SYMBOL INDEX (274 symbols across 22 files)

FILE: demo/app.py
  function multimodal_understanding (line 26) | def multimodal_understanding(image, question, seed, top_p, temperature):
  function generate (line 69) | def generate(input_ids,
  function unpack (line 110) | def unpack(dec, width, height, parallel_size=5):
  function generate_image (line 122) | def generate_image(prompt,

FILE: demo/app_janusflow.py
  function multimodal_understanding (line 25) | def multimodal_understanding(image, question, seed, top_p, temperature):
  function generate (line 70) | def generate(
  function unpack (line 141) | def unpack(dec, width, height, parallel_size=5):
  function generate_image (line 152) | def generate_image(prompt,

FILE: demo/app_januspro.py
  function multimodal_understanding (line 34) | def multimodal_understanding(image, question, seed, top_p, temperature):
  function generate (line 77) | def generate(input_ids,
  function unpack (line 123) | def unpack(dec, width, height, parallel_size=5):
  function generate_image (line 136) | def generate_image(prompt,

FILE: demo/fastapi_app.py
  function multimodal_understanding (line 28) | def multimodal_understanding(image_data, question, seed, top_p, temperat...
  function understand_image_and_question (line 67) | async def understand_image_and_question(
  function generate (line 79) | def generate(input_ids,
  function unpack (line 119) | def unpack(dec, width, height, parallel_size=5):
  function generate_image (line 130) | def generate_image(prompt, seed, guidance):
  function generate_images (line 156) | async def generate_images(

FILE: demo/fastapi_client.py
  function understand_image_and_question (line 12) | def understand_image_and_question(image_path, question, seed=42, top_p=0...
  function generate_images (line 26) | def generate_images(prompt, seed=None, guidance=5.0):

FILE: generation_inference.py
  function generate (line 55) | def generate(

FILE: interactivechat.py
  function create_prompt (line 21) | def create_prompt(user_input: str) -> str:
  function generate (line 40) | def generate(
  function interactive_image_generator (line 113) | def interactive_image_generator():

FILE: janus/janusflow/models/clip_encoder.py
  class CLIPVisionTower (line 30) | class CLIPVisionTower(nn.Module):
    method __init__ (line 31) | def __init__(
    method build_vision_tower (line 70) | def build_vision_tower(self, vision_tower_params):
    method feature_select (line 88) | def feature_select(self, image_forward_outs):
    method forward (line 107) | def forward(self, images):

FILE: janus/janusflow/models/image_processing_vlm.py
  function expand2square (line 41) | def expand2square(pil_img, background_color):
  class VLMImageProcessorConfig (line 55) | class VLMImageProcessorConfig(PretrainedConfig):
    method __init__ (line 64) | def __init__(
  class VLMImageProcessor (line 92) | class VLMImageProcessor(BaseImageProcessor):
    method __init__ (line 95) | def __init__(
    method resize (line 127) | def resize(self, pil_img: Image) -> np.ndarray:
    method preprocess (line 164) | def preprocess(self, images, return_tensors: str = "pt", **kwargs) -> ...
    method default_shape (line 195) | def default_shape(self):

FILE: janus/janusflow/models/modeling_vlm.py
  function model_name_to_cls (line 37) | def model_name_to_cls(cls_name):
  class VisionUnderstandEncoderConfig (line 51) | class VisionUnderstandEncoderConfig(PretrainedConfig):
    method __init__ (line 56) | def __init__(self, **kwargs):
  class VisionGenerationEncoderConfig (line 66) | class VisionGenerationEncoderConfig(PretrainedConfig):
    method __init__ (line 71) | def __init__(self, **kwargs):
  class VisionGenerationDecoderConfig (line 81) | class VisionGenerationDecoderConfig(PretrainedConfig):
    method __init__ (line 86) | def __init__(self, **kwargs):
  class MultiModalityConfig (line 96) | class MultiModalityConfig(PretrainedConfig):
    method __init__ (line 101) | def __init__(self, **kwargs):
  class MultiModalityPreTrainedModel (line 125) | class MultiModalityPreTrainedModel(PreTrainedModel):
  class MultiModalityCausalLM (line 132) | class MultiModalityCausalLM(MultiModalityPreTrainedModel):
    method __init__ (line 134) | def __init__(self, config: MultiModalityConfig):
    method prepare_inputs_embeds (line 171) | def prepare_inputs_embeds(

FILE: janus/janusflow/models/processing_vlm.py
  class DictOutput (line 32) | class DictOutput(object):
    method keys (line 33) | def keys(self):
    method __getitem__ (line 36) | def __getitem__(self, item):
    method __setitem__ (line 39) | def __setitem__(self, key, value):
  class VLChatProcessorOutput (line 44) | class VLChatProcessorOutput(DictOutput):
    method __len__ (line 50) | def __len__(self):
  class BatchedVLChatProcessorOutput (line 55) | class BatchedVLChatProcessorOutput(DictOutput):
    method to (line 63) | def to(self, device, dtype=torch.bfloat16):
  class VLChatProcessor (line 72) | class VLChatProcessor(ProcessorMixin):
    method __init__ (line 84) | def __init__(
    method new_chat_template (line 154) | def new_chat_template(self):
    method apply_sft_template_for_multi_turn_prompts (line 159) | def apply_sft_template_for_multi_turn_prompts(
    method image_token (line 202) | def image_token(self):
    method image_id (line 206) | def image_id(self):
    method image_start_id (line 211) | def image_start_id(self):
    method image_end_id (line 216) | def image_end_id(self):
    method image_start_token (line 221) | def image_start_token(self):
    method image_end_token (line 225) | def image_end_token(self):
    method pad_id (line 229) | def pad_id(self):
    method image_gen_id (line 237) | def image_gen_id(self):
    method add_image_token (line 241) | def add_image_token(
    method process_one (line 289) | def process_one(
    method __call__ (line 352) | def __call__(
    method batchify (line 387) | def batchify(

FILE: janus/janusflow/models/siglip_vit.py
  function _no_grad_trunc_normal_ (line 54) | def _no_grad_trunc_normal_(tensor, mean, std, a, b):
  function trunc_normal_ (line 92) | def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
  function init_weights (line 120) | def init_weights(self):
  function init_weights_vit_timm (line 126) | def init_weights_vit_timm(module: nn.Module, name: str = "") -> None:
  class Attention (line 136) | class Attention(nn.Module):
    method __init__ (line 139) | def __init__(
    method forward (line 164) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class LayerScale (line 194) | class LayerScale(nn.Module):
    method __init__ (line 195) | def __init__(
    method forward (line 205) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class Block (line 209) | class Block(nn.Module):
    method __init__ (line 210) | def __init__(
    method forward (line 253) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class VisionTransformer (line 259) | class VisionTransformer(nn.Module):
    method __init__ (line 268) | def __init__(
    method init_weights (line 434) | def init_weights(self, mode: Literal["jax", "jax_nlhb", "moco", ""] = ...
    method no_weight_decay (line 443) | def no_weight_decay(self) -> Set:
    method group_matcher (line 447) | def group_matcher(self, coarse: bool = False) -> Dict:
    method set_grad_checkpointing (line 454) | def set_grad_checkpointing(self, enable: bool = True) -> None:
    method get_classifier (line 458) | def get_classifier(self) -> nn.Module:
    method reset_classifier (line 461) | def reset_classifier(self, num_classes: int, global_pool=None) -> None:
    method _pos_embed (line 476) | def _pos_embed(self, x: torch.Tensor) -> torch.Tensor:
    method _intermediate_layers (line 509) | def _intermediate_layers(
    method get_intermediate_layers (line 531) | def get_intermediate_layers(
    method forward_features (line 562) | def forward_features(self, x: torch.Tensor) -> torch.Tensor:
    method forward_head (line 574) | def forward_head(self, x: torch.Tensor, pre_logits: bool = False) -> t...
    method forward (line 585) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class SigLIPVisionCfg (line 593) | class SigLIPVisionCfg:
  function create_siglip_vit (line 650) | def create_siglip_vit(

FILE: janus/janusflow/models/uvit.py
  class ImageHead (line 35) | class ImageHead(nn.Module):
    method __init__ (line 37) | def __init__(self, decoder_cfg, gpt_cfg, layer_id=None):
    method forward (line 105) | def forward(
  class GlobalResponseNorm (line 137) | class GlobalResponseNorm(nn.Module):
    method __init__ (line 139) | def __init__(self, dim):
    method forward (line 144) | def forward(self, x):
  class Downsample2D (line 151) | class Downsample2D(nn.Module):
    method __init__ (line 167) | def __init__(
    method forward (line 219) | def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> tor...
  class Upsample2D (line 239) | class Upsample2D(nn.Module):
    method __init__ (line 255) | def __init__(
    method forward (line 318) | def forward(
  class ConvNextBlock (line 373) | class ConvNextBlock(nn.Module):
    method __init__ (line 374) | def __init__(
    method forward (line 405) | def forward(self, x, cond_embeds):
  class Patchify (line 430) | class Patchify(nn.Module):
    method __init__ (line 431) | def __init__(
    method forward (line 453) | def forward(self, x):
  class Unpatchify (line 461) | class Unpatchify(nn.Module):
    method __init__ (line 462) | def __init__(
    method forward (line 476) | def forward(self, x):
  class UVitBlock (line 486) | class UVitBlock(nn.Module):
    method __init__ (line 487) | def __init__(
    method forward (line 559) | def forward(self, x, emb, recompute=False):
  class ShallowUViTEncoder (line 572) | class ShallowUViTEncoder(nn.Module):
    method __init__ (line 573) | def __init__(
    method get_num_extra_tensors (line 625) | def get_num_extra_tensors(self):
    method forward (line 628) | def forward(self, x, timesteps):
  class ShallowUViTDecoder (line 644) | class ShallowUViTDecoder(nn.Module):
    method __init__ (line 645) | def __init__(
    method forward (line 702) | def forward(self, x, hs, t_emb):

FILE: janus/models/clip_encoder.py
  class CLIPVisionTower (line 30) | class CLIPVisionTower(nn.Module):
    method __init__ (line 31) | def __init__(
    method build_vision_tower (line 70) | def build_vision_tower(self, vision_tower_params):
    method feature_select (line 88) | def feature_select(self, image_forward_outs):
    method forward (line 107) | def forward(self, images):

FILE: janus/models/image_processing_vlm.py
  function expand2square (line 41) | def expand2square(pil_img, background_color):
  class VLMImageProcessorConfig (line 55) | class VLMImageProcessorConfig(PretrainedConfig):
    method __init__ (line 64) | def __init__(
  class VLMImageProcessor (line 92) | class VLMImageProcessor(BaseImageProcessor):
    method __init__ (line 95) | def __init__(
    method resize (line 127) | def resize(self, pil_img: Image) -> np.ndarray:
    method preprocess (line 164) | def preprocess(self, images, return_tensors: str = "pt", **kwargs) -> ...
    method default_shape (line 195) | def default_shape(self):

FILE: janus/models/modeling_vlm.py
  class vision_head (line 36) | class vision_head(torch.nn.Module):
    method __init__ (line 37) | def __init__(self, params):
    method forward (line 47) | def forward(self, x):
  function model_name_to_cls (line 54) | def model_name_to_cls(cls_name):
  class VisionConfig (line 73) | class VisionConfig(PretrainedConfig):
    method __init__ (line 78) | def __init__(self, **kwargs):
  class AlignerConfig (line 88) | class AlignerConfig(PretrainedConfig):
    method __init__ (line 93) | def __init__(self, **kwargs):
  class GenVisionConfig (line 103) | class GenVisionConfig(PretrainedConfig):
    method __init__ (line 108) | def __init__(self, **kwargs):
  class GenAlignerConfig (line 118) | class GenAlignerConfig(PretrainedConfig):
    method __init__ (line 123) | def __init__(self, **kwargs):
  class GenHeadConfig (line 133) | class GenHeadConfig(PretrainedConfig):
    method __init__ (line 138) | def __init__(self, **kwargs):
  class MultiModalityConfig (line 148) | class MultiModalityConfig(PretrainedConfig):
    method __init__ (line 159) | def __init__(self, **kwargs):
  class MultiModalityPreTrainedModel (line 183) | class MultiModalityPreTrainedModel(PreTrainedModel):
  class MultiModalityCausalLM (line 190) | class MultiModalityCausalLM(MultiModalityPreTrainedModel):
    method __init__ (line 191) | def __init__(self, config: MultiModalityConfig):
    method prepare_inputs_embeds (line 221) | def prepare_inputs_embeds(
    method prepare_gen_img_embeds (line 262) | def prepare_gen_img_embeds(self, image_ids: torch.LongTensor):

FILE: janus/models/processing_vlm.py
  class DictOutput (line 32) | class DictOutput(object):
    method keys (line 33) | def keys(self):
    method __getitem__ (line 36) | def __getitem__(self, item):
    method __setitem__ (line 39) | def __setitem__(self, key, value):
  class VLChatProcessorOutput (line 44) | class VLChatProcessorOutput(DictOutput):
    method __len__ (line 50) | def __len__(self):
  class BatchedVLChatProcessorOutput (line 55) | class BatchedVLChatProcessorOutput(DictOutput):
    method to (line 63) | def to(self, device, dtype=torch.bfloat16):
  class VLChatProcessor (line 72) | class VLChatProcessor(ProcessorMixin):
    method __init__ (line 84) | def __init__(
    method new_chat_template (line 132) | def new_chat_template(self):
    method apply_sft_template_for_multi_turn_prompts (line 137) | def apply_sft_template_for_multi_turn_prompts(
    method image_token (line 180) | def image_token(self):
    method image_id (line 184) | def image_id(self):
    method image_start_id (line 189) | def image_start_id(self):
    method image_end_id (line 194) | def image_end_id(self):
    method image_start_token (line 199) | def image_start_token(self):
    method image_end_token (line 203) | def image_end_token(self):
    method pad_id (line 207) | def pad_id(self):
    method add_image_token (line 215) | def add_image_token(
    method process_one (line 260) | def process_one(
    method __call__ (line 322) | def __call__(
    method batchify (line 357) | def batchify(

FILE: janus/models/projector.py
  class MlpProjector (line 27) | class MlpProjector(nn.Module):
    method __init__ (line 28) | def __init__(self, cfg):
    method forward (line 63) | def forward(

FILE: janus/models/siglip_vit.py
  function _no_grad_trunc_normal_ (line 54) | def _no_grad_trunc_normal_(tensor, mean, std, a, b):
  function trunc_normal_ (line 92) | def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
  function init_weights (line 120) | def init_weights(self):
  function init_weights_vit_timm (line 126) | def init_weights_vit_timm(module: nn.Module, name: str = "") -> None:
  class Attention (line 136) | class Attention(nn.Module):
    method __init__ (line 139) | def __init__(
    method forward (line 164) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class LayerScale (line 194) | class LayerScale(nn.Module):
    method __init__ (line 195) | def __init__(
    method forward (line 205) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class Block (line 209) | class Block(nn.Module):
    method __init__ (line 210) | def __init__(
    method forward (line 253) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class VisionTransformer (line 259) | class VisionTransformer(nn.Module):
    method __init__ (line 268) | def __init__(
    method init_weights (line 434) | def init_weights(self, mode: Literal["jax", "jax_nlhb", "moco", ""] = ...
    method no_weight_decay (line 443) | def no_weight_decay(self) -> Set:
    method group_matcher (line 447) | def group_matcher(self, coarse: bool = False) -> Dict:
    method set_grad_checkpointing (line 454) | def set_grad_checkpointing(self, enable: bool = True) -> None:
    method get_classifier (line 458) | def get_classifier(self) -> nn.Module:
    method reset_classifier (line 461) | def reset_classifier(self, num_classes: int, global_pool=None) -> None:
    method _pos_embed (line 476) | def _pos_embed(self, x: torch.Tensor) -> torch.Tensor:
    method _intermediate_layers (line 509) | def _intermediate_layers(
    method get_intermediate_layers (line 531) | def get_intermediate_layers(
    method forward_features (line 562) | def forward_features(self, x: torch.Tensor) -> torch.Tensor:
    method forward_head (line 574) | def forward_head(self, x: torch.Tensor, pre_logits: bool = False) -> t...
    method forward (line 585) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class SigLIPVisionCfg (line 593) | class SigLIPVisionCfg:
  function create_siglip_vit (line 640) | def create_siglip_vit(

FILE: janus/models/vq_model.py
  class ModelArgs (line 32) | class ModelArgs:
  class Encoder (line 46) | class Encoder(nn.Module):
    method __init__ (line 47) | def __init__(
    method forward (line 105) | def forward(self, x):
  class Decoder (line 127) | class Decoder(nn.Module):
    method __init__ (line 128) | def __init__(
    method last_layer (line 190) | def last_layer(self):
    method forward (line 193) | def forward(self, z):
  class VectorQuantizer (line 217) | class VectorQuantizer(nn.Module):
    method __init__ (line 218) | def __init__(self, n_e, e_dim, beta, entropy_loss_ratio, l2_norm, show...
    method forward (line 236) | def forward(self, z):
    method get_codebook_entry (line 284) | def get_codebook_entry(self, indices, shape=None, channel_first=True):
  class ResnetBlock (line 302) | class ResnetBlock(nn.Module):
    method __init__ (line 303) | def __init__(
    method forward (line 337) | def forward(self, x):
  class AttnBlock (line 355) | class AttnBlock(nn.Module):
    method __init__ (line 356) | def __init__(self, in_channels, norm_type="group"):
    method forward (line 366) | def forward(self, x):
  function nonlinearity (line 393) | def nonlinearity(x):
  function Normalize (line 398) | def Normalize(in_channels, norm_type="group"):
  class Upsample (line 408) | class Upsample(nn.Module):
    method __init__ (line 409) | def __init__(self, in_channels, with_conv):
    method forward (line 417) | def forward(self, x):
  class Downsample (line 430) | class Downsample(nn.Module):
    method __init__ (line 431) | def __init__(self, in_channels, with_conv):
    method forward (line 440) | def forward(self, x):
  function compute_entropy_loss (line 450) | def compute_entropy_loss(affinity, loss_type="softmax", temperature=0.01):
  class VQModel (line 466) | class VQModel(nn.Module):
    method __init__ (line 467) | def __init__(self, config: ModelArgs):
    method encode (line 494) | def encode(self, x):
    method decode (line 500) | def decode(self, quant):
    method decode_code (line 505) | def decode_code(self, code_b, shape=None, channel_first=True):
    method forward (line 510) | def forward(self, input):
  function VQ_16 (line 519) | def VQ_16(**kwargs):

FILE: janus/utils/conversation.py
  class SeparatorStyle (line 29) | class SeparatorStyle(IntEnum):
  class Conversation (line 52) | class Conversation:
    method get_prompt (line 76) | def get_prompt(self) -> str:
    method get_prompt_for_current_round (line 141) | def get_prompt_for_current_round(self, content=None):
    method set_system_message (line 153) | def set_system_message(self, system_message: str):
    method append_message (line 157) | def append_message(self, role: str, message: str):
    method reset_message (line 161) | def reset_message(self):
    method update_last_message (line 165) | def update_last_message(self, message: str):
    method to_gradio_chatbot (line 173) | def to_gradio_chatbot(self):
    method to_openai_api_messages (line 183) | def to_openai_api_messages(self):
    method copy (line 196) | def copy(self):
    method dict (line 211) | def dict(self):
  function register_conv_template (line 225) | def register_conv_template(template: Conversation, override: bool = False):
  function get_conv_template (line 235) | def get_conv_template(name: str) -> Conversation:

FILE: janus/utils/io.py
  function load_pretrained_model (line 32) | def load_pretrained_model(model_path: str):
  function load_pil_images (line 44) | def load_pil_images(conversations: List[Dict[str, str]]) -> List[PIL.Ima...
  function load_json (line 86) | def load_json(filepath):