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  1. README.md +45 -0
  2. image_0.png +0 -0
  3. image_1.png +0 -0
  4. image_2.png +0 -0
  5. test_model_card.ipynb +211 -0
README.md ADDED
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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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+
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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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+
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+
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+ # controlnet-camaro/test
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+
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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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+
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+
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+
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+ ## Intended uses & limitations
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+
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+ #### How to use
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+
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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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+
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+ #### Limitations and bias
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+
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+ [TODO: provide examples of latent issues and potential remediations]
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+
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+ ## Training details
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+
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+ [TODO: describe the data used to train the model]
image_0.png ADDED
image_1.png ADDED
image_2.png ADDED
test_model_card.ipynb ADDED
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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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+ }