cjwbw/stable-diffusion-high-resolution – Run with an API on Replicate

cjwbw / stable-diffusion-high-resolution

Detailed, higher-resolution images from Stable Diffusion

Cold

Public
73K runs
A100 (80GB)
GitHub
License

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

sports medicine doctor showboating about being a point of care ultrasound gurusports medicine doctor showboating about being a point of care ultrasound guru

The prompt to render.

Default: "female cyborg assimilated by alien fungus, intricate Three-point lighting portrait, by Ching Yeh and Greg Rutkowski, detailed cyberpunk in the style of GitS 1995"

ori_width

integer

Width of original stable-diffusion output image. Final output will double the width. Note that 1024x1024 may run out of memory, if so, please lower the width or height.

Default: 512

ori_height

integer

Height of original stable-diffusion output image. Final output will double the height. Note that 1024x1024 may run out of memory, if so, please lower the width or height.

Default: 512

scale

number

Unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty)).

Default: 7.5

steps

integer

Number of sampling steps.

Default: 50

seed

integer

The seed (for reproducible sampling).

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

npx create-replicate --model=cjwbw/stable-diffusion-high-resolution

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";

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

Run cjwbw/stable-diffusion-high-resolution using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "cjwbw/stable-diffusion-high-resolution:231e401da17b34aac8f8b3685f662f7fdad9ce1cf504ec0828ba4aac19f7882f",
  {
    input: {
      scale: 7.5,
      steps: 50,
      prompt: "sports medicine doctor showboating about being a point of care ultrasound guru",
      ori_width: 512,
      ori_height: 512
    }
  }
);
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 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 cjwbw/stable-diffusion-high-resolution using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "cjwbw/stable-diffusion-high-resolution:231e401da17b34aac8f8b3685f662f7fdad9ce1cf504ec0828ba4aac19f7882f",
    input={
        "scale": 7.5,
        "steps": 50,
        "prompt": "sports medicine doctor showboating about being a point of care ultrasound guru",
        "ori_width": 512,
        "ori_height": 512
    }
)
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 cjwbw/stable-diffusion-high-resolution 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": "231e401da17b34aac8f8b3685f662f7fdad9ce1cf504ec0828ba4aac19f7882f",
    "input": {
      "scale": 7.5,
      "steps": 50,
      "prompt": "sports medicine doctor showboating about being a point of care ultrasound guru",
      "ori_width": 512,
      "ori_height": 512
    }
  }' \
  https://api.replicate.com/v1/predictions

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

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/cjwbw/stable-diffusion-high-resolution@sha256:231e401da17b34aac8f8b3685f662f7fdad9ce1cf504ec0828ba4aac19f7882f \
  -i 'scale=7.5' \
  -i 'steps=50' \
  -i 'prompt="sports medicine doctor showboating about being a point of care ultrasound guru"' \
  -i 'ori_width=512' \
  -i 'ori_height=512'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/cjwbw/stable-diffusion-high-resolution@sha256:231e401da17b34aac8f8b3685f662f7fdad9ce1cf504ec0828ba4aac19f7882f
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "scale": 7.5,
      "steps": 50,
      "prompt": "sports medicine doctor showboating about being a point of care ultrasound guru",
      "ori_width": 512,
      "ori_height": 512
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

{
  "completed_at": "2022-10-12T23:25:15.949286Z",
  "created_at": "2022-10-12T23:24:29.939527Z",
  "data_removed": false,
  "error": null,
  "id": "iwvuhu5lwnbbdnmo3g5bwtuau4",
  "input": {
    "scale": 7.5,
    "steps": 50,
    "prompt": "sports medicine doctor showboating about being a point of care ultrasound guru",
    "ori_width": 512,
    "ori_height": 512
  },
  "logs": "Using seed 3170966120. Enter this in 'seed' if you want to produce the same output again!\nGlobal seed set to 3170966120\n\nSampling:   0%|          | 0/1 [00:00<?, ?it/s]\n\nData shape for DDIM sampling is (1, 4, 64, 64), eta 0\nRunning DDIM Sampling with 50 timesteps\ndata:   0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n\n\nDDIM Sampler:   0%|          | 0/50 [00:00<?, ?it/s]\u001b[A\u001b[A\n\n\nDDIM Sampler:   2%|▏         | 1/50 [00:00<00:05,  8.22it/s]\u001b[A\u001b[A\n\n\nDDIM Sampler:   6%|▌         | 3/50 [00:00<00:04, 10.26it/s]\u001b[A\u001b[A\n\n\nDDIM Sampler:  10%|█         | 5/50 [00:00<00:04, 10.77it/s]\u001b[A\u001b[A\n\n\nDDIM Sampler:  14%|█▍        | 7/50 [00:00<00:03, 10.99it/s]\u001b[A\u001b[A\n\n\nDDIM Sampler:  18%|█▊        | 9/50 [00:00<00:03, 11.13it/s]\u001b[A\u001b[A\n\n\nDDIM Sampler:  22%|██▏       | 11/50 [00:01<00:03, 11.22it/s]\u001b[A\u001b[A\n\n\nDDIM Sampler:  26%|██▌       | 13/50 [00:01<00:03, 11.28it/s]\u001b[A\u001b[A\n\n\nDDIM Sampler:  30%|███       | 15/50 [00:01<00:03, 11.25it/s]\u001b[A\u001b[A\n\n\nDDIM Sampler:  34%|███▍      | 17/50 [00:01<00:02, 11.28it/s]\u001b[A\u001b[A\n\n\nDDIM Sampler:  38%|███▊      | 19/50 [00:01<00:02, 11.32it/s]\u001b[A\u001b[A\n\n\nDDIM Sampler:  42%|████▏     | 21/50 [00:01<00:02, 11.30it/s]\u001b[A\u001b[A\n\n\nDDIM Sampler:  46%|████▌     | 23/50 [00:02<00:02, 11.30it/s]\u001b[A\u001b[A\n\n\nDDIM Sampler:  50%|█████     | 25/50 [00:02<00:02, 11.36it/s]\u001b[A\u001b[A\n\n\nDDIM Sampler:  54%|█████▍    | 27/50 [00:02<00:02, 11.26it/s]\u001b[A\u001b[A\n\n\nDDIM Sampler:  58%|█████▊    | 29/50 [00:02<00:01, 11.30it/s]\u001b[A\u001b[A\n\n\nDDIM Sampler:  62%|██████▏   | 31/50 [00:02<00:01, 11.31it/s]\u001b[A\u001b[A\n\n\nDDIM Sampler:  66%|██████▌   | 33/50 [00:02<00:01, 11.34it/s]\u001b[A\u001b[A\n\n\nDDIM Sampler:  70%|███████   | 35/50 [00:03<00:01, 11.37it/s]\u001b[A\u001b[A\n\n\nDDIM Sampler:  74%|███████▍  | 37/50 [00:03<00:01, 11.37it/s]\u001b[A\u001b[A\n\n\nDDIM Sampler:  78%|███████▊  | 39/50 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11.47it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  98%|█████████▊| 44/45 [00:03<00:00, 11.48it/s]\u001b[A\u001b[A\u001b[A\nDecoding image: 100%|██████████| 45/45 [00:03<00:00, 11.50it/s]\n\n\n\ndata: 100%|██████████| 1/1 [00:04<00:00,  4.03s/it]\u001b[A\u001b[A\ndata: 100%|██████████| 1/1 [00:04<00:00,  4.03s/it]\n\n\nSlices: 1it [00:04,  4.08s/it]\u001b[A\n\n\nRunning DDIM Sampling with 45 timesteps\ndata:   0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n\n\n\nDecoding image:   0%|          | 0/45 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:   4%|▍         | 2/45 [00:00<00:03, 11.10it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:   9%|▉         | 4/45 [00:00<00:03, 11.39it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  13%|█▎        | 6/45 [00:00<00:03, 11.49it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  18%|█▊        | 8/45 [00:00<00:03, 11.51it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  22%|██▏       | 10/45 [00:00<00:03, 11.51it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  27%|██▋       | 12/45 [00:01<00:02, 11.51it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  31%|███       | 14/45 [00:01<00:02, 11.52it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  36%|███▌      | 16/45 [00:01<00:02, 11.52it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  40%|████      | 18/45 [00:01<00:02, 11.52it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  44%|████▍     | 20/45 [00:01<00:02, 11.53it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  49%|████▉     | 22/45 [00:01<00:01, 11.52it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  53%|█████▎    | 24/45 [00:02<00:01, 11.49it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  58%|█████▊    | 26/45 [00:02<00:01, 11.47it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  62%|██████▏   | 28/45 [00:02<00:01, 11.47it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  67%|██████▋   | 30/45 [00:02<00:01, 11.46it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding 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11.44it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  93%|█████████▎| 42/45 [00:03<00:00, 11.45it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  98%|█████████▊| 44/45 [00:03<00:00, 11.44it/s]\u001b[A\u001b[A\u001b[A\nDecoding image: 100%|██████████| 45/45 [00:03<00:00, 11.30it/s]\n\n\n\ndata: 100%|██████████| 1/1 [00:04<00:00,  4.09s/it]\u001b[A\u001b[A\ndata: 100%|██████████| 1/1 [00:04<00:00,  4.10s/it]\n\n\nSlices: 3it [00:12,  4.11s/it]\u001b[A\n\n\nRunning DDIM Sampling with 45 timesteps\ndata:   0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n\n\n\nDecoding image:   0%|          | 0/45 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:   4%|▍         | 2/45 [00:00<00:03, 10.88it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:   9%|▉         | 4/45 [00:00<00:03, 11.14it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  13%|█▎        | 6/45 [00:00<00:03, 11.25it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  18%|█▊        | 8/45 [00:00<00:03, 11.33it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  22%|██▏       | 10/45 [00:00<00:03, 11.38it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  27%|██▋       | 12/45 [00:01<00:02, 11.41it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  31%|███       | 14/45 [00:01<00:02, 11.42it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  36%|███▌      | 16/45 [00:01<00:02, 11.42it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  40%|████      | 18/45 [00:01<00:02, 11.28it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  44%|████▍     | 20/45 [00:01<00:02, 11.34it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  49%|████▉     | 22/45 [00:01<00:02, 11.36it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  53%|█████▎    | 24/45 [00:02<00:01, 11.38it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  58%|█████▊    | 26/45 [00:02<00:01, 11.38it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  62%|██████▏   | 28/45 [00:02<00:01, 11.40it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding 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Sampling with 45 timesteps\ndata:   0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n\n\n\nDecoding image:   0%|          | 0/45 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:   4%|▍         | 2/45 [00:00<00:03, 11.09it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:   9%|▉         | 4/45 [00:00<00:03, 11.37it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  13%|█▎        | 6/45 [00:00<00:03, 11.46it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  18%|█▊        | 8/45 [00:00<00:03, 11.37it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  22%|██▏       | 10/45 [00:00<00:03, 11.32it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  27%|██▋       | 12/45 [00:01<00:02, 11.26it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  31%|███       | 14/45 [00:01<00:02, 10.99it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  36%|███▌      | 16/45 [00:01<00:02, 11.11it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  40%|████      | 18/45 [00:01<00:02, 11.15it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  44%|████▍     | 20/45 [00:01<00:02, 11.22it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  49%|████▉     | 22/45 [00:01<00:02, 11.22it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  53%|█████▎    | 24/45 [00:02<00:01, 11.30it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  58%|█████▊    | 26/45 [00:02<00:01, 11.35it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  62%|██████▏   | 28/45 [00:02<00:01, 11.31it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  67%|██████▋   | 30/45 [00:02<00:01, 11.30it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  71%|███████   | 32/45 [00:02<00:01, 11.30it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  76%|███████▌  | 34/45 [00:03<00:00, 11.28it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  80%|████████  | 36/45 [00:03<00:00, 11.28it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  84%|████████▍ | 38/45 [00:03<00:00, 11.31it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding 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11.40it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  67%|██████▋   | 30/45 [00:02<00:01, 11.39it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  71%|███████   | 32/45 [00:02<00:01, 11.35it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  76%|███████▌  | 34/45 [00:03<00:00, 11.35it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  80%|████████  | 36/45 [00:03<00:00, 11.37it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  84%|████████▍ | 38/45 [00:03<00:00, 11.33it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  89%|████████▉ | 40/45 [00:03<00:00, 11.33it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  93%|█████████▎| 42/45 [00:03<00:00, 11.37it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  98%|█████████▊| 44/45 [00:03<00:00, 11.35it/s]\u001b[A\u001b[A\u001b[A\nDecoding image: 100%|██████████| 45/45 [00:03<00:00, 11.33it/s]\n\n\n\ndata: 100%|██████████| 1/1 [00:04<00:00,  4.08s/it]\u001b[A\u001b[A\ndata: 100%|██████████| 1/1 [00:04<00:00,  4.08s/it]\n\n\nSlices: 6it [00:24,  4.12s/it]\u001b[A\n\n\nRunning DDIM Sampling with 45 timesteps\ndata:   0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n\n\n\nDecoding image:   0%|          | 0/45 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:   4%|▍         | 2/45 [00:00<00:03, 10.91it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:   9%|▉         | 4/45 [00:00<00:03, 11.14it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  13%|█▎        | 6/45 [00:00<00:03, 11.25it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  18%|█▊        | 8/45 [00:00<00:03, 11.34it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  22%|██▏       | 10/45 [00:00<00:03, 11.37it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  27%|██▋       | 12/45 [00:01<00:02, 11.29it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  31%|███       | 14/45 [00:01<00:02, 11.27it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  36%|███▌      | 16/45 [00:01<00:02, 11.33it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  40%|████      | 18/45 [00:01<00:02, 11.34it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  44%|████▍     | 20/45 [00:01<00:02, 11.36it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  49%|████▉     | 22/45 [00:01<00:02, 11.43it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  53%|█████▎    | 24/45 [00:02<00:01, 11.47it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  58%|█████▊    | 26/45 [00:02<00:01, 11.42it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  62%|██████▏   | 28/45 [00:02<00:01, 11.43it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  67%|██████▋   | 30/45 [00:02<00:01, 11.27it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  71%|███████   | 32/45 [00:02<00:01, 11.29it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  76%|███████▌  | 34/45 [00:03<00:01, 10.99it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  80%|████████  | 36/45 [00:03<00:00, 11.14it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  84%|████████▍ | 38/45 [00:03<00:00, 11.23it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  89%|████████▉ | 40/45 [00:03<00:00, 11.32it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  93%|█████████▎| 42/45 [00:03<00:00, 11.36it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  98%|█████████▊| 44/45 [00:03<00:00, 11.44it/s]\u001b[A\u001b[A\u001b[A\nDecoding image: 100%|██████████| 45/45 [00:03<00:00, 11.33it/s]\n\n\n\ndata: 100%|██████████| 1/1 [00:04<00:00,  4.09s/it]\u001b[A\u001b[A\ndata: 100%|██████████| 1/1 [00:04<00:00,  4.09s/it]\n\n\nSlices: 7it [00:28,  4.12s/it]\u001b[A\n\n\nRunning DDIM Sampling with 45 timesteps\ndata:   0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n\n\n\nDecoding image:   0%|          | 0/45 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:   4%|▍         | 2/45 [00:00<00:03, 11.09it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:   9%|▉         | 4/45 [00:00<00:03, 11.18it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  13%|█▎        | 6/45 [00:00<00:03, 11.22it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  18%|█▊        | 8/45 [00:00<00:03, 11.23it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  22%|██▏       | 10/45 [00:00<00:03, 11.29it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  27%|██▋       | 12/45 [00:01<00:02, 11.28it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  31%|███       | 14/45 [00:01<00:02, 11.30it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  36%|███▌      | 16/45 [00:01<00:02, 11.36it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  40%|████      | 18/45 [00:01<00:02, 11.37it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  44%|████▍     | 20/45 [00:01<00:02, 11.39it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  49%|████▉     | 22/45 [00:01<00:02, 11.44it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  53%|█████▎    | 24/45 [00:02<00:01, 11.49it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  58%|█████▊    | 26/45 [00:02<00:01, 11.51it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  62%|██████▏   | 28/45 [00:02<00:01, 11.53it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  67%|██████▋   | 30/45 [00:02<00:01, 11.54it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  71%|███████   | 32/45 [00:02<00:01, 11.54it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  76%|███████▌  | 34/45 [00:02<00:00, 11.55it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  80%|████████  | 36/45 [00:03<00:00, 11.57it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  84%|████████▍ | 38/45 [00:03<00:00, 11.57it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  89%|████████▉ | 40/45 [00:03<00:00, 11.57it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  93%|█████████▎| 42/45 [00:03<00:00, 11.58it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  98%|█████████▊| 44/45 [00:03<00:00, 11.59it/s]\u001b[A\u001b[A\u001b[A\nDecoding image: 100%|██████████| 45/45 [00:03<00:00, 11.46it/s]\n\n\n\ndata: 100%|██████████| 1/1 [00:04<00:00,  4.04s/it]\u001b[A\u001b[A\ndata: 100%|██████████| 1/1 [00:04<00:00,  4.04s/it]\n\n\nSlices: 8it [00:32,  4.11s/it]\u001b[A\n\n\nRunning DDIM Sampling with 45 timesteps\ndata:   0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n\n\n\nDecoding image:   0%|          | 0/45 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:   4%|▍         | 2/45 [00:00<00:03, 10.91it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:   9%|▉         | 4/45 [00:00<00:03, 11.18it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  13%|█▎        | 6/45 [00:00<00:03, 11.18it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  18%|█▊        | 8/45 [00:00<00:03, 11.26it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  22%|██▏       | 10/45 [00:00<00:03, 11.31it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  27%|██▋       | 12/45 [00:01<00:02, 11.36it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  31%|███       | 14/45 [00:01<00:02, 11.38it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  36%|███▌      | 16/45 [00:01<00:02, 11.42it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  40%|████      | 18/45 [00:01<00:02, 11.36it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  44%|████▍     | 20/45 [00:01<00:02, 11.37it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  49%|████▉     | 22/45 [00:01<00:02, 11.35it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  53%|█████▎    | 24/45 [00:02<00:01, 11.28it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  58%|█████▊    | 26/45 [00:02<00:01, 11.30it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  62%|██████▏   | 28/45 [00:02<00:01, 11.33it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  67%|██████▋   | 30/45 [00:02<00:01, 11.32it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  71%|███████   | 32/45 [00:02<00:01, 11.28it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  76%|███████▌  | 34/45 [00:03<00:00, 11.18it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  80%|████████  | 36/45 [00:03<00:00, 11.22it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  84%|████████▍ | 38/45 [00:03<00:00, 11.27it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  89%|████████▉ | 40/45 [00:03<00:00, 11.25it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  93%|█████████▎| 42/45 [00:03<00:00, 11.29it/s]\u001b[A\u001b[A\u001b[A\n\n\n\nDecoding image:  98%|█████████▊| 44/45 [00:03<00:00, 11.33it/s]\u001b[A\u001b[A\u001b[A\nDecoding image: 100%|██████████| 45/45 [00:03<00:00, 11.30it/s]\n\n\n\ndata: 100%|██████████| 1/1 [00:04<00:00,  4.10s/it]\u001b[A\u001b[A\ndata: 100%|██████████| 1/1 [00:04<00:00,  4.10s/it]\n\n\nSlices: 9it [00:37,  4.12s/it]\u001b[A\nSlices: 9it [00:37,  4.11s/it]\n\nPasses: 100%|██████████| 1/1 [00:38<00:00, 38.97s/it]\nPasses: 100%|██████████| 1/1 [00:38<00:00, 38.97s/it]",
  "metrics": {
    "predict_time": 45.75828,
    "total_time": 46.009759
  },
  "output": "https://replicate.com/api/models/cjwbw/stable-diffusion-high-resolution/files/63f7314a-989a-47cb-94b4-969bd9296cf8/out.png",
  "started_at": "2022-10-12T23:24:30.191006Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/iwvuhu5lwnbbdnmo3g5bwtuau4",
    "cancel": "https://api.replicate.com/v1/predictions/iwvuhu5lwnbbdnmo3g5bwtuau4/cancel"
  },
  "version": "231e401da17b34aac8f8b3685f662f7fdad9ce1cf504ec0828ba4aac19f7882f"
}

Generated in

45.8 seconds

Tweak itReport

Using seed 3170966120. Enter this in 'seed' if you want to produce the same output again!
Global seed set to 3170966120

Sampling:   0%|          | 0/1 [00:00<?, ?it/s]

Data shape for DDIM sampling is (1, 4, 64, 64), eta 0
Running DDIM Sampling with 50 timesteps
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DDIM Sampler:  90%|█████████ | 45/50 [00:04<00:00, 11.19it/s]


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DDIM Sampler:  98%|█████████▊| 49/50 [00:04<00:00, 11.30it/s]
DDIM Sampler: 100%|██████████| 50/50 [00:04<00:00, 11.23it/s]


data: 100%|██████████| 1/1 [00:05<00:00,  5.49s/it]
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Sampling: 100%|██████████| 1/1 [00:05<00:00,  5.49s/it]
Sampling: 100%|██████████| 1/1 [00:05<00:00,  5.50s/it]

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Slices: 0it [00:00, ?it/s]


Running DDIM Sampling with 45 timesteps
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data: 100%|██████████| 1/1 [00:04<00:00,  4.03s/it]
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Slices: 1it [00:04,  4.08s/it]


Running DDIM Sampling with 45 timesteps
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data: 100%|██████████| 1/1 [00:04<00:00,  4.06s/it]
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Slices: 2it [00:08,  4.09s/it]


Running DDIM Sampling with 45 timesteps
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Slices: 3it [00:12,  4.11s/it]


Running DDIM Sampling with 45 timesteps
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Slices: 4it [00:16,  4.10s/it]


Running DDIM Sampling with 45 timesteps
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data: 100%|██████████| 1/1 [00:04<00:00,  4.10s/it]


Slices: 5it [00:20,  4.12s/it]


Running DDIM Sampling with 45 timesteps
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data: 100%|██████████| 1/1 [00:04<00:00,  4.08s/it]


Slices: 6it [00:24,  4.12s/it]


Running DDIM Sampling with 45 timesteps
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Decoding image:  98%|█████████▊| 44/45 [00:03<00:00, 11.44it/s]
Decoding image: 100%|██████████| 45/45 [00:03<00:00, 11.33it/s]



data: 100%|██████████| 1/1 [00:04<00:00,  4.09s/it]
data: 100%|██████████| 1/1 [00:04<00:00,  4.09s/it]


Slices: 7it [00:28,  4.12s/it]


Running DDIM Sampling with 45 timesteps
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Decoding image: 100%|██████████| 45/45 [00:03<00:00, 11.46it/s]



data: 100%|██████████| 1/1 [00:04<00:00,  4.04s/it]
data: 100%|██████████| 1/1 [00:04<00:00,  4.04s/it]


Slices: 8it [00:32,  4.11s/it]


Running DDIM Sampling with 45 timesteps
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Decoding image:  58%|█████▊    | 26/45 [00:02<00:01, 11.30it/s]



Decoding image:  62%|██████▏   | 28/45 [00:02<00:01, 11.33it/s]



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Decoding image:  76%|███████▌  | 34/45 [00:03<00:00, 11.18it/s]



Decoding image:  80%|████████  | 36/45 [00:03<00:00, 11.22it/s]



Decoding image:  84%|████████▍ | 38/45 [00:03<00:00, 11.27it/s]



Decoding image:  89%|████████▉ | 40/45 [00:03<00:00, 11.25it/s]



Decoding image:  93%|█████████▎| 42/45 [00:03<00:00, 11.29it/s]



Decoding image:  98%|█████████▊| 44/45 [00:03<00:00, 11.33it/s]
Decoding image: 100%|██████████| 45/45 [00:03<00:00, 11.30it/s]



data: 100%|██████████| 1/1 [00:04<00:00,  4.10s/it]
data: 100%|██████████| 1/1 [00:04<00:00,  4.10s/it]


Slices: 9it [00:37,  4.12s/it]
Slices: 9it [00:37,  4.11s/it]

Passes: 100%|██████████| 1/1 [00:38<00:00, 38.97s/it]
Passes: 100%|██████████| 1/1 [00:38<00:00, 38.97s/it]

Examples

View more examples

Run time and cost

This model costs approximately $0.054 to run on Replicate, or 18 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia A100 (80GB) GPU hardware. Predictions typically complete within 39 seconds. The predict time for this model varies significantly based on the inputs.

Readme

This is a Cog implementation of Detailed, higher-resolution images from Stable-Diffusion, originally implemented by @jquesnelle at https://github.com/jquesnelle/txt2imghd/blob/master/txt2imghd.py Safety checker is added additionally.

txt2imghd is a port of the GOBIG mode from progrockdiffusion applied to Stable Diffusion, with Real-ESRGAN as the upscaler. It creates detailed, higher-resolution images by first generating an image from a prompt, upscaling it, and then running img2img on smaller pieces of the upscaled image, and blending the result back into the original image.

txt2imghd with default settings has the same VRAM requirements as regular Stable Diffusion, although generation of the detailed images will take longer.