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