generate-dict-embeddingsXL.py

#!/bin/env python

"""
    (SDXL counterpart of "cliptextmodel-generate-embeddings.py".
     Not following that name, because we dont use "cliptextmodel")

    Take filenames of an SDXL clip-g type text_encoder2 and config file
    Read in a wordlist from "dictionary"
    Generate the official "embedding" tensor for each one.
    Save the result set to "{outputfile}"

    Defaults to loading openai/clip-vit-large-patch14 from huggingface hub,
    for purposes of tokenizer, since thats what sdxl does anyway
    
    RULES of the loader:
    1. The text_encoder2 model file must appear to be either 
      in current directory or one down. So, do NOT use
        badpath1=some/directory/tree/file.here
        badpath2=/absolutepath
    2. Yes, you MUST have a matching config.json file
    3. if you have no safetensor 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

    Alternatively, 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
    
"""


outputfile="embeddingsXL.temp.safetensors"

import sys
import torch
from safetensors.torch import save_file
from transformers import CLIPProcessor, CLIPTextModel, CLIPTextModelWithProjection

processor=None


tmodel2=None
model_path2=None
model_config2=None

if len(sys.argv) == 3:
    model_path2=sys.argv[1]
    model_config2=sys.argv[2]
else:
    print("You have to give name of modelfile and config file")
    sys.exit(1)

device=torch.device("cuda")



def initXLCLIPmodel(model_path,model_config):
    global tmodel2,processor
    # yes, oddly they all uses the same one, basically
    processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")

    print("loading",model_path)
    tmodel2 = CLIPTextModelWithProjection.from_pretrained(model_path,config=model_config,local_files_only=True,use_safetensors=True)
    tmodel2.to(device)


def embed_from_text2(text):
    global processor,tmodel2
    inputs = processor(text=text, return_tensors="pt")
    inputs.to(device)

    with torch.no_grad():
        outputs = tmodel2(**inputs)
        embeddings = outputs.text_embeds
    return embeddings


# "inputs" == magic pre-embedding format
def embed_from_inputs(inputs):
    global processor,tmodel2
    with torch.no_grad():
        outputs = tmodel2(**inputs)
        embedding = outputs.text_embeds

    return embedding


initXLCLIPmodel(model_path2,model_config2)
inputs = processor(text="dummy", return_tensors="pt")
inputs.to(device)


with open("dictionary","r") as f:
    tokendict = f.readlines()
    tokendict = [token.strip() for token in tokendict]  # Remove trailing newlines


count=1
all_embeddings = []

for word in tokendict:
    emb = embed_from_text2(word)
    emb=emb.unsqueeze(0) # stupid matrix magic to make the cat work
    all_embeddings.append(emb)
    count+=1
    if (count %100) ==0:
        print(count)



"""
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)

print(f"Saving the calculatiuons to {outputfile}...")
save_file({"embeddings": embs}, outputfile)