | 73/163 [00:07<00:06, 13.47it/s]\n 46%|████▌ | 75/163 [00:07<00:06, 13.33it/s]\n 47%|████▋ | 77/163 [00:07<00:06, 13.50it/s]\n 48%|████▊ | 79/163 [00:07<00:06, 13.65it/s]\n 50%|████▉ | 81/163 [00:08<00:06, 13.54it/s]\n 51%|█████ | 83/163 [00:08<00:05, 13.62it/s]\n 52%|█████▏ | 85/163 [00:08<00:05, 13.60it/s]\n 53%|█████▎ | 87/163 [00:08<00:05, 13.42it/s]\n 55%|█████▍ | 89/163 [00:08<00:05, 13.49it/s]\n 56%|█████▌ | 91/163 [00:08<00:05, 13.57it/s]\n 57%|█████▋ | 93/163 [00:09<00:05, 13.35it/s]\n 58%|█████▊ | 95/163 [00:09<00:05, 13.47it/s]\n 60%|█████▉ | 97/163 [00:09<00:04, 13.54it/s]\n 61%|██████ | 99/163 [00:09<00:04, 13.44it/s]\n 62%|██████▏ | 101/163 [00:09<00:04, 13.36it/s]\n 63%|██████▎ | 103/163 [00:09<00:04, 13.49it/s]\n 64%|██████▍ | 105/163 [00:09<00:04, 13.59it/s]\n 66%|██████▌ | 107/163 [00:10<00:04, 12.95it/s]\n 67%|██████▋ | 109/163 [00:10<00:04, 12.16it/s]\n 68%|██████▊ | 111/163 [00:10<00:04, 11.54it/s]\n 69%|██████▉ | 113/163 [00:10<00:04, 11.64it/s]\n 71%|███████ | 115/163 [00:10<00:03, 12.20it/s]\n 72%|███████▏ | 117/163 [00:10<00:03, 12.66it/s]\n 73%|███████▎ | 119/163 [00:11<00:03, 12.86it/s]\n 74%|███████▍ | 121/163 [00:11<00:03, 13.18it/s]\n 75%|███████▌ | 123/163 [00:11<00:02, 13.43it/s]\n 77%|███████▋ | 125/163 [00:11<00:02, 13.43it/s]\n 78%|███████▊ | 127/163 [00:11<00:02, 13.55it/s]\n 79%|███████▉ | 129/163 [00:11<00:02, 13.66it/s]\n 80%|████████ | 131/163 [00:11<00:02, 13.26it/s]\n 82%|████████▏ | 133/163 [00:12<00:02, 13.24it/s]\n 83%|████████▎ | 135/163 [00:12<00:02, 13.45it/s]\n 84%|████████▍ | 137/163 [00:12<00:01, 13.61it/s]\n 85%|████████▌ | 139/163 [00:12<00:01, 13.36it/s]\n 87%|████████▋ | 141/163 [00:12<00:01, 13.48it/s]\n 88%|████████▊ | 143/163 [00:12<00:01, 13.53it/s]\n 89%|████████▉ | 145/163 [00:12<00:01, 13.53it/s]\n 90%|█████████ | 147/163 [00:13<00:01, 13.38it/s]\n 91%|█████████▏| 149/163 [00:13<00:01, 13.45it/s]\n 93%|█████████▎| 151/163 [00:13<00:00, 13.45it/s]\n 94%|█████████▍| 153/163 [00:13<00:00, 13.27it/s]\n 95%|█████████▌| 155/163 [00:13<00:00, 13.31it/s]\n 96%|█████████▋| 157/163 [00:13<00:00, 13.33it/s]\n 98%|█████████▊| 159/163 [00:14<00:00, 13.29it/s]\n 99%|█████████▉| 161/163 [00:14<00:00, 13.44it/s]\n100%|██████████| 163/163 [00:14<00:00, 13.58it/s]\n100%|██████████| 163/163 [00:14<00:00, 11.37it/s]", "metrics": { "predict_time": 16.514487, "total_time": 152.649016 }, "output": [ "https://replicate.delivery/pbxt/GX15APG8RW7kPR0NTMxN3EH6wfXxDrHT9a3P2JVwwnEmX7ePA/out-0.png" ], "started_at": "2022-11-08T02:06:51.555540Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/2pzyvzljubhhfg5sxytodcwbem", "cancel": "https://api.replicate.com/v1/predictions/2pzyvzljubhhfg5sxytodcwbem/cancel" }, "version": "983bec81934c199eac56178cd545e007cf931b6dc90e4c4a6f06378eedca9c1e" }
Generated inUsing seed: 5 0%| | 0/163 [00:00<?, ?it/s] 1%| | 1/163 [00:02<05:57, 2.21s/it] 2%|▏ | 3/163 [00:02<01:40, 1.60it/s] 3%|▎ | 5/163 [00:02<00:54, 2.92it/s] 4%|▍ | 7/163 [00:02<00:35, 4.38it/s] 6%|▌ | 9/163 [00:02<00:26, 5.91it/s] 7%|▋ | 11/163 [00:02<00:20, 7.36it/s] 8%|▊ | 13/163 [00:03<00:17, 8.70it/s] 9%|▉ | 15/163 [00:03<00:15, 9.63it/s] 10%|█ | 17/163 [00:03<00:13, 10.52it/s] 12%|█▏ | 19/163 [00:03<00:12, 11.22it/s] 13%|█▎ | 21/163 [00:03<00:11, 11.91it/s] 14%|█▍ | 23/163 [00:03<00:11, 12.25it/s] 15%|█▌ | 25/163 [00:03<00:10, 12.57it/s] 17%|█▋ | 27/163 [00:04<00:10, 12.94it/s] 18%|█▊ | 29/163 [00:04<00:10, 13.03it/s] 19%|█▉ | 31/163 [00:04<00:10, 13.12it/s] 20%|██ | 33/163 [00:04<00:09, 13.29it/s] 21%|██▏ | 35/163 [00:04<00:09, 13.38it/s] 23%|██▎ | 37/163 [00:04<00:09, 13.30it/s] 24%|██▍ | 39/163 [00:05<00:09, 13.45it/s] 25%|██▌ | 41/163 [00:05<00:09, 13.53it/s] 26%|██▋ | 43/163 [00:05<00:08, 13.39it/s] 28%|██▊ | 45/163 [00:05<00:08, 13.42it/s] 29%|██▉ | 47/163 [00:05<00:08, 13.59it/s] 30%|███ | 49/163 [00:05<00:08, 13.43it/s] 31%|███▏ | 51/163 [00:05<00:08, 13.55it/s] 33%|███▎ | 53/163 [00:06<00:08, 13.65it/s] 34%|███▎ | 55/163 [00:06<00:07, 13.53it/s] 35%|███▍ | 57/163 [00:06<00:07, 13.63it/s] 36%|███▌ | 59/163 [00:06<00:07, 13.59it/s] 37%|███▋ | 61/163 [00:06<00:07, 13.52it/s] 39%|███▊ | 63/163 [00:06<00:07, 13.44it/s] 40%|███▉ | 65/163 [00:06<00:07, 13.51it/s] 41%|████ | 67/163 [00:07<00:07, 13.60it/s] 42%|████▏ | 69/163 [00:07<00:07, 13.40it/s] 44%|████▎ | 71/163 [00:07<00:06, 13.51it/s] 45%|████▍ | 73/163 [00:07<00:06, 13.47it/s] 46%|████▌ | 75/163 [00:07<00:06, 13.33it/s] 47%|████▋ | 77/163 [00:07<00:06, 13.50it/s] 48%|████▊ | 79/163 [00:07<00:06, 13.65it/s] 50%|████▉ | 81/163 [00:08<00:06, 13.54it/s] 51%|█████ | 83/163 [00:08<00:05, 13.62it/s] 52%|█████▏ | 85/163 [00:08<00:05, 13.60it/s] 53%|█████▎ | 87/163 [00:08<00:05, 13.42it/s] 55%|█████▍ | 89/163 [00:08<00:05, 13.49it/s] 56%|█████▌ | 91/163 [00:08<00:05, 13.57it/s] 57%|█████▋ | 93/163 [00:09<00:05, 13.35it/s] 58%|█████▊ | 95/163 [00:09<00:05, 13.47it/s] 60%|█████▉ | 97/163 [00:09<00:04, 13.54it/s] 61%|██████ | 99/163 [00:09<00:04, 13.44it/s] 62%|██████▏ | 101/163 [00:09<00:04, 13.36it/s] 63%|██████▎ | 103/163 [00:09<00:04, 13.49it/s] 64%|██████▍ | 105/163 [00:09<00:04, 13.59it/s] 66%|██████▌ | 107/163 [00:10<00:04, 12.95it/s] 67%|██████▋ | 109/163 [00:10<00:04, 12.16it/s] 68%|██████▊ | 111/163 [00:10<00:04, 11.54it/s] 69%|██████▉ | 113/163 [00:10<00:04, 11.64it/s] 71%|███████ | 115/163 [00:10<00:03, 12.20it/s] 72%|███████▏ | 117/163 [00:10<00:03, 12.66it/s] 73%|███████▎ | 119/163 [00:11<00:03, 12.86it/s] 74%|███████▍ | 121/163 [00:11<00:03, 13.18it/s] 75%|███████▌ | 123/163 [00:11<00:02, 13.43it/s] 77%|███████▋ | 125/163 [00:11<00:02, 13.43it/s] 78%|███████▊ | 127/163 [00:11<00:02, 13.55it/s] 79%|███████▉ | 129/163 [00:11<00:02, 13.66it/s] 80%|████████ | 131/163 [00:11<00:02, 13.26it/s] 82%|████████▏ | 133/163 [00:12<00:02, 13.24it/s] 83%|████████▎ | 135/163 [00:12<00:02, 13.45it/s] 84%|████████▍ | 137/163 [00:12<00:01, 13.61it/s] 85%|████████▌ | 139/163 [00:12<00:01, 13.36it/s] 87%|████████▋ | 141/163 [00:12<00:01, 13.48it/s] 88%|████████▊ | 143/163 [00:12<00:01, 13.53it/s] 89%|████████▉ | 145/163 [00:12<00:01, 13.53it/s] 90%|█████████ | 147/163 [00:13<00:01, 13.38it/s] 91%|█████████▏| 149/163 [00:13<00:01, 13.45it/s] 93%|█████████▎| 151/163 [00:13<00:00, 13.45it/s] 94%|█████████▍| 153/163 [00:13<00:00, 13.27it/s] 95%|█████████▌| 155/163 [00:13<00:00, 13.31it/s] 96%|█████████▋| 157/163 [00:13<00:00, 13.33it/s] 98%|█████████▊| 159/163 [00:14<00:00, 13.29it/s] 99%|█████████▉| 161/163 [00:14<00:00, 13.44it/s] 100%|██████████| 163/163 [00:14<00:00, 13.58it/s] 100%|██████████| 163/163 [00:14<00:00, 11.37it/s]
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