Whatchamacallit.ipynb

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      "source": [
        "# Whatchamacallit\n",
        "by [crumb](https://twitter.com/aicrumb), V0.1\n",
        "\n",
        "A wrapper around the Diffusers Stable Diffusion Pipeline"
      ],
      "metadata": {
        "id": "d6cn2t921MlK"
      }
    },
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      "cell_type": "code",
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        "colab": {
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        "id": "CDBYqtTRdV7u",
        "outputId": "00f0d8a7-b4c3-4769-fff0-9b1412d8b2c2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
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          "metadata": {}
        }
      ],
      "source": [
        "#@markdown `model_name` can either be a repo (for diffusers) or the repo/model.ckpt (wherever the model is). <br>\n",
        "#@markdown it will automatically be converted to a diffusers pipeline for sampling. <br>\n",
        "#@markdown check out https://huggingface.co/sd-dreambooth-library for specific models\n",
        "import os\n",
        "if not os.path.exists(\"installed.txt\"):\n",
        "    # !pip install transformers diffusers -q\n",
        "    !pip install transformers -q\n",
        "    !pip install git+https://github.com/huggingface/diffusers -q\n",
        "    !sudo apt-get install git-lfs\n",
        "    !pip install OmegaConf -q\n",
        "    !curl https://raw.githubusercontent.com/huggingface/diffusers/039958eae55ff0700cfb42a7e72739575ab341f1/scripts/convert_original_stable_diffusion_to_diffusers.py > convert.py\n",
        "    !cat \"true\" > installed.txt\n",
        "\n",
        "from huggingface_hub import notebook_login\n",
        "notebook_login()\n",
        "\n",
        "model_name = \"CompVis/stable-diffusion-v1-4\" #@param {type:\"string\"}"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **setup**"
      ],
      "metadata": {
        "id": "M8Be9aKOfRld"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title import libraries\n",
        "import torch\n",
        "from torch import autocast\n",
        "from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, StableDiffusionInpaintPipeline\n",
        "from diffusers import LMSDiscreteScheduler, DDIMScheduler, KarrasVeScheduler, PNDMScheduler, DDPMScheduler\n",
        "import torchvision.transforms.functional as f\n",
        "from torch import nn\n",
        "from IPython.display import display, Image, clear_output\n",
        "from tqdm.auto import trange\n",
        "from huggingface_hub import hf_hub_download\n",
        "torch.backends.cudnn.benchmark = True"
      ],
      "metadata": {
        "id": "sd4fcme0eMbM",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title convert model if it's just a ckpt file\n",
        "if \".ckpt\" in model_name:\n",
        "    !rm -r output-model\n",
        "    repo_id = '/'.join(model_name.split(\"/\")[:2])\n",
        "    filename = '/'.join(model_name.split(\"/\")[2:])\n",
        "    print(repo_id, filename)\n",
        "    compvis_path = hf_hub_download(repo_id=repo_id, filename=filename)\n",
        "\n",
        "    !python convert.py --checkpoint_path \"$compvis_path\" --dump_path \"output-model\"\n",
        "    model_name = \"output-model\"\n",
        "    clear_output(wait=False)\n",
        "    print(\"Model converted and saved at\", model_name)"
      ],
      "metadata": {
        "id": "Vwls3_w_2k4y",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title setup samplers / download models\n",
        "\n",
        "beta_schedule = \"scaled_linear\" #@param ['scaled_linear', 'linear']\n",
        "sampling_dtype = \"fp16\" #@param ['fp16', 'fp32', 'bf32']\n",
        "dtype_map = {\n",
        "    \"fp16\": torch.float16,\n",
        "    \"fp32\": torch.float32,\n",
        "    \"bf16\": torch.bfloat16\n",
        "}\n",
        "\n",
        "beta_start = 0.00085 #@param\n",
        "beta_end = 0.012 #@param\n",
        "\n",
        "lms = LMSDiscreteScheduler(\n",
        "    beta_start=beta_start, \n",
        "    beta_end=beta_end, \n",
        "    beta_schedule=beta_schedule\n",
        ")\n",
        "ddim = DDIMScheduler(\n",
        "    beta_start=beta_start,\n",
        "    beta_end=beta_end,\n",
        "    beta_schedule=beta_schedule\n",
        ")\n",
        "ddpm = DDPMScheduler( # tune these parameters for whatever reason, they do Not work\n",
        "    beta_start=beta_start,\n",
        "    beta_end=beta_end,\n",
        "    beta_schedule=beta_schedule\n",
        ")\n",
        "pndm = PNDMScheduler(\n",
        "    beta_start=beta_start,\n",
        "    beta_end=beta_end,\n",
        "    beta_schedule=beta_schedule\n",
        ")\n",
        "\n",
        "pipe = StableDiffusionPipeline.from_pretrained(\n",
        "    model_name,\n",
        "    torch_dtype=torch.float32,\n",
        "    scheduler=lms,\n",
        ")\n",
        "pipe = pipe.to(\"cuda\")\n",
        "pipe.enable_attention_slicing()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
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          "height": 67
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        "cellView": "form",
        "id": "4KXxAsThecN2",
        "outputId": "67a7c58a-3c40-4728-83d1-a66797861ab4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Fetching 15 files:   0%|          | 0/15 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ftfy or spacy is not installed using BERT BasicTokenizer instead of ftfy.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **load textual inversion**"
      ],
      "metadata": {
        "id": "ULkFDpKWeuz6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title load textual-inversion concepts from 🤗 hub\n",
        "\n",
        "#@markdown `load_full_concepts_library` if turned on will take a While, it loads every single stable diffusion concept on https://huggingface.co/sd-concepts-library <br>\n",
        "#@markdown `specific_concepts` can be a list of strings, containing the ids of your concepts (from sd-concepts-library or your own repos, example `[\"sd-concepts-library/my-first-inversion\", \"sd-concepts-library/my-second-inversion\"]` etc.)\n",
        "\n",
        "from huggingface_hub import HfApi\n",
        "!pip install wget -q\n",
        "import wget\n",
        "import os\n",
        "import requests\n",
        "from huggingface_hub import hf_hub_download\n",
        "api = HfApi()\n",
        "def load_learned_embed_in_clip(learned_embeds_path, text_encoder, tokenizer, token=None):\n",
        "        loaded_learned_embeds = torch.load(learned_embeds_path, map_location=\"cpu\")\n",
        "        \n",
        "        # separate token and the embeds\n",
        "        trained_token = list(loaded_learned_embeds.keys())[0]\n",
        "        embeds = loaded_learned_embeds[trained_token]\n",
        "\n",
        "        # cast to dtype of text_encoder\n",
        "        dtype = text_encoder.get_input_embeddings().weight.dtype\n",
        "        embeds.to(dtype)\n",
        "\n",
        "        # add the token in tokenizer\n",
        "        token = token if token is not None else trained_token\n",
        "        num_added_tokens = tokenizer.add_tokens(token)\n",
        "        i = 1\n",
        "        # while(num_added_tokens == 0):\n",
        "        #     print(f\"The tokenizer already contains the token {token}.\")\n",
        "        #     token = f\"{token[:-1]}-{i}>\"\n",
        "        #     print(f\"Attempting to add the token {token}.\")\n",
        "        #     num_added_tokens = tokenizer.add_tokens(token)\n",
        "        #     i+=1\n",
        "        \n",
        "        # resize the token embeddings\n",
        "        text_encoder.resize_token_embeddings(len(tokenizer))\n",
        "        \n",
        "        # get the id for the token and assign the embeds\n",
        "        token_id = tokenizer.convert_tokens_to_ids(token)\n",
        "        text_encoder.get_input_embeddings().weight.data[token_id] = embeds\n",
        "        return token\n",
        "\n",
        "specific_concepts = [\"sd-concepts-library/cat-toy\"] #@param\n",
        "\n",
        "models = []\n",
        "for model in specific_concepts:\n",
        "    model_content = {}\n",
        "    model_content[\"id\"] = model\n",
        "    embeds = (model, \"learned_embeds.bin\")\n",
        "    embeds_file = hf_hub_download(embeds[0], embeds[1])\n",
        "    \n",
        "    token_identifier = (model, \"token_identifier.txt\")\n",
        "    tokens_file = hf_hub_download(token_identifier[0], token_identifier[1])\n",
        "    # print(f\"added {token_name}\")\n",
        "    token_name = open(tokens_file, \"r\").read()\n",
        "    print(\"adding\", token_name)\n",
        "\n",
        "    learned_token = load_learned_embed_in_clip(embeds_file, pipe.text_encoder, pipe.tokenizer, token_name)\n",
        "    model_content[\"token\"] = learned_token\n",
        "    models.append(model_content)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "M194x735exFg",
        "outputId": "afbab369-f534-4252-db74-ca377de905a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "adding <cat-toy>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title convert old embedding\n",
        "\n",
        "#@markdown convert original textual-inversion embeds to huggingface-style ones <br> You can run this cell however many times is needed to input all of your inversions.\n",
        "\n",
        "from IPython.display import FileLink\n",
        "import torch\n",
        "\n",
        "input_file = \"\" #@param {\"type\":\"string\"}\n",
        "placeholder_token = \"<my-style>\" #@param {\"type\":\"string\"}\n",
        "\n",
        "def convert_and_load(input_file, placeholder_token):\n",
        "    x = torch.load(input_file, map_location=torch.device('cpu'))\n",
        "\n",
        "    params_dict = {\n",
        "        placeholder_token: torch.tensor(list(x['string_to_param'].items())[0][1])\n",
        "    }\n",
        "    torch.save(params_dict, \"learned_embeds.bin\")\n",
        "    load_learned_embed_in_clip(\"learned_embeds.bin\", pipe.text_encoder, pipe.tokenizer, placeholder_token)\n",
        "    print(\"loaded\", placeholder_token)\n",
        "\n",
        "if input_file != \"\":\n",
        "    convert_and_load(input_file, placeholder_token)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "Ch1itfp_e9sY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown load a few midjourney styles\n",
        "import gdown\n",
        "gdown.download_folder(url=\"https://drive.google.com/drive/u/9/folders/1whqzuBtiAIo9V12I20I1EVkfE9TLb1hS\", quiet=True)\n",
        "\n",
        "try:\n",
        "    folder = \"/content/midj textual inversion\"\n",
        "    files = [folder+\"/\"+i for i in os.listdir(folder) if \".pt\" in i]\n",
        "except: # WHY DOES IT SOMETIMES DO THIS AND SOMETIMES DO IT THE OTHER WAY????? IM SO ANGRY\n",
        "    folder = \"/content\"\n",
        "    files = [folder+\"/\"+i for i in os.listdir(folder) if \".pt\" in i]\n",
        "names = [\"<\"+i.split(\"/\")[-1].split(\".\")[0]+\">\" for i in files]\n",
        "\n",
        "for i,j in zip(files, names):\n",
        "    convert_and_load(i,j)"
      ],
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FtUbivHOfBDK",
        "outputId": "a5b28ffd-c917-4de1-d95a-ab1669cc168e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  from ipykernel import kernelapp as app\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loaded <midj-anthro>\n",
            "loaded <midj-strong>\n",
            "loaded <midj-portrait>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **other model tricks**"
      ],
      "metadata": {
        "id": "SODm06z8aEys"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#@markdown inspired by novelai's approach, works *to an extent* with vanilla SD models\n",
        "#@markdown recommended value = 0, idk it kinda sucks, play around with it\n",
        "del_last_layers = 1 #@param\n",
        "\n",
        "if del_last_layers > 0:\n",
        "    for i in range(del_last_layers):\n",
        "        # del pipe.text_encoder.text_model.encoder.layers[11]\n",
        "        del pipe.text_encoder.text_model.encoder.layers[11-i]"
      ],
      "metadata": {
        "cellView": "form",
        "id": "fD2QNdsQZEy2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **generate**"
      ],
      "metadata": {
        "id": "vg80ewkkfEnr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import PIL.Image as PImage\n",
        "\n",
        "def image_grid(imgs, rows, cols):\n",
        "    assert len(imgs) == rows*cols\n",
        "\n",
        "    w, h = imgs[0].size\n",
        "    grid = PImage.new('RGB', size=(cols*w, rows*h))\n",
        "    grid_w, grid_h = grid.size\n",
        "    \n",
        "    for i, img in enumerate(imgs):\n",
        "        grid.paste(img, box=(i%cols*w, i//cols*h))\n",
        "    return grid\n",
        "\n",
        "if not os.path.exists(\"output\"):\n",
        "    !mkdir output\n",
        "prompt = \"\" #@param {type:\"string\"}\n",
        "negative = \"bad quality\" #@param {type:\"string\"}\n",
        "\n",
        "init_image_url = \"\" #@param {type:\"string\"}\n",
        "init_strength = 0.0 #@param\n",
        "strength = 1-init_strength\n",
        "\n",
        "scheduler = \"lms\" #@param ['lms', 'ddim', 'pndm']\n",
        "\n",
        "scheduler_map = {\n",
        "    \"lms\": lms,\n",
        "    \"ddim\": ddim, # someone please help get the right params for the ddim/ddpm samplers\n",
        "    \"ddpm\": ddpm, # under maintanence\n",
        "    \"pndm\": pndm,\n",
        "    \"default\": pipe.scheduler # going to be useful later for clip guided sampling\n",
        "}\n",
        "pipe.scheduler = scheduler_map[scheduler]\n",
        "\n",
        "steps = 75 #@param\n",
        "# steps = steps - 9 # dude i have NO CLUE LOL\n",
        "width_height = [512, 512] #@param\n",
        "width, height = width_height\n",
        "guidance_scale = 10 #@param\n",
        "seed = 1 #@param\n",
        "if seed == -1:\n",
        "    import random\n",
        "    seed = random.randint(1, 100_000)\n",
        "    print(\"Using random seed:\", seed)\n",
        "\n",
        "\n",
        "batch_size = 1 #@param\n",
        "batches = 1 #@param\n",
        "\n",
        "# negative_prompt = None if len(negative)<=1 else [negative]*batch_size\n",
        "# prompt = [prompt] * batch_size\n",
        "\n",
        "negative_prompt = None if len(negative)<=1 else negative\n",
        "\n",
        "# disable safety checker so that if something is accidentally tagged nsfw it doesn't matter\n",
        "disable_safety_checker = False #@param {type:\"boolean\"}\n",
        "\n",
        "if len(init_image_url)>1:\n",
        "    import requests\n",
        "    from io import BytesIO\n",
        "    response = requests.get(init_image_url)\n",
        "    init_image = PImage.open(BytesIO(response.content)).convert(\"RGB\")\n",
        "    init_image = init_image.resize((width, height))\n",
        "\n",
        "    use_pipe = StableDiffusionImg2ImgPipeline(\n",
        "        vae = pipe.vae,\n",
        "        text_encoder = pipe.text_encoder,\n",
        "        tokenizer = pipe.tokenizer,\n",
        "        unet = pipe.unet,\n",
        "        scheduler = pipe.scheduler,\n",
        "        safety_checker = pipe.safety_checker,\n",
        "        feature_extractor = pipe.feature_extractor\n",
        "    )\n",
        "    use_init = True\n",
        "else:\n",
        "    use_pipe = pipe\n",
        "    use_init = False\n",
        "with autocast(\"cuda\"):\n",
        "    if disable_safety_checker:\n",
        "        # sometimes it's *really* bad and flags innocent fine images\n",
        "        class DummyOutput(nn.Module):\n",
        "            def __init__(self, pixel_values):\n",
        "                super().__init__()\n",
        "                self.hacking_into_the_mainframe = True\n",
        "                self.pixel_values = nn.Parameter(pixel_values)\n",
        "            def forward(self):\n",
        "                return self.pixel_values\n",
        "\n",
        "        class DummyFeatureExtractor:\n",
        "            def __init__(self):\n",
        "                self.mainframe = \"HAAACCKKEDD\"\n",
        "            def __call__(self, x, **extra):\n",
        "                pixel_values = f.to_tensor(x[0])\n",
        "                return DummyOutput(pixel_values)\n",
        "\n",
        "        class DummySafetyChecker:\n",
        "            def __init__(self):\n",
        "                self.hacker_code = \"injected\"\n",
        "            def __call__(self, images, clip_input):\n",
        "                return images, False\n",
        "\n",
        "        use_pipe.feature_extractor = DummyFeatureExtractor()\n",
        "        use_pipe.safety_checker = DummySafetyChecker()\n",
        "    for i in trange(batches):\n",
        "        generator = torch.cuda.manual_seed(seed+i)\n",
        "        if use_init:\n",
        "            image = use_pipe(prompt, num_images_per_prompt=batch_size, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, generator=generator, num_inference_steps=steps,\n",
        "                         init_image=init_image, strength=strength).images  \n",
        "        else:\n",
        "            image = use_pipe(prompt, num_images_per_prompt=batch_size, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, generator=generator, num_inference_steps=steps).images  \n",
        "        filename = \"output/\" + str(i).rjust(5, \"0\") + \".png\"\n",
        "        image = image_grid(image, 1, batch_size)\n",
        "        image.save(filename)\n",
        "        display(Image(filename))\n",
        "        from google.colab import output\n",
        "        output.eval_js('new Audio(\"https://freesound.org/data/previews/80/80921_1022651-lq.ogg\").play()')"
      ],
      "metadata": {
        "cellView": "form",
        "id": "2rfdeODkedBA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}