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# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Conversion script for the Stable Diffusion checkpoints."""
import copy
import os
import re
from contextlib import nullcontext
from io import BytesIO
from urllib.parse import urlparse
import requests
import torch
import yaml
from ..models.modeling_utils import load_state_dict
from ..schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EDMDPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ..utils import (
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
deprecate,
is_accelerate_available,
is_transformers_available,
logging,
)
from ..utils.constants import DIFFUSERS_REQUEST_TIMEOUT
from ..utils.hub_utils import _get_model_file
from ..utils.torch_utils import empty_device_cache
if is_transformers_available():
from transformers import AutoImageProcessor
if is_accelerate_available():
from accelerate import init_empty_weights
from ..models.model_loading_utils import load_model_dict_into_meta
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
CHECKPOINT_KEY_NAMES = {
"v1": "model.diffusion_model.output_blocks.11.0.skip_connection.weight",
"v2": "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight",
"xl_base": "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.bias",
"xl_refiner": "conditioner.embedders.0.model.transformer.resblocks.9.mlp.c_proj.bias",
"upscale": "model.diffusion_model.input_blocks.10.0.skip_connection.bias",
"controlnet": [
"control_model.time_embed.0.weight",
"controlnet_cond_embedding.conv_in.weight",
],
# TODO: find non-Diffusers keys for controlnet_xl
"controlnet_xl": "add_embedding.linear_1.weight",
"controlnet_xl_large": "down_blocks.1.attentions.0.transformer_blocks.0.attn1.to_k.weight",
"controlnet_xl_mid": "down_blocks.1.attentions.0.norm.weight",
"playground-v2-5": "edm_mean",
"inpainting": "model.diffusion_model.input_blocks.0.0.weight",
"clip": "cond_stage_model.transformer.text_model.embeddings.position_embedding.weight",
"clip_sdxl": "conditioner.embedders.0.transformer.text_model.embeddings.position_embedding.weight",
"clip_sd3": "text_encoders.clip_l.transformer.text_model.embeddings.position_embedding.weight",
"open_clip": "cond_stage_model.model.token_embedding.weight",
"open_clip_sdxl": "conditioner.embedders.1.model.positional_embedding",
"open_clip_sdxl_refiner": "conditioner.embedders.0.model.text_projection",
"open_clip_sd3": "text_encoders.clip_g.transformer.text_model.embeddings.position_embedding.weight",
"stable_cascade_stage_b": "down_blocks.1.0.channelwise.0.weight",
"stable_cascade_stage_c": "clip_txt_mapper.weight",
"sd3": [
"joint_blocks.0.context_block.adaLN_modulation.1.bias",
"model.diffusion_model.joint_blocks.0.context_block.adaLN_modulation.1.bias",
],
"sd35_large": [
"joint_blocks.37.x_block.mlp.fc1.weight",
"model.diffusion_model.joint_blocks.37.x_block.mlp.fc1.weight",
],
"animatediff": "down_blocks.0.motion_modules.0.temporal_transformer.transformer_blocks.0.attention_blocks.0.pos_encoder.pe",
"animatediff_v2": "mid_block.motion_modules.0.temporal_transformer.norm.bias",
"animatediff_sdxl_beta": "up_blocks.2.motion_modules.0.temporal_transformer.norm.weight",
"animatediff_scribble": "controlnet_cond_embedding.conv_in.weight",
"animatediff_rgb": "controlnet_cond_embedding.weight",
"auraflow": [
"double_layers.0.attn.w2q.weight",
"double_layers.0.attn.w1q.weight",
"cond_seq_linear.weight",
"t_embedder.mlp.0.weight",
],
"flux": [
"double_blocks.0.img_attn.norm.key_norm.scale",
"model.diffusion_model.double_blocks.0.img_attn.norm.key_norm.scale",
],
"ltx-video": [
"model.diffusion_model.patchify_proj.weight",
"model.diffusion_model.transformer_blocks.27.scale_shift_table",
"patchify_proj.weight",
"transformer_blocks.27.scale_shift_table",
"vae.decoder.last_scale_shift_table", # 0.9.1, 0.9.5, 0.9.7, 0.9.8
"vae.decoder.up_blocks.9.res_blocks.0.conv1.conv.weight", # 0.9.0
],
"autoencoder-dc": "decoder.stages.1.op_list.0.main.conv.conv.bias",
"autoencoder-dc-sana": "encoder.project_in.conv.bias",
"mochi-1-preview": ["model.diffusion_model.blocks.0.attn.qkv_x.weight", "blocks.0.attn.qkv_x.weight"],
"hunyuan-video": "txt_in.individual_token_refiner.blocks.0.adaLN_modulation.1.bias",
"instruct-pix2pix": "model.diffusion_model.input_blocks.0.0.weight",
"lumina2": ["model.diffusion_model.cap_embedder.0.weight", "cap_embedder.0.weight"],
"z-image-turbo": [
"model.diffusion_model.layers.0.adaLN_modulation.0.weight",
"layers.0.adaLN_modulation.0.weight",
],
"z-image-turbo-controlnet": "control_all_x_embedder.2-1.weight",
"z-image-turbo-controlnet-2.x": "control_layers.14.adaLN_modulation.0.weight",
"sana": [
"blocks.0.cross_attn.q_linear.weight",
"blocks.0.cross_attn.q_linear.bias",
"blocks.0.cross_attn.kv_linear.weight",
"blocks.0.cross_attn.kv_linear.bias",
],
"wan": ["model.diffusion_model.head.modulation", "head.modulation"],
"wan_vae": "decoder.middle.0.residual.0.gamma",
"wan_vace": "vace_blocks.0.after_proj.bias",
"wan_animate": "motion_encoder.dec.direction.weight",
"hidream": "double_stream_blocks.0.block.adaLN_modulation.1.bias",
"cosmos-1.0": [
"net.x_embedder.proj.1.weight",
"net.blocks.block1.blocks.0.block.attn.to_q.0.weight",
"net.extra_pos_embedder.pos_emb_h",
],
"cosmos-2.0": [
"net.x_embedder.proj.1.weight",
"net.blocks.0.self_attn.q_proj.weight",
"net.pos_embedder.dim_spatial_range",
],
"flux2": ["model.diffusion_model.single_stream_modulation.lin.weight", "single_stream_modulation.lin.weight"],
"ltx2": [
"model.diffusion_model.av_ca_a2v_gate_adaln_single.emb.timestep_embedder.linear_1.weight",
"vae.per_channel_statistics.mean-of-means",
"audio_vae.per_channel_statistics.mean-of-means",
],
}
DIFFUSERS_DEFAULT_PIPELINE_PATHS = {
"xl_base": {"pretrained_model_name_or_path": "stabilityai/stable-diffusion-xl-base-1.0"},
"xl_refiner": {"pretrained_model_name_or_path": "stabilityai/stable-diffusion-xl-refiner-1.0"},
"xl_inpaint": {"pretrained_model_name_or_path": "diffusers/stable-diffusion-xl-1.0-inpainting-0.1"},
"playground-v2-5": {"pretrained_model_name_or_path": "playgroundai/playground-v2.5-1024px-aesthetic"},
"upscale": {"pretrained_model_name_or_path": "stabilityai/stable-diffusion-x4-upscaler"},
"inpainting": {"pretrained_model_name_or_path": "stable-diffusion-v1-5/stable-diffusion-inpainting"},
"inpainting_v2": {"pretrained_model_name_or_path": "stabilityai/stable-diffusion-2-inpainting"},
"controlnet": {"pretrained_model_name_or_path": "lllyasviel/control_v11p_sd15_canny"},
"controlnet_xl_large": {"pretrained_model_name_or_path": "diffusers/controlnet-canny-sdxl-1.0"},
"controlnet_xl_mid": {"pretrained_model_name_or_path": "diffusers/controlnet-canny-sdxl-1.0-mid"},
"controlnet_xl_small": {"pretrained_model_name_or_path": "diffusers/controlnet-canny-sdxl-1.0-small"},
"v2": {"pretrained_model_name_or_path": "stabilityai/stable-diffusion-2-1"},
"v1": {"pretrained_model_name_or_path": "stable-diffusion-v1-5/stable-diffusion-v1-5"},
"stable_cascade_stage_b": {"pretrained_model_name_or_path": "stabilityai/stable-cascade", "subfolder": "decoder"},
"stable_cascade_stage_b_lite": {
"pretrained_model_name_or_path": "stabilityai/stable-cascade",
"subfolder": "decoder_lite",
},
"stable_cascade_stage_c": {
"pretrained_model_name_or_path": "stabilityai/stable-cascade-prior",
"subfolder": "prior",
},
"stable_cascade_stage_c_lite": {
"pretrained_model_name_or_path": "stabilityai/stable-cascade-prior",
"subfolder": "prior_lite",
},
"sd3": {
"pretrained_model_name_or_path": "stabilityai/stable-diffusion-3-medium-diffusers",
},
"sd35_large": {
"pretrained_model_name_or_path": "stabilityai/stable-diffusion-3.5-large",
},
"sd35_medium": {
"pretrained_model_name_or_path": "stabilityai/stable-diffusion-3.5-medium",
},
"animatediff_v1": {"pretrained_model_name_or_path": "guoyww/animatediff-motion-adapter-v1-5"},
"animatediff_v2": {"pretrained_model_name_or_path": "guoyww/animatediff-motion-adapter-v1-5-2"},
"animatediff_v3": {"pretrained_model_name_or_path": "guoyww/animatediff-motion-adapter-v1-5-3"},
"animatediff_sdxl_beta": {"pretrained_model_name_or_path": "guoyww/animatediff-motion-adapter-sdxl-beta"},
"animatediff_scribble": {"pretrained_model_name_or_path": "guoyww/animatediff-sparsectrl-scribble"},
"animatediff_rgb": {"pretrained_model_name_or_path": "guoyww/animatediff-sparsectrl-rgb"},
"auraflow": {"pretrained_model_name_or_path": "fal/AuraFlow-v0.3"},
"flux-dev": {"pretrained_model_name_or_path": "black-forest-labs/FLUX.1-dev"},
"flux-fill": {"pretrained_model_name_or_path": "black-forest-labs/FLUX.1-Fill-dev"},
"flux-depth": {"pretrained_model_name_or_path": "black-forest-labs/FLUX.1-Depth-dev"},
"flux-schnell": {"pretrained_model_name_or_path": "black-forest-labs/FLUX.1-schnell"},
"flux-2-dev": {"pretrained_model_name_or_path": "black-forest-labs/FLUX.2-dev"},
"ltx-video": {"pretrained_model_name_or_path": "diffusers/LTX-Video-0.9.0"},
"ltx-video-0.9.1": {"pretrained_model_name_or_path": "diffusers/LTX-Video-0.9.1"},
"ltx-video-0.9.5": {"pretrained_model_name_or_path": "Lightricks/LTX-Video-0.9.5"},
"ltx-video-0.9.7": {"pretrained_model_name_or_path": "Lightricks/LTX-Video-0.9.7-dev"},
"autoencoder-dc-f128c512": {"pretrained_model_name_or_path": "mit-han-lab/dc-ae-f128c512-mix-1.0-diffusers"},
"autoencoder-dc-f64c128": {"pretrained_model_name_or_path": "mit-han-lab/dc-ae-f64c128-mix-1.0-diffusers"},
"autoencoder-dc-f32c32": {"pretrained_model_name_or_path": "mit-han-lab/dc-ae-f32c32-mix-1.0-diffusers"},
"autoencoder-dc-f32c32-sana": {"pretrained_model_name_or_path": "mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers"},
"mochi-1-preview": {"pretrained_model_name_or_path": "genmo/mochi-1-preview"},
"hunyuan-video": {"pretrained_model_name_or_path": "hunyuanvideo-community/HunyuanVideo"},
"instruct-pix2pix": {"pretrained_model_name_or_path": "timbrooks/instruct-pix2pix"},
"lumina2": {"pretrained_model_name_or_path": "Alpha-VLLM/Lumina-Image-2.0"},
"sana": {"pretrained_model_name_or_path": "Efficient-Large-Model/Sana_1600M_1024px_diffusers"},
"wan-t2v-1.3B": {"pretrained_model_name_or_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"},
"wan-t2v-14B": {"pretrained_model_name_or_path": "Wan-AI/Wan2.1-T2V-14B-Diffusers"},
"wan-i2v-14B": {"pretrained_model_name_or_path": "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"},
"wan-animate-14B": {"pretrained_model_name_or_path": "Wan-AI/Wan2.2-Animate-14B-Diffusers"},
"wan-vace-1.3B": {"pretrained_model_name_or_path": "Wan-AI/Wan2.1-VACE-1.3B-diffusers"},
"wan-vace-14B": {"pretrained_model_name_or_path": "Wan-AI/Wan2.1-VACE-14B-diffusers"},
"hidream": {"pretrained_model_name_or_path": "HiDream-ai/HiDream-I1-Dev"},
"cosmos-1.0-t2w-7B": {"pretrained_model_name_or_path": "nvidia/Cosmos-1.0-Diffusion-7B-Text2World"},
"cosmos-1.0-t2w-14B": {"pretrained_model_name_or_path": "nvidia/Cosmos-1.0-Diffusion-14B-Text2World"},
"cosmos-1.0-v2w-7B": {"pretrained_model_name_or_path": "nvidia/Cosmos-1.0-Diffusion-7B-Video2World"},
"cosmos-1.0-v2w-14B": {"pretrained_model_name_or_path": "nvidia/Cosmos-1.0-Diffusion-14B-Video2World"},
"cosmos-2.0-t2i-2B": {"pretrained_model_name_or_path": "nvidia/Cosmos-Predict2-2B-Text2Image"},
"cosmos-2.0-t2i-14B": {"pretrained_model_name_or_path": "nvidia/Cosmos-Predict2-14B-Text2Image"},
"cosmos-2.0-v2w-2B": {"pretrained_model_name_or_path": "nvidia/Cosmos-Predict2-2B-Video2World"},
"cosmos-2.0-v2w-14B": {"pretrained_model_name_or_path": "nvidia/Cosmos-Predict2-14B-Video2World"},
"z-image-turbo": {"pretrained_model_name_or_path": "Tongyi-MAI/Z-Image-Turbo"},
"z-image-turbo-controlnet": {"pretrained_model_name_or_path": "hlky/Z-Image-Turbo-Fun-Controlnet-Union"},
"z-image-turbo-controlnet-2.0": {"pretrained_model_name_or_path": "hlky/Z-Image-Turbo-Fun-Controlnet-Union-2.0"},
"z-image-turbo-controlnet-2.1": {"pretrained_model_name_or_path": "hlky/Z-Image-Turbo-Fun-Controlnet-Union-2.1"},
"ltx2-dev": {"pretrained_model_name_or_path": "Lightricks/LTX-2"},
}
# Use to configure model sample size when original config is provided
DIFFUSERS_TO_LDM_DEFAULT_IMAGE_SIZE_MAP = {
"xl_base": 1024,
"xl_refiner": 1024,
"xl_inpaint": 1024,
"playground-v2-5": 1024,
"upscale": 512,
"inpainting": 512,
"inpainting_v2": 512,
"controlnet": 512,
"instruct-pix2pix": 512,
"v2": 768,
"v1": 512,
}
DIFFUSERS_TO_LDM_MAPPING = {
"unet": {
"layers": {
"time_embedding.linear_1.weight": "time_embed.0.weight",
"time_embedding.linear_1.bias": "time_embed.0.bias",
"time_embedding.linear_2.weight": "time_embed.2.weight",
"time_embedding.linear_2.bias": "time_embed.2.bias",
"conv_in.weight": "input_blocks.0.0.weight",
"conv_in.bias": "input_blocks.0.0.bias",
"conv_norm_out.weight": "out.0.weight",
"conv_norm_out.bias": "out.0.bias",
"conv_out.weight": "out.2.weight",
"conv_out.bias": "out.2.bias",
},
"class_embed_type": {
"class_embedding.linear_1.weight": "label_emb.0.0.weight",
"class_embedding.linear_1.bias": "label_emb.0.0.bias",
"class_embedding.linear_2.weight": "label_emb.0.2.weight",
"class_embedding.linear_2.bias": "label_emb.0.2.bias",
},
"addition_embed_type": {
"add_embedding.linear_1.weight": "label_emb.0.0.weight",
"add_embedding.linear_1.bias": "label_emb.0.0.bias",
"add_embedding.linear_2.weight": "label_emb.0.2.weight",
"add_embedding.linear_2.bias": "label_emb.0.2.bias",
},
},
"controlnet": {
"layers": {
"time_embedding.linear_1.weight": "time_embed.0.weight",
"time_embedding.linear_1.bias": "time_embed.0.bias",
"time_embedding.linear_2.weight": "time_embed.2.weight",
"time_embedding.linear_2.bias": "time_embed.2.bias",
"conv_in.weight": "input_blocks.0.0.weight",
"conv_in.bias": "input_blocks.0.0.bias",
"controlnet_cond_embedding.conv_in.weight": "input_hint_block.0.weight",
"controlnet_cond_embedding.conv_in.bias": "input_hint_block.0.bias",
"controlnet_cond_embedding.conv_out.weight": "input_hint_block.14.weight",
"controlnet_cond_embedding.conv_out.bias": "input_hint_block.14.bias",
},
"class_embed_type": {
"class_embedding.linear_1.weight": "label_emb.0.0.weight",
"class_embedding.linear_1.bias": "label_emb.0.0.bias",
"class_embedding.linear_2.weight": "label_emb.0.2.weight",
"class_embedding.linear_2.bias": "label_emb.0.2.bias",
},
"addition_embed_type": {
"add_embedding.linear_1.weight": "label_emb.0.0.weight",
"add_embedding.linear_1.bias": "label_emb.0.0.bias",
"add_embedding.linear_2.weight": "label_emb.0.2.weight",
"add_embedding.linear_2.bias": "label_emb.0.2.bias",
},
},
"vae": {
"encoder.conv_in.weight": "encoder.conv_in.weight",
"encoder.conv_in.bias": "encoder.conv_in.bias",
"encoder.conv_out.weight": "encoder.conv_out.weight",
"encoder.conv_out.bias": "encoder.conv_out.bias",
"encoder.conv_norm_out.weight": "encoder.norm_out.weight",
"encoder.conv_norm_out.bias": "encoder.norm_out.bias",
"decoder.conv_in.weight": "decoder.conv_in.weight",
"decoder.conv_in.bias": "decoder.conv_in.bias",
"decoder.conv_out.weight": "decoder.conv_out.weight",
"decoder.conv_out.bias": "decoder.conv_out.bias",
"decoder.conv_norm_out.weight": "decoder.norm_out.weight",
"decoder.conv_norm_out.bias": "decoder.norm_out.bias",
"quant_conv.weight": "quant_conv.weight",
"quant_conv.bias": "quant_conv.bias",
"post_quant_conv.weight": "post_quant_conv.weight",
"post_quant_conv.bias": "post_quant_conv.bias",
},
"openclip": {
"layers": {
"text_model.embeddings.position_embedding.weight": "positional_embedding",
"text_model.embeddings.token_embedding.weight": "token_embedding.weight",
"text_model.final_layer_norm.weight": "ln_final.weight",
"text_model.final_layer_norm.bias": "ln_final.bias",
"text_projection.weight": "text_projection",
},
"transformer": {
"text_model.encoder.layers.": "resblocks.",
"layer_norm1": "ln_1",
"layer_norm2": "ln_2",
".fc1.": ".c_fc.",
".fc2.": ".c_proj.",
".self_attn": ".attn",
"transformer.text_model.final_layer_norm.": "ln_final.",
"transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight",
"transformer.text_model.embeddings.position_embedding.weight": "positional_embedding",
},
},
}
SD_2_TEXT_ENCODER_KEYS_TO_IGNORE = [
"cond_stage_model.model.transformer.resblocks.23.attn.in_proj_bias",
"cond_stage_model.model.transformer.resblocks.23.attn.in_proj_weight",
"cond_stage_model.model.transformer.resblocks.23.attn.out_proj.bias",
"cond_stage_model.model.transformer.resblocks.23.attn.out_proj.weight",
"cond_stage_model.model.transformer.resblocks.23.ln_1.bias",
"cond_stage_model.model.transformer.resblocks.23.ln_1.weight",
"cond_stage_model.model.transformer.resblocks.23.ln_2.bias",
"cond_stage_model.model.transformer.resblocks.23.ln_2.weight",
"cond_stage_model.model.transformer.resblocks.23.mlp.c_fc.bias",
"cond_stage_model.model.transformer.resblocks.23.mlp.c_fc.weight",
"cond_stage_model.model.transformer.resblocks.23.mlp.c_proj.bias",
"cond_stage_model.model.transformer.resblocks.23.mlp.c_proj.weight",
"cond_stage_model.model.text_projection",
]
# To support legacy scheduler_type argument
SCHEDULER_DEFAULT_CONFIG = {
"beta_schedule": "scaled_linear",
"beta_start": 0.00085,
"beta_end": 0.012,
"interpolation_type": "linear",
"num_train_timesteps": 1000,
"prediction_type": "epsilon",
"sample_max_value": 1.0,
"set_alpha_to_one": False,
"skip_prk_steps": True,
"steps_offset": 1,
"timestep_spacing": "leading",
}
LDM_VAE_KEYS = ["first_stage_model.", "vae."]
LDM_VAE_DEFAULT_SCALING_FACTOR = 0.18215
PLAYGROUND_VAE_SCALING_FACTOR = 0.5
LDM_UNET_KEY = "model.diffusion_model."
LDM_CONTROLNET_KEY = "control_model."
LDM_CLIP_PREFIX_TO_REMOVE = [
"cond_stage_model.transformer.",
"conditioner.embedders.0.transformer.",
]
LDM_OPEN_CLIP_TEXT_PROJECTION_DIM = 1024
SCHEDULER_LEGACY_KWARGS = ["prediction_type", "scheduler_type"]
VALID_URL_PREFIXES = ["https://huggingface.co/", "huggingface.co/", "hf.co/", "https://hf.co/"]
class SingleFileComponentError(Exception):
def __init__(self, message=None):
self.message = message
super().__init__(self.message)
def is_valid_url(url):
result = urlparse(url)
if result.scheme and result.netloc:
return True
return False
def _is_single_file_path_or_url(pretrained_model_name_or_path):
if not os.path.isfile(pretrained_model_name_or_path) or not is_valid_url(pretrained_model_name_or_path):
return False
repo_id, weight_name = _extract_repo_id_and_weights_name(pretrained_model_name_or_path)
return bool(repo_id and weight_name)
def _extract_repo_id_and_weights_name(pretrained_model_name_or_path):
if not is_valid_url(pretrained_model_name_or_path):
raise ValueError("Invalid `pretrained_model_name_or_path` provided. Please set it to a valid URL.")
pattern = r"([^/]+)/([^/]+)/(?:blob/main/)?(.+)"
weights_name = None
repo_id = (None,)
for prefix in VALID_URL_PREFIXES:
pretrained_model_name_or_path = pretrained_model_name_or_path.replace(prefix, "")
match = re.match(pattern, pretrained_model_name_or_path)
if not match:
return repo_id, weights_name
repo_id = f"{match.group(1)}/{match.group(2)}"
weights_name = match.group(3)
return repo_id, weights_name
def _is_model_weights_in_cached_folder(cached_folder, name):
pretrained_model_name_or_path = os.path.join(cached_folder, name)
weights_exist = False
for weights_name in [WEIGHTS_NAME, SAFETENSORS_WEIGHTS_NAME]:
if os.path.isfile(os.path.join(pretrained_model_name_or_path, weights_name)):
weights_exist = True
return weights_exist
def _is_legacy_scheduler_kwargs(kwargs):
return any(k in SCHEDULER_LEGACY_KWARGS for k in kwargs.keys())
def load_single_file_checkpoint(
pretrained_model_link_or_path,
force_download=False,
proxies=None,
token=None,
cache_dir=None,
local_files_only=None,
revision=None,
disable_mmap=False,
user_agent=None,
):
if user_agent is None:
user_agent = {"file_type": "single_file", "framework": "pytorch"}
if os.path.isfile(pretrained_model_link_or_path):
pretrained_model_link_or_path = pretrained_model_link_or_path
else:
repo_id, weights_name = _extract_repo_id_and_weights_name(pretrained_model_link_or_path)
pretrained_model_link_or_path = _get_model_file(
repo_id,
weights_name=weights_name,
force_download=force_download,
cache_dir=cache_dir,
proxies=proxies,
local_files_only=local_files_only,
token=token,
revision=revision,
user_agent=user_agent,
)
checkpoint = load_state_dict(pretrained_model_link_or_path, disable_mmap=disable_mmap)
# some checkpoints contain the model state dict under a "state_dict" key
while "state_dict" in checkpoint:
checkpoint = checkpoint["state_dict"]
return checkpoint
def fetch_original_config(original_config_file, local_files_only=False):
if os.path.isfile(original_config_file):
with open(original_config_file, "r") as fp:
original_config_file = fp.read()
elif is_valid_url(original_config_file):
if local_files_only:
raise ValueError(
"`local_files_only` is set to True, but a URL was provided as `original_config_file`. "
"Please provide a valid local file path."
)
original_config_file = BytesIO(requests.get(original_config_file, timeout=DIFFUSERS_REQUEST_TIMEOUT).content)
else:
raise ValueError("Invalid `original_config_file` provided. Please set it to a valid file path or URL.")
original_config = yaml.safe_load(original_config_file)
return original_config
def is_clip_model(checkpoint):
if CHECKPOINT_KEY_NAMES["clip"] in checkpoint:
return True
return False
def is_clip_sdxl_model(checkpoint):
if CHECKPOINT_KEY_NAMES["clip_sdxl"] in checkpoint:
return True
return False
def is_clip_sd3_model(checkpoint):
if CHECKPOINT_KEY_NAMES["clip_sd3"] in checkpoint:
return True
return False
def is_open_clip_model(checkpoint):
if CHECKPOINT_KEY_NAMES["open_clip"] in checkpoint:
return True
return False
def is_open_clip_sdxl_model(checkpoint):
if CHECKPOINT_KEY_NAMES["open_clip_sdxl"] in checkpoint:
return True
return False
def is_open_clip_sd3_model(checkpoint):
if CHECKPOINT_KEY_NAMES["open_clip_sd3"] in checkpoint:
return True
return False
def is_open_clip_sdxl_refiner_model(checkpoint):
if CHECKPOINT_KEY_NAMES["open_clip_sdxl_refiner"] in checkpoint:
return True
return False
def is_clip_model_in_single_file(class_obj, checkpoint):
is_clip_in_checkpoint = any(
[
is_clip_model(checkpoint),
is_clip_sd3_model(checkpoint),
is_open_clip_model(checkpoint),
is_open_clip_sdxl_model(checkpoint),
is_open_clip_sdxl_refiner_model(checkpoint),
is_open_clip_sd3_model(checkpoint),
]
)
if (
class_obj.__name__ == "CLIPTextModel" or class_obj.__name__ == "CLIPTextModelWithProjection"
) and is_clip_in_checkpoint:
return True
return False
def infer_diffusers_model_type(checkpoint):
if (
CHECKPOINT_KEY_NAMES["inpainting"] in checkpoint
and checkpoint[CHECKPOINT_KEY_NAMES["inpainting"]].shape[1] == 9
):
if CHECKPOINT_KEY_NAMES["v2"] in checkpoint and checkpoint[CHECKPOINT_KEY_NAMES["v2"]].shape[-1] == 1024:
model_type = "inpainting_v2"
elif CHECKPOINT_KEY_NAMES["xl_base"] in checkpoint:
model_type = "xl_inpaint"
else:
model_type = "inpainting"
elif CHECKPOINT_KEY_NAMES["v2"] in checkpoint and checkpoint[CHECKPOINT_KEY_NAMES["v2"]].shape[-1] == 1024:
model_type = "v2"
elif CHECKPOINT_KEY_NAMES["playground-v2-5"] in checkpoint:
model_type = "playground-v2-5"
elif CHECKPOINT_KEY_NAMES["xl_base"] in checkpoint:
model_type = "xl_base"
elif CHECKPOINT_KEY_NAMES["xl_refiner"] in checkpoint:
model_type = "xl_refiner"
elif CHECKPOINT_KEY_NAMES["upscale"] in checkpoint:
model_type = "upscale"
elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["controlnet"]):
if CHECKPOINT_KEY_NAMES["controlnet_xl"] in checkpoint:
if CHECKPOINT_KEY_NAMES["controlnet_xl_large"] in checkpoint:
model_type = "controlnet_xl_large"
elif CHECKPOINT_KEY_NAMES["controlnet_xl_mid"] in checkpoint:
model_type = "controlnet_xl_mid"
else:
model_type = "controlnet_xl_small"
else:
model_type = "controlnet"
elif (
CHECKPOINT_KEY_NAMES["stable_cascade_stage_c"] in checkpoint
and checkpoint[CHECKPOINT_KEY_NAMES["stable_cascade_stage_c"]].shape[0] == 1536
):
model_type = "stable_cascade_stage_c_lite"
elif (
CHECKPOINT_KEY_NAMES["stable_cascade_stage_c"] in checkpoint
and checkpoint[CHECKPOINT_KEY_NAMES["stable_cascade_stage_c"]].shape[0] == 2048
):
model_type = "stable_cascade_stage_c"
elif (
CHECKPOINT_KEY_NAMES["stable_cascade_stage_b"] in checkpoint
and checkpoint[CHECKPOINT_KEY_NAMES["stable_cascade_stage_b"]].shape[-1] == 576
):
model_type = "stable_cascade_stage_b_lite"
elif (
CHECKPOINT_KEY_NAMES["stable_cascade_stage_b"] in checkpoint
and checkpoint[CHECKPOINT_KEY_NAMES["stable_cascade_stage_b"]].shape[-1] == 640
):
model_type = "stable_cascade_stage_b"
elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["sd3"]) and any(
checkpoint[key].shape[-1] == 9216 if key in checkpoint else False for key in CHECKPOINT_KEY_NAMES["sd3"]
):
if "model.diffusion_model.pos_embed" in checkpoint:
key = "model.diffusion_model.pos_embed"
else:
key = "pos_embed"
if checkpoint[key].shape[1] == 36864:
model_type = "sd3"
elif checkpoint[key].shape[1] == 147456:
model_type = "sd35_medium"
elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["sd35_large"]):
model_type = "sd35_large"
elif CHECKPOINT_KEY_NAMES["animatediff"] in checkpoint:
if CHECKPOINT_KEY_NAMES["animatediff_scribble"] in checkpoint:
model_type = "animatediff_scribble"
elif CHECKPOINT_KEY_NAMES["animatediff_rgb"] in checkpoint:
model_type = "animatediff_rgb"
elif CHECKPOINT_KEY_NAMES["animatediff_v2"] in checkpoint:
model_type = "animatediff_v2"
elif checkpoint[CHECKPOINT_KEY_NAMES["animatediff_sdxl_beta"]].shape[-1] == 320:
model_type = "animatediff_sdxl_beta"
elif checkpoint[CHECKPOINT_KEY_NAMES["animatediff"]].shape[1] == 24:
model_type = "animatediff_v1"
else:
model_type = "animatediff_v3"
elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["flux2"]):
model_type = "flux-2-dev"
elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["flux"]):
if any(
g in checkpoint for g in ["guidance_in.in_layer.bias", "model.diffusion_model.guidance_in.in_layer.bias"]
):
if "model.diffusion_model.img_in.weight" in checkpoint:
key = "model.diffusion_model.img_in.weight"
else:
key = "img_in.weight"
if checkpoint[key].shape[1] == 384:
model_type = "flux-fill"
elif checkpoint[key].shape[1] == 128:
model_type = "flux-depth"
else:
model_type = "flux-dev"
else:
model_type = "flux-schnell"
elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["ltx-video"]):
has_vae = "vae.encoder.conv_in.conv.bias" in checkpoint
if any(key.endswith("transformer_blocks.47.scale_shift_table") for key in checkpoint):
model_type = "ltx-video-0.9.7"
elif has_vae and checkpoint["vae.encoder.conv_out.conv.weight"].shape[1] == 2048:
model_type = "ltx-video-0.9.5"
elif "vae.decoder.last_time_embedder.timestep_embedder.linear_1.weight" in checkpoint:
model_type = "ltx-video-0.9.1"
else:
model_type = "ltx-video"
elif CHECKPOINT_KEY_NAMES["autoencoder-dc"] in checkpoint:
encoder_key = "encoder.project_in.conv.conv.bias"
decoder_key = "decoder.project_in.main.conv.weight"
if CHECKPOINT_KEY_NAMES["autoencoder-dc-sana"] in checkpoint:
model_type = "autoencoder-dc-f32c32-sana"
elif checkpoint[encoder_key].shape[-1] == 64 and checkpoint[decoder_key].shape[1] == 32:
model_type = "autoencoder-dc-f32c32"
elif checkpoint[encoder_key].shape[-1] == 64 and checkpoint[decoder_key].shape[1] == 128:
model_type = "autoencoder-dc-f64c128"
else:
model_type = "autoencoder-dc-f128c512"
elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["mochi-1-preview"]):
model_type = "mochi-1-preview"
elif CHECKPOINT_KEY_NAMES["hunyuan-video"] in checkpoint:
model_type = "hunyuan-video"
elif all(key in checkpoint for key in CHECKPOINT_KEY_NAMES["auraflow"]):
model_type = "auraflow"
elif (
CHECKPOINT_KEY_NAMES["instruct-pix2pix"] in checkpoint
and checkpoint[CHECKPOINT_KEY_NAMES["instruct-pix2pix"]].shape[1] == 8
):
model_type = "instruct-pix2pix"
elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["z-image-turbo"]):
model_type = "z-image-turbo"
elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["lumina2"]):
model_type = "lumina2"
elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["sana"]):
model_type = "sana"
elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["wan"]):
if "model.diffusion_model.patch_embedding.weight" in checkpoint:
target_key = "model.diffusion_model.patch_embedding.weight"
else:
target_key = "patch_embedding.weight"
if CHECKPOINT_KEY_NAMES["wan_vace"] in checkpoint:
if checkpoint[target_key].shape[0] == 1536:
model_type = "wan-vace-1.3B"
elif checkpoint[target_key].shape[0] == 5120:
model_type = "wan-vace-14B"
if CHECKPOINT_KEY_NAMES["wan_animate"] in checkpoint:
model_type = "wan-animate-14B"
elif checkpoint[target_key].shape[0] == 1536:
model_type = "wan-t2v-1.3B"
elif checkpoint[target_key].shape[0] == 5120 and checkpoint[target_key].shape[1] == 16:
model_type = "wan-t2v-14B"
else:
model_type = "wan-i2v-14B"
elif CHECKPOINT_KEY_NAMES["wan_vae"] in checkpoint:
# All Wan models use the same VAE so we can use the same default model repo to fetch the config
model_type = "wan-t2v-14B"
elif CHECKPOINT_KEY_NAMES["hidream"] in checkpoint:
model_type = "hidream"
elif all(key in checkpoint for key in CHECKPOINT_KEY_NAMES["cosmos-1.0"]):
x_embedder_shape = checkpoint[CHECKPOINT_KEY_NAMES["cosmos-1.0"][0]].shape
if x_embedder_shape[1] == 68:
model_type = "cosmos-1.0-t2w-7B" if x_embedder_shape[0] == 4096 else "cosmos-1.0-t2w-14B"
elif x_embedder_shape[1] == 72:
model_type = "cosmos-1.0-v2w-7B" if x_embedder_shape[0] == 4096 else "cosmos-1.0-v2w-14B"
else:
raise ValueError(f"Unexpected x_embedder shape: {x_embedder_shape} when loading Cosmos 1.0 model.")
elif all(key in checkpoint for key in CHECKPOINT_KEY_NAMES["cosmos-2.0"]):
x_embedder_shape = checkpoint[CHECKPOINT_KEY_NAMES["cosmos-2.0"][0]].shape
if x_embedder_shape[1] == 68:
model_type = "cosmos-2.0-t2i-2B" if x_embedder_shape[0] == 2048 else "cosmos-2.0-t2i-14B"
elif x_embedder_shape[1] == 72:
model_type = "cosmos-2.0-v2w-2B" if x_embedder_shape[0] == 2048 else "cosmos-2.0-v2w-14B"
else:
raise ValueError(f"Unexpected x_embedder shape: {x_embedder_shape} when loading Cosmos 2.0 model.")
elif CHECKPOINT_KEY_NAMES["z-image-turbo-controlnet-2.x"] in checkpoint:
before_proj_weight = checkpoint.get("control_noise_refiner.0.before_proj.weight", None)
if before_proj_weight is None:
model_type = "z-image-turbo-controlnet-2.0"
elif before_proj_weight is not None and torch.all(before_proj_weight == 0.0):
model_type = "z-image-turbo-controlnet-2.0"
else:
model_type = "z-image-turbo-controlnet-2.1"
elif CHECKPOINT_KEY_NAMES["z-image-turbo-controlnet"] in checkpoint:
model_type = "z-image-turbo-controlnet"
elif any(key in checkpoint for key in CHECKPOINT_KEY_NAMES["ltx2"]):
model_type = "ltx2-dev"
else:
model_type = "v1"
return model_type
def fetch_diffusers_config(checkpoint):
model_type = infer_diffusers_model_type(checkpoint)
model_path = DIFFUSERS_DEFAULT_PIPELINE_PATHS[model_type]
model_path = copy.deepcopy(model_path)
return model_path
def set_image_size(checkpoint, image_size=None):
if image_size:
return image_size
model_type = infer_diffusers_model_type(checkpoint)
image_size = DIFFUSERS_TO_LDM_DEFAULT_IMAGE_SIZE_MAP[model_type]
return image_size
# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.conv_attn_to_linear
def conv_attn_to_linear(checkpoint):
keys = list(checkpoint.keys())
attn_keys = ["query.weight", "key.weight", "value.weight"]
for key in keys:
if ".".join(key.split(".")[-2:]) in attn_keys:
if checkpoint[key].ndim > 2:
checkpoint[key] = checkpoint[key][:, :, 0, 0]
elif "proj_attn.weight" in key:
if checkpoint[key].ndim > 2:
checkpoint[key] = checkpoint[key][:, :, 0]
def create_unet_diffusers_config_from_ldm(
original_config, checkpoint, image_size=None, upcast_attention=None, num_in_channels=None
):
"""
Creates a config for the diffusers based on the config of the LDM model.
"""
if image_size is not None:
deprecation_message = (
"Configuring UNet2DConditionModel with the `image_size` argument to `from_single_file`"
"is deprecated and will be ignored in future versions."
)
deprecate("image_size", "1.0.0", deprecation_message)
image_size = set_image_size(checkpoint, image_size=image_size)
if (
"unet_config" in original_config["model"]["params"]
and original_config["model"]["params"]["unet_config"] is not None
):
unet_params = original_config["model"]["params"]["unet_config"]["params"]
else:
unet_params = original_config["model"]["params"]["network_config"]["params"]
if num_in_channels is not None:
deprecation_message = (
"Configuring UNet2DConditionModel with the `num_in_channels` argument to `from_single_file`"
"is deprecated and will be ignored in future versions."
)
deprecate("image_size", "1.0.0", deprecation_message)
in_channels = num_in_channels
else:
in_channels = unet_params["in_channels"]
vae_params = original_config["model"]["params"]["first_stage_config"]["params"]["ddconfig"]
block_out_channels = [unet_params["model_channels"] * mult for mult in unet_params["channel_mult"]]
down_block_types = []
resolution = 1
for i in range(len(block_out_channels)):
block_type = "CrossAttnDownBlock2D" if resolution in unet_params["attention_resolutions"] else "DownBlock2D"
down_block_types.append(block_type)
if i != len(block_out_channels) - 1:
resolution *= 2
up_block_types = []
for i in range(len(block_out_channels)):
block_type = "CrossAttnUpBlock2D" if resolution in unet_params["attention_resolutions"] else "UpBlock2D"
up_block_types.append(block_type)
resolution //= 2
if unet_params["transformer_depth"] is not None:
transformer_layers_per_block = (
unet_params["transformer_depth"]
if isinstance(unet_params["transformer_depth"], int)
else list(unet_params["transformer_depth"])
)
else:
transformer_layers_per_block = 1
vae_scale_factor = 2 ** (len(vae_params["ch_mult"]) - 1)
head_dim = unet_params["num_heads"] if "num_heads" in unet_params else None
use_linear_projection = (
unet_params["use_linear_in_transformer"] if "use_linear_in_transformer" in unet_params else False
)
if use_linear_projection:
# stable diffusion 2-base-512 and 2-768
if head_dim is None:
head_dim_mult = unet_params["model_channels"] // unet_params["num_head_channels"]
head_dim = [head_dim_mult * c for c in list(unet_params["channel_mult"])]
class_embed_type = None
addition_embed_type = None
addition_time_embed_dim = None
projection_class_embeddings_input_dim = None
context_dim = None
if unet_params["context_dim"] is not None:
context_dim = (
unet_params["context_dim"]
if isinstance(unet_params["context_dim"], int)
else unet_params["context_dim"][0]
)
if "num_classes" in unet_params:
if unet_params["num_classes"] == "sequential":
if context_dim in [2048, 1280]:
# SDXL
addition_embed_type = "text_time"
addition_time_embed_dim = 256
else:
class_embed_type = "projection"
assert "adm_in_channels" in unet_params
projection_class_embeddings_input_dim = unet_params["adm_in_channels"]
config = {
"sample_size": image_size // vae_scale_factor,
"in_channels": in_channels,
"down_block_types": down_block_types,
"block_out_channels": block_out_channels,
"layers_per_block": unet_params["num_res_blocks"],
"cross_attention_dim": context_dim,
"attention_head_dim": head_dim,
"use_linear_projection": use_linear_projection,
"class_embed_type": class_embed_type,
"addition_embed_type": addition_embed_type,
"addition_time_embed_dim": addition_time_embed_dim,
"projection_class_embeddings_input_dim": projection_class_embeddings_input_dim,
"transformer_layers_per_block": transformer_layers_per_block,
}
if upcast_attention is not None:
deprecation_message = (
"Configuring UNet2DConditionModel with the `upcast_attention` argument to `from_single_file`"
"is deprecated and will be ignored in future versions."
)
deprecate("image_size", "1.0.0", deprecation_message)
config["upcast_attention"] = upcast_attention
if "disable_self_attentions" in unet_params:
config["only_cross_attention"] = unet_params["disable_self_attentions"]
if "num_classes" in unet_params and isinstance(unet_params["num_classes"], int):
config["num_class_embeds"] = unet_params["num_classes"]
config["out_channels"] = unet_params["out_channels"]
config["up_block_types"] = up_block_types
return config
def create_controlnet_diffusers_config_from_ldm(original_config, checkpoint, image_size=None, **kwargs):
if image_size is not None:
deprecation_message = (
"Configuring ControlNetModel with the `image_size` argument"
"is deprecated and will be ignored in future versions."
)
deprecate("image_size", "1.0.0", deprecation_message)
image_size = set_image_size(checkpoint, image_size=image_size)
unet_params = original_config["model"]["params"]["control_stage_config"]["params"]
diffusers_unet_config = create_unet_diffusers_config_from_ldm(original_config, image_size=image_size)
controlnet_config = {
"conditioning_channels": unet_params["hint_channels"],
"in_channels": diffusers_unet_config["in_channels"],
"down_block_types": diffusers_unet_config["down_block_types"],
"block_out_channels": diffusers_unet_config["block_out_channels"],
"layers_per_block": diffusers_unet_config["layers_per_block"],