diff --git "a/IllusionDiffusion.ipynb" "b/IllusionDiffusion.ipynb" new file mode 100644--- /dev/null +++ "b/IllusionDiffusion.ipynb" @@ -0,0 +1,4929 @@ +{ + "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", + 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"!pip install -q --upgrade transformers diffusers ftfy mediapy\n", + "\n", + "from base64 import b64encode\n", + "\n", + "import torch\n", + "from diffusers import AutoencoderKL, LMSDiscreteScheduler, UNet2DConditionModel\n", + "from huggingface_hub import notebook_login\n", + "\n", + "import numpy 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": 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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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