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https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
remove dependency on original clipseg library
- This replaces the original clipseg library with the transformers version from HuggingFace. - This should make it possible to register InvokeAI at PyPi and do a fully automated pip-based install. - Minor regression: it is no longer possible to specify which device the clipseg model will be loaded into, and it will reside in CPU. However, performance is more than acceptable.
This commit is contained in:
Lincoln Stein
parent
eb2ca4970b
commit
288e31fc60
@ -30,7 +30,7 @@ from huggingface_hub.utils._errors import RevisionNotFoundError
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from omegaconf import OmegaConf
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from omegaconf.dictconfig import DictConfig
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from tqdm import tqdm
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from transformers import CLIPTokenizer, CLIPTextModel
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from transformers import CLIPTokenizer, CLIPTextModel, AutoProcessor, CLIPSegForImageSegmentation
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from ldm.invoke.globals import Globals, global_cache_dir
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from ldm.invoke.readline import generic_completer
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@ -601,31 +601,10 @@ def download_codeformer():
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#---------------------------------------------
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def download_clipseg():
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print('Installing clipseg model for text-based masking...',end='', file=sys.stderr)
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import zipfile
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CLIPSEG_MODEL = 'CIDAS/clipseg-rd64-refined'
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try:
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model_url = 'https://owncloud.gwdg.de/index.php/s/ioHbRzFx6th32hn/download'
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model_dest = os.path.join(Globals.root,'models/clipseg/clipseg_weights')
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weights_zip = 'models/clipseg/weights.zip'
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if not os.path.exists(model_dest):
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os.makedirs(os.path.dirname(model_dest), exist_ok=True)
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if not os.path.exists(f'{model_dest}/rd64-uni-refined.pth'):
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dest = os.path.join(Globals.root,weights_zip)
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request.urlretrieve(model_url,dest)
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with zipfile.ZipFile(dest,'r') as zip:
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zip.extractall(os.path.join(Globals.root,'models/clipseg'))
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os.remove(dest)
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from clipseg.clipseg import CLIPDensePredT
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model = CLIPDensePredT(version='ViT-B/16', reduce_dim=64, )
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model.eval()
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model.load_state_dict(
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torch.load(
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os.path.join(Globals.root,'models/clipseg/clipseg_weights/rd64-uni-refined.pth'),
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map_location=torch.device('cpu')
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),
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strict=False,
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)
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download_from_hf(AutoProcessor,CLIPSEG_MODEL)
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download_from_hf(CLIPSegForImageSegmentation,CLIPSEG_MODEL)
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except Exception:
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print('Error installing clipseg model:')
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print(traceback.format_exc())
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@ -71,7 +71,8 @@ def global_cache_dir(subdir:Union[str,Path]='')->Path:
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is provided, it will be appended to the end of the path, allowing
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for huggingface-style conventions:
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global_cache_dir('diffusers')
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global_cache_dir('transformers')
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global_cache_dir('hub')
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global_cache_dir('transformers') # not used?
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'''
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home: str = os.getenv('HF_HOME')
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@ -29,16 +29,12 @@ work fine.
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import torch
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import numpy as np
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import os
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from clipseg.clipseg import CLIPDensePredT
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from einops import rearrange, repeat
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from transformers import AutoProcessor, CLIPSegForImageSegmentation
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from PIL import Image, ImageOps
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from torchvision import transforms
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from ldm.invoke.globals import Globals
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from ldm.invoke.globals import global_cache_dir
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CLIP_VERSION = 'ViT-B/16'
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CLIPSEG_WEIGHTS = 'models/clipseg/clipseg_weights/rd64-uni.pth'
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CLIPSEG_WEIGHTS_REFINED = 'models/clipseg/clipseg_weights/rd64-uni-refined.pth'
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CLIPSEG_MODEL = 'CIDAS/clipseg-rd64-refined'
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CLIPSEG_SIZE = 352
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class SegmentedGrayscale(object):
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@ -77,16 +73,15 @@ class Txt2Mask(object):
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'''
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def __init__(self,device='cpu',refined=False):
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print('>> Initializing clipseg model for text to mask inference')
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# BUG: we are not doing anything with the device option at this time
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self.device = device
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self.model = CLIPDensePredT(version=CLIP_VERSION, reduce_dim=64, complex_trans_conv=refined)
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self.model.eval()
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# initially we keep everything in cpu to conserve space
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self.model.to('cpu')
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self.model.load_state_dict(torch.load(os.path.join(Globals.root,CLIPSEG_WEIGHTS_REFINED)
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if refined
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else os.path.join(Globals.root,CLIPSEG_WEIGHTS),
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map_location=torch.device('cpu')), strict=False
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)
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self.processor = AutoProcessor.from_pretrained(CLIPSEG_MODEL,
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cache_dir=global_cache_dir('hub')
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)
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self.model = CLIPSegForImageSegmentation.from_pretrained(CLIPSEG_MODEL,
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cache_dir=global_cache_dir('hub')
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)
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@torch.no_grad()
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def segment(self, image, prompt:str) -> SegmentedGrayscale:
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@ -95,9 +90,6 @@ class Txt2Mask(object):
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provided image and returns a SegmentedGrayscale object in which the brighter
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pixels indicate where the object is inferred to be.
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'''
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self._to_device(self.device)
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prompts = [prompt] # right now we operate on just a single prompt at a time
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transform = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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@ -111,14 +103,15 @@ class Txt2Mask(object):
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img = self._scale_and_crop(image)
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img = transform(img).unsqueeze(0)
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preds = self.model(img.repeat(len(prompts),1,1,1), prompts)[0]
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heatmap = torch.sigmoid(preds[0][0]).cpu()
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self._to_device('cpu')
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inputs = self.processor(text=[prompt],
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images=[image],
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padding=True,
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return_tensors='pt')
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outputs = self.model(**inputs)
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preds = outputs.logits
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heatmap = torch.sigmoid(preds)
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return SegmentedGrayscale(image, heatmap)
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def _to_device(self, device):
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self.model.to(device)
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def _scale_and_crop(self, image:Image)->Image:
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scaled_image = Image.new('RGB',(CLIPSEG_SIZE,CLIPSEG_SIZE))
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if image.width > image.height: # width is constraint
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@ -36,8 +36,7 @@ classifiers = [
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dependencies = [
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"accelerate",
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"albumentations",
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"clip_anytorch", # replaceing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
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"clipseg @ https://github.com/invoke-ai/clipseg/archive/relaxed-python-requirement.zip", # is this still necesarry with diffusers?
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"clip_anytorch", # replacing "clip @ https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip",
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"datasets",
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"diffusers[torch]~=0.11",
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"dnspython==2.2.1",
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@ -53,7 +52,7 @@ dependencies = [
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"huggingface-hub>=0.11.1",
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"imageio",
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"imageio-ffmpeg",
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"k-diffusion", # replaceing "k-diffusion @ https://github.com/Birch-san/k-diffusion/archive/refs/heads/mps.zip",
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"k-diffusion", # replacing "k-diffusion @ https://github.com/Birch-san/k-diffusion/archive/refs/heads/mps.zip",
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"kornia",
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"npyscreen",
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"numpy~=1.23",
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