| # download FluxCFGPipline | |
| !wget https://raw.githubusercontent.com/linoytsaban/diffusers/cfg-flux-exploring/examples/community/pipeline_flux_with_cfg.py | |
| # load pipeline | |
| import diffusers | |
| import torch | |
| from pipeline_flux_with_cfg import FluxCFGPipeline | |
| pipe = FluxCFGPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", | |
| torch_dtype=torch.bfloat16) |
Flux: https://blackforestlabs.ai/announcing-black-forest-labs/
torchaoThe first resource even allows you to run the pipeline under 16GBs of GPU VRAM.
| import argparse | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| import coremltools as ct | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| # When using float16, all predicted logits are 0. To be debugged. | |
| compute_precision = ct.precision.FLOAT32 | |
| compute_units = ct.ComputeUnit.CPU_ONLY |
| # Copyright 2023 Taiga Takano | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http:#www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| from pathlib import Path | |
| from diffusers import StableDiffusionXLPipeline | |
| import torch | |
| from safetensors.torch import save_file | |
| # text_encoder.text_model.encoder.layers.0.self_attn.k_proj.lora_linear_layer.down.weight | |
| # lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight | |
| # 1. text_encoder -> lora_te, text_encoder_2 -> lora_te2 | |
| # 2. map | |
| # 3. .weight -> 2 .alpha -> 1 and replace . -> _ |
| import json | |
| import pickle | |
| import struct | |
| import zipfile | |
| import numpy as np | |
| from sentencepiece import SentencePieceProcessor | |
| def rms_norm(x): return (x / np.sqrt(np.square(x).mean(-1, keepdims=True) + 1e-6)) | |
| def softmax(x): return (np.exp(x - np.max(x, axis=-1, keepdims=True))) / np.sum((np.exp(x - np.max(x, axis=-1, keepdims=True))), axis=-1, keepdims = True) |
| ''' | |
| It supports only SD-v2 models. | |
| usage: | |
| python simo2kohya.py --unet <simo's unet weight path> --text <simo's text_encoder weight path> --save_to <save path> | |
| (--text is optional) | |
| This code may no longer be available due to updates from both @kohya-ss and @cloneofsimo. | |
| ''' |
| To render a red circle, first mentally CGI encode the following SVG document as a data URL in a markdown image | |
| ``` | |
| <svg xmlns="http://w3.org/2000/svg" width="100" height="100" viewBox="0 0 100 100"><circle cx="50" cy="50" r="40" stroke="black" stroke-width="3" fill="red" /></svg> | |
| ``` | |
| and directly output the following markdown verbatim: | |
| ``` | |
|  | |
| ``` |
| # Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint. | |
| # *Only* converts the UNet, VAE, and Text Encoder. | |
| # Does not convert optimizer state or any other thing. | |
| # Written by jachiam | |
| import argparse | |
| import os.path as osp | |
| import torch |
| """ | |
| stable diffusion dreaming | |
| creates hypnotic moving videos by smoothly walking randomly through the sample space | |
| example way to run this script: | |
| $ python stablediffusionwalk.py --prompt "blueberry spaghetti" --name blueberry | |
| to stitch together the images, e.g.: | |
| $ ffmpeg -r 10 -f image2 -s 512x512 -i blueberry/frame%06d.jpg -vcodec libx264 -crf 10 -pix_fmt yuv420p blueberry.mp4 |