diff --git a/docker/Dockerfile.runtime+cuda-py312 b/docker/Dockerfile.runtime+cuda-py312 new file mode 100644 index 0000000..43ad864 --- /dev/null +++ b/docker/Dockerfile.runtime+cuda-py312 @@ -0,0 +1,45 @@ +from nvidia/cuda:12.4.1-devel-ubuntu22.04 +from python:3.12 + +env DEBIAN_FRONTEND=noninteractive + +run apt-get update && apt-get install -y \ + git \ + llvm \ + ffmpeg \ + libsm6 \ + libxext6 \ + ninja-build + +# env CC /usr/bin/clang +# env CXX /usr/bin/clang++ +# +# # install llvm10 as required by llvm-lite +# run git clone https://github.com/llvm/llvm-project.git -b llvmorg-10.0.1 +# workdir /llvm-project +# # this adds a commit from 12.0.0 that fixes build on newer compilers +# run git cherry-pick -n b498303066a63a203d24f739b2d2e0e56dca70d1 +# run cmake -S llvm -B build -G Ninja -DCMAKE_BUILD_TYPE=Release +# run ninja -C build install # -j8 + +run curl -sSL https://install.python-poetry.org | python3 - + +env PATH "/root/.local/bin:$PATH" + +copy . /skynet + +workdir /skynet + +env POETRY_VIRTUALENVS_PATH /skynet/.venv + +run poetry install --with=cuda -v + +workdir /root/target + +env PYTORCH_CUDA_ALLOC_CONF max_split_size_mb:128 +env NVIDIA_VISIBLE_DEVICES=all + +copy docker/entrypoint.sh /entrypoint.sh +entrypoint ["/entrypoint.sh"] + +cmd ["skynet", "--help"] diff --git a/docker/build_docker.sh b/docker/build_docker.sh index 94e07d4..0eaf2a7 100755 --- a/docker/build_docker.sh +++ b/docker/build_docker.sh @@ -1,7 +1,7 @@ docker build \ - -t guilledk/skynet:runtime-cuda-py311 \ - -f docker/Dockerfile.runtime+cuda-py311 . + -t guilledk/skynet:runtime-cuda-py312 \ + -f docker/Dockerfile.runtime+cuda-py312 . -docker build \ - -t guilledk/skynet:runtime-cuda \ - -f docker/Dockerfile.runtime+cuda-py311 . +# docker build \ +# -t guilledk/skynet:runtime-cuda \ +# -f docker/Dockerfile.runtime+cuda-py311 . diff --git a/poetry.lock b/poetry.lock index f1a8e65..0f0d396 100644 --- a/poetry.lock +++ b/poetry.lock @@ -13,32 +13,34 @@ files = [ [[package]] name = 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--git a/pyproject.toml b/pyproject.toml index d8e2dfe..fb0e620 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,21 +1,31 @@ [tool.poetry] name = 'skynet' -version = '0.1a12' +version = '0.1a13' description = 'Decentralized compute platform' authors = ['Guillermo Rodriguez '] license = 'AGPL' readme = 'README.md' [tool.poetry.dependencies] -python = '>=3.10,<3.12' +python = '>=3.10,<3.13' pytz = '^2023.3.post1' trio = '^0.22.2' asks = '^3.0.0' Pillow = '^10.0.1' docker = '^6.1.3' -py-leap = {git = 'https://github.com/guilledk/py-leap.git', rev = 'v0.1a14'} +py-leap = {git = 'https://github.com/guilledk/py-leap.git', rev = 'v0.1a32'} toml = '^0.10.2' msgspec = "^0.19.0" +numpy = "<2.1" +gguf = "^0.14.0" +protobuf = "^5.29.3" +zstandard = "^0.23.0" +diskcache = "^5.6.3" +bitsandbytes = "^0.45.0" +hqq = "^0.2.2" +optimum-quanto = "^0.2.6" +basicsr = "^1.4.2" +realesrgan = "^0.3.0" [tool.poetry.group.frontend] optional = true @@ -39,26 +49,24 @@ pytest-trio = "^0.8.0" optional = true [tool.poetry.group.cuda.dependencies] -torch = {version = '2.0.1+cu118', source = 'torch'} -scipy = {version = '^1.11.2'} -numba = {version = '0.57.0'} +torch = {version = '2.5.1+cu121', source = 'torch'} +scipy = {version = '1.15.1'} +numba = {version = '0.60.0'} quart = {version = '^0.19.3'} -triton = {version = '2.0.0', source = 'torch'} -basicsr = {version = '^1.4.2'} -xformers = {version = '^0.0.22'} +triton = {version = '3.1.0', source = 'torch'} +xformers = {version = '^0.0.29'} hypercorn = {version = '^0.14.4'} -diffusers = {version = '^0.21.2'} -realesrgan = {version = '^0.3.0'} +diffusers = {version = '0.32.1'} quart-trio = {version = '^0.11.0'} -torchvision = {version = '0.15.2+cu118', source = 'torch'} -accelerate = {version = '^0.23.0'} -transformers = {version = '^4.33.2'} -huggingface-hub = {version = '^0.17.3'} +torchvision = {version = '0.20.1+cu121', source = 'torch'} +accelerate = {version = '0.34.0'} +transformers = {version = '4.48.0'} +huggingface-hub = {version = '^0.27.1'} invisible-watermark = {version = '^0.2.0'} [[tool.poetry.source]] name = 'torch' -url = 'https://download.pytorch.org/whl/cu118' +url = 'https://download.pytorch.org/whl/cu121' priority = 'explicit' [build-system] diff --git a/skynet/cli.py b/skynet/cli.py index 9da80de..3adefdb 100755 --- a/skynet/cli.py +++ b/skynet/cli.py @@ -8,7 +8,7 @@ from functools import partial import click -from leap.sugar import Name, asset_from_str +from leap.protocol import Name, Asset from .config import * from .constants import * @@ -178,7 +178,7 @@ def enqueue( 'user': Name(account), 'request_body': req, 'binary_data': binary, - 'reward': asset_from_str(reward), + 'reward': Asset.from_str(reward), 'min_verification': 1 }, account, key, permission, diff --git a/skynet/constants.py b/skynet/constants.py index a4a7bde..32adbe0 100755 --- a/skynet/constants.py +++ b/skynet/constants.py @@ -78,8 +78,20 @@ MODELS: dict[str, ModelDesc] = { 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-inpainting', + short='stablexl-inpaint', mem=8.3, size=Size(w=1024, h=1024), tags=['inpaint'] diff --git a/skynet/dgpu/compute.py b/skynet/dgpu/compute.py index 1393073..4523d48 100644 --- a/skynet/dgpu/compute.py +++ b/skynet/dgpu/compute.py @@ -18,7 +18,6 @@ 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, mode: str, @@ -35,7 +34,11 @@ def prepare_params_for_diffuse( _params['image'] = image _params['mask_image'] = mask - _params['strength'] = float(params['strength']) + + if 'flux' in params['model'].lower(): + _params['max_sequence_length'] = 512 + else: + _params['strength'] = float(params['strength']) case 'img2img': image = crop_image( @@ -66,8 +69,6 @@ def prepare_params_for_diffuse( class SkynetMM: def __init__(self, config: dict): - self.upscaler = init_upscaler() - self.cache_dir = None if 'hf_home' in config: self.cache_dir = config['hf_home'] @@ -88,30 +89,28 @@ class SkynetMM: return False - def load_model( - self, - name: str, - mode: str - ): - logging.info(f'loading model {name}...') - self._model_mode = mode - self._model_name = name - + def unload_model(self): if getattr(self, '_model', None): del self._model gc.collect() torch.cuda.empty_cache() + self._model_name = '' + self._model_mode = '' + + 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 - def get_model(self, name: str, mode: str) -> DiffusionPipeline: - if name not in MODELS: - raise DGPUComputeError(f'Unknown model {model_name}') - - if not self.is_model_loaded(name, mode): - self.load_model(name, mode) def compute_one( self, @@ -127,6 +126,8 @@ class SkynetMM: logging.warn(f'cancelling work at step {step}') raise DGPUInferenceCancelled() + return {} + maybe_cancel_work(0) output_type = 'png' @@ -136,23 +137,29 @@ class SkynetMM: output = None output_hash = None try: + name = params['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 - self.get_model( - params['model'], - method - ) + + 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] @@ -161,7 +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) @@ -173,6 +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') diff --git a/skynet/dgpu/daemon.py b/skynet/dgpu/daemon.py index 005c2a1..d4fecf3 100644 --- a/skynet/dgpu/daemon.py +++ b/skynet/dgpu/daemon.py @@ -125,7 +125,7 @@ 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}') return False @@ -143,11 +143,17 @@ class SkynetDGPUDaemon: statuses = self._snap['requests'][rid] if len(statuses) == 0: - inputs = [ - await self.conn.get_input_data(_input) - for _input in req['binary_data'].split(',') - if _input - ] + 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']) diff --git a/skynet/dgpu/network.py b/skynet/dgpu/network.py index 1e8315d..b40a465 100644 --- a/skynet/dgpu/network.py +++ b/skynet/dgpu/network.py @@ -15,7 +15,7 @@ import anyio from PIL import Image, UnidentifiedImageError from leap.cleos import CLEOS -from leap.sugar import Checksum256, Name, asset_from_str +from leap.protocol import Asset from skynet.constants import DEFAULT_IPFS_DOMAIN from skynet.ipfs import AsyncIPFSHTTP, get_ipfs_file @@ -24,6 +24,225 @@ from skynet.dgpu.errors import DGPUComputeError REQUEST_UPDATE_TIME = 3 +gpu_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": [] +} + + 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 ) diff --git a/skynet/dgpu/pipes/flux.py b/skynet/dgpu/pipes/flux.py new file mode 100644 index 0000000..57642c0 --- /dev/null +++ b/skynet/dgpu/pipes/flux.py @@ -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 diff --git a/skynet/dgpu/pipes/flux_inpaint.py b/skynet/dgpu/pipes/flux_inpaint.py new file mode 100644 index 0000000..ba0b9e7 --- /dev/null +++ b/skynet/dgpu/pipes/flux_inpaint.py @@ -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 diff --git a/skynet/utils.py b/skynet/utils.py index 0ef9c09..0662aca 100755 --- a/skynet/utils.py +++ b/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)