cjwbw

analog-diffusion

Cold

a dreambooth model trained on a diverse set of analog photographs

Public

234.6K runs

A100 (80GB)

Run with an API

Pricing

Playground API Examples README Versions

Prediction

cjwbw/analog-diffusion:1f7f51e8b2e43ade14fb7d6d62385854477e078ac870778aafecf70c0a6de006

Model

cjwbw/analog-diffusion:1f7f51e8

hcdvipfwebgzxe6eirzh77pjp4

Status

Succeeded

Source

Web

Hardware

–

Total duration

2m 31s

Created

over 2 years ago by @chenxwh

Input

width: 512
height: 512
prompt: analog style closeup portrait of cowboy George Washington
scheduler: K_EULER_ANCESTRAL
num_outputs: 1
guidance_scale: 7
negative_prompt: blur haze
num_inference_steps: 20

{
  "width": 512,
  "height": 512,
  "prompt": "analog style closeup portrait of cowboy George Washington",
  "scheduler": "K_EULER_ANCESTRAL",
  "num_outputs": 1,
  "guidance_scale": 7,
  "negative_prompt": "blur haze",
  "num_inference_steps": 20
}

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

const output = await replicate.run(
  "cjwbw/analog-diffusion:1f7f51e8b2e43ade14fb7d6d62385854477e078ac870778aafecf70c0a6de006",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "analog style closeup portrait of cowboy George Washington",
      scheduler: "K_EULER_ANCESTRAL",
      num_outputs: 1,
      guidance_scale: 7,
      negative_prompt: "blur haze",
      num_inference_steps: 20
    }
  }
);

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

output = replicate.run(
    "cjwbw/analog-diffusion:1f7f51e8b2e43ade14fb7d6d62385854477e078ac870778aafecf70c0a6de006",
    input={
        "width": 512,
        "height": 512,
        "prompt": "analog style closeup portrait of cowboy George Washington",
        "scheduler": "K_EULER_ANCESTRAL",
        "num_outputs": 1,
        "guidance_scale": 7,
        "negative_prompt": "blur haze",
        "num_inference_steps": 20
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run cjwbw/analog-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": "cjwbw/analog-diffusion:1f7f51e8b2e43ade14fb7d6d62385854477e078ac870778aafecf70c0a6de006",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "analog style closeup portrait of cowboy George Washington",
      "scheduler": "K_EULER_ANCESTRAL",
      "num_outputs": 1,
      "guidance_scale": 7,
      "negative_prompt": "blur haze",
      "num_inference_steps": 20
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2023-01-01T01:20:46.731310Z",
  "created_at": "2023-01-01T01:18:15.710273Z",
  "data_removed": false,
  "error": null,
  "id": "hcdvipfwebgzxe6eirzh77pjp4",
  "input": {
    "width": 512,
    "height": 512,
    "prompt": "analog style closeup portrait of cowboy George Washington",
    "scheduler": "K_EULER_ANCESTRAL",
    "num_outputs": 1,
    "guidance_scale": 7,
    "negative_prompt": "blur haze",
    "num_inference_steps": 20
  },
  "logs": "Using seed: 52975\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:03<00:57,  3.02s/it]\n 10%|█         | 2/20 [00:03<00:27,  1.52s/it]\n 15%|█▌        | 3/20 [00:03<00:17,  1.05s/it]\n 20%|██        | 4/20 [00:04<00:13,  1.21it/s]\n 25%|██▌       | 5/20 [00:04<00:10,  1.43it/s]\n 30%|███       | 6/20 [00:05<00:08,  1.59it/s]\n 35%|███▌      | 7/20 [00:05<00:07,  1.72it/s]\n 40%|████      | 8/20 [00:06<00:06,  1.81it/s]\n 45%|████▌     | 9/20 [00:06<00:05,  1.88it/s]\n 50%|█████     | 10/20 [00:07<00:05,  1.93it/s]\n 55%|█████▌    | 11/20 [00:07<00:04,  1.97it/s]\n 60%|██████    | 12/20 [00:08<00:04,  1.99it/s]\n 65%|██████▌   | 13/20 [00:08<00:03,  2.01it/s]\n 70%|███████   | 14/20 [00:09<00:02,  2.02it/s]\n 75%|███████▌  | 15/20 [00:09<00:02,  2.03it/s]\n 80%|████████  | 16/20 [00:10<00:01,  2.03it/s]\n 85%|████████▌ | 17/20 [00:10<00:01,  2.04it/s]\n 90%|█████████ | 18/20 [00:11<00:00,  2.04it/s]\n 95%|█████████▌| 19/20 [00:11<00:00,  2.04it/s]\n100%|██████████| 20/20 [00:12<00:00,  2.04it/s]\n100%|██████████| 20/20 [00:12<00:00,  1.63it/s]",
  "metrics": {
    "predict_time": 31.105579,
    "total_time": 151.021037
  },
  "output": [
    "https://replicate.delivery/pbxt/SscVjveDe9lQSkkfmoO3gXtO7K3YgU95O00unSi5GV0cPSfAB/out-0.png"
  ],
  "started_at": "2023-01-01T01:20:15.625731Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/hcdvipfwebgzxe6eirzh77pjp4",
    "cancel": "https://api.replicate.com/v1/predictions/hcdvipfwebgzxe6eirzh77pjp4/cancel"
  },
  "version": "1f7f51e8b2e43ade14fb7d6d62385854477e078ac870778aafecf70c0a6de006"
}

Generated in

31.1 seconds

Tweak it Iterate in playground ShareReport

Prediction

cjwbw/analog-diffusion:1f7f51e8b2e43ade14fb7d6d62385854477e078ac870778aafecf70c0a6de006

Model

cjwbw/analog-diffusion:1f7f51e8

6kmmbkwzrbgsbowlcdaifqim7u

Status

Succeeded

Source

Web

Hardware

–

Total duration

9.6s

Created

over 2 years ago

Input

width: "768"
height: "896"
prompt: analog style closeup portrait of Emma Stone, background futuristic neon at night storefront
scheduler: K_EULER_ANCESTRAL
num_outputs: 1
guidance_scale: 7
negative_prompt: blur haze
num_inference_steps: 20

{
  "width": "768",
  "height": "896",
  "prompt": " analog style closeup portrait of Emma Stone, background futuristic neon at night storefront",
  "scheduler": "K_EULER_ANCESTRAL",
  "num_outputs": 1,
  "guidance_scale": 7,
  "negative_prompt": "blur haze",
  "num_inference_steps": 20
}

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

const output = await replicate.run(
  "cjwbw/analog-diffusion:1f7f51e8b2e43ade14fb7d6d62385854477e078ac870778aafecf70c0a6de006",
  {
    input: {
      width: "768",
      height: "896",
      prompt: " analog style closeup portrait of Emma Stone, background futuristic neon at night storefront",
      scheduler: "K_EULER_ANCESTRAL",
      num_outputs: 1,
      guidance_scale: 7,
      negative_prompt: "blur haze",
      num_inference_steps: 20
    }
  }
);

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

output = replicate.run(
    "cjwbw/analog-diffusion:1f7f51e8b2e43ade14fb7d6d62385854477e078ac870778aafecf70c0a6de006",
    input={
        "width": "768",
        "height": "896",
        "prompt": " analog style closeup portrait of Emma Stone, background futuristic neon at night storefront",
        "scheduler": "K_EULER_ANCESTRAL",
        "num_outputs": 1,
        "guidance_scale": 7,
        "negative_prompt": "blur haze",
        "num_inference_steps": 20
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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

Run cjwbw/analog-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": "cjwbw/analog-diffusion:1f7f51e8b2e43ade14fb7d6d62385854477e078ac870778aafecf70c0a6de006",
    "input": {
      "width": "768",
      "height": "896",
      "prompt": " analog style closeup portrait of Emma Stone, background futuristic neon at night storefront",
      "scheduler": "K_EULER_ANCESTRAL",
      "num_outputs": 1,
      "guidance_scale": 7,
      "negative_prompt": "blur haze",
      "num_inference_steps": 20
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2023-01-01T01:45:13.744060Z",
  "created_at": "2023-01-01T01:45:04.158480Z",
  "data_removed": false,
  "error": null,
  "id": "6kmmbkwzrbgsbowlcdaifqim7u",
  "input": {
    "width": "768",
    "height": "896",
    "prompt": " analog style closeup portrait of Emma Stone, background futuristic neon at night storefront",
    "scheduler": "K_EULER_ANCESTRAL",
    "num_outputs": 1,
    "guidance_scale": 7,
    "negative_prompt": "blur haze",
    "num_inference_steps": 20
  },
  "logs": "Using seed: 62107\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:00<00:08,  2.23it/s]\n 10%|█         | 2/20 [00:00<00:07,  2.35it/s]\n 15%|█▌        | 3/20 [00:01<00:07,  2.40it/s]\n 20%|██        | 4/20 [00:01<00:06,  2.42it/s]\n 25%|██▌       | 5/20 [00:02<00:06,  2.43it/s]\n 30%|███       | 6/20 [00:02<00:05,  2.44it/s]\n 35%|███▌      | 7/20 [00:02<00:05,  2.44it/s]\n 40%|████      | 8/20 [00:03<00:04,  2.44it/s]\n 45%|████▌     | 9/20 [00:03<00:04,  2.45it/s]\n 50%|█████     | 10/20 [00:04<00:04,  2.45it/s]\n 55%|█████▌    | 11/20 [00:04<00:03,  2.45it/s]\n 60%|██████    | 12/20 [00:04<00:03,  2.45it/s]\n 65%|██████▌   | 13/20 [00:05<00:02,  2.45it/s]\n 70%|███████   | 14/20 [00:05<00:02,  2.45it/s]\n 75%|███████▌  | 15/20 [00:06<00:02,  2.45it/s]\n 80%|████████  | 16/20 [00:06<00:01,  2.45it/s]\n 85%|████████▌ | 17/20 [00:06<00:01,  2.45it/s]\n 90%|█████████ | 18/20 [00:07<00:00,  2.45it/s]\n 95%|█████████▌| 19/20 [00:07<00:00,  2.45it/s]\n100%|██████████| 20/20 [00:08<00:00,  2.45it/s]\n100%|██████████| 20/20 [00:08<00:00,  2.44it/s]",
  "metrics": {
    "predict_time": 9.540991,
    "total_time": 9.58558
  },
  "output": [
    "https://replicate.delivery/pbxt/sZO1UbcGe5QnHiMxSNTUe3D1XK0VQX4oAHpjBXeEUSaS9SfAB/out-0.png"
  ],
  "started_at": "2023-01-01T01:45:04.203069Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/6kmmbkwzrbgsbowlcdaifqim7u",
    "cancel": "https://api.replicate.com/v1/predictions/6kmmbkwzrbgsbowlcdaifqim7u/cancel"
  },
  "version": "1f7f51e8b2e43ade14fb7d6d62385854477e078ac870778aafecf70c0a6de006"
}

Generated in

9.6 seconds

Tweak it Iterate in playground ShareReport

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Prediction

Input

Output

Prediction

Input

Output

Logs (hcdvipfwebgzxe6eirzh77pjp4)

Logs (6kmmbkwzrbgsbowlcdaifqim7u)