cswry
/
seesr
SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution
Prediction
cswry/seesr:989cf3a6ModelIDumyas7tbnchzkist6ys3n7xqsaStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 231
- image
- cfg_scale
- 5.5
- user_prompt
- sample_times
- 1
- scale_factor
- 4
- negative_prompt
- dotted, noise, blur, lowres, smooth
- positive_prompt
- clean, high-resolution, 8k
- latent_tiled_size
- 320
- num_inference_steps
- 50
- latent_tiled_overlap
- 4
{ "seed": 231, "image": "https://replicate.delivery/pbxt/KCSXe5GmB6MSQVpdD51dhe60nhlU91yA0MuDkXznmRt7QTXx/179.png", "cfg_scale": 5.5, "user_prompt": "", "sample_times": 1, "scale_factor": 4, "negative_prompt": "dotted, noise, blur, lowres, smooth", "positive_prompt": "clean, high-resolution, 8k", "latent_tiled_size": 320, "num_inference_steps": 50, "latent_tiled_overlap": 4 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cswry/seesr using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cswry/seesr:989cf3a66fd209363de347c3129d95d9fe639e44533ab47e07a6dfb3f250b6e3", { input: { seed: 231, image: "https://replicate.delivery/pbxt/KCSXe5GmB6MSQVpdD51dhe60nhlU91yA0MuDkXznmRt7QTXx/179.png", cfg_scale: 5.5, user_prompt: "", sample_times: 1, scale_factor: 4, negative_prompt: "dotted, noise, blur, lowres, smooth", positive_prompt: "clean, high-resolution, 8k", latent_tiled_size: 320, num_inference_steps: 50, latent_tiled_overlap: 4 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun cswry/seesr using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cswry/seesr:989cf3a66fd209363de347c3129d95d9fe639e44533ab47e07a6dfb3f250b6e3", input={ "seed": 231, "image": "https://replicate.delivery/pbxt/KCSXe5GmB6MSQVpdD51dhe60nhlU91yA0MuDkXznmRt7QTXx/179.png", "cfg_scale": 5.5, "user_prompt": "", "sample_times": 1, "scale_factor": 4, "negative_prompt": "dotted, noise, blur, lowres, smooth", "positive_prompt": "clean, high-resolution, 8k", "latent_tiled_size": 320, "num_inference_steps": 50, "latent_tiled_overlap": 4 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run cswry/seesr 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": "989cf3a66fd209363de347c3129d95d9fe639e44533ab47e07a6dfb3f250b6e3", "input": { "seed": 231, "image": "https://replicate.delivery/pbxt/KCSXe5GmB6MSQVpdD51dhe60nhlU91yA0MuDkXznmRt7QTXx/179.png", "cfg_scale": 5.5, "user_prompt": "", "sample_times": 1, "scale_factor": 4, "negative_prompt": "dotted, noise, blur, lowres, smooth", "positive_prompt": "clean, high-resolution, 8k", "latent_tiled_size": 320, "num_inference_steps": 50, "latent_tiled_overlap": 4 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
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{ "completed_at": "2024-01-10T04:47:56.828664Z", "created_at": "2024-01-10T04:41:06.282807Z", "data_removed": false, "error": null, "id": "umyas7tbnchzkist6ys3n7xqsa", "input": { "seed": 231, "image": "https://replicate.delivery/pbxt/KCSXe5GmB6MSQVpdD51dhe60nhlU91yA0MuDkXznmRt7QTXx/179.png", "cfg_scale": 5.5, "user_prompt": "", "sample_times": 1, "scale_factor": 4, "negative_prompt": "dotted, noise, blur, lowres, smooth", "positive_prompt": "clean, high-resolution, 8k", "latent_tiled_size": 320, "num_inference_steps": 50, "latent_tiled_overlap": 4 }, "logs": "Seed set to 231\n[Tiled VAE]: the input size is tiny and unnecessary to tile.\n[Tiled Latent]: the input size is tiny and unnecessary to tile.\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:09, 5.21it/s]\n 6%|▌ | 3/50 [00:00<00:04, 9.54it/s]\n 10%|█ | 5/50 [00:00<00:03, 11.27it/s]\n 14%|█▍ | 7/50 [00:00<00:03, 12.09it/s]\n 18%|█▊ | 9/50 [00:00<00:03, 12.49it/s]\n 22%|██▏ | 11/50 [00:00<00:03, 12.81it/s]\n 26%|██▌ | 13/50 [00:01<00:02, 13.05it/s]\n 30%|███ | 15/50 [00:01<00:02, 13.18it/s]\n 34%|███▍ | 17/50 [00:01<00:02, 13.29it/s]\n 38%|███▊ | 19/50 [00:01<00:02, 13.35it/s]\n 42%|████▏ | 21/50 [00:01<00:02, 13.38it/s]\n 46%|████▌ | 23/50 [00:01<00:02, 13.34it/s]\n 50%|█████ | 25/50 [00:01<00:01, 13.18it/s]\n 54%|█████▍ | 27/50 [00:02<00:01, 13.29it/s]\n 58%|█████▊ | 29/50 [00:02<00:01, 13.38it/s]\n 62%|██████▏ | 31/50 [00:02<00:01, 13.41it/s]\n 66%|██████▌ | 33/50 [00:02<00:01, 13.39it/s]\n 70%|███████ | 35/50 [00:02<00:01, 13.36it/s]\n 74%|███████▍ | 37/50 [00:02<00:00, 13.39it/s]\n 78%|███████▊ | 39/50 [00:03<00:00, 13.29it/s]\n 82%|████████▏ | 41/50 [00:03<00:00, 13.37it/s]\n 86%|████████▌ | 43/50 [00:03<00:00, 13.43it/s]\n 90%|█████████ | 45/50 [00:03<00:00, 13.48it/s]\n 94%|█████████▍| 47/50 [00:03<00:00, 13.48it/s]\n 98%|█████████▊| 49/50 [00:03<00:00, 13.47it/s]\n100%|██████████| 50/50 [00:03<00:00, 13.01it/s]\n[Tiled VAE]: the input size is tiny and unnecessary to tile.\n[Tiled VAE]: Done in 4.425s, max VRAM alloc 5669.202 MB", "metrics": { "predict_time": 6.151774, "total_time": 410.545857 }, "output": [ "https://replicate.delivery/pbxt/Xx6RRm3aDkqtHNjOIvaxtxHRVjZwoF00KRRQNzmsUjMfmeKSA/0.png" ], "started_at": "2024-01-10T04:47:50.676890Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/umyas7tbnchzkist6ys3n7xqsa", "cancel": "https://api.replicate.com/v1/predictions/umyas7tbnchzkist6ys3n7xqsa/cancel" }, "version": "23b55f9ff1b4f0f254c3d8eb490d4b9d3e3d8961df1b7ea7bddf2641b411d6c9" }Generated inSeed set to 231 [Tiled VAE]: the input size is tiny and unnecessary to tile. [Tiled Latent]: the input size is tiny and unnecessary to tile. 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:09, 5.21it/s] 6%|▌ | 3/50 [00:00<00:04, 9.54it/s] 10%|█ | 5/50 [00:00<00:03, 11.27it/s] 14%|█▍ | 7/50 [00:00<00:03, 12.09it/s] 18%|█▊ | 9/50 [00:00<00:03, 12.49it/s] 22%|██▏ | 11/50 [00:00<00:03, 12.81it/s] 26%|██▌ | 13/50 [00:01<00:02, 13.05it/s] 30%|███ | 15/50 [00:01<00:02, 13.18it/s] 34%|███▍ | 17/50 [00:01<00:02, 13.29it/s] 38%|███▊ | 19/50 [00:01<00:02, 13.35it/s] 42%|████▏ | 21/50 [00:01<00:02, 13.38it/s] 46%|████▌ | 23/50 [00:01<00:02, 13.34it/s] 50%|█████ | 25/50 [00:01<00:01, 13.18it/s] 54%|█████▍ | 27/50 [00:02<00:01, 13.29it/s] 58%|█████▊ | 29/50 [00:02<00:01, 13.38it/s] 62%|██████▏ | 31/50 [00:02<00:01, 13.41it/s] 66%|██████▌ | 33/50 [00:02<00:01, 13.39it/s] 70%|███████ | 35/50 [00:02<00:01, 13.36it/s] 74%|███████▍ | 37/50 [00:02<00:00, 13.39it/s] 78%|███████▊ | 39/50 [00:03<00:00, 13.29it/s] 82%|████████▏ | 41/50 [00:03<00:00, 13.37it/s] 86%|████████▌ | 43/50 [00:03<00:00, 13.43it/s] 90%|█████████ | 45/50 [00:03<00:00, 13.48it/s] 94%|█████████▍| 47/50 [00:03<00:00, 13.48it/s] 98%|█████████▊| 49/50 [00:03<00:00, 13.47it/s] 100%|██████████| 50/50 [00:03<00:00, 13.01it/s] [Tiled VAE]: the input size is tiny and unnecessary to tile. [Tiled VAE]: Done in 4.425s, max VRAM alloc 5669.202 MBPrediction
cswry/seesr:989cf3a6ModelIDystquk3bo7cxjf6sqyir7ngiiqStatusSucceededSourceWebHardwareA40 (Large)Total durationCreatedInput
- seed
- 231
- image
- cfg_scale
- 5.5
- user_prompt
- sample_times
- 1
- scale_factor
- 4
- negative_prompt
- dotted, noise, blur, lowres, smooth
- positive_prompt
- clean, high-resolution, 8k
- latent_tiled_size
- 320
- num_inference_steps
- 50
- latent_tiled_overlap
- 4
{ "seed": 231, "image": "https://replicate.delivery/pbxt/KCSeGq7M1DNJ9JpYxKBoIRbwGcEfery01UhXtGDRCdMeGyFs/lincoln.png", "cfg_scale": 5.5, "user_prompt": "", "sample_times": 1, "scale_factor": 4, "negative_prompt": "dotted, noise, blur, lowres, smooth", "positive_prompt": "clean, high-resolution, 8k", "latent_tiled_size": 320, "num_inference_steps": 50, "latent_tiled_overlap": 4 }Install Replicate’s Node.js client library:npm install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run cswry/seesr using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "cswry/seesr:989cf3a66fd209363de347c3129d95d9fe639e44533ab47e07a6dfb3f250b6e3", { input: { seed: 231, image: "https://replicate.delivery/pbxt/KCSeGq7M1DNJ9JpYxKBoIRbwGcEfery01UhXtGDRCdMeGyFs/lincoln.png", cfg_scale: 5.5, user_prompt: "", sample_times: 1, scale_factor: 4, negative_prompt: "dotted, noise, blur, lowres, smooth", positive_prompt: "clean, high-resolution, 8k", latent_tiled_size: 320, num_inference_steps: 50, latent_tiled_overlap: 4 } } ); console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateSet theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Import the client:import replicateRun cswry/seesr using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "cswry/seesr:989cf3a66fd209363de347c3129d95d9fe639e44533ab47e07a6dfb3f250b6e3", input={ "seed": 231, "image": "https://replicate.delivery/pbxt/KCSeGq7M1DNJ9JpYxKBoIRbwGcEfery01UhXtGDRCdMeGyFs/lincoln.png", "cfg_scale": 5.5, "user_prompt": "", "sample_times": 1, "scale_factor": 4, "negative_prompt": "dotted, noise, blur, lowres, smooth", "positive_prompt": "clean, high-resolution, 8k", "latent_tiled_size": 320, "num_inference_steps": 50, "latent_tiled_overlap": 4 } ) print(output)To learn more, take a look at the guide on getting started with Python.
Set theREPLICATE_API_TOKENenvironment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run cswry/seesr 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": "989cf3a66fd209363de347c3129d95d9fe639e44533ab47e07a6dfb3f250b6e3", "input": { "seed": 231, "image": "https://replicate.delivery/pbxt/KCSeGq7M1DNJ9JpYxKBoIRbwGcEfery01UhXtGDRCdMeGyFs/lincoln.png", "cfg_scale": 5.5, "user_prompt": "", "sample_times": 1, "scale_factor": 4, "negative_prompt": "dotted, noise, blur, lowres, smooth", "positive_prompt": "clean, high-resolution, 8k", "latent_tiled_size": 320, "num_inference_steps": 50, "latent_tiled_overlap": 4 } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
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{ "completed_at": "2024-01-10T04:48:13.846314Z", "created_at": "2024-01-10T04:48:06.072187Z", "data_removed": false, "error": null, "id": "ystquk3bo7cxjf6sqyir7ngiiq", "input": { "seed": 231, "image": "https://replicate.delivery/pbxt/KCSeGq7M1DNJ9JpYxKBoIRbwGcEfery01UhXtGDRCdMeGyFs/lincoln.png", "cfg_scale": 5.5, "user_prompt": "", "sample_times": 1, "scale_factor": 4, "negative_prompt": "dotted, noise, blur, lowres, smooth", "positive_prompt": "clean, high-resolution, 8k", "latent_tiled_size": 320, "num_inference_steps": 50, "latent_tiled_overlap": 4 }, "logs": "Seed set to 231\n[Tiled VAE]: the input size is tiny and unnecessary to tile.\n[Tiled Latent]: the input size is tiny and unnecessary to tile.\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:09, 5.28it/s]\n 4%|▍ | 2/50 [00:00<00:06, 7.05it/s]\n 6%|▌ | 3/50 [00:00<00:05, 7.87it/s]\n 8%|▊ | 4/50 [00:00<00:05, 8.32it/s]\n 10%|█ | 5/50 [00:00<00:05, 8.59it/s]\n 12%|█▏ | 6/50 [00:00<00:05, 8.70it/s]\n 14%|█▍ | 7/50 [00:00<00:04, 8.82it/s]\n 16%|█▌ | 8/50 [00:00<00:04, 8.92it/s]\n 18%|█▊ | 9/50 [00:01<00:04, 8.97it/s]\n 20%|██ | 10/50 [00:01<00:04, 9.01it/s]\n 22%|██▏ | 11/50 [00:01<00:04, 9.03it/s]\n 24%|██▍ | 12/50 [00:01<00:04, 9.06it/s]\n 26%|██▌ | 13/50 [00:01<00:04, 9.06it/s]\n 28%|██▊ | 14/50 [00:01<00:03, 9.07it/s]\n 30%|███ | 15/50 [00:01<00:03, 9.07it/s]\n 32%|███▏ | 16/50 [00:01<00:03, 9.08it/s]\n 34%|███▍ | 17/50 [00:01<00:03, 9.09it/s]\n 36%|███▌ | 18/50 [00:02<00:03, 9.09it/s]\n 38%|███▊ | 19/50 [00:02<00:03, 9.09it/s]\n 40%|████ | 20/50 [00:02<00:03, 9.08it/s]\n 42%|████▏ | 21/50 [00:02<00:03, 9.07it/s]\n 44%|████▍ | 22/50 [00:02<00:03, 9.06it/s]\n 46%|████▌ | 23/50 [00:02<00:02, 9.06it/s]\n 48%|████▊ | 24/50 [00:02<00:02, 9.09it/s]\n 50%|█████ | 25/50 [00:02<00:02, 9.10it/s]\n 52%|█████▏ | 26/50 [00:02<00:02, 9.11it/s]\n 54%|█████▍ | 27/50 [00:03<00:02, 9.11it/s]\n 56%|█████▌ | 28/50 [00:03<00:02, 9.12it/s]\n 58%|█████▊ | 29/50 [00:03<00:02, 9.13it/s]\n 60%|██████ | 30/50 [00:03<00:02, 9.13it/s]\n 62%|██████▏ | 31/50 [00:03<00:02, 9.13it/s]\n 64%|██████▍ | 32/50 [00:03<00:01, 9.14it/s]\n 66%|██████▌ | 33/50 [00:03<00:01, 9.14it/s]\n 68%|██████▊ | 34/50 [00:03<00:01, 9.14it/s]\n 70%|███████ | 35/50 [00:03<00:01, 9.14it/s]\n 72%|███████▏ | 36/50 [00:04<00:01, 9.14it/s]\n 74%|███████▍ | 37/50 [00:04<00:01, 9.13it/s]\n 76%|███████▌ | 38/50 [00:04<00:01, 9.13it/s]\n 78%|███████▊ | 39/50 [00:04<00:01, 9.12it/s]\n 80%|████████ | 40/50 [00:04<00:01, 9.12it/s]\n 82%|████████▏ | 41/50 [00:04<00:00, 9.12it/s]\n 84%|████████▍ | 42/50 [00:04<00:00, 9.12it/s]\n 86%|████████▌ | 43/50 [00:04<00:00, 9.12it/s]\n 88%|████████▊ | 44/50 [00:04<00:00, 9.12it/s]\n 90%|█████████ | 45/50 [00:05<00:00, 9.12it/s]\n 92%|█████████▏| 46/50 [00:05<00:00, 9.13it/s]\n 94%|█████████▍| 47/50 [00:05<00:00, 9.13it/s]\n 96%|█████████▌| 48/50 [00:05<00:00, 9.12it/s]\n 98%|█████████▊| 49/50 [00:05<00:00, 9.11it/s]\n100%|██████████| 50/50 [00:05<00:00, 9.11it/s]\n100%|██████████| 50/50 [00:05<00:00, 8.98it/s]\n[Tiled VAE]: the input size is tiny and unnecessary to tile.\n[Tiled VAE]: Done in 6.098s, max VRAM alloc 6104.595 MB", "metrics": { "predict_time": 7.739778, "total_time": 7.774127 }, "output": [ "https://replicate.delivery/pbxt/C7AwXNlgckpWI1Mb1TgNcA9LUCsCdx6PaFPyrLHT1LCjTviE/0.png" ], "started_at": "2024-01-10T04:48:06.106536Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ystquk3bo7cxjf6sqyir7ngiiq", "cancel": "https://api.replicate.com/v1/predictions/ystquk3bo7cxjf6sqyir7ngiiq/cancel" }, "version": "23b55f9ff1b4f0f254c3d8eb490d4b9d3e3d8961df1b7ea7bddf2641b411d6c9" }Generated inSeed set to 231 [Tiled VAE]: the input size is tiny and unnecessary to tile. [Tiled Latent]: the input size is tiny and unnecessary to tile. 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:09, 5.28it/s] 4%|▍ | 2/50 [00:00<00:06, 7.05it/s] 6%|▌ | 3/50 [00:00<00:05, 7.87it/s] 8%|▊ | 4/50 [00:00<00:05, 8.32it/s] 10%|█ | 5/50 [00:00<00:05, 8.59it/s] 12%|█▏ | 6/50 [00:00<00:05, 8.70it/s] 14%|█▍ | 7/50 [00:00<00:04, 8.82it/s] 16%|█▌ | 8/50 [00:00<00:04, 8.92it/s] 18%|█▊ | 9/50 [00:01<00:04, 8.97it/s] 20%|██ | 10/50 [00:01<00:04, 9.01it/s] 22%|██▏ | 11/50 [00:01<00:04, 9.03it/s] 24%|██▍ | 12/50 [00:01<00:04, 9.06it/s] 26%|██▌ | 13/50 [00:01<00:04, 9.06it/s] 28%|██▊ | 14/50 [00:01<00:03, 9.07it/s] 30%|███ | 15/50 [00:01<00:03, 9.07it/s] 32%|███▏ | 16/50 [00:01<00:03, 9.08it/s] 34%|███▍ | 17/50 [00:01<00:03, 9.09it/s] 36%|███▌ | 18/50 [00:02<00:03, 9.09it/s] 38%|███▊ | 19/50 [00:02<00:03, 9.09it/s] 40%|████ | 20/50 [00:02<00:03, 9.08it/s] 42%|████▏ | 21/50 [00:02<00:03, 9.07it/s] 44%|████▍ | 22/50 [00:02<00:03, 9.06it/s] 46%|████▌ | 23/50 [00:02<00:02, 9.06it/s] 48%|████▊ | 24/50 [00:02<00:02, 9.09it/s] 50%|█████ | 25/50 [00:02<00:02, 9.10it/s] 52%|█████▏ | 26/50 [00:02<00:02, 9.11it/s] 54%|█████▍ | 27/50 [00:03<00:02, 9.11it/s] 56%|█████▌ | 28/50 [00:03<00:02, 9.12it/s] 58%|█████▊ | 29/50 [00:03<00:02, 9.13it/s] 60%|██████ | 30/50 [00:03<00:02, 9.13it/s] 62%|██████▏ | 31/50 [00:03<00:02, 9.13it/s] 64%|██████▍ | 32/50 [00:03<00:01, 9.14it/s] 66%|██████▌ | 33/50 [00:03<00:01, 9.14it/s] 68%|██████▊ | 34/50 [00:03<00:01, 9.14it/s] 70%|███████ | 35/50 [00:03<00:01, 9.14it/s] 72%|███████▏ | 36/50 [00:04<00:01, 9.14it/s] 74%|███████▍ | 37/50 [00:04<00:01, 9.13it/s] 76%|███████▌ | 38/50 [00:04<00:01, 9.13it/s] 78%|███████▊ | 39/50 [00:04<00:01, 9.12it/s] 80%|████████ | 40/50 [00:04<00:01, 9.12it/s] 82%|████████▏ | 41/50 [00:04<00:00, 9.12it/s] 84%|████████▍ | 42/50 [00:04<00:00, 9.12it/s] 86%|████████▌ | 43/50 [00:04<00:00, 9.12it/s] 88%|████████▊ | 44/50 [00:04<00:00, 9.12it/s] 90%|█████████ | 45/50 [00:05<00:00, 9.12it/s] 92%|█████████▏| 46/50 [00:05<00:00, 9.13it/s] 94%|█████████▍| 47/50 [00:05<00:00, 9.13it/s] 96%|█████████▌| 48/50 [00:05<00:00, 9.12it/s] 98%|█████████▊| 49/50 [00:05<00:00, 9.11it/s] 100%|██████████| 50/50 [00:05<00:00, 9.11it/s] 100%|██████████| 50/50 [00:05<00:00, 8.98it/s] [Tiled VAE]: the input size is tiny and unnecessary to tile. [Tiled VAE]: Done in 6.098s, max VRAM alloc 6104.595 MB
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