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--- |
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library_name: diffusers |
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--- |
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Pipeline generated with |
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```python |
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import torch |
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from diffusers import AutoencoderKL, FluxTransformer2DModel, FlowMatchEulerDiscreteScheduler, FluxPipeline |
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from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTextModel,T5EncoderModel, CLIPTokenizer, AutoTokenizer |
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def get_dummy_components(): |
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torch.manual_seed(0) |
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transformer = FluxTransformer2DModel( |
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patch_size=1, |
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in_channels=4, |
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num_layers=1, |
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num_single_layers=1, |
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attention_head_dim=16, |
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num_attention_heads=2, |
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joint_attention_dim=32, |
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pooled_projection_dim=32, |
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axes_dims_rope=[4, 4, 8], |
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) |
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clip_text_encoder_config = CLIPTextConfig( |
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bos_token_id=0, |
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eos_token_id=2, |
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hidden_size=32, |
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intermediate_size=37, |
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layer_norm_eps=1e-05, |
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num_attention_heads=4, |
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num_hidden_layers=5, |
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pad_token_id=1, |
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vocab_size=1000, |
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hidden_act="gelu", |
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projection_dim=32, |
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) |
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torch.manual_seed(0) |
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text_encoder = CLIPTextModel(clip_text_encoder_config) |
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torch.manual_seed(0) |
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text_encoder_2 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5") |
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tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") |
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tokenizer_2 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") |
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torch.manual_seed(0) |
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vae = AutoencoderKL( |
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sample_size=32, |
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in_channels=3, |
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out_channels=3, |
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block_out_channels=(4,), |
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layers_per_block=1, |
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latent_channels=1, |
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norm_num_groups=1, |
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use_quant_conv=False, |
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use_post_quant_conv=False, |
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shift_factor=0.0609, |
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scaling_factor=1.5035, |
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) |
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scheduler = FlowMatchEulerDiscreteScheduler() |
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return { |
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"scheduler": scheduler, |
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"text_encoder": text_encoder, |
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"text_encoder_2": text_encoder_2, |
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"tokenizer": tokenizer, |
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"tokenizer_2": tokenizer_2, |
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"transformer": transformer, |
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"vae": vae, |
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} |
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if __name__ == "__main__": |
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components = get_dummy_components() |
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pipeline = FluxPipeline(**components) |
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pipeline.push_to_hub("hf-internal-testing/tiny-flux-pipe") |
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``` |
