thanks to ai-forever ❤

Kandinsky-2-0-inpainting.pt

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Kandinsky-2-0.pt

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README.md

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+---
+license: apache-2.0
+tags:
+- Kandinsky
+- text-image
+- text2image
+- diffusion
+- latent diffusion
+- mCLIP-XLMR
+- mT5
+---
+
+# Kandinsky 2.0
+
+
+Kandinsky 2.0 — the first multilingual text2image model.
+
+[Open In Colab](https://colab.research.google.com/drive/1uPg9KwGZ2hJBl9taGA_3kyKGw12Rh3ij?usp=sharing)
+
+[GitHub repository](https://github.com/ai-forever/Kandinsky-2.0)
+
+[Habr post](https://habr.com/ru/company/sberbank/blog/701162/)
+
+[Demo](https://rudalle.ru/)
+
+**UNet size: 1.2B parameters**
+
+![NatallE.png](https://s3.amazonaws.com/moonup/production/uploads/1669132577749-5f91b1208a61a359f44e1851.png)
+
+It is a latent diffusion model with two multi-lingual text encoders:
+* mCLIP-XLMR (560M parameters)
+* mT5-encoder-small (146M parameters)
+
+
+These encoders and multilingual training datasets unveil the real multilingual text2image generation experience!
+
+![header.png](https://s3.amazonaws.com/moonup/production/uploads/1669132825912-5f91b1208a61a359f44e1851.png)
+
+# How to use
+
+```python
+pip install "git+https://github.com/ai-forever/Kandinsky-2.0.git"
+
+from kandinsky2 import get_kandinsky2
+model = get_kandinsky2('cuda', task_type='text2img')
+images = model.generate_text2img('кошка в космосе', batch_size=4, h=512, w=512, num_steps=75, denoised_type='dynamic_threshold', dynamic_threshold_v=99.5, sampler='ddim_sampler', ddim_eta=0.01, guidance_scale=10)
+```
+
+# Authors
+
++ Arseniy Shakhmatov: [Github](https://github.com/cene555), [Blog](https://t.me/gradientdip)
++ Anton Razzhigaev: [Github](https://github.com/razzant), [Blog](https://t.me/abstractDL)
++ Aleksandr Nikolich: [Github](https://github.com/AlexWortega), [Blog](https://t.me/lovedeathtransformers)
++ Vladimir Arkhipkin: [Github](https://github.com/oriBetelgeuse)
++ Igor Pavlov: [Github](https://github.com/boomb0om)
++ Andrey Kuznetsov: [Github](https://github.com/kuznetsoffandrey)
++ Denis Dimitrov: [Github](https://github.com/denndimitrov)
+
+
+

text_encoder1/config.json

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+{
+  "architectures": [
+    "XLMRobertaForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.17.0.dev0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

text_encoder1/pytorch_model.bin

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text_encoder1/sentencepiece.bpe.model

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text_encoder1/special_tokens_map.json

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text_encoder1/tokenizer_config.json

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text_encoder2/config.json

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+{
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+    "MT5ForConditionalGeneration"
+  ],
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+  "d_model": 512,
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+  "dropout_rate": 0.1,
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+  "is_encoder_decoder": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "mt5",
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+  "num_heads": 6,
+  "num_layers": 8,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "tokenizer_class": "T5Tokenizer",
+  "vocab_size": 250112
+}

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text_encoder2/spiece.model

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text_encoder2/tokenizer_config.json

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vae.ckpt

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