Create README.md

README.md

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+
+how to use
+
+import torch
+from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
+from diffusers import AutoencoderDC
+from torchvision.utils import save_image
+import numpy as np
+import h5py
+
+
+
+dc_encoder=AutoencoderDC.from_pretrained("mit-han-lab/dc-ae-f64c128-in-1.0-diffusers",  torch_dtype=torch.float32).to('cuda')
+
+
+
+
+with h5py.File('latent_folder/image_latents.hdf5', 'r', libver='latest', swmr=True) as f:
+    print(list(f.keys()))
+    dataset = f['image_latents'][:]
+
+
+
+latents=np.expand_dims(dataset[5]*35, axis=0)
+latents=torch.from_numpy(latents)
+latents=latents.float()
+latents=latents.to('cuda')
+
+
+y = dc_encoder.decode(latents).sample
+
+save_image(y * 0.5 + 0.5, "demo_dc_ae.png")
+
+
+
+
+import clip
+model, _ = clip.load("ViT-L/14")
+
+model.to('cuda')
+
+
+def encode_text(label, model, device):
+    text_tokens = clip.tokenize(label, truncate=True).to(device)
+    text_encoding = model.encode_text(text_tokens)
+    return text_encoding.cpu()
+
+
+import h5py
+
+# Open the file
+f = h5py.File('latent_folder/text_encodings.hdf5', 'r')
+
+# View the contents
+print(list(f.keys()))
+
+# Access specific datasets
+dataset = f['text_encodings'][:]
+
+# Close the file when done
+f.close()
+
+
+
+def cosine_similarity(v1, v2):
+    # Reshape v1 to match v2's dimensions if needed
+    v1 = v1.reshape(-1)  # converts (1,768) to (768,)
+    
+    # Calculate cosine similarity
+    dot_product = np.dot(v1, v2)
+    norm_v1 = np.linalg.norm(v1)
+    norm_v2 = np.linalg.norm(v2)
+    
+    return dot_product / (norm_v1 * norm_v2)
+
+
+
+textembed=encode_text("""The double-cut pork chop at The Ainsworth in""" , model,'cuda')
+
+
+cosine_similarity(textembed.cpu().detach().numpy() , dataset[5])
+
+
+
+dataset link https://huggingface.co/datasets/zzliang/GRIT
+