magiv2-crop-embedder

65
1
1 language
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by
ragavsachdeva
Code Model
OTHER
New
65 downloads
Early-stage
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Quick Summary

AI model with specialized capabilities.

Code Examples

Usagepythontransformers
from transformers import AutoModel
import PIL
import torch

batch_of_images = [PIL.Image.open("image1.jpg"), PIL.Image.open("image2.jpg")]
model = AutoModel.from_pretrained("ragavsachdeva/magiv2-crop-embedder", trust_remote_code=True).cuda().eval()
with torch.no_grad():
    embeddings = model(batch_of_images)

print(embeddings.shape)
Usagepythontransformers
from transformers import AutoModel
import PIL
import torch

batch_of_images = [PIL.Image.open("image1.jpg"), PIL.Image.open("image2.jpg")]
model = AutoModel.from_pretrained("ragavsachdeva/magiv2-crop-embedder", trust_remote_code=True).cuda().eval()
with torch.no_grad():
    embeddings = model(batch_of_images)

print(embeddings.shape)
Usagepythontransformers
from transformers import AutoModel
import PIL
import torch

batch_of_images = [PIL.Image.open("image1.jpg"), PIL.Image.open("image2.jpg")]
model = AutoModel.from_pretrained("ragavsachdeva/magiv2-crop-embedder", trust_remote_code=True).cuda().eval()
with torch.no_grad():
    embeddings = model(batch_of_images)

print(embeddings.shape)
Usagepythontransformers
from transformers import AutoModel
import PIL
import torch

batch_of_images = [PIL.Image.open("image1.jpg"), PIL.Image.open("image2.jpg")]
model = AutoModel.from_pretrained("ragavsachdeva/magiv2-crop-embedder", trust_remote_code=True).cuda().eval()
with torch.no_grad():
    embeddings = model(batch_of_images)

print(embeddings.shape)
Usagepythontransformers
from transformers import AutoModel
import PIL
import torch

batch_of_images = [PIL.Image.open("image1.jpg"), PIL.Image.open("image2.jpg")]
model = AutoModel.from_pretrained("ragavsachdeva/magiv2-crop-embedder", trust_remote_code=True).cuda().eval()
with torch.no_grad():
    embeddings = model(batch_of_images)

print(embeddings.shape)
Usagepythontransformers
from transformers import AutoModel
import PIL
import torch

batch_of_images = [PIL.Image.open("image1.jpg"), PIL.Image.open("image2.jpg")]
model = AutoModel.from_pretrained("ragavsachdeva/magiv2-crop-embedder", trust_remote_code=True).cuda().eval()
with torch.no_grad():
    embeddings = model(batch_of_images)

print(embeddings.shape)
Usagepythontransformers
from transformers import AutoModel
import PIL
import torch

batch_of_images = [PIL.Image.open("image1.jpg"), PIL.Image.open("image2.jpg")]
model = AutoModel.from_pretrained("ragavsachdeva/magiv2-crop-embedder", trust_remote_code=True).cuda().eval()
with torch.no_grad():
    embeddings = model(batch_of_images)

print(embeddings.shape)
Usagepythontransformers
from transformers import AutoModel
import PIL
import torch

batch_of_images = [PIL.Image.open("image1.jpg"), PIL.Image.open("image2.jpg")]
model = AutoModel.from_pretrained("ragavsachdeva/magiv2-crop-embedder", trust_remote_code=True).cuda().eval()
with torch.no_grad():
    embeddings = model(batch_of_images)

print(embeddings.shape)

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