add tokenizer tester script

scripts/test_tokenizer.py

@@ -0,0 +1,22 @@
+import json
+from transformers import CLIPTokenizer
+
+tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
+
+text = "αβ, Γ γ, Δ δ, Ε ε, Ζ ζ, Η η, Θ θ, Ι ι, Κ κ, Λ λ, Μ μ, Ν ν, Ξ ξ, Ο ο, Π π, Ρ ρ, Σ σ/ς, Τ τ, Υ υ, Φ φ, Χ χ, Ψ ψ, Ω ω."
+
+token_dict = json.load(open("SD15/tokenizer/vocab.json", "r"))
+# reverse key and value in token_dict because the token ids are value in vocab.json
+token_dict = {v: k for k, v in token_dict.items()}
+
+
+tokens = tokenizer(text, truncation=False, padding="max_length", return_tensors="pt").input_ids
+
+tokens = tokens.tolist()[0]
+tokens = [t for t in tokens if t not in [49406, 49407]] #remove start/end/pad tokens
+tokens2 = [token_dict[t] for t in tokens]
+print(f"text: {text}")
+print(f"token ids:{tokens}")
+print(f"tokens: {tokens2}")
+print(f"length (special tokens removed, max 75): {len(tokens)}, over limit: {len(tokens) > 75}")
+

scripts/txt2img.py

@@ -24,9 +24,6 @@ from diffusers import StableDiffusionPipeline, AutoencoderKL, UNet2DConditionMod
 from torch.cuda.amp import autocast
 from transformers import CLIPTextModel, CLIPTokenizer
 
-
-# from diffusers.models import AttentionBlock
-
 def __generate_sample(pipe: StableDiffusionPipeline, prompt: str, cfg: float, height: int, width: int, gen,
                       steps: int = 30, batch_size: int = 1):
     """