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Deploy LLMs with Hugging Face Inference Endpoints
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philschmid
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July 4, 2023
|
inference-endpoints-llm
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guide, llm, apps, inference
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https://huggingface.co/blog/inference-endpoints-llm
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Streaming requests with Python First, you need to install the huggingface_hub library: python pip install -U huggingface_hub We can create a InferenceClient providing our endpoint URL and credential alongside the hyperparameters we want to use python from huggingface_hub import InferenceClient # HF Inference Endpoints parameter endpoint_url = "https://YOUR_ENDPOINT.endpoints.huggingface.cloud" hf_token = "hf_YOUR_TOKEN" # Streaming Client client = InferenceClient(endpoint_url, token=hf_token) # generation parameter gen_kwargs = dict( max_new_tokens=512, top_k=30, top_p=0.9, temperature=0.2, repetition_penalty=1.02, stop_sequences=["\nUser:", "<|endoftext|>", "</s>"], ) # prompt prompt = "What can you do in Nuremberg, Germany? Give me 3 Tips" stream = client.text_generation(prompt, stream=True, details=True, **gen_kwargs) # yield each generated token for r in stream: # skip special tokens if r.token.special: continue # stop if we encounter a stop sequence if r.token.text in gen_kwargs["stop_sequences"]: break # yield the generated token print(r.token.text, end = "") # yield r.token.text Replace the print command with the yield or with a function you want to stream the tokens to.
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] |
Making ML-powered web games with Transformers.js
|
Xenova
|
July 5, 2023
|
ml-web-games
|
game-dev, guide, web, javascript, transformers.js
|
https://huggingface.co/blog/ml-web-games
|
# Making ML-powered web games with Transformers.js In this blog post, I'll show you how I made Doodle Dash, a real-time ML-powered web game that runs completely in your browser (thanks to Transformers.js). The goal of this tutorial is to show you how easy it is to make your own ML-powered web game... just in time for the upcoming Open Source AI Game Jam (7-9 July 2023). Join the game jam if you haven't already! Video: Doodle Dash demo video
|
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] |
Making ML-powered web games with Transformers.js
|
Xenova
|
July 5, 2023
|
ml-web-games
|
game-dev, guide, web, javascript, transformers.js
|
https://huggingface.co/blog/ml-web-games
|
3. Game Design In this section, I'll briefly discuss the game design process. As a reminder, you can find the full source code for the project on GitHub, so I won't be going into detail about the code itself.
|
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] |
Making ML-powered web games with Transformers.js
|
Xenova
|
July 5, 2023
|
ml-web-games
|
game-dev, guide, web, javascript, transformers.js
|
https://huggingface.co/blog/ml-web-games
|
Model architecture We'll be finetuning apple/mobilevit-small, a lightweight and mobile-friendly Vision Transformer that has been pre-trained on ImageNet-1k. It has only 5.6M parameters (~20 MB file size), a perfect candidate for running in-browser! For more information, check out the MobileViT paper and the model architecture below.
|
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] |
Making ML-powered web games with Transformers.js
|
Xenova
|
July 5, 2023
|
ml-web-games
|
game-dev, guide, web, javascript, transformers.js
|
https://huggingface.co/blog/ml-web-games
|
Running the model in the browser Running machine learning models is computationally intensive, so it's important to perform inference in a separate thread. This way we won't block the main thread, which is used for rendering the UI and reacting to your drawing gestures 😉. The Web Workers API makes this super simple! Create a new file (e.g., worker.js) in the src directory and add the following code: js import { pipeline, RawImage } from "@xenova/transformers"; const classifier = await pipeline("image-classification", 'Xenova/quickdraw-mobilevit-small', { quantized: false }); const image = await RawImage.read('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/ml-web-games/skateboard.png'); const output = await classifier(image.grayscale()); console.log(output); We can now use this worker in our App.jsx file by adding the following code to the App component: jsx import { useState, useEffect, useRef } from 'react' // ... rest of the imports function App() { // Create a reference to the worker object. const worker = useRef(null); // We use the `useEffect` hook to set up the worker as soon as the `App` component is mounted. useEffect(() => { if (!worker.current) { // Create the worker if it does not yet exist. worker.current = new Worker(new URL('./worker.js', import.meta.url), { type: 'module' }); } // Create a callback function for messages from the worker thread. const onMessageReceived = (e) => { /* See code */ }; // Attach the callback function as an event listener. worker.current.addEventListener('message', onMessageReceived); // Define a cleanup function for when the component is unmounted. return () => worker.current.removeEventListener('message', onMessageReceived); }); // ... rest of the component } You can test that everything is working by running the development server (with npm run dev), visiting the local website (usually http://localhost:5173/), and opening the browser console. You should see the output of the model being logged to the console. js [{ label: "skateboard", score: 0.9980043172836304 }] Woohoo! 🥳 Although the above code is just a small part of the final product, it shows how simple the machine-learning side of it is! The rest is just making it look nice and adding some game logic.
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] |
Making ML-powered web games with Transformers.js
|
Xenova
|
July 5, 2023
|
ml-web-games
|
game-dev, guide, web, javascript, transformers.js
|
https://huggingface.co/blog/ml-web-games
|
Overview Before we start, let's talk about what we'll be creating. The game is inspired by Google's Quick, Draw! game, where you're given a word and a neural network has 20 seconds to guess what you're drawing (repeated 6 times). In fact, we'll be using their training data to train our own sketch detection model! Don't you just love open source? 😍 In our version, you'll have one minute to draw as many items as you can, one prompt at a time. If the model predicts the correct label, the canvas will be cleared and you'll be given a new word. Keep doing this until the timer runs out! Since the game runs locally in your browser, we don't have to worry about server latency at all. The model is able to make real-time predictions as you draw, to the tune of over 60 predictions a second... 🤯 WOW! This tutorial is split into 3 sections: 1. Training the neural network 2. Running in the browser with Transformers.js 3. Game Design
|
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] |
Making ML-powered web games with Transformers.js
|
Xenova
|
July 5, 2023
|
ml-web-games
|
game-dev, guide, web, javascript, transformers.js
|
https://huggingface.co/blog/ml-web-games
|
Taking advantage of real-time performance One of the main advantages of performing in-browser inference is that we can make predictions in real time (over 60 times a second). In the original Quick, Draw! game, the model only makes a new prediction every couple of seconds. We could do the same in our game, but then we wouldn't be taking advantage of its real-time performance! So, I decided to redesign the main game loop: - Instead of six 20-second rounds (where each round corresponds to a new word), our version tasks the player with correctly drawing as many doodles as they can in 60 seconds (one prompt at a time). - If you come across a word you are unable to draw, you can skip it (but this will cost you 3 seconds of your remaining time). - In the original game, since the model would make a guess every few seconds, it could slowly cross labels off the list until it eventually guessed correctly. In our version, we instead decrease the model's scores for the first n incorrect labels, with n increasing over time as the user continues drawing.
|
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] |
Making ML-powered web games with Transformers.js
|
Xenova
|
July 5, 2023
|
ml-web-games
|
game-dev, guide, web, javascript, transformers.js
|
https://huggingface.co/blog/ml-web-games
|
What is Transformers.js? Transformers.js is a JavaScript library that allows you to run 🤗 Transformers directly in your browser (no need for a server)! It's designed to be functionally equivalent to the Python library, meaning you can run the same pre-trained models using a very similar API. Behind the scenes, Transformers.js uses ONNX Runtime, so we need to convert our finetuned PyTorch model to ONNX.
|
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] |
Making ML-powered web games with Transformers.js
|
Xenova
|
July 5, 2023
|
ml-web-games
|
game-dev, guide, web, javascript, transformers.js
|
https://huggingface.co/blog/ml-web-games
|
Quality of life improvements The original dataset contains 345 different classes, and since our model is relatively small (~20MB), it sometimes is unable to correctly guess some of the classes. To solve this problem, we removed some words which are either: - Too similar to other labels (e.g., "barn" vs. "house") - Too difficult to understand (e.g., "animal migration") - Too difficult to draw in sufficient detail (e.g., "brain") - Ambiguous (e.g., "bat") After filtering, we were still left with over 300 different classes!
|
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] |
Making ML-powered web games with Transformers.js
|
Xenova
|
July 5, 2023
|
ml-web-games
|
game-dev, guide, web, javascript, transformers.js
|
https://huggingface.co/blog/ml-web-games
|
Finetuning To keep the blog post (relatively) short, we've prepared a Colab notebook which will show you the exact steps we took to finetune apple/mobilevit-small on our dataset. At a high level, this involves: 1. Loading the "Quick, Draw!" dataset. 2. Transforming the dataset using a MobileViTImageProcessor. 3. Defining our collate function and evaluation metric. 4. Loading the pre-trained MobileVIT model using MobileViTForImageClassification.from_pretrained. 5. Training the model using the Trainer and TrainingArguments helper classes. 6. Evaluating the model using 🤗 Evaluate. NOTE: You can find our finetuned model here on the Hugging Face Hub.
|
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] |
Making ML-powered web games with Transformers.js
|
Xenova
|
July 5, 2023
|
ml-web-games
|
game-dev, guide, web, javascript, transformers.js
|
https://huggingface.co/blog/ml-web-games
|
BONUS: Coming up with the name In the spirit of open-source development, I decided to ask Hugging Chat for some game name ideas... and needless to say, it did not disappoint! I liked the alliteration of "Doodle Dash" (suggestion #4), so I decided to go with that. Thanks Hugging Chat! 🤗 --- I hope you enjoyed building this game with me! If you have any questions or suggestions, you can find me on Twitter, GitHub, or the 🤗 Hub. Also, if you want to improve the game (game modes? power-ups? animations? sound effects?), feel free to fork the project and submit a pull request! I'd love to see what you come up with! PS: Don't forget to join the Open Source AI Game Jam! Hopefully this blog post inspires you to build your own web game with Transformers.js! 😉 See you at the Game Jam! 🚀
|
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] |
Making ML-powered web games with Transformers.js
|
Xenova
|
July 5, 2023
|
ml-web-games
|
game-dev, guide, web, javascript, transformers.js
|
https://huggingface.co/blog/ml-web-games
|
Training data We'll be training our model using a subset of Google's Quick, Draw! dataset, which contains over 5 million drawings across 345 categories. Here are some samples from the dataset:
|
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] |
Making ML-powered web games with Transformers.js
|
Xenova
|
July 5, 2023
|
ml-web-games
|
game-dev, guide, web, javascript, transformers.js
|
https://huggingface.co/blog/ml-web-games
|
2. Running in the browser with Transformers.js
|
[
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] |
Making ML-powered web games with Transformers.js
|
Xenova
|
July 5, 2023
|
ml-web-games
|
game-dev, guide, web, javascript, transformers.js
|
https://huggingface.co/blog/ml-web-games
|
Converting our model to ONNX Fortunately, the 🤗 Optimum library makes it super simple to convert your finetuned model to ONNX! The easiest (and recommended way) is to: 1. Clone the Transformers.js repository and install the necessary dependencies: bash git clone https://github.com/xenova/transformers.js.git cd transformers.js pip install -r scripts/requirements.txt 2. Run the conversion script (it uses Optimum under the hood): bash python -m scripts.convert --model_id <model_id> where <model_id> is the name of the model you want to convert (e.g. Xenova/quickdraw-mobilevit-small).
|
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] |
Making ML-powered web games with Transformers.js
|
Xenova
|
July 5, 2023
|
ml-web-games
|
game-dev, guide, web, javascript, transformers.js
|
https://huggingface.co/blog/ml-web-games
|
Setting up our project Let's start by scaffolding a simple React app using Vite: bash npm create vite@latest doodle-dash -- --template react Next, enter the project directory and install the necessary dependencies: bash cd doodle-dash npm install npm install @xenova/transformers You can then start the development server by running: bash npm run dev
|
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] |
Making ML-powered web games with Transformers.js
|
Xenova
|
July 5, 2023
|
ml-web-games
|
game-dev, guide, web, javascript, transformers.js
|
https://huggingface.co/blog/ml-web-games
|
Quick links - Demo: Doodle Dash - Source code: doodle-dash - Join the game jam: Open Source AI Game Jam
|
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] |
Making ML-powered web games with Transformers.js
|
Xenova
|
July 5, 2023
|
ml-web-games
|
game-dev, guide, web, javascript, transformers.js
|
https://huggingface.co/blog/ml-web-games
|
1. Training the neural network
|
[
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] |
Fine-tuning Stable Diffusion models on Intel CPUs
|
juliensimon
|
July 14, 2023
|
stable-diffusion-finetuning-intel
|
guide, intel, hardware, partnerships
|
https://huggingface.co/blog/stable-diffusion-finetuning-intel
|
# Fine-tuning Stable Diffusion Models on Intel CPUs Diffusion models helped popularize generative AI thanks to their uncanny ability to generate photorealistic images from text prompts. These models have now found their way into enterprise use cases like synthetic data generation or content creation. The Hugging Face hub includes over 5,000 pre-trained text-to-image models. Combining them with the Diffusers library, it's never been easier to start experimenting and building image generation workflows. Like Transformer models, you can fine-tune Diffusion models to help them generate content that matches your business needs. Initially, fine-tuning was only possible on GPU infrastructure, but things are changing! A few months ago, Intel launched the fourth generation of Xeon CPUs, code-named Sapphire Rapids. Sapphire Rapids introduces the Intel Advanced Matrix Extensions (AMX), a new hardware accelerator for deep learning workloads. We've already demonstrated the benefits of AMX in several blog posts: fine-tuning NLP Transformers, inference with NLP Transformers, and inference with Stable Diffusion models. This post will show you how to fine-tune a Stable Diffusion model on an Intel Sapphire Rapids CPU cluster. We will use textual inversion, a technique that only requires a small number of example images. We'll use only five! Let's get started.
|
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] |
Fine-tuning Stable Diffusion models on Intel CPUs
|
juliensimon
|
July 14, 2023
|
stable-diffusion-finetuning-intel
|
guide, intel, hardware, partnerships
|
https://huggingface.co/blog/stable-diffusion-finetuning-intel
|
Setting up the cluster Our friends at Intel provided four servers hosted on the Intel Developer Cloud (IDC), a service platform for developing and running workloads in Intel®-optimized deployment environments with the latest Intel processors and performance-optimized software stacks. Each server is powered by two Intel Sapphire Rapids CPUs with 56 physical cores and 112 threads. Here's the output of lscpu: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 52 bits physical, 57 bits virtual Byte Order: Little Endian CPU(s): 224 On-line CPU(s) list: 0-223 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Platinum 8480+ CPU family: 6 Model: 143 Thread(s) per core: 2 Core(s) per socket: 56 Socket(s): 2 Stepping: 8 CPU max MHz: 3800.0000 CPU min MHz: 800.0000 BogoMIPS: 4000.00 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_per fmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities Let's first list the IP addresses of our servers in nodefile. The first line refers to the primary server. cat << EOF > nodefile 192.168.20.2 192.168.21.2 192.168.22.2 192.168.23.2 EOF Distributed training requires password-less ssh between the primary and other nodes. Here's a good article on how to do this if you're unfamiliar with the process. Next, we create a new environment on each node and install the software dependencies. We notably install two Intel libraries: oneCCL, to manage distributed communication and the Intel Extension for PyTorch (IPEX) to leverage the hardware acceleration features present in Sapphire Rapids. We also add gperftools to install libtcmalloc, a high-performance memory allocation library. conda create -n diffuser python==3.9 conda activate diffuser pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu pip3 install transformers accelerate==0.19.0 pip3 install oneccl_bind_pt -f https://developer.intel.com/ipex-whl-stable-cpu pip3 install intel_extension_for_pytorch conda install gperftools -c conda-forge -y Next, we clone the diffusers repository on each node and install it from source. git clone https://github.com/huggingface/diffusers.git cd diffusers pip install . Next, we add IPEX to the fine-tuning script in diffusers/examples/textual_inversion. We import IPEX and optimize the U-Net and Variable Auto Encoder models. Please make sure this is applied to all nodes. diff --git a/examples/textual_inversion/textual_inversion.py b/examples/textual_inversion/textual_inversion.py index 4a193abc..91c2edd1 100644 --- a/examples/textual_inversion/textual_inversion.py +++ b/examples/textual_inversion/textual_inversion.py @@ -765,6 +765,10 @@ def main(): unet.to(accelerator.device, dtype=weight_dtype) vae.to(accelerator.device, dtype=weight_dtype) + import intel_extension_for_pytorch as ipex + unet = ipex.optimize(unet, dtype=weight_dtype) + vae = ipex.optimize(vae, dtype=weight_dtype) + # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: The last step is downloading the training images. Ideally, we'd use a shared NFS folder, but for the sake of simplicity, we'll download the images on each node. Please ensure they're in the same directory on all nodes (/home/devcloud/dicoo). mkdir /home/devcloud/dicoo cd /home/devcloud/dicoo wget https://huggingface.co/sd-concepts-library/dicoo/resolve/main/concept_images/0.jpeg wget https://huggingface.co/sd-concepts-library/dicoo/resolve/main/concept_images/1.jpeg wget https://huggingface.co/sd-concepts-library/dicoo/resolve/main/concept_images/2.jpeg wget https://huggingface.co/sd-concepts-library/dicoo/resolve/main/concept_images/3.jpeg wget https://huggingface.co/sd-concepts-library/dicoo/resolve/main/concept_images/4.jpeg Here are the images: The system setup is now complete. Let's configure the training job.
|
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] |
Fine-tuning Stable Diffusion models on Intel CPUs
|
juliensimon
|
July 14, 2023
|
stable-diffusion-finetuning-intel
|
guide, intel, hardware, partnerships
|
https://huggingface.co/blog/stable-diffusion-finetuning-intel
|
Troubleshooting Distributed training can be tricky, especially if you're new to the discipline. A minor misconfiguration on a single node is the most likely issue: missing dependency, images stored in a different location, etc. You can quickly pinpoint the troublemaker by logging in to each node and training locally. First, set the same environment as on the primary node, then run: python diffusers/examples/textual_inversion/textual_inversion.py \ --pretrained_model_name_or_path=$MODEL_NAME --train_data_dir=$DATA_DIR \ --learnable_property="object" --placeholder_token="<dicoo>" --initializer_token="toy" \ --resolution=512 --train_batch_size=1 --seed=7 --gradient_accumulation_steps=1 \ --max_train_steps=200 --learning_rate=2.0e-03 --scale_lr --lr_scheduler="constant" \ --lr_warmup_steps=0 --output_dir=./textual_inversion_output --mixed_precision bf16 \ --save_as_full_pipeline If training starts successfully, stop it and move to the next node. If training starts successfully on all nodes, return to the primary node and double-check the node file, the environment, and the mpirun command. Don't worry; you'll find the problem :)
|
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Fine-tuning Stable Diffusion models on Intel CPUs
|
juliensimon
|
July 14, 2023
|
stable-diffusion-finetuning-intel
|
guide, intel, hardware, partnerships
|
https://huggingface.co/blog/stable-diffusion-finetuning-intel
|
Configuring the fine-tuning job The Accelerate library makes it very easy to run distributed training. We need to run it on each node and answer simple questions. Here's a screenshot for the primary node. On the other nodes, you need to set the rank to 1, 2, and 3. All other answers are identical. Finally, we need to set the environment on the primary node. It will be propagated to other nodes as the fine-tuning job starts. The first line sets the name of the network interface connected to the local network where all nodes run. You may need to adapt this usingifconfig to get the appropriate information. export I_MPI_HYDRA_IFACE=ens786f1 oneccl_bindings_for_pytorch_path=$(python -c "from oneccl_bindings_for_pytorch import cwd; print(cwd)") source $oneccl_bindings_for_pytorch_path/env/setvars.sh export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libiomp5.so export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so export CCL_ATL_TRANSPORT=ofi export CCL_WORKER_COUNT=1 export MODEL_NAME="runwayml/stable-diffusion-v1-5" export DATA_DIR="/home/devcloud/dicoo" We can now launch the fine-tuning job.
|
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] |
Fine-tuning Stable Diffusion models on Intel CPUs
|
juliensimon
|
July 14, 2023
|
stable-diffusion-finetuning-intel
|
guide, intel, hardware, partnerships
|
https://huggingface.co/blog/stable-diffusion-finetuning-intel
|
Generating images with the fine-tuned model After 5 minutes training, the model is saved locally. We could load it with a vanilla diffusers pipeline and predict. Instead, let's use Optimum Intel and OpenVINO to optimize the model. As discussed in a previous post, this lets you generate an image on a single CPU in less than 5 seconds! pip install optimum[openvino] Here, we load the model, optimize it for a static shape, and save it: from optimum.intel.openvino import OVStableDiffusionPipeline model_id = "./textual_inversion_output" ov_pipe = OVStableDiffusionPipeline.from_pretrained(model_id, export=True) ov_pipe.reshape(batch_size=5, height=512, width=512, num_images_per_prompt=1) ov_pipe.save_pretrained("./textual_inversion_output_ov") Then, we load the optimized model, generate five different images and save them: from optimum.intel.openvino import OVStableDiffusionPipeline model_id = "./textual_inversion_output_ov" ov_pipe = OVStableDiffusionPipeline.from_pretrained(model_id, num_inference_steps=20) prompt = ["a yellow <dicoo> robot at the beach, high quality"]*5 images = ov_pipe(prompt).images print(images) for idx,img in enumerate(images): img.save(f"image{idx}.png") Here's a generated image. It is impressive that the model only needed five images to learn that dicoos have glasses! If you'd like, you can fine-tune the model some more. Here's a lovely example generated by a 3,000-step model (about an hour of training).
|
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] |
Fine-tuning Stable Diffusion models on Intel CPUs
|
juliensimon
|
July 14, 2023
|
stable-diffusion-finetuning-intel
|
guide, intel, hardware, partnerships
|
https://huggingface.co/blog/stable-diffusion-finetuning-intel
|
Conclusion Thanks to Hugging Face and Intel, you can now use Xeon CPU servers to generate high-quality images adapted to your business needs. They are generally more affordable and widely available than specialized hardware such as GPUs. Xeon CPUs can also be easily repurposed for other production tasks, from web servers to databases, making them a versatile and flexible choice for your IT infrastructure. Here are some resources to help you get started: * Diffusers documentation * Optimum Intel documentation * Intel IPEX on GitHub * Developer resources from Intel and Hugging Face. * Sapphire Rapids servers on Intel Developer Cloud, AWS and GCP. If you have questions or feedback, we'd love to read them on the Hugging Face forum. Thanks for reading!
|
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] |
Fine-tuning Stable Diffusion models on Intel CPUs
|
juliensimon
|
July 14, 2023
|
stable-diffusion-finetuning-intel
|
guide, intel, hardware, partnerships
|
https://huggingface.co/blog/stable-diffusion-finetuning-intel
|
Fine-tuning the model We launch the fine-tuning job with mpirun, which sets up distributed communication across the nodes listed in nodefile. We'll run 16 tasks (-n) with four tasks per node (-ppn). Accelerate automatically sets up distributed training across all tasks. Here, we train for 200 steps, which should take about five minutes. mpirun -f nodefile -n 16 -ppn 4 \ accelerate launch diffusers/examples/textual_inversion/textual_inversion.py \ --pretrained_model_name_or_path=$MODEL_NAME --train_data_dir=$DATA_DIR \ --learnable_property="object" --placeholder_token="<dicoo>" --initializer_token="toy" \ --resolution=512 --train_batch_size=1 --seed=7 --gradient_accumulation_steps=1 \ --max_train_steps=200 --learning_rate=2.0e-03 --scale_lr --lr_scheduler="constant" \ --lr_warmup_steps=0 --output_dir=./textual_inversion_output --mixed_precision bf16 \ --save_as_full_pipeline Here's a screenshot of the busy cluster:
|
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] |
Open-Source Text Generation & LLM Ecosystem at Hugging Face
|
merve
|
July 17, 2023
|
os-llms
|
LLM, inference, nlp
|
https://huggingface.co/blog/os-llms
|
How to find the best model? Hugging Face hosts an LLM leaderboard. This leaderboard is created by evaluating community-submitted models on text generation benchmarks on Hugging Face’s clusters. If you can’t find the language or domain you’re looking for, you can filter them here. You can also check out the LLM Performance leaderboard, which aims to evaluate the latency and throughput of large language models available on Hugging Face Hub.
|
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] |
Open-Source Text Generation & LLM Ecosystem at Hugging Face
|
merve
|
July 17, 2023
|
os-llms
|
LLM, inference, nlp
|
https://huggingface.co/blog/os-llms
|
Text Generation Inference Response time and latency for concurrent users are a big challenge for serving these large models. To tackle this problem, Hugging Face has released text-generation-inference (TGI), an open-source serving solution for large language models built on Rust, Python, and gRPc. TGI is integrated into inference solutions of Hugging Face, Inference Endpoints, and Inference API, so you can directly create an endpoint with optimized inference with few clicks, or simply send a request to Hugging Face's Inference API to benefit from it, instead of integrating TGI to your platform. TGI currently powers HuggingChat, Hugging Face's open-source chat UI for LLMs. This service currently uses one of OpenAssistant's models as the backend model. You can chat as much as you want with HuggingChat and enable the Web search feature for responses that use elements from current Web pages. You can also give feedback to each response for model authors to train better models. The UI of HuggingChat is also open-sourced, and we are working on more features for HuggingChat to allow more functions, like generating images inside the chat. Recently, a Docker template for HuggingChat was released for Hugging Face Spaces. This allows anyone to deploy their instance based on a large language model with only a few clicks and customize it. You can create your large language model instance here based on various LLMs, including Llama 2.
|
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] |
Open-Source Text Generation & LLM Ecosystem at Hugging Face
|
merve
|
July 17, 2023
|
os-llms
|
LLM, inference, nlp
|
https://huggingface.co/blog/os-llms
|
Parameter Efficient Fine Tuning (PEFT) If you’d like to fine-tune one of the existing large models on your instruction dataset, it is nearly impossible to do so on consumer hardware and later deploy them (since the instruction models are the same size as the original checkpoints that are used for fine-tuning). PEFT is a library that allows you to do parameter-efficient fine-tuning techniques. This means that rather than training the whole model, you can train a very small number of additional parameters, enabling much faster training with very little performance degradation. With PEFT, you can do low-rank adaptation (LoRA), prefix tuning, prompt tuning, and p-tuning. You can check out further resources for more information on text generation. Further Resources - Together with AWS we released TGI-based LLM deployment deep learning containers called LLM Inference Containers. Read about them here. - Text Generation task page to find out more about the task itself. - PEFT announcement blog post. - Read about how Inference Endpoints use TGI here. - Read about how to fine-tune Llama 2 transformers and PEFT, and prompt here.
|
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] |
Open-Source Text Generation & LLM Ecosystem at Hugging Face
|
merve
|
July 17, 2023
|
os-llms
|
LLM, inference, nlp
|
https://huggingface.co/blog/os-llms
|
# Open-Source Text Generation & LLM Ecosystem at Hugging Face [Updated on July 24, 2023: Added Llama 2.] Text generation and conversational technologies have been around for ages. Earlier challenges in working with these technologies were controlling both the coherence and diversity of the text through inference parameters and discriminative biases. More coherent outputs were less creative and closer to the original training data and sounded less human. Recent developments overcame these challenges, and user-friendly UIs enabled everyone to try these models out. Services like ChatGPT have recently put the spotlight on powerful models like GPT-4 and caused an explosion of open-source alternatives like Llama to go mainstream. We think these technologies will be around for a long time and become more and more integrated into everyday products. This post is divided into the following sections: 1. Brief background on text generation 2. Licensing 3. Tools in the Hugging Face Ecosystem for LLM Serving 4. Parameter Efficient Fine Tuning (PEFT)
|
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] |
Open-Source Text Generation & LLM Ecosystem at Hugging Face
|
merve
|
July 17, 2023
|
os-llms
|
LLM, inference, nlp
|
https://huggingface.co/blog/os-llms
|
Tools in the Hugging Face Ecosystem for LLM Serving
|
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] |
Open-Source Text Generation & LLM Ecosystem at Hugging Face
|
merve
|
July 17, 2023
|
os-llms
|
LLM, inference, nlp
|
https://huggingface.co/blog/os-llms
|
Models created with love by Hugging Face with BigScience and BigCode 💗 Hugging Face has co-led two science initiatives, BigScience and BigCode. As a result of them, two large language models were created, BLOOM 🌸 and StarCoder 🌟. BLOOM is a causal language model trained on 46 languages and 13 programming languages. It is the first open-source model to have more parameters than GPT-3. You can find all the available checkpoints in the BLOOM documentation. StarCoder is a language model trained on permissive code from GitHub (with 80+ programming languages 🤯) with a Fill-in-the-Middle objective. It’s not fine-tuned on instructions, and thus, it serves more as a coding assistant to complete a given code, e.g., translate Python to C++, explain concepts (what’s recursion), or act as a terminal. You can try all of the StarCoder checkpoints in this application. It also comes with a VSCode extension. Snippets to use all models mentioned in this blog post are given in either the model repository or the documentation page of that model type in Hugging Face.
|
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] |
Open-Source Text Generation & LLM Ecosystem at Hugging Face
|
merve
|
July 17, 2023
|
os-llms
|
LLM, inference, nlp
|
https://huggingface.co/blog/os-llms
|
Brief Background on Text Generation Text generation models are essentially trained with the objective of completing an incomplete text or generating text from scratch as a response to a given instruction or question. Models that complete incomplete text are called Causal Language Models, and famous examples are GPT-3 by OpenAI and Llama by Meta AI. One concept you need to know before we move on is fine-tuning. This is the process of taking a very large model and transferring the knowledge contained in this base model to another use case, which we call a downstream task. These tasks can come in the form of instructions. As the model size grows, it can generalize better to instructions that do not exist in the pre-training data, but were learned during fine-tuning. Causal language models are adapted using a process called reinforcement learning from human feedback (RLHF). This optimization is mainly made over how natural and coherent the text sounds rather than the validity of the answer. Explaining how RLHF works is outside the scope of this blog post, but you can find more information about this process here. For example, GPT-3 is a causal language base model, while the models in the backend of ChatGPT (which is the UI for GPT-series models) are fine-tuned through RLHF on prompts that can consist of conversations or instructions. It’s an important distinction to make between these models. On the Hugging Face Hub, you can find both causal language models and causal language models fine-tuned on instructions (which we’ll give links to later in this blog post). Llama is one of the first open-source LLMs to have outperformed/matched closed-source ones. A research group led by Together has created a reproduction of Llama's dataset, called Red Pajama, and trained LLMs and instruction fine-tuned models on it. You can read more about it here and find the model checkpoints on Hugging Face Hub. By the time this blog post is written, three of the largest causal language models with open-source licenses are MPT-30B by MosaicML, XGen by Salesforce and Falcon by TII UAE, available completely open on Hugging Face Hub. Recently, Meta released Llama 2, an open-access model with a license that allows commercial use. As of now, Llama 2 outperforms all of the other open-source large language models on different benchmarks. Llama 2 checkpoints on Hugging Face Hub are compatible with transformers, and the largest checkpoint is available for everyone to try at HuggingChat. You can read more about how to fine-tune, deploy and prompt with Llama 2 in this blog post. The second type of text generation model is commonly referred to as the text-to-text generation model. These models are trained on text pairs, which can be questions and answers or instructions and responses. The most popular ones are T5 and BART (which, as of now, aren’t state-of-the-art). Google has recently released the FLAN-T5 series of models. FLAN is a recent technique developed for instruction fine-tuning, and FLAN-T5 is essentially T5 fine-tuned using FLAN. As of now, the FLAN-T5 series of models are state-of-the-art and open-source, available on the Hugging Face Hub. Note that these are different from instruction-tuned causal language models, although the input-output format might seem similar. Below you can see an illustration of how these models work. Having more variation of open-source text generation models enables companies to keep their data private, to adapt models to their domains faster, and to cut costs for inference instead of relying on closed paid APIs. All open-source causal language models on Hugging Face Hub can be found here, and text-to-text generation models can be found here.
|
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] |
Open-Source Text Generation & LLM Ecosystem at Hugging Face
|
merve
|
July 17, 2023
|
os-llms
|
LLM, inference, nlp
|
https://huggingface.co/blog/os-llms
|
Licensing Many text generation models are either closed-source or the license limits commercial use. Fortunately, open-source alternatives are starting to appear and being embraced by the community as building blocks for further development, fine-tuning, or integration with other projects. Below you can find a list of some of the large causal language models with fully open-source licenses: - Falcon 40B - XGen - MPT-30B - Pythia-12B - RedPajama-INCITE-7B - OpenAssistant (Falcon variant) There are two code generation models, StarCoder by BigCode and Codegen by Salesforce. There are model checkpoints in different sizes and open-source or open RAIL licenses for both, except for Codegen fine-tuned on instruction. The Hugging Face Hub also hosts various models fine-tuned for instruction or chat use. They come in various styles and sizes depending on your needs. - MPT-30B-Chat, by Mosaic ML, uses the CC-BY-NC-SA license, which does not allow commercial use. However, MPT-30B-Instruct uses CC-BY-SA 3.0, which can be used commercially. - Falcon-40B-Instruct and Falcon-7B-Instruct both use the Apache 2.0 license, so commercial use is also permitted. - Another popular family of models is OpenAssistant, some of which are built on Meta's Llama model using a custom instruction-tuning dataset. Since the original Llama model can only be used for research, the OpenAssistant checkpoints built on Llama don’t have full open-source licenses. However, there are OpenAssistant models built on open-source models like Falcon or pythia that use permissive licenses. - StarChat Beta is the instruction fine-tuned version of StarCoder, and has BigCode Open RAIL-M v1 license, which allows commercial use. Instruction-tuned coding model of Salesforce, XGen model, only allows research use. If you're looking to fine-tune a model on an existing instruction dataset, you need to know how a dataset was compiled. Some of the existing instruction datasets are either crowd-sourced or use outputs of existing models (e.g., the models behind ChatGPT). ALPACA dataset created by Stanford is created through the outputs of models behind ChatGPT. Moreover, there are various crowd-sourced instruction datasets with open-source licenses, like oasst1 (created by thousands of people voluntarily!) or databricks/databricks-dolly-15k. If you'd like to create a dataset yourself, you can check out the dataset card of Dolly on how to create an instruction dataset. Models fine-tuned on these datasets can be distributed. You can find a comprehensive table of some open-source/open-access models below. | Model | Dataset | License | Use | |------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------|-------------------------| | Falcon 40B | Falcon RefinedWeb | Apache-2.0 | Text Generation | | SalesForce XGen 7B | Mix of C4, RedPajama and more | Apache-2.0 | Text Generation | | MPT-30B | Mix of C4, RedPajama and more | Apache-2.0 | Text Generation | | Pythia-12B | Pile | Apache-2.0 | Text Generation | | RedPajama INCITE 7B | RedPajama | Apache-2.0 | Text Generation | | OpenAssistant Falcon 40B | oasst1 and Dolly | Apache-2.0 | Text Generation | | StarCoder | The Stack | BigCode OpenRAIL-M | Code Generation | | Salesforce CodeGen | Starcoder Data | Apache-2.0 | Code Generation | | FLAN-T5-XXL | gsm8k, lambada, and esnli | Apache-2.0 | Text-to-text Generation | | MPT-30B Chat | ShareGPT-Vicuna, OpenAssistant Guanaco and more | CC-By-NC-SA-4.0 | Chat | | MPT-30B Instruct | duorc, competition_math, dolly_hhrlhf | CC-By-SA-3.0 | Instruction | | Falcon 40B Instruct | baize | Apache-2.0 | Instruction | | Dolly v2 | Dolly | MIT | Text Generation | | StarChat-β | OpenAssistant Guanaco | BigCode OpenRAIL-M | Code Instruction | | Llama 2 | Undisclosed dataset | Custom Meta License (Allows commercial use) | Text Generation |
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] |
Building an AI WebTV
|
jbilcke-hf
|
July 17, 2023
|
ai-webtv
|
text-to-video, guide
|
https://huggingface.co/blog/ai-webtv
|
# Building an AI WebTV The AI WebTV is an experimental demo to showcase the latest advancements in automatic video and music synthesis. 👉 Watch the stream now by going to the AI WebTV Space. If you are using a mobile device, you can view the stream from the Twitch mirror. # Concept The motivation for the AI WebTV is to demo videos generated with open-source text-to-video models such as Zeroscope and MusicGen, in an entertaining and accessible way. You can find those open-source models on the Hugging Face hub: - For video: zeroscope_v2_576 and zeroscope_v2_XL - For music: musicgen-melody The individual video sequences are purposely made to be short, meaning the WebTV should be seen as a tech demo/showreel rather than an actual show (with an art direction or programming). # Architecture The AI WebTV works by taking a sequence of video shot prompts and passing them to a text-to-video model to generate a sequence of takes. Additionally, a base theme and idea (written by a human) are passed through a LLM (in this case, ChatGPT), in order to generate a variety of individual prompts for each video clip. Here's a diagram of the current architecture of the AI WebTV: # Implementing the pipeline The WebTV is implemented in NodeJS and TypeScript, and uses various services hosted on Hugging Face.
|
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] |
Building an AI WebTV
|
jbilcke-hf
|
July 17, 2023
|
ai-webtv
|
text-to-video, guide
|
https://huggingface.co/blog/ai-webtv
|
Leverage frame interpolation Frame interpolation is a powerful tool which can repair small rendering errors and turn many defects into features, especially in scenes with a lot of animation, or where a cartoon effect is acceptable. The FILM algorithm will smoothen out elements of a frame with previous and following events in the video clip. This works great to displace the background when the camera is panning or rotating, and will also give you creative freedom, such as control over the number of frames after the generation, to make slow-motion effects. # Future work We hope you enjoyed watching the AI WebTV stream and that it will inspire you to build more in this space. As this was a first trial, a lot of things were not the focus of the tech demo: generating longer and more varied sequences, adding audio (sound effects, dialogue), generating and orchestrating complex scenarios, or letting a language model agent have more control over the pipeline. Some of these ideas may make their way into future updates to the AI WebTV, but we also can’t wait to see what the community of researchers, engineers and builders will come up with!
|
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] |
Building an AI WebTV
|
jbilcke-hf
|
July 17, 2023
|
ai-webtv
|
text-to-video, guide
|
https://huggingface.co/blog/ai-webtv
|
Using video-specific prompt keywords You may already know that if you don’t prompt a specific aspect of the image with Stable Diffusion, things like the color of clothes or the time of the day might become random, or be assigned a generic value such as a neutral mid-day light. The same is true for video models: you will want to be specific about things. Examples include camera and character movement, their orientation, speed and direction. You can leave it unspecified for creative purposes (idea generation), but this might not always give you the results you want (e.g., entities animated in reverse).
|
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] |
Building an AI WebTV
|
jbilcke-hf
|
July 17, 2023
|
ai-webtv
|
text-to-video, guide
|
https://huggingface.co/blog/ai-webtv
|
Calling the video chain To make a quick prototype, the WebTV runs Zeroscope from two duplicated Hugging Face Spaces running Gradio, which are called using the @gradio/client NPM package. You can find the original spaces here: - zeroscope-v2 by @hysts - Zeroscope XL by @fffiloni Other spaces deployed by the community can also be found if you search for Zeroscope on the Hub. 👉 Public Spaces may become overcrowded and paused at any time. If you intend to deploy your own system, please duplicate those Spaces and run them under your own account.
|
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] |
Building an AI WebTV
|
jbilcke-hf
|
July 17, 2023
|
ai-webtv
|
text-to-video, guide
|
https://huggingface.co/blog/ai-webtv
|
Using a model hosted on a Space Spaces using Gradio have the ability to expose a REST API, which can then be called from Node using the @gradio/client module. Here is an example: typescript import { client } from "@gradio/client" export const generateVideo = async (prompt: string) => { const api = await client("*** URL OF THE SPACE ***") // call the "run()" function with an array of parameters const { data } = await api.predict("/run", [ prompt, 42, // seed 24, // nbFrames 35 // nbSteps ]) const { orig_name } = data[0][0] const remoteUrl = `${instance}/file=${orig_name}` // the file can then be downloaded and stored locally }
|
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] |
Building an AI WebTV
|
jbilcke-hf
|
July 17, 2023
|
ai-webtv
|
text-to-video, guide
|
https://huggingface.co/blog/ai-webtv
|
The text-to-video model The central video model is Zeroscope V2, a model based on ModelScope. Zeroscope is comprised of two parts that can be chained together: - A first pass with zeroscope_v2_576, to generate a 576x320 video clip - An optional second pass with zeroscope_v2_XL to upscale the video to 1024x576 👉 You will need to use the same prompt for both the generation and upscaling.
|
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] |
Building an AI WebTV
|
jbilcke-hf
|
July 17, 2023
|
ai-webtv
|
text-to-video, guide
|
https://huggingface.co/blog/ai-webtv
|
Maintaining consistency between scenes If you plan to create sequences of multiple videos, you will want to make sure you add as many details as possible in each prompt, otherwise you may lose important details from one sequence to another, such as the color. 💡 This will also improve the quality of the image since the prompt is used for the upscaling part with Zeroscope XL.
|
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] |
Building an AI WebTV
|
jbilcke-hf
|
July 17, 2023
|
ai-webtv
|
text-to-video, guide
|
https://huggingface.co/blog/ai-webtv
|
Styling and effects Prompt: 3D rendered video of a friendly broccoli character wearing a hat, walking in a candy-filled city street with gingerbread houses, under a bright sun and blue skies, Pixar's style, cinematic, photorealistic, movie, ambient lighting, natural lighting, CGI, wide-angle view, daytime, ultra realistic. Prompt: Cinematic movie, shot of an astronaut and a llama at dawn, the mountain landscape bathed in soft muted colors, early morning fog, dew glistening on fur, craggy peaks, vintage NASA suit, Canon EOS, high detailed skin, epic composition, high quality, 4K, trending on artstation, beautiful Prompt: Panda and black cat navigating down the flowing river in a small boat, Studio Ghibli style > Cinematic, beautiful composition > IMAX camera panning following the boat > High quality, cinematic, movie, mist effect, film grain, trending on Artstation
|
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] |
Building an AI WebTV
|
jbilcke-hf
|
July 17, 2023
|
ai-webtv
|
text-to-video, guide
|
https://huggingface.co/blog/ai-webtv
|
Broadcasting the stream Note: there are multiple tools you can use to create a video stream. The AI WebTV currently uses FFmpeg to read a playlist made of mp4 videos files and m4a audio files. Here is an example of creating such a playlist: typescript import { promises as fs } from "fs" import path from "path" const allFiles = await fs.readdir("** PATH TO VIDEO FOLDER **") const allVideos = allFiles .map(file => path.join(dir, file)) .filter(filePath => filePath.endsWith('.mp4')) let playlist = 'ffconcat version 1.0\n' allFilePaths.forEach(filePath => { playlist += `file '${filePath}'\n` }) await fs.promises.writeFile("playlist.txt", playlist) This will generate the following playlist content: bash ffconcat version 1.0 file 'video1.mp4' file 'video2.mp4' ... FFmpeg is then used again to read this playlist and send a FLV stream to a RTMP server. FLV is an old format but still popular in the world of real-time streaming due to its low latency. bash ffmpeg -y -nostdin \ -re \ -f concat \ -safe 0 -i channel_random.txt -stream_loop -1 \ -loglevel error \ -c:v libx264 -preset veryfast -tune zerolatency \ -shortest \ -f flv rtmp://<SERVER> There are many different configuration options for FFmpeg, for more information in the official documentation. For the RTMP server, you can find open-source implementations on GitHub, such as the NGINX-RTMP module. The AI WebTV itself uses node-media-server. 💡 You can also directly stream to one of the Twitch RTMP entrypoints. Check out the Twitch documentation for more details. # Observations and examples Here are some examples of the generated content. The first thing we notice is that applying the second pass of Zeroscope XL significantly improves the quality of the image. The impact of frame interpolation is also clearly visible.
|
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] |
Building an AI WebTV
|
jbilcke-hf
|
July 17, 2023
|
ai-webtv
|
text-to-video, guide
|
https://huggingface.co/blog/ai-webtv
|
Failure cases Wrong direction: the model sometimes has trouble with movement and direction. For instance, here the clip seems to be played in reverse. Also the modifier keyword green was not taken into account. Prompt: Movie showing a green pumpkin falling into a bed of nails, slow-mo explosion with chunks flying all over, ambient fog adding to the dramatic lighting, filmed with IMAX camera, 8k ultra high definition, high quality, trending on artstation. Rendering errors on realistic scenes: sometimes we can see artifacts such as moving vertical lines or waves. It is unclear what causes this, but it may be due to the combination of keywords used. Prompt: Film shot of a captivating flight above the Grand Canyon, ledges and plateaus etched in orange and red. Deep shadows contrast with the fiery landscape under the midday sun, shot with DJI Phantom 4 Pro. The camera rotates to capture the vastness, textures and colors, in imax quality. Film grain, cinematic, movie. Text or objects inserted into the image: the model sometimes injects words from the prompt into the scene, such as "IMAX". Mentioning "Canon EOS" or "Drone footage" in the prompt can also make those objects appear in the video. In the following example, we notice the word "llama" inserts a llama but also two occurrences of the word llama in flames. Prompt: Movie scene of a llama acting as a firefighter, in firefighter uniform, dramatically spraying water at roaring flames, amidst a chaotic urban scene, Canon EOS, ambient lighting, high quality, award winning, highly detailed fur, cinematic, trending on artstation. # Recommendations Here are some early recommendations that can be made from the previous observations:
|
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] |
Building an AI WebTV
|
jbilcke-hf
|
July 17, 2023
|
ai-webtv
|
text-to-video, guide
|
https://huggingface.co/blog/ai-webtv
|
Post-processing Once an individual take (a video clip) is upscaled, it is then passed to FILM (Frame Interpolation for Large Motion), a frame interpolation algorithm: - Original links: website, source code - Model on Hugging Face: /frame-interpolation-film-style - A Hugging Face Space you can duplicate: video_frame_interpolation by @fffiloni During post-processing, we also add music generated with MusicGen: - Original links: website, source code - Hugging Face Space you can duplicate: MusicGen
|
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Building an AI WebTV
|
jbilcke-hf
|
July 17, 2023
|
ai-webtv
|
text-to-video, guide
|
https://huggingface.co/blog/ai-webtv
|
Simulation of dynamic scenes Something truly fascinating about text-to-video models is their ability to emulate real-life phenomena they have been trained on. We've seen it with large language models and their ability to synthesize convincing content that mimics human responses, but this takes things to a whole new dimension when applied to video. A video model predicts the next frames of a scene, which might include objects in motion such as fluids, people, animals, or vehicles. Today, this emulation isn't perfect, but it will be interesting to evaluate future models (trained on larger or specialized datasets, such as animal locomotion) for their accuracy when reproducing physical phenomena, and also their ability to simulate the behavior of agents. Prompt: Cinematic movie shot of bees energetically buzzing around a flower, sun rays illuminating the scene, captured in 4k IMAX with a soft bokeh background. Prompt: Dynamic footage of a grizzly bear catching a salmon in a rushing river, ambient lighting highlighting the splashing water, low angle, IMAX camera, 4K movie quality, golden hour, film grain. Prompt: Aerial footage of a quiet morning at the coast of California, with waves gently crashing against the rocky shore. A startling sunrise illuminates the coast with vibrant colors, captured beautifully with a DJI Phantom 4 Pro. Colors and textures of the landscape come alive under the soft morning light. Film grain, cinematic, imax, movie 💡 It will be interesting to see these capabilities explored more in the future, for instance by training video models on larger video datasets covering more phenomena.
|
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] |
Building an AI WebTV
|
jbilcke-hf
|
July 17, 2023
|
ai-webtv
|
text-to-video, guide
|
https://huggingface.co/blog/ai-webtv
|
Characters and scene composition Prompt: Photorealistic movie of a llama acting as a programmer, wearing glasses and a hoodie, intensely staring at a screen with lines of code, in a cozy, dimly lit room, Canon EOS, ambient lighting, high details, cinematic, trending on artstation Prompt: 3D rendered animation showing a group of food characters forming a pyramid, with a banana standing triumphantly on top. In a city with cotton candy clouds and chocolate road, Pixar's style, CGI, ambient lighting, direct sunlight, rich color scheme, ultra realistic, cinematic, photorealistic. Prompt: Intimate close-up of a red fox, gazing into the camera with sharp eyes, ambient lighting creating a high contrast silhouette, IMAX camera, high detail, cinematic effect, golden hour, film grain.
|
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] |
Llama 2 is here - get it on Hugging Face
|
osanseviero
|
July 18, 2023
|
llama2
|
nlp, community, research, LLM
|
https://huggingface.co/blog/llama2
|
Table of Contents - Why Llama 2? - Demo - Inference - With Transformers - With Inference Endpoints - Fine-tuning with PEFT - How to Prompt Llama 2 - Additional Resources - Conclusion
|
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] |
Llama 2 is here - get it on Hugging Face
|
osanseviero
|
July 18, 2023
|
llama2
|
nlp, community, research, LLM
|
https://huggingface.co/blog/llama2
|
How to Prompt Llama 2 One of the unsung advantages of open-access models is that you have full control over the system prompt in chat applications. This is essential to specify the behavior of your chat assistant –and even imbue it with some personality–, but it's unreachable in models served behind APIs. We're adding this section just a few days after the initial release of Llama 2, as we've had many questions from the community about how to prompt the models and how to change the system prompt. We hope this helps! The prompt template for the first turn looks like this: <s>[INST] <<SYS>> {{ system_prompt }} <</SYS>> {{ user_message }} [/INST] This template follows the model's training procedure, as described in the Llama 2 paper. We can use any system_prompt we want, but it's crucial that the format matches the one used during training. To spell it out in full clarity, this is what is actually sent to the language model when the user enters some text (There's a llama in my garden 😱 What should I do?) in our 13B chat demo to initiate a chat: b <s>[INST] <<SYS>> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <</SYS>> There's a llama in my garden 😱 What should I do? [/INST] As you can see, the instructions between the special <<SYS>> tokens provide context for the model so it knows how we expect it to respond. This works because exactly the same format was used during training with a wide variety of system prompts intended for different tasks. As the conversation progresses, all the interactions between the human and the "bot" are appended to the previous prompt, enclosed between [INST] delimiters. The template used during multi-turn conversations follows this structure (🎩 h/t Arthur Zucker for some final clarifications): b <s>[INST] <<SYS>> {{ system_prompt }} <</SYS>> {{ user_msg_1 }} [/INST] {{ model_answer_1 }} </s><s>[INST] {{ user_msg_2 }} [/INST] The model is stateless and does not "remember" previous fragments of the conversation, we must always supply it with all the context so the conversation can continue. This is the reason why context length is a very important parameter to maximize, as it allows for longer conversations and larger amounts of information to be used.
|
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] |
Llama 2 is here - get it on Hugging Face
|
osanseviero
|
July 18, 2023
|
llama2
|
nlp, community, research, LLM
|
https://huggingface.co/blog/llama2
|
Why Llama 2? The Llama 2 release introduces a family of pretrained and fine-tuned LLMs, ranging in scale from 7B to 70B parameters (7B, 13B, 70B). The pretrained models come with significant improvements over the Llama 1 models, including being trained on 40% more tokens, having a much longer context length (4k tokens 🤯), and using grouped-query attention for fast inference of the 70B model🔥! However, the most exciting part of this release is the fine-tuned models (Llama 2-Chat), which have been optimized for dialogue applications using Reinforcement Learning from Human Feedback (RLHF). Across a wide range of helpfulness and safety benchmarks, the Llama 2-Chat models perform better than most open models and achieve comparable performance to ChatGPT according to human evaluations. You can read the paper here. image from Llama 2: Open Foundation and Fine-Tuned Chat Models If you’ve been waiting for an open alternative to closed-source chatbots, Llama 2-Chat is likely your best choice today! | Model | License | Commercial use? | Pretraining length [tokens] | Leaderboard score | | --- | --- | --- | --- | --- | | Falcon-7B | Apache 2.0 | ✅ | 1,500B | 47.01 | | MPT-7B | Apache 2.0 | ✅ | 1,000B | 48.7 | | Llama-7B | Llama license | ❌ | 1,000B | 49.71 | | Llama-2-7B | Llama 2 license | ✅ | 2,000B | 54.32 | | Llama-33B | Llama license | ❌ | 1,500B | * | | Llama-2-13B | Llama 2 license | ✅ | 2,000B | 58.67 | | mpt-30B | Apache 2.0 | ✅ | 1,000B | 55.7 | | Falcon-40B | Apache 2.0 | ✅ | 1,000B | 61.5 | | Llama-65B | Llama license | ❌ | 1,500B | 62.1 | | Llama-2-70B | Llama 2 license | ✅ | 2,000B | * | | Llama-2-70B-chat | Llama 2 license | ✅ | 2,000B | 66.8 | we’re currently running evaluation of the Llama 2 70B (non chatty version). This table will be updated with the results.
|
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] |
Llama 2 is here - get it on Hugging Face
|
osanseviero
|
July 18, 2023
|
llama2
|
nlp, community, research, LLM
|
https://huggingface.co/blog/llama2
|
# Llama 2 is here - get it on Hugging Face
|
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] |
Llama 2 is here - get it on Hugging Face
|
osanseviero
|
July 18, 2023
|
llama2
|
nlp, community, research, LLM
|
https://huggingface.co/blog/llama2
|
Inference In this section, we’ll go through different approaches to running inference of the Llama2 models. Before using these models, make sure you have requested access to one of the models in the official Meta Llama 2 repositories. Note: Make sure to also fill the official Meta form. Users are provided access to the repository once both forms are filled after few hours.
|
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] |
Llama 2 is here - get it on Hugging Face
|
osanseviero
|
July 18, 2023
|
llama2
|
nlp, community, research, LLM
|
https://huggingface.co/blog/llama2
|
Using text-generation-inference and Inference Endpoints Text Generation Inference is a production-ready inference container developed by Hugging Face to enable easy deployment of large language models. It has features such as continuous batching, token streaming, tensor parallelism for fast inference on multiple GPUs, and production-ready logging and tracing. You can try out Text Generation Inference on your own infrastructure, or you can use Hugging Face's Inference Endpoints. To deploy a Llama 2 model, go to the model page and click on the Deploy -> Inference Endpoints widget. - For 7B models, we advise you to select "GPU [medium] - 1x Nvidia A10G". - For 13B models, we advise you to select "GPU [xlarge] - 1x Nvidia A100". - For 70B models, we advise you to select "GPU [2xlarge] - 2x Nvidia A100" with bitsandbytes quantization enabled or "GPU [4xlarge] - 4x Nvidia A100" Note: You might need to request a quota upgrade via email to [email protected] to access A100s You can learn more on how to Deploy LLMs with Hugging Face Inference Endpoints in our blog. The blog includes information about supported hyperparameters and how to stream your response using Python and Javascript.
|
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] |
Llama 2 is here - get it on Hugging Face
|
osanseviero
|
July 18, 2023
|
llama2
|
nlp, community, research, LLM
|
https://huggingface.co/blog/llama2
|
Additional Resources - Paper Page - Models on the Hub - Leaderboard - Meta Examples and recipes for Llama model - Chat demo (7B) - Chat demo (13B) - Chat demo (70B) on TGI
|
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] |
Llama 2 is here - get it on Hugging Face
|
osanseviero
|
July 18, 2023
|
llama2
|
nlp, community, research, LLM
|
https://huggingface.co/blog/llama2
|
Using transformers With transformers release 4.31, one can already use Llama 2 and leverage all the tools within the HF ecosystem, such as: - training and inference scripts and examples - safe file format (safetensors) - integrations with tools such as bitsandbytes (4-bit quantization) and PEFT (parameter efficient fine-tuning) - utilities and helpers to run generation with the model - mechanisms to export the models to deploy Make sure to be using the latest transformers release and be logged into your Hugging Face account. pip install transformers huggingface-cli login In the following code snippet, we show how to run inference with transformers. It runs on the free tier of Colab, as long as you select a GPU runtime. python from transformers import AutoTokenizer import transformers import torch model = "meta-llama/Llama-2-7b-chat-hf" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) sequences = pipeline( 'I liked "Breaking Bad" and "Band of Brothers". Do you have any recommendations of other shows I might like?\n', do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=200, ) for seq in sequences: print(f"Result: {seq['generated_text']}") Result: I liked "Breaking Bad" and "Band of Brothers". Do you have any recommendations of other shows I might like? Answer: Of course! If you enjoyed "Breaking Bad" and "Band of Brothers," here are some other TV shows you might enjoy: 1. "The Sopranos" - This HBO series is a crime drama that explores the life of a New Jersey mob boss, Tony Soprano, as he navigates the criminal underworld and deals with personal and family issues. 2. "The Wire" - This HBO series is a gritty and realistic portrayal of the drug trade in Baltimore, exploring the impact of drugs on individuals, communities, and the criminal justice system. 3. "Mad Men" - Set in the 1960s, this AMC series follows the lives of advertising executives on Madison Avenue, expl And although the model has only 4k tokens of context, you can use techniques supported in transformers such as rotary position embedding scaling (tweet) to push it further!
|
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] |
Llama 2 is here - get it on Hugging Face
|
osanseviero
|
July 18, 2023
|
llama2
|
nlp, community, research, LLM
|
https://huggingface.co/blog/llama2
|
Fine-tuning with PEFT Training LLMs can be technically and computationally challenging. In this section, we look at the tools available in the Hugging Face ecosystem to efficiently train Llama 2 on simple hardware and show how to fine-tune the 7B version of Llama 2 on a single NVIDIA T4 (16GB - Google Colab). You can learn more about it in the Making LLMs even more accessible blog. We created a script to instruction-tune Llama 2 using QLoRA and the SFTTrainer from trl. An example command for fine-tuning Llama 2 7B on the timdettmers/openassistant-guanaco can be found below. The script can merge the LoRA weights into the model weights and save them as safetensor weights by providing the merge_and_push argument. This allows us to deploy our fine-tuned model after training using text-generation-inference and inference endpoints. First pip install trl and clone the script: bash pip install trl git clone https://github.com/lvwerra/trl Then you can run the script: bash python trl/examples/scripts/sft_trainer.py \ --model_name meta-llama/Llama-2-7b-hf \ --dataset_name timdettmers/openassistant-guanaco \ --load_in_4bit \ --use_peft \ --batch_size 4 \ --gradient_accumulation_steps 2
|
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] |
Llama 2 is here - get it on Hugging Face
|
osanseviero
|
July 18, 2023
|
llama2
|
nlp, community, research, LLM
|
https://huggingface.co/blog/llama2
|
Introduction Llama 2 is a family of state-of-the-art open-access large language models released by Meta today, and we’re excited to fully support the launch with comprehensive integration in Hugging Face. Llama 2 is being released with a very permissive community license and is available for commercial use. The code, pretrained models, and fine-tuned models are all being released today 🔥 We’ve collaborated with Meta to ensure smooth integration into the Hugging Face ecosystem. You can find the 12 open-access models (3 base models & 3 fine-tuned ones with the original Meta checkpoints, plus their corresponding transformers models) on the Hub. Among the features and integrations being released, we have: - Models on the Hub with their model cards and license. - Transformers integration - Examples to fine-tune the small variants of the model with a single GPU - Integration with Text Generation Inference for fast and efficient production-ready inference - Integration with Inference Endpoints
|
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Llama 2 is here - get it on Hugging Face
|
osanseviero
|
July 18, 2023
|
llama2
|
nlp, community, research, LLM
|
https://huggingface.co/blog/llama2
|
Ignore previous instructions In API-based models, people resort to tricks in an attempt to override the system prompt and change the default model behaviour. As imaginative as these solutions are, this is not necessary in open-access models: anyone can use a different prompt, as long as it follows the format described above. We believe that this will be an important tool for researchers to study the impact of prompts on both desired and unwanted characteristics. For example, when people are surprised with absurdly cautious generations, you can explore whether maybe a different prompt would work. (🎩 h/t Clémentine Fourrier for the links to this example). In our 13B and 7B demos, you can easily explore this feature by disclosing the "Advanced Options" UI and simply writing your desired instructions. You can also duplicate those demos and use them privately for fun or research!
|
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] |
Llama 2 is here - get it on Hugging Face
|
osanseviero
|
July 18, 2023
|
llama2
|
nlp, community, research, LLM
|
https://huggingface.co/blog/llama2
|
Conclusion We're very excited about Llama 2 being out! In the incoming days, be ready to learn more about ways to run your own fine-tuning, execute the smallest models on-device, and many other exciting updates we're prepating for you!
|
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] |
Llama 2 is here - get it on Hugging Face
|
osanseviero
|
July 18, 2023
|
llama2
|
nlp, community, research, LLM
|
https://huggingface.co/blog/llama2
|
Demo You can easily try the Big Llama 2 Model (70 billion parameters!) in this Space or in the playground embedded below: Under the hood, this playground uses Hugging Face's Text Generation Inference, the same technology that powers HuggingChat, and which we'll share more in the following sections.
|
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] |
Happy 1st anniversary 🤗 Diffusers!
|
stevhliu
|
July 20, 2023
|
diffusers-turns-1
|
community, open-source-collab, diffusion, diffusers
|
https://huggingface.co/blog/diffusers-turns-1
|
Community highlights One of the most gratifying experiences of the past year has been seeing how the community is incorporating 🤗 Diffusers into their projects. From adapting Low-rank adaptation (LoRA) for faster training of text-to-image models to building a state-of-the-art inpainting tool, here are a few of our favorite projects: We built Core ML Stable Diffusion to make it easier for developers to add state-of-the-art generative AI capabilities in their iOS, iPadOS and macOS apps with the highest efficiency on Apple Silicon. We built on top of 🤗 Diffusers instead of from scratch as 🤗 Diffusers consistently stays on top of a rapidly evolving field and promotes much needed interoperability of new and old ideas. Atila Orhon 🤗 Diffusers has been absolutely developer-friendly for me to dive right into stable diffusion models. Main differentiating factor clearly being that 🤗 Diffusers implementation is often not some code from research lab, that are mostly focused on high velocity driven. While research codes are often poorly written and difficult to understand (lack of typing, assertions, inconsistent design patterns and conventions), 🤗 Diffusers was a breeze to use for me to hack my ideas within couple of hours. Without it, I would have needed to invest significantly more amount of time to start hacking. Well-written documentations and examples are extremely helpful as well. Simo BentoML is the unified framework for for building, shipping, and scaling production-ready AI applications incorporating traditional ML, pre-trained AI models, Generative and Large Language Models. All Hugging Face Diffuser models and pipelines can be seamlessly integrated into BentoML applications, enabling the running of models on the most suitable hardware and independent scaling based on usage. BentoML Invoke AI is an open-source Generative AI tool built to empower professional creatives, from game designers and photographers to architects and product designers. Invoke recently launched their hosted offering at invoke.ai, allowing users to generate assets from any computer, powered by the latest research in open-source. InvokeAI TaskMatrix connects Large Language Model and a series of Visual Models to enable sending and receiving images during chatting. Chenfei Wu Lama Cleaner is a powerful image inpainting tool that uses Stable Diffusion technology to remove unwanted objects, defects, or people from your pictures. It can also erase and replace anything in your images with ease. Qing Grounded-SAM combines a powerful Zero-Shot detector Grounding-DINO and Segment-Anything-Model (SAM) to build a strong pipeline to detect and segment everything with text inputs. When combined with 🤗 Diffusers inpainting models, Grounded-SAM can do highly controllable image editing tasks, including replacing specific objects, inpainting the background, etc. Tianhe Ren Stable-Dreamfusion leverages the convenient implementations of 2D diffusion models in 🤗 Diffusers to replicate recent text-to-3D and image-to-3D methods. kiui MMagic (Multimodal Advanced, Generative, and Intelligent Creation) is an advanced and comprehensive Generative AI toolbox that provides state-of-the-art AI models (e.g., diffusion models powered by 🤗 Diffusers and GAN) to synthesize, edit and enhance images and videos. In MMagic, users can use rich components to customize their own models like playing with Legos and manage the training loop easily. mmagic Tune-A-Video, developed by Jay Zhangjie Wu and his team at Show Lab, is the first to fine-tune a pre-trained text-to-image diffusion model using a single text-video pair and enables changing video content while preserving motion. Jay Zhangjie Wu We also collaborated with Google Cloud (who generously provided the compute) to provide technical guidance and mentorship to help the community train diffusion models with TPUs (check out a summary of the event here). There were many cool models such as this demo that combines ControlNet with Segment Anything. Finally, we were delighted to receive contributions to our codebase from over 300 contributors, which allowed us to collaborate together in the most open way possible. Here are just a few of the contributions from our community: - Model editing by @bahjat-kawar, a pipeline for editing a model’s implicit assumptions - LDM3D by @estelleafl, a diffusion model for 3D images - DPMSolver by @LuChengTHU, improvements for significantly improving inference speed - Custom Diffusion by @nupurkmr9, a technique for generating personalized images with only a few images of a subject Besides these, a heartfelt shoutout to the following contributors who helped us ship some of the most powerful features of Diffusers (in no particular order): * @takuma104 * @nipunjindal * @isamu-isozaki * @piEsposito * @Birch-san * @LuChengTHU * @duongna21 * @clarencechen * @dg845 * @Abhinay1997 * @camenduru * @ayushtues
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] |
Happy 1st anniversary 🤗 Diffusers!
|
stevhliu
|
July 20, 2023
|
diffusers-turns-1
|
community, open-source-collab, diffusion, diffusers
|
https://huggingface.co/blog/diffusers-turns-1
|
Text-to-3D models In addition to text-to-video, we also have text-to-3D generation now thanks to OpenAI’s Shap-E model. Shap-E is trained by encoding a large dataset of 3D-text pairs, and a diffusion model is conditioned on the encoder’s outputs. You can design 3D assets for video games, interior design, and architecture. Try it out today with the ShapEPipeline and ShapEImg2ImgPipeline.
|
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] |
Happy 1st anniversary 🤗 Diffusers!
|
stevhliu
|
July 20, 2023
|
diffusers-turns-1
|
community, open-source-collab, diffusion, diffusers
|
https://huggingface.co/blog/diffusers-turns-1
|
Building products with 🤗 Diffusers Over the last year, we also saw many companies choosing to build their products on top of 🤗 Diffusers. Here are a couple of products that have caught our attention: - PlaiDay: “PlaiDay is a Generative AI experience where people collaborate, create, and connect. Our platform unlocks the limitless creativity of the human mind, and provides a safe, fun social canvas for expression.” - Previs One: “Previs One is a diffuser pipeline for cinematic storyboarding and previsualization — it understands film and television compositional rules just as a director would speak them.” - Zust.AI: “We leverage Generative AI to create studio-quality product photos for brands and marketing agencies.” - Dashtoon: “Dashtoon is building a platform to create and consume visual content. We have multiple pipelines that load multiple LORAs, multiple control-nets and even multiple models powered by diffusers. Diffusers has made the gap between a product engineer and a ML engineer super low allowing dashtoon to ship user value faster and better.” - Virtual Staging AI: "Filling empty rooms with beautiful furniture using generative models.” - Hexo.AI: “Hexo AI helps brands get higher ROI on marketing spends through Personalized Marketing at Scale. Hexo is building a proprietary campaign generation engine which ingests customer data and generates brand compliant personalized creatives.” If you’re building products on top of 🤗 Diffusers, we’d love to chat to understand how we can make the library better together! Feel free to reach out to [email protected] or [email protected].
|
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] |
Happy 1st anniversary 🤗 Diffusers!
|
stevhliu
|
July 20, 2023
|
diffusers-turns-1
|
community, open-source-collab, diffusion, diffusers
|
https://huggingface.co/blog/diffusers-turns-1
|
Torch 2.0 optimizations PyTorch 2.0 introduced support for torch.compile()and scaled_dot_product_attention(), a more efficient implementation of the attention mechanism. 🤗 Diffusers provides first-class support for these features resulting in massive speedups in inference latency, which can sometimes be more than twice as fast! In addition to visual content (images, videos, 3D assets, etc.), we also added support for audio! Check out the documentation to learn more.
|
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] |
Happy 1st anniversary 🤗 Diffusers!
|
stevhliu
|
July 20, 2023
|
diffusers-turns-1
|
community, open-source-collab, diffusion, diffusers
|
https://huggingface.co/blog/diffusers-turns-1
|
# Happy 1st anniversary 🤗 Diffusers! 🤗 Diffusers is happy to celebrate its first anniversary! It has been an exciting year, and we're proud and grateful for how far we've come thanks to our community and open-source contributors. Last year, text-to-image models like DALL-E 2, Imagen, and Stable Diffusion captured the world's attention with their ability to generate stunningly photorealistic images from text, sparking a massive surge of interest and development in generative AI. But access to these powerful models was limited. At Hugging Face, our mission is to democratize good machine learning by collaborating and helping each other build an open and ethical AI future together. Our mission motivated us to create the 🤗 Diffusers library so everyone can experiment, research, or simply play with text-to-image models. That’s why we designed the library as a modular toolbox, so you can customize a diffusion model’s components or just start using it out-of-the-box. As 🤗 Diffusers turns 1, here’s an overview of some of the most notable features we’ve added to the library with the help of our community. We are proud and immensely grateful for being part of an engaged community that promotes accessible usage, pushes diffusion models beyond just text-to-image generation, and is an all-around inspiration. Table of Contents * Striving for photorealism * Video pipelines * Text-to-3D models * Image editing pipelines * Faster diffusion models * Ethics and safety * Support for LoRA * Torch 2.0 optimizations * Community highlights * Building products with 🤗 Diffusers * Looking forward
|
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] |
Happy 1st anniversary 🤗 Diffusers!
|
stevhliu
|
July 20, 2023
|
diffusers-turns-1
|
community, open-source-collab, diffusion, diffusers
|
https://huggingface.co/blog/diffusers-turns-1
|
Faster diffusion models Diffusion models are known to be time-intensive because of their iterative steps. With OpenAI’s Consistency Models, the image generation process is significantly faster. Generating a single 256x256 resolution image only takes 3/4 of a second on a modern CPU! You can try this out in 🤗 Diffusers with the ConsistencyModelPipeline. On top of speedier diffusion models, we also offer many optimization techniques for faster inference like PyTorch 2.0’s scaled_dot_product_attention() (SDPA) and torch.compile(), sliced attention, feed-forward chunking, VAE tiling, CPU and model offloading, and more. These optimizations save memory, which translates to faster generation, and allow you to run inference on consumer GPUs. When you distribute a model with 🤗 Diffusers, all of these optimizations are immediately supported! In addition to that, we also support specific hardware and formats like ONNX, the mps PyTorch device for Apple Silicon computers, Core ML, and others. To learn more about how we optimize inference with 🤗 Diffusers, check out the docs!
|
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] |
Happy 1st anniversary 🤗 Diffusers!
|
stevhliu
|
July 20, 2023
|
diffusers-turns-1
|
community, open-source-collab, diffusion, diffusers
|
https://huggingface.co/blog/diffusers-turns-1
|
Looking forward As we celebrate our first anniversary, we're grateful to our community and open-source contributors who have helped us come so far in such a short time. We're happy to share that we'll be presenting a 🤗 Diffusers demo at ICCV 2023 this fall – if you're attending, do come and see us! We'll continue to develop and improve our library, making it easier for everyone to use. We're also excited to see what the community will create next with our tools and resources. Thank you for being a part of our journey so far, and we look forward to continuing to democratize good machine learning together! 🥳 ❤️ Diffusers team --- Acknowledgements: Thank you to Omar Sanseviero, Patrick von Platen, Giada Pistilli for their reviews, and Chunte Lee for designing the thumbnail.
|
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] |
Happy 1st anniversary 🤗 Diffusers!
|
stevhliu
|
July 20, 2023
|
diffusers-turns-1
|
community, open-source-collab, diffusion, diffusers
|
https://huggingface.co/blog/diffusers-turns-1
|
Image editing pipelines Image editing is one of the most practical use cases in fashion, material design, and photography. With diffusion models, the possibilities of image editing continue to expand. We have many pipelines in 🤗 Diffusers to support image editing. There are image editing pipelines that allow you to describe your desired edit as a prompt, removing concepts from an image, and even a pipeline that unifies multiple generation methods to create high-quality images like panoramas. With 🤗 Diffusers, you can experiment with the future of photo editing now!
|
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] |
Happy 1st anniversary 🤗 Diffusers!
|
stevhliu
|
July 20, 2023
|
diffusers-turns-1
|
community, open-source-collab, diffusion, diffusers
|
https://huggingface.co/blog/diffusers-turns-1
|
Support for LoRA Fine-tuning diffusion models is expensive and out of reach for most consumer GPUs. We added the Low-Rank Adaptation (LoRA) technique to close this gap. With LoRA, which is a method for parameter-efficient fine-tuning, you can fine-tune large diffusion models faster and consume less memory. The resulting model weights are also very lightweight compared to the original model, so you can easily share your custom models. If you want to learn more, our documentation shows how to perform fine-tuning and inference on Stable Diffusion with LoRA. In addition to LoRA, we support other training techniques for personalized generation, including DreamBooth, textual inversion, custom diffusion, and more!
|
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] |
Happy 1st anniversary 🤗 Diffusers!
|
stevhliu
|
July 20, 2023
|
diffusers-turns-1
|
community, open-source-collab, diffusion, diffusers
|
https://huggingface.co/blog/diffusers-turns-1
|
Video pipelines Text-to-image pipelines are cool, but text-to-video is even cooler! We currently support two text-to-video pipelines, VideoFusion and Text2Video-Zero. If you’re already familiar with text-to-image pipelines, using a text-to-video pipeline is very similar: py import torch from diffusers import DiffusionPipeline from diffusers.utils import export_to_video pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16) pipe.enable_model_cpu_offload() prompt = "Darth Vader surfing a wave" video_frames = pipe(prompt, num_frames=24).frames video_path = export_to_video(video_frames) We expect text-to-video to go through a revolution during 🤗 Diffusers second year, and we are excited to see what the community builds on top of these to push the boundaries of video generation from language!
|
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] |
Happy 1st anniversary 🤗 Diffusers!
|
stevhliu
|
July 20, 2023
|
diffusers-turns-1
|
community, open-source-collab, diffusion, diffusers
|
https://huggingface.co/blog/diffusers-turns-1
|
Striving for photorealism Generative AI models are known for creating photorealistic images, but if you look closely, you may notice certain things that don't look right, like generating extra fingers on a hand. This year, the DeepFloyd IF and Stability AI SDXL models made a splash by improving the quality of generated images to be even more photorealistic. DeepFloyd IF - A modular diffusion model that includes different processes for generating an image (for example, an image is upscaled 3x to produce a higher resolution image). Unlike Stable Diffusion, the IF model works directly on the pixel level, and it uses a large language model to encode text. Stable Diffusion XL (SDXL) - The latest Stable Diffusion model from Stability AI, with significantly more parameters than its predecessor Stable Diffusion 2. It generates hyper-realistic images, leveraging a base model for close adherence to the prompt, and a refiner model specialized in the fine details and high-frequency content. Head over to the DeepFloyd IF docs and the SDXL docs today to learn how to start generating your own images!
|
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] |
Happy 1st anniversary 🤗 Diffusers!
|
stevhliu
|
July 20, 2023
|
diffusers-turns-1
|
community, open-source-collab, diffusion, diffusers
|
https://huggingface.co/blog/diffusers-turns-1
|
Ethics and safety Generative models are cool, but they also have the ability to produce harmful and NSFW content. To help users interact with these models responsibly and ethically, we’ve added a safety_checker component that flags inappropriate content generated during inference. Model creators can choose to incorporate this component into their models if they want. In addition, generative models can also be used to produce disinformation. Earlier this year, the Balenciaga Pope went viral for how realistic the image was despite it being fake. This underscores the importance and need for a mechanism to distinguish between generated and human content. That’s why we’ve added an invisible watermark for images generated by the SDXL model, which helps users be better informed. The development of these features is guided by our ethical charter, which you can find in our documentation.
|
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] |
Results of the Open Source AI Game Jam
|
ThomasSimonini
|
July 21, 2023
|
game-jam-first-edition-results
|
game-dev
|
https://huggingface.co/blog/game-jam-first-edition-results
|
The Theme: Expanding To inspire creativity, we decided on the theme of "EXPANDING." We left it open to interpretation, allowing developers to explore and experiment with their ideas, leading to a diverse range of games. The games were evaluated by their peers and contributors based on three key criteria: fun, creativity, and adherence to the theme. The top 10 games were then presented to three judges (Dylan Ebert, Thomas Simonini and Omar Sanseviero), who selected the best game.
|
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] |
Results of the Open Source AI Game Jam
|
ThomasSimonini
|
July 21, 2023
|
game-jam-first-edition-results
|
game-dev
|
https://huggingface.co/blog/game-jam-first-edition-results
|
#4: Everchanging Quest In this game, you are the village's last hope. Arm yourself before embarking on your adventure, and don't hesitate to ask the locals for guidance. The world beyond the portal will never be the same, so be prepared. Defeat your enemies to collect points and find your way to the end. 🤖 Used GPT-4 to place the tiles and objects (proprietary) but also Starcoder to code (open source). 🎮👉 https://jofthomas.itch.io/everchanging-quest
|
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] |
Results of the Open Source AI Game Jam
|
ThomasSimonini
|
July 21, 2023
|
game-jam-first-edition-results
|
game-dev
|
https://huggingface.co/blog/game-jam-first-edition-results
|
#7: Hexagon Tactics: The Expanding Arena In this game, you are dropped into an arena battle. Defeat your opponents, then upgrade your deck and the arena expands. 🤖 Stable Diffusion 1.5 to generate your own character (executable version of the game). 🎮👉 https://dgeisert.itch.io/hextactics
|
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] |
Results of the Open Source AI Game Jam
|
ThomasSimonini
|
July 21, 2023
|
game-jam-first-edition-results
|
game-dev
|
https://huggingface.co/blog/game-jam-first-edition-results
|
#9: Apocalypse Expansion In this game, you'll step into the decaying shoes of a zombie, driven by an insatiable hunger for human flesh. Your objective? To build the largest horde of zombies ever seen, while evading the relentless pursuit of the determined police force. 🤖 Used Stable Diffusion to generate the images 🤖 Used MusicGen (melody 1.5B) for the music 🎮👉 https://mad25.itch.io/apocalypse-expansion
|
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] |
Results of the Open Source AI Game Jam
|
ThomasSimonini
|
July 21, 2023
|
game-jam-first-edition-results
|
game-dev
|
https://huggingface.co/blog/game-jam-first-edition-results
|
#10: Singularity This demo is a conceptual demonstration of what could soon be the generation of experiences/games in the near future. 🤖 Used Stable Diffusion 🎮👉 https://ilumine-ai.itch.io/dreamlike-hugging-face-open-source-ai-game-jam In addition to this top 10, don't hesitate to check the other amazing games (Ghost In Smoke, Outopolis, Dungeons and Decoders...). You can find the whole list here 👉 https://itch.io/jam/open-source-ai-game-jam/entries --- The first-ever Open Source AI Game Jam proved to be an astounding success, exceeding our expectations in terms of community engagement and the quality of games produced. The overwhelming response has reinforced our belief in the potential of open-source AI tools to revolutionize the gaming industry. We are eager to continue this initiative and plan to host more sessions in the future, providing game developers with an opportunity to showcase their skills and explore the power of AI in game development. For those interested in AI for games, we have compiled a list of valuable resources, including AI tools for game development and tutorials on integrating AI into game engines like Unity: - Compilation of AI tools for Game Dev - How to install the Unity Hugging Face API: https://huggingface.co/blog/unity-api - AI Speech Recognition in Unity: https://huggingface.co/blog/unity-asr - Making ML-powered web games with Transformers.js: https://huggingface.co/blog/ml-web-games - Building a smart Robot AI using Hugging Face 🤗 and Unity: https://thomassimonini.substack.com/p/building-a-smart-robot-ai-using-hugging To stay connected and stay updated on future events, feel free to drop by our Discord server, where you can find channels dedicated to exchanging ideas about AI for games. Join our Discord Server 👉 https://hf.co/join/discord Thank you to all the participants, contributors, and supporters who made this event a memorable success!
|
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] |
Results of the Open Source AI Game Jam
|
ThomasSimonini
|
July 21, 2023
|
game-jam-first-edition-results
|
game-dev
|
https://huggingface.co/blog/game-jam-first-edition-results
|
#10: Galactic Bride: Bullet Ballet In this game, you dive into an exhilarating bullet-hell journey to become the Star Prince's bride and fulfill your wishes. 🎮👉 https://n30hrtgdv.itch.io/galactic-bride-bullet-ballet
|
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] |
Results of the Open Source AI Game Jam
|
ThomasSimonini
|
July 21, 2023
|
game-jam-first-edition-results
|
game-dev
|
https://huggingface.co/blog/game-jam-first-edition-results
|
Participants Selection: Top 10 🥈🥉🏅 Out of the 88 fantastic submissions, these impressive games emerged as the Top 11 finalists.
|
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] |
Results of the Open Source AI Game Jam
|
ThomasSimonini
|
July 21, 2023
|
game-jam-first-edition-results
|
game-dev
|
https://huggingface.co/blog/game-jam-first-edition-results
|
The Winner 🏆🥇 After careful deliberation, the judges crowned one outstanding game as the Winner of the Open Source AI Game Jam. It's Snip It by ohmlet 👏👏👏. Code: Ruben Gres AI assets: Philippe Saade Music / SFX: Matthieu Deloffre In this AI-generated game, you visit a museum where the paintings come to life. Snip the objects in the paintings to uncover their hidden secrets. You can play it here 👉 https://ohmlet.itch.io/snip-it
|
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] |
Results of the Open Source AI Game Jam
|
ThomasSimonini
|
July 21, 2023
|
game-jam-first-edition-results
|
game-dev
|
https://huggingface.co/blog/game-jam-first-edition-results
|
#1: Snip It In addition to be the winner of the Game Jam, Snip it has been selected as the top participant selection. 🤖 Open Source Model Used: Stable Diffusion to generate the assets. 🎮👉 https://ohmlet.itch.io/snip-it
|
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] |
Results of the Open Source AI Game Jam
|
ThomasSimonini
|
July 21, 2023
|
game-jam-first-edition-results
|
game-dev
|
https://huggingface.co/blog/game-jam-first-edition-results
|
#3: Fish Dang Bot Rolling Land In this game, you take control of a fish-shaped robot named Fein, who is abandoned in a garbage dump with mechanical legs. Unexpectedly, it develops self-awareness, and upon awakening, it sees a dung beetle pushing a dung ball. Naturally, Fein assumes himself to be a dung beetle and harbours a dream of pushing the largest dung ball. With this dream in mind, it decides to embark on its own adventure. 🤖 Used Text To Speech model to generate the voices. 🎮👉 https://zeenaz.itch.io/fish-dang-rolling-laud
|
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] |
Results of the Open Source AI Game Jam
|
ThomasSimonini
|
July 21, 2023
|
game-jam-first-edition-results
|
game-dev
|
https://huggingface.co/blog/game-jam-first-edition-results
|
#2: Yabbit Attack In Yabbit Attack, your goal is to beat the constantly adapting neural network behind the Yabbits. 🤖 Used genetic algorithms in the context of natural selection and evolution. 🤖 Backgrounds visuals were generated using Stable Diffusion 🎮👉 https://visionistx.itch.io/yabbit-attack
|
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] |
Results of the Open Source AI Game Jam
|
ThomasSimonini
|
July 21, 2023
|
game-jam-first-edition-results
|
game-dev
|
https://huggingface.co/blog/game-jam-first-edition-results
|
#6: Expanding Universe In this sandbox gravity game, you create an expanding universe and try to complete the challenges. 🤖 Used Dream Textures Blender (Stable Diffusion) add-on to create textures for all of the planets and stars and an LLM model to generate descriptions of the stars and planets. 🎮👉 https://carsonkatri.itch.io/expanding-universe
|
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] |
Results of the Open Source AI Game Jam
|
ThomasSimonini
|
July 21, 2023
|
game-jam-first-edition-results
|
game-dev
|
https://huggingface.co/blog/game-jam-first-edition-results
|
# Results of the Open Source AI Game Jam From July 7th to July 11th, we hosted our first Open Source AI Game Jam, an exciting event that challenged game developers to create innovative games within a tight 48-hour window using AI. The primary objective was to create games that incorporate at least one Open Source AI Tool. Although proprietary AI tools were allowed, we encouraged participants to integrate open-source tools into their game or workflow. The response to our initiative was beyond our expectations, with over 1300 signups and the submission of 88 amazing games. You can try them here 👉 https://itch.io/jam/open-source-ai-game-jam/entries
|
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] |
Results of the Open Source AI Game Jam
|
ThomasSimonini
|
July 21, 2023
|
game-jam-first-edition-results
|
game-dev
|
https://huggingface.co/blog/game-jam-first-edition-results
|
#8: Galactic Domination In this game, you embark on an interstellar journey as a spaceship captain, pitted against formidable spaceships in a battle for dominance. Your goal is to be the first to construct a powerful space station that will expand your influence and secure your supremacy in the vast expanse of the cosmos. As you navigate the treacherous battlefield, you must gather essential resources to fuel the growth of your space station. It's a construction race! 🤖 Unity ML-Agents (bot-AI works with reinforcement learning) 🤖 Charmed - Texture Generator 🤖 Soundful - Music generator 🤖 Elevenlabs - Voice generator 🤖 Scenario - Image generator 🎮👉 https://blastergames.itch.io/galactic-domination
|
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] |
Results of the Open Source AI Game Jam
|
ThomasSimonini
|
July 21, 2023
|
game-jam-first-edition-results
|
game-dev
|
https://huggingface.co/blog/game-jam-first-edition-results
|
#5: Word Conquest In this game, you need to write as many unrelated words as you can to conquer the map. The more unrelated, the farther away and the more score you get. 🤖 Used embeddings from all-MiniLM-L6-v2 model and GloVe to generate the map. 🎮👉 https://danielquelali.itch.io/wordconquest
|
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] |
Introducing Agents.js: Give tools to your LLMs using JavaScript
|
nsarrazin
|
July 24, 2023
|
agents-js
|
agents, javascript, web
|
https://huggingface.co/blog/agents-js
|
# Introducing Agents.js: Give tools to your LLMs using JavaScript We have recently been working on Agents.js at huggingface.js. It's a new library for giving tool access to LLMs from JavaScript in either the browser or the server. It ships with a few multi-modal tools out of the box and can easily be extended with your own tools and language models.
|
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] |
Introducing Agents.js: Give tools to your LLMs using JavaScript
|
nsarrazin
|
July 24, 2023
|
agents-js
|
agents, javascript, web
|
https://huggingface.co/blog/agents-js
|
Usage warning Currently using this library will mean evaluating arbitrary code in the browser (or in Node). This is a security risk and should not be done in an untrusted environment. We recommend that you use generateCode and evaluateCode instead of run in order to check what code you are running.
|
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] |
Introducing Agents.js: Give tools to your LLMs using JavaScript
|
nsarrazin
|
July 24, 2023
|
agents-js
|
agents, javascript, web
|
https://huggingface.co/blog/agents-js
|
Custom Tools 🛠️ Agents.js was designed to be easily expanded with custom tools & examples. For example if you wanted to add a tool that would translate text from English to German you could do it like this: ts import type { Tool } from "@huggingface/agents/src/types"; const englishToGermanTool: Tool = { name: "englishToGerman", description: "Takes an input string in english and returns a german translation. ", examples: [ { prompt: "translate the string 'hello world' to german", code: `const output = englishToGerman("hello world")`, tools: ["englishToGerman"], }, { prompt: "translate the string 'The quick brown fox jumps over the lazy dog` into german", code: `const output = englishToGerman("The quick brown fox jumps over the lazy dog")`, tools: ["englishToGerman"], }, ], call: async (input, inference) => { const data = await input; if (typeof data !== "string") { throw new Error("Input must be a string"); } const result = await inference.translation({ model: "t5-base", inputs: input, }); return result.translation_text; }, }; Now this tool can be added to the list of tools when initiating your agent. ts import { HfAgent, LLMFromHub, defaultTools } from "@huggingface/agents"; const HF_ACCESS_TOKEN = "hf_..."; const agent = new HfAgent(HF_ACCESS_TOKEN, LLMFromHub("hf_..."), [ englishToGermanTool, ...defaultTools, ]);
|
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] |
Introducing Agents.js: Give tools to your LLMs using JavaScript
|
nsarrazin
|
July 24, 2023
|
agents-js
|
agents, javascript, web
|
https://huggingface.co/blog/agents-js
|
Demo 🎉 We've been working on a demo for Agents.js that you can try out here. It's powered by the same Open Assistant 30B model that we use on HuggingChat and uses tools called from the hub. 🚀
|
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] |
Introducing Agents.js: Give tools to your LLMs using JavaScript
|
nsarrazin
|
July 24, 2023
|
agents-js
|
agents, javascript, web
|
https://huggingface.co/blog/agents-js
|
Passing input files to the agent 🖼️ The agent can also take input files to pass along to the tools. You can pass an optional FileList to generateCode and evaluateCode as such: If you have the following html: html <input id="fileItem" type="file" /> Then you can do: ts const agent = new HfAgent(HF_ACCESS_TOKEN); const files = document.getElementById("fileItem").files; // FileList type const code = agent.generateCode( "Caption the image and then read the text out loud.", files ); Which generated the following code when passing an image: ts // code generated by the LLM async function generate(image) { const caption = await imageToText(image); message("First we caption the image", caption); const output = await textToSpeech(caption); message("Then we read the caption out loud", output); return output; }
|
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] |
Introducing Agents.js: Give tools to your LLMs using JavaScript
|
nsarrazin
|
July 24, 2023
|
agents-js
|
agents, javascript, web
|
https://huggingface.co/blog/agents-js
|
Usage The library exposes the HfAgent object which is the entry point to the library. You can instantiate it like this: ts import { HfAgent } from "@huggingface/agents"; const HF_ACCESS_TOKEN = "hf_..."; // get your token at https://huggingface.co/settings/tokens const agent = new HfAgent(HF_ACCESS_TOKEN); Afterward, using the agent is easy. You give it a plain-text command and it will return some messages. ts const code = await agent.generateCode( "Draw a picture of a rubber duck with a top hat, then caption this picture." ); which in this case generated the following code js // code generated by the LLM async function generate() { const output = await textToImage("rubber duck with a top hat"); message("We generate the duck picture", output); const caption = await imageToText(output); message("Now we caption the image", caption); return output; } Then the code can be evaluated as such: ts const messages = await agent.evaluateCode(code); The messages returned by the agent are objects with the following shape: ts export interface Update { message: string; data: undefined | string | Blob; where message is an info text and data can contain either a string or a blob. The blob can be used to display images or audio. If you trust your environment (see warning), you can also run the code directly from the prompt with run : ts const messages = await agent.run( "Draw a picture of a rubber duck with a top hat, then caption this picture." );
|
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] |
Introducing Agents.js: Give tools to your LLMs using JavaScript
|
nsarrazin
|
July 24, 2023
|
agents-js
|
agents, javascript, web
|
https://huggingface.co/blog/agents-js
|
Custom LLMs 💬 By default HfAgent will use OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5 hosted Inference API as the LLM. This can be customized however. When instancing your HfAgent you can pass a custom LLM. A LLM in this context is any async function that takes a string input and returns a promise for a string. For example if you have an OpenAI API key you could make use of it like this: ts import { Configuration, OpenAIApi } from "openai"; const HF_ACCESS_TOKEN = "hf_..."; const api = new OpenAIApi(new Configuration({ apiKey: "sk-..." })); const llmOpenAI = async (prompt: string): Promise<string> => { return ( ( await api.createCompletion({ model: "text-davinci-003", prompt: prompt, max_tokens: 1000, }) ).data.choices[0].text ?? "" ); }; const agent = new HfAgent(HF_ACCESS_TOKEN, llmOpenAI);
|
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] |
Introducing Agents.js: Give tools to your LLMs using JavaScript
|
nsarrazin
|
July 24, 2023
|
agents-js
|
agents, javascript, web
|
https://huggingface.co/blog/agents-js
|
Installation Getting started is very easy, you can grab the library from npm with the following: npm install @huggingface/agents
|
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] |
AI Policy @🤗: Open ML Considerations in the EU AI Act
|
yjernite
|
July 24, 2023
|
eu-ai-act-oss
|
ethics
|
https://huggingface.co/blog/eu-ai-act-oss
|
# AI Policy @🤗: Open ML Considerations in the EU AI Act Like everyone else in Machine Learning, we’ve been following the EU AI Act closely at Hugging Face. It’s a ground-breaking piece of legislation that is poised to shape how democratic inputs interact with AI technology development around the world. It’s also the outcome of extensive work and negotiations between organizations representing many different components of society – a process we’re particularly sensitive to as a community-driven company. In the present position paper written in coalition with Creative Commons, Eleuther AI, GitHub, LAION, and Open Future, we aim to contribute to this process by sharing our experience of the necessary role of open ML development in supporting the goals of the Act – and conversely, by outlining specific ways in which the regulation can better account for the needs of open, modular, and collaborative ML development. Hugging Face is where it is today thanks to its community of developers, so we’ve seen firsthand what open development brings to the table to support more robust innovation for more diverse and context-specific use cases; where developers can easily share innovative new techniques, mix and match ML components to suit their own needs, and reliably work with full visibility into their entire stack. We’re also acutely aware of the necessary role of transparency in supporting more accountability and inclusivity of the technology – which we’ve worked on fostering through better documentation and accessibility of ML artifacts, education efforts, and hosting large-scale multidisciplinary collaborations, among others. Thus, as the EU AI Act moves toward its final phase, we believe accounting for the specific needs and strengths of open and open-source development of ML systems will be instrumental in supporting its long-term goals. Along with our co-signed partner organizations, we make the following five recommendations to that end: 1. Define AI components clearly, 2. Clarify that collaborative development of open source AI components and making them available in public repositories does not subject developers to the requirements in the AI Act, building on and improving the Parliament text’s Recitals 12a-c and Article 2(5e), 3. Support the AI Office’s coordination and inclusive governance with the open source ecosystem, building on the Parliament’s text, 4. Ensure the R&D exception is practical and effective, by permitting limited testing in real-world conditions, combining aspects of the Council’s approach and an amended version of the Parliament’s Article 2(5d), 5. Set proportional requirements for “foundation models,” recognizing and distinctly treating different uses and development modalities, including open source approaches, tailoring the Parliament’s Article 28b. You can find more detail and context for those in the full paper here!
|
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] |
Stable Diffusion XL on Mac with Advanced Core ML Quantization
|
pcuenq
|
July 27, 2023
|
stable-diffusion-xl-coreml
|
coreml, stable-diffusion, stable-diffusion-xl, diffusers
|
https://huggingface.co/blog/stable-diffusion-xl-coreml
|
Contents - Using SD XL Models from the Hugging Face Hub - What is Mixed-Bit Palettization? - How are Mixed-Bit Recipes Created? - Converting Fine-Tuned Models - Published Resources
|
[
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] |
Stable Diffusion XL on Mac with Advanced Core ML Quantization
|
pcuenq
|
July 27, 2023
|
stable-diffusion-xl-coreml
|
coreml, stable-diffusion, stable-diffusion-xl, diffusers
|
https://huggingface.co/blog/stable-diffusion-xl-coreml
|
What is Mixed-Bit Palettization? Last month we discussed 6-bit palettization, a post-training quantization method that converts 16-bit weights to just 6-bit per parameter. This achieves an important reduction in model size, but going beyond that is tricky because model quality becomes more and more impacted as the number of bits is decreased. One option to decrease model size further is to use training time quantization, which consists of learning the quantization tables while we fine-tune the model. This works great, but you need to run a fine-tuning phase for every model you want to convert. We explored a different alternative instead: mixed-bit palettization. Instead of using 6 bits per parameter, we examine the model and decide how many quantization bits to use per layer. We make the decision based on how much each layer contributes to the overall quality degradation, which we measure by comparing the PSNR between the quantized model and the original model in float16 mode, for a set of a few inputs. We explore several bit depths, per layer: 1 (!), 2, 4 and 8. If a layer degrades significantly when using, say, 2 bits, we move to 4 and so on. Some layers might be kept in 16-bit mode if they are critical to preserving quality. Using this method, we can achieve effective quantizations of, for example, 2.8 bits on average, and we measure the impact on degradation for every combination we try. This allows us to be better informed about the best quantization to use for our target quality and size budgets. To illustrate the method, let’s consider the following quantization “recipes” that we got from one of our analysis runs (we’ll explain later how they were generated): json { "model_version": "stabilityai/stable-diffusion-xl-base-1.0", "baselines": { "original": 82.2, "linear_8bit": 66.025, "recipe_6.55_bit_mixedpalette": 79.9, "recipe_4.50_bit_mixedpalette": 75.8, "recipe_3.41_bit_mixedpalette": 71.7, }, } What this tells us is that the original model quality, as measured by PSNR in float16, is about 82 dB. Performing a naïve 8-bit linear quantization drops it to 66 dB. But then we have a recipe that compresses to 6.55 bits per parameter, on average, while keeping PSNR at 80 dB. The second and third recipes further reduce the model size, while still sustaining a PSNR larger than that of the 8-bit linear quantization. For visual examples, these are the results on prompt a high quality photo of a surfing dog running each one of the three recipes with the same seed: | 3.41-bit | 4.50-bit | 6.55-bit | 16-bit (original) | | :-------:| :-------:| :-------:| :----------------:| | | | | | Some initial conclusions: - In our opinion, all the images have good quality in terms of how realistic they look. The 6.55 and 4.50 versions are close to the 16-bit version in this aspect. - The same seed produces an equivalent composition, but will not preserve the same details. Dog breeds may be different, for example. - Adherence to the prompt may degrade as we increase compression. In this example, the aggressive 3.41 version loses the board. PSNR only compares how much pixels differ overall, but does not care about the subjects in the images. You need to examine results and assess them for your use case. This technique is great for Stable Diffusion XL because we can keep about the same UNet size even though the number of parameters tripled with respect to the previous version. But it's not exclusive to it! You can apply the method to any Stable Diffusion Core ML model.
|
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] |
Stable Diffusion XL on Mac with Advanced Core ML Quantization
|
pcuenq
|
July 27, 2023
|
stable-diffusion-xl-coreml
|
coreml, stable-diffusion, stable-diffusion-xl, diffusers
|
https://huggingface.co/blog/stable-diffusion-xl-coreml
|
Running Mixed-Bit Palettization After converting Stable Diffusion or Stable Diffusion XL models to Core ML, you can optionally apply mixed-bit palettization using the scripts mentioned above. Because the analysis process is slow, we have prepared recipes for the most popular models: * Recipes for Stable Diffusion 1.5 * Recipes for Stable Diffusion 2.1 * Recipes for Stable Diffusion XL 1.0 base You can download and apply them locally to experiment. In addition, we also applied the three best recipes from the Stable Diffusion XL analysis to the Core ML version of the UNet, and published them here. Feel free to play with them and see how they work for you! Finally, as mentioned in the introduction, we created a complete Stable Diffusion XL Core ML pipeline that uses a 4.5-bit recipe.
|
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] |
Stable Diffusion XL on Mac with Advanced Core ML Quantization
|
pcuenq
|
July 27, 2023
|
stable-diffusion-xl-coreml
|
coreml, stable-diffusion, stable-diffusion-xl, diffusers
|
https://huggingface.co/blog/stable-diffusion-xl-coreml
|
Converting Fine-Tuned Models If you’ve previously converted Stable Diffusion models to Core ML, the process for XL using the command line converter is very similar. There’s a new flag to indicate whether the model belongs to the XL family, and you have to use --attention-implementation ORIGINAL if that’s the case. For an introduction to the process, check the instructions in the repo or one of our previous blog posts, and make sure you use the flags above.
|
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] |
Stable Diffusion XL on Mac with Advanced Core ML Quantization
|
pcuenq
|
July 27, 2023
|
stable-diffusion-xl-coreml
|
coreml, stable-diffusion, stable-diffusion-xl, diffusers
|
https://huggingface.co/blog/stable-diffusion-xl-coreml
|
Published Resources * apple/ml-stable-diffusion, by Apple. Conversion and inference library for Swift (and Python). * huggingface/swift-coreml-diffusers. Hugging Face demo app, built on top of Apple's package. * Stable Diffusion XL 1.0 base (Core ML version). Model ready to run using the repos above and other third-party apps. * Stable Diffusion XL 1.0 base, with mixed-bit palettization (Core ML). Same model as above, with UNet quantized with an effective palettization of 4.5 bits (on average). * Additional UNets with mixed-bit palettizaton. * Mixed-bit palettization recipes, pre-computed for popular models and ready to use. * mixed_bit_compression_pre_analysis.py. Script to run mixed-bit analysis and recipe generation. * mixed_bit_compression_apply.py. Script to apply recipes computed during the analysis phase.
|
[
0.023195313289761543,
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] |
Stable Diffusion XL on Mac with Advanced Core ML Quantization
|
pcuenq
|
July 27, 2023
|
stable-diffusion-xl-coreml
|
coreml, stable-diffusion, stable-diffusion-xl, diffusers
|
https://huggingface.co/blog/stable-diffusion-xl-coreml
|
How are Mixed-Bit Recipes Created? The following plot shows the signal strength (PSNR in dB) versus model size reduction (% of float16 size) for stabilityai/stable-diffusion-xl-base-1.0. The {1,2,4,6,8}-bit curves are generated by progressively palettizing more layers using a palette with a fixed number of bits. The layers were ordered in ascending order of their isolated impact to end-to-end signal strength, so the cumulative compression's impact is delayed as much as possible. The mixed-bit curve is based on falling back to a higher number of bits as soon as a layer's isolated impact to end-to-end signal integrity drops below a threshold. Note that all curves based on palettization outperform linear 8-bit quantization at the same model size except for 1-bit. Mixed-bit palettization runs in two phases: analysis and application. The goal of the analysis phase is to find points in the mixed-bit curve (the brown one above all the others in the figure) so we can choose our desired quality-vs-size tradeoff. As mentioned in the previous section, we iterate through the layers and select the lowest bit depths that yield results above a given PSNR threshold. We repeat the process for various thresholds to get different quantization strategies. The result of the process is thus a set of quantization recipes, where each recipe is just a JSON dictionary detailing the number of bits to use for each layer in the model. Layers with few parameters are ignored and kept in float16 for simplicity. The application phase simply goes over the recipe and applies palettization with the number of bits specified in the JSON structure. Analysis is a lengthy process and requires a GPU (mps or cuda), as we have to run inference multiple times. Once it’s done, recipe application can be performed in a few minutes. We provide scripts for each one of these phases: * mixed_bit_compression_pre_analysis.py * mixed_bit_compression_apply.py
|
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