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Merge branch 'modelscope:main' into main

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lzws
2025-08-26 15:08:43 +08:00
committed by GitHub
parent f7c01f1367 04e39f7de5
commit 822e80ec2f
3 changed files with 46 additions and 4 deletions
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29
diffsynth/pipelines/qwen_image.py
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@@ -150,6 +150,35 @@ class QwenImagePipeline(BasePipeline):
return loss
def _enable_fp8_lora_training(self, dtype):
from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import Qwen2_5_VLRotaryEmbedding, Qwen2RMSNorm, Qwen2_5_VisionPatchEmbed, Qwen2_5_VisionRotaryEmbedding
from ..models.qwen_image_dit import RMSNorm
from ..models.qwen_image_vae import QwenImageRMS_norm
module_map = {
RMSNorm: AutoWrappedModule,
torch.nn.Linear: AutoWrappedLinear,
torch.nn.Conv3d: AutoWrappedModule,
torch.nn.Conv2d: AutoWrappedModule,
torch.nn.Embedding: AutoWrappedModule,
Qwen2_5_VLRotaryEmbedding: AutoWrappedModule,
Qwen2RMSNorm: AutoWrappedModule,
Qwen2_5_VisionPatchEmbed: AutoWrappedModule,
Qwen2_5_VisionRotaryEmbedding: AutoWrappedModule,
QwenImageRMS_norm: AutoWrappedModule,
}
model_config = dict(
offload_dtype=dtype,
offload_device="cuda",
onload_dtype=dtype,
onload_device="cuda",
computation_dtype=self.torch_dtype,
computation_device="cuda",
)
enable_vram_management(self.text_encoder, module_map=module_map, module_config=model_config)
enable_vram_management(self.dit, module_map=module_map, module_config=model_config)
enable_vram_management(self.vae, module_map=module_map, module_config=model_config)
def enable_vram_management(self, num_persistent_param_in_dit=None, vram_limit=None, vram_buffer=0.5, enable_dit_fp8_computation=False):
self.vram_management_enabled = True
if vram_limit is None:

7
diffsynth/trainers/utils.py
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@@ -338,11 +338,15 @@ class DiffusionTrainingModule(torch.nn.Module):
return trainable_param_names
def add_lora_to_model(self, model, target_modules, lora_rank, lora_alpha=None):
def add_lora_to_model(self, model, target_modules, lora_rank, lora_alpha=None, upcast_dtype=None):
if lora_alpha is None:
lora_alpha = lora_rank
lora_config = LoraConfig(r=lora_rank, lora_alpha=lora_alpha, target_modules=target_modules)
model = inject_adapter_in_model(lora_config, model)
if upcast_dtype is not None:
for param in model.parameters():
if param.requires_grad:
param.data = param.to(upcast_dtype)
return model
@@ -555,4 +559,5 @@ def qwen_image_parser():
parser.add_argument("--dataset_num_workers", type=int, default=0, help="Number of workers for data loading.")
parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay.")
parser.add_argument("--processor_path", type=str, default=None, help="Path to the processor. If provided, the processor will be used for image editing.")
parser.add_argument("--enable_fp8_training", default=False, action="store_true", help="Whether to enable FP8 training. Only available for LoRA training on a single GPU.")
return parser

14
examples/qwen_image/model_training/train.py
View File

@@ -17,21 +17,27 @@ class QwenImageTrainingModule(DiffusionTrainingModule):
use_gradient_checkpointing=True,
use_gradient_checkpointing_offload=False,
extra_inputs=None,
enable_fp8_training=False,
):
super().__init__()
# Load models
offload_dtype = torch.float8_e4m3fn if enable_fp8_training else None
model_configs = []
if model_paths is not None:
model_paths = json.loads(model_paths)
model_configs += [ModelConfig(path=path) for path in model_paths]
model_configs += [ModelConfig(path=path, offload_dtype=offload_dtype) for path in model_paths]
if model_id_with_origin_paths is not None:
model_id_with_origin_paths = model_id_with_origin_paths.split(",")
model_configs += [ModelConfig(model_id=i.split(":")[0], origin_file_pattern=i.split(":")[1]) for i in model_id_with_origin_paths]
model_configs += [ModelConfig(model_id=i.split(":")[0], origin_file_pattern=i.split(":")[1], offload_dtype=offload_dtype) for i in model_id_with_origin_paths]
tokenizer_config = ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/") if tokenizer_path is None else ModelConfig(tokenizer_path)
processor_config = ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/") if processor_path is None else ModelConfig(processor_path)
self.pipe = QwenImagePipeline.from_pretrained(torch_dtype=torch.bfloat16, device="cpu", model_configs=model_configs, tokenizer_config=tokenizer_config, processor_config=processor_config)
# Enable FP8
if enable_fp8_training:
self.pipe._enable_fp8_lora_training(torch.float8_e4m3fn)
# Reset training scheduler (do it in each training step)
self.pipe.scheduler.set_timesteps(1000, training=True)
@@ -43,7 +49,8 @@ class QwenImageTrainingModule(DiffusionTrainingModule):
model = self.add_lora_to_model(
getattr(self.pipe, lora_base_model),
target_modules=lora_target_modules.split(","),
lora_rank=lora_rank
lora_rank=lora_rank,
upcast_dtype=self.pipe.torch_dtype,
)
if lora_checkpoint is not None:
state_dict = load_state_dict(lora_checkpoint)
@@ -126,6 +133,7 @@ if __name__ == "__main__":
use_gradient_checkpointing=args.use_gradient_checkpointing,
use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload,
extra_inputs=args.extra_inputs,
enable_fp8_training=args.enable_fp8_training,
)
model_logger = ModelLogger(args.output_path, remove_prefix_in_ckpt=args.remove_prefix_in_ckpt)
optimizer = torch.optim.AdamW(model.trainable_modules(), lr=args.learning_rate, weight_decay=args.weight_decay)