machine-learning/stable-diffusion-models/GenerateImagesFromText_StableDiffusion_PythonCodeTutorial.ipynb

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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZgIU4Ga56Tiq",
        "outputId": "764ce650-379a-4bed-d5fb-b5052af024c9"
      },
      "outputs": [],
      "source": [
        "%pip install --quiet --upgrade diffusers transformers accelerate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S919oAK46Z8x",
        "outputId": "74fe51b4-157d-48a0-9067-6947e2a71bb8"
      },
      "outputs": [],
      "source": [
        "# The xformers package is mandatory to be able to create several 768x768 images.\n",
        "%pip install -q xformers==0.0.16rc425"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dn2_-E5Sa9Rn"
      },
      "source": [
        "# Using Dreamlike Photoreal"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WGIvJ0hE6Z_B"
      },
      "outputs": [],
      "source": [
        "from diffusers import StableDiffusionPipeline\n",
        "import torch"
      ]
    },
    {
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        },
        "id": "JzcSCwsF6aBT",
        "outputId": "1f223f71-54ed-49fe-8cb5-dcfc183a7c3f"
      },
      "outputs": [],
      "source": [
        "model_id = \"dreamlike-art/dreamlike-photoreal-2.0\"\n",
        "pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\n",
        "pipe = pipe.to(\"cuda\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sz6SRmjd6pBb"
      },
      "outputs": [],
      "source": [
        "prompts = [\"Cute Rabbit, Ultra HD, realistic, futuristic, sharp, octane render, photoshopped, photorealistic, soft, pastel, Aesthetic, Magical background\",\n",
        "           \"Anime style aesthetic landscape, 90's vintage style, digital art, ultra HD, 8k, photoshopped, sharp focus, surrealism, akira style, detailed line art\",\n",
        "           \"Beautiful, abstract art of a human mind, 3D, highly detailed, 8K, aesthetic\"]\n",
        "\n",
        "images = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
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        "id": "ovvyensy6pDl",
        "outputId": "2b0269af-4978-4a8b-eea9-96c12401dc62"
      },
      "outputs": [],
      "source": [
        "for i, prompt in enumerate(prompts):\n",
        "    image = pipe(prompt).images[0]\n",
        "    image.save(f'result_{i}.jpg')\n",
        "    images.append(image)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "id": "vd532OSA8Md7",
        "outputId": "a8ddd5b1-376b-4036-d87d-af9dc71c88e0"
      },
      "outputs": [],
      "source": [
        "images[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "id": "ZpVbvylE8OEt",
        "outputId": "5a577720-b68e-4657-9cbb-4112287afa23"
      },
      "outputs": [],
      "source": [
        "images[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "id": "R1DNPbbz8PU-",
        "outputId": "893bb392-96f0-4106-e3d7-f6def830ede1"
      },
      "outputs": [],
      "source": [
        "images[2]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jd-5c7bouD-_"
      },
      "source": [
        "# Manually working with the different components"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "01bGNP1n6aF4"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import autocast\n",
        "import numpy as np\n",
        "\n",
        "from transformers import CLIPTextModel, CLIPTokenizer\n",
        "\n",
        "from diffusers import AutoencoderKL\n",
        "from diffusers import LMSDiscreteScheduler\n",
        "from diffusers import UNet2DConditionModel\n",
        "from diffusers.schedulers.scheduling_ddim import DDIMScheduler\n",
        "\n",
        "from tqdm import tqdm\n",
        "from PIL import Image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3yBgKeUs8LWU"
      },
      "outputs": [],
      "source": [
        "class ImageDiffusionModel:\n",
        "\n",
        "    def __init__(self, vae, tokenizer, text_encoder, unet, \n",
        "                 scheduler_LMS, scheduler_DDIM):\n",
        "        self.vae = vae\n",
        "        self.tokenizer = tokenizer\n",
        "        self.text_encoder = text_encoder\n",
        "        self.unet = unet\n",
        "        self.scheduler_LMS = scheduler_LMS\n",
        "        self.scheduler_DDIM = scheduler_DDIM\n",
        "        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "    \n",
        "    \n",
        "    def get_text_embeds(self, text):\n",
        "        # tokenize the text\n",
        "        text_input = self.tokenizer(text, \n",
        "                                    padding='max_length', \n",
        "                                    max_length=tokenizer.model_max_length, \n",
        "                                    truncation=True, \n",
        "                                    return_tensors='pt')\n",
        "        # embed the text\n",
        "        with torch.no_grad():\n",
        "            text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0]\n",
        "\n",
        "        return text_embeds\n",
        "\n",
        "    def get_prompt_embeds(self, prompt):\n",
        "        # get conditional prompt embeddings\n",
        "        cond_embeds = self.get_text_embeds(prompt)\n",
        "        # get unconditional prompt embeddings\n",
        "        uncond_embeds = self.get_text_embeds([''] * len(prompt))\n",
        "        # concatenate the above 2 embeds\n",
        "        prompt_embeds = torch.cat([uncond_embeds, cond_embeds])\n",
        "        return prompt_embeds\n",
        "\n",
        "    def get_img_latents(self, \n",
        "                        text_embeds, \n",
        "                        height=512, width=512, \n",
        "                        num_inference_steps=50, \n",
        "                        guidance_scale=7.5, \n",
        "                        img_latents=None):\n",
        "        # if no image latent is passed, start reverse diffusion with random noise\n",
        "        if img_latents is None:\n",
        "            img_latents = torch.randn((text_embeds.shape[0] // 2, self.unet.in_channels,\\\n",
        "                                       height // 8, width // 8)).to(self.device)\n",
        "        # set the number of inference steps for the scheduler\n",
        "        self.scheduler_LMS.set_timesteps(num_inference_steps)\n",
        "        # scale the latent embeds\n",
        "        img_latents = img_latents * self.scheduler_LMS.sigmas[0]\n",
        "        # use autocast for automatic mixed precision (AMP) inference\n",
        "        with autocast('cuda'):\n",
        "            for i, t in tqdm(enumerate(self.scheduler_LMS.timesteps)):\n",
        "                # do a single forward pass for both the conditional and unconditional latents\n",
        "                latent_model_input = torch.cat([img_latents] * 2)\n",
        "                sigma = self.scheduler_LMS.sigmas[i]\n",
        "                latent_model_input = latent_model_input / ((sigma ** 2 + 1) ** 0.5)\n",
        "                \n",
        "                # predict noise residuals\n",
        "                with torch.no_grad():\n",
        "                    noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeds)['sample']\n",
        "\n",
        "                # separate predictions for unconditional and conditional outputs\n",
        "                noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)\n",
        "                # perform guidance\n",
        "                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)\n",
        "\n",
        "                # remove the noise from the current sample i.e. go from x_t to x_{t-1}\n",
        "                img_latents = self.scheduler_LMS.step(noise_pred, t, img_latents)['prev_sample']\n",
        "\n",
        "        return img_latents\n",
        "\n",
        "\n",
        "    def decode_img_latents(self, img_latents):\n",
        "        img_latents = img_latents / 0.18215\n",
        "        with torch.no_grad():\n",
        "            imgs = self.vae.decode(img_latents)[\"sample\"]\n",
        "        # load image in the CPU\n",
        "        imgs = imgs.detach().cpu()\n",
        "        return imgs\n",
        "\n",
        "\n",
        "\n",
        "    def transform_imgs(self, imgs):\n",
        "        # transform images from the range [-1, 1] to [0, 1]\n",
        "        imgs = (imgs / 2 + 0.5).clamp(0, 1)\n",
        "        # permute the channels and convert to numpy arrays\n",
        "        imgs = imgs.permute(0, 2, 3, 1).numpy()\n",
        "        # scale images to the range [0, 255] and convert to int\n",
        "        imgs = (imgs * 255).round().astype('uint8')        \n",
        "        # convert to PIL Image objects\n",
        "        imgs = [Image.fromarray(img) for img in imgs]\n",
        "        return imgs\n",
        "        \n",
        "    \n",
        "    \n",
        "    def prompt_to_img(self, \n",
        "                      prompts, \n",
        "                      height=512, width=512, \n",
        "                      num_inference_steps=50, \n",
        "                      guidance_scale=7.5, \n",
        "                      img_latents=None):\n",
        "        \n",
        "        # convert prompt to a list\n",
        "        if isinstance(prompts, str):\n",
        "            prompts = [prompts]\n",
        "        \n",
        "        # get prompt embeddings\n",
        "        text_embeds = self.get_prompt_embeds(prompts)\n",
        "\n",
        "        # get image embeddings\n",
        "        img_latents = self.get_img_latents(text_embeds,\n",
        "                                      height, width,\n",
        "                                      num_inference_steps,\n",
        "                                      guidance_scale, \n",
        "                                      img_latents)\n",
        "        # decode the image embeddings\n",
        "        imgs = self.decode_img_latents(img_latents)\n",
        "        # convert decoded image to suitable PIL Image format\n",
        "        imgs = self.transform_imgs(imgs)\n",
        "\n",
        "        return imgs\n",
        "\n",
        "\n",
        "\n",
        "    def encode_img_latents(self, imgs):\n",
        "        if not isinstance(imgs, list):\n",
        "            imgs = [imgs]\n",
        "        \n",
        "        imgs = np.stack([np.array(img) for img in imgs], axis=0)\n",
        "        # scale images to the range [-1, 1]\n",
        "        imgs = 2 * ((imgs / 255.0) - 0.5)\n",
        "        imgs = torch.from_numpy(imgs).float().permute(0, 3, 1, 2)\n",
        "\n",
        "        # encode images\n",
        "        img_latents_dist = self.vae.encode(imgs.to(self.device))\n",
        "        # img_latents = img_latents_dist.sample()\n",
        "        img_latents = img_latents_dist[\"latent_dist\"].mean.clone()\n",
        "        # scale images\n",
        "        img_latents *= 0.18215\n",
        "\n",
        "        return img_latents\n",
        "\n",
        "\n",
        "    def get_img_latents_similar(self,\n",
        "                                img_latents,\n",
        "                                text_embeds, \n",
        "                                height=512, width=512, \n",
        "                                num_inference_steps=50, \n",
        "                                guidance_scale=7.5,\n",
        "                                start_step=10):        \n",
        "        \n",
        "        # set the number of inference steps for the scheduler\n",
        "        self.scheduler_DDIM.set_timesteps(num_inference_steps)\n",
        "\n",
        "        if start_step > 0:\n",
        "            start_timestep = self.scheduler_DDIM.timesteps[start_step]\n",
        "            start_timesteps = start_timestep.repeat(img_latents.shape[0]).long()\n",
        "\n",
        "            noise = torch.randn_like(img_latents)\n",
        "            img_latents = scheduler_DDIM.add_noise(img_latents, noise, start_timesteps)\n",
        "        \n",
        "        # use autocast for automatic mixed precision (AMP) inference\n",
        "        with autocast('cuda'):\n",
        "            for i, t in tqdm(enumerate(self.scheduler_DDIM.timesteps[start_step:])):\n",
        "                # do a single forward pass for both the conditional and unconditional latents\n",
        "                latent_model_input = torch.cat([img_latents] * 2)\n",
        "                \n",
        "                # predict noise residuals\n",
        "                with torch.no_grad():\n",
        "                    noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeds)['sample']\n",
        "\n",
        "                # separate predictions for unconditional and conditional outputs\n",
        "                noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)\n",
        "                # perform guidance\n",
        "                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)\n",
        "\n",
        "                # remove the noise from the current sample i.e. go from x_t to x_{t-1}\n",
        "                img_latents = self.scheduler_DDIM.step(noise_pred, t, img_latents)['prev_sample']\n",
        "\n",
        "        return img_latents\n",
        "\n",
        "    \n",
        "    def similar_imgs(self, \n",
        "                     img, \n",
        "                     prompt, \n",
        "                     height=512, width=512,\n",
        "                     num_inference_steps=50, \n",
        "                     guidance_scale=7.5,\n",
        "                     start_step=10):\n",
        "        \n",
        "        # get image latents\n",
        "        img_latents = self.encode_img_latents(img)\n",
        "\n",
        "        if isinstance(prompt, str):\n",
        "            prompt = [prompt]\n",
        "\n",
        "        text_embeds = self.get_prompt_embeds(prompt)\n",
        "        \n",
        "        img_latents = self.get_img_latents_similar(img_latents=img_latents,\n",
        "                                                   text_embeds=text_embeds,\n",
        "                                                height=height, width=width,\n",
        "                                                num_inference_steps=num_inference_steps,\n",
        "                                                guidance_scale=guidance_scale,\n",
        "                                                start_step=start_step) \n",
        "\n",
        "        imgs = self.decode_img_latents(img_latents)\n",
        "        imgs = self.transform_imgs(imgs)\n",
        "        # Clear the CUDA cache\n",
        "        torch.cuda.empty_cache()\n",
        "\n",
        "        return imgs\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kd6TwWqEs4Me"
      },
      "outputs": [],
      "source": [
        "device = 'cuda'\n",
        "\n",
        "# model_name = \"dreamlike-art/dreamlike-photoreal-2.0\"\n",
        "model_name = \"CompVis/stable-diffusion-v1-4\"\n",
        "# Load autoencoder\n",
        "vae = AutoencoderKL.from_pretrained(model_name, \n",
        "                                    subfolder='vae').to(device)\n",
        "\n",
        "# Load tokenizer and the text encoder\n",
        "tokenizer = CLIPTokenizer.from_pretrained(model_name, subfolder=\"tokenizer\")\n",
        "text_encoder = CLIPTextModel.from_pretrained(model_name, subfolder=\"text_encoder\").to(device)\n",
        "\n",
        "# Load UNet model\n",
        "unet = UNet2DConditionModel.from_pretrained(model_name, subfolder='unet').to(device)\n",
        "\n",
        "# Load scheduler\n",
        "scheduler_LMS = LMSDiscreteScheduler(beta_start=0.00085, \n",
        "                                 beta_end=0.012, \n",
        "                                 beta_schedule='scaled_linear', \n",
        "                                 num_train_timesteps=1000)\n",
        "\n",
        "scheduler_DDIM = DDIMScheduler(beta_start=0.00085, \n",
        "                               beta_end=0.012, \n",
        "                               beta_schedule='scaled_linear', \n",
        "                               num_train_timesteps=1000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SigUHp47f14I",
        "outputId": "bad874ae-1e68-45fe-ef31-9fe887780582"
      },
      "outputs": [],
      "source": [
        "model = ImageDiffusionModel(vae, tokenizer, text_encoder, unet, scheduler_LMS, scheduler_DDIM)\n",
        "\n",
        "prompts = [\"A really giant cute pink barbie doll on the top of Burj Khalifa\", \n",
        "           \"A green, scary aesthetic dragon breathing fire near a group of heroic firefighters\"]\n",
        "\n",
        "imgs = model.prompt_to_img(prompts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "8UpQ8gIWf17j",
        "outputId": "165f5a5d-fe20-4303-c46f-b247efd05181"
      },
      "outputs": [],
      "source": [
        "imgs[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "NAS1yD8vZym_",
        "outputId": "ef57db7c-a6c9-437f-d27e-94b2bab06ea9"
      },
      "outputs": [],
      "source": [
        "imgs[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603
        },
        "id": "nj8pcEOupRES",
        "outputId": "0ced4046-ed46-4bd0-8b77-1c23ca73dab6"
      },
      "outputs": [],
      "source": [
        "prompt = [\"Aesthetic star wars spaceship with an aethethic background, Ultra HD, futuristic, sharp, octane render, neon\"]\n",
        "\n",
        "imgs = model.prompt_to_img(prompt)\n",
        "\n",
        "imgs[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GmXyduZ1npqg"
      },
      "outputs": [],
      "source": [
        "# saving the image\n",
        "imgs[0].save(\"spaceship1.png\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "RuAHYae4r3MC",
        "outputId": "c4be8be3-cacb-48f6-b70c-15ec69afe5b0"
      },
      "outputs": [],
      "source": [
        "# loading the image again\n",
        "original_img = Image.open(\"spaceship1.png\")\n",
        "original_img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qMcpCt20RyKi"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import gc\n",
        "\n",
        "### If you get OOM errors, execute this cell\n",
        "# del model\n",
        "# Clear the CUDA cache \n",
        "torch.cuda.empty_cache()\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1TQNiEE86Y6E",
        "outputId": "2b87847d-6a63-4ec7-9cc1-7ac6a3396a48"
      },
      "outputs": [],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "1vIVmpL4rPmK",
        "outputId": "4bbc1c35-6850-41f0-a430-39d764a59f2a"
      },
      "outputs": [],
      "source": [
        "model = ImageDiffusionModel(vae, tokenizer, text_encoder, unet, scheduler_LMS, scheduler_DDIM)\n",
        "\n",
        "prompt = \"Aesthetic star wars spaceship with an aethethic background, Ultra HD, futuristic, sharp, octane render, neon\"\n",
        "\n",
        "imgs = model.similar_imgs(original_img, prompt, num_inference_steps=50, start_step=30)\n",
        "imgs[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "zOL-Y7BFai7d",
        "outputId": "666384a3-667d-4715-cbe1-07566afa242d"
      },
      "outputs": [],
      "source": [
        "# model = ImageDiffusionModel(vae, tokenizer, text_encoder, unet, scheduler_LMS, scheduler_DDIM)\n",
        "\n",
        "prompt = \"Aesthetic dark star wars spaceship, Ultra HD, futuristic, sharp, octane render, neon\"\n",
        "\n",
        "imgs = model.similar_imgs(original_img, prompt,\n",
        "                          num_inference_steps=50,\n",
        "                          start_step=40)\n",
        "imgs[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "thiXQYcG8Ekv"
      },
      "outputs": [],
      "source": []
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xwtu2l3-8EnJ"
      },
      "outputs": [],
      "source": []
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yb0H_X6i8Eqj"
      },
      "outputs": [],
      "source": []
    }
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