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Browse files- README.md +45 -0
- image_0.png +0 -0
- image_1.png +0 -0
- image_2.png +0 -0
- test_model_card.ipynb +211 -0
README.md
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---
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license: creativeml-openrail-m
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library_name: diffusers
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tags:
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- stable-diffusion
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- stable-diffusion-diffusers
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- text-to-image
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- diffusers
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- controlnet
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inference: true
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base_model: runwayml/stable-diffusion-v1-5
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---
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<!-- This model card has been generated automatically according to the information the training script had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# controlnet-camaro/test
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These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
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You can find some example images below.
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prompt: validation_prompt
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![images_0)](./images_0.png)
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prompt: validation_prompt
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![images_1)](./images_1.png)
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prompt: validation_prompt
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![images_2)](./images_2.png)
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## Intended uses & limitations
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#### How to use
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```python
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# TODO: add an example code snippet for running this diffusion pipeline
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```
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#### Limitations and bias
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[TODO: provide examples of latent issues and potential remediations]
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## Training details
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[TODO: describe the data used to train the model]
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image_0.png
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image_1.png
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image_2.png
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test_model_card.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"source": [
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"!git clone https://github.com/bamps53/diffusers\n",
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"%cd diffusers\n",
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"!git checkout 2b8f0991f46749f22aa1394f19a80275d27d3fb9\n",
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"!pip install -e \".[dev]\""
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],
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"metadata": {
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"id": "ZNgWSJQ-BViA",
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"outputId": "3956d806-fc52-467d-e73f-a09961ed147d",
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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},
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"id": "ZNgWSJQ-BViA",
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"execution_count": 1,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"/content/diffusers\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"!wget https://huggingface.co/sayakpaul/test-model-card-template-dreambooth/resolve/main/image_0.png\n",
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"!wget https://huggingface.co/sayakpaul/test-model-card-template-dreambooth/resolve/main/image_1.png\n",
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"!wget https://huggingface.co/sayakpaul/test-model-card-template-dreambooth/resolve/main/image_2.png"
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],
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"metadata": {
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"id": "pdpDJAul40ZB",
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"outputId": "d6dbe2fa-c611-48dc-ed28-af4341f0e33e",
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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},
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"id": "pdpDJAul40ZB",
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"execution_count": 8,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"--2024-02-08 13:23:49-- https://huggingface.co/sayakpaul/test-model-card-template-dreambooth/resolve/main/image_1.png\n",
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"Resolving huggingface.co (huggingface.co)... 18.67.181.126, 18.67.181.36, 18.67.181.124, ...\n",
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"Connecting to huggingface.co (huggingface.co)|18.67.181.126|:443... connected.\n",
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"HTTP request sent, awaiting response... 200 OK\n",
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"Length: 386612 (378K) [image/png]\n",
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"Saving to: ‘image_1.png’\n",
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"\n",
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"image_1.png 100%[===================>] 377.55K 501KB/s in 0.8s \n",
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"\n",
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"2024-02-08 13:23:50 (501 KB/s) - ‘image_1.png’ saved [386612/386612]\n",
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"\n",
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"--2024-02-08 13:23:51-- https://huggingface.co/sayakpaul/test-model-card-template-dreambooth/resolve/main/image_2.png\n",
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"Resolving huggingface.co (huggingface.co)... 18.67.181.126, 18.67.181.36, 18.67.181.124, ...\n",
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"Connecting to huggingface.co (huggingface.co)|18.67.181.126|:443... connected.\n",
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"HTTP request sent, awaiting response... 200 OK\n",
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"Length: 386612 (378K) [image/png]\n",
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"Saving to: ‘image_2.png’\n",
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"\n",
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"image_2.png 100%[===================>] 377.55K 503KB/s in 0.8s \n",
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"\n",
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"2024-02-08 13:23:52 (503 KB/s) - ‘image_2.png’ saved [386612/386612]\n",
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"\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"from diffusers.utils import load_image\n",
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"from examples.controlnet.train_controlnet import save_model_card\n",
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"\n",
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"images = [load_image(f\"image_{i}.png\") for i in range(3)]\n",
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"\n",
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"image_logs = [\n",
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" dict(\n",
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" images=[image],\n",
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" validation_prompt=\"validation_prompt\",\n",
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" validation_image=image,\n",
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" )\n",
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" for image in images\n",
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"]\n",
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"save_model_card(\n",
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" repo_id=\"camaro/test\",\n",
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" image_logs=image_logs,\n",
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" base_model=\"runwayml/stable-diffusion-v1-5\",\n",
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" repo_folder=\".\",\n",
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")"
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],
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"metadata": {
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"id": "v7vs7eQyDeX1"
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},
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"id": "v7vs7eQyDeX1",
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"execution_count": 9,
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"outputs": []
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "b19ff19c-fff0-469f-b004-dd731edbffa9",
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"metadata": {
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"id": "b19ff19c-fff0-469f-b004-dd731edbffa9",
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"outputId": "b8682622-e0ce-4015-91a3-2e5b018f1c4f",
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"---\n",
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"license: creativeml-openrail-m\n",
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"library_name: diffusers\n",
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"tags:\n",
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"- stable-diffusion\n",
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"- stable-diffusion-diffusers\n",
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"- text-to-image\n",
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"- diffusers\n",
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"- controlnet\n",
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"inference: true\n",
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"base_model: runwayml/stable-diffusion-v1-5\n",
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"---\n",
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"\n",
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"<!-- This model card has been generated automatically according to the information the training script had access to. You\n",
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"should probably proofread and complete it, then remove this comment. -->\n",
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"\n",
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"\n",
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"# controlnet-camaro/test\n",
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"\n",
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"These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.\n",
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"You can find some example images below.\n",
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"prompt: validation_prompt\n",
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"![images_0)](./images_0.png)\n",
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"prompt: validation_prompt\n",
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"![images_1)](./images_1.png)\n",
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"prompt: validation_prompt\n",
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"![images_2)](./images_2.png)\n",
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"\n",
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"\n",
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"\n",
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"## Intended uses & limitations\n",
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"\n",
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"#### How to use\n",
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"\n",
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"```python\n",
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"# TODO: add an example code snippet for running this diffusion pipeline\n",
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"```\n",
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"\n",
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"#### Limitations and bias\n",
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"\n",
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"[TODO: provide examples of latent issues and potential remediations]\n",
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"\n",
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"## Training details\n",
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"\n",
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"[TODO: describe the data used to train the model]"
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]
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}
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],
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"source": [
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"!cat README.md"
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]
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},
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{
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"cell_type": "code",
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"source": [],
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"metadata": {
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"id": "Y3uTj1bv5Amw"
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},
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"id": "Y3uTj1bv5Amw",
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"execution_count": null,
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"outputs": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.17"
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},
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"colab": {
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"provenance": [],
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"gpuType": "T4"
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},
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"accelerator": "GPU"
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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