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#!/bin/env python
"""
Take a CLIPTextModel compatible text encoder.
Go through the official range of tokens IDs (0-49405)
Generate the official "embedding" tensor for each one.
Save the result set to "temp.allids.safetensors"
Defaults to loading openai/clip-vit-large-patch14 from huggingface hub.
However, can take optional pair of arguments to a .safetensor model, and config file
RULES of the loader:
1. the model file must appear to be either in current directory or one down. So,
badpath1=some/directory/tree/file.here
badpath2=/absolutepath
2. yes, you MUST have a matching config.json file
3. if you have no alternative, you can get away with using pytorch_model.bin
Sample location for such things that you can download:
https://huggingface.co/stablediffusionapi/edge-of-realism/tree/main/text_encoder/
If there is a .safetensors AND a .bin file, ignore the .bin file
You can also convert a singlefile model, such as is downloaded from civitai,
by using the utility at
https://github.com/huggingface/diffusers/blob/main/scripts/convert_original_stable_diffusion_to_diffusers.py
Args should look like
convert_original_stable_diffusion_to_diffusers.py --checkpoint_file somemodel.safetensors \
--dump_path extractdir --to_safetensors --from_safetensors
"""
import sys
import json
import torch
from safetensors.torch import save_file
from transformers import CLIPProcessor,CLIPModel,CLIPTextModel
clipsrc="openai/clip-vit-large-patch14"
processor=None
model=None
encfile=None
configfile=None
if len(sys.argv) == 3:
encfile=sys.argv[1]
configfile=sys.argv[2]
device=torch.device("cuda")
def init():
global processor
global model
global encfile
global configfile
# Load the processor and model
print("loading processor from "+clipsrc,file=sys.stderr)
processor = CLIPProcessor.from_pretrained(clipsrc)
print("done",file=sys.stderr)
# originally done this way. But its not the right one to use
#print("loading model from "+clipsrc,file=sys.stderr)
#model = CLIPModel.from_pretrained(clipsrc)
#print("done",file=sys.stderr)
if encfile != None:
print("loading model from "+encfile,file=sys.stderr)
model = CLIPTextModel.from_pretrained(
encfile,config=configfile,local_files_only=True,use_safetensors=True
)
else:
print("loading model from "+clipsrc,file=sys.stderr)
model = CLIPTextModel.from_pretrained(clipsrc)
print("done",file=sys.stderr)
model = model.to(device)
# "inputs" == magic pre-embedding format
def embed_from_inputs(inputs):
with torch.no_grad():
# This way is for CLIPModel
#text_features = model.get_text_features(**inputs)
#embedding = text_features[0]
outputs = model(**inputs)
embeddings = outputs.pooler_output
embedding = embeddings
return embedding
init()
inputs = processor(text="dummy", return_tensors="pt")
inputs.to(device)
all_embeddings = []
for id in range(49405):
inputs.input_ids[0][1]=id
emb=embed_from_inputs(inputs)
all_embeddings.append(emb)
if (id %100) ==0:
print(id)
embs = torch.cat(all_embeddings,dim=0)
print("Shape of result = ",embs.shape)
outputfile="cliptextmodel.temp.allids.safetensors"
print(f"Saving the calculatiuons to {outputfile}...")
save_file({"embeddings": embs}, outputfile)
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