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Functions153 in github.com/bytedance/UNO

⨍ Functions 153◇ Types & classes 41
↓ 12 callersMethod__init__
(self, dim: int)
uno/flux/modules/layers.py:91
↓ 7 callersMethod__init__
( self, resolution: int, in_channels: int, ch: int, ch_mult: list[int]
uno/flux/modules/autoencoder.py:125
↓ 6 callersFunctionattention
(q: Tensor, k: Tensor, v: Tensor, pe: Tensor)
uno/flux/math.py:21
↓ 6 callersFunctionextract_all_images
(images, model, datasetclass, device, batch_size=64, num_workers=8)
eval/evaluate_clip_dino_score_single_subject.py:109
↓ 6 callersFunctionextract_all_images
(images, model, datasetclass, device, batch_size=64, num_workers=8)
eval/evaluate_clip_dino_score_multi_subject.py:109
↓ 5 callersMethodencode
(self, x: Tensor)
uno/flux/modules/autoencoder.py:317
↓ 4 callersMethodoffload_model_to_cpu
(self, *models)
uno/flux/pipeline.py:306
↓ 4 callersFunctionswish
(x: Tensor)
uno/flux/modules/autoencoder.py:36
↓ 3 callersMethoddecode
(self, z: Tensor)
uno/flux/modules/autoencoder.py:322
↓ 3 callersFunctionget_schedule
( num_steps: int, image_seq_len: int, base_shift: float = 0.5, max_shift: float = 1.15, sh
uno/flux/sampling.py:191
↓ 3 callersFunctionprepare_multi_ip
( t5: HFEmbedder, clip: HFEmbedder, img: Tensor, prompt: str | list[str], ref_imgs: list[T
uno/flux/sampling.py:113
↓ 3 callersFunctionprint_load_warning
(missing: list[str], unexpected: list[str])
uno/flux/util.py:218
↓ 3 callersMethodset_processor
(self, processor)
uno/flux/modules/layers.py:300
↓ 2 callersFunctionbucket_images
(images: list[torch.Tensor], resolution: int = 512)
uno/dataset/uno.py:27
↓ 2 callersFunctiondenoise
( model: Flux, # model input img: Tensor, img_ids: Tensor, txt: Tensor, txt_ids: Tenso
uno/flux/sampling.py:210
↓ 2 callersFunctionget_noise
( num_samples: int, height: int, width: int, device: torch.device, dtype: torch.dtype,
uno/flux/sampling.py:28
↓ 2 callersFunctioninference
( batch: dict, model: "Flux", t5: "HFEmbedder", clip: "HFEmbedder", ae: "AutoEncoder", accelerator
train.py:56
↓ 2 callersFunctionload_ae
(name: str, device: str | torch.device = "cuda", hf_download: bool = True)
uno/flux/util.py:392
↓ 2 callersFunctionload_checkpoint
(local_path, repo_id, name)
uno/flux/util.py:55
↓ 2 callersFunctionload_clip
(device: str | torch.device = "cuda")
uno/flux/util.py:387
↓ 2 callersFunctionload_flow_model
(name: str, device: str | torch.device = "cuda", hf_download: bool = True)
uno/flux/util.py:233
↓ 2 callersFunctionload_t5
(device: str | torch.device = "cuda", max_length: int = 512)
uno/flux/util.py:382
↓ 2 callersFunctionpreprocess_ref
(raw_image: Image.Image, long_size: int = 512)
uno/flux/pipeline.py:70
↓ 2 callersMethodset_attn_processor
r""" Sets the attention processor to use to compute attention. Parameters: processor (`dict` of `AttentionProcessor` or o
uno/flux/model.py:115
↓ 2 callersFunctionset_lora
( model: Flux, lora_rank: int, double_blocks_indices: list[int] | None = None, single_blocks_i
uno/flux/util.py:324
↓ 2 callersFunctiontimestep_embedding
Create sinusoidal timestep embeddings. :param t: a 1-D Tensor of N indices, one per batch element. These may be fractio
uno/flux/modules/layers.py:43
↓ 2 callersFunctionunpack
(x: Tensor, height: int, width: int)
uno/flux/sampling.py:244
↓ 2 callersMethodupdate_model_with_lora
(self, checkpoint, lora_weight)
uno/flux/pipeline.py:156
↓ 1 callersMethod_transform_test
(self, n_px)
eval/evaluate_clip_dino_score_single_subject.py:50
↓ 1 callersMethod_transform_test
(self, n_px)
eval/evaluate_clip_dino_score_single_subject.py:76
↓ 1 callersMethod_transform_test
(self, n_px)
eval/evaluate_clip_dino_score_multi_subject.py:50
↓ 1 callersMethod_transform_test
(self, n_px)
eval/evaluate_clip_dino_score_multi_subject.py:76
↓ 1 callersFunctionapply_rope
(xq: Tensor, xk: Tensor, freqs_cis: Tensor)
uno/flux/math.py:40
↓ 1 callersMethodattention
(self, h_: Tensor)
uno/flux/modules/autoencoder.py:52
↓ 1 callersFunctionclipeval
(image_paths, prompts, model, device)
eval/evaluate_clip_dino_score_single_subject.py:153
↓ 1 callersFunctionclipeval
(image_paths, prompts, model, device)
eval/evaluate_clip_dino_score_multi_subject.py:153
↓ 1 callersFunctionclipeval_image
(image_paths, image_dir_ref, model, device)
eval/evaluate_clip_dino_score_single_subject.py:166
↓ 1 callersFunctionclipeval_image
(image_paths, image_dir_ref, model, device)
eval/evaluate_clip_dino_score_multi_subject.py:166
↓ 1 callersFunctioncreate_demo
( model_type: str, device: str = "cuda" if torch.cuda.is_available() else "cpu", offload: bool = F
app.py:52
↓ 1 callersFunctiondinoeval_image
(image_paths, image_dir_ref, model, device)
eval/evaluate_clip_dino_score_single_subject.py:181
↓ 1 callersFunctiondinoeval_image
(image_paths, image_dir_ref, model, device)
eval/evaluate_clip_dino_score_multi_subject.py:181
↓ 1 callersFunctionextract_all_captions
(captions, model, device, batch_size=256, num_workers=8, append=False)
eval/evaluate_clip_dino_score_single_subject.py:95
↓ 1 callersFunctionextract_all_captions
(captions, model, device, batch_size=256, num_workers=8, append=False)
eval/evaluate_clip_dino_score_multi_subject.py:95
↓ 1 callersFunctionfilter_and_convert_data
Filter JSON data based on score_final and convert to target format. Args: input_file (str): Path to input JSON file outp
uno/utils/filter_uno_1m_dataset.py:24
↓ 1 callersMethodforward
( self, prompt: str, width: int, height: int, guidance: float,
uno/flux/pipeline.py:244
↓ 1 callersFunctionget_clip_score
get standard image-text clipscore. images can either be: - a list of strings specifying filepaths for images - a precomputed, ordered
eval/evaluate_clip_dino_score_single_subject.py:128
↓ 1 callersFunctionget_clip_score
get standard image-text clipscore. images can either be: - a list of strings specifying filepaths for images - a precomputed, ordered
eval/evaluate_clip_dino_score_multi_subject.py:128
↓ 1 callersFunctionget_examples
(examples_dir: str = "assets/examples")
app.py:25
↓ 1 callersFunctionget_lin_function
( x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15 )
uno/flux/sampling.py:183
↓ 1 callersFunctionget_lora_rank
(checkpoint)
uno/flux/util.py:50
↓ 1 callersFunctionget_models
(name: str, device, offload: bool=False)
train.py:49
↓ 1 callersFunctionhorizontal_concat
(images)
inference.py:28
↓ 1 callersFunctioninfinite_dataloader
(dataloader)
train.py:328
↓ 1 callersFunctionload_flow_model_only_lora
( name: str, device: str | torch.device = "cuda", hf_download: bool = True, lora_rank: int = 1
uno/flux/util.py:256
↓ 1 callersFunctionload_from_repo_id
(repo_id, checkpoint_name)
uno/flux/util.py:228
↓ 1 callersFunctionload_model
(ckpt, device='cpu')
uno/flux/util.py:32
↓ 1 callersFunctionload_safetensors
(path)
uno/flux/util.py:43
↓ 1 callersFunctionmain
( args: TrainArgs, )
train.py:211
↓ 1 callersFunctionmain
(args: InferenceArgs)
inference.py:64
↓ 1 callersFunctionmain
Main function to parse arguments and run the filter.
uno/utils/filter_uno_1m_dataset.py:63
↓ 1 callersFunctionresume_from_checkpoint
( resume_from_checkpoint: str | None | Literal["latest"], project_dir: str, accelerator: Accelerat
train.py:107
↓ 1 callersFunctionrope
(pos: Tensor, dim: int, theta: int)
uno/flux/math.py:30
↓ 1 callersMethodset_processor
(self, processor)
uno/flux/modules/layers.py:403
↓ 1 callersFunctiontime_shift
(mu: float, sigma: float, t: Tensor)
uno/flux/sampling.py:179
FunctionConvert
(image)
eval/evaluate_clip_dino_score_single_subject.py:40
FunctionConvert
(image)
eval/evaluate_clip_dino_score_multi_subject.py:40
FunctionHWC3
(x)
uno/flux/util.py:85
Method__call__
( self, prompt: str, width: int = 512, height: int = 512, guidance: fl
uno/flux/pipeline.py:182
Method__call__
(self, attn, x, pe, **attention_kwargs)
uno/flux/modules/layers.py:128
Method__call__
(self, attn, x, pe, **attention_kwargs)
uno/flux/modules/layers.py:145
Method__call__
(self, attn, img, txt, vec, pe, **attention_kwargs)
uno/flux/modules/layers.py:233
Method__call__
(self, attn: nn.Module, x: Tensor, vec: Tensor, pe: Tensor, **attention_kwargs)
uno/flux/modules/layers.py:349
Method__getitem__
(self, idx)
eval/evaluate_clip_dino_score_single_subject.py:31
Method__getitem__
(self, idx)
eval/evaluate_clip_dino_score_single_subject.py:60
Method__getitem__
(self, idx)
eval/evaluate_clip_dino_score_single_subject.py:85
Method__getitem__
(self, idx)
eval/evaluate_clip_dino_score_multi_subject.py:31
Method__getitem__
(self, idx)
eval/evaluate_clip_dino_score_multi_subject.py:60
Method__getitem__
(self, idx)
eval/evaluate_clip_dino_score_multi_subject.py:85
Method__getitem__
(self, idx)
uno/dataset/uno.py:76
Method__init__
(self, data, append=False, prefix='A photo depicts')
eval/evaluate_clip_dino_score_single_subject.py:23
Method__init__
(self, data)
eval/evaluate_clip_dino_score_single_subject.py:45
Method__init__
(self, data)
eval/evaluate_clip_dino_score_single_subject.py:71
Method__init__
(self, data, append=False, prefix='A photo depicts')
eval/evaluate_clip_dino_score_multi_subject.py:23
Method__init__
(self, data)
eval/evaluate_clip_dino_score_multi_subject.py:45
Method__init__
(self, data)
eval/evaluate_clip_dino_score_multi_subject.py:71
Method__init__
( self, model_type: str, device: torch.device, offload: bool = False,
uno/flux/pipeline.py:101
Method__init__
(self, params: FluxParams)
uno/flux/model.py:46
Method__init__
(self, dim: int, theta: int, axes_dim: list[int])
uno/flux/modules/layers.py:27
Method__init__
(self, in_dim: int, hidden_dim: int)
uno/flux/modules/layers.py:68
Method__init__
(self, dim: int)
uno/flux/modules/layers.py:79
Method__init__
(self, in_features, out_features, rank=4, network_alpha=None, device=None, dtype=None)
uno/flux/modules/layers.py:102
Method__init__
(self, dim: int, rank=4, network_alpha=None, lora_weight=1)
uno/flux/modules/layers.py:138
Method__init__
(self, dim: int, num_heads: int = 8, qkv_bias: bool = False)
uno/flux/modules/layers.py:154
Method__init__
(self, dim: int, double: bool)
uno/flux/modules/layers.py:174
Method__init__
(self, dim: int, rank=4, network_alpha=None, lora_weight=1)
uno/flux/modules/layers.py:189
Method__init__
(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False)
uno/flux/modules/layers.py:269
Method__init__
(self, dim: int, rank: int = 4, network_alpha = None, lora_weight: float = 1)
uno/flux/modules/layers.py:322
Method__init__
( self, hidden_size: int, num_heads: int, mlp_ratio: float = 4.0, qk_s
uno/flux/modules/layers.py:372
Method__init__
(self, hidden_size: int, patch_size: int, out_channels: int)
uno/flux/modules/layers.py:425
Method__init__
(self, version: str, max_length: int, **hf_kwargs)
uno/flux/modules/conditioner.py:22
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