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demo.py
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15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
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17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
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Also add information on how to contact you by electronic and paper mail.
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<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
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under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
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might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
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<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
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<https://www.gnu.org/licenses/why-not-lgpl.html>.

21
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# HuggingFace Guess
A simple tool to guess an HuggingFace repo URL from a state dict.
This repo does almost the same thing as `from diffusers.loaders.single_file_utils import fetch_diffusers_config` but a bit stronger and more robust.
The main model detection logics are extracted from Diffusers and stolen from ComfyUI.
```python
import safetensors.torch as sf
import huggingface_guess
state_dict = sf.load_file('./realisticVisionV51_v51VAE.safetensors')
repo_name = huggingface_guess.guess_repo_name(state_dict)
print(repo_name)
```
The above codes will print `runwayml/stable-diffusion-v1-5`.
Then you can download (or prefetch configs) from HuggingFace to instantiate models and load weights.

8
huggingface_guess/__init__.py Normal file
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from huggingface_guess.detection import model_config_from_unet, unet_prefix_from_state_dict, model_config_from_diffusers_unet
def guess_repo_name(state_dict):
unet_key_prefix = unet_prefix_from_state_dict(state_dict)
model_config = model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=True)
repo_id = model_config.huggingface_repo
return repo_id

543
huggingface_guess/detection.py Normal file
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import math
import logging
import torch
from huggingface_guess import utils, model_list
def count_blocks(state_dict_keys, prefix_string):
count = 0
while True:
c = False
for k in state_dict_keys:
if k.startswith(prefix_string.format(count)):
c = True
break
if c == False:
break
count += 1
return count
def calculate_transformer_depth(prefix, state_dict_keys, state_dict):
context_dim = None
use_linear_in_transformer = False
transformer_prefix = prefix + "1.transformer_blocks."
transformer_keys = sorted(list(filter(lambda a: a.startswith(transformer_prefix), state_dict_keys)))
if len(transformer_keys) > 0:
last_transformer_depth = count_blocks(state_dict_keys, transformer_prefix + '{}')
context_dim = state_dict['{}0.attn2.to_k.weight'.format(transformer_prefix)].shape[1]
use_linear_in_transformer = len(state_dict['{}1.proj_in.weight'.format(prefix)].shape) == 2
time_stack = '{}1.time_stack.0.attn1.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn1.to_q.weight'.format(prefix) in state_dict
time_stack_cross = '{}1.time_stack.0.attn2.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn2.to_q.weight'.format(prefix) in state_dict
return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack, time_stack_cross
return None
def detect_unet_config(state_dict, key_prefix):
state_dict_keys = list(state_dict.keys())
if '{}joint_blocks.0.context_block.attn.qkv.weight'.format(key_prefix) in state_dict_keys: # mmdit model
unet_config = {}
unet_config["in_channels"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[1]
patch_size = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[2]
unet_config["patch_size"] = patch_size
final_layer = '{}final_layer.linear.weight'.format(key_prefix)
if final_layer in state_dict:
unet_config["out_channels"] = state_dict[final_layer].shape[0] // (patch_size * patch_size)
unet_config["depth"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] // 64
unet_config["input_size"] = None
y_key = '{}y_embedder.mlp.0.weight'.format(key_prefix)
if y_key in state_dict_keys:
unet_config["adm_in_channels"] = state_dict[y_key].shape[1]
context_key = '{}context_embedder.weight'.format(key_prefix)
if context_key in state_dict_keys:
in_features = state_dict[context_key].shape[1]
out_features = state_dict[context_key].shape[0]
unet_config["context_embedder_config"] = {"target": "torch.nn.Linear", "params": {"in_features": in_features, "out_features": out_features}}
num_patches_key = '{}pos_embed'.format(key_prefix)
if num_patches_key in state_dict_keys:
num_patches = state_dict[num_patches_key].shape[1]
unet_config["num_patches"] = num_patches
unet_config["pos_embed_max_size"] = round(math.sqrt(num_patches))
rms_qk = '{}joint_blocks.0.context_block.attn.ln_q.weight'.format(key_prefix)
if rms_qk in state_dict_keys:
unet_config["qk_norm"] = "rms"
unet_config["pos_embed_scaling_factor"] = None # unused for inference
context_processor = '{}context_processor.layers.0.attn.qkv.weight'.format(key_prefix)
if context_processor in state_dict_keys:
unet_config["context_processor_layers"] = count_blocks(state_dict_keys, '{}context_processor.layers.'.format(key_prefix) + '{}.')
return unet_config
if '{}clf.1.weight'.format(key_prefix) in state_dict_keys: # stable cascade
unet_config = {}
text_mapper_name = '{}clip_txt_mapper.weight'.format(key_prefix)
if text_mapper_name in state_dict_keys:
unet_config['stable_cascade_stage'] = 'c'
w = state_dict[text_mapper_name]
if w.shape[0] == 1536: # stage c lite
unet_config['c_cond'] = 1536
unet_config['c_hidden'] = [1536, 1536]
unet_config['nhead'] = [24, 24]
unet_config['blocks'] = [[4, 12], [12, 4]]
elif w.shape[0] == 2048: # stage c full
unet_config['c_cond'] = 2048
elif '{}clip_mapper.weight'.format(key_prefix) in state_dict_keys:
unet_config['stable_cascade_stage'] = 'b'
w = state_dict['{}down_blocks.1.0.channelwise.0.weight'.format(key_prefix)]
if w.shape[-1] == 640:
unet_config['c_hidden'] = [320, 640, 1280, 1280]
unet_config['nhead'] = [-1, -1, 20, 20]
unet_config['blocks'] = [[2, 6, 28, 6], [6, 28, 6, 2]]
unet_config['block_repeat'] = [[1, 1, 1, 1], [3, 3, 2, 2]]
elif w.shape[-1] == 576: # stage b lite
unet_config['c_hidden'] = [320, 576, 1152, 1152]
unet_config['nhead'] = [-1, 9, 18, 18]
unet_config['blocks'] = [[2, 4, 14, 4], [4, 14, 4, 2]]
unet_config['block_repeat'] = [[1, 1, 1, 1], [2, 2, 2, 2]]
return unet_config
if '{}transformer.rotary_pos_emb.inv_freq'.format(key_prefix) in state_dict_keys: # stable audio dit
unet_config = {}
unet_config["audio_model"] = "dit1.0"
return unet_config
if '{}double_layers.0.attn.w1q.weight'.format(key_prefix) in state_dict_keys: # aura flow dit
unet_config = {}
unet_config["max_seq"] = state_dict['{}positional_encoding'.format(key_prefix)].shape[1]
unet_config["cond_seq_dim"] = state_dict['{}cond_seq_linear.weight'.format(key_prefix)].shape[1]
double_layers = count_blocks(state_dict_keys, '{}double_layers.'.format(key_prefix) + '{}.')
single_layers = count_blocks(state_dict_keys, '{}single_layers.'.format(key_prefix) + '{}.')
unet_config["n_double_layers"] = double_layers
unet_config["n_layers"] = double_layers + single_layers
return unet_config
if '{}mlp_t5.0.weight'.format(key_prefix) in state_dict_keys: # Hunyuan DiT
unet_config = {}
unet_config["image_model"] = "hydit"
unet_config["depth"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.')
unet_config["hidden_size"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0]
if unet_config["hidden_size"] == 1408 and unet_config["depth"] == 40: # DiT-g/2
unet_config["mlp_ratio"] = 4.3637
if state_dict['{}extra_embedder.0.weight'.format(key_prefix)].shape[1] == 3968:
unet_config["size_cond"] = True
unet_config["use_style_cond"] = True
unet_config["image_model"] = "hydit1"
return unet_config
if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys: # Flux
dit_config = {}
dit_config["image_model"] = "flux"
dit_config["in_channels"] = 64
dit_config["vec_in_dim"] = 768
dit_config["context_in_dim"] = 4096
dit_config["hidden_size"] = 3072
dit_config["mlp_ratio"] = 4.0
dit_config["num_heads"] = 24
dit_config["depth"] = 19
dit_config["depth_single_blocks"] = 38
dit_config["axes_dim"] = [16, 56, 56]
dit_config["theta"] = 10000
dit_config["qkv_bias"] = True
dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
return dit_config
if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
return None
unet_config = {
"use_checkpoint": False,
"image_size": 32,
"use_spatial_transformer": True,
"legacy": False
}
y_input = '{}label_emb.0.0.weight'.format(key_prefix)
if y_input in state_dict_keys:
unet_config["num_classes"] = "sequential"
unet_config["adm_in_channels"] = state_dict[y_input].shape[1]
else:
unet_config["adm_in_channels"] = None
model_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[0]
in_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[1]
out_key = '{}out.2.weight'.format(key_prefix)
if out_key in state_dict:
out_channels = state_dict[out_key].shape[0]
else:
out_channels = 4
num_res_blocks = []
channel_mult = []
attention_resolutions = []
transformer_depth = []
transformer_depth_output = []
context_dim = None
use_linear_in_transformer = False
video_model = False
video_model_cross = False
current_res = 1
count = 0
last_res_blocks = 0
last_channel_mult = 0
input_block_count = count_blocks(state_dict_keys, '{}input_blocks'.format(key_prefix) + '.{}.')
for count in range(input_block_count):
prefix = '{}input_blocks.{}.'.format(key_prefix, count)
prefix_output = '{}output_blocks.{}.'.format(key_prefix, input_block_count - count - 1)
block_keys = sorted(list(filter(lambda a: a.startswith(prefix), state_dict_keys)))
if len(block_keys) == 0:
break
block_keys_output = sorted(list(filter(lambda a: a.startswith(prefix_output), state_dict_keys)))
if "{}0.op.weight".format(prefix) in block_keys: # new layer
num_res_blocks.append(last_res_blocks)
channel_mult.append(last_channel_mult)
current_res *= 2
last_res_blocks = 0
last_channel_mult = 0
out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict)
if out is not None:
transformer_depth_output.append(out[0])
else:
transformer_depth_output.append(0)
else:
res_block_prefix = "{}0.in_layers.0.weight".format(prefix)
if res_block_prefix in block_keys:
last_res_blocks += 1
last_channel_mult = state_dict["{}0.out_layers.3.weight".format(prefix)].shape[0] // model_channels
out = calculate_transformer_depth(prefix, state_dict_keys, state_dict)
if out is not None:
transformer_depth.append(out[0])
if context_dim is None:
context_dim = out[1]
use_linear_in_transformer = out[2]
video_model = out[3]
video_model_cross = out[4]
else:
transformer_depth.append(0)
res_block_prefix = "{}0.in_layers.0.weight".format(prefix_output)
if res_block_prefix in block_keys_output:
out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict)
if out is not None:
transformer_depth_output.append(out[0])
else:
transformer_depth_output.append(0)
num_res_blocks.append(last_res_blocks)
channel_mult.append(last_channel_mult)
if "{}middle_block.1.proj_in.weight".format(key_prefix) in state_dict_keys:
transformer_depth_middle = count_blocks(state_dict_keys, '{}middle_block.1.transformer_blocks.'.format(key_prefix) + '{}')
elif "{}middle_block.0.in_layers.0.weight".format(key_prefix) in state_dict_keys:
transformer_depth_middle = -1
else:
transformer_depth_middle = -2
unet_config["in_channels"] = in_channels
unet_config["out_channels"] = out_channels
unet_config["model_channels"] = model_channels
unet_config["num_res_blocks"] = num_res_blocks
unet_config["transformer_depth"] = transformer_depth
unet_config["transformer_depth_output"] = transformer_depth_output
unet_config["channel_mult"] = channel_mult
unet_config["transformer_depth_middle"] = transformer_depth_middle
unet_config['use_linear_in_transformer'] = use_linear_in_transformer
unet_config["context_dim"] = context_dim
if video_model:
unet_config["extra_ff_mix_layer"] = True
unet_config["use_spatial_context"] = True
unet_config["merge_strategy"] = "learned_with_images"
unet_config["merge_factor"] = 0.0
unet_config["video_kernel_size"] = [3, 1, 1]
unet_config["use_temporal_resblock"] = True
unet_config["use_temporal_attention"] = True
unet_config["disable_temporal_crossattention"] = not video_model_cross
else:
unet_config["use_temporal_resblock"] = False
unet_config["use_temporal_attention"] = False
return unet_config
def model_config_from_unet_config(unet_config, state_dict=None):
for model_config in model_list.models:
if model_config.matches(unet_config, state_dict):
return model_config(unet_config)
logging.error("no match {}".format(unet_config))
return None
def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False):
unet_config = detect_unet_config(state_dict, unet_key_prefix)
if unet_config is None:
return None
model_config = model_config_from_unet_config(unet_config, state_dict)
if model_config is None and use_base_if_no_match:
return model_list.BASE(unet_config)
else:
return model_config
def unet_prefix_from_state_dict(state_dict):
candidates = [
"model.diffusion_model.", # ldm/sgm models
"model.model.", # audio models
]
counts = {k: 0 for k in candidates}
for k in state_dict:
for c in candidates:
if k.startswith(c):
counts[c] += 1
break
top = max(counts, key=counts.get)
if counts[top] > 5:
return top
else:
return "model." # aura flow and others
def convert_config(unet_config):
new_config = unet_config.copy()
num_res_blocks = new_config.get("num_res_blocks", None)
channel_mult = new_config.get("channel_mult", None)
if isinstance(num_res_blocks, int):
num_res_blocks = len(channel_mult) * [num_res_blocks]
if "attention_resolutions" in new_config:
attention_resolutions = new_config.pop("attention_resolutions")
transformer_depth = new_config.get("transformer_depth", None)
transformer_depth_middle = new_config.get("transformer_depth_middle", None)
if isinstance(transformer_depth, int):
transformer_depth = len(channel_mult) * [transformer_depth]
if transformer_depth_middle is None:
transformer_depth_middle = transformer_depth[-1]
t_in = []
t_out = []
s = 1
for i in range(len(num_res_blocks)):
res = num_res_blocks[i]
d = 0
if s in attention_resolutions:
d = transformer_depth[i]
t_in += [d] * res
t_out += [d] * (res + 1)
s *= 2
transformer_depth = t_in
transformer_depth_output = t_out
new_config["transformer_depth"] = t_in
new_config["transformer_depth_output"] = t_out
new_config["transformer_depth_middle"] = transformer_depth_middle
new_config["num_res_blocks"] = num_res_blocks
return new_config
def unet_config_from_diffusers_unet(state_dict, dtype=None):
match = {}
transformer_depth = []
attn_res = 1
down_blocks = count_blocks(state_dict, "down_blocks.{}")
for i in range(down_blocks):
attn_blocks = count_blocks(state_dict, "down_blocks.{}.attentions.".format(i) + '{}')
res_blocks = count_blocks(state_dict, "down_blocks.{}.resnets.".format(i) + '{}')
for ab in range(attn_blocks):
transformer_count = count_blocks(state_dict, "down_blocks.{}.attentions.{}.transformer_blocks.".format(i, ab) + '{}')
transformer_depth.append(transformer_count)
if transformer_count > 0:
match["context_dim"] = state_dict["down_blocks.{}.attentions.{}.transformer_blocks.0.attn2.to_k.weight".format(i, ab)].shape[1]
attn_res *= 2
if attn_blocks == 0:
for i in range(res_blocks):
transformer_depth.append(0)
match["transformer_depth"] = transformer_depth
match["model_channels"] = state_dict["conv_in.weight"].shape[0]
match["in_channels"] = state_dict["conv_in.weight"].shape[1]
match["adm_in_channels"] = None
if "class_embedding.linear_1.weight" in state_dict:
match["adm_in_channels"] = state_dict["class_embedding.linear_1.weight"].shape[1]
elif "add_embedding.linear_1.weight" in state_dict:
match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1]
SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
'use_temporal_attention': False, 'use_temporal_resblock': False}
SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2560, 'dtype': dtype, 'in_channels': 4, 'model_channels': 384,
'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [0, 0, 4, 4, 4, 4, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 4,
'use_linear_in_transformer': True, 'context_dim': 1280, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0],
'use_temporal_attention': False, 'use_temporal_resblock': False}
SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2],
'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True,
'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
'use_temporal_attention': False, 'use_temporal_resblock': False}
SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2048, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1,
'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
'use_temporal_attention': False, 'use_temporal_resblock': False}
SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 1536, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1,
'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
'use_temporal_attention': False, 'use_temporal_resblock': False}
SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None,
'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0],
'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8,
'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
'use_temporal_attention': False, 'use_temporal_resblock': False}
SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 1,
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1],
'use_temporal_attention': False, 'use_temporal_resblock': False}
SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 0, 0], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 0,
'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0],
'use_temporal_attention': False, 'use_temporal_resblock': False}
SDXL_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
'use_temporal_attention': False, 'use_temporal_resblock': False}
SDXL_diffusers_ip2p = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 8, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
'use_temporal_attention': False, 'use_temporal_resblock': False}
SSD_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 4, 4], 'transformer_depth_output': [0, 0, 0, 1, 1, 2, 10, 4, 4],
'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
'use_temporal_attention': False, 'use_temporal_resblock': False}
Segmind_Vega = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 1, 1, 2, 2], 'transformer_depth_output': [0, 0, 0, 1, 1, 1, 2, 2, 2],
'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
'use_temporal_attention': False, 'use_temporal_resblock': False}
KOALA_700M = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 5], 'transformer_depth_output': [0, 0, 2, 2, 5, 5],
'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
'use_temporal_attention': False, 'use_temporal_resblock': False}
KOALA_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 6], 'transformer_depth_output': [0, 0, 2, 2, 6, 6],
'channel_mult': [1, 2, 4], 'transformer_depth_middle': 6, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
'use_temporal_attention': False, 'use_temporal_resblock': False}
SD09_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1],
'transformer_depth': [1, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True,
'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1],
'use_temporal_attention': False, 'use_temporal_resblock': False, 'disable_self_attentions': [True, False, False]}
SD_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1],
'transformer_depth': [0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': False,
'context_dim': 768, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 1, 1, 1, 1],
'use_temporal_attention': False, 'use_temporal_resblock': False}
supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SD_XS, SDXL_diffusers_ip2p]
for unet_config in supported_models:
matches = True
for k in match:
if match[k] != unet_config[k]:
matches = False
break
if matches:
return convert_config(unet_config)
return None
def model_config_from_diffusers_unet(state_dict):
unet_config = unet_config_from_diffusers_unet(state_dict)
if unet_config is not None:
return model_config_from_unet_config(unet_config)
return None
def convert_diffusers_mmdit(state_dict, output_prefix=""):
out_sd = {}
if 'transformer_blocks.0.attn.add_q_proj.weight' in state_dict: # SD3
num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.')
depth = state_dict["pos_embed.proj.weight"].shape[0] // 64
sd_map = utils.mmdit_to_diffusers({"depth": depth, "num_blocks": num_blocks}, output_prefix=output_prefix)
elif 'joint_transformer_blocks.0.attn.add_k_proj.weight' in state_dict: # AuraFlow
num_joint = count_blocks(state_dict, 'joint_transformer_blocks.{}.')
num_single = count_blocks(state_dict, 'single_transformer_blocks.{}.')
sd_map = utils.auraflow_to_diffusers({"n_double_layers": num_joint, "n_layers": num_joint + num_single}, output_prefix=output_prefix)
else:
return None
for k in sd_map:
weight = state_dict.get(k, None)
if weight is not None:
t = sd_map[k]
if not isinstance(t, str):
if len(t) > 2:
fun = t[2]
else:
fun = lambda a: a
offset = t[1]
if offset is not None:
old_weight = out_sd.get(t[0], None)
if old_weight is None:
old_weight = torch.empty_like(weight)
old_weight = old_weight.repeat([3] + [1] * (len(old_weight.shape) - 1))
w = old_weight.narrow(offset[0], offset[1], offset[2])
else:
old_weight = weight
w = weight
w[:] = fun(weight)
t = t[0]
out_sd[t] = old_weight
else:
out_sd[t] = weight
state_dict.pop(k)
return out_sd

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import re
import torch
import logging
# conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py
# =================#
# UNet Conversion #
# =================#
unet_conversion_map = [
# (stable-diffusion, HF Diffusers)
("time_embed.0.weight", "time_embedding.linear_1.weight"),
("time_embed.0.bias", "time_embedding.linear_1.bias"),
("time_embed.2.weight", "time_embedding.linear_2.weight"),
("time_embed.2.bias", "time_embedding.linear_2.bias"),
("input_blocks.0.0.weight", "conv_in.weight"),
("input_blocks.0.0.bias", "conv_in.bias"),
("out.0.weight", "conv_norm_out.weight"),
("out.0.bias", "conv_norm_out.bias"),
("out.2.weight", "conv_out.weight"),
("out.2.bias", "conv_out.bias"),
]
unet_conversion_map_resnet = [
# (stable-diffusion, HF Diffusers)
("in_layers.0", "norm1"),
("in_layers.2", "conv1"),
("out_layers.0", "norm2"),
("out_layers.3", "conv2"),
("emb_layers.1", "time_emb_proj"),
("skip_connection", "conv_shortcut"),
]
unet_conversion_map_layer = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
# loop over downblocks/upblocks
for j in range(2):
# loop over resnets/attentions for downblocks
hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0."
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
if i < 3:
# no attention layers in down_blocks.3
hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1."
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
for j in range(3):
# loop over resnets/attentions for upblocks
hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
sd_up_res_prefix = f"output_blocks.{3 * i + j}.0."
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
if i > 0:
# no attention layers in up_blocks.0
hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1."
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
if i < 3:
# no downsample in down_blocks.3
hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op."
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
# no upsample in up_blocks.3
hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}."
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
hf_mid_atn_prefix = "mid_block.attentions.0."
sd_mid_atn_prefix = "middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
for j in range(2):
hf_mid_res_prefix = f"mid_block.resnets.{j}."
sd_mid_res_prefix = f"middle_block.{2 * j}."
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def convert_unet_state_dict(unet_state_dict):
# buyer beware: this is a *brittle* function,
# and correct output requires that all of these pieces interact in
# the exact order in which I have arranged them.
mapping = {k: k for k in unet_state_dict.keys()}
for sd_name, hf_name in unet_conversion_map:
mapping[hf_name] = sd_name
for k, v in mapping.items():
if "resnets" in k:
for sd_part, hf_part in unet_conversion_map_resnet:
v = v.replace(hf_part, sd_part)
mapping[k] = v
for k, v in mapping.items():
for sd_part, hf_part in unet_conversion_map_layer:
v = v.replace(hf_part, sd_part)
mapping[k] = v
new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
return new_state_dict
# ================#
# VAE Conversion #
# ================#
vae_conversion_map = [
# (stable-diffusion, HF Diffusers)
("nin_shortcut", "conv_shortcut"),
("norm_out", "conv_norm_out"),
("mid.attn_1.", "mid_block.attentions.0."),
]
for i in range(4):
# down_blocks have two resnets
for j in range(2):
hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
sd_down_prefix = f"encoder.down.{i}.block.{j}."
vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
if i < 3:
hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
sd_downsample_prefix = f"down.{i}.downsample."
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
sd_upsample_prefix = f"up.{3 - i}.upsample."
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
# up_blocks have three resnets
# also, up blocks in hf are numbered in reverse from sd
for j in range(3):
hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
sd_up_prefix = f"decoder.up.{3 - i}.block.{j}."
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
hf_mid_res_prefix = f"mid_block.resnets.{i}."
sd_mid_res_prefix = f"mid.block_{i + 1}."
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
vae_conversion_map_attn = [
# (stable-diffusion, HF Diffusers)
("norm.", "group_norm."),
("q.", "query."),
("k.", "key."),
("v.", "value."),
("q.", "to_q."),
("k.", "to_k."),
("v.", "to_v."),
("proj_out.", "to_out.0."),
("proj_out.", "proj_attn."),
]
def reshape_weight_for_sd(w):
# convert HF linear weights to SD conv2d weights
return w.reshape(*w.shape, 1, 1)
def convert_vae_state_dict(vae_state_dict):
mapping = {k: k for k in vae_state_dict.keys()}
for k, v in mapping.items():
for sd_part, hf_part in vae_conversion_map:
v = v.replace(hf_part, sd_part)
mapping[k] = v
for k, v in mapping.items():
if "attentions" in k:
for sd_part, hf_part in vae_conversion_map_attn:
v = v.replace(hf_part, sd_part)
mapping[k] = v
new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
weights_to_convert = ["q", "k", "v", "proj_out"]
for k, v in new_state_dict.items():
for weight_name in weights_to_convert:
if f"mid.attn_1.{weight_name}.weight" in k:
logging.debug(f"Reshaping {k} for SD format")
new_state_dict[k] = reshape_weight_for_sd(v)
return new_state_dict
# =========================#
# Text Encoder Conversion #
# =========================#
textenc_conversion_lst = [
# (stable-diffusion, HF Diffusers)
("resblocks.", "text_model.encoder.layers."),
("ln_1", "layer_norm1"),
("ln_2", "layer_norm2"),
(".c_fc.", ".fc1."),
(".c_proj.", ".fc2."),
(".attn", ".self_attn"),
("ln_final.", "transformer.text_model.final_layer_norm."),
("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
textenc_pattern = re.compile("|".join(protected.keys()))
# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
code2idx = {"q": 0, "k": 1, "v": 2}
# This function exists because at the time of writing torch.cat can't do fp8 with cuda
def cat_tensors(tensors):
x = 0
for t in tensors:
x += t.shape[0]
shape = [x] + list(tensors[0].shape)[1:]
out = torch.empty(shape, device=tensors[0].device, dtype=tensors[0].dtype)
x = 0
for t in tensors:
out[x:x + t.shape[0]] = t
x += t.shape[0]
return out
def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""):
new_state_dict = {}
capture_qkv_weight = {}
capture_qkv_bias = {}
for k, v in text_enc_dict.items():
if not k.startswith(prefix):
continue
if (
k.endswith(".self_attn.q_proj.weight")
or k.endswith(".self_attn.k_proj.weight")
or k.endswith(".self_attn.v_proj.weight")
):
k_pre = k[: -len(".q_proj.weight")]
k_code = k[-len("q_proj.weight")]
if k_pre not in capture_qkv_weight:
capture_qkv_weight[k_pre] = [None, None, None]
capture_qkv_weight[k_pre][code2idx[k_code]] = v
continue
if (
k.endswith(".self_attn.q_proj.bias")
or k.endswith(".self_attn.k_proj.bias")
or k.endswith(".self_attn.v_proj.bias")
):
k_pre = k[: -len(".q_proj.bias")]
k_code = k[-len("q_proj.bias")]
if k_pre not in capture_qkv_bias:
capture_qkv_bias[k_pre] = [None, None, None]
capture_qkv_bias[k_pre][code2idx[k_code]] = v
continue
text_proj = "transformer.text_projection.weight"
if k.endswith(text_proj):
new_state_dict[k.replace(text_proj, "text_projection")] = v.transpose(0, 1).contiguous()
else:
relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k)
new_state_dict[relabelled_key] = v
for k_pre, tensors in capture_qkv_weight.items():
if None in tensors:
raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
new_state_dict[relabelled_key + ".in_proj_weight"] = cat_tensors(tensors)
for k_pre, tensors in capture_qkv_bias.items():
if None in tensors:
raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
new_state_dict[relabelled_key + ".in_proj_bias"] = cat_tensors(tensors)
return new_state_dict
def convert_text_enc_state_dict(text_enc_dict):
return text_enc_dict

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import torch
class LatentFormat:
scale_factor = 1.0
latent_channels = 4
latent_rgb_factors = None
taesd_decoder_name = None
def process_in(self, latent):
return latent * self.scale_factor
def process_out(self, latent):
return latent / self.scale_factor
class SD15(LatentFormat):
def __init__(self, scale_factor=0.18215):
self.scale_factor = scale_factor
self.latent_rgb_factors = [
# R G B
[0.3512, 0.2297, 0.3227],
[0.3250, 0.4974, 0.2350],
[-0.2829, 0.1762, 0.2721],
[-0.2120, -0.2616, -0.7177]
]
self.taesd_decoder_name = "taesd_decoder"
class SDXL(LatentFormat):
scale_factor = 0.13025
def __init__(self):
self.latent_rgb_factors = [
# R G B
[0.3920, 0.4054, 0.4549],
[-0.2634, -0.0196, 0.0653],
[0.0568, 0.1687, -0.0755],
[-0.3112, -0.2359, -0.2076]
]
self.taesd_decoder_name = "taesdxl_decoder"
class SDXL_Playground_2_5(LatentFormat):
def __init__(self):
self.scale_factor = 0.5
self.latents_mean = torch.tensor([-1.6574, 1.886, -1.383, 2.5155]).view(1, 4, 1, 1)
self.latents_std = torch.tensor([8.4927, 5.9022, 6.5498, 5.2299]).view(1, 4, 1, 1)
self.latent_rgb_factors = [
# R G B
[0.3920, 0.4054, 0.4549],
[-0.2634, -0.0196, 0.0653],
[0.0568, 0.1687, -0.0755],
[-0.3112, -0.2359, -0.2076]
]
self.taesd_decoder_name = "taesdxl_decoder"
def process_in(self, latent):
latents_mean = self.latents_mean.to(latent.device, latent.dtype)
latents_std = self.latents_std.to(latent.device, latent.dtype)
return (latent - latents_mean) * self.scale_factor / latents_std
def process_out(self, latent):
latents_mean = self.latents_mean.to(latent.device, latent.dtype)
latents_std = self.latents_std.to(latent.device, latent.dtype)
return latent * latents_std / self.scale_factor + latents_mean
class SD_X4(LatentFormat):
def __init__(self):
self.scale_factor = 0.08333
self.latent_rgb_factors = [
[-0.2340, -0.3863, -0.3257],
[0.0994, 0.0885, -0.0908],
[-0.2833, -0.2349, -0.3741],
[0.2523, -0.0055, -0.1651]
]
class SC_Prior(LatentFormat):
latent_channels = 16
def __init__(self):
self.scale_factor = 1.0
self.latent_rgb_factors = [
[-0.0326, -0.0204, -0.0127],
[-0.1592, -0.0427, 0.0216],
[0.0873, 0.0638, -0.0020],
[-0.0602, 0.0442, 0.1304],
[0.0800, -0.0313, -0.1796],
[-0.0810, -0.0638, -0.1581],
[0.1791, 0.1180, 0.0967],
[0.0740, 0.1416, 0.0432],
[-0.1745, -0.1888, -0.1373],
[0.2412, 0.1577, 0.0928],
[0.1908, 0.0998, 0.0682],
[0.0209, 0.0365, -0.0092],
[0.0448, -0.0650, -0.1728],
[-0.1658, -0.1045, -0.1308],
[0.0542, 0.1545, 0.1325],
[-0.0352, -0.1672, -0.2541]
]
class SC_B(LatentFormat):
def __init__(self):
self.scale_factor = 1.0 / 0.43
self.latent_rgb_factors = [
[0.1121, 0.2006, 0.1023],
[-0.2093, -0.0222, -0.0195],
[-0.3087, -0.1535, 0.0366],
[0.0290, -0.1574, -0.4078]
]
class SD3(LatentFormat):
latent_channels = 16
def __init__(self):
self.scale_factor = 1.5305
self.shift_factor = 0.0609
self.latent_rgb_factors = [
[-0.0645, 0.0177, 0.1052],
[0.0028, 0.0312, 0.0650],
[0.1848, 0.0762, 0.0360],
[0.0944, 0.0360, 0.0889],
[0.0897, 0.0506, -0.0364],
[-0.0020, 0.1203, 0.0284],
[0.0855, 0.0118, 0.0283],
[-0.0539, 0.0658, 0.1047],
[-0.0057, 0.0116, 0.0700],
[-0.0412, 0.0281, -0.0039],
[0.1106, 0.1171, 0.1220],
[-0.0248, 0.0682, -0.0481],
[0.0815, 0.0846, 0.1207],
[-0.0120, -0.0055, -0.0867],
[-0.0749, -0.0634, -0.0456],
[-0.1418, -0.1457, -0.1259]
]
self.taesd_decoder_name = "taesd3_decoder"
def process_in(self, latent):
return (latent - self.shift_factor) * self.scale_factor
def process_out(self, latent):
return (latent / self.scale_factor) + self.shift_factor
class StableAudio1(LatentFormat):
latent_channels = 64
class Flux(SD3):
def __init__(self):
self.scale_factor = 0.3611
self.shift_factor = 0.1159
self.latent_rgb_factors = [
[-0.0404, 0.0159, 0.0609],
[0.0043, 0.0298, 0.0850],
[0.0328, -0.0749, -0.0503],
[-0.0245, 0.0085, 0.0549],
[0.0966, 0.0894, 0.0530],
[0.0035, 0.0399, 0.0123],
[0.0583, 0.1184, 0.1262],
[-0.0191, -0.0206, -0.0306],
[-0.0324, 0.0055, 0.1001],
[0.0955, 0.0659, -0.0545],
[-0.0504, 0.0231, -0.0013],
[0.0500, -0.0008, -0.0088],
[0.0982, 0.0941, 0.0976],
[-0.1233, -0.0280, -0.0897],
[-0.0005, -0.0530, -0.0020],
[-0.1273, -0.0932, -0.0680]
]
def process_in(self, latent):
return (latent - self.shift_factor) * self.scale_factor
def process_out(self, latent):
return (latent / self.scale_factor) + self.shift_factor

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huggingface_guess/model_list.py Normal file
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import torch
from enum import Enum
from huggingface_guess import latent, utils, diffusers_convert
class ModelType(Enum):
EPS = 1
V_PREDICTION = 2
V_PREDICTION_EDM = 3
STABLE_CASCADE = 4
EDM = 5
FLOW = 6
V_PREDICTION_CONTINUOUS = 7
FLUX = 8
class BASE:
huggingface_repo = None
unet_config = {}
unet_extra_config = {
"num_heads": -1,
"num_head_channels": 64,
}
required_keys = {}
clip_prefix = []
clip_vision_prefix = None
noise_aug_config = None
sampling_settings = {}
latent_format = latent.LatentFormat
vae_key_prefix = ["first_stage_model."]
text_encoder_key_prefix = ["cond_stage_model."]
supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
memory_usage_factor = 2.0
manual_cast_dtype = None
@classmethod
def matches(s, unet_config, state_dict=None):
for k in s.unet_config:
if k not in unet_config or s.unet_config[k] != unet_config[k]:
return False
if state_dict is not None:
for k in s.required_keys:
if k not in state_dict:
return False
return True
def model_type(self, state_dict, prefix=""):
return ModelType.EPS
def inpaint_model(self):
return self.unet_config["in_channels"] > 4
def __init__(self, unet_config):
self.unet_config = unet_config.copy()
self.sampling_settings = self.sampling_settings.copy()
self.latent_format = self.latent_format()
for x in self.unet_extra_config:
self.unet_config[x] = self.unet_extra_config[x]
def process_clip_state_dict(self, state_dict):
state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True)
return state_dict
def process_unet_state_dict(self, state_dict):
return state_dict
def process_vae_state_dict(self, state_dict):
return state_dict
def process_clip_state_dict_for_saving(self, state_dict):
replace_prefix = {"": self.text_encoder_key_prefix[0]}
return utils.state_dict_prefix_replace(state_dict, replace_prefix)
def process_clip_vision_state_dict_for_saving(self, state_dict):
replace_prefix = {}
if self.clip_vision_prefix is not None:
replace_prefix[""] = self.clip_vision_prefix
return utils.state_dict_prefix_replace(state_dict, replace_prefix)
def process_unet_state_dict_for_saving(self, state_dict):
replace_prefix = {"": "model.diffusion_model."}
return utils.state_dict_prefix_replace(state_dict, replace_prefix)
def process_vae_state_dict_for_saving(self, state_dict):
replace_prefix = {"": self.vae_key_prefix[0]}
return utils.state_dict_prefix_replace(state_dict, replace_prefix)
class SD15(BASE):
huggingface_repo = "runwayml/stable-diffusion-v1-5"
unet_config = {
"context_dim": 768,
"model_channels": 320,
"use_linear_in_transformer": False,
"adm_in_channels": None,
"use_temporal_attention": False,
}
unet_extra_config = {
"num_heads": 8,
"num_head_channels": -1,
}
latent_format = latent.SD15
memory_usage_factor = 1.0
def process_clip_state_dict(self, state_dict):
k = list(state_dict.keys())
for x in k:
if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."):
y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.")
state_dict[y] = state_dict.pop(x)
if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in state_dict:
ids = state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids']
if ids.dtype == torch.float32:
state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()
replace_prefix = {}
replace_prefix["cond_stage_model."] = "clip_l."
state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
return state_dict
def process_clip_state_dict_for_saving(self, state_dict):
pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"]
for p in pop_keys:
if p in state_dict:
state_dict.pop(p)
replace_prefix = {"clip_l.": "cond_stage_model."}
return utils.state_dict_prefix_replace(state_dict, replace_prefix)
def clip_target(self, state_dict={}):
return ['clip_l']
class SD20(BASE):
huggingface_repo = "stabilityai/stable-diffusion-2-1"
unet_config = {
"context_dim": 1024,
"model_channels": 320,
"use_linear_in_transformer": True,
"adm_in_channels": None,
"use_temporal_attention": False,
}
unet_extra_config = {
"num_heads": -1,
"num_head_channels": 64,
"attn_precision": torch.float32,
}
latent_format = latent.SD15
memory_usage_factor = 1.0
def model_type(self, state_dict, prefix=""):
if self.unet_config["in_channels"] == 4: # SD2.0 inpainting models are not v prediction
k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix)
out = state_dict.get(k, None)
if out is not None and torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out.
return ModelType.V_PREDICTION
return ModelType.EPS
def process_clip_state_dict(self, state_dict):
replace_prefix = {}
replace_prefix["conditioner.embedders.0.model."] = "clip_h." # SD2 in sgm format
replace_prefix["cond_stage_model.model."] = "clip_h."
state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
state_dict = utils.clip_text_transformers_convert(state_dict, "clip_h.", "clip_h.transformer.")
return state_dict
def process_clip_state_dict_for_saving(self, state_dict):
replace_prefix = {}
replace_prefix["clip_h"] = "cond_stage_model.model"
state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix)
state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict)
return state_dict
def clip_target(self, state_dict={}):
return ['clip_h']
class SD21UnclipL(SD20):
unet_config = {
"context_dim": 1024,
"model_channels": 320,
"use_linear_in_transformer": True,
"adm_in_channels": 1536,
"use_temporal_attention": False,
}
clip_vision_prefix = "embedder.model.visual."
noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 768}
class SD21UnclipH(SD20):
unet_config = {
"context_dim": 1024,
"model_channels": 320,
"use_linear_in_transformer": True,
"adm_in_channels": 2048,
"use_temporal_attention": False,
}
clip_vision_prefix = "embedder.model.visual."
noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1024}
class SDXLRefiner(BASE):
huggingface_repo = "stabilityai/stable-diffusion-xl-refiner-1.0"
unet_config = {
"model_channels": 384,
"use_linear_in_transformer": True,
"context_dim": 1280,
"adm_in_channels": 2560,
"transformer_depth": [0, 0, 4, 4, 4, 4, 0, 0],
"use_temporal_attention": False,
}
latent_format = latent.SDXL
memory_usage_factor = 1.0
def process_clip_state_dict(self, state_dict):
keys_to_replace = {}
replace_prefix = {}
replace_prefix["conditioner.embedders.0.model."] = "clip_g."
state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.")
state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace)
return state_dict
def process_clip_state_dict_for_saving(self, state_dict):
replace_prefix = {}
state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g")
if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g:
state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids")
replace_prefix["clip_g"] = "conditioner.embedders.0.model"
state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix)
return state_dict_g
def clip_target(self, state_dict={}):
return ["clip_g"]
class SDXL(BASE):
huggingface_repo = "stabilityai/stable-diffusion-xl-base-1.0"
unet_config = {
"model_channels": 320,
"use_linear_in_transformer": True,
"transformer_depth": [0, 0, 2, 2, 10, 10],
"context_dim": 2048,
"adm_in_channels": 2816,
"use_temporal_attention": False,
}
latent_format = latent.SDXL
memory_usage_factor = 0.7
def model_type(self, state_dict, prefix=""):
if 'edm_mean' in state_dict and 'edm_std' in state_dict: # Playground V2.5
self.latent_format = latent.SDXL_Playground_2_5()
self.sampling_settings["sigma_data"] = 0.5
self.sampling_settings["sigma_max"] = 80.0
self.sampling_settings["sigma_min"] = 0.002
return ModelType.EDM
elif "edm_vpred.sigma_max" in state_dict:
self.sampling_settings["sigma_max"] = float(state_dict["edm_vpred.sigma_max"].item())
if "edm_vpred.sigma_min" in state_dict:
self.sampling_settings["sigma_min"] = float(state_dict["edm_vpred.sigma_min"].item())
return ModelType.V_PREDICTION_EDM
elif "v_pred" in state_dict:
return ModelType.V_PREDICTION
else:
return ModelType.EPS
def process_clip_state_dict(self, state_dict):
keys_to_replace = {}
replace_prefix = {}
replace_prefix["conditioner.embedders.0.transformer.text_model"] = "clip_l.transformer.text_model"
replace_prefix["conditioner.embedders.1.model."] = "clip_g."
state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace)
state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.")
return state_dict
def process_clip_state_dict_for_saving(self, state_dict):
replace_prefix = {}
keys_to_replace = {}
state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g")
for k in state_dict:
if k.startswith("clip_l"):
state_dict_g[k] = state_dict[k]
state_dict_g["clip_l.transformer.text_model.embeddings.position_ids"] = torch.arange(77).expand((1, -1))
pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"]
for p in pop_keys:
if p in state_dict_g:
state_dict_g.pop(p)
replace_prefix["clip_g"] = "conditioner.embedders.1.model"
replace_prefix["clip_l"] = "conditioner.embedders.0"
state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix)
return state_dict_g
def clip_target(self, state_dict={}):
return ['clip_l', 'clip_g']
class SSD1B(SDXL):
unet_config = {
"model_channels": 320,
"use_linear_in_transformer": True,
"transformer_depth": [0, 0, 2, 2, 4, 4],
"context_dim": 2048,
"adm_in_channels": 2816,
"use_temporal_attention": False,
}
class Segmind_Vega(SDXL):
unet_config = {
"model_channels": 320,
"use_linear_in_transformer": True,
"transformer_depth": [0, 0, 1, 1, 2, 2],
"context_dim": 2048,
"adm_in_channels": 2816,
"use_temporal_attention": False,
}
class KOALA_700M(SDXL):
unet_config = {
"model_channels": 320,
"use_linear_in_transformer": True,
"transformer_depth": [0, 2, 5],
"context_dim": 2048,
"adm_in_channels": 2816,
"use_temporal_attention": False,
}
class KOALA_1B(SDXL):
unet_config = {
"model_channels": 320,
"use_linear_in_transformer": True,
"transformer_depth": [0, 2, 6],
"context_dim": 2048,
"adm_in_channels": 2816,
"use_temporal_attention": False,
}
class SVD_img2vid(BASE):
unet_config = {
"model_channels": 320,
"in_channels": 8,
"use_linear_in_transformer": True,
"transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0],
"context_dim": 1024,
"adm_in_channels": 768,
"use_temporal_attention": True,
"use_temporal_resblock": True
}
unet_extra_config = {
"num_heads": -1,
"num_head_channels": 64,
"attn_precision": torch.float32,
}
clip_vision_prefix = "conditioner.embedders.0.open_clip.model.visual."
latent_format = latent.SD15
sampling_settings = {"sigma_max": 700.0, "sigma_min": 0.002}
def clip_target(self, state_dict={}):
return None
class SV3D_u(SVD_img2vid):
unet_config = {
"model_channels": 320,
"in_channels": 8,
"use_linear_in_transformer": True,
"transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0],
"context_dim": 1024,
"adm_in_channels": 256,
"use_temporal_attention": True,
"use_temporal_resblock": True
}
vae_key_prefix = ["conditioner.embedders.1.encoder."]
class SV3D_p(SV3D_u):
unet_config = {
"model_channels": 320,
"in_channels": 8,
"use_linear_in_transformer": True,
"transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0],
"context_dim": 1024,
"adm_in_channels": 1280,
"use_temporal_attention": True,
"use_temporal_resblock": True
}
class Stable_Zero123(BASE):
unet_config = {
"context_dim": 768,
"model_channels": 320,
"use_linear_in_transformer": False,
"adm_in_channels": None,
"use_temporal_attention": False,
"in_channels": 8,
}
unet_extra_config = {
"num_heads": 8,
"num_head_channels": -1,
}
required_keys = {
"cc_projection.weight": None,
"cc_projection.bias": None,
}
clip_vision_prefix = "cond_stage_model.model.visual."
latent_format = latent.SD15
def clip_target(self, state_dict={}):
return None
class SD_X4Upscaler(SD20):
unet_config = {
"context_dim": 1024,
"model_channels": 256,
'in_channels': 7,
"use_linear_in_transformer": True,
"adm_in_channels": None,
"use_temporal_attention": False,
}
unet_extra_config = {
"disable_self_attentions": [True, True, True, False],
"num_classes": 1000,
"num_heads": 8,
"num_head_channels": -1,
}
latent_format = latent.SD_X4
sampling_settings = {
"linear_start": 0.0001,
"linear_end": 0.02,
}
class Stable_Cascade_C(BASE):
unet_config = {
"stable_cascade_stage": 'c',
}
unet_extra_config = {}
latent_format = latent.SC_Prior
supported_inference_dtypes = [torch.bfloat16, torch.float32]
sampling_settings = {
"shift": 2.0,
}
vae_key_prefix = ["vae."]
text_encoder_key_prefix = ["text_encoder."]
clip_vision_prefix = "clip_l_vision."
def process_unet_state_dict(self, state_dict):
key_list = list(state_dict.keys())
for y in ["weight", "bias"]:
suffix = "in_proj_{}".format(y)
keys = filter(lambda a: a.endswith(suffix), key_list)
for k_from in keys:
weights = state_dict.pop(k_from)
prefix = k_from[:-(len(suffix) + 1)]
shape_from = weights.shape[0] // 3
for x in range(3):
p = ["to_q", "to_k", "to_v"]
k_to = "{}.{}.{}".format(prefix, p[x], y)
state_dict[k_to] = weights[shape_from * x:shape_from * (x + 1)]
return state_dict
def process_clip_state_dict(self, state_dict):
state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True)
if "clip_g.text_projection" in state_dict:
state_dict["clip_g.transformer.text_projection.weight"] = state_dict.pop("clip_g.text_projection").transpose(0, 1)
return state_dict
def clip_target(self, state_dict={}):
return ['clip_g']
class Stable_Cascade_B(Stable_Cascade_C):
unet_config = {
"stable_cascade_stage": 'b',
}
unet_extra_config = {}
latent_format = latent.SC_B
supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
sampling_settings = {
"shift": 1.0,
}
clip_vision_prefix = None
class SD15_instructpix2pix(SD15):
unet_config = {
"context_dim": 768,
"model_channels": 320,
"use_linear_in_transformer": False,
"adm_in_channels": None,
"use_temporal_attention": False,
"in_channels": 8,
}
class SDXL_instructpix2pix(SDXL):
unet_config = {
"model_channels": 320,
"use_linear_in_transformer": True,
"transformer_depth": [0, 0, 2, 2, 10, 10],
"context_dim": 2048,
"adm_in_channels": 2816,
"use_temporal_attention": False,
"in_channels": 8,
}
class SD3(BASE):
huggingface_repo = "stabilityai/stable-diffusion-3-medium-diffusers"
unet_config = {
"in_channels": 16,
"pos_embed_scaling_factor": None,
}
sampling_settings = {
"shift": 3.0,
}
unet_extra_config = {}
latent_format = latent.SD3
memory_usage_factor = 1.2
text_encoder_key_prefix = ["text_encoders."]
def clip_target(self, state_dict={}):
clip_l = False
clip_g = False
t5 = False
dtype_t5 = None
pref = self.text_encoder_key_prefix[0]
if "{}clip_l.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict:
clip_l = True
if "{}clip_g.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict:
clip_g = True
t5_key = "{}t5xxl.transformer.encoder.final_layer_norm.weight".format(pref)
if t5_key in state_dict:
t5 = True
dtype_t5 = state_dict[t5_key].dtype
return dict(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5)
class StableAudio(BASE):
unet_config = {
"audio_model": "dit1.0",
}
sampling_settings = {"sigma_max": 500.0, "sigma_min": 0.03}
unet_extra_config = {}
latent_format = latent.StableAudio1
text_encoder_key_prefix = ["text_encoders."]
vae_key_prefix = ["pretransform.model."]
def process_unet_state_dict(self, state_dict):
for k in list(state_dict.keys()):
if k.endswith(".cross_attend_norm.beta") or k.endswith(".ff_norm.beta") or k.endswith(".pre_norm.beta"): # These weights are all zero
state_dict.pop(k)
return state_dict
def process_unet_state_dict_for_saving(self, state_dict):
replace_prefix = {"": "model.model."}
return utils.state_dict_prefix_replace(state_dict, replace_prefix)
def clip_target(self, state_dict={}):
return ['sa_t5']
class AuraFlow(BASE):
unet_config = {
"cond_seq_dim": 2048,
}
sampling_settings = {
"multiplier": 1.0,
"shift": 1.73,
}
unet_extra_config = {}
latent_format = latent.SDXL
vae_key_prefix = ["vae."]
text_encoder_key_prefix = ["text_encoders."]
def clip_target(self, state_dict={}):
return ['aura_t5']
class HunyuanDiT(BASE):
unet_config = {
"image_model": "hydit",
}
unet_extra_config = {
"attn_precision": torch.float32,
}
sampling_settings = {
"linear_start": 0.00085,
"linear_end": 0.018,
}
latent_format = latent.SDXL
vae_key_prefix = ["vae."]
text_encoder_key_prefix = ["text_encoders."]
def clip_target(self, state_dict={}):
return ['hunyuan_t5']
class HunyuanDiT1(HunyuanDiT):
unet_config = {
"image_model": "hydit1",
}
unet_extra_config = {}
sampling_settings = {
"linear_start": 0.00085,
"linear_end": 0.03,
}
class Flux(BASE):
unet_config = {
"image_model": "flux",
"guidance_embed": True,
}
sampling_settings = {
}
unet_extra_config = {}
latent_format = latent.Flux
memory_usage_factor = 2.6
supported_inference_dtypes = [torch.bfloat16, torch.float32]
vae_key_prefix = ["vae."]
text_encoder_key_prefix = ["text_encoders."]
def clip_target(self, state_dict={}):
dtype_t5 = None
pref = self.text_encoder_key_prefix[0]
t5_key = "{}t5xxl.transformer.encoder.final_layer_norm.weight".format(pref)
if t5_key in state_dict:
dtype_t5 = state_dict[t5_key].dtype
return dict(t5=True, dtype_t5=dtype_t5)
class FluxSchnell(Flux):
unet_config = {
"image_model": "flux",
"guidance_embed": False,
}
sampling_settings = {
"multiplier": 1.0,
"shift": 1.0,
}
models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, HunyuanDiT, HunyuanDiT1, Flux, FluxSchnell]
models += [SVD_img2vid]

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import torch
import math
import struct
def calculate_parameters(sd, prefix=""):
params = 0
for k in sd.keys():
if k.startswith(prefix):
w = sd[k]
params += w.nelement()
return params
def weight_dtype(sd, prefix=""):
dtypes = {}
for k in sd.keys():
if k.startswith(prefix):
w = sd[k]
dtypes[w.dtype] = dtypes.get(w.dtype, 0) + 1
return max(dtypes, key=dtypes.get)
def state_dict_key_replace(state_dict, keys_to_replace):
for x in keys_to_replace:
if x in state_dict:
state_dict[keys_to_replace[x]] = state_dict.pop(x)
return state_dict
def state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=False):
if filter_keys:
out = {}
else:
out = state_dict
for rp in replace_prefix:
replace = list(map(lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp):])), filter(lambda a: a.startswith(rp), state_dict.keys())))
for x in replace:
w = state_dict.pop(x[0])
out[x[1]] = w
return out
def transformers_convert(sd, prefix_from, prefix_to, number):
keys_to_replace = {
"{}positional_embedding": "{}embeddings.position_embedding.weight",
"{}token_embedding.weight": "{}embeddings.token_embedding.weight",
"{}ln_final.weight": "{}final_layer_norm.weight",
"{}ln_final.bias": "{}final_layer_norm.bias",
}
for k in keys_to_replace:
x = k.format(prefix_from)
if x in sd:
sd[keys_to_replace[k].format(prefix_to)] = sd.pop(x)
resblock_to_replace = {
"ln_1": "layer_norm1",
"ln_2": "layer_norm2",
"mlp.c_fc": "mlp.fc1",
"mlp.c_proj": "mlp.fc2",
"attn.out_proj": "self_attn.out_proj",
}
for resblock in range(number):
for x in resblock_to_replace:
for y in ["weight", "bias"]:
k = "{}transformer.resblocks.{}.{}.{}".format(prefix_from, resblock, x, y)
k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, resblock_to_replace[x], y)
if k in sd:
sd[k_to] = sd.pop(k)
for y in ["weight", "bias"]:
k_from = "{}transformer.resblocks.{}.attn.in_proj_{}".format(prefix_from, resblock, y)
if k_from in sd:
weights = sd.pop(k_from)
shape_from = weights.shape[0] // 3
for x in range(3):
p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"]
k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, p[x], y)
sd[k_to] = weights[shape_from * x:shape_from * (x + 1)]
return sd
def clip_text_transformers_convert(sd, prefix_from, prefix_to):
sd = transformers_convert(sd, prefix_from, "{}text_model.".format(prefix_to), 32)
tp = "{}text_projection.weight".format(prefix_from)
if tp in sd:
sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp)
tp = "{}text_projection".format(prefix_from)
if tp in sd:
sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp).transpose(0, 1).contiguous()
return sd
UNET_MAP_ATTENTIONS = {
"proj_in.weight",
"proj_in.bias",
"proj_out.weight",
"proj_out.bias",
"norm.weight",
"norm.bias",
}
TRANSFORMER_BLOCKS = {
"norm1.weight",
"norm1.bias",
"norm2.weight",
"norm2.bias",
"norm3.weight",
"norm3.bias",
"attn1.to_q.weight",
"attn1.to_k.weight",
"attn1.to_v.weight",
"attn1.to_out.0.weight",
"attn1.to_out.0.bias",
"attn2.to_q.weight",
"attn2.to_k.weight",
"attn2.to_v.weight",
"attn2.to_out.0.weight",
"attn2.to_out.0.bias",
"ff.net.0.proj.weight",
"ff.net.0.proj.bias",
"ff.net.2.weight",
"ff.net.2.bias",
}
UNET_MAP_RESNET = {
"in_layers.2.weight": "conv1.weight",
"in_layers.2.bias": "conv1.bias",
"emb_layers.1.weight": "time_emb_proj.weight",
"emb_layers.1.bias": "time_emb_proj.bias",
"out_layers.3.weight": "conv2.weight",
"out_layers.3.bias": "conv2.bias",
"skip_connection.weight": "conv_shortcut.weight",
"skip_connection.bias": "conv_shortcut.bias",
"in_layers.0.weight": "norm1.weight",
"in_layers.0.bias": "norm1.bias",
"out_layers.0.weight": "norm2.weight",
"out_layers.0.bias": "norm2.bias",
}
UNET_MAP_BASIC = {
("label_emb.0.0.weight", "class_embedding.linear_1.weight"),
("label_emb.0.0.bias", "class_embedding.linear_1.bias"),
("label_emb.0.2.weight", "class_embedding.linear_2.weight"),
("label_emb.0.2.bias", "class_embedding.linear_2.bias"),
("label_emb.0.0.weight", "add_embedding.linear_1.weight"),
("label_emb.0.0.bias", "add_embedding.linear_1.bias"),
("label_emb.0.2.weight", "add_embedding.linear_2.weight"),
("label_emb.0.2.bias", "add_embedding.linear_2.bias"),
("input_blocks.0.0.weight", "conv_in.weight"),
("input_blocks.0.0.bias", "conv_in.bias"),
("out.0.weight", "conv_norm_out.weight"),
("out.0.bias", "conv_norm_out.bias"),
("out.2.weight", "conv_out.weight"),
("out.2.bias", "conv_out.bias"),
("time_embed.0.weight", "time_embedding.linear_1.weight"),
("time_embed.0.bias", "time_embedding.linear_1.bias"),
("time_embed.2.weight", "time_embedding.linear_2.weight"),
("time_embed.2.bias", "time_embedding.linear_2.bias")
}
def unet_to_diffusers(unet_config):
if "num_res_blocks" not in unet_config:
return {}
num_res_blocks = unet_config["num_res_blocks"]
channel_mult = unet_config["channel_mult"]
transformer_depth = unet_config["transformer_depth"][:]
transformer_depth_output = unet_config["transformer_depth_output"][:]
num_blocks = len(channel_mult)
transformers_mid = unet_config.get("transformer_depth_middle", None)
diffusers_unet_map = {}
for x in range(num_blocks):
n = 1 + (num_res_blocks[x] + 1) * x
for i in range(num_res_blocks[x]):
for b in UNET_MAP_RESNET:
diffusers_unet_map["down_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "input_blocks.{}.0.{}".format(n, b)
num_transformers = transformer_depth.pop(0)
if num_transformers > 0:
for b in UNET_MAP_ATTENTIONS:
diffusers_unet_map["down_blocks.{}.attentions.{}.{}".format(x, i, b)] = "input_blocks.{}.1.{}".format(n, b)
for t in range(num_transformers):
for b in TRANSFORMER_BLOCKS:
diffusers_unet_map["down_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "input_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b)
n += 1
for k in ["weight", "bias"]:
diffusers_unet_map["down_blocks.{}.downsamplers.0.conv.{}".format(x, k)] = "input_blocks.{}.0.op.{}".format(n, k)
i = 0
for b in UNET_MAP_ATTENTIONS:
diffusers_unet_map["mid_block.attentions.{}.{}".format(i, b)] = "middle_block.1.{}".format(b)
for t in range(transformers_mid):
for b in TRANSFORMER_BLOCKS:
diffusers_unet_map["mid_block.attentions.{}.transformer_blocks.{}.{}".format(i, t, b)] = "middle_block.1.transformer_blocks.{}.{}".format(t, b)
for i, n in enumerate([0, 2]):
for b in UNET_MAP_RESNET:
diffusers_unet_map["mid_block.resnets.{}.{}".format(i, UNET_MAP_RESNET[b])] = "middle_block.{}.{}".format(n, b)
num_res_blocks = list(reversed(num_res_blocks))
for x in range(num_blocks):
n = (num_res_blocks[x] + 1) * x
l = num_res_blocks[x] + 1
for i in range(l):
c = 0
for b in UNET_MAP_RESNET:
diffusers_unet_map["up_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "output_blocks.{}.0.{}".format(n, b)
c += 1
num_transformers = transformer_depth_output.pop()
if num_transformers > 0:
c += 1
for b in UNET_MAP_ATTENTIONS:
diffusers_unet_map["up_blocks.{}.attentions.{}.{}".format(x, i, b)] = "output_blocks.{}.1.{}".format(n, b)
for t in range(num_transformers):
for b in TRANSFORMER_BLOCKS:
diffusers_unet_map["up_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "output_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b)
if i == l - 1:
for k in ["weight", "bias"]:
diffusers_unet_map["up_blocks.{}.upsamplers.0.conv.{}".format(x, k)] = "output_blocks.{}.{}.conv.{}".format(n, c, k)
n += 1
for k in UNET_MAP_BASIC:
diffusers_unet_map[k[1]] = k[0]
return diffusers_unet_map
def swap_scale_shift(weight):
shift, scale = weight.chunk(2, dim=0)
new_weight = torch.cat([scale, shift], dim=0)
return new_weight
MMDIT_MAP_BASIC = {
("context_embedder.bias", "context_embedder.bias"),
("context_embedder.weight", "context_embedder.weight"),
("t_embedder.mlp.0.bias", "time_text_embed.timestep_embedder.linear_1.bias"),
("t_embedder.mlp.0.weight", "time_text_embed.timestep_embedder.linear_1.weight"),
("t_embedder.mlp.2.bias", "time_text_embed.timestep_embedder.linear_2.bias"),
("t_embedder.mlp.2.weight", "time_text_embed.timestep_embedder.linear_2.weight"),
("x_embedder.proj.bias", "pos_embed.proj.bias"),
("x_embedder.proj.weight", "pos_embed.proj.weight"),
("y_embedder.mlp.0.bias", "time_text_embed.text_embedder.linear_1.bias"),
("y_embedder.mlp.0.weight", "time_text_embed.text_embedder.linear_1.weight"),
("y_embedder.mlp.2.bias", "time_text_embed.text_embedder.linear_2.bias"),
("y_embedder.mlp.2.weight", "time_text_embed.text_embedder.linear_2.weight"),
("pos_embed", "pos_embed.pos_embed"),
("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift),
("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift),
("final_layer.linear.bias", "proj_out.bias"),
("final_layer.linear.weight", "proj_out.weight"),
}
MMDIT_MAP_BLOCK = {
("context_block.adaLN_modulation.1.bias", "norm1_context.linear.bias"),
("context_block.adaLN_modulation.1.weight", "norm1_context.linear.weight"),
("context_block.attn.proj.bias", "attn.to_add_out.bias"),
("context_block.attn.proj.weight", "attn.to_add_out.weight"),
("context_block.mlp.fc1.bias", "ff_context.net.0.proj.bias"),
("context_block.mlp.fc1.weight", "ff_context.net.0.proj.weight"),
("context_block.mlp.fc2.bias", "ff_context.net.2.bias"),
("context_block.mlp.fc2.weight", "ff_context.net.2.weight"),
("x_block.adaLN_modulation.1.bias", "norm1.linear.bias"),
("x_block.adaLN_modulation.1.weight", "norm1.linear.weight"),
("x_block.attn.proj.bias", "attn.to_out.0.bias"),
("x_block.attn.proj.weight", "attn.to_out.0.weight"),
("x_block.mlp.fc1.bias", "ff.net.0.proj.bias"),
("x_block.mlp.fc1.weight", "ff.net.0.proj.weight"),
("x_block.mlp.fc2.bias", "ff.net.2.bias"),
("x_block.mlp.fc2.weight", "ff.net.2.weight"),
}
def mmdit_to_diffusers(mmdit_config, output_prefix=""):
key_map = {}
depth = mmdit_config.get("depth", 0)
num_blocks = mmdit_config.get("num_blocks", depth)
for i in range(num_blocks):
block_from = "transformer_blocks.{}".format(i)
block_to = "{}joint_blocks.{}".format(output_prefix, i)
offset = depth * 64
for end in ("weight", "bias"):
k = "{}.attn.".format(block_from)
qkv = "{}.x_block.attn.qkv.{}".format(block_to, end)
key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset))
key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset))
key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
qkv = "{}.context_block.attn.qkv.{}".format(block_to, end)
key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, offset))
key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, offset, offset))
key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
for k in MMDIT_MAP_BLOCK:
key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0])
map_basic = MMDIT_MAP_BASIC.copy()
map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.bias".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.bias".format(depth - 1), swap_scale_shift))
map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.weight".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.weight".format(depth - 1), swap_scale_shift))
for k in map_basic:
if len(k) > 2:
key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2])
else:
key_map[k[1]] = "{}{}".format(output_prefix, k[0])
return key_map
def auraflow_to_diffusers(mmdit_config, output_prefix=""):
n_double_layers = mmdit_config.get("n_double_layers", 0)
n_layers = mmdit_config.get("n_layers", 0)
key_map = {}
for i in range(n_layers):
if i < n_double_layers:
index = i
prefix_from = "joint_transformer_blocks"
prefix_to = "{}double_layers".format(output_prefix)
block_map = {
"attn.to_q.weight": "attn.w2q.weight",
"attn.to_k.weight": "attn.w2k.weight",
"attn.to_v.weight": "attn.w2v.weight",
"attn.to_out.0.weight": "attn.w2o.weight",
"attn.add_q_proj.weight": "attn.w1q.weight",
"attn.add_k_proj.weight": "attn.w1k.weight",
"attn.add_v_proj.weight": "attn.w1v.weight",
"attn.to_add_out.weight": "attn.w1o.weight",
"ff.linear_1.weight": "mlpX.c_fc1.weight",
"ff.linear_2.weight": "mlpX.c_fc2.weight",
"ff.out_projection.weight": "mlpX.c_proj.weight",
"ff_context.linear_1.weight": "mlpC.c_fc1.weight",
"ff_context.linear_2.weight": "mlpC.c_fc2.weight",
"ff_context.out_projection.weight": "mlpC.c_proj.weight",
"norm1.linear.weight": "modX.1.weight",
"norm1_context.linear.weight": "modC.1.weight",
}
else:
index = i - n_double_layers
prefix_from = "single_transformer_blocks"
prefix_to = "{}single_layers".format(output_prefix)
block_map = {
"attn.to_q.weight": "attn.w1q.weight",
"attn.to_k.weight": "attn.w1k.weight",
"attn.to_v.weight": "attn.w1v.weight",
"attn.to_out.0.weight": "attn.w1o.weight",
"norm1.linear.weight": "modCX.1.weight",
"ff.linear_1.weight": "mlp.c_fc1.weight",
"ff.linear_2.weight": "mlp.c_fc2.weight",
"ff.out_projection.weight": "mlp.c_proj.weight"
}
for k in block_map:
key_map["{}.{}.{}".format(prefix_from, index, k)] = "{}.{}.{}".format(prefix_to, index, block_map[k])
MAP_BASIC = {
("positional_encoding", "pos_embed.pos_embed"),
("register_tokens", "register_tokens"),
("t_embedder.mlp.0.weight", "time_step_proj.linear_1.weight"),
("t_embedder.mlp.0.bias", "time_step_proj.linear_1.bias"),
("t_embedder.mlp.2.weight", "time_step_proj.linear_2.weight"),
("t_embedder.mlp.2.bias", "time_step_proj.linear_2.bias"),
("cond_seq_linear.weight", "context_embedder.weight"),
("init_x_linear.weight", "pos_embed.proj.weight"),
("init_x_linear.bias", "pos_embed.proj.bias"),
("final_linear.weight", "proj_out.weight"),
("modF.1.weight", "norm_out.linear.weight", swap_scale_shift),
}
for k in MAP_BASIC:
if len(k) > 2:
key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2])
else:
key_map[k[1]] = "{}{}".format(output_prefix, k[0])
return key_map
def repeat_to_batch_size(tensor, batch_size, dim=0):
if tensor.shape[dim] > batch_size:
return tensor.narrow(dim, 0, batch_size)
elif tensor.shape[dim] < batch_size:
return tensor.repeat(dim * [1] + [math.ceil(batch_size / tensor.shape[dim])] + [1] * (len(tensor.shape) - 1 - dim)).narrow(dim, 0, batch_size)
return tensor
def resize_to_batch_size(tensor, batch_size):
in_batch_size = tensor.shape[0]
if in_batch_size == batch_size:
return tensor
if batch_size <= 1:
return tensor[:batch_size]
output = torch.empty([batch_size] + list(tensor.shape)[1:], dtype=tensor.dtype, device=tensor.device)
if batch_size < in_batch_size:
scale = (in_batch_size - 1) / (batch_size - 1)
for i in range(batch_size):
output[i] = tensor[min(round(i * scale), in_batch_size - 1)]
else:
scale = in_batch_size / batch_size
for i in range(batch_size):
output[i] = tensor[min(math.floor((i + 0.5) * scale), in_batch_size - 1)]
return output
def convert_sd_to(state_dict, dtype):
keys = list(state_dict.keys())
for k in keys:
state_dict[k] = state_dict[k].to(dtype)
return state_dict
def safetensors_header(safetensors_path, max_size=100 * 1024 * 1024):
with open(safetensors_path, "rb") as f:
header = f.read(8)
length_of_header = struct.unpack('<Q', header)[0]
if length_of_header > max_size:
return None
return f.read(length_of_header)
def set_attr(obj, attr, value):
attrs = attr.split(".")
for name in attrs[:-1]:
obj = getattr(obj, name)
prev = getattr(obj, attrs[-1])
setattr(obj, attrs[-1], value)
return prev
def set_attr_param(obj, attr, value):
return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False))
def copy_to_param(obj, attr, value):
# inplace update tensor instead of replacing it
attrs = attr.split(".")
for name in attrs[:-1]:
obj = getattr(obj, name)
prev = getattr(obj, attrs[-1])
prev.data.copy_(value)
def get_attr(obj, attr):
attrs = attr.split(".")
for name in attrs:
obj = getattr(obj, name)
return obj