File size: 3,226 Bytes
4df5f4d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 |
#!/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, 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)
print("getting embeddings2")
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)
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.tempXL.allids.safetensors"
print(f"Saving the calculatiuons to {outputfile}...")
save_file({"embeddings": embs}, outputfile)
|