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Functions316 in github.com/ToTheBeginning/PuLID

⨍ Functions 316◇ Types & classes 80
↓ 1 callersMethodinit_parameters
(self)
eva_clip/transformer.py:686
↓ 1 callersFunctionis_global_master
(args)
eva_clip/utils.py:293
↓ 1 callersFunctionis_local_master
(args)
eva_clip/utils.py:296
↓ 1 callersFunctionis_pretrained_cfg
(model: str, tag: str)
eva_clip/pretrained.py:221
↓ 1 callersFunctionlist_models
enumerate available model architectures based on config files
eva_clip/factory.py:53
↓ 1 callersFunctionlist_openai_models
Returns the names of available CLIP models
eva_clip/openai.py:18
↓ 1 callersFunctionlist_pretrained_models_by_tag
return all models having the specified pretrain tag
eva_clip/pretrained.py:203
↓ 1 callersFunctionlist_pretrained_tags_by_model
return all pretrain tags for the specified model architecture
eva_clip/pretrained.py:213
↓ 1 callersFunctionload_ae
(name: str, device: str = "cuda", hf_download: bool = True)
flux/util.py:204
↓ 1 callersFunctionload_checkpoint
(model, checkpoint_path, model_key="model|module|state_dict", strict=True)
eva_clip/factory.py:110
↓ 1 callersFunctionload_clip
(device: str = "cuda")
flux/util.py:200
↓ 1 callersFunctionload_flow_model
(name: str, device: str = "cuda", hf_download: bool = True)
flux/util.py:146
↓ 1 callersFunctionload_flow_model_quintized
(name: str, device: str = "cuda", hf_download: bool = True)
flux/util.py:170
↓ 1 callersFunctionload_openai_model
Load a CLIP model Parameters ---------- name : str A model name listed by `clip.available_models()`, or the path to a model check
eva_clip/openai.py:23
↓ 1 callersMethodload_pretrain
(self)
pulid/pipeline_v1_1.py:151
↓ 1 callersFunctionload_pretrained_checkpoint
( model, visual_checkpoint_path, text_checkpoint_path, strict=True, vi
eva_clip/factory.py:163
↓ 1 callersFunctionload_t5
(device: str = "cuda", max_length: int = 512)
flux/util.py:195
↓ 1 callersMethodmask
(self, input_ids, vocab_size, device, targets=None, masked_indices=None, probability_matrix=None)
eva_clip/hf_model.py:152
↓ 1 callersFunctionresize_clip_pos_embed
(state_dict, model, interpolation: str = 'bicubic', seq_dim=1)
eva_clip/utils.py:13
↓ 1 callersFunctionresize_eva_pos_embed
(state_dict, model, interpolation: str = 'bicubic', seq_dim=1)
eva_clip/utils.py:109
↓ 1 callersFunctionresize_evaclip_pos_embed
(state_dict, model, interpolation: str = 'bicubic', seq_dim=1)
eva_clip/utils.py:78
↓ 1 callersFunctionresize_visual_pos_embed
(state_dict, model, interpolation: str = 'bicubic', seq_dim=1)
eva_clip/utils.py:46
↓ 1 callersFunctionrope
(pos: Tensor, dim: int, theta: int)
flux/math.py:16
↓ 1 callersFunctionseed_everything
(seed)
pulid/utils.py:16
↓ 1 callersMethodstem
(self, x)
eva_clip/modified_resnet.py:166
↓ 1 callersFunctiontime_shift
(mu: float, sigma: float, t: Tensor)
flux/sampling.py:66
↓ 1 callersMethodtimestep
(self, sigma)
pulid/pipeline_v1_1.py:117
↓ 1 callersFunctionto_d
Converts a denoiser output to a Karras ODE derivative.
pulid/utils.py:186
↓ 1 callersMethodto_gray
(self, img)
pulid/pipeline_flux.py:114
↓ 1 callersMethodto_gray
(self, img)
pulid/pipeline_v1_1.py:166
↓ 1 callersMethodto_gray
(self, img)
pulid/pipeline.py:139
↓ 1 callersFunctionunpack
(x: Tensor, height: int, width: int)
flux/sampling.py:156
Method__call__
( self, attn, hidden_states, encoder_hidden_states=None, attention_mas
pulid/attention_processor.py:15
Method__call__
( self, attn, hidden_states, encoder_hidden_states=None, attention_mas
pulid/attention_processor.py:95
Method__call__
( self, attn, hidden_states, encoder_hidden_states=None, attention_mas
pulid/attention_processor.py:200
Method__call__
( self, attn, hidden_states, encoder_hidden_states=None, attention_mas
pulid/attention_processor.py:294
Method__call__
(self, x, sigma, **extra_args)
pulid/pipeline_v1_1.py:261
Method__call__
(self, t0, t1)
pulid/utils.py:228
Method__call__
(self, sigma, sigma_next)
pulid/utils.py:260
Method__call__
(self, texts:Union[str, List[str]], context_length:int=77)
eva_clip/tokenizer.py:194
Method__init__
(self, model_name: str, device: str, offload: bool, aggressive_offload: bool, args)
app_flux.py:34
Method__init__
(self, params: FluxParams)
flux/model.py:38
Method__init__
(self, dim: int, theta: int, axes_dim: list[int])
flux/modules/layers.py:12
Method__init__
(self, in_dim: int, hidden_dim: int)
flux/modules/layers.py:53
Method__init__
(self, dim: int)
flux/modules/layers.py:64
Method__init__
(self, dim: int, num_heads: int = 8, qkv_bias: bool = False)
flux/modules/layers.py:88
Method__init__
(self, dim: int, double: bool)
flux/modules/layers.py:114
Method__init__
(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False)
flux/modules/layers.py:130
Method__init__
( self, hidden_size: int, num_heads: int, mlp_ratio: float = 4.0, qk_s
flux/modules/layers.py:200
Method__init__
(self, hidden_size: int, patch_size: int, out_channels: int)
flux/modules/layers.py:243
Method__init__
(self, version: str, max_length: int, **hf_kwargs)
flux/modules/conditioner.py:6
Method__init__
(self, in_channels: int)
flux/modules/autoencoder.py:26
Method__init__
(self, in_channels: int, out_channels: int)
flux/modules/autoencoder.py:56
Method__init__
(self, in_channels: int)
flux/modules/autoencoder.py:86
Method__init__
(self, in_channels: int)
flux/modules/autoencoder.py:99
Method__init__
( self, ch: int, out_ch: int, ch_mult: list[int], num_res_blocks: int,
flux/modules/autoencoder.py:184
Method__init__
(self, sample: bool = True, chunk_dim: int = 1)
flux/modules/autoencoder.py:263
Method__init__
(self, params: AutoEncoderParams)
flux/modules/autoencoder.py:278
Method__init__
(self, hidden_size, cross_attention_dim=None)
pulid/attention_processor.py:90
Method__init__
(self)
pulid/attention_processor.py:195
Method__init__
(self, hidden_size, cross_attention_dim=None)
pulid/attention_processor.py:286
Method__init__
(self, dit, device, weight_dtype=torch.bfloat16, onnx_provider='gpu', *args, **kwargs)
pulid/pipeline_flux.py:22
Method__init__
(self, width=1280, context_dim=2048, num_token=5)
pulid/encoders.py:6
Method__init__
(self, sdxl_repo='Lykon/dreamshaper-xl-lightning', sampler='dpmpp_sde', *args, **kwargs)
pulid/pipeline_v1_1.py:32
Method__init__
(self, x, t0, t1, seed=None, **kwargs)
pulid/utils.py:204
Method__init__
(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False)
pulid/utils.py:255
Method__init__
(self, *args, **kwargs)
pulid/pipeline.py:33
Method__init__
(self, *, dim=3072, dim_head=128, heads=16, kv_dim=2048)
pulid/encoders_transformer.py:30
Method__init__
(self, *, dim, dim_head=64, heads=8, kv_dim=None)
pulid/encoders_transformer.py:76
Method__init__
( self, local_loss=False, gather_with_grad=False, cache_labels
eva_clip/loss.py:72
Method__init__
( self, dim, pt_seq_len, ft_seq_len=None, custom_freqs = None,
eva_clip/rope.py:80
Method__init__
(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None)
eva_clip/modified_resnet.py:59
Method__init__
(self, layers, output_dim, heads, image_size=224, width=64)
eva_clip/modified_resnet.py:103
Method__init__
(self, bpe_path: str = default_bpe(), special_tokens=None)
eva_clip/tokenizer.py:73
Method__init__
(self, tokenizer_name:str)
eva_clip/tokenizer.py:190
Method__init__
(self, max_size, interpolation=InterpolationMode.BICUBIC, fn='max', fill=0)
eva_clip/transform.py:15
Method__init__
( self, model_name, embed_dim, image_size=224, poo
eva_clip/timm_model.py:33
Method__init__
( self, model_name_or_path: str, output_dim: int, tokenizer_n
eva_clip/hf_model.py:77
Method__init__
(self, *args, **kwargs)
eva_clip/transformer.py:38
Method__init__
(self, dim, init_values=1e-5, inplace=False)
eva_clip/transformer.py:67
Method__init__
(self, prob, exclude_first_token=True)
eva_clip/transformer.py:80
Method__init__
( self, dim, num_heads=8, qkv_bias=True, scaled_co
eva_clip/transformer.py:244
Method__init__
( self, d_model: int, n_head: int, mlp_ratio: float = 4.0,
eva_clip/transformer.py:340
Method__init__
( self, width: int, layers: int, heads: int, mlp_r
eva_clip/transformer.py:390
Method__init__
( self, d_model: int, n_head: int, mlp_ratio: float = 4.0,
eva_clip/transformer.py:444
Method__init__
( self, width: int, layers: int, heads: int, mlp_r
eva_clip/transformer.py:486
Method__init__
( self, image_size: int, patch_size: int, width: int,
eva_clip/transformer.py:521
Method__init__
( self, context_length: int = 77, vocab_size: int = 49408, wid
eva_clip/transformer.py:643
Method__init__
(self, drop_prob=None)
eva_clip/eva_vit_model.py:36
Method__init__
(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0., norm
eva_clip/eva_vit_model.py:82
Method__init__
( self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., w
eva_clip/eva_vit_model.py:107
Method__init__
(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., dr
eva_clip/eva_vit_model.py:248
Method__init__
(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768)
eva_clip/eva_vit_model.py:308
Method__init__
(self, window_size, num_heads)
eva_clip/eva_vit_model.py:331
Method__init__
(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, nu
eva_clip/eva_vit_model.py:369
Method__init__
( self, embed_dim: int, vision_cfg: CLIPVisionCfg, text_cfg: C
eva_clip/model.py:271
Function_convert_to_rgb
(image)
eva_clip/transform.py:39
Function_convert_weights
(l)
eva_clip/model.py:318
Method_init_weights
(self, m)
eva_clip/eva_vit_model.py:461
Method_unlock
(x)
eva_clip/transformer.py:587
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