mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
fix: Black linting
This commit is contained in:
blessedcoolant
parent
6ed1bf7084
commit
6d82a1019a
@ -6,8 +6,7 @@ from pydantic import Field
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from invokeai.app.invocations.prompt import PromptOutput
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from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
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InvocationConfig, InvocationContext)
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from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
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from .math import FloatOutput, IntOutput
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# Pass-through parameter nodes - used by subgraphs
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@ -68,6 +67,7 @@ class ParamStringInvocation(BaseInvocation):
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def invoke(self, context: InvocationContext) -> StringOutput:
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return StringOutput(text=self.text)
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class ParamPromptInvocation(BaseInvocation):
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"""A prompt input parameter"""
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@ -80,4 +80,4 @@ class ParamPromptInvocation(BaseInvocation):
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}
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def invoke(self, context: InvocationContext) -> PromptOutput:
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return PromptOutput(prompt=self.prompt)
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return PromptOutput(prompt=self.prompt)
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@ -1,281 +1,283 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "ycYWcsEKc6w7"
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},
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"source": [
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"# Stable Diffusion AI Notebook (Release 2.0.0)\n",
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"\n",
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"<img src=\"https://user-images.githubusercontent.com/60411196/186547976-d9de378a-9de8-4201-9c25-c057a9c59bad.jpeg\" alt=\"stable-diffusion-ai\" width=\"170px\"/> <br>\n",
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"#### Instructions:\n",
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"1. Execute each cell in order to mount a Dream bot and create images from text. <br>\n",
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"2. Once cells 1-8 were run correctly you'll be executing a terminal in cell #9, you'll need to enter `python scripts/dream.py` command to run Dream bot.<br> \n",
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"3. After launching dream bot, you'll see: <br> `Dream > ` in terminal. <br> Insert a command, eg. `Dream > Astronaut floating in a distant galaxy`, or type `-h` for help.\n",
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"3. After completion you'll see your generated images in path `stable-diffusion/outputs/img-samples/`, you can also show last generated images in cell #10.\n",
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"4. To quit Dream bot use `q` command. <br> \n",
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"---\n",
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"<font color=\"red\">Note:</font> It takes some time to load, but after installing all dependencies you can use the bot all time you want while colab instance is up. <br>\n",
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"<font color=\"red\">Requirements:</font> For this notebook to work you need to have [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) stored in your Google Drive, it will be needed in cell #7\n",
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"##### For more details visit Github repository: [invoke-ai/InvokeAI](https://github.com/invoke-ai/InvokeAI)\n",
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"---\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "dr32VLxlnouf"
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},
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"source": [
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"## ◢ Installation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "a2Z5Qu_o8VtQ"
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},
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"outputs": [],
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"source": [
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"#@title 1. Check current GPU assigned\n",
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"!nvidia-smi -L\n",
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"!nvidia-smi"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "vbI9ZsQHzjqF"
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},
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"outputs": [],
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"source": [
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"#@title 2. Download stable-diffusion Repository\n",
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"from os.path import exists\n",
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"\n",
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"!git clone --quiet https://github.com/invoke-ai/InvokeAI.git # Original repo\n",
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"%cd /content/InvokeAI/\n",
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"!git checkout --quiet tags/v2.0.0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "QbXcGXYEFSNB"
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},
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"outputs": [],
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"source": [
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"#@title 3. Install dependencies\n",
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"import gc\n",
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"\n",
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"!wget https://raw.githubusercontent.com/invoke-ai/InvokeAI/development/environments-and-requirements/requirements-base.txt\n",
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"!wget https://raw.githubusercontent.com/invoke-ai/InvokeAI/development/environments-and-requirements/requirements-win-colab-cuda.txt\n",
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"!pip install colab-xterm\n",
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"!pip install -r requirements-lin-win-colab-CUDA.txt\n",
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"!pip install clean-fid torchtext\n",
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"!pip install transformers\n",
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"gc.collect()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "8rSMhgnAttQa"
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},
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"outputs": [],
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"source": [
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"#@title 4. Restart Runtime\n",
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"exit()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "ChIDWxLVHGGJ"
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},
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"outputs": [],
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"source": [
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"#@title 5. Load small ML models required\n",
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"import gc\n",
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"%cd /content/InvokeAI/\n",
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"!python scripts/preload_models.py\n",
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"gc.collect()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "795x1tMoo8b1"
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},
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"source": [
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"## ◢ Configuration"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "YEWPV-sF1RDM"
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},
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"outputs": [],
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"source": [
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"#@title 6. Mount google Drive\n",
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"from google.colab import drive\n",
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"drive.mount('/content/drive')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "zRTJeZ461WGu"
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},
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"outputs": [],
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"source": [
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"#@title 7. Drive Path to model\n",
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"#@markdown Path should start with /content/drive/path-to-your-file <br>\n",
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"#@markdown <font color=\"red\">Note:</font> Model should be downloaded from https://huggingface.co <br>\n",
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"#@markdown Lastest release: [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)\n",
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"from os.path import exists\n",
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"\n",
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"model_path = \"\" #@param {type:\"string\"}\n",
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"if exists(model_path):\n",
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" print(\"✅ Valid directory\")\n",
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"else: \n",
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" print(\"❌ File doesn't exist\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "UY-NNz4I8_aG"
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},
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"outputs": [],
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"source": [
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"#@title 8. Symlink to model\n",
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"\n",
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"from os.path import exists\n",
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"import os \n",
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"\n",
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"# Folder creation if it doesn't exist\n",
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"if exists(\"/content/InvokeAI/models/ldm/stable-diffusion-v1\"):\n",
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" print(\"❗ Dir stable-diffusion-v1 already exists\")\n",
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"else:\n",
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" %mkdir /content/InvokeAI/models/ldm/stable-diffusion-v1\n",
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" print(\"✅ Dir stable-diffusion-v1 created\")\n",
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"\n",
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"# Symbolic link if it doesn't exist\n",
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"if exists(\"/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt\"):\n",
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" print(\"❗ Symlink already created\")\n",
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"else: \n",
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" src = model_path\n",
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" dst = '/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt'\n",
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" os.symlink(src, dst) \n",
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" print(\"✅ Symbolic link created successfully\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Mc28N0_NrCQH"
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},
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"source": [
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"## ◢ Execution"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "ir4hCrMIuUpl"
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},
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"outputs": [],
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"source": [
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"#@title 9. Run Terminal and Execute Dream bot\n",
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"#@markdown <font color=\"blue\">Steps:</font> <br>\n",
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"#@markdown 1. Execute command `python scripts/invoke.py` to run InvokeAI.<br>\n",
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"#@markdown 2. After initialized you'll see `Dream>` line.<br>\n",
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"#@markdown 3. Example text: `Astronaut floating in a distant galaxy` <br>\n",
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"#@markdown 4. To quit Dream bot use: `q` command.<br>\n",
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"\n",
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"%load_ext colabxterm\n",
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"%xterm\n",
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"gc.collect()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "qnLohSHmKoGk"
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},
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"outputs": [],
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"source": [
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"#@title 10. Show the last 15 generated images\n",
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"import glob\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.image as mpimg\n",
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"%matplotlib inline\n",
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"\n",
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"images = []\n",
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"for img_path in sorted(glob.glob('/content/InvokeAI/outputs/img-samples/*.png'), reverse=True):\n",
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" images.append(mpimg.imread(img_path))\n",
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"\n",
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"images = images[:15] \n",
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"\n",
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"plt.figure(figsize=(20,10))\n",
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"\n",
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"columns = 5\n",
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"for i, image in enumerate(images):\n",
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" ax = plt.subplot(len(images) / columns + 1, columns, i + 1)\n",
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" ax.axes.xaxis.set_visible(False)\n",
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" ax.axes.yaxis.set_visible(False)\n",
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" ax.axis('off')\n",
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" plt.imshow(image)\n",
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" gc.collect()\n",
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"\n"
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]
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}
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],
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"collapsed_sections": [],
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"private_outputs": true,
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"provenance": []
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},
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"gpuClass": "standard",
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"kernelspec": {
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"display_name": "Python 3.9.12 64-bit",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.9.12"
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},
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"vscode": {
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"interpreter": {
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"hash": "4e870c5c5fe42db7e2c5647ae5af656ff3391bf8c2b729cbf7fa0e16ca8cb5af"
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}
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}
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "ycYWcsEKc6w7"
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},
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"source": [
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"# Stable Diffusion AI Notebook (Release 2.0.0)\n",
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"\n",
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"<img src=\"https://user-images.githubusercontent.com/60411196/186547976-d9de378a-9de8-4201-9c25-c057a9c59bad.jpeg\" alt=\"stable-diffusion-ai\" width=\"170px\"/> <br>\n",
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"#### Instructions:\n",
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"1. Execute each cell in order to mount a Dream bot and create images from text. <br>\n",
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"2. Once cells 1-8 were run correctly you'll be executing a terminal in cell #9, you'll need to enter `python scripts/dream.py` command to run Dream bot.<br> \n",
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"3. After launching dream bot, you'll see: <br> `Dream > ` in terminal. <br> Insert a command, eg. `Dream > Astronaut floating in a distant galaxy`, or type `-h` for help.\n",
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"3. After completion you'll see your generated images in path `stable-diffusion/outputs/img-samples/`, you can also show last generated images in cell #10.\n",
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"4. To quit Dream bot use `q` command. <br> \n",
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"---\n",
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"<font color=\"red\">Note:</font> It takes some time to load, but after installing all dependencies you can use the bot all time you want while colab instance is up. <br>\n",
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"<font color=\"red\">Requirements:</font> For this notebook to work you need to have [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) stored in your Google Drive, it will be needed in cell #7\n",
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"##### For more details visit Github repository: [invoke-ai/InvokeAI](https://github.com/invoke-ai/InvokeAI)\n",
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"---\n"
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]
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},
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"nbformat": 4,
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"nbformat_minor": 0
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "dr32VLxlnouf"
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},
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"source": [
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"## ◢ Installation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "a2Z5Qu_o8VtQ"
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},
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"outputs": [],
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"source": [
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"# @title 1. Check current GPU assigned\n",
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"!nvidia-smi -L\n",
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"!nvidia-smi"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "vbI9ZsQHzjqF"
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},
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"outputs": [],
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"source": [
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"# @title 2. Download stable-diffusion Repository\n",
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"from os.path import exists\n",
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"\n",
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"!git clone --quiet https://github.com/invoke-ai/InvokeAI.git # Original repo\n",
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"%cd /content/InvokeAI/\n",
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"!git checkout --quiet tags/v2.0.0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "QbXcGXYEFSNB"
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},
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"outputs": [],
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"source": [
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"# @title 3. Install dependencies\n",
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"import gc\n",
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"\n",
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"!wget https://raw.githubusercontent.com/invoke-ai/InvokeAI/development/environments-and-requirements/requirements-base.txt\n",
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"!wget https://raw.githubusercontent.com/invoke-ai/InvokeAI/development/environments-and-requirements/requirements-win-colab-cuda.txt\n",
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"!pip install colab-xterm\n",
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"!pip install -r requirements-lin-win-colab-CUDA.txt\n",
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"!pip install clean-fid torchtext\n",
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"!pip install transformers\n",
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"gc.collect()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "8rSMhgnAttQa"
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},
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"outputs": [],
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"source": [
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"# @title 4. Restart Runtime\n",
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"exit()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "ChIDWxLVHGGJ"
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},
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"outputs": [],
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"source": [
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"# @title 5. Load small ML models required\n",
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"import gc\n",
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"\n",
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"%cd /content/InvokeAI/\n",
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"!python scripts/preload_models.py\n",
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"gc.collect()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "795x1tMoo8b1"
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},
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"source": [
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"## ◢ Configuration"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "YEWPV-sF1RDM"
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},
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"outputs": [],
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"source": [
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"# @title 6. Mount google Drive\n",
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"from google.colab import drive\n",
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"\n",
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"drive.mount(\"/content/drive\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "zRTJeZ461WGu"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title 7. Drive Path to model\n",
|
||||
"# @markdown Path should start with /content/drive/path-to-your-file <br>\n",
|
||||
"# @markdown <font color=\"red\">Note:</font> Model should be downloaded from https://huggingface.co <br>\n",
|
||||
"# @markdown Lastest release: [Stable-Diffusion-v-1-4](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)\n",
|
||||
"from os.path import exists\n",
|
||||
"\n",
|
||||
"model_path = \"\" # @param {type:\"string\"}\n",
|
||||
"if exists(model_path):\n",
|
||||
" print(\"✅ Valid directory\")\n",
|
||||
"else:\n",
|
||||
" print(\"❌ File doesn't exist\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "UY-NNz4I8_aG"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title 8. Symlink to model\n",
|
||||
"\n",
|
||||
"from os.path import exists\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"# Folder creation if it doesn't exist\n",
|
||||
"if exists(\"/content/InvokeAI/models/ldm/stable-diffusion-v1\"):\n",
|
||||
" print(\"❗ Dir stable-diffusion-v1 already exists\")\n",
|
||||
"else:\n",
|
||||
" %mkdir /content/InvokeAI/models/ldm/stable-diffusion-v1\n",
|
||||
" print(\"✅ Dir stable-diffusion-v1 created\")\n",
|
||||
"\n",
|
||||
"# Symbolic link if it doesn't exist\n",
|
||||
"if exists(\"/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt\"):\n",
|
||||
" print(\"❗ Symlink already created\")\n",
|
||||
"else:\n",
|
||||
" src = model_path\n",
|
||||
" dst = \"/content/InvokeAI/models/ldm/stable-diffusion-v1/model.ckpt\"\n",
|
||||
" os.symlink(src, dst)\n",
|
||||
" print(\"✅ Symbolic link created successfully\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "Mc28N0_NrCQH"
|
||||
},
|
||||
"source": [
|
||||
"## ◢ Execution"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "ir4hCrMIuUpl"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# @title 9. Run Terminal and Execute Dream bot\n",
|
||||
"# @markdown <font color=\"blue\">Steps:</font> <br>\n",
|
||||
"# @markdown 1. Execute command `python scripts/invoke.py` to run InvokeAI.<br>\n",
|
||||
"# @markdown 2. After initialized you'll see `Dream>` line.<br>\n",
|
||||
"# @markdown 3. Example text: `Astronaut floating in a distant galaxy` <br>\n",
|
||||
"# @markdown 4. To quit Dream bot use: `q` command.<br>\n",
|
||||
"\n",
|
||||
"%load_ext colabxterm\n",
|
||||
"%xterm\n",
|
||||
"gc.collect()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "qnLohSHmKoGk"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title 10. Show the last 15 generated images\n",
|
||||
"import glob\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import matplotlib.image as mpimg\n",
|
||||
"%matplotlib inline\n",
|
||||
"\n",
|
||||
"images = []\n",
|
||||
"for img_path in sorted(glob.glob('/content/InvokeAI/outputs/img-samples/*.png'), reverse=True):\n",
|
||||
" images.append(mpimg.imread(img_path))\n",
|
||||
"\n",
|
||||
"images = images[:15] \n",
|
||||
"\n",
|
||||
"plt.figure(figsize=(20,10))\n",
|
||||
"\n",
|
||||
"columns = 5\n",
|
||||
"for i, image in enumerate(images):\n",
|
||||
" ax = plt.subplot(len(images) / columns + 1, columns, i + 1)\n",
|
||||
" ax.axes.xaxis.set_visible(False)\n",
|
||||
" ax.axes.yaxis.set_visible(False)\n",
|
||||
" ax.axis('off')\n",
|
||||
" plt.imshow(image)\n",
|
||||
" gc.collect()\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"accelerator": "GPU",
|
||||
"colab": {
|
||||
"collapsed_sections": [],
|
||||
"private_outputs": true,
|
||||
"provenance": []
|
||||
},
|
||||
"gpuClass": "standard",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.9.12 64-bit",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python",
|
||||
"version": "3.9.12"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "4e870c5c5fe42db7e2c5647ae5af656ff3391bf8c2b729cbf7fa0e16ca8cb5af"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
|
||||
@ -52,17 +52,17 @@
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"Cloning into 'latent-diffusion'...\n",
|
||||
"remote: Enumerating objects: 992, done.\u001B[K\n",
|
||||
"remote: Counting objects: 100% (695/695), done.\u001B[K\n",
|
||||
"remote: Compressing objects: 100% (397/397), done.\u001B[K\n",
|
||||
"remote: Total 992 (delta 375), reused 564 (delta 253), pack-reused 297\u001B[K\n",
|
||||
"remote: Enumerating objects: 992, done.\u001b[K\n",
|
||||
"remote: Counting objects: 100% (695/695), done.\u001b[K\n",
|
||||
"remote: Compressing objects: 100% (397/397), done.\u001b[K\n",
|
||||
"remote: Total 992 (delta 375), reused 564 (delta 253), pack-reused 297\u001b[K\n",
|
||||
"Receiving objects: 100% (992/992), 30.78 MiB | 29.43 MiB/s, done.\n",
|
||||
"Resolving deltas: 100% (510/510), done.\n",
|
||||
"Cloning into 'taming-transformers'...\n",
|
||||
"remote: Enumerating objects: 1335, done.\u001B[K\n",
|
||||
"remote: Counting objects: 100% (525/525), done.\u001B[K\n",
|
||||
"remote: Compressing objects: 100% (493/493), done.\u001B[K\n",
|
||||
"remote: Total 1335 (delta 58), reused 481 (delta 30), pack-reused 810\u001B[K\n",
|
||||
"remote: Enumerating objects: 1335, done.\u001b[K\n",
|
||||
"remote: Counting objects: 100% (525/525), done.\u001b[K\n",
|
||||
"remote: Compressing objects: 100% (493/493), done.\u001b[K\n",
|
||||
"remote: Total 1335 (delta 58), reused 481 (delta 30), pack-reused 810\u001b[K\n",
|
||||
"Receiving objects: 100% (1335/1335), 412.35 MiB | 30.53 MiB/s, done.\n",
|
||||
"Resolving deltas: 100% (267/267), done.\n",
|
||||
"Obtaining file:///content/taming-transformers\n",
|
||||
@ -73,23 +73,24 @@
|
||||
"Installing collected packages: taming-transformers\n",
|
||||
" Running setup.py develop for taming-transformers\n",
|
||||
"Successfully installed taming-transformers-0.0.1\n",
|
||||
"\u001B[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
|
||||
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
|
||||
"tensorflow 2.8.0 requires tf-estimator-nightly==2.8.0.dev2021122109, which is not installed.\n",
|
||||
"arviz 0.11.4 requires typing-extensions<4,>=3.7.4.3, but you have typing-extensions 4.1.1 which is incompatible.\u001B[0m\n"
|
||||
"arviz 0.11.4 requires typing-extensions<4,>=3.7.4.3, but you have typing-extensions 4.1.1 which is incompatible.\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#@title Installation\n",
|
||||
"# @title Installation\n",
|
||||
"!git clone https://github.com/CompVis/latent-diffusion.git\n",
|
||||
"!git clone https://github.com/CompVis/taming-transformers\n",
|
||||
"!pip install -e ./taming-transformers\n",
|
||||
"!pip install omegaconf>=2.0.0 pytorch-lightning>=1.0.8 torch-fidelity einops\n",
|
||||
"\n",
|
||||
"import sys\n",
|
||||
"\n",
|
||||
"sys.path.append(\".\")\n",
|
||||
"sys.path.append('./taming-transformers')\n",
|
||||
"from taming.models import vqgan "
|
||||
"sys.path.append(\"./taming-transformers\")\n",
|
||||
"from taming.models import vqgan"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -104,11 +105,11 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title Download\n",
|
||||
"%cd latent-diffusion/ \n",
|
||||
"# @title Download\n",
|
||||
"%cd latent-diffusion/\n",
|
||||
"\n",
|
||||
"!mkdir -p models/ldm/cin256-v2/\n",
|
||||
"!wget -O models/ldm/cin256-v2/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/cin/model.ckpt "
|
||||
"!wget -O models/ldm/cin256-v2/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/cin/model.ckpt"
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
@ -203,7 +204,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title loading utils\n",
|
||||
"# @title loading utils\n",
|
||||
"import torch\n",
|
||||
"from omegaconf import OmegaConf\n",
|
||||
"\n",
|
||||
@ -212,7 +213,7 @@
|
||||
"\n",
|
||||
"def load_model_from_config(config, ckpt):\n",
|
||||
" print(f\"Loading model from {ckpt}\")\n",
|
||||
" pl_sd = torch.load(ckpt)#, map_location=\"cpu\")\n",
|
||||
" pl_sd = torch.load(ckpt) # , map_location=\"cpu\")\n",
|
||||
" sd = pl_sd[\"state_dict\"]\n",
|
||||
" model = instantiate_from_config(config.model)\n",
|
||||
" m, u = model.load_state_dict(sd, strict=False)\n",
|
||||
@ -222,7 +223,7 @@
|
||||
"\n",
|
||||
"\n",
|
||||
"def get_model():\n",
|
||||
" config = OmegaConf.load(\"configs/latent-diffusion/cin256-v2.yaml\") \n",
|
||||
" config = OmegaConf.load(\"configs/latent-diffusion/cin256-v2.yaml\")\n",
|
||||
" model = load_model_from_config(config, \"models/ldm/cin256-v2/model.ckpt\")\n",
|
||||
" return model"
|
||||
],
|
||||
@ -276,18 +277,18 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"import numpy as np \n",
|
||||
"import numpy as np\n",
|
||||
"from PIL import Image\n",
|
||||
"from einops import rearrange\n",
|
||||
"from torchvision.utils import make_grid\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"classes = [25, 187, 448, 992] # define classes to be sampled here\n",
|
||||
"classes = [25, 187, 448, 992] # define classes to be sampled here\n",
|
||||
"n_samples_per_class = 6\n",
|
||||
"\n",
|
||||
"ddim_steps = 20\n",
|
||||
"ddim_eta = 0.0\n",
|
||||
"scale = 3.0 # for unconditional guidance\n",
|
||||
"scale = 3.0 # for unconditional guidance\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"all_samples = list()\n",
|
||||
@ -295,36 +296,39 @@
|
||||
"with torch.no_grad():\n",
|
||||
" with model.ema_scope():\n",
|
||||
" uc = model.get_learned_conditioning(\n",
|
||||
" {model.cond_stage_key: torch.tensor(n_samples_per_class*[1000]).to(model.device)}\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" {model.cond_stage_key: torch.tensor(n_samples_per_class * [1000]).to(model.device)}\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" for class_label in classes:\n",
|
||||
" print(f\"rendering {n_samples_per_class} examples of class '{class_label}' in {ddim_steps} steps and using s={scale:.2f}.\")\n",
|
||||
" xc = torch.tensor(n_samples_per_class*[class_label])\n",
|
||||
" print(\n",
|
||||
" f\"rendering {n_samples_per_class} examples of class '{class_label}' in {ddim_steps} steps and using s={scale:.2f}.\"\n",
|
||||
" )\n",
|
||||
" xc = torch.tensor(n_samples_per_class * [class_label])\n",
|
||||
" c = model.get_learned_conditioning({model.cond_stage_key: xc.to(model.device)})\n",
|
||||
" \n",
|
||||
" samples_ddim, _ = sampler.sample(S=ddim_steps,\n",
|
||||
" conditioning=c,\n",
|
||||
" batch_size=n_samples_per_class,\n",
|
||||
" shape=[3, 64, 64],\n",
|
||||
" verbose=False,\n",
|
||||
" unconditional_guidance_scale=scale,\n",
|
||||
" unconditional_conditioning=uc, \n",
|
||||
" eta=ddim_eta)\n",
|
||||
"\n",
|
||||
" samples_ddim, _ = sampler.sample(\n",
|
||||
" S=ddim_steps,\n",
|
||||
" conditioning=c,\n",
|
||||
" batch_size=n_samples_per_class,\n",
|
||||
" shape=[3, 64, 64],\n",
|
||||
" verbose=False,\n",
|
||||
" unconditional_guidance_scale=scale,\n",
|
||||
" unconditional_conditioning=uc,\n",
|
||||
" eta=ddim_eta,\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" x_samples_ddim = model.decode_first_stage(samples_ddim)\n",
|
||||
" x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, \n",
|
||||
" min=0.0, max=1.0)\n",
|
||||
" x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)\n",
|
||||
" all_samples.append(x_samples_ddim)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# display as grid\n",
|
||||
"grid = torch.stack(all_samples, 0)\n",
|
||||
"grid = rearrange(grid, 'n b c h w -> (n b) c h w')\n",
|
||||
"grid = rearrange(grid, \"n b c h w -> (n b) c h w\")\n",
|
||||
"grid = make_grid(grid, nrow=n_samples_per_class)\n",
|
||||
"\n",
|
||||
"# to image\n",
|
||||
"grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()\n",
|
||||
"grid = 255.0 * rearrange(grid, \"c h w -> h w c\").cpu().numpy()\n",
|
||||
"Image.fromarray(grid.astype(np.uint8))"
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
Loading…
Reference in New Issue
Block a user