write_own_pipeline.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ryukijano/Self.Diffusion/blob/main/write_own_pipeline.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_0rKxycEGYRK"
      },
      "source": [
        "# Understanding pipelines, models and schedulers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qe0Uu32NGYRN"
      },
      "source": [
        "🧨 Diffusers is designed to be a user-friendly and flexible toolbox for building diffusion systems tailored to your use-case. At the core of the toolbox are models and schedulers. While the [DiffusionPipeline](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline) bundles these components together for convenience, you can also unbundle the pipeline and use the models and schedulers separately to create new diffusion systems. \n",
        "\n",
        "In this tutorial, you'll learn how to use models and schedulers to assemble a diffusion system for inference, starting with a basic pipeline and then progressing to the Stable Diffusion pipeline."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a9lpRRdOGYRO"
      },
      "source": [
        "## Deconstruct a basic pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "knC3tcKhGYRP"
      },
      "source": [
        "A pipeline is a quick and easy way to run a model for inference, requiring no more than four lines of code to generate an image:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install diffusers"
      ],
      "metadata": {
        "id": "Wu6NyexafW6S",
        "outputId": "ad1d7301-17b9-48e4-ab96-1453dc21c19c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting diffusers\n",
            "  Downloading diffusers-0.15.0-py3-none-any.whl (851 kB)\n",
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            "Installing collected packages: huggingface-hub, diffusers\n",
            "Successfully installed diffusers-0.15.0 huggingface-hub-0.13.4\n"
          ]
        }
      ]
    },
    {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "text": [
            "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \n",
            "```\n",
            "pip install accelerate\n",
            "```\n",
            ".\n"
          ]
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\n"
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "from diffusers import DDPMPipeline\n",
        "\n",
        "ddpm = DDPMPipeline.from_pretrained(\"google/ddpm-cat-256\").to(\"cuda\")\n",
        "image = ddpm(num_inference_steps=25).images[0]\n",
        "image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osETKFGoGYRQ"
      },
      "source": [
        "<div class=\"flex justify-center\">\n",
        "    <img src=\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ddpm-cat.png\" alt=\"Image of cat created from DDPMPipeline\"/>\n",
        "</div>\n",
        "\n",
        "That was super easy, but how did the pipeline do that? Let's breakdown the pipeline and take a look at what's happening under the hood.\n",
        "\n",
        "In the example above, the pipeline contains a UNet model and a DDPM scheduler. The pipeline denoises an image by taking random noise the size of the desired output and passing it through the model several times. At each timestep, the model predicts the *noise residual* and the scheduler uses it to predict a less noisy image. The pipeline repeats this process until it reaches the end of the specified number of inference steps. \n",
        "\n",
        "To recreate the pipeline with the model and scheduler separately, let's write our own denoising process.\n",
        "\n",
        "1. Load the model and scheduler:\n",
        "\n",
        "    ```py\n",
        "    >>> from diffusers import DDPMScheduler, UNet2DModel\n",
        "\n",
        "    >>> scheduler = DDPMScheduler.from_pretrained(\"google/ddpm-cat-256\")\n",
        "    >>> model = UNet2DModel.from_pretrained(\"google/ddpm-cat-256\").to(\"cuda\")\n",
        "    ```\n",
        "\n",
        "2. Set the number of timesteps to run the denoising process for:\n",
        "\n",
        "    ```py\n",
        "    >>> scheduler.set_timesteps(50)\n",
        "    ```\n",
        "\n",
        "3. Setting the scheduler timesteps creates a tensor with evenly spaced elements in it, 50 in this example. Each element corresponds to a timestep at which the model denoises an image. When you create the denoising loop later, you'll iterate over this tensor to denoise an image:\n",
        "\n",
        "    ```py\n",
        "    >>> scheduler.timesteps\n",
        "    tensor([980, 960, 940, 920, 900, 880, 860, 840, 820, 800, 780, 760, 740, 720,\n",
        "        700, 680, 660, 640, 620, 600, 580, 560, 540, 520, 500, 480, 460, 440,\n",
        "        420, 400, 380, 360, 340, 320, 300, 280, 260, 240, 220, 200, 180, 160,\n",
        "        140, 120, 100,  80,  60,  40,  20,   0])\n",
        "    ```\n",
        "\n",
        "4. Create some random noise with the same shape as the desired output:\n",
        "\n",
        "    ```py\n",
        "    >>> import torch\n",
        "\n",
        "    >>> sample_size = model.config.sample_size\n",
        "    >>> noise = torch.randn((1, 3, sample_size, sample_size)).to(\"cuda\")\n",
        "    ```\n",
        "\n",
        "4. Now write a loop to iterate over the timesteps. At each timestep, the model does a [UNet2DModel.forward()](https://huggingface.co/docs/diffusers/main/en/api/models#diffusers.UNet2DModel.forward) pass and returns the noisy residual. The scheduler's [step()](https://huggingface.co/docs/diffusers/main/en/api/schedulers/ddpm#diffusers.DDPMScheduler.step) method takes the noisy residual, timestep, and input and it predicts the image at the previous timestep. This output becomes the next input to the model in the denoising loop, and it'll repeat until it reaches the end of the `timesteps` array.\n",
        "\n",
        "    ```py\n",
        "    >>> input = noise\n",
        "\n",
        "    >>> for t in scheduler.timesteps:\n",
        "    ...     with torch.no_grad():\n",
        "    ...         noisy_residual = model(input, t).sample\n",
        "    >>> previous_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample\n",
        "    >>> input = previous_noisy_sample\n",
        "    ```\n",
        "\n",
        "    This is the entire denoising process, and you can use this same pattern to write any diffusion system.\n",
        "\n",
        "5. The last step is to convert the denoised output into an image:\n",
        "\n",
        "    ```py\n",
        "    >>> from PIL import Image\n",
        "    >>> import numpy as np\n",
        "\n",
        "    >>> image = (input / 2 + 0.5).clamp(0, 1)\n",
        "    >>> image = image.cpu().permute(0, 2, 3, 1).numpy()[0]\n",
        "    >>> image = Image.fromarray((image * 255)).round().astype(\"uint8\")\n",
        "    >>> image\n",
        "    ```\n",
        "\n",
        "In the next section, you'll put your skills to the test and breakdown the more complex Stable Diffusion pipeline. The steps are more or less the same. You'll initialize the necessary components, and set the number of timesteps to create a `timestep` array. The `timestep` array is used in the denoising loop, and for each element in this array, the model predicts a less noisy image. The denoising loop iterates over the `timestep`'s, and at each timestep, it outputs a noisy residual and the scheduler uses it to predict a less noisy image at the previous timestep. This process is repeated until you reach the end of the `timestep` array.\n",
        "\n",
        "Let's try it out!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nO-r52DaGYRR"
      },
      "source": [
        "## Deconstruct the Stable Diffusion pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AXWBw1vJGYRR"
      },
      "source": [
        "Stable Diffusion is a text-to-image *latent diffusion* model. It is called a latent diffusion model because it works with a lower-dimensional representation of the image instead of the actual pixel space, which makes it more memory efficient. The encoder compresses the image into a smaller representation, and a decoder to convert the compressed representation back into an image. For text-to-image models, you'll need a tokenizer and an encoder to generate text embeddings. From the previous example, you already know you need a UNet model and a scheduler.\n",
        "\n",
        "As you can see, this is already more complex than the DDPM pipeline which only contains a UNet model. The Stable Diffusion model has three separate pretrained models.\n",
        "\n",
        "<Tip>\n",
        "\n",
        "💡 Read the [How does Stable Diffusion work?](https://huggingface.co/blog/stable_diffusion#how-does-stable-diffusion-work) blog for more details about how the VAE, UNet, and text encoder models.\n",
        "\n",
        "</Tip>\n",
        "\n",
        "Now that you know what you need for the Stable Diffusion pipeline, load all these components with the [from_pretrained()](https://huggingface.co/docs/diffusers/main/en/api/models#diffusers.ModelMixin.from_pretrained) method. You can find them in the pretrained [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) checkpoint, and each component is stored in a separate subfolder:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GtATqXCFrR-u",
        "outputId": "d39d8b7d-0a7d-4d03-80d7-75b214bee936"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Installing collected packages: tokenizers, transformers\n",
            "Successfully installed tokenizers-0.13.3 transformers-4.28.1\n"
          ]
        }
      ]
    },
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
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            }
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          "metadata": {}
        },
        {
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          "text": [
            "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \n",
            "```\n",
            "pip install accelerate\n",
            "```\n",
            ".\n"
          ]
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          "metadata": {}
        },
        {
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          "name": "stderr",
          "text": [
            "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install `accelerate` for faster and less memory-intense model loading. You can do so with: \n",
            "```\n",
            "pip install accelerate\n",
            "```\n",
            ".\n"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)on_pytorch_model.bin:   0%|          | 0.00/3.44G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "81ec92d25dc44c93b4329dcce91a7c8e"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from PIL import Image\n",
        "import torch\n",
        "from transformers import CLIPTextModel, CLIPTokenizer\n",
        "from diffusers import AutoencoderKL, UNet2DConditionModel, PNDMScheduler\n",
        "\n",
        "vae = AutoencoderKL.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"vae\")\n",
        "tokenizer = CLIPTokenizer.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"tokenizer\")\n",
        "text_encoder = CLIPTextModel.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"text_encoder\")\n",
        "unet = UNet2DConditionModel.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"unet\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Me7KwJdAGYRS"
      },
      "source": [
        "Instead of the default [PNDMScheduler](https://huggingface.co/docs/diffusers/main/en/api/schedulers/pndm#diffusers.PNDMScheduler), exchange it for the [UniPCMultistepScheduler](https://huggingface.co/docs/diffusers/main/en/api/schedulers/unipc#diffusers.UniPCMultistepScheduler) to see how easy it is to plug a different scheduler in:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "IVmohuTLGYRT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "846a63fb46f840b38df46c7b47f8502e",
            "f5cdbf379fb043c9885b9fb3391c8caf",
            "b51e2a4185e848fe99d1c68a74333326",
            "721a2d024ec9421bb2d8f7e8d63c604d",
            "afb5622f9d674a38815d94c5b86bfc5c",
            "9a735c2e4fa949068b49895d4ff761b6",
            "1c4fb590b08149c590389ae86c8fdb7a",
            "7f4ad74d3a2d48119a6ca4d5ef7282d9",
            "98988fce075c4618b7cb414b35f0a6c3",
            "7b30502644fb4eaeb18e12ce7c50f351",
            "fbd4b27a3ab14de0bd94580d8f0f5477"
          ]
        },
        "outputId": "ec74c23e-590a-4522-ef1f-c03af9e51c54"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cheduler_config.json:   0%|          | 0.00/313 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "846a63fb46f840b38df46c7b47f8502e"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from diffusers import UniPCMultistepScheduler\n",
        "\n",
        "scheduler = UniPCMultistepScheduler.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"scheduler\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z-gA-jXIGYRT"
      },
      "source": [
        "To speed up inference, move the models to a GPU since, unlike the scheduler, they have trainable weights:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "e4hfgfGlGYRT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d759afaa-dea3-4c6d-e103-1b7b7c3f3678"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "UNet2DConditionModel(\n",
              "  (conv_in): Conv2d(4, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "  (time_proj): Timesteps()\n",
              "  (time_embedding): TimestepEmbedding(\n",
              "    (linear_1): Linear(in_features=320, out_features=1280, bias=True)\n",
              "    (act): SiLU()\n",
              "    (linear_2): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "  )\n",
              "  (down_blocks): ModuleList(\n",
              "    (0): CrossAttnDownBlock2D(\n",
              "      (attentions): ModuleList(\n",
              "        (0-1): 2 x Transformer2DModel(\n",
              "          (norm): GroupNorm(32, 320, eps=1e-06, affine=True)\n",
              "          (proj_in): Conv2d(320, 320, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (transformer_blocks): ModuleList(\n",
              "            (0): BasicTransformerBlock(\n",
              "              (norm1): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
              "              (attn1): Attention(\n",
              "                (to_q): Linear(in_features=320, out_features=320, bias=False)\n",
              "                (to_k): Linear(in_features=320, out_features=320, bias=False)\n",
              "                (to_v): Linear(in_features=320, out_features=320, bias=False)\n",
              "                (to_out): ModuleList(\n",
              "                  (0): Linear(in_features=320, out_features=320, bias=True)\n",
              "                  (1): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (norm2): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
              "              (attn2): Attention(\n",
              "                (to_q): Linear(in_features=320, out_features=320, bias=False)\n",
              "                (to_k): Linear(in_features=768, out_features=320, bias=False)\n",
              "                (to_v): Linear(in_features=768, out_features=320, bias=False)\n",
              "                (to_out): ModuleList(\n",
              "                  (0): Linear(in_features=320, out_features=320, bias=True)\n",
              "                  (1): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (norm3): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
              "              (ff): FeedForward(\n",
              "                (net): ModuleList(\n",
              "                  (0): GEGLU(\n",
              "                    (proj): Linear(in_features=320, out_features=2560, bias=True)\n",
              "                  )\n",
              "                  (1): Dropout(p=0.0, inplace=False)\n",
              "                  (2): Linear(in_features=1280, out_features=320, bias=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (proj_out): Conv2d(320, 320, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "      )\n",
              "      (resnets): ModuleList(\n",
              "        (0-1): 2 x ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=1280, out_features=320, bias=True)\n",
              "          (norm2): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "        )\n",
              "      )\n",
              "      (downsamplers): ModuleList(\n",
              "        (0): Downsample2D(\n",
              "          (conv): Conv2d(320, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (1): CrossAttnDownBlock2D(\n",
              "      (attentions): ModuleList(\n",
              "        (0-1): 2 x Transformer2DModel(\n",
              "          (norm): GroupNorm(32, 640, eps=1e-06, affine=True)\n",
              "          (proj_in): Conv2d(640, 640, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (transformer_blocks): ModuleList(\n",
              "            (0): BasicTransformerBlock(\n",
              "              (norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
              "              (attn1): Attention(\n",
              "                (to_q): Linear(in_features=640, out_features=640, bias=False)\n",
              "                (to_k): Linear(in_features=640, out_features=640, bias=False)\n",
              "                (to_v): Linear(in_features=640, out_features=640, bias=False)\n",
              "                (to_out): ModuleList(\n",
              "                  (0): Linear(in_features=640, out_features=640, bias=True)\n",
              "                  (1): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
              "              (attn2): Attention(\n",
              "                (to_q): Linear(in_features=640, out_features=640, bias=False)\n",
              "                (to_k): Linear(in_features=768, out_features=640, bias=False)\n",
              "                (to_v): Linear(in_features=768, out_features=640, bias=False)\n",
              "                (to_out): ModuleList(\n",
              "                  (0): Linear(in_features=640, out_features=640, bias=True)\n",
              "                  (1): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
              "              (ff): FeedForward(\n",
              "                (net): ModuleList(\n",
              "                  (0): GEGLU(\n",
              "                    (proj): Linear(in_features=640, out_features=5120, bias=True)\n",
              "                  )\n",
              "                  (1): Dropout(p=0.0, inplace=False)\n",
              "                  (2): Linear(in_features=2560, out_features=640, bias=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (proj_out): Conv2d(640, 640, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "      )\n",
              "      (resnets): ModuleList(\n",
              "        (0): ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(320, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\n",
              "          (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "          (conv_shortcut): Conv2d(320, 640, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (1): ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\n",
              "          (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "        )\n",
              "      )\n",
              "      (downsamplers): ModuleList(\n",
              "        (0): Downsample2D(\n",
              "          (conv): Conv2d(640, 640, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (2): CrossAttnDownBlock2D(\n",
              "      (attentions): ModuleList(\n",
              "        (0-1): 2 x Transformer2DModel(\n",
              "          (norm): GroupNorm(32, 1280, eps=1e-06, affine=True)\n",
              "          (proj_in): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (transformer_blocks): ModuleList(\n",
              "            (0): BasicTransformerBlock(\n",
              "              (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (attn1): Attention(\n",
              "                (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
              "                (to_k): Linear(in_features=1280, out_features=1280, bias=False)\n",
              "                (to_v): Linear(in_features=1280, out_features=1280, bias=False)\n",
              "                (to_out): ModuleList(\n",
              "                  (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "                  (1): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (attn2): Attention(\n",
              "                (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
              "                (to_k): Linear(in_features=768, out_features=1280, bias=False)\n",
              "                (to_v): Linear(in_features=768, out_features=1280, bias=False)\n",
              "                (to_out): ModuleList(\n",
              "                  (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "                  (1): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (ff): FeedForward(\n",
              "                (net): ModuleList(\n",
              "                  (0): GEGLU(\n",
              "                    (proj): Linear(in_features=1280, out_features=10240, bias=True)\n",
              "                  )\n",
              "                  (1): Dropout(p=0.0, inplace=False)\n",
              "                  (2): Linear(in_features=5120, out_features=1280, bias=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (proj_out): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "      )\n",
              "      (resnets): ModuleList(\n",
              "        (0): ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(640, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "          (conv_shortcut): Conv2d(640, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (1): ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "        )\n",
              "      )\n",
              "      (downsamplers): ModuleList(\n",
              "        (0): Downsample2D(\n",
              "          (conv): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (3): DownBlock2D(\n",
              "      (resnets): ModuleList(\n",
              "        (0-1): 2 x ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (up_blocks): ModuleList(\n",
              "    (0): UpBlock2D(\n",
              "      (resnets): ModuleList(\n",
              "        (0-2): 3 x ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 2560, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(2560, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "          (conv_shortcut): Conv2d(2560, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "      )\n",
              "      (upsamplers): ModuleList(\n",
              "        (0): Upsample2D(\n",
              "          (conv): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (1): CrossAttnUpBlock2D(\n",
              "      (attentions): ModuleList(\n",
              "        (0-2): 3 x Transformer2DModel(\n",
              "          (norm): GroupNorm(32, 1280, eps=1e-06, affine=True)\n",
              "          (proj_in): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (transformer_blocks): ModuleList(\n",
              "            (0): BasicTransformerBlock(\n",
              "              (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (attn1): Attention(\n",
              "                (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
              "                (to_k): Linear(in_features=1280, out_features=1280, bias=False)\n",
              "                (to_v): Linear(in_features=1280, out_features=1280, bias=False)\n",
              "                (to_out): ModuleList(\n",
              "                  (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "                  (1): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (attn2): Attention(\n",
              "                (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
              "                (to_k): Linear(in_features=768, out_features=1280, bias=False)\n",
              "                (to_v): Linear(in_features=768, out_features=1280, bias=False)\n",
              "                (to_out): ModuleList(\n",
              "                  (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "                  (1): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "              (ff): FeedForward(\n",
              "                (net): ModuleList(\n",
              "                  (0): GEGLU(\n",
              "                    (proj): Linear(in_features=1280, out_features=10240, bias=True)\n",
              "                  )\n",
              "                  (1): Dropout(p=0.0, inplace=False)\n",
              "                  (2): Linear(in_features=5120, out_features=1280, bias=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (proj_out): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "      )\n",
              "      (resnets): ModuleList(\n",
              "        (0-1): 2 x ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 2560, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(2560, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "          (conv_shortcut): Conv2d(2560, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (2): ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 1920, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(1920, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "          (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "          (conv_shortcut): Conv2d(1920, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "      )\n",
              "      (upsamplers): ModuleList(\n",
              "        (0): Upsample2D(\n",
              "          (conv): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (2): CrossAttnUpBlock2D(\n",
              "      (attentions): ModuleList(\n",
              "        (0-2): 3 x Transformer2DModel(\n",
              "          (norm): GroupNorm(32, 640, eps=1e-06, affine=True)\n",
              "          (proj_in): Conv2d(640, 640, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (transformer_blocks): ModuleList(\n",
              "            (0): BasicTransformerBlock(\n",
              "              (norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
              "              (attn1): Attention(\n",
              "                (to_q): Linear(in_features=640, out_features=640, bias=False)\n",
              "                (to_k): Linear(in_features=640, out_features=640, bias=False)\n",
              "                (to_v): Linear(in_features=640, out_features=640, bias=False)\n",
              "                (to_out): ModuleList(\n",
              "                  (0): Linear(in_features=640, out_features=640, bias=True)\n",
              "                  (1): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
              "              (attn2): Attention(\n",
              "                (to_q): Linear(in_features=640, out_features=640, bias=False)\n",
              "                (to_k): Linear(in_features=768, out_features=640, bias=False)\n",
              "                (to_v): Linear(in_features=768, out_features=640, bias=False)\n",
              "                (to_out): ModuleList(\n",
              "                  (0): Linear(in_features=640, out_features=640, bias=True)\n",
              "                  (1): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True)\n",
              "              (ff): FeedForward(\n",
              "                (net): ModuleList(\n",
              "                  (0): GEGLU(\n",
              "                    (proj): Linear(in_features=640, out_features=5120, bias=True)\n",
              "                  )\n",
              "                  (1): Dropout(p=0.0, inplace=False)\n",
              "                  (2): Linear(in_features=2560, out_features=640, bias=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (proj_out): Conv2d(640, 640, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "      )\n",
              "      (resnets): ModuleList(\n",
              "        (0): ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 1920, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(1920, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\n",
              "          (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "          (conv_shortcut): Conv2d(1920, 640, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (1): ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(1280, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\n",
              "          (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "          (conv_shortcut): Conv2d(1280, 640, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (2): ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 960, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(960, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=1280, out_features=640, bias=True)\n",
              "          (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "          (conv_shortcut): Conv2d(960, 640, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "      )\n",
              "      (upsamplers): ModuleList(\n",
              "        (0): Upsample2D(\n",
              "          (conv): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (3): CrossAttnUpBlock2D(\n",
              "      (attentions): ModuleList(\n",
              "        (0-2): 3 x Transformer2DModel(\n",
              "          (norm): GroupNorm(32, 320, eps=1e-06, affine=True)\n",
              "          (proj_in): Conv2d(320, 320, kernel_size=(1, 1), stride=(1, 1))\n",
              "          (transformer_blocks): ModuleList(\n",
              "            (0): BasicTransformerBlock(\n",
              "              (norm1): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
              "              (attn1): Attention(\n",
              "                (to_q): Linear(in_features=320, out_features=320, bias=False)\n",
              "                (to_k): Linear(in_features=320, out_features=320, bias=False)\n",
              "                (to_v): Linear(in_features=320, out_features=320, bias=False)\n",
              "                (to_out): ModuleList(\n",
              "                  (0): Linear(in_features=320, out_features=320, bias=True)\n",
              "                  (1): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (norm2): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
              "              (attn2): Attention(\n",
              "                (to_q): Linear(in_features=320, out_features=320, bias=False)\n",
              "                (to_k): Linear(in_features=768, out_features=320, bias=False)\n",
              "                (to_v): Linear(in_features=768, out_features=320, bias=False)\n",
              "                (to_out): ModuleList(\n",
              "                  (0): Linear(in_features=320, out_features=320, bias=True)\n",
              "                  (1): Dropout(p=0.0, inplace=False)\n",
              "                )\n",
              "              )\n",
              "              (norm3): LayerNorm((320,), eps=1e-05, elementwise_affine=True)\n",
              "              (ff): FeedForward(\n",
              "                (net): ModuleList(\n",
              "                  (0): GEGLU(\n",
              "                    (proj): Linear(in_features=320, out_features=2560, bias=True)\n",
              "                  )\n",
              "                  (1): Dropout(p=0.0, inplace=False)\n",
              "                  (2): Linear(in_features=1280, out_features=320, bias=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (proj_out): Conv2d(320, 320, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "      )\n",
              "      (resnets): ModuleList(\n",
              "        (0): ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 960, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(960, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=1280, out_features=320, bias=True)\n",
              "          (norm2): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "          (conv_shortcut): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "        (1-2): 2 x ResnetBlock2D(\n",
              "          (norm1): GroupNorm(32, 640, eps=1e-05, affine=True)\n",
              "          (conv1): Conv2d(640, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (time_emb_proj): Linear(in_features=1280, out_features=320, bias=True)\n",
              "          (norm2): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
              "          (dropout): Dropout(p=0.0, inplace=False)\n",
              "          (conv2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "          (nonlinearity): SiLU()\n",
              "          (conv_shortcut): Conv2d(640, 320, kernel_size=(1, 1), stride=(1, 1))\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (mid_block): UNetMidBlock2DCrossAttn(\n",
              "    (attentions): ModuleList(\n",
              "      (0): Transformer2DModel(\n",
              "        (norm): GroupNorm(32, 1280, eps=1e-06, affine=True)\n",
              "        (proj_in): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
              "        (transformer_blocks): ModuleList(\n",
              "          (0): BasicTransformerBlock(\n",
              "            (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn1): Attention(\n",
              "              (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
              "              (to_k): Linear(in_features=1280, out_features=1280, bias=False)\n",
              "              (to_v): Linear(in_features=1280, out_features=1280, bias=False)\n",
              "              (to_out): ModuleList(\n",
              "                (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "                (1): Dropout(p=0.0, inplace=False)\n",
              "              )\n",
              "            )\n",
              "            (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "            (attn2): Attention(\n",
              "              (to_q): Linear(in_features=1280, out_features=1280, bias=False)\n",
              "              (to_k): Linear(in_features=768, out_features=1280, bias=False)\n",
              "              (to_v): Linear(in_features=768, out_features=1280, bias=False)\n",
              "              (to_out): ModuleList(\n",
              "                (0): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "                (1): Dropout(p=0.0, inplace=False)\n",
              "              )\n",
              "            )\n",
              "            (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
              "            (ff): FeedForward(\n",
              "              (net): ModuleList(\n",
              "                (0): GEGLU(\n",
              "                  (proj): Linear(in_features=1280, out_features=10240, bias=True)\n",
              "                )\n",
              "                (1): Dropout(p=0.0, inplace=False)\n",
              "                (2): Linear(in_features=5120, out_features=1280, bias=True)\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "        )\n",
              "        (proj_out): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))\n",
              "      )\n",
              "    )\n",
              "    (resnets): ModuleList(\n",
              "      (0-1): 2 x ResnetBlock2D(\n",
              "        (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
              "        (conv1): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (time_emb_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
              "        (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)\n",
              "        (dropout): Dropout(p=0.0, inplace=False)\n",
              "        (conv2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "        (nonlinearity): SiLU()\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (conv_norm_out): GroupNorm(32, 320, eps=1e-05, affine=True)\n",
              "  (conv_act): SiLU()\n",
              "  (conv_out): Conv2d(320, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "torch_device = \"cuda\"\n",
        "vae.to(torch_device)\n",
        "text_encoder.to(torch_device)\n",
        "unet.to(torch_device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SDFFk9CQGYRT"
      },
      "source": [
        "### Create text embeddings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wmdCj2pZGYRT"
      },
      "source": [
        "The next step is to tokenize the text to generate embeddings. The text is used to condition the UNet model and steer the diffusion process towards something that resembles the input prompt. \n",
        "\n",
        "<Tip>\n",
        "\n",
        "💡 The `guidance_scale` parameter determines how much weight should be given to the prompt when generating an image.\n",
        "\n",
        "</Tip>\n",
        "\n",
        "Feel free to choose any prompt you like if you want to generate something else!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "uBS0dYbbGYRU"
      },
      "outputs": [],
      "source": [
        "prompt = [\"red dead redemption 2\"]\n",
        "height = 512  # default height of Stable Diffusion\n",
        "width = 512  # default width of Stable Diffusion\n",
        "num_inference_steps = 25  # Number of denoising steps\n",
        "guidance_scale = 7.5  # Scale for classifier-free guidance\n",
        "generator = torch.manual_seed(0)  # Seed generator to create the inital latent noise\n",
        "batch_size = len(prompt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EYyuTxhfGYRU"
      },
      "source": [
        "Tokenize the text and generate the embeddings from the prompt:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "kFFnX6KjGYRU"
      },
      "outputs": [],
      "source": [
        "text_input = tokenizer(\n",
        "    prompt, padding=\"max_length\", max_length=tokenizer.model_max_length, truncation=True, return_tensors=\"pt\"\n",
        ")\n",
        "\n",
        "with torch.no_grad():\n",
        "    text_embeddings = text_encoder(text_input.input_ids.to(torch_device))[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3TKwAwQzGYRU"
      },
      "source": [
        "You'll also need to generate the *unconditional text embeddings* which are the embeddings for the padding token. These need to have the same shape (`batch_size` and `seq_length`) as the conditional `text_embeddings`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "9Dlqb1ylGYRU"
      },
      "outputs": [],
      "source": [
        "max_length = text_input.input_ids.shape[-1]\n",
        "uncond_input = tokenizer([\"\"] * batch_size, padding=\"max_length\", max_length=max_length, return_tensors=\"pt\")\n",
        "uncond_embeddings = text_encoder(uncond_input.input_ids.to(torch_device))[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q4tUOroZGYRU"
      },
      "source": [
        "Let's concatenate the conditional and unconditional embeddings into a batch to avoid doing two forward passes:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "uQdzJ3A1GYRU"
      },
      "outputs": [],
      "source": [
        "text_embeddings = torch.cat([uncond_embeddings, text_embeddings])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PMbfveTFGYRV"
      },
      "source": [
        "### Create random noise"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mwhzGbgAGYRV"
      },
      "source": [
        "Next, generate some initial random noise as a starting point for the diffusion process. This is the latent representation of the image, and it'll be gradually denoised. At this point, the `latent` image is smaller than the final image size but that's okay though because the model will transform it into the final 512x512 image dimensions later.\n",
        "\n",
        "<Tip>\n",
        "\n",
        "💡 The height and width are divided by 8 because the `vae` model has 3 down-sampling layers. You can check by running the following:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "tma7oF_-GYRV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c0a15d5-c9d1-4ff3-ae6c-622b4640c2b5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "2 ** (len(vae.config.block_out_channels) - 1) == 8"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gruo3l-BGYRV"
      },
      "source": [
        "</Tip>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "x2-SYY-qGYRW"
      },
      "outputs": [],
      "source": [
        "latents = torch.randn(\n",
        "    (batch_size, unet.in_channels, height // 8, width // 8),\n",
        "    generator=generator,\n",
        ")\n",
        "latents = latents.to(torch_device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XRXmhv1qGYRY"
      },
      "source": [
        "### Denoise the image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mz79_FGPGYRY"
      },
      "source": [
        "Start by scaling the input with the initial noise distribution, *sigma*, the noise scale value, which is required for improved schedulers like [UniPCMultistepScheduler](https://huggingface.co/docs/diffusers/main/en/api/schedulers/unipc#diffusers.UniPCMultistepScheduler):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "uIM7rVwhGYRY"
      },
      "outputs": [],
      "source": [
        "latents = latents * scheduler.init_noise_sigma"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0xoEy0kMGYRZ"
      },
      "source": [
        "The last step is to create the denoising loop that'll progressively transform the pure noise in `latents` to an image described by your prompt. Remember, the denoising loop needs to do three things:\n",
        "\n",
        "1. Set the scheduler's timesteps to use during denoising.\n",
        "2. Iterate over the timesteps.\n",
        "3. At each timestep, call the UNet model to predict the noise residual and pass it to the scheduler to compute the previous noisy sample."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "mmWCiyoOGYRZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "4954d68b4b914db29869bf33042734ed",
            "f126f1d1aa1c4581a5ea2096c73b875e",
            "08b661f1aeb64ac5b39b3ad9d9ebf624",
            "1784138e09274b7cb87cb6431c5ac788",
            "06317185518b45cf94e3345212497145",
            "c5bc24c6f99c441da242c12172ba20ad",
            "88af1cef08dc47c1bd95f7010713e2e7",
            "9d0ee5de847246e6885690b5b66bca0f",
            "19d922b8fe3c4383b11222e6420615d2",
            "f82f37dd5221408187cd06627745459d",
            "5285813543c14e5eabbf519a61b7d44f"
          ]
        },
        "outputId": "d06deb8b-25a9-49d0-c581-36f5e9341f44"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/25 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4954d68b4b914db29869bf33042734ed"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from tqdm.auto import tqdm\n",
        "\n",
        "scheduler.set_timesteps(num_inference_steps)\n",
        "\n",
        "for t in tqdm(scheduler.timesteps):\n",
        "    # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.\n",
        "    latent_model_input = torch.cat([latents] * 2)\n",
        "\n",
        "    latent_model_input = scheduler.scale_model_input(latent_model_input, timestep=t)\n",
        "\n",
        "    # predict the noise residual\n",
        "    with torch.no_grad():\n",
        "        noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample\n",
        "\n",
        "    # perform guidance\n",
        "    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n",
        "    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)\n",
        "\n",
        "    # compute the previous noisy sample x_t -> x_t-1\n",
        "    latents = scheduler.step(noise_pred, t, latents).prev_sample"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u5TS2137GYRZ"
      },
      "source": [
        "### Decode the image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dsP0n6aqGYRZ"
      },
      "source": [
        "The final step is to use the `vae` to decode the latent representation into an image and get the decoded output with `sample`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "Ei8s3p-XGYRZ"
      },
      "outputs": [],
      "source": [
        "# scale and decode the image latents with vae\n",
        "latents = 1 / 0.18215 * latents\n",
        "with torch.no_grad():\n",
        "    image = vae.decode(latents).sample"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Ofh-oBmGYRZ"
      },
      "source": [
        "Lastly, convert the image to a `PIL.Image` to see your generated image!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "UVfMh1vHGYRa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "outputId": "e9e24ab9-e15f-480f-bcb1-50230c79063a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=512x512 at 0x7F7E88C06070>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "image = (image / 2 + 0.5).clamp(0, 1)\n",
        "image = image.detach().cpu().permute(0, 2, 3, 1).numpy()\n",
        "images = (image * 255).round().astype(\"uint8\")\n",
        "pil_images = [Image.fromarray(image) for image in images]\n",
        "pil_images[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Dpyi4D7GYRa"
      },
      "source": [
        "<div class=\"flex justify-center\">\n",
        "    <img src=\"https://huggingface.co/blog/assets/98_stable_diffusion/stable_diffusion_k_lms.png\"/>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xmARN6HbGYRa"
      },
      "source": [
        "## Next steps"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bXyjJukeGYRa"
      },
      "source": [
        "From basic to complex pipelines, you've seen that all you really need to write your own diffusion system is a denoising loop. The loop should set the scheduler's timesteps, iterate over them, and alternate between calling the UNet model to predict the noise residual and passing it to the scheduler to compute the previous noisy sample. \n",
        "\n",
        "This is really what 🧨 Diffusers is designed for: to make it intuitive and easy to write your own diffusion system using models and schedulers.\n",
        "\n",
        "For your next steps, feel free to:\n",
        "\n",
        "* Learn how to [build and contribute a pipeline](https://huggingface.co/docs/diffusers/main/en/using-diffusers/using-diffusers/#contribute_pipeline) to 🧨 Diffusers. We can't wait and see what you'll come up with!\n",
        "* Explore [existing pipelines](https://huggingface.co/docs/diffusers/main/en/using-diffusers/./api/pipelines/overview) in the library, and see if you can deconstruct and build a pipeline from scratch using the models and schedulers separately."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "language_info": {
      "name": "python"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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