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cloneofsimo /analog_diffusion_lora:b71669ee
Input
Run this model in Node.js with one line of code:
npm install replicate
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import Replicate from "replicate";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});
Run cloneofsimo/analog_diffusion_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"cloneofsimo/analog_diffusion_lora:b71669eeda89a08ce8294c3e79fa203417918161b961c6de3215fce20ff9bc87",
{
input: {
width: 448,
height: 640,
prompt: "analog style closeup portrait of <1> cowboy hat, sitting on a desk",
lora_urls: "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors",
scheduler: "DPMSolverMultistep",
lora_scales: "0.6",
num_outputs: 1,
adapter_type: "depth",
guidance_scale: 6.5,
negative_prompt: "blur, haze",
prompt_strength: 0.8,
num_inference_steps: 50,
adapter_condition_image: "https://replicate.delivery/pbxt/IOpTvFxWf0Tiu8aOHcjBbXhucssJJ7wFFqkLP80snon5NtP7/depth_0.png"
}
}
);
console.log(output);
To learn more, take a look at the guide on getting started with Node.js.
pip install replicate
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
import replicate
Run cloneofsimo/analog_diffusion_lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"cloneofsimo/analog_diffusion_lora:b71669eeda89a08ce8294c3e79fa203417918161b961c6de3215fce20ff9bc87",
input={
"width": 448,
"height": 640,
"prompt": "analog style closeup portrait of <1> cowboy hat, sitting on a desk",
"lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors",
"scheduler": "DPMSolverMultistep",
"lora_scales": "0.6",
"num_outputs": 1,
"adapter_type": "depth",
"guidance_scale": 6.5,
"negative_prompt": "blur, haze",
"prompt_strength": 0.8,
"num_inference_steps": 50,
"adapter_condition_image": "https://replicate.delivery/pbxt/IOpTvFxWf0Tiu8aOHcjBbXhucssJJ7wFFqkLP80snon5NtP7/depth_0.png"
}
)
print(output)
To learn more, take a look at the guide on getting started with Python.
REPLICATE_API_TOKEN
environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>
Find your API token in your account settings.
Run cloneofsimo/analog_diffusion_lora 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": "b71669eeda89a08ce8294c3e79fa203417918161b961c6de3215fce20ff9bc87",
"input": {
"width": 448,
"height": 640,
"prompt": "analog style closeup portrait of <1> cowboy hat, sitting on a desk",
"lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors",
"scheduler": "DPMSolverMultistep",
"lora_scales": "0.6",
"num_outputs": 1,
"adapter_type": "depth",
"guidance_scale": 6.5,
"negative_prompt": "blur, haze",
"prompt_strength": 0.8,
"num_inference_steps": 50,
"adapter_condition_image": "https://replicate.delivery/pbxt/IOpTvFxWf0Tiu8aOHcjBbXhucssJJ7wFFqkLP80snon5NtP7/depth_0.png"
}
}' \
https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
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/cloneofsimo/analog_diffusion_lora@sha256:b71669eeda89a08ce8294c3e79fa203417918161b961c6de3215fce20ff9bc87 \
-i 'width=448' \
-i 'height=640' \
-i 'prompt="analog style closeup portrait of <1> cowboy hat, sitting on a desk"' \
-i 'lora_urls="https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors"' \
-i 'scheduler="DPMSolverMultistep"' \
-i 'lora_scales="0.6"' \
-i 'num_outputs=1' \
-i 'adapter_type="depth"' \
-i 'guidance_scale=6.5' \
-i 'negative_prompt="blur, haze"' \
-i 'prompt_strength=0.8' \
-i 'num_inference_steps=50' \
-i 'adapter_condition_image="https://replicate.delivery/pbxt/IOpTvFxWf0Tiu8aOHcjBbXhucssJJ7wFFqkLP80snon5NtP7/depth_0.png"'
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/cloneofsimo/analog_diffusion_lora@sha256:b71669eeda89a08ce8294c3e79fa203417918161b961c6de3215fce20ff9bc87
curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "width": 448, "height": 640, "prompt": "analog style closeup portrait of <1> cowboy hat, sitting on a desk", "lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors", "scheduler": "DPMSolverMultistep", "lora_scales": "0.6", "num_outputs": 1, "adapter_type": "depth", "guidance_scale": 6.5, "negative_prompt": "blur, haze", "prompt_strength": 0.8, "num_inference_steps": 50, "adapter_condition_image": "https://replicate.delivery/pbxt/IOpTvFxWf0Tiu8aOHcjBbXhucssJJ7wFFqkLP80snon5NtP7/depth_0.png" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Add a payment method to run this model.
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Output
{
"completed_at": "2023-03-01T16:56:37.124433Z",
"created_at": "2023-03-01T16:56:22.601102Z",
"data_removed": false,
"error": null,
"id": "nhbqzwzbrzd6xdyz343o57pkwa",
"input": {
"width": "448",
"height": "640",
"prompt": "analog style closeup portrait of <1> cowboy hat, sitting on a desk",
"lora_urls": "https://replicate.delivery/pbxt/IzbeguwVsW3PcC1gbiLy5SeALwk4sGgWroHagcYIn9I960bQA/tmpjlodd7vazekezip.safetensors",
"scheduler": "DPMSolverMultistep",
"lora_scales": "0.6",
"num_outputs": 1,
"adapter_type": "depth",
"guidance_scale": "6.5",
"negative_prompt": "blur, haze",
"prompt_strength": 0.8,
"num_inference_steps": 50,
"adapter_condition_image": "https://replicate.delivery/pbxt/IOpTvFxWf0Tiu8aOHcjBbXhucssJJ7wFFqkLP80snon5NtP7/depth_0.png"
},
"logs": "Using seed: 18066\nGenerating image of 448 x 640 with prompt: analog style closeup portrait of <1> cowboy hat, sitting on a desk\nThe requested LoRAs are loaded.\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:16, 3.01it/s]\n 4%|▍ | 2/50 [00:00<00:13, 3.52it/s]\n 6%|▌ | 3/50 [00:00<00:12, 3.71it/s]\n 8%|▊ | 4/50 [00:01<00:12, 3.81it/s]\n 10%|█ | 5/50 [00:01<00:11, 3.86it/s]\n 12%|█▏ | 6/50 [00:01<00:11, 3.89it/s]\n 14%|█▍ | 7/50 [00:01<00:10, 3.93it/s]\n 16%|█▌ | 8/50 [00:02<00:10, 3.94it/s]\n 18%|█▊ | 9/50 [00:02<00:10, 3.94it/s]\n 20%|██ | 10/50 [00:02<00:10, 3.96it/s]\n 22%|██▏ | 11/50 [00:02<00:09, 3.96it/s]\n 24%|██▍ | 12/50 [00:03<00:09, 3.96it/s]\n 26%|██▌ | 13/50 [00:03<00:09, 3.94it/s]\n 28%|██▊ | 14/50 [00:03<00:09, 3.96it/s]\n 30%|███ | 15/50 [00:03<00:08, 3.96it/s]\n 32%|███▏ | 16/50 [00:04<00:08, 3.96it/s]\n 34%|███▍ | 17/50 [00:04<00:08, 3.94it/s]\n 36%|███▌ | 18/50 [00:04<00:08, 3.96it/s]\n 38%|███▊ | 19/50 [00:04<00:07, 3.94it/s]\n 40%|████ | 20/50 [00:05<00:07, 3.97it/s]\n 42%|████▏ | 21/50 [00:05<00:07, 3.95it/s]\n 44%|████▍ | 22/50 [00:05<00:07, 3.96it/s]\n 46%|████▌ | 23/50 [00:05<00:06, 3.94it/s]\n 48%|████▊ | 24/50 [00:06<00:06, 3.96it/s]\n 50%|█████ | 25/50 [00:06<00:06, 3.94it/s]\n 52%|█████▏ | 26/50 [00:06<00:06, 3.95it/s]\n 54%|█████▍ | 27/50 [00:06<00:05, 3.95it/s]\n 56%|█████▌ | 28/50 [00:07<00:05, 3.95it/s]\n 58%|█████▊ | 29/50 [00:07<00:05, 3.95it/s]\n 60%|██████ | 30/50 [00:07<00:05, 3.95it/s]\n 62%|██████▏ | 31/50 [00:07<00:04, 3.94it/s]\n 64%|██████▍ | 32/50 [00:08<00:04, 3.95it/s]\n 66%|██████▌ | 33/50 [00:08<00:04, 3.95it/s]\n 68%|██████▊ | 34/50 [00:08<00:04, 3.95it/s]\n 70%|███████ | 35/50 [00:08<00:03, 3.94it/s]\n 72%|███████▏ | 36/50 [00:09<00:03, 3.94it/s]\n 74%|███████▍ | 37/50 [00:09<00:03, 3.94it/s]\n 76%|███████▌ | 38/50 [00:09<00:03, 3.93it/s]\n 78%|███████▊ | 39/50 [00:09<00:02, 3.94it/s]\n 80%|████████ | 40/50 [00:10<00:02, 3.93it/s]\n 82%|████████▏ | 41/50 [00:10<00:02, 3.93it/s]\n 84%|████████▍ | 42/50 [00:10<00:02, 3.92it/s]\n 86%|████████▌ | 43/50 [00:10<00:01, 3.94it/s]\n 88%|████████▊ | 44/50 [00:11<00:01, 3.93it/s]\n 90%|█████████ | 45/50 [00:11<00:01, 3.93it/s]\n 92%|█████████▏| 46/50 [00:11<00:01, 3.92it/s]\n 94%|█████████▍| 47/50 [00:11<00:00, 3.92it/s]\n 96%|█████████▌| 48/50 [00:12<00:00, 3.93it/s]\n 98%|█████████▊| 49/50 [00:12<00:00, 3.92it/s]\n100%|██████████| 50/50 [00:12<00:00, 3.92it/s]\n100%|██████████| 50/50 [00:12<00:00, 3.92it/s]",
"metrics": {
"predict_time": 14.441374,
"total_time": 14.523331
},
"output": [
"https://replicate.delivery/pbxt/yF7aZDG41n5YEFf9eDYgDS6HULdvT4l1XtUIi1AjetfScNNCB/out-0.png"
],
"started_at": "2023-03-01T16:56:22.683059Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/nhbqzwzbrzd6xdyz343o57pkwa",
"cancel": "https://api.replicate.com/v1/predictions/nhbqzwzbrzd6xdyz343o57pkwa/cancel"
},
"version": "b71669eeda89a08ce8294c3e79fa203417918161b961c6de3215fce20ff9bc87"
}
Using seed: 18066
Generating image of 448 x 640 with prompt: analog style closeup portrait of <1> cowboy hat, sitting on a desk
The requested LoRAs are loaded.
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