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flux compatibility patch

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Artiprocher
2026-01-05 16:04:20 +08:00
parent ab8580f77e
commit 7604ca1e52
2 changed files with 110 additions and 0 deletions
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7
diffsynth/configs/model_configs.py
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@@ -317,6 +317,13 @@ flux_series = [
"model_class": "diffsynth.models.flux_dit.FluxDiT",
"state_dict_converter": "diffsynth.utils.state_dict_converters.flux_dit.FluxDiTStateDictConverter",
},
{
# Supported due to historical reasons.
"model_hash": "605c56eab23e9e2af863ad8f0813a25d",
"model_name": "flux_dit",
"model_class": "diffsynth.models.flux_dit.FluxDiT",
"state_dict_converter": "diffsynth.utils.state_dict_converters.flux_dit.FluxDiTStateDictConverterFromDiffusers",
},
{
# Example: ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors")
"model_hash": "94eefa3dac9cec93cb1ebaf1747d7b78",

103
diffsynth/utils/state_dict_converters/flux_dit.py
View File

@@ -89,4 +89,107 @@ def FluxDiTStateDictConverter(state_dict):
state_dict_[rename] = state_dict[original_name]
else:
pass
return state_dict_
def FluxDiTStateDictConverterFromDiffusers(state_dict):
global_rename_dict = {
"context_embedder": "context_embedder",
"x_embedder": "x_embedder",
"time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0",
"time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2",
"time_text_embed.guidance_embedder.linear_1": "guidance_embedder.timestep_embedder.0",
"time_text_embed.guidance_embedder.linear_2": "guidance_embedder.timestep_embedder.2",
"time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0",
"time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2",
"norm_out.linear": "final_norm_out.linear",
"proj_out": "final_proj_out",
}
rename_dict = {
"proj_out": "proj_out",
"norm1.linear": "norm1_a.linear",
"norm1_context.linear": "norm1_b.linear",
"attn.to_q": "attn.a_to_q",
"attn.to_k": "attn.a_to_k",
"attn.to_v": "attn.a_to_v",
"attn.to_out.0": "attn.a_to_out",
"attn.add_q_proj": "attn.b_to_q",
"attn.add_k_proj": "attn.b_to_k",
"attn.add_v_proj": "attn.b_to_v",
"attn.to_add_out": "attn.b_to_out",
"ff.net.0.proj": "ff_a.0",
"ff.net.2": "ff_a.2",
"ff_context.net.0.proj": "ff_b.0",
"ff_context.net.2": "ff_b.2",
"attn.norm_q": "attn.norm_q_a",
"attn.norm_k": "attn.norm_k_a",
"attn.norm_added_q": "attn.norm_q_b",
"attn.norm_added_k": "attn.norm_k_b",
}
rename_dict_single = {
"attn.to_q": "a_to_q",
"attn.to_k": "a_to_k",
"attn.to_v": "a_to_v",
"attn.norm_q": "norm_q_a",
"attn.norm_k": "norm_k_a",
"norm.linear": "norm.linear",
"proj_mlp": "proj_in_besides_attn",
"proj_out": "proj_out",
}
state_dict_ = {}
for name in state_dict:
param = state_dict[name]
if name.endswith(".weight") or name.endswith(".bias"):
suffix = ".weight" if name.endswith(".weight") else ".bias"
prefix = name[:-len(suffix)]
if prefix in global_rename_dict:
state_dict_[global_rename_dict[prefix] + suffix] = param
elif prefix.startswith("transformer_blocks."):
names = prefix.split(".")
names[0] = "blocks"
middle = ".".join(names[2:])
if middle in rename_dict:
name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]])
state_dict_[name_] = param
elif prefix.startswith("single_transformer_blocks."):
names = prefix.split(".")
names[0] = "single_blocks"
middle = ".".join(names[2:])
if middle in rename_dict_single:
name_ = ".".join(names[:2] + [rename_dict_single[middle]] + [suffix[1:]])
state_dict_[name_] = param
else:
pass
else:
pass
for name in list(state_dict_.keys()):
if "single_blocks." in name and ".a_to_q." in name:
mlp = state_dict_.get(name.replace(".a_to_q.", ".proj_in_besides_attn."), None)
if mlp is None:
mlp = torch.zeros(4 * state_dict_[name].shape[0],
*state_dict_[name].shape[1:],
dtype=state_dict_[name].dtype)
else:
state_dict_.pop(name.replace(".a_to_q.", ".proj_in_besides_attn."))
param = torch.concat([
state_dict_.pop(name),
state_dict_.pop(name.replace(".a_to_q.", ".a_to_k.")),
state_dict_.pop(name.replace(".a_to_q.", ".a_to_v.")),
mlp,
], dim=0)
name_ = name.replace(".a_to_q.", ".to_qkv_mlp.")
state_dict_[name_] = param
for name in list(state_dict_.keys()):
for component in ["a", "b"]:
if f".{component}_to_q." in name:
name_ = name.replace(f".{component}_to_q.", f".{component}_to_qkv.")
param = torch.concat([
state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")],
state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")],
state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")],
], dim=0)
state_dict_[name_] = param
state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_q."))
state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_k."))
state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_v."))
return state_dict_