Hugging Face's logo

Datasets:
John6666
/
twodgirl_diffusers_to_flux_script

Tags:
text-to-image
flux
Merge
License:
Dataset card Files
xet
 Community
twodgirl_diffusers_to_flux_script / map_from_diffusers.py
John6666's picture
John6666
Upload 6 files
b716b6e verified
Raw History Blame Contribute Delete
16.8 kB
import torch
##
# Code from huggingface/twodgirl
# License: apache-2.0
#
# Reverse of the script from
# https://github.com/huggingface/diffusers/blob/main/scripts/convert_flux_to_diffusers.py
def swap_scale_shift(weight):
shift, scale = weight.chunk(2, dim=0)
new_weight = torch.cat([scale, shift], dim=0)
return new_weight
def convert_diffusers_to_flux_checkpoint(
converted_state_dict,
num_layers=19,
num_single_layers=38,
inner_dim=3072,
mlp_ratio=4.0
):
"""
84c3df90-9df5-48c2-9fa0-1e81324e61bf
Reverses the conversion from Diffusers checkpoint to Flux Transformer format.
This function takes a state dictionary that has been converted to the Diffusers format
and transforms it back to the original Flux Transformer checkpoint format. It systematically
maps each parameter from the Diffusers naming and structure back to the original format,
handling different components such as embeddings, transformer blocks, and normalization layers.
Args:
converted_state_dict (dict): The state dictionary in Diffusers format to be converted back.
num_layers (int, optional): Number of transformer layers in the original model. Default is 19.
num_single_layers (int, optional): Number of single transformer layers. Default is 38.
inner_dim (int, optional): The inner dimension size for MLP layers. Default is 3072.
mlp_ratio (float, optional): The ratio to compute the MLP hidden dimension. Default is 4.0.
Returns:
dict: The original state dictionary in Flux Transformer checkpoint format.
"""
# Initialize an empty dictionary to store the original state dictionary.
original_state_dict = {}
# -------------------------
# Handle Time Text Embeddings
# -------------------------
# Map the timestep embedder weights and biases back to "time_in.in_layer"
original_state_dict["time_in.in_layer.weight"] = converted_state_dict.pop(
"time_text_embed.timestep_embedder.linear_1.weight"
)
original_state_dict["time_in.in_layer.bias"] = converted_state_dict.pop(
"time_text_embed.timestep_embedder.linear_1.bias"
)
original_state_dict["time_in.out_layer.weight"] = converted_state_dict.pop(
"time_text_embed.timestep_embedder.linear_2.weight"
)
original_state_dict["time_in.out_layer.bias"] = converted_state_dict.pop(
"time_text_embed.timestep_embedder.linear_2.bias"
)
# Map the text embedder weights and biases back to "vector_in.in_layer"
original_state_dict["vector_in.in_layer.weight"] = converted_state_dict.pop(
"time_text_embed.text_embedder.linear_1.weight"
)
original_state_dict["vector_in.in_layer.bias"] = converted_state_dict.pop(
"time_text_embed.text_embedder.linear_1.bias"
)
original_state_dict["vector_in.out_layer.weight"] = converted_state_dict.pop(
"time_text_embed.text_embedder.linear_2.weight"
)
original_state_dict["vector_in.out_layer.bias"] = converted_state_dict.pop(
"time_text_embed.text_embedder.linear_2.bias"
)
# -------------------------
# Handle Guidance Embeddings (if present)
# -------------------------
# Check if any keys related to guidance are present in the converted_state_dict
has_guidance = any("guidance_embedder" in k for k in converted_state_dict)
if has_guidance:
# Map the guidance embedder weights and biases back to "guidance_in.in_layer"
original_state_dict["guidance_in.in_layer.weight"] = converted_state_dict.pop(
"time_text_embed.guidance_embedder.linear_1.weight"
)
original_state_dict["guidance_in.in_layer.bias"] = converted_state_dict.pop(
"time_text_embed.guidance_embedder.linear_1.bias"
)
original_state_dict["guidance_in.out_layer.weight"] = converted_state_dict.pop(
"time_text_embed.guidance_embedder.linear_2.weight"
)
original_state_dict["guidance_in.out_layer.bias"] = converted_state_dict.pop(
"time_text_embed.guidance_embedder.linear_2.bias"
)
# -------------------------
# Handle Context and Image Embeddings
# -------------------------
# Map the context embedder weights and biases back to "txt_in"
original_state_dict["txt_in.weight"] = converted_state_dict.pop("context_embedder.weight")
original_state_dict["txt_in.bias"] = converted_state_dict.pop("context_embedder.bias")
# Map the image embedder weights and biases back to "img_in"
original_state_dict["img_in.weight"] = converted_state_dict.pop("x_embedder.weight")
original_state_dict["img_in.bias"] = converted_state_dict.pop("x_embedder.bias")
# -------------------------
# Handle Transformer Blocks
# -------------------------
for i in range(num_layers):
# Define the prefix for the current transformer block in the converted_state_dict
block_prefix = f"transformer_blocks.{i}."
# -------------------------
# Map Norm1 Layers
# -------------------------
# Map the norm1 linear layer weights and biases back to "double_blocks.{i}.img_mod.lin"
original_state_dict[f"double_blocks.{i}.img_mod.lin.weight"] = converted_state_dict.pop(
f"{block_prefix}norm1.linear.weight"
)
original_state_dict[f"double_blocks.{i}.img_mod.lin.bias"] = converted_state_dict.pop(
f"{block_prefix}norm1.linear.bias"
)
# Map the norm1_context linear layer weights and biases back to "double_blocks.{i}.txt_mod.lin"
original_state_dict[f"double_blocks.{i}.txt_mod.lin.weight"] = converted_state_dict.pop(
f"{block_prefix}norm1_context.linear.weight"
)
original_state_dict[f"double_blocks.{i}.txt_mod.lin.bias"] = converted_state_dict.pop(
f"{block_prefix}norm1_context.linear.bias"
)
# -------------------------
# Handle Q, K, V Projections for Image Attention
# -------------------------
# Retrieve and combine the Q, K, V weights for image attention
q_weight = converted_state_dict.pop(f"{block_prefix}attn.to_q.weight")
k_weight = converted_state_dict.pop(f"{block_prefix}attn.to_k.weight")
v_weight = converted_state_dict.pop(f"{block_prefix}attn.to_v.weight")
# Concatenate along the first dimension to form the combined QKV weight
original_state_dict[f"double_blocks.{i}.img_attn.qkv.weight"] = torch.cat([q_weight, k_weight, v_weight], dim=0)
# Retrieve and combine the Q, K, V biases for image attention
q_bias = converted_state_dict.pop(f"{block_prefix}attn.to_q.bias")
k_bias = converted_state_dict.pop(f"{block_prefix}attn.to_k.bias")
v_bias = converted_state_dict.pop(f"{block_prefix}attn.to_v.bias")
# Concatenate along the first dimension to form the combined QKV bias
original_state_dict[f"double_blocks.{i}.img_attn.qkv.bias"] = torch.cat([q_bias, k_bias, v_bias], dim=0)
# -------------------------
# Handle Q, K, V Projections for Text Attention
# -------------------------
# Retrieve and combine the additional Q, K, V projections for context (text) attention
add_q_weight = converted_state_dict.pop(f"{block_prefix}attn.add_q_proj.weight")
add_k_weight = converted_state_dict.pop(f"{block_prefix}attn.add_k_proj.weight")
add_v_weight = converted_state_dict.pop(f"{block_prefix}attn.add_v_proj.weight")
# Concatenate along the first dimension to form the combined QKV weight for text
original_state_dict[f"double_blocks.{i}.txt_attn.qkv.weight"] = torch.cat([add_q_weight, add_k_weight, add_v_weight], dim=0)
add_q_bias = converted_state_dict.pop(f"{block_prefix}attn.add_q_proj.bias")
add_k_bias = converted_state_dict.pop(f"{block_prefix}attn.add_k_proj.bias")
add_v_bias = converted_state_dict.pop(f"{block_prefix}attn.add_v_proj.bias")
# Concatenate along the first dimension to form the combined QKV bias for text
original_state_dict[f"double_blocks.{i}.txt_attn.qkv.bias"] = torch.cat([add_q_bias, add_k_bias, add_v_bias], dim=0)
# -------------------------
# Map Attention Norm Layers
# -------------------------
# Map the attention query norm weights back to "double_blocks.{i}.img_attn.norm.query_norm.scale"
original_state_dict[f"double_blocks.{i}.img_attn.norm.query_norm.scale"] = converted_state_dict.pop(
f"{block_prefix}attn.norm_q.weight"
)
# Map the attention key norm weights back to "double_blocks.{i}.img_attn.norm.key_norm.scale"
original_state_dict[f"double_blocks.{i}.img_attn.norm.key_norm.scale"] = converted_state_dict.pop(
f"{block_prefix}attn.norm_k.weight"
)
# Map the added attention query norm weights back to "double_blocks.{i}.txt_attn.norm.query_norm.scale"
original_state_dict[f"double_blocks.{i}.txt_attn.norm.query_norm.scale"] = converted_state_dict.pop(
f"{block_prefix}attn.norm_added_q.weight"
)
# Map the added attention key norm weights back to "double_blocks.{i}.txt_attn.norm.key_norm.scale"
original_state_dict[f"double_blocks.{i}.txt_attn.norm.key_norm.scale"] = converted_state_dict.pop(
f"{block_prefix}attn.norm_added_k.weight"
)
# -------------------------
# Handle Feed-Forward Networks (FFNs) for Image and Text
# -------------------------
# Map the image MLP projection layers back to "double_blocks.{i}.img_mlp"
original_state_dict[f"double_blocks.{i}.img_mlp.0.weight"] = converted_state_dict.pop(
f"{block_prefix}ff.net.0.proj.weight"
)
original_state_dict[f"double_blocks.{i}.img_mlp.0.bias"] = converted_state_dict.pop(
f"{block_prefix}ff.net.0.proj.bias"
)
original_state_dict[f"double_blocks.{i}.img_mlp.2.weight"] = converted_state_dict.pop(
f"{block_prefix}ff.net.2.weight"
)
original_state_dict[f"double_blocks.{i}.img_mlp.2.bias"] = converted_state_dict.pop(
f"{block_prefix}ff.net.2.bias"
)
# Map the text MLP projection layers back to "double_blocks.{i}.txt_mlp"
original_state_dict[f"double_blocks.{i}.txt_mlp.0.weight"] = converted_state_dict.pop(
f"{block_prefix}ff_context.net.0.proj.weight"
)
original_state_dict[f"double_blocks.{i}.txt_mlp.0.bias"] = converted_state_dict.pop(
f"{block_prefix}ff_context.net.0.proj.bias"
)
original_state_dict[f"double_blocks.{i}.txt_mlp.2.weight"] = converted_state_dict.pop(
f"{block_prefix}ff_context.net.2.weight"
)
original_state_dict[f"double_blocks.{i}.txt_mlp.2.bias"] = converted_state_dict.pop(
f"{block_prefix}ff_context.net.2.bias"
)
# -------------------------
# Handle Attention Output Projections
# -------------------------
# Map the image attention output projection weights and biases back to "double_blocks.{i}.img_attn.proj"
original_state_dict[f"double_blocks.{i}.img_attn.proj.weight"] = converted_state_dict.pop(
f"{block_prefix}attn.to_out.0.weight"
)
original_state_dict[f"double_blocks.{i}.img_attn.proj.bias"] = converted_state_dict.pop(
f"{block_prefix}attn.to_out.0.bias"
)
# Map the text attention output projection weights and biases back to "double_blocks.{i}.txt_attn.proj"
original_state_dict[f"double_blocks.{i}.txt_attn.proj.weight"] = converted_state_dict.pop(
f"{block_prefix}attn.to_add_out.weight"
)
original_state_dict[f"double_blocks.{i}.txt_attn.proj.bias"] = converted_state_dict.pop(
f"{block_prefix}attn.to_add_out.bias"
)
# -------------------------
# Handle Single Transformer Blocks
# -------------------------
for i in range(num_single_layers):
# Define the prefix for the current single transformer block in the converted_state_dict
block_prefix = f"single_transformer_blocks.{i}."
# -------------------------
# Map Norm Layers
# -------------------------
# Map the normalization linear layer weights and biases back to "single_blocks.{i}.modulation.lin"
original_state_dict[f"single_blocks.{i}.modulation.lin.weight"] = converted_state_dict.pop(
f"{block_prefix}norm.linear.weight"
)
original_state_dict[f"single_blocks.{i}.modulation.lin.bias"] = converted_state_dict.pop(
f"{block_prefix}norm.linear.bias"
)
# -------------------------
# Handle Q, K, V Projections and MLP
# -------------------------
# Retrieve the Q, K, V weights and the MLP projection weight
q_weight = converted_state_dict.pop(f"{block_prefix}attn.to_q.weight")
k_weight = converted_state_dict.pop(f"{block_prefix}attn.to_k.weight")
v_weight = converted_state_dict.pop(f"{block_prefix}attn.to_v.weight")
proj_mlp_weight = converted_state_dict.pop(f"{block_prefix}proj_mlp.weight")
# Concatenate Q, K, V, and MLP weights to form the combined linear1.weight
combined_weight = torch.cat([q_weight, k_weight, v_weight, proj_mlp_weight], dim=0)
original_state_dict[f"single_blocks.{i}.linear1.weight"] = combined_weight
# Retrieve the Q, K, V biases and the MLP projection bias
q_bias = converted_state_dict.pop(f"{block_prefix}attn.to_q.bias")
k_bias = converted_state_dict.pop(f"{block_prefix}attn.to_k.bias")
v_bias = converted_state_dict.pop(f"{block_prefix}attn.to_v.bias")
proj_mlp_bias = converted_state_dict.pop(f"{block_prefix}proj_mlp.bias")
# Concatenate Q, K, V, and MLP biases to form the combined linear1.bias
combined_bias = torch.cat([q_bias, k_bias, v_bias, proj_mlp_bias], dim=0)
original_state_dict[f"single_blocks.{i}.linear1.bias"] = combined_bias
# -------------------------
# Map Attention Normalization Weights
# -------------------------
# Map the attention query norm weights back to "single_blocks.{i}.norm.query_norm.scale"
original_state_dict[f"single_blocks.{i}.norm.query_norm.scale"] = converted_state_dict.pop(
f"{block_prefix}attn.norm_q.weight"
)
# Map the attention key norm weights back to "single_blocks.{i}.norm.key_norm.scale"
original_state_dict[f"single_blocks.{i}.norm.key_norm.scale"] = converted_state_dict.pop(
f"{block_prefix}attn.norm_k.weight"
)
# -------------------------
# Handle Projection Output
# -------------------------
# Map the projection output weights and biases back to "single_blocks.{i}.linear2"
original_state_dict[f"single_blocks.{i}.linear2.weight"] = converted_state_dict.pop(
f"{block_prefix}proj_out.weight"
)
original_state_dict[f"single_blocks.{i}.linear2.bias"] = converted_state_dict.pop(
f"{block_prefix}proj_out.bias"
)
# -------------------------
# Handle Final Output Projection and Normalization
# -------------------------
# Map the final output projection weights and biases back to "final_layer.linear"
original_state_dict["final_layer.linear.weight"] = converted_state_dict.pop("proj_out.weight")
original_state_dict["final_layer.linear.bias"] = converted_state_dict.pop("proj_out.bias")
# Reverse the swap_scale_shift transformation for normalization weights and biases
original_state_dict["final_layer.adaLN_modulation.1.weight"] = swap_scale_shift(
converted_state_dict.pop("norm_out.linear.weight")
)
original_state_dict["final_layer.adaLN_modulation.1.bias"] = swap_scale_shift(
converted_state_dict.pop("norm_out.linear.bias")
)
# -------------------------
# Handle Remaining Parameters (if any)
# -------------------------
# It's possible that there are remaining parameters that were not mapped.
# Depending on your use case, you can handle them here or raise an error.
if len(converted_state_dict) > 0:
# For debugging purposes, you might want to log or print the remaining keys
remaining_keys = list(converted_state_dict.keys())
print(f"Warning: The following keys were not mapped and remain in the state dict: {remaining_keys}")
# Optionally, you can choose to include them or exclude them from the original_state_dict
return original_state_dict