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---
license: mit
base_model:
- stable-diffusion-v1-5/stable-diffusion-v1-5
language:
- hi
- bn
- as
- gu
- kn
- ml
- mr
- ne
- or
- pa
- sa
- ta
- te
- ur
- ks
- es
- fr
- ja
- zh
- tr
- de
- ar
- pt
- ru
- vi
- it
- ko
---
**Use with the Stable Diffusion Pipeline**
```python
import torch
from diffusers import StableDiffusionPipeline
from transformers import CLIPTokenizer, CLIPTextModel
device = "cuda" if torch.cuda.is_available() else "cpu"
lang = "hin_Deva" # Hindi
# Load pipeline
pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
# Load the multilingual tokenizer
tokenizer = CLIPTokenizer.from_pretrained("tokenizers/multilingual")
pipe.tokenizer = tokenizer
pipe.text_encoder.resize_token_embeddings(len(tokenizer))
# Load the fine-tuned text encoder
state_dict = torch.load(f"models/{lang}/{lang}_text_encoder.pth")
new_text_encoder = CLIPTextModel(config=pipe.text_encoder.config)
new_text_encoder.load_state_dict(state_dict)
new_text_encoder = new_text_encoder.to(device)
pipe.text_encoder = new_text_encoder
pipe = pipe.to(device)
# Generate and save image
caption = "गाँव का शांतिपूर्ण दृश्य|"
image = pipe(caption).images[0]
image.save(f"example.png") |