astramlco/diffbir:f7a6e783 | Run with an API on Replicate

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astramlco /diffbir:f7a6e783

Playground API

Input

input

*file

Path to the input image you want to enhance.

upscaling_model_type

string

Choose the type of model best suited for the primary content of the image: 'faces' for portraits and 'general_scenes' for everything else.

Default: "general_scenes"

restoration_model_type

string

Select the restoration model that aligns with the content of your image. This model is responsible for image restoration which removes degradations.

Default: "general_scenes"

reload_restoration_model

boolean

Reload the image restoration model (SwinIR) if set to True. This can be useful if you've updated or changed the underlying SwinIR model.

Default: false

steps

integer

(minimum: 1, maximum: 100)

The number of enhancement iterations to perform. More steps might result in a clearer image but can also introduce artifacts.

Default: 50

super_resolution_factor

integer

(minimum: 1, maximum: 4)

Factor by which the input image resolution should be increased. For instance, a factor of 4 will make the resolution 4 times greater in both height and width.

Default: 4

repeat_times

integer

(minimum: 1, maximum: 10)

Number of times the enhancement process is repeated by feeding the output back as input. This can refine the result but might also introduce over-enhancement issues.

Default: 1

disable_preprocess_model

boolean

Disables the initial preprocessing step using SwinIR. Turn this off if your input image is already of high quality and doesn't require restoration.

Default: false

tiled

boolean

Whether to use patch-based sampling. This can be useful for very large images to enhance them in smaller chunks rather than all at once.

Default: false

tile_size

integer

Size of each tile (or patch) when 'tiled' option is enabled. Determines how the image is divided during patch-based enhancement.

Default: 512

tile_stride

integer

Distance between the start of each tile when the image is divided for patch-based enhancement. A smaller stride means more overlap between tiles.

Default: 256

use_guidance

boolean

Use latent image guidance for enhancement. This can help in achieving more accurate and contextually relevant enhancements.

Default: false

guidance_scale

number

For 'general_scenes': Scale factor for the guidance mechanism. Adjusts the influence of guidance on the enhancement process.

Default: 0

guidance_time_start

integer

For 'general_scenes': Specifies when (at which step) the guidance mechanism starts influencing the enhancement.

Default: 1001

guidance_time_stop

integer

For 'general_scenes': Specifies when (at which step) the guidance mechanism stops influencing the enhancement.

Default: -1

guidance_space

string

For 'general_scenes': Determines in which space (RGB or latent) the guidance operates. 'latent' can often provide more subtle and context-aware enhancements.

Default: "latent"

guidance_repeat

integer

For 'general_scenes': Number of times the guidance process is repeated during enhancement.

Default: 5

color_fix_type

string

Method used for color correction post enhancement. 'wavelet' and 'adain' offer different styles of color correction, while 'none' skips this step.

Default: "wavelet"

seed

integer

Random seed to ensure reproducibility. Setting this ensures that multiple runs with the same input produce the same output.

Default: 231

has_aligned

boolean

For 'faces' mode: Indicates if the input images are already cropped and aligned to faces. If not, the model will attempt to do this.

Default: false

only_center_face

boolean

For 'faces' mode: If multiple faces are detected, only enhance the center-most face in the image.

Default: false

face_detection_model

string

For 'faces' mode: Model used for detecting faces in the image. Choose based on accuracy and speed preferences.

Default: "retinaface_resnet50"

background_upsampler

string

For 'faces' mode: Model used to upscale the background in images where the primary subject is a face.

Default: "RealESRGAN"

background_upsampler_tile

integer

For 'faces' mode: Size of each tile used by the background upsampler when dividing the image into patches.

Default: 400

background_upsampler_tile_stride

integer

For 'faces' mode: Distance between the start of each tile when the background is divided for upscaling. A smaller stride means more overlap between tiles.

Default: 400

Run this model in Node.js with one line of code:

npx create-replicate --model=astramlco/diffbir

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment 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";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run astramlco/diffbir using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "astramlco/diffbir:f7a6e7832fee8d2593be566723295b80ed14b424f8365f8647e19775f617e205",
  {
    input: {
      seed: 231,
      input: "https://replicate.delivery/pbxt/Ketx7Rc2HPkgBgsBAmZ14gWWKc6aiKsA783giEEf4qiHDtOb/C.L.A.I.R.E._everhart_s.jpg",
      steps: 50,
      tiled: false,
      tile_size: 512,
      has_aligned: false,
      tile_stride: 256,
      repeat_times: 1,
      use_guidance: false,
      color_fix_type: "wavelet",
      guidance_scale: 0,
      guidance_space: "latent",
      guidance_repeat: 5,
      only_center_face: false,
      guidance_time_stop: -1,
      guidance_time_start: 1001,
      background_upsampler: "RealESRGAN",
      face_detection_model: "retinaface_resnet50",
      upscaling_model_type: "faces",
      restoration_model_type: "faces",
      super_resolution_factor: 2,
      disable_preprocess_model: false,
      reload_restoration_model: false,
      background_upsampler_tile: 400,
      background_upsampler_tile_stride: 400
    }
  }
);

// 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

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run astramlco/diffbir using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "astramlco/diffbir:f7a6e7832fee8d2593be566723295b80ed14b424f8365f8647e19775f617e205",
    input={
        "seed": 231,
        "input": "https://replicate.delivery/pbxt/Ketx7Rc2HPkgBgsBAmZ14gWWKc6aiKsA783giEEf4qiHDtOb/C.L.A.I.R.E._everhart_s.jpg",
        "steps": 50,
        "tiled": False,
        "tile_size": 512,
        "has_aligned": False,
        "tile_stride": 256,
        "repeat_times": 1,
        "use_guidance": False,
        "color_fix_type": "wavelet",
        "guidance_scale": 0,
        "guidance_space": "latent",
        "guidance_repeat": 5,
        "only_center_face": False,
        "guidance_time_stop": -1,
        "guidance_time_start": 1001,
        "background_upsampler": "RealESRGAN",
        "face_detection_model": "retinaface_resnet50",
        "upscaling_model_type": "faces",
        "restoration_model_type": "faces",
        "super_resolution_factor": 2,
        "disable_preprocess_model": False,
        "reload_restoration_model": False,
        "background_upsampler_tile": 400,
        "background_upsampler_tile_stride": 400
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run astramlco/diffbir 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": "astramlco/diffbir:f7a6e7832fee8d2593be566723295b80ed14b424f8365f8647e19775f617e205",
    "input": {
      "seed": 231,
      "input": "https://replicate.delivery/pbxt/Ketx7Rc2HPkgBgsBAmZ14gWWKc6aiKsA783giEEf4qiHDtOb/C.L.A.I.R.E._everhart_s.jpg",
      "steps": 50,
      "tiled": false,
      "tile_size": 512,
      "has_aligned": false,
      "tile_stride": 256,
      "repeat_times": 1,
      "use_guidance": false,
      "color_fix_type": "wavelet",
      "guidance_scale": 0,
      "guidance_space": "latent",
      "guidance_repeat": 5,
      "only_center_face": false,
      "guidance_time_stop": -1,
      "guidance_time_start": 1001,
      "background_upsampler": "RealESRGAN",
      "face_detection_model": "retinaface_resnet50",
      "upscaling_model_type": "faces",
      "restoration_model_type": "faces",
      "super_resolution_factor": 2,
      "disable_preprocess_model": false,
      "reload_restoration_model": false,
      "background_upsampler_tile": 400,
      "background_upsampler_tile_stride": 400
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-03-30T07:49:57.477422Z",
  "created_at": "2024-03-30T07:46:10.719345Z",
  "data_removed": false,
  "error": null,
  "id": "lg6q6ydbytjofvmc7o474gdleu",
  "input": {
    "seed": 231,
    "input": "https://replicate.delivery/pbxt/Ketx7Rc2HPkgBgsBAmZ14gWWKc6aiKsA783giEEf4qiHDtOb/C.L.A.I.R.E._everhart_s.jpg",
    "steps": 50,
    "tiled": false,
    "tile_size": 512,
    "has_aligned": false,
    "tile_stride": 256,
    "repeat_times": 1,
    "use_guidance": false,
    "color_fix_type": "wavelet",
    "guidance_scale": 0,
    "guidance_space": "latent",
    "guidance_repeat": 5,
    "only_center_face": false,
    "guidance_time_stop": -1,
    "guidance_time_start": 1001,
    "background_upsampler": "RealESRGAN",
    "face_detection_model": "retinaface_resnet50",
    "upscaling_model_type": "faces",
    "restoration_model_type": "faces",
    "super_resolution_factor": 2,
    "disable_preprocess_model": false,
    "reload_restoration_model": false,
    "background_upsampler_tile": 400,
    "background_upsampler_tile_stride": 400
  },
  "logs": "ckptckptckpt weights/face_full_v1.ckpt\nSwitching from mode 'FULL' to 'FACE'...\nBuilding and loading 'FACE' mode model...\nControlLDM: Running in eps-prediction mode\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.\nDiffusionWrapper has 865.91 M params.\nmaking attention of type 'vanilla-xformers' with 512 in_channels\nbuilding MemoryEfficientAttnBlock with 512 in_channels...\nWorking with z of shape (1, 4, 32, 32) = 4096 dimensions.\nmaking attention of type 'vanilla-xformers' with 512 in_channels\nbuilding MemoryEfficientAttnBlock with 512 in_channels...\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.\nSetting up [LPIPS] perceptual loss: trunk [alex], v[0.1], spatial [off]\nLoading model from: /root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/lpips/weights/v0.1/alex.pth\nreload swinir model from weights/face_swinir_v1.ckpt\nENABLE XFORMERS!\nModel successfully switched to 'FACE' mode.\n{'bg_tile': 400,\n'bg_tile_stride': 400,\n'bg_upsampler': 'RealESRGAN',\n'ckpt': 'weights/face_full_v1.ckpt',\n'color_fix_type': 'wavelet',\n'config': 'configs/model/cldm.yaml',\n'detection_model': 'retinaface_resnet50',\n'device': 'cuda',\n'disable_preprocess_model': False,\n'g_repeat': 5,\n'g_scale': 0.0,\n'g_space': 'latent',\n'g_t_start': 1001,\n'g_t_stop': -1,\n'has_aligned': False,\n'image_size': 512,\n'input': '/tmp/tmpcdoq1b8cC.L.A.I.R.E._everhart_s.jpg',\n'only_center_face': False,\n'output': '.',\n'reload_swinir': False,\n'repeat_times': 1,\n'seed': 231,\n'show_lq': False,\n'skip_if_exist': False,\n'sr_scale': 2,\n'steps': 50,\n'swinir_ckpt': 'weights/face_swinir_v1.ckpt',\n'tile_size': 512,\n'tile_stride': 256,\n'tiled': False,\n'use_guidance': False}\nGlobal seed set to 231\n/root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\nwarnings.warn(msg)\nDownloading: \"https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth\" to /root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/facexlib/weights/detection_Resnet50_Final.pth\n  0%|          | 0.00/104M [00:00<?, ?B/s]\n 39%|███▉      | 40.5M/104M [00:00<00:00, 425MB/s]\n 78%|███████▊  | 81.1M/104M [00:00<00:00, 98.1MB/s]\n100%|██████████| 104M/104M [00:00<00:00, 127MB/s]\nDownloading: \"https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth\" to /root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/facexlib/weights/parsing_parsenet.pth\n  0%|          | 0.00/81.4M [00:00<?, ?B/s]\n 54%|█████▍    | 43.8M/81.4M [00:00<00:00, 459MB/s]\n100%|██████████| 81.4M/81.4M [00:00<00:00, 464MB/s]\nLoading RealESRGAN_x2plus.pth for background upsampling...\nDownloading: \"https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth\" to /src/weights/realesrgan/RealESRGAN_x2plus.pth\n  0%|          | 0.00/64.0M [00:00<?, ?B/s]\n 52%|█████▏    | 33.4M/64.0M [00:00<00:00, 351MB/s]\n100%|██████████| 64.0M/64.0M [00:00<00:00, 386MB/s]\ntimesteps used in spaced sampler:\n[0, 20, 41, 61, 82, 102, 122, 143, 163, 183, 204, 224, 245, 265, 285, 306, 326, 347, 367, 387, 408, 428, 449, 469, 489, 510, 530, 550, 571, 591, 612, 632, 652, 673, 693, 714, 734, 754, 775, 795, 816, 836, 856, 877, 897, 917, 938, 958, 979, 999]\nSpaced Sampler:   0%|          | 0/50 [00:00<?, ?it/s]\nSpaced Sampler:   2%|▏         | 1/50 [00:00<00:10,  4.79it/s]\nSpaced Sampler:   6%|▌         | 3/50 [00:00<00:05,  8.70it/s]\nSpaced Sampler:  10%|█         | 5/50 [00:00<00:04, 10.19it/s]\nSpaced Sampler:  14%|█▍        | 7/50 [00:00<00:03, 10.93it/s]\nSpaced Sampler:  18%|█▊        | 9/50 [00:00<00:03, 11.34it/s]\nSpaced Sampler:  22%|██▏       | 11/50 [00:01<00:03, 11.59it/s]\nSpaced Sampler:  26%|██▌       | 13/50 [00:01<00:03, 11.81it/s]\nSpaced Sampler:  30%|███       | 15/50 [00:01<00:02, 11.96it/s]\nSpaced Sampler:  34%|███▍      | 17/50 [00:01<00:02, 12.07it/s]\nSpaced Sampler:  38%|███▊      | 19/50 [00:01<00:02, 12.13it/s]\nSpaced Sampler:  42%|████▏     | 21/50 [00:01<00:02, 12.17it/s]\nSpaced Sampler:  46%|████▌     | 23/50 [00:02<00:02, 12.14it/s]\nSpaced Sampler:  50%|█████     | 25/50 [00:02<00:02, 12.13it/s]\nSpaced Sampler:  54%|█████▍    | 27/50 [00:02<00:01, 12.11it/s]\nSpaced Sampler:  58%|█████▊    | 29/50 [00:02<00:01, 12.10it/s]\nSpaced Sampler:  62%|██████▏   | 31/50 [00:02<00:01, 12.07it/s]\nSpaced Sampler:  66%|██████▌   | 33/50 [00:02<00:01, 12.08it/s]\nSpaced Sampler:  70%|███████   | 35/50 [00:03<00:01, 12.06it/s]\nSpaced Sampler:  74%|███████▍  | 37/50 [00:03<00:01, 12.05it/s]\nSpaced Sampler:  78%|███████▊  | 39/50 [00:03<00:00, 11.95it/s]\nSpaced Sampler:  82%|████████▏ | 41/50 [00:03<00:00, 11.97it/s]\nSpaced Sampler:  86%|████████▌ | 43/50 [00:03<00:00, 12.01it/s]\nSpaced Sampler:  90%|█████████ | 45/50 [00:03<00:00, 12.06it/s]\nSpaced Sampler:  94%|█████████▍| 47/50 [00:04<00:00, 12.12it/s]\nSpaced Sampler:  98%|█████████▊| 49/50 [00:04<00:00, 12.17it/s]\nSpaced Sampler: 100%|██████████| 50/50 [00:04<00:00, 11.77it/s]\nupsampling the background image using RealESRGAN...\nFace image tmpcdoq1b8cC.L.A.I.R.E._everhart_s saved to ./..",
  "metrics": {
    "predict_time": 43.867453,
    "total_time": 226.758077
  },
  "output": [
    "https://replicate.delivery/pbxt/ZebgzumoHLy2ZCL5ppfJnICvW5SBKGZ2emNQxPeS35KNidVKB/tmpcdoq1b8cC.L.A.I.R.E._everhart_s_00.png",
    "https://replicate.delivery/pbxt/e1wupnukQOzFYaCzdEemITU2qoHPTUcDRHyfiWepxncNidVKB/tmpcdoq1b8cC.L.A.I.R.E._everhart_s_00.png",
    "https://replicate.delivery/pbxt/a35MV4BcyI7wEdbFav8hrbX8CuOt3IU8jEr39S6E9geSsrSJA/tmpcdoq1b8cC.L.A.I.R.E._everhart_s.png"
  ],
  "started_at": "2024-03-30T07:49:13.609969Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/lg6q6ydbytjofvmc7o474gdleu",
    "cancel": "https://api.replicate.com/v1/predictions/lg6q6ydbytjofvmc7o474gdleu/cancel"
  },
  "version": "f7a6e7832fee8d2593be566723295b80ed14b424f8365f8647e19775f617e205"
}

Generated in

43.9 seconds

Tweak it Report

ckptckptckpt weights/face_full_v1.ckpt
Switching from mode 'FULL' to 'FACE'...
Building and loading 'FACE' mode model...
ControlLDM: Running in eps-prediction mode
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.
DiffusionWrapper has 865.91 M params.
making attention of type 'vanilla-xformers' with 512 in_channels
building MemoryEfficientAttnBlock with 512 in_channels...
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
making attention of type 'vanilla-xformers' with 512 in_channels
building MemoryEfficientAttnBlock with 512 in_channels...
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.
Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.
Setting up [LPIPS] perceptual loss: trunk [alex], v[0.1], spatial [off]
Loading model from: /root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/lpips/weights/v0.1/alex.pth
reload swinir model from weights/face_swinir_v1.ckpt
ENABLE XFORMERS!
Model successfully switched to 'FACE' mode.
{'bg_tile': 400,
'bg_tile_stride': 400,
'bg_upsampler': 'RealESRGAN',
'ckpt': 'weights/face_full_v1.ckpt',
'color_fix_type': 'wavelet',
'config': 'configs/model/cldm.yaml',
'detection_model': 'retinaface_resnet50',
'device': 'cuda',
'disable_preprocess_model': False,
'g_repeat': 5,
'g_scale': 0.0,
'g_space': 'latent',
'g_t_start': 1001,
'g_t_stop': -1,
'has_aligned': False,
'image_size': 512,
'input': '/tmp/tmpcdoq1b8cC.L.A.I.R.E._everhart_s.jpg',
'only_center_face': False,
'output': '.',
'reload_swinir': False,
'repeat_times': 1,
'seed': 231,
'show_lq': False,
'skip_if_exist': False,
'sr_scale': 2,
'steps': 50,
'swinir_ckpt': 'weights/face_swinir_v1.ckpt',
'tile_size': 512,
'tile_stride': 256,
'tiled': False,
'use_guidance': False}
Global seed set to 231
/root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.
warnings.warn(msg)
Downloading: "https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth" to /root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/facexlib/weights/detection_Resnet50_Final.pth
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Downloading: "https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth" to /root/.pyenv/versions/3.9.19/lib/python3.9/site-packages/facexlib/weights/parsing_parsenet.pth
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Loading RealESRGAN_x2plus.pth for background upsampling...
Downloading: "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth" to /src/weights/realesrgan/RealESRGAN_x2plus.pth
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timesteps used in spaced sampler:
[0, 20, 41, 61, 82, 102, 122, 143, 163, 183, 204, 224, 245, 265, 285, 306, 326, 347, 367, 387, 408, 428, 449, 469, 489, 510, 530, 550, 571, 591, 612, 632, 652, 673, 693, 714, 734, 754, 775, 795, 816, 836, 856, 877, 897, 917, 938, 958, 979, 999]
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upsampling the background image using RealESRGAN...
Face image tmpcdoq1b8cC.L.A.I.R.E._everhart_s saved to ./..