{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "view-in-github" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "REl-Wc9qXMIm" }, "source": [ "# Illusion Diffusion\n", "By [Matt Tancik](https://www.matthewtancik.com/)\n", "\n", "Based on fast.ai [diffusion notebooks](https://github.com/fastai/diffusion-nbs).\n", "\n", "![ChessUrl](https://user-images.githubusercontent.com/3310961/218217058-4c3daad4-1e3f-4f14-8c50-782d40c70501.gif \"chess\")\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/", "height": 331, "referenced_widgets": [ "ec93e6f38614427dbcfce3f90080b704", "250a79dff54c49208331e56446788a34", "4a679f608885425a8c3fb68e8bf0e59e", "384806faeaa64d0e91ede64bd693734c", "217f74737ced4a0bb1b0b0f7b32212df", "02e9ad5c65a64a65b6ffa1cc9db2bc74", "6f6d302f6b3642688dcd856861d17f00", "7dfc93de0bab415aa60d342041d9045c", 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as np\n", "import mediapy as mp\n", "import cv2\n", "\n", "from pathlib import Path\n", "from torch import autocast\n", "from tqdm.auto import tqdm\n", "from transformers import CLIPTextModel, CLIPTokenizer, logging\n", "\n", "torch.manual_seed(1)\n", "if not (Path.home()/'.huggingface'/'token').exists(): notebook_login()\n", "\n", "# Supress some unnecessary warnings when loading the CLIPTextModel\n", "logging.set_verbosity_error()\n", "\n", "# Set device\n", "torch_device = \"cuda\" if torch.cuda.is_available() else \"cpu\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/", "height": 536, "referenced_widgets": [ "556705183bd543d3901536cec18ded0d", "fc44fc81699e436b911fbdf15ea9f3f0", "70a47ab819084ef8bb2a02436e41d947", "c9a5967a8619496d846bea386bbb839a", "d8931b9edd064f549202ab04e4dd3e0c", "81a80b12037a4fc6bfe4200ed386c38a", "e495eea5d6a845a5aefe815e3e037786", "a61f0edcb520432aad103469576c299f", 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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" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "556705183bd543d3901536cec18ded0d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading (…)_pytorch_model.bin\";: 0%| | 0.00/335M [00:00" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#@title #Generate Images\n", "first_prompt = \"A cartoon drawing of a frog\" #@param {type:\"string\"}\n", "second_prompt = \"A cartoon drawing of a bird\" #@param {type:\"string\"}\n", "rotation_deg = 90 #@param [90, 180]\n", "height = 512 # default height of Stable Diffusion\n", "width = 512 # default width of Stable Diffusion\n", "#@markdown More steps improves quality but takes longer.\n", "num_inference_steps = 500 #@param {type:\"integer\"}\n", "guidance_scale = 7.5 #@param {type:\"slider\", min:2, max:15, step:0.5}\n", "seed = 42 #@param {type:\"integer\"}\n", "generator = torch.manual_seed(seed) \n", "batch_size = 1\n", "rotate = int(rotation_deg) // 90\n", "\n", "# for seed in [0,1,2,3 ,4,5,6,7]:\n", "# 0,3,5 \n", "for seed in [0]:\n", " print(seed)\n", " generator = torch.manual_seed(seed)\n", "\n", " # Prep text \n", " text_input = tokenizer([first_prompt], padding=\"max_length\", max_length=tokenizer.model_max_length, truncation=True, return_tensors=\"pt\")\n", " text_input_2 = tokenizer([second_prompt], padding=\"max_length\", max_length=tokenizer.model_max_length, truncation=True, return_tensors=\"pt\")\n", "\n", " with torch.no_grad():\n", " text_embeddings = text_encoder(text_input.input_ids.to(torch_device))[0]\n", " test_embeddings_2 = text_encoder(text_input_2.input_ids.to(torch_device))[0]\n", " max_length = text_input.input_ids.shape[-1]\n", " uncond_input = tokenizer(\n", " [\"\"] * batch_size, padding=\"max_length\", max_length=max_length, return_tensors=\"pt\"\n", " )\n", " with torch.no_grad():\n", " uncond_embeddings = text_encoder(uncond_input.input_ids.to(torch_device))[0] \n", " text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n", " text_embeddings_2 = torch.cat([uncond_embeddings, test_embeddings_2])\n", "\n", " # Prep Scheduler\n", " scheduler.set_timesteps(num_inference_steps)\n", "\n", " # Prep latents\n", " latents = torch.randn(\n", " (batch_size, unet.in_channels, height // 8, width // 8),\n", " generator=generator,\n", " )\n", " latents = latents.half()\n", " latents = latents.to(torch_device)\n", " latents = latents * scheduler.init_noise_sigma # Scaling (previous versions did latents = latents * self.scheduler.sigmas[0]\n", "\n", " # Loop\n", " with autocast(\"cuda\"):\n", " for i, t in tqdm(enumerate(scheduler.timesteps)):\n", " # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.\n", " \n", " \n", " if i % 2 == 0:\n", " latents = torch.rot90(latents, rotate, [2, 3])\n", " latent_model_input = torch.cat([latents] * 2)\n", " else:\n", " latent_model_input = torch.cat([latents] * 2)\n", " \n", " sigma = scheduler.sigmas[i]\n", " # Scale the latents (preconditioning):\n", " # latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5) # Diffusers 0.3 and below\n", " latent_model_input = scheduler.scale_model_input(latent_model_input, t)\n", "\n", " # predict the noise residual\n", " with torch.no_grad():\n", " if i % 2 == 0:\n", " noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample\n", " else:\n", " noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings_2).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", " if i % 2 == 0:\n", " noise_pred = torch.rot90(noise_pred, -rotate, [2, 3])\n", " latents = torch.rot90(latents, -rotate, [2, 3])\n", "\n", " # compute the previous noisy sample x_t -> x_t-1\n", " latents = scheduler.step(noise_pred, t, latents).prev_sample\n", "\n", " # 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\n", "\n", " # Display\n", " image = (image / 2 + 0.5).clamp(0, 1)\n", " image = image.detach().cpu().permute(0, 2, 3, 1).numpy()\n", " image = (image * 255).round().astype(\"uint8\")[0, ...]\n", "\n", " mp.show_images([image, np.rot90(image, rotate, (0, 1))])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/", "height": 535 }, "id": "7VgQ_ftXeC27", "outputId": "0da218b6-c8a9-4ad0-b343-2600927f17ca" }, "outputs": [ { "data": { "text/html": [ "
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