feature: support sdxl (#431)
- adds support for (SDXL)[https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0] - adds sliced encoding/decoding to refiners sdxl pipeline - doesn't support inpainting, controlnets - monkeypatches self_attention_guidance to use sliced attention - adds a bunch of model weight translation utilities and weightmaps - add [opendalle 1.1](https://huggingface.co/dataautogpt3/OpenDalleV1.1) - change default model to opendalle - fix: better handle special characters in path inputs on command line **todo** - add testspull/433/head
Bryce Drennan
5 months ago
committed by
GitHub
parent
fb7cff3684
commit
42a045e8e6
@ -0,0 +1,206 @@
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import logging
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import re
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from dataclasses import asdict, dataclass, field
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from typing import Dict
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import torch
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from refiners.fluxion import load_from_safetensors
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from torch import device as Device
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logger = logging.getLogger(__name__)
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TensorDict = Dict[str, torch.Tensor]
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@dataclass
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class WeightTranslationMap:
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name_map: dict[str, str | None] = field(default_factory=dict)
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regex_map: dict[str, str | None] = field(default_factory=dict)
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ignore_prefixes: list[str] = field(default_factory=list)
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source_aliases: dict[str, str] = field(default_factory=dict)
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reshapes: dict[str, tuple[int, ...]] = field(default_factory=dict)
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def load_and_translate_weights(
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self, source_path: str, device: Device | str = "cpu"
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) -> TensorDict:
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extension = source_path.split(".")[-1]
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if extension in ["pth", "pt", "bin"]:
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source_weights = torch.load(source_path, map_location="cpu")
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elif extension in ["safetensors"]:
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source_weights = load_from_safetensors(source_path, device=device)
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else:
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msg = f"Unsupported extension {extension}"
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raise ValueError(msg)
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return self.translate_weights(source_weights)
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def translate_weights(self, source_weights: TensorDict) -> TensorDict:
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return translate_weights(source_weights=source_weights, weight_map=self)
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def add_regex_replacer(self, find_pattern: str, replace_pattern: str):
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orig_size = len(self.name_map)
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# see which keys of name_map
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find_pattern_c = re.compile(find_pattern)
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matched_keys = [k for k in self.name_map if find_pattern_c.match(k)]
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# print(f"Pattern {find_pattern} matched {len(matched_keys)} keys")
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for k in matched_keys:
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match = find_pattern_c.match(k)
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if not match:
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continue
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data = match.groupdict()
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new_k = render_fstring(replace_pattern, data)
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expected_k = self.name_map[k]
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assert new_k == expected_k
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# print(f"Replacing {k} with {new_k}")
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del self.name_map[k]
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self.regex_map[find_pattern] = replace_pattern
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print(
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f"Adding pattern reduced name_map from {orig_size} to {len(self.name_map)}"
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)
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def save(self, path):
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import json
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import os
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os.makedirs(os.path.dirname(path), exist_ok=True)
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with open(path, "w") as f:
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json.dump(asdict(self), f, indent=4)
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@classmethod
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def load(cls, path):
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import json
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with open(path) as f:
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d = json.load(f)
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return cls(**d)
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def translate_weights(
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source_weights: TensorDict, weight_map: WeightTranslationMap
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) -> TensorDict:
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new_state_dict: TensorDict = {}
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# print(f"Translating {len(source_weights)} weights")
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# print(f"Using {len(weight_map.name_map)} name mappings")
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# print(source_weights.keys())
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source_weights = flatten_dict(source_weights)
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for source_key in list(source_weights.keys()):
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source_key = weight_map.source_aliases.get(source_key, source_key)
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try:
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target_key = weight_map.name_map[source_key]
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# print(f"Found {source_prefix} -> {target_prefix}")
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except KeyError:
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continue
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if target_key is None:
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# mapped to None means we ignore it
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source_weights.pop(source_key)
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else:
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# print(f"Adding {target_key}")
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new_state_dict[target_key] = source_weights.pop(source_key)
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for source_key in list(source_weights.keys()):
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try:
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source_prefix, suffix = source_key.rsplit(sep=".", maxsplit=1)
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except ValueError:
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# no dots
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continue
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# print(f"Checking {source_prefix} {suffix}")
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source_prefix = weight_map.source_aliases.get(source_prefix, source_prefix)
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try:
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target_prefix = weight_map.name_map[source_prefix]
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# print(f"Found {source_prefix} -> {target_prefix}")
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except KeyError:
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continue
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if target_prefix is None:
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# mapped to None means we ignore it
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source_weights.pop(source_key)
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continue
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else:
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target_key = ".".join([target_prefix, suffix])
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# print(f"Adding {target_key}")
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new_state_dict[target_key] = source_weights.pop(source_key)
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for source_key in list(source_weights.keys()):
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try:
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source_prefix, suffix = source_key.rsplit(sep=".", maxsplit=1)
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except ValueError:
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# no dots
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continue
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for pattern, replace_pattern in weight_map.regex_map.items():
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match = re.match(pattern, source_prefix)
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if match:
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match_data = match.groupdict()
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new_k = render_fstring(replace_pattern, match_data)
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new_k = ".".join([new_k, suffix])
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new_state_dict[new_k] = source_weights.pop(source_key)
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if source_weights:
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msg = f"Unmapped keys: {list(source_weights.keys())}"
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print(msg)
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for k in source_weights:
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if isinstance(source_weights[k], torch.Tensor):
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print(f" {k}: {source_weights[k].shape}")
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else:
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print(f" {k}: {repr(source_weights[k])[:100]}")
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if weight_map.reshapes:
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for key, new_shape in weight_map.reshapes.items():
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if key in new_state_dict:
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new_state_dict[key] = new_state_dict[key].reshape(new_shape)
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return new_state_dict
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def flatten_dict(d, sep="."):
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"""
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Flattens a nested dictionary into a dictionary with dot-separated keys.
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This function removes items from the original dictionary as they are added to the new one.
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The function uses an iterative approach instead of recursion.
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Parameters:
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d (dict): The dictionary to flatten.
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sep (str): The separator to use between keys.
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Returns:
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dict: A flattened dictionary.
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"""
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flat_dict = {}
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stack = [("", d)]
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while stack:
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parent_key, current_dict = stack.pop()
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keys = list(current_dict.keys()) # Create a list of keys to avoid RuntimeError
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for k in keys:
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new_key = f"{parent_key}{sep}{k}" if parent_key else k
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if isinstance(current_dict[k], dict):
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stack.append((new_key, current_dict[k]))
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else:
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flat_dict[new_key] = current_dict.pop(k)
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return flat_dict
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def render_fstring(fstring, variables):
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"""
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Render a string formatted like an f-string using the provided variables.
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DANGER: This is a security risk if the fstring is user-provided.
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Args:
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fstring (str): The template string with placeholders for variables.
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variables (dict): A dictionary containing the variables to be used in the f-string.
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Returns:
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str: The rendered string with variables substituted.
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"""
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# Use locals().update to add the variables to the local scope
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locals().update(variables)
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# Evaluate the f-string using eval with an f-string formatted string
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return eval(f'f"""{fstring}"""')
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@ -0,0 +1,115 @@
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import os
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from functools import lru_cache
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from torch import device as Device
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from imaginairy.weight_management.translation import TensorDict, WeightTranslationMap
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_current_folder = os.path.dirname(os.path.abspath(__file__))
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weight_map_folder = os.path.join(_current_folder, "weight_maps")
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@lru_cache
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def _weight_map(map_name: str) -> WeightTranslationMap:
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map_path = os.path.join(weight_map_folder, f"{map_name}.weightmap.json")
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return WeightTranslationMap.load(map_path)
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def transformers_text_encoder_to_refiners_translator() -> WeightTranslationMap:
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return _weight_map("Transformers-ClipTextEncoder")
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def transformers_image_encoder_to_refiners_translator() -> WeightTranslationMap:
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return _weight_map("Transformers-ClipImageEncoder-SD21")
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def diffusers_autoencoder_kl_to_refiners_translator() -> WeightTranslationMap:
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return _weight_map("Diffusers-AutoencoderKL-SD")
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def diffusers_unet_sd15_to_refiners_translator() -> WeightTranslationMap:
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return _weight_map("Diffusers-UNet-SD15")
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def diffusers_unet_sdxl_to_refiners_translator() -> WeightTranslationMap:
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return _weight_map("Diffusers-UNet-SDXL")
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def informative_drawings_to_refiners_translator() -> WeightTranslationMap:
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return _weight_map("InformativeDrawings")
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def diffusers_controlnet_sd15_to_refiners_translator() -> WeightTranslationMap:
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return _weight_map("Diffusers-Controlnet-SD15")
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def diffusers_ip_adapter_sd15_to_refiners_translator() -> WeightTranslationMap:
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return _weight_map("Diffusers-IPAdapter-SD15")
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def diffusers_ip_adapter_sdxl_to_refiners_translator() -> WeightTranslationMap:
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return _weight_map("Diffusers-IPAdapter-SDXL")
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def diffusers_ip_adapter_plus_sd15_to_refiners_translator() -> WeightTranslationMap:
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return _weight_map("Diffusers-IPAdapterPlus-SD15")
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def diffusers_ip_adapter_plus_sdxl_to_refiners_translator() -> WeightTranslationMap:
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return _weight_map("Diffusers-IPAdapterPlus-SDXL")
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def diffusers_t2i_adapter_sd15_to_refiners_translator() -> WeightTranslationMap:
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return _weight_map("Diffusers-T2IAdapter-SD15")
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def diffusers_t2i_adapter_sdxl_to_refiners_translator() -> WeightTranslationMap:
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return _weight_map("Diffusers-T2IAdapter-SDXL")
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class DoubleTextEncoderTranslator:
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def __init__(self):
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self.translator = transformers_text_encoder_to_refiners_translator()
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def load_and_translate_weights(
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self,
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text_encoder_l_weights_path: str,
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text_encoder_g_weights_path: str,
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device: Device | str = "cpu",
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) -> TensorDict:
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text_encoder_l_weights = self.translator.load_and_translate_weights(
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text_encoder_l_weights_path, device=device
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)
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text_encoder_g_weights = self.translator.load_and_translate_weights(
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text_encoder_g_weights_path, device=device
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)
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return self.translate_weights(text_encoder_l_weights, text_encoder_g_weights)
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def translate_weights(
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self, text_encoder_l_weights: TensorDict, text_encoder_g_weights: TensorDict
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) -> TensorDict:
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new_sd: TensorDict = {}
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for k in list(text_encoder_l_weights.keys()):
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if k.startswith("TransformerLayer_12"):
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text_encoder_l_weights.pop(k)
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elif k.startswith("LayerNorm"):
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text_encoder_l_weights.pop(k)
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else:
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new_key = f"Parallel.CLIPTextEncoderL.{k}"
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new_sd[new_key] = text_encoder_l_weights.pop(k)
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new_sd[
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"Parallel.TextEncoderWithPooling.Parallel.Chain.Linear.weight"
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] = text_encoder_g_weights.pop("Linear.weight")
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for k in list(text_encoder_g_weights.keys()):
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if k.startswith("TransformerLayer_32"):
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new_key = f"Parallel.TextEncoderWithPooling.Parallel.Chain.CLIPTextEncoderG.TransformerLayer{k[19:]}"
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elif k.startswith("LayerNorm"):
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new_key = f"Parallel.TextEncoderWithPooling.Parallel.Chain.CLIPTextEncoderG.{k}"
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else:
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new_key = f"Parallel.TextEncoderWithPooling.CLIPTextEncoderG.{k}"
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new_sd[new_key] = text_encoder_g_weights.pop(k)
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return new_sd
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@ -0,0 +1,174 @@
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{
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"name_map": {
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"encoder.conv_in": "Encoder.Conv2d",
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"encoder.down_blocks.0.resnets.0.norm1": "Encoder.Chain_1.Chain_1.Resnet_1.Chain.GroupNorm_1",
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"encoder.down_blocks.0.resnets.0.norm2": "Encoder.Chain_1.Chain_1.Resnet_1.Chain.GroupNorm_2",
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"encoder.down_blocks.0.resnets.1.norm1": "Encoder.Chain_1.Chain_1.Resnet_2.Chain.GroupNorm_1",
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"encoder.down_blocks.0.resnets.1.norm2": "Encoder.Chain_1.Chain_1.Resnet_2.Chain.GroupNorm_2",
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"encoder.down_blocks.1.resnets.0.norm1": "Encoder.Chain_1.Chain_2.Resnet_1.Chain.GroupNorm_1",
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"decoder.up_blocks.3.resnets.0.norm2": "Decoder.Chain_1.Chain_5.Resnet_1.Chain.GroupNorm_2",
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"decoder.up_blocks.3.resnets.1.norm1": "Decoder.Chain_1.Chain_5.Resnet_2.Chain.GroupNorm_1",
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"decoder.up_blocks.3.resnets.1.norm2": "Decoder.Chain_1.Chain_5.Resnet_2.Chain.GroupNorm_2",
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"decoder.up_blocks.3.resnets.2.norm1": "Decoder.Chain_1.Chain_5.Resnet_3.Chain.GroupNorm_1",
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"decoder.up_blocks.3.resnets.2.norm2": "Decoder.Chain_1.Chain_5.Resnet_3.Chain.GroupNorm_2",
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"decoder.conv_norm_out": "Decoder.Chain_2.GroupNorm",
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"encoder.down_blocks.0.resnets.0.conv1": "Encoder.Chain_1.Chain_1.Resnet_1.Chain.Conv2d_1",
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"encoder.down_blocks.0.resnets.0.conv2": "Encoder.Chain_1.Chain_1.Resnet_1.Chain.Conv2d_2",
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"encoder.down_blocks.0.resnets.1.conv1": "Encoder.Chain_1.Chain_1.Resnet_2.Chain.Conv2d_1",
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"encoder.down_blocks.0.resnets.1.conv2": "Encoder.Chain_1.Chain_1.Resnet_2.Chain.Conv2d_2",
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"encoder.down_blocks.0.downsamplers.0.conv": "Encoder.Chain_1.Chain_1.Downsample.Conv2d",
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"decoder.up_blocks.3.resnets.0.conv2": "Decoder.Chain_1.Chain_5.Resnet_1.Chain.Conv2d_2",
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"decoder.up_blocks.3.resnets.1.conv1": "Decoder.Chain_1.Chain_5.Resnet_2.Chain.Conv2d_1",
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"decoder.up_blocks.3.resnets.1.conv2": "Decoder.Chain_1.Chain_5.Resnet_2.Chain.Conv2d_2",
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"decoder.up_blocks.3.resnets.2.conv1": "Decoder.Chain_1.Chain_5.Resnet_3.Chain.Conv2d_1",
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"decoder.up_blocks.3.resnets.2.conv2": "Decoder.Chain_1.Chain_5.Resnet_3.Chain.Conv2d_2",
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"encoder.down_blocks.1.resnets.0.conv1": "Encoder.Chain_1.Chain_2.Resnet_1.Chain.Conv2d_1",
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"encoder.down_blocks.1.resnets.0.norm2": "Encoder.Chain_1.Chain_2.Resnet_1.Chain.GroupNorm_2",
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"encoder.down_blocks.1.resnets.1.norm1": "Encoder.Chain_1.Chain_2.Resnet_2.Chain.GroupNorm_1",
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"encoder.down_blocks.1.resnets.1.norm2": "Encoder.Chain_1.Chain_2.Resnet_2.Chain.GroupNorm_2",
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"encoder.down_blocks.2.resnets.0.norm1": "Encoder.Chain_1.Chain_3.Resnet_1.Chain.GroupNorm_1",
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"decoder.up_blocks.2.resnets.0.norm2": "Decoder.Chain_1.Chain_4.Resnet_1.Chain.GroupNorm_2",
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"decoder.up_blocks.2.resnets.1.norm1": "Decoder.Chain_1.Chain_4.Resnet_2.Chain.GroupNorm_1",
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"decoder.up_blocks.2.resnets.1.norm2": "Decoder.Chain_1.Chain_4.Resnet_2.Chain.GroupNorm_2",
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"decoder.up_blocks.2.resnets.2.norm1": "Decoder.Chain_1.Chain_4.Resnet_3.Chain.GroupNorm_1",
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"decoder.up_blocks.2.resnets.2.norm2": "Decoder.Chain_1.Chain_4.Resnet_3.Chain.GroupNorm_2",
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"decoder.up_blocks.3.resnets.0.norm1": "Decoder.Chain_1.Chain_5.Resnet_1.Chain.GroupNorm_1",
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"encoder.down_blocks.1.resnets.0.conv2": "Encoder.Chain_1.Chain_2.Resnet_1.Chain.Conv2d_2",
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"encoder.down_blocks.1.resnets.1.conv1": "Encoder.Chain_1.Chain_2.Resnet_2.Chain.Conv2d_1",
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"encoder.down_blocks.1.resnets.1.conv2": "Encoder.Chain_1.Chain_2.Resnet_2.Chain.Conv2d_2",
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"encoder.down_blocks.1.downsamplers.0.conv": "Encoder.Chain_1.Chain_2.Downsample.Conv2d",
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"decoder.up_blocks.2.resnets.0.conv2": "Decoder.Chain_1.Chain_4.Resnet_1.Chain.Conv2d_2",
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"decoder.up_blocks.2.resnets.1.conv1": "Decoder.Chain_1.Chain_4.Resnet_2.Chain.Conv2d_1",
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"decoder.up_blocks.2.resnets.1.conv2": "Decoder.Chain_1.Chain_4.Resnet_2.Chain.Conv2d_2",
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"decoder.up_blocks.2.resnets.2.conv1": "Decoder.Chain_1.Chain_4.Resnet_3.Chain.Conv2d_1",
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"decoder.up_blocks.2.resnets.2.conv2": "Decoder.Chain_1.Chain_4.Resnet_3.Chain.Conv2d_2",
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"decoder.up_blocks.2.upsamplers.0.conv": "Decoder.Chain_1.Chain_4.Upsample.Conv2d",
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"encoder.down_blocks.1.resnets.0.conv_shortcut": "Encoder.Chain_1.Chain_2.Resnet_1.Conv2d",
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"encoder.down_blocks.2.resnets.0.conv1": "Encoder.Chain_1.Chain_3.Resnet_1.Chain.Conv2d_1",
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"encoder.down_blocks.2.resnets.0.norm2": "Encoder.Chain_1.Chain_3.Resnet_1.Chain.GroupNorm_2",
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"encoder.down_blocks.2.resnets.1.norm1": "Encoder.Chain_1.Chain_3.Resnet_2.Chain.GroupNorm_1",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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||||
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|
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|
||||
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|
||||
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|
||||
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|
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
}
|
||||
}
|
||||
@ -0,0 +1,199 @@
|
||||
{
|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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@ -0,0 +1,44 @@
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|
||||
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||||
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||||
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||||
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||||
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|
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,20 @@
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"model3.0": "Chain_4.ConvTranspose2d",
|
||||
"model3.3": "Chain_5.ConvTranspose2d",
|
||||
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|
||||
},
|
||||
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|
||||
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|
||||
"source_aliases": {},
|
||||
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|
||||
}
|
||||
@ -0,0 +1,275 @@
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"vision_model.encoder.layers.27.self_attn.out_proj": "Chain.TransformerLayer_28.Residual_1.SelfAttention.Linear",
|
||||
"vision_model.encoder.layers.28.self_attn.q_proj": "Chain.TransformerLayer_29.Residual_1.SelfAttention.Distribute.Linear_1",
|
||||
"vision_model.encoder.layers.28.self_attn.k_proj": "Chain.TransformerLayer_29.Residual_1.SelfAttention.Distribute.Linear_2",
|
||||
"vision_model.encoder.layers.28.self_attn.v_proj": "Chain.TransformerLayer_29.Residual_1.SelfAttention.Distribute.Linear_3",
|
||||
"vision_model.encoder.layers.28.self_attn.out_proj": "Chain.TransformerLayer_29.Residual_1.SelfAttention.Linear",
|
||||
"vision_model.encoder.layers.29.self_attn.q_proj": "Chain.TransformerLayer_30.Residual_1.SelfAttention.Distribute.Linear_1",
|
||||
"vision_model.encoder.layers.29.self_attn.k_proj": "Chain.TransformerLayer_30.Residual_1.SelfAttention.Distribute.Linear_2",
|
||||
"vision_model.encoder.layers.29.self_attn.v_proj": "Chain.TransformerLayer_30.Residual_1.SelfAttention.Distribute.Linear_3",
|
||||
"vision_model.encoder.layers.29.self_attn.out_proj": "Chain.TransformerLayer_30.Residual_1.SelfAttention.Linear",
|
||||
"vision_model.encoder.layers.30.self_attn.q_proj": "Chain.TransformerLayer_31.Residual_1.SelfAttention.Distribute.Linear_1",
|
||||
"vision_model.encoder.layers.30.self_attn.k_proj": "Chain.TransformerLayer_31.Residual_1.SelfAttention.Distribute.Linear_2",
|
||||
"vision_model.encoder.layers.30.self_attn.v_proj": "Chain.TransformerLayer_31.Residual_1.SelfAttention.Distribute.Linear_3",
|
||||
"vision_model.encoder.layers.30.self_attn.out_proj": "Chain.TransformerLayer_31.Residual_1.SelfAttention.Linear",
|
||||
"vision_model.encoder.layers.31.self_attn.q_proj": "Chain.TransformerLayer_32.Residual_1.SelfAttention.Distribute.Linear_1",
|
||||
"vision_model.encoder.layers.31.self_attn.k_proj": "Chain.TransformerLayer_32.Residual_1.SelfAttention.Distribute.Linear_2",
|
||||
"vision_model.encoder.layers.31.self_attn.v_proj": "Chain.TransformerLayer_32.Residual_1.SelfAttention.Distribute.Linear_3",
|
||||
"vision_model.encoder.layers.31.self_attn.out_proj": "Chain.TransformerLayer_32.Residual_1.SelfAttention.Linear",
|
||||
"vision_model.encoder.layers.0.mlp.fc1": "Chain.TransformerLayer_1.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.1.mlp.fc1": "Chain.TransformerLayer_2.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.2.mlp.fc1": "Chain.TransformerLayer_3.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.3.mlp.fc1": "Chain.TransformerLayer_4.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.4.mlp.fc1": "Chain.TransformerLayer_5.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.5.mlp.fc1": "Chain.TransformerLayer_6.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.6.mlp.fc1": "Chain.TransformerLayer_7.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.7.mlp.fc1": "Chain.TransformerLayer_8.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.8.mlp.fc1": "Chain.TransformerLayer_9.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.9.mlp.fc1": "Chain.TransformerLayer_10.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.10.mlp.fc1": "Chain.TransformerLayer_11.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.11.mlp.fc1": "Chain.TransformerLayer_12.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.12.mlp.fc1": "Chain.TransformerLayer_13.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.13.mlp.fc1": "Chain.TransformerLayer_14.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.14.mlp.fc1": "Chain.TransformerLayer_15.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.15.mlp.fc1": "Chain.TransformerLayer_16.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.16.mlp.fc1": "Chain.TransformerLayer_17.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.17.mlp.fc1": "Chain.TransformerLayer_18.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.18.mlp.fc1": "Chain.TransformerLayer_19.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.19.mlp.fc1": "Chain.TransformerLayer_20.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.20.mlp.fc1": "Chain.TransformerLayer_21.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.21.mlp.fc1": "Chain.TransformerLayer_22.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.22.mlp.fc1": "Chain.TransformerLayer_23.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.23.mlp.fc1": "Chain.TransformerLayer_24.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.24.mlp.fc1": "Chain.TransformerLayer_25.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.25.mlp.fc1": "Chain.TransformerLayer_26.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.26.mlp.fc1": "Chain.TransformerLayer_27.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.27.mlp.fc1": "Chain.TransformerLayer_28.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.28.mlp.fc1": "Chain.TransformerLayer_29.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.29.mlp.fc1": "Chain.TransformerLayer_30.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.30.mlp.fc1": "Chain.TransformerLayer_31.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.31.mlp.fc1": "Chain.TransformerLayer_32.Residual_2.FeedForward.Linear_1",
|
||||
"vision_model.encoder.layers.0.mlp.fc2": "Chain.TransformerLayer_1.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.1.mlp.fc2": "Chain.TransformerLayer_2.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.2.mlp.fc2": "Chain.TransformerLayer_3.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.3.mlp.fc2": "Chain.TransformerLayer_4.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.4.mlp.fc2": "Chain.TransformerLayer_5.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.5.mlp.fc2": "Chain.TransformerLayer_6.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.6.mlp.fc2": "Chain.TransformerLayer_7.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.7.mlp.fc2": "Chain.TransformerLayer_8.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.8.mlp.fc2": "Chain.TransformerLayer_9.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.9.mlp.fc2": "Chain.TransformerLayer_10.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.10.mlp.fc2": "Chain.TransformerLayer_11.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.11.mlp.fc2": "Chain.TransformerLayer_12.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.12.mlp.fc2": "Chain.TransformerLayer_13.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.13.mlp.fc2": "Chain.TransformerLayer_14.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.14.mlp.fc2": "Chain.TransformerLayer_15.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.15.mlp.fc2": "Chain.TransformerLayer_16.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.16.mlp.fc2": "Chain.TransformerLayer_17.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.17.mlp.fc2": "Chain.TransformerLayer_18.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.18.mlp.fc2": "Chain.TransformerLayer_19.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.19.mlp.fc2": "Chain.TransformerLayer_20.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.20.mlp.fc2": "Chain.TransformerLayer_21.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.21.mlp.fc2": "Chain.TransformerLayer_22.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.22.mlp.fc2": "Chain.TransformerLayer_23.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.23.mlp.fc2": "Chain.TransformerLayer_24.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.24.mlp.fc2": "Chain.TransformerLayer_25.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.25.mlp.fc2": "Chain.TransformerLayer_26.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.26.mlp.fc2": "Chain.TransformerLayer_27.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.27.mlp.fc2": "Chain.TransformerLayer_28.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.28.mlp.fc2": "Chain.TransformerLayer_29.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.29.mlp.fc2": "Chain.TransformerLayer_30.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.30.mlp.fc2": "Chain.TransformerLayer_31.Residual_2.FeedForward.Linear_2",
|
||||
"vision_model.encoder.layers.31.mlp.fc2": "Chain.TransformerLayer_32.Residual_2.FeedForward.Linear_2",
|
||||
"visual_projection": "Linear"
|
||||
},
|
||||
"regex_map": {},
|
||||
"ignore_prefixes": [],
|
||||
"source_aliases": {},
|
||||
"reshapes": {
|
||||
"ViTEmbeddings.Concatenate.ClassToken.Parameter.weight": [
|
||||
1,
|
||||
1280
|
||||
]
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,19 @@
|
||||
{
|
||||
"name_map": {
|
||||
"text_model.embeddings.token_embedding": "Sum.TokenEncoder",
|
||||
"text_model.embeddings.position_embedding": "Sum.PositionalEncoder.Embedding",
|
||||
"text_model.final_layer_norm": "LayerNorm",
|
||||
"text_projection": "Linear"
|
||||
},
|
||||
"regex_map": {
|
||||
"text_model\\.encoder\\.layers\\.(?P<layer>\\d+)\\.layer_norm(?P<norm>\\d+)": "TransformerLayer_{int(layer) + 1}.Residual_{norm}.LayerNorm",
|
||||
"text_model\\.encoder\\.layers\\.(?P<layer_number>\\d+)\\.self_attn\\.q_proj": "TransformerLayer_{int(layer_number) + 1}.Residual_1.SelfAttention.Distribute.Linear_1",
|
||||
"text_model\\.encoder\\.layers\\.(?P<layer_number>\\d+)\\.self_attn\\.k_proj": "TransformerLayer_{int(layer_number) + 1}.Residual_1.SelfAttention.Distribute.Linear_2",
|
||||
"text_model\\.encoder\\.layers\\.(?P<layer_number>\\d+)\\.self_attn\\.v_proj": "TransformerLayer_{int(layer_number) + 1}.Residual_1.SelfAttention.Distribute.Linear_3",
|
||||
"text_model\\.encoder\\.layers\\.(?P<layer_number>\\d+)\\.self_attn\\.out_proj": "TransformerLayer_{int(layer_number) + 1}.Residual_1.SelfAttention.Linear",
|
||||
"text_model\\.encoder\\.layers\\.(?P<layer_number>\\d+)\\.mlp\\.fc(?P<fc_num>\\d+)": "TransformerLayer_{int(layer_number) + 1}.Residual_2.FeedForward.Linear_{fc_num}"
|
||||
},
|
||||
"ignore_prefixes": [],
|
||||
"source_aliases": {},
|
||||
"reshapes": {}
|
||||
}
|
||||
|
@ -0,0 +1,2 @@
|
||||
def test_sdxl_vae():
|
||||
pass
|
||||
Loading…
Reference in New Issue