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qwen-image-controlnet

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Artiprocher
2025-08-06 14:18:38 +08:00
parent bc739c78cd
commit 5b1444282e
6 changed files with 283 additions and 4 deletions
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34
examples/qwen_image/model_training/full/Qwen-Image-ControlNet.sh Normal file
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@@ -0,0 +1,34 @@
accelerate launch examples/qwen_image/model_training/train.py \
--dataset_base_path data/example_image_dataset \
--dataset_metadata_path data/example_image_dataset/metadata_controlnet_upscale.csv \
--data_file_keys "image,controlnet_image" \
--max_pixels 1048576 \
--dataset_repeat 8000 \
--model_paths '[
[
"models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00001-of-00009.safetensors",
"models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00002-of-00009.safetensors",
"models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00003-of-00009.safetensors",
"models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00004-of-00009.safetensors",
"models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00005-of-00009.safetensors",
"models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00006-of-00009.safetensors",
"models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00007-of-00009.safetensors",
"models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00008-of-00009.safetensors",
"models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00009-of-00009.safetensors"
],
[
"models/Qwen/Qwen-Image/text_encoder/model-00001-of-00004.safetensors",
"models/Qwen/Qwen-Image/text_encoder/model-00002-of-00004.safetensors",
"models/Qwen/Qwen-Image/text_encoder/model-00003-of-00004.safetensors",
"models/Qwen/Qwen-Image/text_encoder/model-00004-of-00004.safetensors"
],
"models/Qwen/Qwen-Image/vae/diffusion_pytorch_model.safetensors",
"models/controlnet.safetensors"
]' \
--learning_rate 1e-5 \
--num_epochs 2 \
--remove_prefix_in_ckpt "pipe.controlnet.models.0." \
--output_path "./models/train/Qwen-Image-ControlNet_full" \
--trainable_models "controlnet" \
--extra_inputs "controlnet_image" \
--use_gradient_checkpointing

33
examples/qwen_image/model_training/full/others/initialize_controlnet.py Normal file
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# This script is for initializing a Qwen-Image-ControlNet
from diffsynth import load_state_dict, hash_state_dict_keys
from diffsynth.pipelines.qwen_image import QwenImageControlNet
import torch
from safetensors.torch import save_file
state_dict_dit = {}
for i in range(1, 10):
state_dict_dit.update(load_state_dict(f"models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-0000{i}-of-00009.safetensors", torch_dtype=torch.bfloat16, device="cuda"))
controlnet = QwenImageControlNet().to(dtype=torch.bfloat16, device="cuda")
state_dict_controlnet = controlnet.state_dict()
state_dict_init = {}
for k in state_dict_controlnet:
if k in state_dict_dit:
if state_dict_dit[k].shape == state_dict_controlnet[k].shape:
state_dict_init[k] = state_dict_dit[k]
elif k == "img_in.weight":
state_dict_init[k] = torch.concat(
[
state_dict_dit[k],
state_dict_dit[k],
],
dim=-1
)
elif k == "alpha":
state_dict_init[k] = torch.zeros_like(state_dict_controlnet[k])
controlnet.load_state_dict(state_dict_init)
print(hash_state_dict_keys(state_dict_controlnet))
save_file(state_dict_controlnet, "models/controlnet.safetensors")

10
examples/qwen_image/model_training/train.py
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@@ -1,5 +1,5 @@
import torch, os, json
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
from diffsynth.trainers.utils import DiffusionTrainingModule, ImageDataset, ModelLogger, launch_training_task, qwen_image_parser
os.environ["TOKENIZERS_PARALLELISM"] = "false"
@@ -72,8 +72,14 @@ class QwenImageTrainingModule(DiffusionTrainingModule):
}
# Extra inputs
controlnet_input = {}
for extra_input in self.extra_inputs:
inputs_shared[extra_input] = data[extra_input]
if extra_input.startswith("controlnet_"):
controlnet_input[extra_input.replace("controlnet_", "")] = data[extra_input]
else:
inputs_shared[extra_input] = data[extra_input]
if len(controlnet_input) > 0:
inputs_shared["controlnet_inputs"] = [ControlNetInput(**controlnet_input)]
# Pipeline units will automatically process the input parameters.
for unit in self.pipe.units: