Xl version of generate-allid-embeddings.py

generate-allid-embeddingsXL.py

@@ -0,0 +1,108 @@
+#!/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)
+
+