initial commit

app.py

@@ -0,0 +1,115 @@
+# %%
+import gradio.components as gc
+import gradio as gr
+
+import numpy as np
+import pandas as pd
+import torch
+from PIL import Image
+from transformers import CLIPModel, CLIPProcessor
+device = 'cpu'
+torch.no_grad().__enter__()
+torch.autocast('cuda').__enter__()
+
+# %%
+
+t = pd.read_pickle("clip_texts_1_fp16.pkl")
+words = t.reset_index().word
+wordsv = torch.tensor(t.values).to(device)
+
+# %%
+
+# %%
+model_name = "openai/clip-vit-large-patch14"
+mmm = CLIPModel.from_pretrained(model_name)
+mmm.eval()
+mmm.to(device)
+
+processor = CLIPProcessor.from_pretrained(model_name)
+
+# %%
+
+
+def slerp(t, v0, v1, DOT_THRESHOLD=0.9995):
+    """ helper function to spherically interpolate two arrays v1 v2 """
+    inputs_are_torch = False
+    if not isinstance(v0, np.ndarray):
+        inputs_are_torch = True
+        input_device = v0.device
+        v0 = v0.cpu().numpy()
+        v1 = v1.cpu().numpy()
+
+    dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
+    if np.abs(dot) > DOT_THRESHOLD:
+        v2 = (1 - t) * v0 + t * v1
+    else:
+        theta_0 = np.arccos(dot)
+        sin_theta_0 = np.sin(theta_0)
+        theta_t = theta_0 * t
+        sin_theta_t = np.sin(theta_t)
+        s0 = np.sin(theta_0 - theta_t) / sin_theta_0
+        s1 = sin_theta_t / sin_theta_0
+        v2 = s0 * v0 + s1 * v1
+
+    if inputs_are_torch:
+        v2 = torch.from_numpy(v2).to(input_device)
+
+    return v2
+
+
+def query(text: str, img: Image.Image, limit: int, score_threshold: float, slerp_degree: float):
+    if text != '':
+        inp = processor(text=text, return_tensors='pt').to(device)
+        rout = mmm.get_text_features(**inp)
+        tout = rout.detach().cpu().numpy()[0]
+        out = tout
+
+    if img is not None:
+        inp = processor(images=[img], return_tensors="pt",).to(device)
+        rout = mmm.get_image_features(**inp)
+        iout = rout.detach().cpu().numpy()[0]
+        out = iout
+
+    if text != '' and img is not None:
+        out = slerp(slerp_degree, tout, iout)
+
+    if out is not None:
+        # calculate cosine similarity
+        scores = np.dot(out, wordsv.T)
+        # sort by score
+        topk = (
+            pd.concat(
+                [words, pd.Series(scores, name='score')],
+                axis=1
+            )
+            .sort_values('score', ascending=False)
+            .query(f'score > {score_threshold}')
+            .head(limit)
+        )
+
+        topwords = "\n".join(
+            f'{word}: {score:.2f} '
+            for _, word, score in topk.itertuples()
+        )
+
+        return topwords
+
+
+searchtext = gc.Textbox(lines=2, placeholder="Search text")
+searchimage = gc.Image(shape=(224, 224), label="Search image", type='pil')
+inp_limit = gc.Slider(1, 50, 10, step=1, label='Limit')
+score_threshold = gc.Slider(0, 30, 0, step=.5, label='Score threshold')
+slerp_degree = gc.Slider(
+    0, 1, 0.5, step=.01, label='Slerp degree (if both text and image are provided)\nFinds a midpoint between image and text embeddings')
+
+
+dsurl = 'https://www.kaggle.com/datasets/yk1598/479k-english-words'
+gr.Interface(
+    query,
+    [searchtext, searchimage, inp_limit, score_threshold, slerp_degree],
+    [gc.Textbox(label='Top words')],
+    title="Initial Token Finder for Textual Inversion",
+    description=f"find the closest single token word for a given text and/or image.\nbased on {model_name}.\n\nData: {dsurl}",
+    analytics_enabled=False,
+    allow_flagging='never',
+).launch()

clip_texts_1_fp16.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bbcc5d3d464979b764c0b8a69a58f28f5bf941bf10b3501b513d3b28fcb17876
+size 39828901