From b460c3e003491d2ae1b4ccef1e8945487499af45 Mon Sep 17 00:00:00 2001 From: Soumik Rakshit <19soumik.rakshit96@gmail.com> Date: Mon, 4 Dec 2023 08:35:38 +0000 Subject: [PATCH 1/2] add: pixart alpha notebook --- colabs/diffusers/pixart-alpha-diffusers.ipynb | 7142 +++++++++++++++++ 1 file changed, 7142 insertions(+) create mode 100644 colabs/diffusers/pixart-alpha-diffusers.ipynb diff --git a/colabs/diffusers/pixart-alpha-diffusers.ipynb b/colabs/diffusers/pixart-alpha-diffusers.ipynb new file mode 100644 index 00000000..ddb6954a --- /dev/null +++ b/colabs/diffusers/pixart-alpha-diffusers.ipynb @@ -0,0 +1,7142 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Image Generation with Pixart-α using 🤗 Diffusers\n", + "\n", + "This notebook demonstrates the following:\n", + "- Performing text-conditional image-generations with the [Pixart-α model](https://huggingface.co/docs/diffusers/v0.23.1/en/api/pipelines/pixart) using [🤗 Diffusers](https://huggingface.co/docs/diffusers).\n", + "- Manage image generation experiments using [Weights & Biases](http://wandb.ai/site).\n", + "- Log the prompts, generated images and experiment configs to [Weigts & Biases](http://wandb.ai/site) for visalization." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0_F8JWIgNfqy", + "outputId": "bd6cdb65-e29c-4f93-b7a9-406993a94d62" + }, + "outputs": [], + "source": [ + "!pip install diffusers transformers accelerate sentencepiece ftfy > install.log\n", + "!pip install git+https://github.com/wandb/wandb@feat/diffusers-autologger > install.log" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 87, + "referenced_widgets": [ + "95616ad88fc84e80a99f7aa0987a4372", + "2d4b6035778d4bac8a2159504533272e", + "35675cfe3b5d4c4f83fddd70c0cf8c50", + 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"id": "O4WktKfiQ0z7", + "outputId": "0be1b5e0-cfb2-4b1d-f9e4-18d97b92377c" + }, + "outputs": [], + "source": [ + "# Load the pre-trained checkpoints from HuggingFace Hub to the PixArtAlphaPipeline\n", + "pipe = PixArtAlphaPipeline.from_pretrained(\n", + " \"PixArt-alpha/PixArt-XL-2-1024-MS\", torch_dtype=torch.float16\n", + ")\n", + "\n", + "# Enable offloading the weights to the CPU and only loading them on the GPU when\n", + "# performing the forward pass can also save memory.\n", + "pipe.enable_model_cpu_offload()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MQIZo4F_Q1qG" + }, + "outputs": [], + "source": [ + "wandb_project = \"pixart-alpha\" # @param {type:\"string\"}\n", + "\n", + "prompt = \"a traveler navigating via a boat in countless mountains, Chinese ink painting\" # @param {type:\"string\"}\n", + "negative_prompt = \"\" # @param {type:\"string\"}\n", + "num_inference_steps = 25 # @param {type:\"slider\", min:10, max:50, step:1}\n", + "guidance_scale = 4.5 # @param {type:\"slider\", min:0, max:10, step:0.1}\n", + "num_images_per_prompt = 1 # @param {type:\"slider\", min:0, max:10, step:0.1}\n", + "height = 1024 # @param {type:\"slider\", min:512, max:2560, step:32}\n", + "width = 1024 # @param {type:\"slider\", min:512, max:2560, step:32}\n", + "seed = None # @param {type:\"raw\"}\n", + "\n", + "\n", + "def autogenerate_seed():\n", + " max_seed = int(1024 * 1024 * 1024)\n", + " seed = random.randint(1, max_seed)\n", + " seed = -seed if seed < 0 else seed\n", + " seed = seed % max_seed\n", + " return seed\n", + "\n", + "\n", + "seed = autogenerate_seed() if seed is None else seed\n", + "\n", + "# Make the experiment reproducible by controlling randomness.\n", + "# The seed would be automatically logged to WandB.\n", + "generator = torch.Generator(device=\"cuda\").manual_seed(seed)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": 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This would automatically log\n", + "# the prompts, generated images, pipeline architecture and all\n", + "# associated experiment configs to Weights & Biases, thus making your\n", + "# image generation experiments easy to reproduce, share and analyze.\n", + "autolog(init=dict(project=wandb_project))\n", + "\n", + "# Generate the images by calling the PixArtAlphaPipeline\n", + "image = pipe(\n", + " prompt=prompt,\n", + " negative_prompt=negative_prompt,\n", + " num_inference_steps=num_inference_steps,\n", + " guidance_scale=guidance_scale,\n", + " num_images_per_prompt=num_images_per_prompt,\n", + " height=height,\n", + " width=width,\n", + " generator=generator,\n", + ").images[0]\n", + "\n", + "# End the experiment\n", + "wandb.finish()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "machine_shape": "hm", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + 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