mirror of https://github.com/skygpu/skynet.git
First attempt at adding flux models, update all deps, upgrade to cuda 12, add custom pipe sys
parent
00dcccf2bb
commit
07b211514d
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@ -0,0 +1,45 @@
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from nvidia/cuda:12.4.1-devel-ubuntu22.04
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from python:3.12
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env DEBIAN_FRONTEND=noninteractive
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run apt-get update && apt-get install -y \
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git \
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llvm \
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ffmpeg \
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libsm6 \
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libxext6 \
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ninja-build
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# env CC /usr/bin/clang
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# env CXX /usr/bin/clang++
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#
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# # install llvm10 as required by llvm-lite
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# run git clone https://github.com/llvm/llvm-project.git -b llvmorg-10.0.1
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# workdir /llvm-project
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# # this adds a commit from 12.0.0 that fixes build on newer compilers
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# run git cherry-pick -n b498303066a63a203d24f739b2d2e0e56dca70d1
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# run cmake -S llvm -B build -G Ninja -DCMAKE_BUILD_TYPE=Release
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# run ninja -C build install # -j8
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run curl -sSL https://install.python-poetry.org | python3 -
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env PATH "/root/.local/bin:$PATH"
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copy . /skynet
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workdir /skynet
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env POETRY_VIRTUALENVS_PATH /skynet/.venv
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run poetry install --with=cuda -v
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workdir /root/target
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env PYTORCH_CUDA_ALLOC_CONF max_split_size_mb:128
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env NVIDIA_VISIBLE_DEVICES=all
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copy docker/entrypoint.sh /entrypoint.sh
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entrypoint ["/entrypoint.sh"]
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cmd ["skynet", "--help"]
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@ -1,7 +1,7 @@
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docker build \
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-t guilledk/skynet:runtime-cuda-py311 \
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-f docker/Dockerfile.runtime+cuda-py311 .
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-t guilledk/skynet:runtime-cuda-py312 \
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-f docker/Dockerfile.runtime+cuda-py312 .
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docker build \
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-t guilledk/skynet:runtime-cuda \
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-f docker/Dockerfile.runtime+cuda-py311 .
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# docker build \
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# -t guilledk/skynet:runtime-cuda \
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# -f docker/Dockerfile.runtime+cuda-py311 .
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File diff suppressed because it is too large
Load Diff
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@ -1,21 +1,31 @@
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[tool.poetry]
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name = 'skynet'
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version = '0.1a12'
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version = '0.1a13'
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description = 'Decentralized compute platform'
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authors = ['Guillermo Rodriguez <guillermo@telos.net>']
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license = 'AGPL'
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readme = 'README.md'
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[tool.poetry.dependencies]
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python = '>=3.10,<3.12'
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python = '>=3.10,<3.13'
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pytz = '^2023.3.post1'
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trio = '^0.22.2'
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asks = '^3.0.0'
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Pillow = '^10.0.1'
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docker = '^6.1.3'
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py-leap = {git = 'https://github.com/guilledk/py-leap.git', rev = 'v0.1a14'}
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py-leap = {git = 'https://github.com/guilledk/py-leap.git', rev = 'v0.1a32'}
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toml = '^0.10.2'
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msgspec = "^0.19.0"
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numpy = "<2.1"
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gguf = "^0.14.0"
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protobuf = "^5.29.3"
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zstandard = "^0.23.0"
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diskcache = "^5.6.3"
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bitsandbytes = "^0.45.0"
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hqq = "^0.2.2"
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optimum-quanto = "^0.2.6"
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basicsr = "^1.4.2"
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realesrgan = "^0.3.0"
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[tool.poetry.group.frontend]
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optional = true
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@ -39,26 +49,24 @@ pytest-trio = "^0.8.0"
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optional = true
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[tool.poetry.group.cuda.dependencies]
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torch = {version = '2.0.1+cu118', source = 'torch'}
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scipy = {version = '^1.11.2'}
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numba = {version = '0.57.0'}
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torch = {version = '2.5.1+cu121', source = 'torch'}
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scipy = {version = '1.15.1'}
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numba = {version = '0.60.0'}
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quart = {version = '^0.19.3'}
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triton = {version = '2.0.0', source = 'torch'}
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basicsr = {version = '^1.4.2'}
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xformers = {version = '^0.0.22'}
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triton = {version = '3.1.0', source = 'torch'}
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xformers = {version = '^0.0.29'}
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hypercorn = {version = '^0.14.4'}
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diffusers = {version = '^0.21.2'}
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realesrgan = {version = '^0.3.0'}
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diffusers = {version = '0.32.1'}
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quart-trio = {version = '^0.11.0'}
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torchvision = {version = '0.15.2+cu118', source = 'torch'}
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accelerate = {version = '^0.23.0'}
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transformers = {version = '^4.33.2'}
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huggingface-hub = {version = '^0.17.3'}
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torchvision = {version = '0.20.1+cu121', source = 'torch'}
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accelerate = {version = '0.34.0'}
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transformers = {version = '4.48.0'}
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huggingface-hub = {version = '^0.27.1'}
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invisible-watermark = {version = '^0.2.0'}
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[[tool.poetry.source]]
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name = 'torch'
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url = 'https://download.pytorch.org/whl/cu118'
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url = 'https://download.pytorch.org/whl/cu121'
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priority = 'explicit'
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[build-system]
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@ -8,7 +8,7 @@ from functools import partial
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import click
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from leap.sugar import Name, asset_from_str
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from leap.protocol import Name, Asset
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from .config import *
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from .constants import *
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@ -178,7 +178,7 @@ def enqueue(
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'user': Name(account),
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'request_body': req,
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'binary_data': binary,
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'reward': asset_from_str(reward),
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'reward': Asset.from_str(reward),
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'min_verification': 1
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},
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account, key, permission,
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@ -78,8 +78,20 @@ MODELS: dict[str, ModelDesc] = {
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size=Size(w=512, h=512),
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tags=['txt2img']
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),
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'black-forest-labs/FLUX.1-schnell': ModelDesc(
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short='flux',
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mem=24,
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size=Size(w=1024, h=1024),
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tags=['txt2img']
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),
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'black-forest-labs/FLUX.1-Fill-dev': ModelDesc(
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short='flux-inpaint',
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mem=24,
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size=Size(w=1024, h=1024),
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tags=['inpaint']
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),
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'diffusers/stable-diffusion-xl-1.0-inpainting-0.1': ModelDesc(
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short='stablexl-inpainting',
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short='stablexl-inpaint',
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mem=8.3,
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size=Size(w=1024, h=1024),
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tags=['inpaint']
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@ -18,7 +18,6 @@ from skynet.dgpu.errors import DGPUComputeError, DGPUInferenceCancelled
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from skynet.utils import crop_image, convert_from_cv2_to_image, convert_from_image_to_cv2, convert_from_img_to_bytes, init_upscaler, pipeline_for
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def prepare_params_for_diffuse(
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params: dict,
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mode: str,
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@ -35,7 +34,11 @@ def prepare_params_for_diffuse(
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_params['image'] = image
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_params['mask_image'] = mask
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_params['strength'] = float(params['strength'])
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if 'flux' in params['model'].lower():
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_params['max_sequence_length'] = 512
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else:
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_params['strength'] = float(params['strength'])
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case 'img2img':
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image = crop_image(
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@ -66,8 +69,6 @@ def prepare_params_for_diffuse(
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class SkynetMM:
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def __init__(self, config: dict):
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self.upscaler = init_upscaler()
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self.cache_dir = None
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if 'hf_home' in config:
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self.cache_dir = config['hf_home']
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@ -88,30 +89,28 @@ class SkynetMM:
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return False
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def load_model(
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self,
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name: str,
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mode: str
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):
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logging.info(f'loading model {name}...')
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self._model_mode = mode
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self._model_name = name
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def unload_model(self):
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if getattr(self, '_model', None):
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del self._model
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gc.collect()
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torch.cuda.empty_cache()
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self._model_name = ''
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self._model_mode = ''
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def load_model(
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self,
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name: str,
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mode: str
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):
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logging.info(f'loading model {name}...')
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self.unload_model()
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self._model = pipeline_for(
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name, mode, cache_dir=self.cache_dir)
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self._model_mode = mode
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self._model_name = name
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def get_model(self, name: str, mode: str) -> DiffusionPipeline:
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if name not in MODELS:
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raise DGPUComputeError(f'Unknown model {model_name}')
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if not self.is_model_loaded(name, mode):
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self.load_model(name, mode)
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def compute_one(
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self,
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@ -127,6 +126,8 @@ class SkynetMM:
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logging.warn(f'cancelling work at step {step}')
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raise DGPUInferenceCancelled()
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return {}
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maybe_cancel_work(0)
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output_type = 'png'
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output = None
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output_hash = None
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try:
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name = params['model']
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match method:
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case 'diffuse' | 'txt2img' | 'img2img' | 'inpaint':
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if not self.is_model_loaded(name, method):
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self.load_model(name, method)
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arguments = prepare_params_for_diffuse(
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params, method, inputs)
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prompt, guidance, step, seed, upscaler, extra_params = arguments
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self.get_model(
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params['model'],
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method
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)
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if 'flux' in name.lower():
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extra_params['callback_on_step_end'] = maybe_cancel_work
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else:
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extra_params['callback'] = maybe_cancel_work
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extra_params['callback_steps'] = 1
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output = self._model(
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prompt,
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guidance_scale=guidance,
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num_inference_steps=step,
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generator=seed,
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callback=maybe_cancel_work,
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callback_steps=1,
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**extra_params
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).images[0]
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@ -161,7 +168,7 @@ class SkynetMM:
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case 'png':
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if upscaler == 'x4':
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input_img = output.convert('RGB')
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up_img, _ = self.upscaler.enhance(
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up_img, _ = init_upscaler().enhance(
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convert_from_image_to_cv2(input_img), outscale=4)
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output = convert_from_cv2_to_image(up_img)
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@ -173,6 +180,22 @@ class SkynetMM:
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output_hash = sha256(output_binary).hexdigest()
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case 'upscale':
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if self._model_mode != 'upscale':
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self.unload_model()
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self._model = init_upscaler()
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self._model_mode = 'upscale'
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self._model_name = 'realesrgan'
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input_img = inputs[0].convert('RGB')
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up_img, _ = self._model.enhance(
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convert_from_image_to_cv2(input_img), outscale=4)
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output = convert_from_cv2_to_image(up_img)
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output_binary = convert_from_img_to_bytes(output)
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output_hash = sha256(output_binary).hexdigest()
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case _:
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raise DGPUComputeError('Unsupported compute method')
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@ -125,7 +125,7 @@ class SkynetDGPUDaemon:
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model = body['params']['model']
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# if model not known
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if model not in MODELS:
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if model != 'RealESRGAN_x4plus' and model not in MODELS:
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logging.warning(f'Unknown model {model}')
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return False
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@ -143,11 +143,17 @@ class SkynetDGPUDaemon:
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statuses = self._snap['requests'][rid]
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if len(statuses) == 0:
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inputs = [
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await self.conn.get_input_data(_input)
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for _input in req['binary_data'].split(',')
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if _input
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]
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inputs = []
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for _input in req['binary_data'].split(','):
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if _input:
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for _ in range(3):
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try:
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img = await self.conn.get_input_data(_input)
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inputs.append(img)
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break
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except:
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...
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hash_str = (
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str(req['nonce'])
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@ -15,7 +15,7 @@ import anyio
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from PIL import Image, UnidentifiedImageError
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from leap.cleos import CLEOS
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from leap.sugar import Checksum256, Name, asset_from_str
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from leap.protocol import Asset
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from skynet.constants import DEFAULT_IPFS_DOMAIN
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from skynet.ipfs import AsyncIPFSHTTP, get_ipfs_file
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@ -24,6 +24,225 @@ from skynet.dgpu.errors import DGPUComputeError
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REQUEST_UPDATE_TIME = 3
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gpu_abi = {
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"version": "eosio::abi/1.2",
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"types": [],
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"structs": [
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{
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"name": "account",
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"base": "",
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"fields": [
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{"name": "user", "type": "name"},
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{"name": "balance", "type": "asset"},
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{"name": "nonce", "type": "uint64"}
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]
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},
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{
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"name": "card",
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"base": "",
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"fields": [
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{"name": "id", "type": "uint64"},
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{"name": "owner", "type": "name"},
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{"name": "card_name", "type": "string"},
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{"name": "version", "type": "string"},
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{"name": "total_memory", "type": "uint64"},
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{"name": "mp_count", "type": "uint32"},
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{"name": "extra", "type": "string"}
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]
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},
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{
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"name": "clean",
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"base": "",
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"fields": []
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},
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{
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"name": "config",
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"base": "",
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"fields": [
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{"name": "token_contract", "type": "name"},
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{"name": "token_symbol", "type": "symbol"}
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]
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},
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{
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"name": "dequeue",
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"base": "",
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"fields": [
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{"name": "user", "type": "name"},
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{"name": "request_id", "type": "uint64"}
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]
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},
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{
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"name": "enqueue",
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"base": "",
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"fields": [
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{"name": "user", "type": "name"},
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{"name": "request_body", "type": "string"},
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{"name": "binary_data", "type": "string"},
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{"name": "reward", "type": "asset"},
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{"name": "min_verification", "type": "uint32"}
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]
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},
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{
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"name": "gcfgstruct",
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"base": "",
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"fields": [
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{"name": "token_contract", "type": "name"},
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{"name": "token_symbol", "type": "symbol"}
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]
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},
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{
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"name": "submit",
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"base": "",
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"fields": [
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{"name": "worker", "type": "name"},
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{"name": "request_id", "type": "uint64"},
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{"name": "request_hash", "type": "checksum256"},
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{"name": "result_hash", "type": "checksum256"},
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{"name": "ipfs_hash", "type": "string"}
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]
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},
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{
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"name": "withdraw",
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"base": "",
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"fields": [
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{"name": "user", "type": "name"},
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{"name": "quantity", "type": "asset"}
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]
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},
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{
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"name": "work_request_struct",
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"base": "",
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"fields": [
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{"name": "id", "type": "uint64"},
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{"name": "user", "type": "name"},
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{"name": "reward", "type": "asset"},
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{"name": "min_verification", "type": "uint32"},
|
||||
{"name": "nonce", "type": "uint64"},
|
||||
{"name": "body", "type": "string"},
|
||||
{"name": "binary_data", "type": "string"},
|
||||
{"name": "timestamp", "type": "time_point_sec"}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "work_result_struct",
|
||||
"base": "",
|
||||
"fields": [
|
||||
{"name": "id", "type": "uint64"},
|
||||
{"name": "request_id", "type": "uint64"},
|
||||
{"name": "user", "type": "name"},
|
||||
{"name": "worker", "type": "name"},
|
||||
{"name": "result_hash", "type": "checksum256"},
|
||||
{"name": "ipfs_hash", "type": "string"},
|
||||
{"name": "submited", "type": "time_point_sec"}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "workbegin",
|
||||
"base": "",
|
||||
"fields": [
|
||||
{"name": "worker", "type": "name"},
|
||||
{"name": "request_id", "type": "uint64"},
|
||||
{"name": "max_workers", "type": "uint32"}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "workcancel",
|
||||
"base": "",
|
||||
"fields": [
|
||||
{"name": "worker", "type": "name"},
|
||||
{"name": "request_id", "type": "uint64"},
|
||||
{"name": "reason", "type": "string"}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "worker",
|
||||
"base": "",
|
||||
"fields": [
|
||||
{"name": "account", "type": "name"},
|
||||
{"name": "joined", "type": "time_point_sec"},
|
||||
{"name": "left", "type": "time_point_sec"},
|
||||
{"name": "url", "type": "string"}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "worker_status_struct",
|
||||
"base": "",
|
||||
"fields": [
|
||||
{"name": "worker", "type": "name"},
|
||||
{"name": "status", "type": "string"},
|
||||
{"name": "started", "type": "time_point_sec"}
|
||||
]
|
||||
}
|
||||
],
|
||||
"actions": [
|
||||
{"name": "clean", "type": "clean", "ricardian_contract": ""},
|
||||
{"name": "config", "type": "config", "ricardian_contract": ""},
|
||||
{"name": "dequeue", "type": "dequeue", "ricardian_contract": ""},
|
||||
{"name": "enqueue", "type": "enqueue", "ricardian_contract": ""},
|
||||
{"name": "submit", "type": "submit", "ricardian_contract": ""},
|
||||
{"name": "withdraw", "type": "withdraw", "ricardian_contract": ""},
|
||||
{"name": "workbegin", "type": "workbegin", "ricardian_contract": ""},
|
||||
{"name": "workcancel", "type": "workcancel", "ricardian_contract": ""}
|
||||
],
|
||||
"tables": [
|
||||
{
|
||||
"name": "cards",
|
||||
"index_type": "i64",
|
||||
"key_names": [],
|
||||
"key_types": [],
|
||||
"type": "card"
|
||||
},
|
||||
{
|
||||
"name": "gcfgstruct",
|
||||
"index_type": "i64",
|
||||
"key_names": [],
|
||||
"key_types": [],
|
||||
"type": "gcfgstruct"
|
||||
},
|
||||
{
|
||||
"name": "queue",
|
||||
"index_type": "i64",
|
||||
"key_names": [],
|
||||
"key_types": [],
|
||||
"type": "work_request_struct"
|
||||
},
|
||||
{
|
||||
"name": "results",
|
||||
"index_type": "i64",
|
||||
"key_names": [],
|
||||
"key_types": [],
|
||||
"type": "work_result_struct"
|
||||
},
|
||||
{
|
||||
"name": "status",
|
||||
"index_type": "i64",
|
||||
"key_names": [],
|
||||
"key_types": [],
|
||||
"type": "worker_status_struct"
|
||||
},
|
||||
{
|
||||
"name": "users",
|
||||
"index_type": "i64",
|
||||
"key_names": [],
|
||||
"key_types": [],
|
||||
"type": "account"
|
||||
},
|
||||
{
|
||||
"name": "workers",
|
||||
"index_type": "i64",
|
||||
"key_names": [],
|
||||
"key_types": [],
|
||||
"type": "worker"
|
||||
}
|
||||
],
|
||||
"ricardian_clauses": [],
|
||||
"error_messages": [],
|
||||
"abi_extensions": [],
|
||||
"variants": [],
|
||||
"action_results": []
|
||||
}
|
||||
|
||||
|
||||
|
||||
async def failable(fn: partial, ret_fail=None):
|
||||
try:
|
||||
|
|
@ -35,22 +254,22 @@ async def failable(fn: partial, ret_fail=None):
|
|||
asks.errors.RequestTimeout,
|
||||
asks.errors.BadHttpResponse,
|
||||
anyio.BrokenResourceError
|
||||
):
|
||||
) as e:
|
||||
return ret_fail
|
||||
|
||||
|
||||
class SkynetGPUConnector:
|
||||
|
||||
def __init__(self, config: dict):
|
||||
self.account = Name(config['account'])
|
||||
self.account = config['account']
|
||||
self.permission = config['permission']
|
||||
self.key = config['key']
|
||||
|
||||
self.node_url = config['node_url']
|
||||
self.hyperion_url = config['hyperion_url']
|
||||
|
||||
self.cleos = CLEOS(
|
||||
None, None, self.node_url, remote=self.node_url)
|
||||
self.cleos = CLEOS(endpoint=self.node_url)
|
||||
self.cleos.load_abi('gpu.scd', gpu_abi)
|
||||
|
||||
self.ipfs_gateway_url = None
|
||||
if 'ipfs_gateway_url' in config:
|
||||
|
|
@ -151,11 +370,11 @@ class SkynetGPUConnector:
|
|||
self.cleos.a_push_action,
|
||||
'gpu.scd',
|
||||
'workbegin',
|
||||
{
|
||||
list({
|
||||
'worker': self.account,
|
||||
'request_id': request_id,
|
||||
'max_workers': 2
|
||||
},
|
||||
}.values()),
|
||||
self.account, self.key,
|
||||
permission=self.permission
|
||||
)
|
||||
|
|
@ -168,11 +387,11 @@ class SkynetGPUConnector:
|
|||
self.cleos.a_push_action,
|
||||
'gpu.scd',
|
||||
'workcancel',
|
||||
{
|
||||
list({
|
||||
'worker': self.account,
|
||||
'request_id': request_id,
|
||||
'reason': reason
|
||||
},
|
||||
}.values()),
|
||||
self.account, self.key,
|
||||
permission=self.permission
|
||||
)
|
||||
|
|
@ -191,10 +410,10 @@ class SkynetGPUConnector:
|
|||
self.cleos.a_push_action,
|
||||
'gpu.scd',
|
||||
'withdraw',
|
||||
{
|
||||
list({
|
||||
'user': self.account,
|
||||
'quantity': asset_from_str(balance)
|
||||
},
|
||||
'quantity': Asset.from_str(balance)
|
||||
}.values()),
|
||||
self.account, self.key,
|
||||
permission=self.permission
|
||||
)
|
||||
|
|
@ -226,13 +445,13 @@ class SkynetGPUConnector:
|
|||
self.cleos.a_push_action,
|
||||
'gpu.scd',
|
||||
'submit',
|
||||
{
|
||||
list({
|
||||
'worker': self.account,
|
||||
'request_id': request_id,
|
||||
'request_hash': Checksum256(request_hash),
|
||||
'result_hash': Checksum256(result_hash),
|
||||
'request_hash': request_hash,
|
||||
'result_hash': result_hash,
|
||||
'ipfs_hash': ipfs_hash
|
||||
},
|
||||
}.values()),
|
||||
self.account, self.key,
|
||||
permission=self.permission
|
||||
)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,50 @@
|
|||
#!/usr/bin/python
|
||||
|
||||
import torch
|
||||
|
||||
from diffusers import (
|
||||
DiffusionPipeline,
|
||||
FluxPipeline,
|
||||
FluxTransformer2DModel
|
||||
)
|
||||
from transformers import T5EncoderModel, BitsAndBytesConfig
|
||||
|
||||
from huggingface_hub import hf_hub_download
|
||||
|
||||
__model = {
|
||||
'name': 'black-forest-labs/FLUX.1-schnell'
|
||||
}
|
||||
|
||||
def pipeline_for(
|
||||
model: str,
|
||||
mode: str,
|
||||
mem_fraction: float = 1.0,
|
||||
cache_dir: str | None = None
|
||||
) -> DiffusionPipeline:
|
||||
qonfig = BitsAndBytesConfig(
|
||||
load_in_4bit=True,
|
||||
bnb_4bit_quant_type="nf4",
|
||||
)
|
||||
params = {
|
||||
'torch_dtype': torch.bfloat16,
|
||||
'cache_dir': cache_dir,
|
||||
'device_map': 'balanced',
|
||||
'max_memory': {'cpu': '10GiB', 0: '11GiB'}
|
||||
# 'max_memory': {0: '11GiB'}
|
||||
}
|
||||
|
||||
text_encoder = T5EncoderModel.from_pretrained(
|
||||
'black-forest-labs/FLUX.1-schnell',
|
||||
subfolder="text_encoder_2",
|
||||
torch_dtype=torch.bfloat16,
|
||||
quantization_config=qonfig
|
||||
)
|
||||
params['text_encoder_2'] = text_encoder
|
||||
|
||||
pipe = FluxPipeline.from_pretrained(
|
||||
model, **params)
|
||||
|
||||
pipe.vae.enable_tiling()
|
||||
pipe.vae.enable_slicing()
|
||||
|
||||
return pipe
|
||||
|
|
@ -0,0 +1,55 @@
|
|||
#!/usr/bin/python
|
||||
|
||||
import torch
|
||||
|
||||
from diffusers import (
|
||||
DiffusionPipeline,
|
||||
FluxFillPipeline,
|
||||
FluxTransformer2DModel
|
||||
)
|
||||
from transformers import T5EncoderModel, BitsAndBytesConfig
|
||||
|
||||
__model = {
|
||||
'name': 'black-forest-labs/FLUX.1-Fill-dev'
|
||||
}
|
||||
|
||||
def pipeline_for(
|
||||
model: str,
|
||||
mode: str,
|
||||
mem_fraction: float = 1.0,
|
||||
cache_dir: str | None = None
|
||||
) -> DiffusionPipeline:
|
||||
qonfig = BitsAndBytesConfig(
|
||||
load_in_4bit=True,
|
||||
bnb_4bit_quant_type="nf4",
|
||||
)
|
||||
params = {
|
||||
'torch_dtype': torch.bfloat16,
|
||||
'cache_dir': cache_dir,
|
||||
'device_map': 'balanced',
|
||||
'max_memory': {'cpu': '10GiB', 0: '11GiB'}
|
||||
# 'max_memory': {0: '11GiB'}
|
||||
}
|
||||
|
||||
text_encoder = T5EncoderModel.from_pretrained(
|
||||
'sayakpaul/FLUX.1-Fill-dev-nf4',
|
||||
subfolder="text_encoder_2",
|
||||
torch_dtype=torch.bfloat16,
|
||||
quantization_config=qonfig
|
||||
)
|
||||
params['text_encoder_2'] = text_encoder
|
||||
|
||||
transformer = FluxTransformer2DModel.from_pretrained(
|
||||
'sayakpaul/FLUX.1-Fill-dev-nf4',
|
||||
subfolder="transformer",
|
||||
torch_dtype=torch.bfloat16,
|
||||
quantization_config=qonfig
|
||||
)
|
||||
|
||||
pipe = FluxFillPipeline.from_pretrained(
|
||||
model, **params)
|
||||
|
||||
pipe.vae.enable_tiling()
|
||||
pipe.vae.enable_slicing()
|
||||
|
||||
return pipe
|
||||
103
skynet/utils.py
103
skynet/utils.py
|
|
@ -6,29 +6,42 @@ import sys
|
|||
import time
|
||||
import random
|
||||
import logging
|
||||
import importlib
|
||||
|
||||
from typing import Optional
|
||||
from pathlib import Path
|
||||
import asks
|
||||
|
||||
import trio
|
||||
import torch
|
||||
import numpy as np
|
||||
|
||||
from PIL import Image
|
||||
from basicsr.archs.rrdbnet_arch import RRDBNet
|
||||
from diffusers import (
|
||||
DiffusionPipeline,
|
||||
AutoPipelineForText2Image,
|
||||
AutoPipelineForImage2Image,
|
||||
AutoPipelineForInpainting,
|
||||
EulerAncestralDiscreteScheduler
|
||||
EulerAncestralDiscreteScheduler,
|
||||
)
|
||||
from realesrgan import RealESRGANer
|
||||
from huggingface_hub import login
|
||||
import trio
|
||||
|
||||
from .constants import MODELS
|
||||
|
||||
# Hack to fix a changed import in torchvision 0.17+, which otherwise breaks
|
||||
# basicsr; see https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/13985
|
||||
try:
|
||||
import torchvision.transforms.functional_tensor # noqa: F401
|
||||
except ImportError:
|
||||
try:
|
||||
import torchvision.transforms.functional as functional
|
||||
sys.modules["torchvision.transforms.functional_tensor"] = functional
|
||||
except ImportError:
|
||||
pass # shrug...
|
||||
|
||||
from basicsr.archs.rrdbnet_arch import RRDBNet
|
||||
from realesrgan import RealESRGANer
|
||||
|
||||
|
||||
|
||||
def time_ms():
|
||||
return int(time.time() * 1000)
|
||||
|
|
@ -72,6 +85,7 @@ def pipeline_for(
|
|||
cache_dir: str | None = None
|
||||
) -> DiffusionPipeline:
|
||||
|
||||
logging.info(f'pipeline_for {model} {mode}')
|
||||
assert torch.cuda.is_available()
|
||||
torch.cuda.empty_cache()
|
||||
torch.backends.cuda.matmul.allow_tf32 = True
|
||||
|
|
@ -85,21 +99,35 @@ def pipeline_for(
|
|||
torch.use_deterministic_algorithms(True)
|
||||
|
||||
model_info = MODELS[model]
|
||||
shortname = model_info.short
|
||||
|
||||
# disable for compat with "diffuse" method
|
||||
# assert mode in model_info.tags
|
||||
|
||||
# default to checking if custom pipeline exist and return that if not, attempt generic
|
||||
try:
|
||||
normalized_shortname = shortname.replace('-', '_')
|
||||
custom_pipeline = importlib.import_module(f'skynet.dgpu.pipes.{normalized_shortname}')
|
||||
assert custom_pipeline.__model['name'] == model
|
||||
return custom_pipeline.pipeline_for(model, mode, mem_fraction=mem_fraction, cache_dir=cache_dir)
|
||||
|
||||
except ImportError:
|
||||
...
|
||||
|
||||
|
||||
req_mem = model_info.mem
|
||||
|
||||
mem_gb = torch.cuda.mem_get_info()[1] / (10**9)
|
||||
mem_gb *= mem_fraction
|
||||
over_mem = mem_gb < req_mem
|
||||
if over_mem:
|
||||
logging.warn(f'model requires {req_mem} but card has {mem_gb}, model will run slower..')
|
||||
|
||||
shortname = model_info.short
|
||||
|
||||
params = {
|
||||
'safety_checker': None,
|
||||
'torch_dtype': torch.float16,
|
||||
'cache_dir': cache_dir,
|
||||
'variant': 'fp16'
|
||||
'variant': 'fp16',
|
||||
}
|
||||
|
||||
match shortname:
|
||||
|
|
@ -108,6 +136,7 @@ def pipeline_for(
|
|||
|
||||
torch.cuda.set_per_process_memory_fraction(mem_fraction)
|
||||
|
||||
pipe_class = DiffusionPipeline
|
||||
match mode:
|
||||
case 'inpaint':
|
||||
pipe_class = AutoPipelineForInpainting
|
||||
|
|
@ -115,7 +144,7 @@ def pipeline_for(
|
|||
case 'img2img':
|
||||
pipe_class = AutoPipelineForImage2Image
|
||||
|
||||
case 'txt2img' | 'diffuse':
|
||||
case 'txt2img':
|
||||
pipe_class = AutoPipelineForText2Image
|
||||
|
||||
pipe = pipe_class.from_pretrained(
|
||||
|
|
@ -124,20 +153,20 @@ def pipeline_for(
|
|||
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(
|
||||
pipe.scheduler.config)
|
||||
|
||||
pipe.enable_xformers_memory_efficient_attention()
|
||||
# pipe.enable_xformers_memory_efficient_attention()
|
||||
|
||||
if over_mem:
|
||||
if mode == 'txt2img':
|
||||
pipe.enable_vae_slicing()
|
||||
pipe.enable_vae_tiling()
|
||||
|
||||
pipe.vae.enable_tiling()
|
||||
pipe.vae.enable_slicing()
|
||||
|
||||
pipe.enable_model_cpu_offload()
|
||||
|
||||
else:
|
||||
if sys.version_info[1] < 11:
|
||||
# torch.compile only supported on python < 3.11
|
||||
pipe.unet = torch.compile(
|
||||
pipe.unet, mode='reduce-overhead', fullgraph=True)
|
||||
# if sys.version_info[1] < 11:
|
||||
# # torch.compile only supported on python < 3.11
|
||||
# pipe.unet = torch.compile(
|
||||
# pipe.unet, mode='reduce-overhead', fullgraph=True)
|
||||
|
||||
pipe = pipe.to('cuda')
|
||||
|
||||
|
|
@ -155,7 +184,7 @@ def txt2img(
|
|||
seed: Optional[int] = None
|
||||
):
|
||||
login(token=hf_token)
|
||||
pipe = pipeline_for(model)
|
||||
pipe = pipeline_for(model, 'txt2img')
|
||||
|
||||
seed = seed if seed else random.randint(0, 2 ** 64)
|
||||
prompt = prompt
|
||||
|
|
@ -182,7 +211,7 @@ def img2img(
|
|||
seed: Optional[int] = None
|
||||
):
|
||||
login(token=hf_token)
|
||||
pipe = pipeline_for(model, image=True)
|
||||
pipe = pipeline_for(model, 'img2img')
|
||||
|
||||
model_info = MODELS[model]
|
||||
|
||||
|
|
@ -215,7 +244,7 @@ def inpaint(
|
|||
seed: Optional[int] = None
|
||||
):
|
||||
login(token=hf_token)
|
||||
pipe = pipeline_for(model, image=True, inpainting=True)
|
||||
pipe = pipeline_for(model, 'inpaint')
|
||||
|
||||
model_info = MODELS[model]
|
||||
|
||||
|
|
@ -225,21 +254,25 @@ def inpaint(
|
|||
with open(mask_path, 'rb') as mask_file:
|
||||
mask_img = convert_from_bytes_and_crop(mask_file.read(), model_info.size.w, model_info.size.h)
|
||||
|
||||
var_params = {}
|
||||
if 'flux' not in model.lower():
|
||||
var_params['strength'] = strength
|
||||
|
||||
seed = seed if seed else random.randint(0, 2 ** 64)
|
||||
prompt = prompt
|
||||
image = pipe(
|
||||
prompt,
|
||||
image=input_img,
|
||||
mask_image=mask_img,
|
||||
strength=strength,
|
||||
guidance_scale=guidance, num_inference_steps=steps,
|
||||
generator=torch.Generator("cuda").manual_seed(seed)
|
||||
generator=torch.Generator("cuda").manual_seed(seed),
|
||||
**var_params
|
||||
).images[0]
|
||||
|
||||
image.save(output)
|
||||
|
||||
|
||||
def init_upscaler(model_path: str = 'weights/RealESRGAN_x4plus.pth'):
|
||||
def init_upscaler(model_path: str = 'hf_home/RealESRGAN_x4plus.pth'):
|
||||
return RealESRGANer(
|
||||
scale=4,
|
||||
model_path=model_path,
|
||||
|
|
@ -258,7 +291,7 @@ def init_upscaler(model_path: str = 'weights/RealESRGAN_x4plus.pth'):
|
|||
def upscale(
|
||||
img_path: str = 'input.png',
|
||||
output: str = 'output.png',
|
||||
model_path: str = 'weights/RealESRGAN_x4plus.pth'
|
||||
model_path: str = 'hf_home/RealESRGAN_x4plus.pth'
|
||||
):
|
||||
input_img = Image.open(img_path).convert('RGB')
|
||||
|
||||
|
|
@ -269,25 +302,3 @@ def upscale(
|
|||
|
||||
image = convert_from_cv2_to_image(up_img)
|
||||
image.save(output)
|
||||
|
||||
|
||||
async def download_upscaler():
|
||||
print('downloading upscaler...')
|
||||
weights_path = Path('weights')
|
||||
weights_path.mkdir(exist_ok=True)
|
||||
upscaler_url = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth'
|
||||
save_path = weights_path / 'RealESRGAN_x4plus.pth'
|
||||
response = await asks.get(upscaler_url)
|
||||
with open(save_path, 'wb') as f:
|
||||
f.write(response.content)
|
||||
print('done')
|
||||
|
||||
def download_all_models(hf_token: str, hf_home: str):
|
||||
assert torch.cuda.is_available()
|
||||
|
||||
trio.run(download_upscaler)
|
||||
|
||||
login(token=hf_token)
|
||||
for model in MODELS:
|
||||
print(f'DOWNLOADING {model.upper()}')
|
||||
pipeline_for(model, cache_dir=hf_home)
|
||||
|
|
|
|||
Loading…
Reference in New Issue