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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,45 +1,57 @@ | ||
| import os | ||
| from transformers import AutoTokenizer, T5EncoderModel | ||
| from diffusers import AutoencoderKLHunyuanVideo | ||
|
|
||
| import torch | ||
| from diffusers import AutoencoderKLHunyuanVideo | ||
| from transformers import AutoTokenizer, T5EncoderModel | ||
|
|
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| init_dict = { | ||
| "in_channels": 3, | ||
| "out_channels": 3, | ||
| "latent_channels": 4, | ||
| "down_block_types": ( | ||
| "HunyuanVideoDownBlock3D", | ||
| "HunyuanVideoDownBlock3D", | ||
| "HunyuanVideoDownBlock3D", | ||
| "HunyuanVideoDownBlock3D", | ||
| ), | ||
| "up_block_types": ( | ||
| "HunyuanVideoUpBlock3D", | ||
| "HunyuanVideoUpBlock3D", | ||
| "HunyuanVideoUpBlock3D", | ||
| "HunyuanVideoUpBlock3D", | ||
| ), | ||
| "block_out_channels": (8, 8, 8, 8), | ||
| "layers_per_block": 1, | ||
| "act_fn": "silu", | ||
| "norm_num_groups": 4, | ||
| "scaling_factor": 0.476986, | ||
| "spatial_compression_ratio": 8, | ||
| "temporal_compression_ratio": 4, | ||
| "mid_block_add_attention": True, | ||
| } | ||
| init_dict = { | ||
| "in_channels": | ||
| 3, | ||
| "out_channels": | ||
| 3, | ||
| "latent_channels": | ||
| 4, | ||
| "down_block_types": ( | ||
| "HunyuanVideoDownBlock3D", | ||
| "HunyuanVideoDownBlock3D", | ||
| "HunyuanVideoDownBlock3D", | ||
| "HunyuanVideoDownBlock3D", | ||
| ), | ||
| "up_block_types": ( | ||
| "HunyuanVideoUpBlock3D", | ||
| "HunyuanVideoUpBlock3D", | ||
| "HunyuanVideoUpBlock3D", | ||
| "HunyuanVideoUpBlock3D", | ||
| ), | ||
| "block_out_channels": (8, 8, 8, 8), | ||
| "layers_per_block": | ||
| 1, | ||
| "act_fn": | ||
| "silu", | ||
| "norm_num_groups": | ||
| 4, | ||
| "scaling_factor": | ||
| 0.476986, | ||
| "spatial_compression_ratio": | ||
| 8, | ||
| "temporal_compression_ratio": | ||
| 4, | ||
| "mid_block_add_attention": | ||
| True, | ||
| } | ||
| os.environ["HF_HUB_DISABLE_PROGRESS_BARS"] = "1" | ||
| text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5") | ||
| text_encoder = T5EncoderModel.from_pretrained( | ||
| "hf-internal-testing/tiny-random-t5") | ||
| tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") | ||
|
|
||
| model = AutoencoderKLHunyuanVideo(**init_dict) | ||
|
|
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| input_tensor = torch.rand(1, 3, 9, 16, 16) | ||
|
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| vae_encoder_output = model.encoder(input_tensor) | ||
| vae_encoder_output = model.encoder(input_tensor) | ||
|
|
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| # vae_decoder_output = model.decoder(vae_encoder_output) | ||
|
|
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| assert vae_encoder_output.shape == (1,8,3,2,2) | ||
| assert vae_encoder_output.shape == (1, 8, 3, 2, 2) | ||
|
|
||
| # print(vae_decoder_output.shape) | ||
| # print(vae_decoder_output.shape) |