mirror of https://github.com/skygpu/skynet.git
GitHub
commit
b5f52b3b5b
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@ -29,9 +29,6 @@ poetry shell
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# test you can run this command
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skynet --help
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# launch ipfs node
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skynet run ipfs
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# to launch worker
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skynet run dgpu
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@ -77,9 +74,6 @@ docker pull guilledk/skynet:runtime-cuda
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# or build it (takes a bit of time)
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./build_docker.sh
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# launch simple ipfs node
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./launch_ipfs.sh
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# run worker with all gpus
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docker run \
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-it \
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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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@ -1,20 +1,7 @@
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docker build \
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-t guilledk/skynet:runtime \
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-f docker/Dockerfile.runtime .
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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-frontend \
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-f docker/Dockerfile.runtime+frontend .
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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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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-py310 \
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-f docker/Dockerfile.runtime+cuda-py310 .
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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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@ -1,20 +1,25 @@
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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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protobuf = "^5.29.3"
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zstandard = "^0.23.0"
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click = "^8.1.8"
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httpx = "^0.28.1"
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[tool.poetry.group.frontend]
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optional = true
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@ -32,31 +37,33 @@ optional = true
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[tool.poetry.group.dev.dependencies]
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pdbpp = {version = '^0.10.3'}
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pytest = {version = '^7.4.2'}
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pytest-trio = "^0.8.0"
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[tool.poetry.group.cuda]
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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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bitsandbytes = "^0.45.0"
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basicsr = "^1.4.2"
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realesrgan = "^0.3.0"
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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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@ -65,3 +72,7 @@ build-backend = 'poetry.core.masonry.api'
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[tool.poetry.scripts]
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skynet = 'skynet.cli:skynet'
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txt2img = 'skynet.cli:txt2img'
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img2img = 'skynet.cli:img2img'
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upscale = 'skynet.cli:upscale'
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inpaint = 'skynet.cli:inpaint'
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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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@ -20,7 +20,7 @@ def skynet(*args, **kwargs):
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@click.command()
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@click.option('--model', '-m', default='midj')
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@click.option('--model', '-m', default=list(MODELS.keys())[-1])
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@click.option(
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'--prompt', '-p', default='a red old tractor in a sunny wheat field')
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@click.option('--output', '-o', default='output.png')
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@ -39,7 +39,7 @@ def txt2img(*args, **kwargs):
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utils.txt2img(hf_token, **kwargs)
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@click.command()
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@click.option('--model', '-m', default=list(MODELS.keys())[0])
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@click.option('--model', '-m', default=list(MODELS.keys())[-2])
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@click.option(
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'--prompt', '-p', default='a red old tractor in a sunny wheat field')
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@click.option('--input', '-i', default='input.png')
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@ -66,6 +66,37 @@ def img2img(model, prompt, input, output, strength, guidance, steps, seed):
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seed=seed
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)
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@click.command()
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@click.option('--model', '-m', default=list(MODELS.keys())[-3])
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@click.option(
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'--prompt', '-p', default='a red old tractor in a sunny wheat field')
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@click.option('--input', '-i', default='input.png')
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@click.option('--mask', '-M', default='mask.png')
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@click.option('--output', '-o', default='output.png')
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@click.option('--strength', '-Z', default=1.0)
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@click.option('--guidance', '-g', default=10.0)
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@click.option('--steps', '-s', default=26)
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@click.option('--seed', '-S', default=None)
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def inpaint(model, prompt, input, mask, output, strength, guidance, steps, seed):
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from . import utils
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config = load_skynet_toml()
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hf_token = load_key(config, 'skynet.dgpu.hf_token')
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hf_home = load_key(config, 'skynet.dgpu.hf_home')
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set_hf_vars(hf_token, hf_home)
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utils.inpaint(
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hf_token,
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model=model,
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prompt=prompt,
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img_path=input,
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mask_path=mask,
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output=output,
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strength=strength,
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guidance=guidance,
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steps=steps,
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seed=seed
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)
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@click.command()
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@click.option('--input', '-i', default='input.png')
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@click.option('--output', '-o', default='output.png')
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@ -147,7 +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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|
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@ -4,31 +4,120 @@ VERSION = '0.1a12'
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DOCKER_RUNTIME_CUDA = 'skynet:runtime-cuda'
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MODELS = {
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'prompthero/openjourney': {'short': 'midj', 'mem': 6},
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'runwayml/stable-diffusion-v1-5': {'short': 'stable', 'mem': 6},
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'stabilityai/stable-diffusion-2-1-base': {'short': 'stable2', 'mem': 6},
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'snowkidy/stable-diffusion-xl-base-0.9': {'short': 'stablexl0.9', 'mem': 8.3},
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'Linaqruf/anything-v3.0': {'short': 'hdanime', 'mem': 6},
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'hakurei/waifu-diffusion': {'short': 'waifu', 'mem': 6},
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'nitrosocke/Ghibli-Diffusion': {'short': 'ghibli', 'mem': 6},
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'dallinmackay/Van-Gogh-diffusion': {'short': 'van-gogh', 'mem': 6},
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'lambdalabs/sd-pokemon-diffusers': {'short': 'pokemon', 'mem': 6},
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'Envvi/Inkpunk-Diffusion': {'short': 'ink', 'mem': 6},
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'nousr/robo-diffusion': {'short': 'robot', 'mem': 6},
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import msgspec
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from typing import Literal
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# default is always last
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'stabilityai/stable-diffusion-xl-base-1.0': {'short': 'stablexl', 'mem': 8.3},
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class Size(msgspec.Struct):
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w: int
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h: int
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class ModelDesc(msgspec.Struct):
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short: str
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mem: float
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size: Size
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tags: list[Literal['txt2img', 'img2img', 'inpaint']]
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MODELS: dict[str, ModelDesc] = {
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'runwayml/stable-diffusion-v1-5': ModelDesc(
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short='stable',
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mem=6,
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size=Size(w=512, h=512),
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tags=['txt2img']
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),
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'stabilityai/stable-diffusion-2-1-base': ModelDesc(
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short='stable2',
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mem=6,
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size=Size(w=512, h=512),
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tags=['txt2img']
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),
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'snowkidy/stable-diffusion-xl-base-0.9': ModelDesc(
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short='stablexl0.9',
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mem=8.3,
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size=Size(w=1024, h=1024),
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tags=['txt2img']
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),
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'Linaqruf/anything-v3.0': ModelDesc(
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short='hdanime',
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mem=6,
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size=Size(w=512, h=512),
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tags=['txt2img']
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),
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'hakurei/waifu-diffusion': ModelDesc(
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short='waifu',
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mem=6,
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size=Size(w=512, h=512),
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tags=['txt2img']
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),
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'nitrosocke/Ghibli-Diffusion': ModelDesc(
|
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short='ghibli',
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mem=6,
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size=Size(w=512, h=512),
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tags=['txt2img']
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),
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'dallinmackay/Van-Gogh-diffusion': ModelDesc(
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short='van-gogh',
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mem=6,
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size=Size(w=512, h=512),
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tags=['txt2img']
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),
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'lambdalabs/sd-pokemon-diffusers': ModelDesc(
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short='pokemon',
|
||||
mem=6,
|
||||
size=Size(w=512, h=512),
|
||||
tags=['txt2img']
|
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),
|
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'Envvi/Inkpunk-Diffusion': ModelDesc(
|
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short='ink',
|
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mem=6,
|
||||
size=Size(w=512, h=512),
|
||||
tags=['txt2img']
|
||||
),
|
||||
'nousr/robo-diffusion': ModelDesc(
|
||||
short='robot',
|
||||
mem=6,
|
||||
size=Size(w=512, h=512),
|
||||
tags=['txt2img']
|
||||
),
|
||||
'black-forest-labs/FLUX.1-schnell': ModelDesc(
|
||||
short='flux',
|
||||
mem=24,
|
||||
size=Size(w=1024, h=1024),
|
||||
tags=['txt2img']
|
||||
),
|
||||
'black-forest-labs/FLUX.1-Fill-dev': ModelDesc(
|
||||
short='flux-inpaint',
|
||||
mem=24,
|
||||
size=Size(w=1024, h=1024),
|
||||
tags=['inpaint']
|
||||
),
|
||||
'diffusers/stable-diffusion-xl-1.0-inpainting-0.1': ModelDesc(
|
||||
short='stablexl-inpaint',
|
||||
mem=8.3,
|
||||
size=Size(w=1024, h=1024),
|
||||
tags=['inpaint']
|
||||
),
|
||||
'prompthero/openjourney': ModelDesc(
|
||||
short='midj',
|
||||
mem=6,
|
||||
size=Size(w=512, h=512),
|
||||
tags=['txt2img', 'img2img']
|
||||
),
|
||||
'stabilityai/stable-diffusion-xl-base-1.0': ModelDesc(
|
||||
short='stablexl',
|
||||
mem=8.3,
|
||||
size=Size(w=1024, h=1024),
|
||||
tags=['txt2img']
|
||||
),
|
||||
}
|
||||
|
||||
SHORT_NAMES = [
|
||||
model_info['short']
|
||||
model_info.short
|
||||
for model_info in MODELS.values()
|
||||
]
|
||||
|
||||
def get_model_by_shortname(short: str):
|
||||
for model, info in MODELS.items():
|
||||
if short == info['short']:
|
||||
if short == info.short:
|
||||
return model
|
||||
|
||||
N = '\n'
|
||||
|
|
@ -166,9 +255,7 @@ DEFAULT_UPSCALER = None
|
|||
|
||||
DEFAULT_CONFIG_PATH = 'skynet.toml'
|
||||
|
||||
DEFAULT_INITAL_MODELS = [
|
||||
'stabilityai/stable-diffusion-xl-base-1.0'
|
||||
]
|
||||
DEFAULT_INITAL_MODEL = list(MODELS.keys())[-1]
|
||||
|
||||
DATE_FORMAT = '%B the %dth %Y, %H:%M:%S'
|
||||
|
||||
|
|
@ -193,3 +280,221 @@ TG_MAX_WIDTH = 1280
|
|||
TG_MAX_HEIGHT = 1280
|
||||
|
||||
DEFAULT_SINGLE_CARD_MAP = 'cuda:0'
|
||||
|
||||
GPU_CONTRACT_ABI = {
|
||||
"version": "eosio::abi/1.2",
|
||||
"types": [],
|
||||
"structs": [
|
||||
{
|
||||
"name": "account",
|
||||
"base": "",
|
||||
"fields": [
|
||||
{"name": "user", "type": "name"},
|
||||
{"name": "balance", "type": "asset"},
|
||||
{"name": "nonce", "type": "uint64"}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "card",
|
||||
"base": "",
|
||||
"fields": [
|
||||
{"name": "id", "type": "uint64"},
|
||||
{"name": "owner", "type": "name"},
|
||||
{"name": "card_name", "type": "string"},
|
||||
{"name": "version", "type": "string"},
|
||||
{"name": "total_memory", "type": "uint64"},
|
||||
{"name": "mp_count", "type": "uint32"},
|
||||
{"name": "extra", "type": "string"}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "clean",
|
||||
"base": "",
|
||||
"fields": []
|
||||
},
|
||||
{
|
||||
"name": "config",
|
||||
"base": "",
|
||||
"fields": [
|
||||
{"name": "token_contract", "type": "name"},
|
||||
{"name": "token_symbol", "type": "symbol"}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "dequeue",
|
||||
"base": "",
|
||||
"fields": [
|
||||
{"name": "user", "type": "name"},
|
||||
{"name": "request_id", "type": "uint64"}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "enqueue",
|
||||
"base": "",
|
||||
"fields": [
|
||||
{"name": "user", "type": "name"},
|
||||
{"name": "request_body", "type": "string"},
|
||||
{"name": "binary_data", "type": "string"},
|
||||
{"name": "reward", "type": "asset"},
|
||||
{"name": "min_verification", "type": "uint32"}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "gcfgstruct",
|
||||
"base": "",
|
||||
"fields": [
|
||||
{"name": "token_contract", "type": "name"},
|
||||
{"name": "token_symbol", "type": "symbol"}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "submit",
|
||||
"base": "",
|
||||
"fields": [
|
||||
{"name": "worker", "type": "name"},
|
||||
{"name": "request_id", "type": "uint64"},
|
||||
{"name": "request_hash", "type": "checksum256"},
|
||||
{"name": "result_hash", "type": "checksum256"},
|
||||
{"name": "ipfs_hash", "type": "string"}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "withdraw",
|
||||
"base": "",
|
||||
"fields": [
|
||||
{"name": "user", "type": "name"},
|
||||
{"name": "quantity", "type": "asset"}
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "work_request_struct",
|
||||
"base": "",
|
||||
"fields": [
|
||||
{"name": "id", "type": "uint64"},
|
||||
{"name": "user", "type": "name"},
|
||||
{"name": "reward", "type": "asset"},
|
||||
{"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": []
|
||||
}
|
||||
|
|
|
|||
|
|
@ -13,36 +13,48 @@ from diffusers import DiffusionPipeline
|
|||
import trio
|
||||
import torch
|
||||
|
||||
from skynet.constants import DEFAULT_INITAL_MODELS, MODELS
|
||||
from skynet.constants import DEFAULT_INITAL_MODEL, MODELS
|
||||
from skynet.dgpu.errors import DGPUComputeError, DGPUInferenceCancelled
|
||||
|
||||
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
|
||||
|
||||
|
||||
def prepare_params_for_diffuse(
|
||||
params: dict,
|
||||
input_type: str,
|
||||
binary = None
|
||||
mode: str,
|
||||
inputs: list[bytes]
|
||||
):
|
||||
_params = {}
|
||||
if binary != None:
|
||||
match input_type:
|
||||
case 'png':
|
||||
match mode:
|
||||
case 'inpaint':
|
||||
image = crop_image(
|
||||
binary, params['width'], params['height'])
|
||||
inputs[0], params['width'], params['height'])
|
||||
|
||||
mask = crop_image(
|
||||
inputs[1], params['width'], params['height'])
|
||||
|
||||
_params['image'] = image
|
||||
_params['mask_image'] = mask
|
||||
|
||||
if 'flux' in params['model'].lower():
|
||||
_params['max_sequence_length'] = 512
|
||||
else:
|
||||
_params['strength'] = float(params['strength'])
|
||||
|
||||
case 'img2img':
|
||||
image = crop_image(
|
||||
inputs[0], params['width'], params['height'])
|
||||
|
||||
_params['image'] = image
|
||||
_params['strength'] = float(params['strength'])
|
||||
|
||||
case 'none':
|
||||
case 'txt2img' | 'diffuse':
|
||||
...
|
||||
|
||||
case _:
|
||||
raise DGPUComputeError(f'Unknown input_type {input_type}')
|
||||
raise DGPUComputeError(f'Unknown mode {mode}')
|
||||
|
||||
else:
|
||||
_params['width'] = int(params['width'])
|
||||
_params['height'] = int(params['height'])
|
||||
# _params['width'] = int(params['width'])
|
||||
# _params['height'] = int(params['height'])
|
||||
|
||||
return (
|
||||
params['prompt'],
|
||||
|
|
@ -57,95 +69,55 @@ def prepare_params_for_diffuse(
|
|||
class SkynetMM:
|
||||
|
||||
def __init__(self, config: dict):
|
||||
self.upscaler = init_upscaler()
|
||||
self.initial_models = (
|
||||
config['initial_models']
|
||||
if 'initial_models' in config else DEFAULT_INITAL_MODELS
|
||||
)
|
||||
|
||||
self.cache_dir = None
|
||||
if 'hf_home' in config:
|
||||
self.cache_dir = config['hf_home']
|
||||
|
||||
self._models = {}
|
||||
for model in self.initial_models:
|
||||
self.load_model(model, False, force=True)
|
||||
self._model_name = ''
|
||||
self._model_mode = ''
|
||||
|
||||
# self.load_model(DEFAULT_INITAL_MODEL, 'txt2img')
|
||||
|
||||
def log_debug_info(self):
|
||||
logging.info('memory summary:')
|
||||
logging.info('\n' + torch.cuda.memory_summary())
|
||||
|
||||
def is_model_loaded(self, model_name: str, image: bool):
|
||||
for model_key, model_data in self._models.items():
|
||||
if (model_key == model_name and
|
||||
model_data['image'] == image):
|
||||
def is_model_loaded(self, name: str, mode: str):
|
||||
if (name == self._model_name and
|
||||
mode == self._model_mode):
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
def load_model(
|
||||
self,
|
||||
model_name: str,
|
||||
image: bool,
|
||||
force=False
|
||||
):
|
||||
logging.info(f'loading model {model_name}...')
|
||||
if force or len(self._models.keys()) == 0:
|
||||
pipe = pipeline_for(
|
||||
model_name, image=image, cache_dir=self.cache_dir)
|
||||
|
||||
self._models[model_name] = {
|
||||
'pipe': pipe,
|
||||
'generated': 0,
|
||||
'image': image
|
||||
}
|
||||
|
||||
else:
|
||||
least_used = list(self._models.keys())[0]
|
||||
|
||||
for model in self._models:
|
||||
if self._models[
|
||||
least_used]['generated'] > self._models[model]['generated']:
|
||||
least_used = model
|
||||
|
||||
del self._models[least_used]
|
||||
|
||||
logging.info(f'swapping model {least_used} for {model_name}...')
|
||||
def unload_model(self):
|
||||
if getattr(self, '_model', None):
|
||||
del self._model
|
||||
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
pipe = pipeline_for(
|
||||
model_name, image=image, cache_dir=self.cache_dir)
|
||||
self._model_name = ''
|
||||
self._model_mode = ''
|
||||
|
||||
self._models[model_name] = {
|
||||
'pipe': pipe,
|
||||
'generated': 0,
|
||||
'image': image
|
||||
}
|
||||
def load_model(
|
||||
self,
|
||||
name: str,
|
||||
mode: str
|
||||
):
|
||||
logging.info(f'loading model {name}...')
|
||||
self.unload_model()
|
||||
self._model = pipeline_for(
|
||||
name, mode, cache_dir=self.cache_dir)
|
||||
self._model_mode = mode
|
||||
self._model_name = name
|
||||
|
||||
logging.info(f'loaded model {model_name}')
|
||||
return pipe
|
||||
|
||||
def get_model(self, model_name: str, image: bool) -> DiffusionPipeline:
|
||||
if model_name not in MODELS:
|
||||
raise DGPUComputeError(f'Unknown model {model_name}')
|
||||
|
||||
if not self.is_model_loaded(model_name, image):
|
||||
pipe = self.load_model(model_name, image=image)
|
||||
|
||||
else:
|
||||
pipe = self._models[model_name]['pipe']
|
||||
|
||||
return pipe
|
||||
|
||||
def compute_one(
|
||||
self,
|
||||
request_id: int,
|
||||
method: str,
|
||||
params: dict,
|
||||
input_type: str = 'png',
|
||||
binary: bytes | None = None
|
||||
inputs: list[bytes] = []
|
||||
):
|
||||
def maybe_cancel_work(step, *args, **kwargs):
|
||||
if self._should_cancel:
|
||||
|
|
@ -154,6 +126,8 @@ class SkynetMM:
|
|||
logging.warn(f'cancelling work at step {step}')
|
||||
raise DGPUInferenceCancelled()
|
||||
|
||||
return {}
|
||||
|
||||
maybe_cancel_work(0)
|
||||
|
||||
output_type = 'png'
|
||||
|
|
@ -163,20 +137,29 @@ class SkynetMM:
|
|||
output = None
|
||||
output_hash = None
|
||||
try:
|
||||
match method:
|
||||
case 'diffuse':
|
||||
arguments = prepare_params_for_diffuse(
|
||||
params, input_type, binary=binary)
|
||||
prompt, guidance, step, seed, upscaler, extra_params = arguments
|
||||
model = self.get_model(params['model'], 'image' in extra_params)
|
||||
name = params['model']
|
||||
|
||||
output = model(
|
||||
match method:
|
||||
case 'diffuse' | 'txt2img' | 'img2img' | 'inpaint':
|
||||
if not self.is_model_loaded(name, method):
|
||||
self.load_model(name, method)
|
||||
|
||||
arguments = prepare_params_for_diffuse(
|
||||
params, method, inputs)
|
||||
prompt, guidance, step, seed, upscaler, extra_params = arguments
|
||||
|
||||
if 'flux' in name.lower():
|
||||
extra_params['callback_on_step_end'] = maybe_cancel_work
|
||||
|
||||
else:
|
||||
extra_params['callback'] = maybe_cancel_work
|
||||
extra_params['callback_steps'] = 1
|
||||
|
||||
output = self._model(
|
||||
prompt,
|
||||
guidance_scale=guidance,
|
||||
num_inference_steps=step,
|
||||
generator=seed,
|
||||
callback=maybe_cancel_work,
|
||||
callback_steps=1,
|
||||
**extra_params
|
||||
).images[0]
|
||||
|
||||
|
|
@ -185,7 +168,7 @@ class SkynetMM:
|
|||
case 'png':
|
||||
if upscaler == 'x4':
|
||||
input_img = output.convert('RGB')
|
||||
up_img, _ = self.upscaler.enhance(
|
||||
up_img, _ = init_upscaler().enhance(
|
||||
convert_from_image_to_cv2(input_img), outscale=4)
|
||||
|
||||
output = convert_from_cv2_to_image(up_img)
|
||||
|
|
@ -197,6 +180,22 @@ class SkynetMM:
|
|||
|
||||
output_hash = sha256(output_binary).hexdigest()
|
||||
|
||||
case 'upscale':
|
||||
if self._model_mode != 'upscale':
|
||||
self.unload_model()
|
||||
self._model = init_upscaler()
|
||||
self._model_mode = 'upscale'
|
||||
self._model_name = 'realesrgan'
|
||||
|
||||
input_img = inputs[0].convert('RGB')
|
||||
up_img, _ = self._model.enhance(
|
||||
convert_from_image_to_cv2(input_img), outscale=4)
|
||||
|
||||
output = convert_from_cv2_to_image(up_img)
|
||||
|
||||
output_binary = convert_from_img_to_bytes(output)
|
||||
output_hash = sha256(output_binary).hexdigest()
|
||||
|
||||
case _:
|
||||
raise DGPUComputeError('Unsupported compute method')
|
||||
|
||||
|
|
|
|||
|
|
@ -117,22 +117,7 @@ class SkynetDGPUDaemon:
|
|||
|
||||
return app
|
||||
|
||||
async def serve_forever(self):
|
||||
try:
|
||||
while True:
|
||||
if self.auto_withdraw:
|
||||
await self.conn.maybe_withdraw_all()
|
||||
|
||||
queue = self._snap['queue']
|
||||
|
||||
random.shuffle(queue)
|
||||
queue = sorted(
|
||||
queue,
|
||||
key=lambda req: convert_reward_to_int(req['reward']),
|
||||
reverse=True
|
||||
)
|
||||
|
||||
for req in queue:
|
||||
async def maybe_serve_one(self, req):
|
||||
rid = req['id']
|
||||
|
||||
# parse request
|
||||
|
|
@ -140,25 +125,35 @@ class SkynetDGPUDaemon:
|
|||
model = body['params']['model']
|
||||
|
||||
# if model not known
|
||||
if model not in MODELS:
|
||||
if model != 'RealESRGAN_x4plus' and model not in MODELS:
|
||||
logging.warning(f'Unknown model {model}')
|
||||
continue
|
||||
return False
|
||||
|
||||
# if whitelist enabled and model not in it continue
|
||||
if (len(self.model_whitelist) > 0 and
|
||||
not model in self.model_whitelist):
|
||||
continue
|
||||
return False
|
||||
|
||||
# if blacklist contains model skip
|
||||
if model in self.model_blacklist:
|
||||
continue
|
||||
return False
|
||||
|
||||
my_results = [res['id'] for res in self._snap['my_results']]
|
||||
if rid not in my_results and rid in self._snap['requests']:
|
||||
statuses = self._snap['requests'][rid]
|
||||
|
||||
if len(statuses) == 0:
|
||||
binary, input_type = await self.conn.get_input_data(req['binary_data'])
|
||||
inputs = []
|
||||
for _input in req['binary_data'].split(','):
|
||||
if _input:
|
||||
for _ in range(3):
|
||||
try:
|
||||
img = await self.conn.get_input_data(_input)
|
||||
inputs.append(img)
|
||||
break
|
||||
|
||||
except:
|
||||
...
|
||||
|
||||
hash_str = (
|
||||
str(req['nonce'])
|
||||
|
|
@ -176,7 +171,7 @@ class SkynetDGPUDaemon:
|
|||
logging.info(f'working on {body}')
|
||||
|
||||
resp = await self.conn.begin_work(rid)
|
||||
if 'code' in resp:
|
||||
if not resp or 'code' in resp:
|
||||
logging.info(f'probably being worked on already... skip.')
|
||||
|
||||
else:
|
||||
|
|
@ -195,8 +190,7 @@ class SkynetDGPUDaemon:
|
|||
self.mm.compute_one,
|
||||
rid,
|
||||
body['method'], body['params'],
|
||||
input_type=input_type,
|
||||
binary=binary
|
||||
inputs=inputs
|
||||
)
|
||||
)
|
||||
|
||||
|
|
@ -215,11 +209,30 @@ class SkynetDGPUDaemon:
|
|||
await self.conn.cancel_work(rid, str(e))
|
||||
|
||||
finally:
|
||||
break
|
||||
return True
|
||||
|
||||
else:
|
||||
logging.info(f'request {rid} already beign worked on, skip...')
|
||||
|
||||
async def serve_forever(self):
|
||||
try:
|
||||
while True:
|
||||
if self.auto_withdraw:
|
||||
await self.conn.maybe_withdraw_all()
|
||||
|
||||
queue = self._snap['queue']
|
||||
|
||||
random.shuffle(queue)
|
||||
queue = sorted(
|
||||
queue,
|
||||
key=lambda req: convert_reward_to_int(req['reward']),
|
||||
reverse=True
|
||||
)
|
||||
|
||||
for req in queue:
|
||||
if (await self.maybe_serve_one(req)):
|
||||
break
|
||||
|
||||
await trio.sleep(1)
|
||||
|
||||
except KeyboardInterrupt:
|
||||
|
|
|
|||
|
|
@ -8,15 +8,16 @@ import logging
|
|||
from pathlib import Path
|
||||
from functools import partial
|
||||
|
||||
import asks
|
||||
import trio
|
||||
import leap
|
||||
import anyio
|
||||
import httpx
|
||||
|
||||
from PIL import Image, UnidentifiedImageError
|
||||
|
||||
from leap.cleos import CLEOS
|
||||
from leap.sugar import Checksum256, Name, asset_from_str
|
||||
from skynet.constants import DEFAULT_IPFS_DOMAIN
|
||||
from leap.protocol import Asset
|
||||
from skynet.constants import DEFAULT_IPFS_DOMAIN, GPU_CONTRACT_ABI
|
||||
|
||||
from skynet.ipfs import AsyncIPFSHTTP, get_ipfs_file
|
||||
from skynet.dgpu.errors import DGPUComputeError
|
||||
|
|
@ -32,25 +33,25 @@ async def failable(fn: partial, ret_fail=None):
|
|||
except (
|
||||
OSError,
|
||||
json.JSONDecodeError,
|
||||
asks.errors.RequestTimeout,
|
||||
asks.errors.BadHttpResponse,
|
||||
anyio.BrokenResourceError
|
||||
):
|
||||
anyio.BrokenResourceError,
|
||||
httpx.ReadError,
|
||||
leap.errors.TransactionPushError
|
||||
) 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_CONTRACT_ABI)
|
||||
|
||||
self.ipfs_gateway_url = None
|
||||
if 'ipfs_gateway_url' in config:
|
||||
|
|
@ -151,11 +152,11 @@ 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 +169,11 @@ 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 +192,10 @@ 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 +227,13 @@ 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
|
||||
)
|
||||
|
|
@ -267,46 +268,15 @@ class SkynetGPUConnector:
|
|||
|
||||
return file_cid
|
||||
|
||||
async def get_input_data(self, ipfs_hash: str) -> tuple[bytes, str]:
|
||||
input_type = 'none'
|
||||
async def get_input_data(self, ipfs_hash: str) -> Image:
|
||||
link = f'https://{self.ipfs_domain}/ipfs/{ipfs_hash}'
|
||||
|
||||
if ipfs_hash == '':
|
||||
return b'', input_type
|
||||
|
||||
results = {}
|
||||
ipfs_link = f'https://{self.ipfs_domain}/ipfs/{ipfs_hash}'
|
||||
ipfs_link_legacy = ipfs_link + '/image.png'
|
||||
|
||||
async with trio.open_nursery() as n:
|
||||
async def get_and_set_results(link: str):
|
||||
res = await get_ipfs_file(link, timeout=1)
|
||||
logging.info(f'got response from {link}')
|
||||
if not res or res.status_code != 200:
|
||||
logging.warning(f'couldn\'t get ipfs binary data at {link}!')
|
||||
|
||||
else:
|
||||
try:
|
||||
# attempt to decode as image
|
||||
results[link] = Image.open(io.BytesIO(res.raw))
|
||||
input_type = 'png'
|
||||
n.cancel_scope.cancel()
|
||||
input_data = Image.open(io.BytesIO(res.raw))
|
||||
|
||||
except UnidentifiedImageError:
|
||||
logging.warning(f'couldn\'t get ipfs binary data at {link}!')
|
||||
|
||||
n.start_soon(
|
||||
get_and_set_results, ipfs_link)
|
||||
n.start_soon(
|
||||
get_and_set_results, ipfs_link_legacy)
|
||||
|
||||
input_data = None
|
||||
if ipfs_link_legacy in results:
|
||||
input_data = results[ipfs_link_legacy]
|
||||
|
||||
if ipfs_link in results:
|
||||
input_data = results[ipfs_link]
|
||||
|
||||
if input_data == None:
|
||||
raise DGPUComputeError('Couldn\'t gather input data from ipfs')
|
||||
|
||||
return input_data, input_type
|
||||
return input_data
|
||||
|
|
|
|||
|
|
@ -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 +1,56 @@
|
|||
#!/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
|
||||
)
|
||||
params['transformer'] = transformer
|
||||
|
||||
pipe = FluxFillPipeline.from_pretrained(
|
||||
model, **params)
|
||||
|
||||
pipe.vae.enable_tiling()
|
||||
pipe.vae.enable_slicing()
|
||||
|
||||
return pipe
|
||||
|
|
@ -39,7 +39,7 @@ def validate_user_config_request(req: str):
|
|||
case 'model' | 'algo':
|
||||
attr = 'model'
|
||||
val = params[2]
|
||||
shorts = [model_info['short'] for model_info in MODELS.values()]
|
||||
shorts = [model_info.short for model_info in MODELS.values()]
|
||||
if val not in shorts:
|
||||
raise ConfigUnknownAlgorithm(f'no model named {val}')
|
||||
|
||||
|
|
@ -112,20 +112,10 @@ def validate_user_config_request(req: str):
|
|||
|
||||
|
||||
def perform_auto_conf(config: dict) -> dict:
|
||||
model = config['model']
|
||||
prefered_size_w = 512
|
||||
prefered_size_h = 512
|
||||
|
||||
if 'xl' in model:
|
||||
prefered_size_w = 1024
|
||||
prefered_size_h = 1024
|
||||
|
||||
else:
|
||||
prefered_size_w = 512
|
||||
prefered_size_h = 512
|
||||
model = MODELS[config['model']]
|
||||
|
||||
config['step'] = random.randint(20, 35)
|
||||
config['width'] = prefered_size_w
|
||||
config['height'] = prefered_size_h
|
||||
config['width'] = model.size.w
|
||||
config['height'] = model.size.h
|
||||
|
||||
return config
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@ from contextlib import AsyncExitStack
|
|||
from contextlib import asynccontextmanager as acm
|
||||
|
||||
from leap.cleos import CLEOS
|
||||
from leap.sugar import Name, asset_from_str, collect_stdout
|
||||
from leap.protocol import Name, Asset
|
||||
from leap.hyperion import HyperionAPI
|
||||
|
||||
from telebot.types import InputMediaPhoto
|
||||
|
|
@ -43,7 +43,6 @@ class SkynetTelegramFrontend:
|
|||
db_user: str,
|
||||
db_pass: str,
|
||||
ipfs_node: str,
|
||||
remote_ipfs_node: str | None,
|
||||
key: str,
|
||||
explorer_domain: str,
|
||||
ipfs_domain: str
|
||||
|
|
@ -56,22 +55,19 @@ class SkynetTelegramFrontend:
|
|||
self.db_host = db_host
|
||||
self.db_user = db_user
|
||||
self.db_pass = db_pass
|
||||
self.remote_ipfs_node = remote_ipfs_node
|
||||
self.key = key
|
||||
self.explorer_domain = explorer_domain
|
||||
self.ipfs_domain = ipfs_domain
|
||||
|
||||
self.bot = AsyncTeleBot(token, exception_handler=SKYExceptionHandler)
|
||||
self.cleos = CLEOS(None, None, url=node_url, remote=node_url)
|
||||
self.cleos = CLEOS(endpoint=node_url)
|
||||
self.cleos.load_abi('gpu.scd', GPU_CONTRACT_ABI)
|
||||
self.hyperion = HyperionAPI(hyperion_url)
|
||||
self.ipfs_node = AsyncIPFSHTTP(ipfs_node)
|
||||
|
||||
self._async_exit_stack = AsyncExitStack()
|
||||
|
||||
async def start(self):
|
||||
if self.remote_ipfs_node:
|
||||
await self.ipfs_node.connect(self.remote_ipfs_node)
|
||||
|
||||
self.db_call = await self._async_exit_stack.enter_async_context(
|
||||
open_database_connection(
|
||||
self.db_user, self.db_pass, self.db_host))
|
||||
|
|
@ -116,7 +112,7 @@ class SkynetTelegramFrontend:
|
|||
method: str,
|
||||
params: dict,
|
||||
file_id: str | None = None,
|
||||
binary_data: str = ''
|
||||
inputs: list[str] = []
|
||||
) -> bool:
|
||||
if params['seed'] == None:
|
||||
params['seed'] = random.randint(0, 0xFFFFFFFF)
|
||||
|
|
@ -145,13 +141,13 @@ class SkynetTelegramFrontend:
|
|||
res = await self.cleos.a_push_action(
|
||||
'gpu.scd',
|
||||
'enqueue',
|
||||
{
|
||||
list({
|
||||
'user': Name(self.account),
|
||||
'request_body': body,
|
||||
'binary_data': binary_data,
|
||||
'reward': asset_from_str(reward),
|
||||
'binary_data': ','.join(inputs),
|
||||
'reward': Asset.from_str(reward),
|
||||
'min_verification': 1
|
||||
},
|
||||
}.values()),
|
||||
self.account, self.key, permission=self.permission
|
||||
)
|
||||
|
||||
|
|
@ -176,12 +172,12 @@ class SkynetTelegramFrontend:
|
|||
parse_mode='HTML'
|
||||
)
|
||||
|
||||
out = collect_stdout(res)
|
||||
out = res['processed']['action_traces'][0]['console']
|
||||
|
||||
request_id, nonce = out.split(':')
|
||||
|
||||
request_hash = sha256(
|
||||
(nonce + body + binary_data).encode('utf-8')).hexdigest().upper()
|
||||
(nonce + body + ','.join(inputs)).encode('utf-8')).hexdigest().upper()
|
||||
|
||||
request_id = int(request_id)
|
||||
|
||||
|
|
@ -189,7 +185,7 @@ class SkynetTelegramFrontend:
|
|||
|
||||
tx_hash = None
|
||||
ipfs_hash = None
|
||||
for i in range(60):
|
||||
for i in range(60 * 3):
|
||||
try:
|
||||
submits = await self.hyperion.aget_actions(
|
||||
account=self.account,
|
||||
|
|
@ -241,15 +237,12 @@ class SkynetTelegramFrontend:
|
|||
user, params, tx_hash, worker, reward, self.explorer_domain)
|
||||
|
||||
# attempt to get the image and send it
|
||||
results = {}
|
||||
ipfs_link = f'https://{self.ipfs_domain}/ipfs/{ipfs_hash}'
|
||||
ipfs_link_legacy = ipfs_link + '/image.png'
|
||||
|
||||
async def get_and_set_results(link: str):
|
||||
res = await get_ipfs_file(link)
|
||||
logging.info(f'got response from {link}')
|
||||
res = await get_ipfs_file(ipfs_link)
|
||||
logging.info(f'got response from {ipfs_link}')
|
||||
if not res or res.status_code != 200:
|
||||
logging.warning(f'couldn\'t get ipfs binary data at {link}!')
|
||||
logging.warning(f'couldn\'t get ipfs binary data at {ipfs_link}!')
|
||||
|
||||
else:
|
||||
try:
|
||||
|
|
@ -264,23 +257,8 @@ class SkynetTelegramFrontend:
|
|||
image.save(tmp_buf, format='PNG')
|
||||
png_img = tmp_buf.getvalue()
|
||||
|
||||
results[link] = png_img
|
||||
|
||||
except UnidentifiedImageError:
|
||||
logging.warning(f'couldn\'t get ipfs binary data at {link}!')
|
||||
|
||||
tasks = [
|
||||
get_and_set_results(ipfs_link),
|
||||
get_and_set_results(ipfs_link_legacy)
|
||||
]
|
||||
await asyncio.gather(*tasks)
|
||||
|
||||
png_img = None
|
||||
if ipfs_link_legacy in results:
|
||||
png_img = results[ipfs_link_legacy]
|
||||
|
||||
if ipfs_link in results:
|
||||
png_img = results[ipfs_link]
|
||||
logging.warning(f'couldn\'t get ipfs binary data at {ipfs_link}!')
|
||||
|
||||
if not png_img:
|
||||
await self.update_status_message(
|
||||
|
|
|
|||
|
|
@ -254,7 +254,7 @@ def create_handler_context(frontend: 'SkynetTelegramFrontend'):
|
|||
success = await work_request(
|
||||
user, status_msg, 'img2img', params,
|
||||
file_id=file_id,
|
||||
binary_data=ipfs_hash
|
||||
inputs=ipfs_hash
|
||||
)
|
||||
|
||||
if success:
|
||||
|
|
@ -320,7 +320,7 @@ def create_handler_context(frontend: 'SkynetTelegramFrontend'):
|
|||
success = await work_request(
|
||||
user, status_msg, 'redo', params,
|
||||
file_id=file_id,
|
||||
binary_data=binary
|
||||
inputs=binary
|
||||
)
|
||||
|
||||
if success:
|
||||
|
|
|
|||
|
|
@ -72,7 +72,7 @@ def generate_reply_caption(
|
|||
):
|
||||
explorer_link = hlink(
|
||||
'SKYNET Transaction Explorer',
|
||||
f'https://explorer.{explorer_domain}/v2/explore/transaction/{tx_hash}'
|
||||
f'https://{explorer_domain}/v2/explore/transaction/{tx_hash}'
|
||||
)
|
||||
|
||||
meta_info = prepare_metainfo_caption(tguser, worker, reward, params)
|
||||
|
|
|
|||
|
|
@ -3,10 +3,10 @@
|
|||
import logging
|
||||
from pathlib import Path
|
||||
|
||||
import asks
|
||||
import httpx
|
||||
|
||||
|
||||
class IPFSClientException(BaseException):
|
||||
class IPFSClientException(Exception):
|
||||
...
|
||||
|
||||
|
||||
|
|
@ -16,7 +16,8 @@ class AsyncIPFSHTTP:
|
|||
self.endpoint = endpoint
|
||||
|
||||
async def _post(self, sub_url: str, *args, **kwargs):
|
||||
resp = await asks.post(
|
||||
async with httpx.AsyncClient() as client:
|
||||
resp = await client.post(
|
||||
self.endpoint + sub_url,
|
||||
*args, **kwargs
|
||||
)
|
||||
|
|
@ -25,10 +26,10 @@ class AsyncIPFSHTTP:
|
|||
raise IPFSClientException(resp.text)
|
||||
|
||||
return resp.json()
|
||||
|
||||
#!/usr/bin/python
|
||||
async def add(self, file_path: Path, **kwargs):
|
||||
files = {
|
||||
'file': file_path
|
||||
'file': (file_path.name, file_path.open('rb'))
|
||||
}
|
||||
return await self._post(
|
||||
'/api/v0/add',
|
||||
|
|
@ -55,18 +56,19 @@ class AsyncIPFSHTTP:
|
|||
))['Peers']
|
||||
|
||||
|
||||
async def get_ipfs_file(ipfs_link: str, timeout: int = 60):
|
||||
async def get_ipfs_file(ipfs_link: str, timeout: int = 60 * 5):
|
||||
logging.info(f'attempting to get image at {ipfs_link}')
|
||||
resp = None
|
||||
for i in range(timeout):
|
||||
for _ in range(timeout):
|
||||
try:
|
||||
resp = await asks.get(ipfs_link, timeout=3)
|
||||
async with httpx.AsyncClient() as client:
|
||||
resp = await client.get(ipfs_link, timeout=3)
|
||||
|
||||
except asks.errors.RequestTimeout:
|
||||
logging.warning('timeout...')
|
||||
except httpx.RequestError as e:
|
||||
logging.warning(f'Request error: {e}')
|
||||
|
||||
except asks.errors.BadHttpResponse as e:
|
||||
logging.error(f'ifps gateway exception: \n{e}')
|
||||
if resp is not None:
|
||||
break
|
||||
|
||||
if resp:
|
||||
logging.info(f'status_code: {resp.status_code}')
|
||||
|
|
|
|||
|
|
@ -55,7 +55,7 @@ class SkynetPinner:
|
|||
|
||||
cids = []
|
||||
for action in enqueues['actions']:
|
||||
cid = action['act']['data']['binary_data']
|
||||
for cid in action['act']['data']['binary_data'].split(','):
|
||||
if cid and not self.is_pinned(cid):
|
||||
cids.append(cid)
|
||||
|
||||
|
|
|
|||
162
skynet/utils.py
162
skynet/utils.py
|
|
@ -6,26 +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,
|
||||
EulerAncestralDiscreteScheduler
|
||||
AutoPipelineForText2Image,
|
||||
AutoPipelineForImage2Image,
|
||||
AutoPipelineForInpainting,
|
||||
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)
|
||||
|
|
@ -58,14 +74,18 @@ def crop_image(image: Image, max_w: int, max_h: int) -> Image:
|
|||
|
||||
return image.convert('RGB')
|
||||
|
||||
def convert_from_bytes_and_crop(raw: bytes, max_w: int, max_h: int) -> Image:
|
||||
return crop_image(convert_from_bytes_to_img(raw), max_w, max_h)
|
||||
|
||||
|
||||
def pipeline_for(
|
||||
model: str,
|
||||
mode: str,
|
||||
mem_fraction: float = 1.0,
|
||||
image: bool = False,
|
||||
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
|
||||
|
|
@ -79,21 +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
|
||||
|
||||
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:
|
||||
|
|
@ -102,26 +136,37 @@ def pipeline_for(
|
|||
|
||||
torch.cuda.set_per_process_memory_fraction(mem_fraction)
|
||||
|
||||
pipe = DiffusionPipeline.from_pretrained(
|
||||
pipe_class = DiffusionPipeline
|
||||
match mode:
|
||||
case 'inpaint':
|
||||
pipe_class = AutoPipelineForInpainting
|
||||
|
||||
case 'img2img':
|
||||
pipe_class = AutoPipelineForImage2Image
|
||||
|
||||
case 'txt2img':
|
||||
pipe_class = AutoPipelineForText2Image
|
||||
|
||||
pipe = pipe_class.from_pretrained(
|
||||
model, **params)
|
||||
|
||||
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(
|
||||
pipe.scheduler.config)
|
||||
|
||||
pipe.enable_xformers_memory_efficient_attention()
|
||||
# pipe.enable_xformers_memory_efficient_attention()
|
||||
|
||||
if over_mem:
|
||||
if not image:
|
||||
pipe.enable_vae_slicing()
|
||||
pipe.enable_vae_tiling()
|
||||
if mode == 'txt2img':
|
||||
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')
|
||||
|
||||
|
|
@ -130,7 +175,7 @@ def pipeline_for(
|
|||
|
||||
def txt2img(
|
||||
hf_token: str,
|
||||
model: str = 'prompthero/openjourney',
|
||||
model: str = list(MODELS.keys())[-1],
|
||||
prompt: str = 'a red old tractor in a sunny wheat field',
|
||||
output: str = 'output.png',
|
||||
width: int = 512, height: int = 512,
|
||||
|
|
@ -139,7 +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
|
||||
|
|
@ -156,7 +201,7 @@ def txt2img(
|
|||
|
||||
def img2img(
|
||||
hf_token: str,
|
||||
model: str = 'prompthero/openjourney',
|
||||
model: str = list(MODELS.keys())[-2],
|
||||
prompt: str = 'a red old tractor in a sunny wheat field',
|
||||
img_path: str = 'input.png',
|
||||
output: str = 'output.png',
|
||||
|
|
@ -166,10 +211,12 @@ 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]
|
||||
|
||||
with open(img_path, 'rb') as img_file:
|
||||
input_img = convert_from_bytes_and_crop(img_file.read(), 512, 512)
|
||||
input_img = convert_from_bytes_and_crop(img_file.read(), model_info.size.w, model_info.size.h)
|
||||
|
||||
seed = seed if seed else random.randint(0, 2 ** 64)
|
||||
prompt = prompt
|
||||
|
|
@ -184,7 +231,48 @@ def img2img(
|
|||
image.save(output)
|
||||
|
||||
|
||||
def init_upscaler(model_path: str = 'weights/RealESRGAN_x4plus.pth'):
|
||||
def inpaint(
|
||||
hf_token: str,
|
||||
model: str = list(MODELS.keys())[-3],
|
||||
prompt: str = 'a red old tractor in a sunny wheat field',
|
||||
img_path: str = 'input.png',
|
||||
mask_path: str = 'mask.png',
|
||||
output: str = 'output.png',
|
||||
strength: float = 1.0,
|
||||
guidance: float = 10,
|
||||
steps: int = 28,
|
||||
seed: Optional[int] = None
|
||||
):
|
||||
login(token=hf_token)
|
||||
pipe = pipeline_for(model, 'inpaint')
|
||||
|
||||
model_info = MODELS[model]
|
||||
|
||||
with open(img_path, 'rb') as img_file:
|
||||
input_img = convert_from_bytes_and_crop(img_file.read(), model_info.size.w, model_info.size.h)
|
||||
|
||||
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,
|
||||
guidance_scale=guidance, num_inference_steps=steps,
|
||||
generator=torch.Generator("cuda").manual_seed(seed),
|
||||
**var_params
|
||||
).images[0]
|
||||
|
||||
image.save(output)
|
||||
|
||||
|
||||
def init_upscaler(model_path: str = 'hf_home/RealESRGAN_x4plus.pth'):
|
||||
return RealESRGANer(
|
||||
scale=4,
|
||||
model_path=model_path,
|
||||
|
|
@ -203,7 +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')
|
||||
|
||||
|
|
@ -214,25 +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)
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
import pytest
|
||||
|
||||
from skynet.db import open_new_database
|
||||
from skynet.config import *
|
||||
from skynet.ipfs import AsyncIPFSHTTP
|
||||
from skynet.ipfs.docker import open_ipfs_node
|
||||
from skynet.nodeos import open_nodeos
|
||||
|
|
@ -15,6 +15,7 @@ def ipfs_client():
|
|||
|
||||
@pytest.fixture(scope='session')
|
||||
def postgres_db():
|
||||
from skynet.db import open_new_database
|
||||
with open_new_database() as db_params:
|
||||
yield db_params
|
||||
|
||||
|
|
@ -22,3 +23,20 @@ def postgres_db():
|
|||
def cleos():
|
||||
with open_nodeos() as cli:
|
||||
yield cli
|
||||
|
||||
@pytest.fixture(scope='session')
|
||||
def dgpu():
|
||||
from skynet.dgpu.network import SkynetGPUConnector
|
||||
from skynet.dgpu.compute import SkynetMM
|
||||
from skynet.dgpu.daemon import SkynetDGPUDaemon
|
||||
|
||||
config = load_skynet_toml(file_path='skynet.toml')
|
||||
hf_token = load_key(config, 'skynet.dgpu.hf_token')
|
||||
hf_home = load_key(config, 'skynet.dgpu.hf_home')
|
||||
set_hf_vars(hf_token, hf_home)
|
||||
config = config['skynet']['dgpu']
|
||||
conn = SkynetGPUConnector(config)
|
||||
mm = SkynetMM(config)
|
||||
daemon = SkynetDGPUDaemon(mm, conn, config)
|
||||
|
||||
yield conn, mm, daemon
|
||||
|
|
|
|||
|
|
@ -0,0 +1,112 @@
|
|||
import json
|
||||
|
||||
from skynet.dgpu.compute import SkynetMM
|
||||
from skynet.constants import *
|
||||
from skynet.config import *
|
||||
|
||||
|
||||
async def test_diffuse(dgpu):
|
||||
conn, mm, daemon = dgpu
|
||||
await conn.cancel_work(0, 'testing')
|
||||
|
||||
daemon._snap['requests'][0] = {}
|
||||
req = {
|
||||
'id': 0,
|
||||
'nonce': 0,
|
||||
'body': json.dumps({
|
||||
"method": "diffuse",
|
||||
"params": {
|
||||
"prompt": "Kronos God Realistic 4k",
|
||||
"model": list(MODELS.keys())[-1],
|
||||
"step": 21,
|
||||
"width": 1024,
|
||||
"height": 1024,
|
||||
"seed": 168402949,
|
||||
"guidance": "7.5"
|
||||
}
|
||||
}),
|
||||
'binary_data': '',
|
||||
}
|
||||
|
||||
await daemon.maybe_serve_one(req)
|
||||
|
||||
|
||||
async def test_txt2img(dgpu):
|
||||
conn, mm, daemon = dgpu
|
||||
await conn.cancel_work(0, 'testing')
|
||||
|
||||
daemon._snap['requests'][0] = {}
|
||||
req = {
|
||||
'id': 0,
|
||||
'nonce': 0,
|
||||
'body': json.dumps({
|
||||
"method": "txt2img",
|
||||
"params": {
|
||||
"prompt": "Kronos God Realistic 4k",
|
||||
"model": list(MODELS.keys())[-1],
|
||||
"step": 21,
|
||||
"width": 1024,
|
||||
"height": 1024,
|
||||
"seed": 168402949,
|
||||
"guidance": "7.5"
|
||||
}
|
||||
}),
|
||||
'binary_data': '',
|
||||
}
|
||||
|
||||
await daemon.maybe_serve_one(req)
|
||||
|
||||
|
||||
async def test_img2img(dgpu):
|
||||
conn, mm, daemon = dgpu
|
||||
await conn.cancel_work(0, 'testing')
|
||||
|
||||
daemon._snap['requests'][0] = {}
|
||||
req = {
|
||||
'id': 0,
|
||||
'nonce': 0,
|
||||
'body': json.dumps({
|
||||
"method": "img2img",
|
||||
"params": {
|
||||
"prompt": "a hindu cat god feline god on a house roof",
|
||||
"model": list(MODELS.keys())[-2],
|
||||
"step": 21,
|
||||
"width": 1024,
|
||||
"height": 1024,
|
||||
"seed": 168402949,
|
||||
"guidance": "7.5",
|
||||
"strength": "0.5"
|
||||
}
|
||||
}),
|
||||
'binary_data': 'QmZcGdXXVQfpco1G3tr2CGFBtv8xVsCwcwuq9gnJBWDymi',
|
||||
}
|
||||
|
||||
await daemon.maybe_serve_one(req)
|
||||
|
||||
async def test_inpaint(dgpu):
|
||||
conn, mm, daemon = dgpu
|
||||
await conn.cancel_work(0, 'testing')
|
||||
|
||||
daemon._snap['requests'][0] = {}
|
||||
req = {
|
||||
'id': 0,
|
||||
'nonce': 0,
|
||||
'body': json.dumps({
|
||||
"method": "inpaint",
|
||||
"params": {
|
||||
"prompt": "a black panther on a sunny roof",
|
||||
"model": list(MODELS.keys())[-3],
|
||||
"step": 21,
|
||||
"width": 1024,
|
||||
"height": 1024,
|
||||
"seed": 168402949,
|
||||
"guidance": "7.5",
|
||||
"strength": "0.5"
|
||||
}
|
||||
}),
|
||||
'binary_data':
|
||||
'QmZcGdXXVQfpco1G3tr2CGFBtv8xVsCwcwuq9gnJBWDymi,' +
|
||||
'Qmccx1aXNmq5mZDS3YviUhgGHXWhQeHvca3AgA7MDjj2hR'
|
||||
}
|
||||
|
||||
await daemon.maybe_serve_one(req)
|
||||
Loading…
Reference in New Issue