repo_id stringlengths 15 89 | file_path stringlengths 27 180 | content stringlengths 1 2.23M | __index_level_0__ int64 0 0 |
|---|---|---|---|
hf_public_repos/candle/candle-book/src | hf_public_repos/candle/candle-book/src/inference/hub.md | # Using the hub
Install the [`hf-hub`](https://github.com/huggingface/hf-hub) crate:
```bash
cargo add hf-hub
```
Then let's start by downloading the [model file](https://huggingface.co/bert-base-uncased/tree/main).
```rust
# extern crate candle_core;
# extern crate hf_hub;
use hf_hub::api::sync::Api;
use candle_c... | 0 |
hf_public_repos/candle/candle-book/src | hf_public_repos/candle/candle-book/src/inference/inference.md | # Running a model
In order to run an existing model, you will need to download and use existing weights.
Most models are already available on https://huggingface.co/ in [`safetensors`](https://github.com/huggingface/safetensors) format.
Let's get started by running an old model : `bert-base-uncased`.
| 0 |
hf_public_repos/candle/candle-book/src/inference | hf_public_repos/candle/candle-book/src/inference/cuda/porting.md | # Porting a custom kernel
| 0 |
hf_public_repos/candle/candle-book/src/inference | hf_public_repos/candle/candle-book/src/inference/cuda/README.md | # Advanced Cuda usage
| 0 |
hf_public_repos/candle/candle-book/src/inference | hf_public_repos/candle/candle-book/src/inference/cuda/writing.md | # Writing a custom kernel
| 0 |
hf_public_repos/candle/candle-book/src | hf_public_repos/candle/candle-book/src/guide/installation.md | # Installation
**With Cuda support**:
1. First, make sure that Cuda is correctly installed.
- `nvcc --version` should print information about your Cuda compiler driver.
- `nvidia-smi --query-gpu=compute_cap --format=csv` should print your GPUs compute capability, e.g. something
like:
```bash
compute_cap
8.9
```
You... | 0 |
hf_public_repos/candle/candle-book/src | hf_public_repos/candle/candle-book/src/guide/cheatsheet.md | # Pytorch cheatsheet
{{#include ../../../README.md:cheatsheet}}
| 0 |
hf_public_repos/candle/candle-book/src | hf_public_repos/candle/candle-book/src/guide/hello_world.md | # Hello world!
We will now create the hello world of the ML world, building a model capable of solving MNIST dataset.
Open `src/main.rs` and fill in this content:
```rust
# extern crate candle_core;
use candle_core::{Device, Result, Tensor};
struct Model {
first: Tensor,
second: Tensor,
}
impl Model {
... | 0 |
hf_public_repos/candle/candle-book/src | hf_public_repos/candle/candle-book/src/training/mnist.md | # MNIST
So we now have downloaded the MNIST parquet files, let's put them in a simple struct.
```rust,ignore
{{#include ../lib.rs:book_training_3}}
```
The parsing of the file and putting it into single tensors requires the dataset to fit the entire memory.
It is quite rudimentary, but simple enough for a small data... | 0 |
hf_public_repos/candle/candle-book/src | hf_public_repos/candle/candle-book/src/training/finetuning.md | # Fine-tuning
| 0 |
hf_public_repos/candle/candle-book/src | hf_public_repos/candle/candle-book/src/training/serialization.md | # Serialization
| 0 |
hf_public_repos/candle/candle-book/src | hf_public_repos/candle/candle-book/src/training/training.md | # Training
Training starts with data. We're going to use the huggingface hub and
start with the Hello world dataset of machine learning, MNIST.
Let's start with downloading `MNIST` from [huggingface](https://huggingface.co/datasets/mnist).
This requires [`hf-hub`](https://github.com/huggingface/hf-hub).
```bash
ca... | 0 |
hf_public_repos/candle/candle-book/src | hf_public_repos/candle/candle-book/src/training/simplified.md | # Simplified
## How its works
This program implements a neural network to predict the winner of the second round of elections based on the results of the first round.
Basic moments:
1. A multilayer perceptron with two hidden layers is used. The first hidden layer has 4 neurons, the second has 2 neurons.
2. The inpu... | 0 |
hf_public_repos/candle/candle-book/src | hf_public_repos/candle/candle-book/src/apps/rest.md | # Creating a REST api webserver
| 0 |
hf_public_repos/candle/candle-book/src | hf_public_repos/candle/candle-book/src/apps/README.md | # Creating apps
| 0 |
hf_public_repos/candle/candle-book/src | hf_public_repos/candle/candle-book/src/apps/wasm.md | # Creating a WASM app
| 0 |
hf_public_repos/candle/candle-book/src | hf_public_repos/candle/candle-book/src/apps/desktop.md | # Creating a desktop Tauri app
| 0 |
hf_public_repos/candle/candle-book/src | hf_public_repos/candle/candle-book/src/cuda/porting.md | # Porting a custom kernel
| 0 |
hf_public_repos/candle/candle-book/src | hf_public_repos/candle/candle-book/src/cuda/README.md | # Advanced Cuda usage
| 0 |
hf_public_repos/candle/candle-book/src | hf_public_repos/candle/candle-book/src/cuda/writing.md | # Writing a custom kernel
| 0 |
hf_public_repos/candle | hf_public_repos/candle/candle-datasets/README.md | # candle-datasets
| 0 |
hf_public_repos/candle | hf_public_repos/candle/candle-datasets/Cargo.toml | [package]
name = "candle-datasets"
version.workspace = true
edition.workspace = true
description.workspace = true
repository.workspace = true
keywords.workspace = true
categories.workspace = true
license.workspace = true
readme = "README.md"
[dependencies]
byteorder = { workspace = true }
candle = { workspace = true }... | 0 |
hf_public_repos/candle/candle-datasets | hf_public_repos/candle/candle-datasets/src/lib.rs | //! Datasets & Dataloaders for Candle
pub mod batcher;
pub mod hub;
pub mod nlp;
pub mod vision;
pub use batcher::Batcher;
| 0 |
hf_public_repos/candle/candle-datasets | hf_public_repos/candle/candle-datasets/src/batcher.rs | use candle::{Result, Tensor};
pub struct Batcher<I> {
inner: I,
batch_size: usize,
return_last_incomplete_batch: bool,
}
impl<I> Batcher<I> {
fn new(inner: I) -> Self {
Self {
inner,
batch_size: 16,
return_last_incomplete_batch: false,
}
}
p... | 0 |
hf_public_repos/candle/candle-datasets | hf_public_repos/candle/candle-datasets/src/hub.rs | use hf_hub::{
api::sync::{Api, ApiRepo},
Repo, RepoType,
};
use parquet::file::reader::SerializedFileReader;
use std::fs::File;
#[derive(thiserror::Error, Debug)]
pub enum Error {
#[error("ApiError : {0}")]
ApiError(#[from] hf_hub::api::sync::ApiError),
#[error("IoError : {0}")]
IoError(#[from... | 0 |
hf_public_repos/candle/candle-datasets/src | hf_public_repos/candle/candle-datasets/src/nlp/mod.rs | pub mod tinystories;
| 0 |
hf_public_repos/candle/candle-datasets/src | hf_public_repos/candle/candle-datasets/src/nlp/tinystories.rs | //! Helper functions for the tinystories dataset. This uses the pre-tokenized version as generated
//! by the tools from https://github.com/karpathy/llama2.c
use candle::{Device, Result, Tensor};
pub struct Dataset {
valid_tokens: Vec<memmap2::Mmap>,
train_tokens: Vec<memmap2::Mmap>,
}
fn mmap_file(p: &std::p... | 0 |
hf_public_repos/candle/candle-datasets/src | hf_public_repos/candle/candle-datasets/src/vision/mod.rs | use candle::Tensor;
pub struct Dataset {
pub train_images: Tensor,
pub train_labels: Tensor,
pub test_images: Tensor,
pub test_labels: Tensor,
pub labels: usize,
}
pub mod cifar;
pub mod mnist;
| 0 |
hf_public_repos/candle/candle-datasets/src | hf_public_repos/candle/candle-datasets/src/vision/mnist.rs | //! The MNIST hand-written digit dataset.
//!
//! The files can be obtained from the following link:
//! <http://yann.lecun.com/exdb/mnist/>
use candle::{DType, Device, Error, Result, Tensor};
use hf_hub::{api::sync::Api, Repo, RepoType};
use parquet::file::reader::{FileReader, SerializedFileReader};
use std::fs::File;... | 0 |
hf_public_repos/candle/candle-datasets/src | hf_public_repos/candle/candle-datasets/src/vision/cifar.rs | //! The CIFAR-10 dataset.
//!
//! The files can be downloaded from the following page:
//! <https://www.cs.toronto.edu/~kriz/cifar.html>
//! The binary version of the dataset is used.
use crate::vision::Dataset;
use candle::{DType, Device, Error, Result, Tensor};
use hf_hub::{api::sync::Api, Repo, RepoType};
use parque... | 0 |
hf_public_repos | hf_public_repos/diffusers/CODE_OF_CONDUCT.md |
# Contributor Covenant Code of Conduct
## Our Pledge
We as members, contributors, and leaders pledge to make participation in our
community a harassment-free experience for everyone, regardless of age, body
size, visible or invisible disability, ethnicity, sex characteristics, gender
identity and expression, level o... | 0 |
hf_public_repos | hf_public_repos/diffusers/README.md | <!---
Copyright 2022 - The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law o... | 0 |
hf_public_repos | hf_public_repos/diffusers/_typos.toml | # Files for typos
# Instruction: https://github.com/marketplace/actions/typos-action#getting-started
[default.extend-identifiers]
[default.extend-words]
NIN="NIN" # NIN is used in scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py
nd="np" # nd may be np (numpy)
parms="parms" # parms is used in scripts/conver... | 0 |
hf_public_repos | hf_public_repos/diffusers/pyproject.toml | [tool.ruff]
# Never enforce `E501` (line length violations).
ignore = ["C901", "E501", "E741", "F402", "F823"]
select = ["C", "E", "F", "I", "W"]
line-length = 119
# Ignore import violations in all `__init__.py` files.
[tool.ruff.per-file-ignores]
"__init__.py" = ["E402", "F401", "F403", "F811"]
"src/diffusers/utils/d... | 0 |
hf_public_repos | hf_public_repos/diffusers/CONTRIBUTING.md | <!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | 0 |
hf_public_repos | hf_public_repos/diffusers/setup.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 0 |
hf_public_repos | hf_public_repos/diffusers/CITATION.cff | cff-version: 1.2.0
title: 'Diffusers: State-of-the-art diffusion models'
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Patrick
family-names: von Platen
- given-names: Suraj
family-names: Patil
- given-names: Anton
fam... | 0 |
hf_public_repos | hf_public_repos/diffusers/MANIFEST.in | include LICENSE
include src/diffusers/utils/model_card_template.md
| 0 |
hf_public_repos | hf_public_repos/diffusers/Makefile | .PHONY: deps_table_update modified_only_fixup extra_style_checks quality style fixup fix-copies test test-examples
# make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!)
export PYTHONPATH = src
check_dirs := examples scripts src tests utils benchmarks
modified_only_fixup:... | 0 |
hf_public_repos | hf_public_repos/diffusers/PHILOSOPHY.md | <!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed... | 0 |
hf_public_repos | hf_public_repos/diffusers/LICENSE | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/benchmarks/benchmark_sd_inpainting.py | import argparse
import sys
sys.path.append(".")
from base_classes import InpaintingBenchmark # noqa: E402
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--ckpt",
type=str,
default="runwayml/stable-diffusion-v1-5",
choices=[
"r... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/benchmarks/benchmark_sd_img.py | import argparse
import sys
sys.path.append(".")
from base_classes import ImageToImageBenchmark, TurboImageToImageBenchmark # noqa: E402
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--ckpt",
type=str,
default="runwayml/stable-diffusion-v1-5",
... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/benchmarks/benchmark_t2i_lcm_lora.py | import argparse
import sys
sys.path.append(".")
from base_classes import LCMLoRATextToImageBenchmark # noqa: E402
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--ckpt",
type=str,
default="stabilityai/stable-diffusion-xl-base-1.0",
)
pars... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/benchmarks/utils.py | import argparse
import csv
import gc
import os
from dataclasses import dataclass
from typing import Dict, List, Union
import torch
import torch.utils.benchmark as benchmark
GITHUB_SHA = os.getenv("GITHUB_SHA", None)
BENCHMARK_FIELDS = [
"pipeline_cls",
"ckpt_id",
"batch_size",
"num_inference_steps",
... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/benchmarks/run_all.py | import glob
import subprocess
import sys
from typing import List
sys.path.append(".")
from benchmark_text_to_image import ALL_T2I_CKPTS # noqa: E402
PATTERN = "benchmark_*.py"
class SubprocessCallException(Exception):
pass
# Taken from `test_examples_utils.py`
def run_command(command: List[str], return_std... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/benchmarks/benchmark_text_to_image.py | import argparse
import sys
sys.path.append(".")
from base_classes import TextToImageBenchmark, TurboTextToImageBenchmark # noqa: E402
ALL_T2I_CKPTS = [
"runwayml/stable-diffusion-v1-5",
"segmind/SSD-1B",
"stabilityai/stable-diffusion-xl-base-1.0",
"kandinsky-community/kandinsky-2-2-decoder",
"w... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/benchmarks/push_results.py | import glob
import sys
import pandas as pd
from huggingface_hub import hf_hub_download, upload_file
from huggingface_hub.utils._errors import EntryNotFoundError
sys.path.append(".")
from utils import BASE_PATH, FINAL_CSV_FILE, GITHUB_SHA, REPO_ID, collate_csv # noqa: E402
def has_previous_benchmark() -> str:
... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/benchmarks/benchmark_t2i_adapter.py | import argparse
import sys
sys.path.append(".")
from base_classes import T2IAdapterBenchmark, T2IAdapterSDXLBenchmark # noqa: E402
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--ckpt",
type=str,
default="TencentARC/t2iadapter_canny_sd14v1",
... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/benchmarks/benchmark_controlnet.py | import argparse
import sys
sys.path.append(".")
from base_classes import ControlNetBenchmark, ControlNetSDXLBenchmark # noqa: E402
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--ckpt",
type=str,
default="lllyasviel/sd-controlnet-canny",
... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/benchmarks/base_classes.py | import os
import sys
import torch
from diffusers import (
AutoPipelineForImage2Image,
AutoPipelineForInpainting,
AutoPipelineForText2Image,
ControlNetModel,
LCMScheduler,
StableDiffusionAdapterPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionXLAdapterPipeline,
StableDiff... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_original_audioldm2_to_diffusers.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_kakao_brain_unclip_to_diffusers.py | import argparse
import tempfile
import torch
from accelerate import load_checkpoint_and_dispatch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import UnCLIPPipeline, UNet2DConditionModel, UNet2DModel
from diffusers.models.prior_transformer import PriorTransformer
from diffusers.pi... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_pixart_alpha_to_diffusers.py | import argparse
import os
import torch
from transformers import T5EncoderModel, T5Tokenizer
from diffusers import AutoencoderKL, DPMSolverMultistepScheduler, PixArtAlphaPipeline, Transformer2DModel
ckpt_id = "PixArt-alpha/PixArt-alpha"
# https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_zero123_to_diffusers.py | """
This script modified from
https://github.com/huggingface/diffusers/blob/bc691231360a4cbc7d19a58742ebb8ed0f05e027/scripts/convert_original_stable_diffusion_to_diffusers.py
Convert original Zero1to3 checkpoint to diffusers checkpoint.
# run the convert script
$ python convert_zero123_to_diffusers.py \
--checkpoi... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/conversion_ldm_uncond.py | import argparse
import torch
import yaml
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def convert_ldm_original(checkpoint_path, config_path, output_path):
config = yaml.safe_load(config_path)
state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
keys = list(sta... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_vae_pt_to_diffusers.py | import argparse
import io
import requests
import torch
import yaml
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_original_stable_diffusion_to_diffusers.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_animatediff_motion_module_to_diffusers.py | import argparse
import torch
from diffusers import MotionAdapter
def convert_motion_module(original_state_dict):
converted_state_dict = {}
for k, v in original_state_dict.items():
if "pos_encoder" in k:
continue
else:
converted_state_dict[
k.replace("... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_gligen_to_diffusers.py | import argparse
import re
import torch
import yaml
from transformers import (
CLIPProcessor,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionModelWithProjection,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionGLIGENPipeline,
StableDiffusionGLIGENTextImagePipeline,
U... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_unidiffuser_to_diffusers.py | # Convert the original UniDiffuser checkpoints into diffusers equivalents.
import argparse
from argparse import Namespace
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
GPT2Tokenizer... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_dit_to_diffusers.py | import argparse
import os
import torch
from torchvision.datasets.utils import download_url
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, Transformer2DModel
pretrained_models = {512: "DiT-XL-2-512x512.pt", 256: "DiT-XL-2-256x256.pt"}
def download_model(model_name):
"""
Downloads a pre-tr... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_if.py | import argparse
import inspect
import os
import numpy as np
import torch
import yaml
from torch.nn import functional as F
from transformers import CLIPConfig, CLIPImageProcessor, CLIPVisionModelWithProjection, T5EncoderModel, T5Tokenizer
from diffusers import DDPMScheduler, IFPipeline, IFSuperResolutionPipeline, UNet... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_original_musicldm_to_diffusers.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_vq_diffusion_to_diffusers.py | """
This script ports models from VQ-diffusion (https://github.com/microsoft/VQ-Diffusion) to diffusers.
It currently only supports porting the ITHQ dataset.
ITHQ dataset:
```sh
# From the root directory of diffusers.
# Download the VQVAE checkpoint
$ wget https://facevcstandard.blob.core.windows.net/v-zhictang/Impr... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_tiny_autoencoder_to_diffusers.py | import argparse
import safetensors.torch
from diffusers import AutoencoderTiny
"""
Example - From the diffusers root directory:
Download the weights:
```sh
$ wget -q https://huggingface.co/madebyollin/taesd/resolve/main/taesd_encoder.safetensors
$ wget -q https://huggingface.co/madebyollin/taesd/resolve/main/taesd... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_original_controlnet_to_diffusers.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_blipdiffusion_to_diffusers.py | """
This script requires you to build `LAVIS` from source, since the pip version doesn't have BLIP Diffusion. Follow instructions here: https://github.com/salesforce/LAVIS/tree/main.
"""
import argparse
import os
import tempfile
import torch
from lavis.models import load_model_and_preprocess
from transformers import ... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/change_naming_configs_and_checkpoints.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_kandinsky3_unet.py | #!/usr/bin/env python3
import argparse
import fnmatch
from safetensors.torch import load_file
from diffusers import Kandinsky3UNet
MAPPING = {
"to_time_embed.1": "time_embedding.linear_1",
"to_time_embed.3": "time_embedding.linear_2",
"in_layer": "conv_in",
"out_layer.0": "conv_norm_out",
"out_l... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_amused.py | import inspect
import os
from argparse import ArgumentParser
import numpy as np
import torch
from muse import MaskGiTUViT, VQGANModel
from muse import PipelineMuse as OldPipelineMuse
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import VQModel
from diffusers.models.attention_proce... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_original_t2i_adapter.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_lora_safetensor_to_diffusers.py | # coding=utf-8
# Copyright 2023, Haofan Wang, Qixun Wang, All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_shap_e_to_diffusers.py | import argparse
import tempfile
import torch
from accelerate import load_checkpoint_and_dispatch
from diffusers.models.prior_transformer import PriorTransformer
from diffusers.pipelines.shap_e import ShapERenderer
"""
Example - From the diffusers root directory:
Download weights:
```sh
$ wget "https://openaipubli... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_svd_to_diffusers.py | from diffusers.utils import is_accelerate_available, logging
if is_accelerate_available():
pass
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def create_unet_diffusers_config(original_config, image_size: int, controlnet=False):
"""
Creates a config for the diffusers based on the... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_stable_diffusion_controlnet_to_tensorrt.py | import argparse
import sys
import tensorrt as trt
def convert_models(onnx_path: str, num_controlnet: int, output_path: str, fp16: bool = False, sd_xl: bool = False):
"""
Function to convert models in stable diffusion controlnet pipeline into TensorRT format
Example:
python convert_stable_diffusion_c... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_stable_diffusion_checkpoint_to_onnx.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_wuerstchen.py | # Run inside root directory of official source code: https://github.com/dome272/wuerstchen/
import os
import torch
from transformers import AutoTokenizer, CLIPTextModel
from vqgan import VQModel
from diffusers import (
DDPMWuerstchenScheduler,
WuerstchenCombinedPipeline,
WuerstchenDecoderPipeline,
Wue... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_music_spectrogram_to_diffusers.py | #!/usr/bin/env python3
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from t5x import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogr... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_diffusers_to_original_sdxl.py | # Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
# *Only* converts the UNet, VAE, and Text Encoder.
# Does not convert optimizer state or any other thing.
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# ========... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_stable_diffusion_controlnet_to_onnx.py | import argparse
import os
import shutil
from pathlib import Path
import onnx
import onnx_graphsurgeon as gs
import torch
from onnx import shape_inference
from packaging import version
from polygraphy.backend.onnx.loader import fold_constants
from torch.onnx import export
from diffusers import (
ControlNetModel,
... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_dance_diffusion_to_diffusers.py | #!/usr/bin/env python3
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnet1D
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNet1DModel
MODELS_MAP = {
"gwf-440k": {
... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_vae_diff_to_onnx.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_consistency_decoder.py | import math
import os
import urllib
import warnings
from argparse import ArgumentParser
import torch
import torch.nn as nn
import torch.nn.functional as F
from huggingface_hub.utils import insecure_hashlib
from safetensors.torch import load_file as stl
from tqdm import tqdm
from diffusers import AutoencoderKL, Consis... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_ddpm_original_checkpoint_to_diffusers.py | import argparse
import json
import torch
from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNet2DModel, VQModel
def shave_segments(path, n_shave_prefix_segments=1):
"""
Removes segments. Positive values shave the first segments, negative shave the last segments.
"""
if n_shave_prefix... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_models_diffuser_to_diffusers.py | import json
import os
import torch
from diffusers import UNet1DModel
os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)
def unet(hor):
if hor == 128:
down_block_... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_diffusers_to_original_stable_diffusion.py | # Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
# *Only* converts the UNet, VAE, and Text Encoder.
# Does not convert optimizer state or any other thing.
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# ========... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_kandinsky_to_diffusers.py | import argparse
import os
import tempfile
import torch
from accelerate import load_checkpoint_and_dispatch
from diffusers import UNet2DConditionModel
from diffusers.models.prior_transformer import PriorTransformer
from diffusers.models.vq_model import VQModel
"""
Example - From the diffusers root directory:
Downlo... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_ldm_original_checkpoint_to_diffusers.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/generate_logits.py | import random
import torch
from huggingface_hub import HfApi
from diffusers import UNet2DModel
api = HfApi()
results = {}
# fmt: off
results["google_ddpm_cifar10_32"] = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.849... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_k_upscaler_to_diffusers.py | import argparse
import huggingface_hub
import k_diffusion as K
import torch
from diffusers import UNet2DConditionModel
UPSCALER_REPO = "pcuenq/k-upscaler"
def resnet_to_diffusers_checkpoint(resnet, checkpoint, *, diffusers_resnet_prefix, resnet_prefix):
rv = {
# norm1
f"{diffusers_resnet_prefi... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_diffusers_sdxl_lora_to_webui.py | # Script for converting a Hugging Face Diffusers trained SDXL LoRAs to Kohya format
# This means that you can input your diffusers-trained LoRAs and
# Get the output to work with WebUIs such as AUTOMATIC1111, ComfyUI, SD.Next and others.
# To get started you can find some cool `diffusers` trained LoRAs such as this cu... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_original_audioldm_to_diffusers.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_asymmetric_vqgan_to_diffusers.py | import argparse
import time
from pathlib import Path
from typing import Any, Dict, Literal
import torch
from diffusers import AsymmetricAutoencoderKL
ASYMMETRIC_AUTOENCODER_KL_x_1_5_CONFIG = {
"in_channels": 3,
"out_channels": 3,
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBl... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_unclip_txt2img_to_image_variation.py | import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_ms_text_to_video_to_diffusers.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_versatile_diffusion_to_diffusers.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_consistency_to_diffusers.py | import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNet2DModel,
)
TEST_UNET_CONFIG = {
"sample_size": 32,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 2,
"num_class_embeds": 1000,
"block_out_channel... | 0 |
hf_public_repos/diffusers | hf_public_repos/diffusers/scripts/convert_animatediff_motion_lora_to_diffusers.py | import argparse
import torch
from safetensors.torch import save_file
def convert_motion_module(original_state_dict):
converted_state_dict = {}
for k, v in original_state_dict.items():
if "pos_encoder" in k:
continue
else:
converted_state_dict[
k.replac... | 0 |
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