monster/Qwen-Image-Layered-MRP-MLX
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Fix broken links with emojis

Browse Source
This commit is contained in:
filipstrand 2024-09-23 20:00:32 +02:00
parent dfdd543227
commit 895754df51
1 changed files with 16 additions and 16 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

32
README.md
View File

@ -13,11 +13,11 @@ Run the powerful [FLUX](https://blackforestlabs.ai/#get-flux) models from [Black
- [Philosophy](#philosophy) - [Philosophy](#philosophy)
- [💿 Installation](#-installation) - [💿 Installation](#-installation)
- [🖼️ Generating an image](#-generating-an-image) - [🖼️ Generating an image](#%EF%B8%8F-generating-an-image)
* [📜 Full list of Command-Line Arguments](#-full-list-of-command-line-arguments) * [📜 Full list of Command-Line Arguments](#-full-list-of-command-line-arguments)
- [⏱️ Image generation speed (updated)](#-image-generation-speed-updated) - [⏱️ Image generation speed (updated)](#%EF%B8%8F-image-generation-speed-updated)
- [↔️ Equivalent to Diffusers implementation](#-equivalent-to-diffusers-implementation) - [↔️ Equivalent to Diffusers implementation](#%EF%B8%8F-equivalent-to-diffusers-implementation)
- [🗜️ Quantization](#-quantization) - [🗜️ Quantization](#%EF%B8%8F-quantization)
* [📊 Size comparisons for quantized models](#-size-comparisons-for-quantized-models) * [📊 Size comparisons for quantized models](#-size-comparisons-for-quantized-models)
* [💾 Saving a quantized version to disk](#-saving-a-quantized-version-to-disk) * [💾 Saving a quantized version to disk](#-saving-a-quantized-version-to-disk)
* [💽 Loading and running a quantized version from disk](#-loading-and-running-a-quantized-version-from-disk) * [💽 Loading and running a quantized version from disk](#-loading-and-running-a-quantized-version-from-disk)
@ -25,7 +25,7 @@ Run the powerful [FLUX](https://blackforestlabs.ai/#get-flux) models from [Black
- [🔌 LoRA](#-lora) - [🔌 LoRA](#-lora)
* [Multi-LoRA](#multi-lora) * [Multi-LoRA](#multi-lora)
* [Supported LoRA formats (updated)](#supported-lora-formats-updated) * [Supported LoRA formats (updated)](#supported-lora-formats-updated)
- [🕹️ Controlnet](#-controlnet) - [🕹️ Controlnet](#%EF%B8%8F-controlnet)
- [🚧 Current limitations](#-current-limitations) - [🚧 Current limitations](#-current-limitations)
- [✅ TODO](#-todo) - [✅ TODO](#-todo)
@ -36,7 +36,7 @@ Run the powerful [FLUX](https://blackforestlabs.ai/#get-flux) models from [Black
MFLUX is a line-by-line port of the FLUX implementation in the [Huggingface Diffusers](https://github.com/huggingface/diffusers) library to [Apple MLX](https://github.com/ml-explore/mlx). MFLUX is a line-by-line port of the FLUX implementation in the [Huggingface Diffusers](https://github.com/huggingface/diffusers) library to [Apple MLX](https://github.com/ml-explore/mlx).
MFLUX is purposefully kept minimal and explicit - Network architectures are hardcoded and no config files are used MFLUX is purposefully kept minimal and explicit - Network architectures are hardcoded and no config files are used
except for the tokenizers. The aim is to have a tiny codebase with the single purpose of expressing these models except for the tokenizers. The aim is to have a tiny codebase with the single purpose of expressing these models
(thereby avoiding too many abstractions). While MFLUX priorities readability over generality and performance, [it can still be quite fast](#image-generation-speed-updated), [and even faster quantized](#quantization). (thereby avoiding too many abstractions). While MFLUX priorities readability over generality and performance, [it can still be quite fast](#%EF%B8%8F-image-generation-speed-updated), [and even faster quantized](#%EF%B8%8F-quantization).
All models are implemented from scratch in MLX and only the tokenizers are used via the All models are implemented from scratch in MLX and only the tokenizers are used via the
[Huggingface Transformers](https://github.com/huggingface/transformers) library. Other than that, there are only minimal dependencies [Huggingface Transformers](https://github.com/huggingface/transformers) library. Other than that, there are only minimal dependencies
@ -105,7 +105,7 @@ This example uses the more powerful `dev` model with 25 time steps:
mflux-generate --model dev --prompt "Luxury food photograph" --steps 25 --seed 2 -q 8 mflux-generate --model dev --prompt "Luxury food photograph" --steps 25 --seed 2 -q 8
``` ```
⚠️ *If the specific model is not already downloaded on your machine, it will start the download process and fetch the model weights (~34GB in size for the Schnell or Dev model respectively). See the [quantization](#quantization) section for running compressed versions of the model.* ⚠️ ⚠️ *If the specific model is not already downloaded on your machine, it will start the download process and fetch the model weights (~34GB in size for the Schnell or Dev model respectively). See the [quantization](#%EF%B8%8F-quantization) section for running compressed versions of the model.* ⚠️
*By default, model files are downloaded to the `.cache` folder within your home directory. For example, in my setup, the path looks like this:* *By default, model files are downloaded to the `.cache` folder within your home directory. For example, in my setup, the path looks like this:*
@ -137,11 +137,11 @@ mflux-generate --model dev --prompt "Luxury food photograph" --steps 25 --seed 2
- **`--path`** (optional, `str`, default: `None`): Path to a local model on disk. - **`--path`** (optional, `str`, default: `None`): Path to a local model on disk.
- **`--quantize`** or **`-q`** (optional, `int`, default: `None`): [Quantization](#quantization) (choose between `4` or `8`). - **`--quantize`** or **`-q`** (optional, `int`, default: `None`): [Quantization](#%EF%B8%8F-quantization) (choose between `4` or `8`).
- **`--lora-paths`** (optional, `[str]`, default: `None`): The paths to the [LoRA](#LoRA) weights. - **`--lora-paths`** (optional, `[str]`, default: `None`): The paths to the [LoRA](#-LoRA) weights.
- **`--lora-scales`** (optional, `[float]`, default: `None`): The scale for each respective [LoRA](#LoRA) (will default to `1.0` if not specified and only one LoRA weight is loaded.) - **`--lora-scales`** (optional, `[float]`, default: `None`): The scale for each respective [LoRA](#-LoRA) (will default to `1.0` if not specified and only one LoRA weight is loaded.)
- **`--metadata`** (optional): Exports a `.json` file containing the metadata for the image with the same name. (Even without this flag, the image metadata is saved and can be viewed using `exiftool image.png`) - **`--metadata`** (optional): Exports a `.json` file containing the metadata for the image with the same name. (Even without this flag, the image metadata is saved and can be viewed using `exiftool image.png`)
@ -259,7 +259,7 @@ Luxury food photograph of an italian Linguine pasta alle vongole dish with lots
### 🗜️ Quantization ### 🗜️ Quantization
MFLUX supports running FLUX in 4-bit or 8-bit quantized mode. Running a quantized version can greatly speed up the MFLUX supports running FLUX in 4-bit or 8-bit quantized mode. Running a quantized version can greatly speed up the
generation process and reduce the memory consumption by several gigabytes. [Quantized models also take up less disk space](#size-comparisons-for-quantized-models). generation process and reduce the memory consumption by several gigabytes. [Quantized models also take up less disk space](#-size-comparisons-for-quantized-models).
```sh ```sh
mflux-generate \ mflux-generate \
@ -273,7 +273,7 @@ mflux-generate \
``` ```
![image](src/mflux/assets/comparison6.jpg) ![image](src/mflux/assets/comparison6.jpg)
*In this example, weights are quantized at **runtime** - this is convenient if you don't want to [save a quantized copy of the weights to disk](#saving-a-quantized-version-to-disk), but still want to benefit from the potential speedup and RAM reduction quantization might bring.* *In this example, weights are quantized at **runtime** - this is convenient if you don't want to [save a quantized copy of the weights to disk](#-saving-a-quantized-version-to-disk), but still want to benefit from the potential speedup and RAM reduction quantization might bring.*
By selecting the `--quantize` or `-q` flag to be `4`, `8`, or removing it entirely, we get all 3 images above. As can be seen, there is very little difference between the images (especially between the 8-bit, and the non-quantized result). By selecting the `--quantize` or `-q` flag to be `4`, `8`, or removing it entirely, we get all 3 images above. As can be seen, there is very little difference between the images (especially between the 8-bit, and the non-quantized result).
@ -303,7 +303,7 @@ mflux-save \
*Note that when saving a quantized version, you will need the original huggingface weights.* *Note that when saving a quantized version, you will need the original huggingface weights.*
It is also possible to specify [LoRA](#lora) adapters when saving the model, e.g It is also possible to specify [LoRA](#-lora) adapters when saving the model, e.g
```sh ```sh
mflux-save \ mflux-save \
@ -334,8 +334,8 @@ mflux-generate \
*Note: When loading a quantized model from disk, there is no need to pass in `-q` flag, since we can infer this from the weight metadata.* *Note: When loading a quantized model from disk, there is no need to pass in `-q` flag, since we can infer this from the weight metadata.*
*Also Note: Once we have a local model (quantized [or not](#running-a-non-quantized-model-directly-from-disk)) specified via the `--path` argument, the huggingface cache models are not required to launch the model. *Also Note: Once we have a local model (quantized [or not](#-running-a-non-quantized-model-directly-from-disk)) specified via the `--path` argument, the huggingface cache models are not required to launch the model.
In other words, you can reclaim the 34GB diskspace (per model) by deleting the full 16-bit model from the [Huggingface cache](#generating-an-image) if you choose.* In other words, you can reclaim the 34GB diskspace (per model) by deleting the full 16-bit model from the [Huggingface cache](#%EF%B8%8F-generating-an-image) if you choose.*
*If you don't want to download the full models and quantize them yourself, the 4-bit weights are available here for a direct download:* *If you don't want to download the full models and quantize them yourself, the 4-bit weights are available here for a direct download:*
- [madroid/flux.1-schnell-mflux-4bit](https://huggingface.co/madroid/flux.1-schnell-mflux-4bit) - [madroid/flux.1-schnell-mflux-4bit](https://huggingface.co/madroid/flux.1-schnell-mflux-4bit)
@ -480,7 +480,7 @@ It can work well with `schnell`, but performance is not guaranteed.*
Too high settings will corrupt the image. A recommended starting point a value like 0.4 and to play around with the strength.* Too high settings will corrupt the image. A recommended starting point a value like 0.4 and to play around with the strength.*
Controlnet can also work well together with [LoRA adapters](#lora). In the example below the same reference image is used as a controlnet input Controlnet can also work well together with [LoRA adapters](#-lora). In the example below the same reference image is used as a controlnet input
with different prompts and LoRA adapters active. with different prompts and LoRA adapters active.
![image](src/mflux/assets/controlnet2.jpg) ![image](src/mflux/assets/controlnet2.jpg)