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41
README.md
41
README.md
@@ -760,6 +760,37 @@ Example code for Wan is available at: [/examples/wanvideo/](/examples/wanvideo/)
|
||||
|
||||
DiffSynth-Studio is not just an engineered model framework, but also an incubator for innovative achievements.
|
||||
|
||||
<details>
|
||||
|
||||
<summary>Spectral Evolution Search: Efficient Inference-Time Scaling for Reward-Aligned Image Generation</summary>
|
||||
|
||||
- Paper: [Spectral Evolution Search: Efficient Inference-Time Scaling for Reward-Aligned Image Generation
|
||||
](https://arxiv.org/abs/2602.03208)
|
||||
- Sample Code: coming soon
|
||||
|
||||
|FLUX.1-dev|FLUX.1-dev + SES|Qwen-Image|Qwen-Image + SES|
|
||||
|-|-|-|-|
|
||||
|||||
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
<details>
|
||||
|
||||
<summary>VIRAL: Visual In-Context Reasoning via Analogy in Diffusion Transformers</summary>
|
||||
|
||||
- Paper: [VIRAL: Visual In-Context Reasoning via Analogy in Diffusion Transformers
|
||||
](https://arxiv.org/abs/2602.03210)
|
||||
- Sample code: [/examples/qwen_image/model_inference/Qwen-Image-Edit-2511-ICEdit.py](/examples/qwen_image/model_inference/Qwen-Image-Edit-2511-ICEdit.py)
|
||||
- Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-2511-ICEdit-LoRA)
|
||||
|
||||
|Example 1|Example 2|Query|Output|
|
||||
|-|-|-|-|
|
||||
|||||
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
<details>
|
||||
|
||||
<summary>AttriCtrl: Attribute Intensity Control for Image Generation Models</summary>
|
||||
@@ -770,7 +801,7 @@ DiffSynth-Studio is not just an engineered model framework, but also an incubato
|
||||
|
||||
|brightness scale = 0.1|brightness scale = 0.3|brightness scale = 0.5|brightness scale = 0.7|brightness scale = 0.9|
|
||||
|-|-|-|-|-|
|
||||
||||||
|
||||
||||||
|
||||
|
||||
</details>
|
||||
|
||||
@@ -785,10 +816,10 @@ DiffSynth-Studio is not just an engineered model framework, but also an incubato
|
||||
|
||||
||[LoRA 1](https://modelscope.cn/models/cancel13/cxsk)|[LoRA 2](https://modelscope.cn/models/wy413928499/xuancai2)|[LoRA 3](https://modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1)|[LoRA 4](https://modelscope.cn/models/hongyanbujian/JPL)|
|
||||
|-|-|-|-|-|
|
||||
|[LoRA 1](https://modelscope.cn/models/cancel13/cxsk) |||||
|
||||
|[LoRA 2](https://modelscope.cn/models/wy413928499/xuancai2) |||||
|
||||
|[LoRA 3](https://modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1) |||||
|
||||
|[LoRA 4](https://modelscope.cn/models/hongyanbujian/JPL) |||||
|
||||
|[LoRA 1](https://modelscope.cn/models/cancel13/cxsk) |||||
|
||||
|[LoRA 2](https://modelscope.cn/models/wy413928499/xuancai2) |||||
|
||||
|[LoRA 3](https://modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1) |||||
|
||||
|[LoRA 4](https://modelscope.cn/models/hongyanbujian/JPL) |||||
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
41
README_zh.md
41
README_zh.md
@@ -760,6 +760,37 @@ Wan 的示例代码位于：[/examples/wanvideo/](/examples/wanvideo/)
|
||||
|
||||
DiffSynth-Studio 不仅仅是一个工程化的模型框架，更是创新成果的孵化器。
|
||||
|
||||
<details>
|
||||
|
||||
<summary>Spectral Evolution Search: 用于奖励对齐图像生成的高效推理阶段缩放</summary>
|
||||
|
||||
- 论文：[Spectral Evolution Search: Efficient Inference-Time Scaling for Reward-Aligned Image Generation
|
||||
](https://arxiv.org/abs/2602.03208)
|
||||
- 代码样例：coming soon
|
||||
|
||||
|FLUX.1-dev|FLUX.1-dev + SES|Qwen-Image|Qwen-Image + SES|
|
||||
|-|-|-|-|
|
||||
|||||
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
<details>
|
||||
|
||||
<summary>VIRAL：基于DiT模型的类比视觉上下文推理</summary>
|
||||
|
||||
- 论文：[VIRAL: Visual In-Context Reasoning via Analogy in Diffusion Transformers
|
||||
](https://arxiv.org/abs/2602.03210)
|
||||
- 代码样例：[/examples/qwen_image/model_inference/Qwen-Image-Edit-2511-ICEdit.py](/examples/qwen_image/model_inference/Qwen-Image-Edit-2511-ICEdit.py)
|
||||
- 模型：[ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-2511-ICEdit-LoRA)
|
||||
|
||||
|Example 1|Example 2|Query|Output|
|
||||
|-|-|-|-|
|
||||
|||||
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
<details>
|
||||
|
||||
<summary>AttriCtrl: 图像生成模型的属性强度控制</summary>
|
||||
@@ -771,7 +802,7 @@ DiffSynth-Studio 不仅仅是一个工程化的模型框架，更是创新成果
|
||||
|
||||
|brightness scale = 0.1|brightness scale = 0.3|brightness scale = 0.5|brightness scale = 0.7|brightness scale = 0.9|
|
||||
|-|-|-|-|-|
|
||||
||||||
|
||||
||||||
|
||||
|
||||
</details>
|
||||
|
||||
@@ -787,10 +818,10 @@ DiffSynth-Studio 不仅仅是一个工程化的模型框架，更是创新成果
|
||||
|
||||
||[LoRA 1](https://modelscope.cn/models/cancel13/cxsk)|[LoRA 2](https://modelscope.cn/models/wy413928499/xuancai2)|[LoRA 3](https://modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1)|[LoRA 4](https://modelscope.cn/models/hongyanbujian/JPL)|
|
||||
|-|-|-|-|-|
|
||||
|[LoRA 1](https://modelscope.cn/models/cancel13/cxsk) |||||
|
||||
|[LoRA 2](https://modelscope.cn/models/wy413928499/xuancai2) |||||
|
||||
|[LoRA 3](https://modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1) |||||
|
||||
|[LoRA 4](https://modelscope.cn/models/hongyanbujian/JPL) |||||
|
||||
|[LoRA 1](https://modelscope.cn/models/cancel13/cxsk) |||||
|
||||
|[LoRA 2](https://modelscope.cn/models/wy413928499/xuancai2) |||||
|
||||
|[LoRA 3](https://modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1) |||||
|
||||
|[LoRA 4](https://modelscope.cn/models/hongyanbujian/JPL) |||||
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
@@ -3,14 +3,14 @@ from ..vram.disk_map import DiskMap
|
||||
from ..vram.layers import enable_vram_management
|
||||
from .file import load_state_dict
|
||||
import torch
|
||||
from contextlib import contextmanager
|
||||
from transformers.integrations import is_deepspeed_zero3_enabled
|
||||
from transformers.utils import ContextManagers
|
||||
|
||||
|
||||
def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, device="cpu", state_dict_converter=None, use_disk_map=False, module_map=None, vram_config=None, vram_limit=None, state_dict=None):
|
||||
config = {} if config is None else config
|
||||
# Why do we use `skip_model_initialization`?
|
||||
# It skips the random initialization of model parameters,
|
||||
# thereby speeding up model loading and avoiding excessive memory usage.
|
||||
with skip_model_initialization():
|
||||
with ContextManagers(get_init_context(torch_dtype=torch_dtype, device=device)):
|
||||
model = model_class(**config)
|
||||
# What is `module_map`?
|
||||
# This is a module mapping table for VRAM management.
|
||||
@@ -48,7 +48,14 @@ def load_model(model_class, path, config=None, torch_dtype=torch.bfloat16, devic
|
||||
state_dict = state_dict_converter(state_dict)
|
||||
else:
|
||||
state_dict = {i: state_dict[i] for i in state_dict}
|
||||
model.load_state_dict(state_dict, assign=True)
|
||||
# Why does DeepSpeed ZeRO Stage 3 need to be handled separately?
|
||||
# Because at this stage, model parameters are partitioned across multiple GPUs.
|
||||
# Loading them directly could lead to excessive GPU memory consumption.
|
||||
if is_deepspeed_zero3_enabled():
|
||||
from transformers.integrations.deepspeed import _load_state_dict_into_zero3_model
|
||||
_load_state_dict_into_zero3_model(model, state_dict)
|
||||
else:
|
||||
model.load_state_dict(state_dict, assign=True)
|
||||
# Why do we call `to()`?
|
||||
# Because some models override the behavior of `to()`,
|
||||
# especially those from libraries like Transformers.
|
||||
@@ -79,3 +86,20 @@ def load_model_with_disk_offload(model_class, path, config=None, torch_dtype=tor
|
||||
}
|
||||
enable_vram_management(model, module_map, vram_config=vram_config, disk_map=disk_map, vram_limit=80)
|
||||
return model
|
||||
|
||||
|
||||
def get_init_context(torch_dtype, device):
|
||||
if is_deepspeed_zero3_enabled():
|
||||
from transformers.modeling_utils import set_zero3_state
|
||||
import deepspeed
|
||||
# Why do we use "deepspeed.zero.Init"?
|
||||
# Weight segmentation of the model can be performed on the CPU side
|
||||
# and loading the segmented weights onto the computing card
|
||||
init_contexts = [deepspeed.zero.Init(remote_device=device, dtype=torch_dtype), set_zero3_state()]
|
||||
else:
|
||||
# Why do we use `skip_model_initialization`?
|
||||
# It skips the random initialization of model parameters,
|
||||
# thereby speeding up model loading and avoiding excessive memory usage.
|
||||
init_contexts = [skip_model_initialization()]
|
||||
|
||||
return init_contexts
|
||||
|
||||
30
diffsynth/core/npu_patch/npu_fused_operator.py
Normal file
30
diffsynth/core/npu_patch/npu_fused_operator.py
Normal file
@@ -0,0 +1,30 @@
|
||||
import torch
|
||||
from ..device.npu_compatible_device import get_device_type
|
||||
try:
|
||||
import torch_npu
|
||||
except:
|
||||
pass
|
||||
|
||||
|
||||
def rms_norm_forward_npu(self, hidden_states):
|
||||
"npu rms fused operator for RMSNorm.forward from diffsynth\models\general_modules.py"
|
||||
if hidden_states.dtype != self.weight.dtype:
|
||||
hidden_states = hidden_states.to(self.weight.dtype)
|
||||
return torch_npu.npu_rms_norm(hidden_states, self.weight, self.eps)[0]
|
||||
|
||||
|
||||
def rms_norm_forward_transformers_npu(self, hidden_states):
|
||||
"npu rms fused operator for transformers"
|
||||
if hidden_states.dtype != self.weight.dtype:
|
||||
hidden_states = hidden_states.to(self.weight.dtype)
|
||||
return torch_npu.npu_rms_norm(hidden_states, self.weight, self.variance_epsilon)[0]
|
||||
|
||||
|
||||
def rotary_emb_Zimage_npu(self, x_in: torch.Tensor, freqs_cis: torch.Tensor):
|
||||
"npu rope fused operator for Zimage"
|
||||
with torch.amp.autocast(get_device_type(), enabled=False):
|
||||
freqs_cis = freqs_cis.unsqueeze(2)
|
||||
cos, sin = torch.chunk(torch.view_as_real(freqs_cis), 2, dim=-1)
|
||||
cos = cos.expand(-1, -1, -1, -1, 2).flatten(-2)
|
||||
sin = sin.expand(-1, -1, -1, -1, 2).flatten(-2)
|
||||
return torch_npu.npu_rotary_mul(x_in, cos, sin, rotary_mode="interleave").to(x_in)
|
||||
@@ -18,8 +18,8 @@ class ModelLogger:
|
||||
|
||||
def on_epoch_end(self, accelerator: Accelerator, model: torch.nn.Module, epoch_id):
|
||||
accelerator.wait_for_everyone()
|
||||
state_dict = accelerator.get_state_dict(model)
|
||||
if accelerator.is_main_process:
|
||||
state_dict = accelerator.get_state_dict(model)
|
||||
state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=self.remove_prefix_in_ckpt)
|
||||
state_dict = self.state_dict_converter(state_dict)
|
||||
os.makedirs(self.output_path, exist_ok=True)
|
||||
@@ -34,8 +34,8 @@ class ModelLogger:
|
||||
|
||||
def save_model(self, accelerator: Accelerator, model: torch.nn.Module, file_name):
|
||||
accelerator.wait_for_everyone()
|
||||
state_dict = accelerator.get_state_dict(model)
|
||||
if accelerator.is_main_process:
|
||||
state_dict = accelerator.get_state_dict(model)
|
||||
state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=self.remove_prefix_in_ckpt)
|
||||
state_dict = self.state_dict_converter(state_dict)
|
||||
os.makedirs(self.output_path, exist_ok=True)
|
||||
|
||||
@@ -27,7 +27,7 @@ def launch_training_task(
|
||||
optimizer = torch.optim.AdamW(model.trainable_modules(), lr=learning_rate, weight_decay=weight_decay)
|
||||
scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer)
|
||||
dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0], num_workers=num_workers)
|
||||
|
||||
model.to(device=accelerator.device)
|
||||
model, optimizer, dataloader, scheduler = accelerator.prepare(model, optimizer, dataloader, scheduler)
|
||||
|
||||
for epoch_id in range(num_epochs):
|
||||
@@ -59,6 +59,7 @@ def launch_data_process_task(
|
||||
num_workers = args.dataset_num_workers
|
||||
|
||||
dataloader = torch.utils.data.DataLoader(dataset, shuffle=False, collate_fn=lambda x: x[0], num_workers=num_workers)
|
||||
model.to(device=accelerator.device)
|
||||
model, dataloader = accelerator.prepare(model, dataloader)
|
||||
|
||||
for data_id, data in enumerate(tqdm(dataloader)):
|
||||
|
||||
@@ -407,6 +407,7 @@ class Flux2AttnProcessor:
|
||||
query = apply_rotary_emb(query, image_rotary_emb, sequence_dim=1)
|
||||
key = apply_rotary_emb(key, image_rotary_emb, sequence_dim=1)
|
||||
|
||||
query, key, value = query.to(hidden_states.dtype), key.to(hidden_states.dtype), value.to(hidden_states.dtype)
|
||||
hidden_states = attention_forward(
|
||||
query,
|
||||
key,
|
||||
@@ -536,6 +537,7 @@ class Flux2ParallelSelfAttnProcessor:
|
||||
query = apply_rotary_emb(query, image_rotary_emb, sequence_dim=1)
|
||||
key = apply_rotary_emb(key, image_rotary_emb, sequence_dim=1)
|
||||
|
||||
query, key, value = query.to(hidden_states.dtype), key.to(hidden_states.dtype), value.to(hidden_states.dtype)
|
||||
hidden_states = attention_forward(
|
||||
query,
|
||||
key,
|
||||
|
||||
@@ -5,6 +5,7 @@ import math
|
||||
from typing import Tuple, Optional
|
||||
from einops import rearrange
|
||||
from .wan_video_camera_controller import SimpleAdapter
|
||||
from ..core.gradient import gradient_checkpoint_forward
|
||||
|
||||
try:
|
||||
import flash_attn_interface
|
||||
@@ -379,27 +380,15 @@ class WanModel(torch.nn.Module):
|
||||
self.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
|
||||
self.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
|
||||
], dim=-1).reshape(f * h * w, 1, -1).to(x.device)
|
||||
|
||||
def create_custom_forward(module):
|
||||
def custom_forward(*inputs):
|
||||
return module(*inputs)
|
||||
return custom_forward
|
||||
|
||||
for block in self.blocks:
|
||||
if self.training and use_gradient_checkpointing:
|
||||
if use_gradient_checkpointing_offload:
|
||||
with torch.autograd.graph.save_on_cpu():
|
||||
x = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
x, context, t_mod, freqs,
|
||||
use_reentrant=False,
|
||||
)
|
||||
else:
|
||||
x = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
x, context, t_mod, freqs,
|
||||
use_reentrant=False,
|
||||
)
|
||||
if self.training:
|
||||
x = gradient_checkpoint_forward(
|
||||
block,
|
||||
use_gradient_checkpointing,
|
||||
use_gradient_checkpointing_offload,
|
||||
x, context, t_mod, freqs
|
||||
)
|
||||
else:
|
||||
x = block(x, context, t_mod, freqs)
|
||||
|
||||
|
||||
@@ -4,6 +4,7 @@ import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from typing import Tuple
|
||||
from .wan_video_dit import rearrange, precompute_freqs_cis_3d, DiTBlock, Head, CrossAttention, modulate, sinusoidal_embedding_1d
|
||||
from ..core.gradient import gradient_checkpoint_forward
|
||||
|
||||
|
||||
def torch_dfs(model: nn.Module, parent_name='root'):
|
||||
@@ -545,46 +546,19 @@ class WanS2VModel(torch.nn.Module):
|
||||
t = self.time_embedding(sinusoidal_embedding_1d(self.freq_dim, timestep))
|
||||
t_mod = self.time_projection(t).unflatten(1, (6, self.dim)).unsqueeze(2).transpose(0, 2)
|
||||
|
||||
def create_custom_forward(module):
|
||||
def custom_forward(*inputs):
|
||||
return module(*inputs)
|
||||
return custom_forward
|
||||
|
||||
for block_id, block in enumerate(self.blocks):
|
||||
if use_gradient_checkpointing_offload:
|
||||
with torch.autograd.graph.save_on_cpu():
|
||||
x = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
x,
|
||||
context,
|
||||
t_mod,
|
||||
seq_len_x,
|
||||
pre_compute_freqs[0],
|
||||
use_reentrant=False,
|
||||
)
|
||||
x = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(lambda x: self.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x)),
|
||||
x,
|
||||
use_reentrant=False,
|
||||
)
|
||||
elif use_gradient_checkpointing:
|
||||
x = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
x,
|
||||
context,
|
||||
t_mod,
|
||||
seq_len_x,
|
||||
pre_compute_freqs[0],
|
||||
use_reentrant=False,
|
||||
)
|
||||
x = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(lambda x: self.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x)),
|
||||
x,
|
||||
use_reentrant=False,
|
||||
)
|
||||
else:
|
||||
x = block(x, context, t_mod, seq_len_x, pre_compute_freqs[0])
|
||||
x = self.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x)
|
||||
x = gradient_checkpoint_forward(
|
||||
block,
|
||||
use_gradient_checkpointing,
|
||||
use_gradient_checkpointing_offload,
|
||||
x, context, t_mod, seq_len_x, pre_compute_freqs[0]
|
||||
)
|
||||
x = gradient_checkpoint_forward(
|
||||
lambda x: self.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x),
|
||||
use_gradient_checkpointing,
|
||||
use_gradient_checkpointing_offload,
|
||||
x
|
||||
)
|
||||
|
||||
x = x[:, :seq_len_x]
|
||||
x = self.head(x, t[:-1])
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
import torch
|
||||
from .wan_video_dit import DiTBlock
|
||||
|
||||
from ..core.gradient import gradient_checkpoint_forward
|
||||
|
||||
class VaceWanAttentionBlock(DiTBlock):
|
||||
def __init__(self, has_image_input, dim, num_heads, ffn_dim, eps=1e-6, block_id=0):
|
||||
@@ -62,26 +62,13 @@ class VaceWanModel(torch.nn.Module):
|
||||
dim=1) for u in c
|
||||
])
|
||||
|
||||
def create_custom_forward(module):
|
||||
def custom_forward(*inputs):
|
||||
return module(*inputs)
|
||||
return custom_forward
|
||||
|
||||
for block in self.vace_blocks:
|
||||
if use_gradient_checkpointing_offload:
|
||||
with torch.autograd.graph.save_on_cpu():
|
||||
c = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
c, x, context, t_mod, freqs,
|
||||
use_reentrant=False,
|
||||
)
|
||||
elif use_gradient_checkpointing:
|
||||
c = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
c, x, context, t_mod, freqs,
|
||||
use_reentrant=False,
|
||||
)
|
||||
else:
|
||||
c = block(c, x, context, t_mod, freqs)
|
||||
c = gradient_checkpoint_forward(
|
||||
block,
|
||||
use_gradient_checkpointing,
|
||||
use_gradient_checkpointing_offload,
|
||||
c, x, context, t_mod, freqs
|
||||
)
|
||||
|
||||
hints = torch.unbind(c)[:-1]
|
||||
return hints
|
||||
|
||||
@@ -171,7 +171,7 @@ class Resample(nn.Module):
|
||||
torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))
|
||||
feat_cache[idx] = cache_x
|
||||
feat_idx[0] += 1
|
||||
return x
|
||||
return x, feat_cache, feat_idx
|
||||
|
||||
def init_weight(self, conv):
|
||||
conv_weight = conv.weight
|
||||
@@ -298,7 +298,7 @@ class ResidualBlock(nn.Module):
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = layer(x)
|
||||
return x + h
|
||||
return x + h, feat_cache, feat_idx
|
||||
|
||||
|
||||
class AttentionBlock(nn.Module):
|
||||
@@ -471,7 +471,7 @@ class Down_ResidualBlock(nn.Module):
|
||||
for module in self.downsamples:
|
||||
x = module(x, feat_cache, feat_idx)
|
||||
|
||||
return x + self.avg_shortcut(x_copy)
|
||||
return x + self.avg_shortcut(x_copy), feat_cache, feat_idx
|
||||
|
||||
|
||||
class Up_ResidualBlock(nn.Module):
|
||||
@@ -511,7 +511,7 @@ class Up_ResidualBlock(nn.Module):
|
||||
x_shortcut = self.avg_shortcut(x, first_chunk)
|
||||
return x_main + x_shortcut
|
||||
else:
|
||||
return x_main
|
||||
return x_main, feat_cache, feat_idx
|
||||
|
||||
|
||||
class Encoder3d(nn.Module):
|
||||
@@ -586,14 +586,14 @@ class Encoder3d(nn.Module):
|
||||
## downsamples
|
||||
for layer in self.downsamples:
|
||||
if feat_cache is not None:
|
||||
x = layer(x, feat_cache, feat_idx)
|
||||
x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx)
|
||||
else:
|
||||
x = layer(x)
|
||||
|
||||
## middle
|
||||
for layer in self.middle:
|
||||
if check_is_instance(layer, ResidualBlock) and feat_cache is not None:
|
||||
x = layer(x, feat_cache, feat_idx)
|
||||
x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx)
|
||||
else:
|
||||
x = layer(x)
|
||||
|
||||
@@ -614,7 +614,7 @@ class Encoder3d(nn.Module):
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = layer(x)
|
||||
return x
|
||||
return x, feat_cache, feat_idx
|
||||
|
||||
|
||||
class Encoder3d_38(nn.Module):
|
||||
@@ -698,14 +698,14 @@ class Encoder3d_38(nn.Module):
|
||||
## downsamples
|
||||
for layer in self.downsamples:
|
||||
if feat_cache is not None:
|
||||
x = layer(x, feat_cache, feat_idx)
|
||||
x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx)
|
||||
else:
|
||||
x = layer(x)
|
||||
|
||||
## middle
|
||||
for layer in self.middle:
|
||||
if isinstance(layer, ResidualBlock) and feat_cache is not None:
|
||||
x = layer(x, feat_cache, feat_idx)
|
||||
x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx)
|
||||
else:
|
||||
x = layer(x)
|
||||
|
||||
@@ -730,7 +730,7 @@ class Encoder3d_38(nn.Module):
|
||||
else:
|
||||
x = layer(x)
|
||||
|
||||
return x
|
||||
return x, feat_cache, feat_idx
|
||||
|
||||
|
||||
class Decoder3d(nn.Module):
|
||||
@@ -807,14 +807,14 @@ class Decoder3d(nn.Module):
|
||||
## middle
|
||||
for layer in self.middle:
|
||||
if check_is_instance(layer, ResidualBlock) and feat_cache is not None:
|
||||
x = layer(x, feat_cache, feat_idx)
|
||||
x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx)
|
||||
else:
|
||||
x = layer(x)
|
||||
|
||||
## upsamples
|
||||
for layer in self.upsamples:
|
||||
if feat_cache is not None:
|
||||
x = layer(x, feat_cache, feat_idx)
|
||||
x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx)
|
||||
else:
|
||||
x = layer(x)
|
||||
|
||||
@@ -835,7 +835,7 @@ class Decoder3d(nn.Module):
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = layer(x)
|
||||
return x
|
||||
return x, feat_cache, feat_idx
|
||||
|
||||
|
||||
|
||||
@@ -906,14 +906,14 @@ class Decoder3d_38(nn.Module):
|
||||
|
||||
for layer in self.middle:
|
||||
if check_is_instance(layer, ResidualBlock) and feat_cache is not None:
|
||||
x = layer(x, feat_cache, feat_idx)
|
||||
x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx)
|
||||
else:
|
||||
x = layer(x)
|
||||
|
||||
## upsamples
|
||||
for layer in self.upsamples:
|
||||
if feat_cache is not None:
|
||||
x = layer(x, feat_cache, feat_idx, first_chunk)
|
||||
x, feat_cache, feat_idx = layer(x, feat_cache, feat_idx, first_chunk)
|
||||
else:
|
||||
x = layer(x)
|
||||
|
||||
@@ -937,7 +937,7 @@ class Decoder3d_38(nn.Module):
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = layer(x)
|
||||
return x
|
||||
return x, feat_cache, feat_idx
|
||||
|
||||
|
||||
def count_conv3d(model):
|
||||
@@ -990,11 +990,11 @@ class VideoVAE_(nn.Module):
|
||||
for i in range(iter_):
|
||||
self._enc_conv_idx = [0]
|
||||
if i == 0:
|
||||
out = self.encoder(x[:, :, :1, :, :],
|
||||
out, self._enc_feat_map, self._enc_conv_idx = self.encoder(x[:, :, :1, :, :],
|
||||
feat_cache=self._enc_feat_map,
|
||||
feat_idx=self._enc_conv_idx)
|
||||
else:
|
||||
out_ = self.encoder(x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
|
||||
out_, self._enc_feat_map, self._enc_conv_idx = self.encoder(x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
|
||||
feat_cache=self._enc_feat_map,
|
||||
feat_idx=self._enc_conv_idx)
|
||||
out = torch.cat([out, out_], 2)
|
||||
@@ -1023,11 +1023,11 @@ class VideoVAE_(nn.Module):
|
||||
for i in range(iter_):
|
||||
self._conv_idx = [0]
|
||||
if i == 0:
|
||||
out = self.decoder(x[:, :, i:i + 1, :, :],
|
||||
out, self._feat_map, self._conv_idx = self.decoder(x[:, :, i:i + 1, :, :],
|
||||
feat_cache=self._feat_map,
|
||||
feat_idx=self._conv_idx)
|
||||
else:
|
||||
out_ = self.decoder(x[:, :, i:i + 1, :, :],
|
||||
out_, self._feat_map, self._conv_idx = self.decoder(x[:, :, i:i + 1, :, :],
|
||||
feat_cache=self._feat_map,
|
||||
feat_idx=self._conv_idx)
|
||||
out = torch.cat([out, out_], 2) # may add tensor offload
|
||||
@@ -1303,11 +1303,11 @@ class VideoVAE38_(VideoVAE_):
|
||||
for i in range(iter_):
|
||||
self._enc_conv_idx = [0]
|
||||
if i == 0:
|
||||
out = self.encoder(x[:, :, :1, :, :],
|
||||
out, self._enc_feat_map, self._enc_conv_idx = self.encoder(x[:, :, :1, :, :],
|
||||
feat_cache=self._enc_feat_map,
|
||||
feat_idx=self._enc_conv_idx)
|
||||
else:
|
||||
out_ = self.encoder(x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
|
||||
out_, self._enc_feat_map, self._enc_conv_idx = self.encoder(x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
|
||||
feat_cache=self._enc_feat_map,
|
||||
feat_idx=self._enc_conv_idx)
|
||||
out = torch.cat([out, out_], 2)
|
||||
@@ -1337,12 +1337,12 @@ class VideoVAE38_(VideoVAE_):
|
||||
for i in range(iter_):
|
||||
self._conv_idx = [0]
|
||||
if i == 0:
|
||||
out = self.decoder(x[:, :, i:i + 1, :, :],
|
||||
out, self._feat_map, self._conv_idx = self.decoder(x[:, :, i:i + 1, :, :],
|
||||
feat_cache=self._feat_map,
|
||||
feat_idx=self._conv_idx,
|
||||
first_chunk=True)
|
||||
else:
|
||||
out_ = self.decoder(x[:, :, i:i + 1, :, :],
|
||||
out_, self._feat_map, self._conv_idx = self.decoder(x[:, :, i:i + 1, :, :],
|
||||
feat_cache=self._feat_map,
|
||||
feat_idx=self._conv_idx)
|
||||
out = torch.cat([out, out_], 2)
|
||||
|
||||
@@ -88,6 +88,14 @@ class Attention(torch.nn.Module):
|
||||
self.norm_q = RMSNorm(head_dim, eps=1e-5)
|
||||
self.norm_k = RMSNorm(head_dim, eps=1e-5)
|
||||
|
||||
# Apply RoPE
|
||||
def apply_rotary_emb(self, x_in: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
|
||||
with torch.amp.autocast(get_device_type(), enabled=False):
|
||||
x = torch.view_as_complex(x_in.float().reshape(*x_in.shape[:-1], -1, 2))
|
||||
freqs_cis = freqs_cis.unsqueeze(2)
|
||||
x_out = torch.view_as_real(x * freqs_cis).flatten(3)
|
||||
return x_out.type_as(x_in) # todo
|
||||
|
||||
def forward(self, hidden_states, freqs_cis, attention_mask):
|
||||
query = self.to_q(hidden_states)
|
||||
key = self.to_k(hidden_states)
|
||||
@@ -103,17 +111,9 @@ class Attention(torch.nn.Module):
|
||||
if self.norm_k is not None:
|
||||
key = self.norm_k(key)
|
||||
|
||||
# Apply RoPE
|
||||
def apply_rotary_emb(x_in: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
|
||||
with torch.amp.autocast(get_device_type(), enabled=False):
|
||||
x = torch.view_as_complex(x_in.float().reshape(*x_in.shape[:-1], -1, 2))
|
||||
freqs_cis = freqs_cis.unsqueeze(2)
|
||||
x_out = torch.view_as_real(x * freqs_cis).flatten(3)
|
||||
return x_out.type_as(x_in) # todo
|
||||
|
||||
if freqs_cis is not None:
|
||||
query = apply_rotary_emb(query, freqs_cis)
|
||||
key = apply_rotary_emb(key, freqs_cis)
|
||||
query = self.apply_rotary_emb(query, freqs_cis)
|
||||
key = self.apply_rotary_emb(key, freqs_cis)
|
||||
|
||||
# Cast to correct dtype
|
||||
dtype = query.dtype
|
||||
|
||||
@@ -348,13 +348,12 @@ class Flux2Unit_Qwen3PromptEmbedder(PipelineUnit):
|
||||
attention_mask = torch.cat(all_attention_masks, dim=0).to(device)
|
||||
|
||||
# Forward pass through the model
|
||||
with torch.inference_mode():
|
||||
output = text_encoder(
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
output_hidden_states=True,
|
||||
use_cache=False,
|
||||
)
|
||||
output = text_encoder(
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
output_hidden_states=True,
|
||||
use_cache=False,
|
||||
)
|
||||
|
||||
# Only use outputs from intermediate layers and stack them
|
||||
out = torch.stack([output.hidden_states[k] for k in self.hidden_states_layers], dim=1)
|
||||
|
||||
@@ -1321,11 +1321,6 @@ def model_fn_wan_video(
|
||||
if tea_cache_update:
|
||||
x = tea_cache.update(x)
|
||||
else:
|
||||
def create_custom_forward(module):
|
||||
def custom_forward(*inputs):
|
||||
return module(*inputs)
|
||||
return custom_forward
|
||||
|
||||
def create_custom_forward_vap(block, vap):
|
||||
def custom_forward(*inputs):
|
||||
return vap(block, *inputs)
|
||||
@@ -1339,32 +1334,24 @@ def model_fn_wan_video(
|
||||
x, x_vap = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward_vap(block, vap),
|
||||
x, context, t_mod, freqs, x_vap, context_vap, t_mod_vap, freqs_vap, block_id,
|
||||
use_reentrant=False,
|
||||
use_reentrant=False
|
||||
)
|
||||
elif use_gradient_checkpointing:
|
||||
x, x_vap = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward_vap(block, vap),
|
||||
x, context, t_mod, freqs, x_vap, context_vap, t_mod_vap, freqs_vap, block_id,
|
||||
use_reentrant=False,
|
||||
use_reentrant=False
|
||||
)
|
||||
else:
|
||||
x, x_vap = vap(block, x, context, t_mod, freqs, x_vap, context_vap, t_mod_vap, freqs_vap, block_id)
|
||||
else:
|
||||
if use_gradient_checkpointing_offload:
|
||||
with torch.autograd.graph.save_on_cpu():
|
||||
x = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
x, context, t_mod, freqs,
|
||||
use_reentrant=False,
|
||||
)
|
||||
elif use_gradient_checkpointing:
|
||||
x = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
x, context, t_mod, freqs,
|
||||
use_reentrant=False,
|
||||
)
|
||||
else:
|
||||
x = block(x, context, t_mod, freqs)
|
||||
x = gradient_checkpoint_forward(
|
||||
block,
|
||||
use_gradient_checkpointing,
|
||||
use_gradient_checkpointing_offload,
|
||||
x, context, t_mod, freqs
|
||||
)
|
||||
|
||||
|
||||
# VACE
|
||||
if vace_context is not None and block_id in vace.vace_layers_mapping:
|
||||
@@ -1487,32 +1474,18 @@ def model_fn_wans2v(
|
||||
return custom_forward
|
||||
|
||||
for block_id, block in enumerate(dit.blocks):
|
||||
if use_gradient_checkpointing_offload:
|
||||
with torch.autograd.graph.save_on_cpu():
|
||||
x = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
x, context, t_mod, seq_len_x, pre_compute_freqs[0],
|
||||
use_reentrant=False,
|
||||
)
|
||||
x = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(lambda x: dit.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x)),
|
||||
x,
|
||||
use_reentrant=False,
|
||||
)
|
||||
elif use_gradient_checkpointing:
|
||||
x = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
x, context, t_mod, seq_len_x, pre_compute_freqs[0],
|
||||
use_reentrant=False,
|
||||
x = gradient_checkpoint_forward(
|
||||
block,
|
||||
use_gradient_checkpointing,
|
||||
use_gradient_checkpointing_offload,
|
||||
x, context, t_mod, seq_len_x, pre_compute_freqs[0]
|
||||
)
|
||||
x = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(lambda x: dit.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x)),
|
||||
x,
|
||||
use_reentrant=False,
|
||||
)
|
||||
else:
|
||||
x = block(x, context, t_mod, seq_len_x, pre_compute_freqs[0])
|
||||
x = dit.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x_global, use_unified_sequence_parallel)
|
||||
x = gradient_checkpoint_forward(
|
||||
lambda x: dit.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x),
|
||||
use_gradient_checkpointing,
|
||||
use_gradient_checkpointing_offload,
|
||||
x
|
||||
)
|
||||
|
||||
if use_unified_sequence_parallel and dist.is_initialized() and dist.get_world_size() > 1:
|
||||
x = get_sp_group().all_gather(x, dim=1)
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
import torch, math
|
||||
import torch, math, warnings
|
||||
from PIL import Image
|
||||
from typing import Union
|
||||
from tqdm import tqdm
|
||||
@@ -6,7 +6,7 @@ from einops import rearrange
|
||||
import numpy as np
|
||||
from typing import Union, List, Optional, Tuple, Iterable, Dict
|
||||
|
||||
from ..core.device.npu_compatible_device import get_device_type
|
||||
from ..core.device.npu_compatible_device import get_device_type, IS_NPU_AVAILABLE
|
||||
from ..diffusion import FlowMatchScheduler
|
||||
from ..core import ModelConfig, gradient_checkpoint_forward
|
||||
from ..core.data.operators import ImageCropAndResize
|
||||
@@ -63,6 +63,7 @@ class ZImagePipeline(BasePipeline):
|
||||
model_configs: list[ModelConfig] = [],
|
||||
tokenizer_config: ModelConfig = ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
|
||||
vram_limit: float = None,
|
||||
enable_npu_patch: bool = True,
|
||||
):
|
||||
# Initialize pipeline
|
||||
pipe = ZImagePipeline(device=device, torch_dtype=torch_dtype)
|
||||
@@ -84,6 +85,8 @@ class ZImagePipeline(BasePipeline):
|
||||
|
||||
# VRAM Management
|
||||
pipe.vram_management_enabled = pipe.check_vram_management_state()
|
||||
# NPU patch
|
||||
apply_npu_patch(enable_npu_patch)
|
||||
return pipe
|
||||
|
||||
|
||||
@@ -667,3 +670,19 @@ def model_fn_z_image_turbo(
|
||||
x = rearrange(x, "C B H W -> B C H W")
|
||||
x = -x
|
||||
return x
|
||||
|
||||
|
||||
def apply_npu_patch(enable_npu_patch: bool=True):
|
||||
if IS_NPU_AVAILABLE and enable_npu_patch:
|
||||
from ..models.general_modules import RMSNorm
|
||||
from transformers.models.qwen3.modeling_qwen3 import Qwen3RMSNorm
|
||||
from ..models.z_image_dit import Attention
|
||||
from ..core.npu_patch.npu_fused_operator import (
|
||||
rms_norm_forward_npu,
|
||||
rms_norm_forward_transformers_npu,
|
||||
rotary_emb_Zimage_npu
|
||||
)
|
||||
warnings.warn("Replacing RMSNorm and Rope with NPU fusion operators to improve the performance of the model on NPU.Set enable_npu_patch=False to disable this feature.")
|
||||
RMSNorm.forward = rms_norm_forward_npu
|
||||
Qwen3RMSNorm.forward = rms_norm_forward_transformers_npu
|
||||
Attention.apply_rotary_emb = rotary_emb_Zimage_npu
|
||||
|
||||
@@ -9,6 +9,7 @@ from xfuser.core.long_ctx_attention import xFuserLongContextAttention
|
||||
|
||||
from ... import IS_NPU_AVAILABLE
|
||||
from ...core.device import parse_nccl_backend, parse_device_type
|
||||
from ...core.gradient import gradient_checkpoint_forward
|
||||
|
||||
|
||||
def initialize_usp(device_type):
|
||||
@@ -87,11 +88,6 @@ def usp_dit_forward(self,
|
||||
self.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
|
||||
self.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
|
||||
], dim=-1).reshape(f * h * w, 1, -1).to(x.device)
|
||||
|
||||
def create_custom_forward(module):
|
||||
def custom_forward(*inputs):
|
||||
return module(*inputs)
|
||||
return custom_forward
|
||||
|
||||
# Context Parallel
|
||||
chunks = torch.chunk(x, get_sequence_parallel_world_size(), dim=1)
|
||||
@@ -100,20 +96,13 @@ def usp_dit_forward(self,
|
||||
x = chunks[get_sequence_parallel_rank()]
|
||||
|
||||
for block in self.blocks:
|
||||
if self.training and use_gradient_checkpointing:
|
||||
if use_gradient_checkpointing_offload:
|
||||
with torch.autograd.graph.save_on_cpu():
|
||||
x = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
x, context, t_mod, freqs,
|
||||
use_reentrant=False,
|
||||
)
|
||||
else:
|
||||
x = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(block),
|
||||
x, context, t_mod, freqs,
|
||||
use_reentrant=False,
|
||||
)
|
||||
if self.training:
|
||||
x = gradient_checkpoint_forward(
|
||||
block,
|
||||
use_gradient_checkpointing,
|
||||
use_gradient_checkpointing_offload,
|
||||
x, context, t_mod, freqs
|
||||
)
|
||||
else:
|
||||
x = block(x, context, t_mod, freqs)
|
||||
|
||||
|
||||
@@ -107,6 +107,11 @@ Special Training Scripts:
|
||||
* Two-stage Split Training: [doc](/docs/en/Training/Split_Training.md), [code](/examples/qwen_image/model_training/special/split_training/)
|
||||
* End-to-end Direct Distillation: [doc](/docs/en/Training/Direct_Distill.md), [code](/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh)
|
||||
|
||||
DeepSpeed ZeRO Stage 3 Training: The Qwen-Image series models support DeepSpeed ZeRO Stage 3 training, which partitions the model across multiple GPUs. Taking full parameter training of the Qwen-Image model as an example, the following modifications are required:
|
||||
|
||||
* `--config_file examples/qwen_image/model_training/full/accelerate_config_zero3.yaml`
|
||||
* `--initialize_model_on_cpu`
|
||||
|
||||
## Model Inference
|
||||
|
||||
Models are loaded via `QwenImagePipeline.from_pretrained`, see [Loading Models](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models).
|
||||
|
||||
@@ -142,6 +142,11 @@ graph LR;
|
||||
* Two-stage Split Training: [doc](/docs/en/Training/Split_Training.md), [code](/examples/wanvideo/model_training/special/split_training/)
|
||||
* End-to-end Direct Distillation: [doc](/docs/en/Training/Direct_Distill.md), [code](/examples/wanvideo/model_training/special/direct_distill/)
|
||||
|
||||
DeepSpeed ZeRO Stage 3 Training: The Wan series models support DeepSpeed ZeRO Stage 3 training, which partitions the model across multiple GPUs. Taking full parameter training of the Wan2.1-T2V-14B model as an example, the following modifications are required:
|
||||
|
||||
* `--config_file examples/wanvideo/model_training/full/accelerate_config_zero3.yaml`
|
||||
* `--initialize_model_on_cpu`
|
||||
|
||||
## Model Inference
|
||||
|
||||
Models are loaded via `WanVideoPipeline.from_pretrained`, see [Loading Models](/docs/en/Pipeline_Usage/Model_Inference.md#loading-models).
|
||||
|
||||
@@ -89,4 +89,5 @@ Set 0 or not set: indicates not enabling the binding function
|
||||
#### Parameters for specific models
|
||||
| Model | Parameter | Note |
|
||||
|----------------|---------------------------|-------------------|
|
||||
| Wan 14B series | --initialize_model_on_cpu | The 14B model needs to be initialized on the CPU |
|
||||
| Wan 14B series | --initialize_model_on_cpu | The 14B model needs to be initialized on the CPU |
|
||||
| Qwen-Image series | --initialize_model_on_cpu | The model needs to be initialized on the CPU |
|
||||
@@ -102,4 +102,65 @@ image.save("image.jpg")
|
||||
|
||||
Each model `Pipeline` has different input parameters. Please refer to the documentation for each model.
|
||||
|
||||
If the model parameters are too large, causing insufficient VRAM, please enable [VRAM management](/docs/en/Pipeline_Usage/VRAM_management.md).
|
||||
If the model parameters are too large, causing insufficient VRAM, please enable [VRAM management](/docs/en/Pipeline_Usage/VRAM_management.md).
|
||||
|
||||
## Loading LoRA
|
||||
|
||||
LoRA is a lightweight model training method that produces a small number of parameters to extend model capabilities. DiffSynth-Studio supports two ways to load LoRA: cold loading and hot loading.
|
||||
|
||||
* Cold loading: When the base model does not have [VRAM management](/docs/en/Pipeline_Usage/VRAM_management.md) enabled, LoRA will be fused into the base model weights. In this case, inference speed remains unchanged, but LoRA cannot be unloaded after loading.
|
||||
|
||||
```python
|
||||
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
|
||||
import torch
|
||||
|
||||
pipe = QwenImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
|
||||
],
|
||||
tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
|
||||
)
|
||||
lora = ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-LoRA-ArtAug-v1", origin_file_pattern="model.safetensors")
|
||||
pipe.load_lora(pipe.dit, lora, alpha=1)
|
||||
prompt = "Exquisite portrait, underwater girl, blue dress flowing, hair floating, translucent light, bubbles surrounding, peaceful face, intricate details, dreamy and ethereal."
|
||||
image = pipe(prompt, seed=0, num_inference_steps=40)
|
||||
image.save("image.jpg")
|
||||
```
|
||||
|
||||
* Hot loading: When the base model has [VRAM management](/docs/en/Pipeline_Usage/VRAM_management.md) enabled, LoRA will not be fused into the base model weights. In this case, inference speed will be slower, but LoRA can be unloaded through `pipe.clear_lora()` after loading.
|
||||
|
||||
```python
|
||||
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
|
||||
import torch
|
||||
|
||||
vram_config = {
|
||||
"offload_dtype": torch.bfloat16,
|
||||
"offload_device": "cuda",
|
||||
"onload_dtype": torch.bfloat16,
|
||||
"onload_device": "cuda",
|
||||
"preparing_dtype": torch.bfloat16,
|
||||
"preparing_device": "cuda",
|
||||
"computation_dtype": torch.bfloat16,
|
||||
"computation_device": "cuda",
|
||||
}
|
||||
pipe = QwenImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
|
||||
],
|
||||
tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
|
||||
)
|
||||
lora = ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-LoRA-ArtAug-v1", origin_file_pattern="model.safetensors")
|
||||
pipe.load_lora(pipe.dit, lora, alpha=1)
|
||||
prompt = "Exquisite portrait, underwater girl, blue dress flowing, hair floating, translucent light, bubbles surrounding, peaceful face, intricate details, dreamy and ethereal."
|
||||
image = pipe(prompt, seed=0, num_inference_steps=40)
|
||||
image.save("image.jpg")
|
||||
pipe.clear_lora()
|
||||
```
|
||||
|
||||
@@ -25,4 +25,11 @@ Even with suitable hardware conditions, we currently have no plans to support na
|
||||
* The main challenge of native FP8 precision training is precision overflow caused by gradient explosion. To ensure training stability, the model structure needs to be redesigned accordingly. However, no model developers are willing to do so at present.
|
||||
* Additionally, models trained with native FP8 precision can only be computed with BF16 precision during inference without Hopper architecture GPUs, theoretically resulting in generation quality inferior to FP8.
|
||||
|
||||
Therefore, native FP8 precision training technology is extremely immature. We will observe the technological developments in the open-source community.
|
||||
Therefore, native FP8 precision training technology is extremely immature. We will observe the technological developments in the open-source community.
|
||||
|
||||
## How to dynamically load LoRA models during inference?
|
||||
|
||||
We support two loading methods for LoRA models. See [LoRA Loading](/docs/en/Pipeline_Usage/Model_Inference.md#loading-lora) for details:
|
||||
|
||||
* Cold Loading: When [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md) is not enabled for the base model, LoRA will be fused into the base model weights. In this case, inference speed remains unchanged, and LoRA cannot be unloaded after loading.
|
||||
* Hot Loading: When [VRAM Management](/docs/en/Pipeline_Usage/VRAM_management.md) is enabled for the base model, LoRA will not be fused into the base model weights. In this case, inference speed will slow down, and LoRA can be unloaded after loading via `pipe.clear_lora()`.
|
||||
|
||||
@@ -107,6 +107,11 @@ graph LR;
|
||||
* 两阶段拆分训练：[doc](/docs/zh/Training/Split_Training.md)、[code](/examples/qwen_image/model_training/special/split_training/)
|
||||
* 端到端直接蒸馏：[doc](/docs/zh/Training/Direct_Distill.md)、[code](/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh)
|
||||
|
||||
DeepSpeed ZeRO 3 训练：Qwen-Image 系列模型支持 DeepSpeed ZeRO 3 训练，将模型拆分到多个 GPU 上，以 Qwen-Image 模型的全量训练为例，需修改：
|
||||
|
||||
* `--config_file examples/qwen_image/model_training/full/accelerate_config_zero3.yaml`
|
||||
* `--initialize_model_on_cpu`
|
||||
|
||||
## 模型推理
|
||||
|
||||
模型通过 `QwenImagePipeline.from_pretrained` 加载，详见[加载模型](/docs/zh/Pipeline_Usage/Model_Inference.md#加载模型)。
|
||||
|
||||
@@ -143,6 +143,11 @@ graph LR;
|
||||
* 两阶段拆分训练：[doc](/docs/zh/Training/Split_Training.md)、[code](/examples/wanvideo/model_training/special/split_training/)
|
||||
* 端到端直接蒸馏：[doc](/docs/zh/Training/Direct_Distill.md)、[code](/examples/wanvideo/model_training/special/direct_distill/)
|
||||
|
||||
DeepSpeed ZeRO 3 训练：Wan 系列模型支持 DeepSpeed ZeRO 3 训练，将模型拆分到多个 GPU 上，以 Wan2.1-T2V-14B 模型的全量训练为例，需修改：
|
||||
|
||||
* `--config_file examples/wanvideo/model_training/full/accelerate_config_zero3.yaml`
|
||||
* `--initialize_model_on_cpu`
|
||||
|
||||
## 模型推理
|
||||
|
||||
模型通过 `WanVideoPipeline.from_pretrained` 加载，详见[加载模型](/docs/zh/Pipeline_Usage/Model_Inference.md#加载模型)。
|
||||
|
||||
@@ -88,4 +88,5 @@ export CPU_AFFINITY_CONF=1
|
||||
#### 特定模型需要开启的参数
|
||||
| 模型 | 参数 | 备注 |
|
||||
|-----------|------|-------------------|
|
||||
| Wan 14B系列 | --initialize_model_on_cpu | 14B模型需要在cpu上进行初始化 |
|
||||
| Wan 14B系列 | --initialize_model_on_cpu | 14B模型需要在cpu上进行初始化 |
|
||||
| Qwen-Image系列 | --initialize_model_on_cpu | 模型需要在cpu上进行初始化 |
|
||||
@@ -103,3 +103,64 @@ image.save("image.jpg")
|
||||
每个模型 `Pipeline` 的输入参数不同，请参考各模型的文档。
|
||||
|
||||
如果模型参数量太大，导致显存不足，请开启[显存管理](/docs/zh/Pipeline_Usage/VRAM_management.md)。
|
||||
|
||||
## 加载 LoRA
|
||||
|
||||
LoRA 是一种轻量化的模型训练方式，产生少量参数，扩展模型的能力。DiffSynth-Studio 的 LoRA 加载有两种方式：冷加载和热加载。
|
||||
|
||||
* 冷加载：当基础模型未开启[显存管理](/docs/zh/Pipeline_Usage/VRAM_management.md)时，LoRA 会融合进基础模型权重，此时推理速度没有变化，LoRA 加载后无法卸载。
|
||||
|
||||
```python
|
||||
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
|
||||
import torch
|
||||
|
||||
pipe = QwenImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
|
||||
],
|
||||
tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
|
||||
)
|
||||
lora = ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-LoRA-ArtAug-v1", origin_file_pattern="model.safetensors")
|
||||
pipe.load_lora(pipe.dit, lora, alpha=1)
|
||||
prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。"
|
||||
image = pipe(prompt, seed=0, num_inference_steps=40)
|
||||
image.save("image.jpg")
|
||||
```
|
||||
|
||||
* 热加载：当基础模型开启[显存管理](/docs/zh/Pipeline_Usage/VRAM_management.md)时，LoRA 不会融合进基础模型权重，此时推理速度会变慢，LoRA 加载后可通过 `pipe.clear_lora()` 卸载。
|
||||
|
||||
```python
|
||||
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
|
||||
import torch
|
||||
|
||||
vram_config = {
|
||||
"offload_dtype": torch.bfloat16,
|
||||
"offload_device": "cuda",
|
||||
"onload_dtype": torch.bfloat16,
|
||||
"onload_device": "cuda",
|
||||
"preparing_dtype": torch.bfloat16,
|
||||
"preparing_device": "cuda",
|
||||
"computation_dtype": torch.bfloat16,
|
||||
"computation_device": "cuda",
|
||||
}
|
||||
pipe = QwenImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
|
||||
],
|
||||
tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
|
||||
)
|
||||
lora = ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-LoRA-ArtAug-v1", origin_file_pattern="model.safetensors")
|
||||
pipe.load_lora(pipe.dit, lora, alpha=1)
|
||||
prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。"
|
||||
image = pipe(prompt, seed=0, num_inference_steps=40)
|
||||
image.save("image.jpg")
|
||||
pipe.clear_lora()
|
||||
```
|
||||
|
||||
@@ -26,3 +26,10 @@
|
||||
* 此外，使用原生 FP8 精度训练的模型，在推理时若没有 Hopper 架构 GPU，则只能以 BF16 精度进行计算，理论上其生成效果反而不如 FP8。
|
||||
|
||||
因此，原生 FP8 精度训练技术是极不成熟的，我们静观开源社区的技术发展。
|
||||
|
||||
## 如何在推理时动态加载 LoRA 模型？
|
||||
|
||||
我们支持 LoRA 模型的两种加载方式，详见[LoRA 加载](/docs/zh/Pipeline_Usage/Model_Inference.md#加载-lora)：
|
||||
|
||||
* 冷加载：当基础模型未开启[显存管理](/docs/zh/Pipeline_Usage/VRAM_management.md)时，LoRA 会融合进基础模型权重，此时推理速度没有变化，LoRA 加载后无法卸载。
|
||||
* 热加载：当基础模型开启[显存管理](/docs/zh/Pipeline_Usage/VRAM_management.md)时，LoRA 不会融合进基础模型权重，此时推理速度会变慢，LoRA 加载后可通过 `pipe.clear_lora()` 卸载。
|
||||
|
||||
@@ -0,0 +1,23 @@
|
||||
compute_environment: LOCAL_MACHINE
|
||||
debug: false
|
||||
deepspeed_config:
|
||||
gradient_accumulation_steps: 1
|
||||
offload_optimizer_device: none
|
||||
offload_param_device: none
|
||||
zero3_init_flag: true
|
||||
zero3_save_16bit_model: true
|
||||
zero_stage: 3
|
||||
distributed_type: DEEPSPEED
|
||||
downcast_bf16: 'no'
|
||||
enable_cpu_affinity: false
|
||||
machine_rank: 0
|
||||
main_training_function: main
|
||||
mixed_precision: bf16
|
||||
num_machines: 1
|
||||
num_processes: 8
|
||||
rdzv_backend: static
|
||||
same_network: true
|
||||
tpu_env: []
|
||||
tpu_use_cluster: false
|
||||
tpu_use_sudo: false
|
||||
use_cpu: false
|
||||
@@ -0,0 +1,23 @@
|
||||
compute_environment: LOCAL_MACHINE
|
||||
debug: false
|
||||
deepspeed_config:
|
||||
gradient_accumulation_steps: 1
|
||||
offload_optimizer_device: none
|
||||
offload_param_device: none
|
||||
zero3_init_flag: true
|
||||
zero3_save_16bit_model: true
|
||||
zero_stage: 3
|
||||
distributed_type: DEEPSPEED
|
||||
downcast_bf16: 'no'
|
||||
enable_cpu_affinity: false
|
||||
machine_rank: 0
|
||||
main_training_function: main
|
||||
mixed_precision: bf16
|
||||
num_machines: 1
|
||||
num_processes: 8
|
||||
rdzv_backend: static
|
||||
same_network: true
|
||||
tpu_env: []
|
||||
tpu_use_cluster: false
|
||||
tpu_use_sudo: false
|
||||
use_cpu: false
|
||||
@@ -0,0 +1,36 @@
|
||||
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
|
||||
export CPU_AFFINITY_CONF=1
|
||||
|
||||
accelerate launch examples/flux2/model_training/train.py \
|
||||
--dataset_base_path data/example_image_dataset \
|
||||
--dataset_metadata_path data/example_image_dataset/metadata.csv \
|
||||
--max_pixels 1048576 \
|
||||
--dataset_repeat 1 \
|
||||
--model_id_with_origin_paths "black-forest-labs/FLUX.2-dev:text_encoder/*.safetensors,black-forest-labs/FLUX.2-dev:vae/diffusion_pytorch_model.safetensors" \
|
||||
--learning_rate 1e-4 \
|
||||
--num_epochs 5 \
|
||||
--remove_prefix_in_ckpt "pipe.dit." \
|
||||
--output_path "./models/train/FLUX.2-dev-LoRA-splited-cache" \
|
||||
--lora_base_model "dit" \
|
||||
--lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_qkv_mlp_proj,to_out.0,to_add_out,linear_in,linear_out,single_transformer_blocks.0.attn.to_out,single_transformer_blocks.1.attn.to_out,single_transformer_blocks.2.attn.to_out,single_transformer_blocks.3.attn.to_out,single_transformer_blocks.4.attn.to_out,single_transformer_blocks.5.attn.to_out,single_transformer_blocks.6.attn.to_out,single_transformer_blocks.7.attn.to_out,single_transformer_blocks.8.attn.to_out,single_transformer_blocks.9.attn.to_out,single_transformer_blocks.10.attn.to_out,single_transformer_blocks.11.attn.to_out,single_transformer_blocks.12.attn.to_out,single_transformer_blocks.13.attn.to_out,single_transformer_blocks.14.attn.to_out,single_transformer_blocks.15.attn.to_out,single_transformer_blocks.16.attn.to_out,single_transformer_blocks.17.attn.to_out,single_transformer_blocks.18.attn.to_out,single_transformer_blocks.19.attn.to_out,single_transformer_blocks.20.attn.to_out,single_transformer_blocks.21.attn.to_out,single_transformer_blocks.22.attn.to_out,single_transformer_blocks.23.attn.to_out,single_transformer_blocks.24.attn.to_out,single_transformer_blocks.25.attn.to_out,single_transformer_blocks.26.attn.to_out,single_transformer_blocks.27.attn.to_out,single_transformer_blocks.28.attn.to_out,single_transformer_blocks.29.attn.to_out,single_transformer_blocks.30.attn.to_out,single_transformer_blocks.31.attn.to_out,single_transformer_blocks.32.attn.to_out,single_transformer_blocks.33.attn.to_out,single_transformer_blocks.34.attn.to_out,single_transformer_blocks.35.attn.to_out,single_transformer_blocks.36.attn.to_out,single_transformer_blocks.37.attn.to_out,single_transformer_blocks.38.attn.to_out,single_transformer_blocks.39.attn.to_out,single_transformer_blocks.40.attn.to_out,single_transformer_blocks.41.attn.to_out,single_transformer_blocks.42.attn.to_out,single_transformer_blocks.43.attn.to_out,single_transformer_blocks.44.attn.to_out,single_transformer_blocks.45.attn.to_out,single_transformer_blocks.46.attn.to_out,single_transformer_blocks.47.attn.to_out" \
|
||||
--lora_rank 32 \
|
||||
--use_gradient_checkpointing \
|
||||
--dataset_num_workers 8 \
|
||||
--task "sft:data_process"
|
||||
|
||||
accelerate launch --config_file examples/flux2/model_training/full/accelerate_config_zero3.yaml examples/flux2/model_training/train.py \
|
||||
--dataset_base_path "./models/train/FLUX.2-dev-LoRA-splited-cache" \
|
||||
--max_pixels 1048576 \
|
||||
--dataset_repeat 50 \
|
||||
--model_id_with_origin_paths "black-forest-labs/FLUX.2-dev:transformer/*.safetensors" \
|
||||
--learning_rate 1e-4 \
|
||||
--num_epochs 5 \
|
||||
--remove_prefix_in_ckpt "pipe.dit." \
|
||||
--output_path "./models/train/FLUX.2-dev-LoRA-splited" \
|
||||
--lora_base_model "dit" \
|
||||
--lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_qkv_mlp_proj,to_out.0,to_add_out,linear_in,linear_out,single_transformer_blocks.0.attn.to_out,single_transformer_blocks.1.attn.to_out,single_transformer_blocks.2.attn.to_out,single_transformer_blocks.3.attn.to_out,single_transformer_blocks.4.attn.to_out,single_transformer_blocks.5.attn.to_out,single_transformer_blocks.6.attn.to_out,single_transformer_blocks.7.attn.to_out,single_transformer_blocks.8.attn.to_out,single_transformer_blocks.9.attn.to_out,single_transformer_blocks.10.attn.to_out,single_transformer_blocks.11.attn.to_out,single_transformer_blocks.12.attn.to_out,single_transformer_blocks.13.attn.to_out,single_transformer_blocks.14.attn.to_out,single_transformer_blocks.15.attn.to_out,single_transformer_blocks.16.attn.to_out,single_transformer_blocks.17.attn.to_out,single_transformer_blocks.18.attn.to_out,single_transformer_blocks.19.attn.to_out,single_transformer_blocks.20.attn.to_out,single_transformer_blocks.21.attn.to_out,single_transformer_blocks.22.attn.to_out,single_transformer_blocks.23.attn.to_out,single_transformer_blocks.24.attn.to_out,single_transformer_blocks.25.attn.to_out,single_transformer_blocks.26.attn.to_out,single_transformer_blocks.27.attn.to_out,single_transformer_blocks.28.attn.to_out,single_transformer_blocks.29.attn.to_out,single_transformer_blocks.30.attn.to_out,single_transformer_blocks.31.attn.to_out,single_transformer_blocks.32.attn.to_out,single_transformer_blocks.33.attn.to_out,single_transformer_blocks.34.attn.to_out,single_transformer_blocks.35.attn.to_out,single_transformer_blocks.36.attn.to_out,single_transformer_blocks.37.attn.to_out,single_transformer_blocks.38.attn.to_out,single_transformer_blocks.39.attn.to_out,single_transformer_blocks.40.attn.to_out,single_transformer_blocks.41.attn.to_out,single_transformer_blocks.42.attn.to_out,single_transformer_blocks.43.attn.to_out,single_transformer_blocks.44.attn.to_out,single_transformer_blocks.45.attn.to_out,single_transformer_blocks.46.attn.to_out,single_transformer_blocks.47.attn.to_out" \
|
||||
--lora_rank 32 \
|
||||
--use_gradient_checkpointing \
|
||||
--dataset_num_workers 8 \
|
||||
--initialize_model_on_cpu \
|
||||
--task "sft:train"
|
||||
@@ -0,0 +1,34 @@
|
||||
# This script is tested on 8*910B(NPU)
|
||||
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
|
||||
export CPU_AFFINITY_CONF=1
|
||||
|
||||
accelerate launch --config_file examples/flux2/model_training/full/accelerate_config.yaml examples/flux2/model_training/train.py \
|
||||
--dataset_base_path data/example_image_dataset \
|
||||
--dataset_metadata_path data/example_image_dataset/metadata.csv \
|
||||
--max_pixels 1048576 \
|
||||
--dataset_repeat 50 \
|
||||
--model_id_with_origin_paths "black-forest-labs/FLUX.2-klein-9B:text_encoder/*.safetensors,black-forest-labs/FLUX.2-klein-9B:transformer/*.safetensors,black-forest-labs/FLUX.2-klein-9B:vae/diffusion_pytorch_model.safetensors" \
|
||||
--tokenizer_path "black-forest-labs/FLUX.2-klein-9B:tokenizer/" \
|
||||
--learning_rate 1e-5 \
|
||||
--num_epochs 2 \
|
||||
--remove_prefix_in_ckpt "pipe.dit." \
|
||||
--output_path "./models/train/FLUX.2-klein-9B_full" \
|
||||
--trainable_models "dit" \
|
||||
--use_gradient_checkpointing
|
||||
|
||||
# Edit
|
||||
# accelerate launch --config_file examples/flux2/model_training/full/accelerate_config.yaml examples/flux2/model_training/train.py \
|
||||
# --dataset_base_path data/example_image_dataset \
|
||||
# --dataset_metadata_path data/example_image_dataset/metadata_qwen_imgae_edit_multi.json \
|
||||
# --data_file_keys "image,edit_image" \
|
||||
# --extra_inputs "edit_image" \
|
||||
# --max_pixels 1048576 \
|
||||
# --dataset_repeat 50 \
|
||||
# --model_id_with_origin_paths "black-forest-labs/FLUX.2-klein-9B:text_encoder/*.safetensors,black-forest-labs/FLUX.2-klein-9B:transformer/*.safetensors,black-forest-labs/FLUX.2-klein-9B:vae/diffusion_pytorch_model.safetensors" \
|
||||
# --tokenizer_path "black-forest-labs/FLUX.2-klein-9B:tokenizer/" \
|
||||
# --learning_rate 1e-5 \
|
||||
# --num_epochs 2 \
|
||||
# --remove_prefix_in_ckpt "pipe.dit." \
|
||||
# --output_path "./models/train/FLUX.2-klein-9B_full" \
|
||||
# --trainable_models "dit" \
|
||||
# --use_gradient_checkpointing
|
||||
@@ -85,6 +85,7 @@ def flux2_parser():
|
||||
parser = add_general_config(parser)
|
||||
parser = add_image_size_config(parser)
|
||||
parser.add_argument("--tokenizer_path", type=str, default=None, help="Path to tokenizer.")
|
||||
parser.add_argument("--initialize_model_on_cpu", default=False, action="store_true", help="Whether to initialize models on CPU.")
|
||||
return parser
|
||||
|
||||
|
||||
@@ -126,7 +127,7 @@ if __name__ == "__main__":
|
||||
fp8_models=args.fp8_models,
|
||||
offload_models=args.offload_models,
|
||||
task=args.task,
|
||||
device=accelerator.device,
|
||||
device="cpu" if args.initialize_model_on_cpu else accelerator.device,
|
||||
)
|
||||
model_logger = ModelLogger(
|
||||
args.output_path,
|
||||
|
||||
@@ -0,0 +1,47 @@
|
||||
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
|
||||
from modelscope import snapshot_download
|
||||
from PIL import Image
|
||||
import torch
|
||||
|
||||
# Load models
|
||||
pipe = QwenImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="Qwen/Qwen-Image-Edit-2511", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
|
||||
],
|
||||
processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
|
||||
)
|
||||
lora = ModelConfig(
|
||||
model_id="DiffSynth-Studio/Qwen-Image-Edit-2511-ICEdit-LoRA",
|
||||
origin_file_pattern="model.safetensors"
|
||||
)
|
||||
pipe.load_lora(pipe.dit, lora)
|
||||
|
||||
# Load images
|
||||
snapshot_download(
|
||||
"DiffSynth-Studio/Qwen-Image-Edit-2511-ICEdit-LoRA",
|
||||
local_dir="./data",
|
||||
allow_file_pattern="assets/*"
|
||||
)
|
||||
edit_image = [
|
||||
Image.open("data/assets/image1_original.png"),
|
||||
Image.open("data/assets/image1_edit_1.png"),
|
||||
Image.open("data/assets/image2_original.png")
|
||||
]
|
||||
prompt = "Edit image 3 based on the transformation from image 1 to image 2."
|
||||
negative_prompt = "泛黄，AI感，不真实，丑陋，油腻的皮肤，异常的肢体，不协调的肢体"
|
||||
|
||||
# Generate
|
||||
image_4 = pipe(
|
||||
prompt=prompt, negative_prompt=negative_prompt,
|
||||
edit_image=edit_image,
|
||||
seed=1,
|
||||
num_inference_steps=50,
|
||||
height=1280,
|
||||
width=720,
|
||||
zero_cond_t=True,
|
||||
)
|
||||
image_4.save("image.png")
|
||||
@@ -0,0 +1,58 @@
|
||||
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
|
||||
from modelscope import snapshot_download
|
||||
from PIL import Image
|
||||
import torch
|
||||
|
||||
# Load models
|
||||
vram_config = {
|
||||
"offload_dtype": "disk",
|
||||
"offload_device": "disk",
|
||||
"onload_dtype": torch.float8_e4m3fn,
|
||||
"onload_device": "cpu",
|
||||
"preparing_dtype": torch.float8_e4m3fn,
|
||||
"preparing_device": "cuda",
|
||||
"computation_dtype": torch.bfloat16,
|
||||
"computation_device": "cuda",
|
||||
}
|
||||
pipe = QwenImagePipeline.from_pretrained(
|
||||
torch_dtype=torch.bfloat16,
|
||||
device="cuda",
|
||||
model_configs=[
|
||||
ModelConfig(model_id="Qwen/Qwen-Image-Edit-2511", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
|
||||
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
|
||||
],
|
||||
processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
|
||||
)
|
||||
|
||||
lora = ModelConfig(
|
||||
model_id="DiffSynth-Studio/Qwen-Image-Edit-2511-ICEdit-LoRA",
|
||||
origin_file_pattern="model.safetensors"
|
||||
)
|
||||
pipe.load_lora(pipe.dit, lora)
|
||||
|
||||
# Load images
|
||||
snapshot_download(
|
||||
"DiffSynth-Studio/Qwen-Image-Edit-2511-ICEdit-LoRA",
|
||||
local_dir="./data",
|
||||
allow_file_pattern="assets/*"
|
||||
)
|
||||
edit_image = [
|
||||
Image.open("data/assets/image1_original.png"),
|
||||
Image.open("data/assets/image1_edit_1.png"),
|
||||
Image.open("data/assets/image2_original.png")
|
||||
]
|
||||
prompt = "Edit image 3 based on the transformation from image 1 to image 2."
|
||||
negative_prompt = "泛黄，AI感，不真实，丑陋，油腻的皮肤，异常的肢体，不协调的肢体"
|
||||
|
||||
# Generate
|
||||
image_4 = pipe(
|
||||
prompt=prompt, negative_prompt=negative_prompt,
|
||||
edit_image=edit_image,
|
||||
seed=1,
|
||||
num_inference_steps=50,
|
||||
height=1280,
|
||||
width=720,
|
||||
zero_cond_t=True,
|
||||
)
|
||||
image_4.save("image.png")
|
||||
@@ -0,0 +1,23 @@
|
||||
compute_environment: LOCAL_MACHINE
|
||||
debug: false
|
||||
deepspeed_config:
|
||||
gradient_accumulation_steps: 1
|
||||
offload_optimizer_device: none
|
||||
offload_param_device: none
|
||||
zero3_init_flag: true
|
||||
zero3_save_16bit_model: true
|
||||
zero_stage: 3
|
||||
distributed_type: DEEPSPEED
|
||||
downcast_bf16: 'no'
|
||||
enable_cpu_affinity: false
|
||||
machine_rank: 0
|
||||
main_training_function: main
|
||||
mixed_precision: bf16
|
||||
num_machines: 1
|
||||
num_processes: 8
|
||||
rdzv_backend: static
|
||||
same_network: true
|
||||
tpu_env: []
|
||||
tpu_use_cluster: false
|
||||
tpu_use_sudo: false
|
||||
use_cpu: false
|
||||
@@ -0,0 +1,20 @@
|
||||
# This script was tested using zero3 and on 8*910B(NPU)
|
||||
export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
|
||||
export CPU_AFFINITY_CONF=1
|
||||
|
||||
accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero3.yaml examples/qwen_image/model_training/train.py \
|
||||
--dataset_base_path data/example_image_dataset \
|
||||
--dataset_metadata_path data/example_image_dataset/metadata_qwen_imgae_edit_multi.json \
|
||||
--data_file_keys "image,edit_image" \
|
||||
--extra_inputs "edit_image" \
|
||||
--max_pixels 1048576 \
|
||||
--dataset_repeat 50 \
|
||||
--model_id_with_origin_paths "Qwen/Qwen-Image-Edit-2509:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \
|
||||
--learning_rate 1e-5 \
|
||||
--num_epochs 2 \
|
||||
--remove_prefix_in_ckpt "pipe.dit." \
|
||||
--output_path "./models/train/Qwen-Image-Edit-2509_full" \
|
||||
--trainable_models "dit" \
|
||||
--use_gradient_checkpointing \
|
||||
--find_unused_parameters \
|
||||
--initialize_model_on_cpu
|
||||
@@ -101,6 +101,7 @@ def qwen_image_parser():
|
||||
parser.add_argument("--tokenizer_path", type=str, default=None, help="Path to tokenizer.")
|
||||
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("--zero_cond_t", default=False, action="store_true", help="A special parameter introduced by Qwen-Image-Edit-2511. Please enable it for this model.")
|
||||
parser.add_argument("--initialize_model_on_cpu", default=False, action="store_true", help="Whether to initialize models on CPU.")
|
||||
return parser
|
||||
|
||||
|
||||
@@ -151,7 +152,7 @@ if __name__ == "__main__":
|
||||
fp8_models=args.fp8_models,
|
||||
offload_models=args.offload_models,
|
||||
task=args.task,
|
||||
device=accelerator.device,
|
||||
device="cpu" if args.initialize_model_on_cpu else accelerator.device,
|
||||
zero_cond_t=args.zero_cond_t,
|
||||
)
|
||||
model_logger = ModelLogger(
|
||||
|
||||
@@ -7,6 +7,7 @@ accelerate launch --config_file examples/wanvideo/model_training/full/accelerate
|
||||
--num_frames 81 \
|
||||
--dataset_repeat 100 \
|
||||
--model_id_with_origin_paths "Wan-AI/Wan2.2-S2V-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-S2V-14B:wav2vec2-large-xlsr-53-english/model.safetensors,Wan-AI/Wan2.2-S2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-S2V-14B:Wan2.1_VAE.pth" \
|
||||
--audio_processor_path "Wan-AI/Wan2.2-S2V-14B:wav2vec2-large-xlsr-53-english/" \
|
||||
--learning_rate 1e-5 \
|
||||
--num_epochs 1 \
|
||||
--trainable_models "dit" \
|
||||
|
||||
@@ -0,0 +1,23 @@
|
||||
compute_environment: LOCAL_MACHINE
|
||||
debug: false
|
||||
deepspeed_config:
|
||||
gradient_accumulation_steps: 1
|
||||
offload_optimizer_device: none
|
||||
offload_param_device: none
|
||||
zero3_init_flag: true
|
||||
zero3_save_16bit_model: true
|
||||
zero_stage: 3
|
||||
distributed_type: DEEPSPEED
|
||||
downcast_bf16: 'no'
|
||||
enable_cpu_affinity: false
|
||||
machine_rank: 0
|
||||
main_training_function: main
|
||||
mixed_precision: bf16
|
||||
num_machines: 1
|
||||
num_processes: 8
|
||||
rdzv_backend: static
|
||||
same_network: true
|
||||
tpu_env: []
|
||||
tpu_use_cluster: false
|
||||
tpu_use_sudo: false
|
||||
use_cpu: false
|
||||
@@ -7,6 +7,7 @@ accelerate launch --config_file examples/wanvideo/model_training/full/accelerate
|
||||
--num_frames 81 \
|
||||
--dataset_repeat 100 \
|
||||
--model_id_with_origin_paths "Wan-AI/Wan2.2-S2V-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-S2V-14B:wav2vec2-large-xlsr-53-english/model.safetensors,Wan-AI/Wan2.2-S2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-S2V-14B:Wan2.1_VAE.pth" \
|
||||
--audio_processor_path "Wan-AI/Wan2.2-S2V-14B:wav2vec2-large-xlsr-53-english/" \
|
||||
--learning_rate 1e-4 \
|
||||
--num_epochs 5 \
|
||||
--remove_prefix_in_ckpt "pipe.dit." \
|
||||
|
||||
@@ -33,7 +33,7 @@ class WanTrainingModule(DiffusionTrainingModule):
|
||||
# Load models
|
||||
model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, fp8_models=fp8_models, offload_models=offload_models, device=device)
|
||||
tokenizer_config = ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/") if tokenizer_path is None else ModelConfig(tokenizer_path)
|
||||
audio_processor_config = ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/") if audio_processor_path is None else ModelConfig(audio_processor_path)
|
||||
audio_processor_config = self.parse_path_or_model_id(audio_processor_path)
|
||||
self.pipe = WanVideoPipeline.from_pretrained(torch_dtype=torch.bfloat16, device=device, model_configs=model_configs, tokenizer_config=tokenizer_config, audio_processor_config=audio_processor_config)
|
||||
self.pipe = self.split_pipeline_units(task, self.pipe, trainable_models, lora_base_model)
|
||||
|
||||
|
||||
@@ -0,0 +1,23 @@
|
||||
compute_environment: LOCAL_MACHINE
|
||||
debug: false
|
||||
deepspeed_config:
|
||||
gradient_accumulation_steps: 1
|
||||
offload_optimizer_device: none
|
||||
offload_param_device: none
|
||||
zero3_init_flag: true
|
||||
zero3_save_16bit_model: true
|
||||
zero_stage: 3
|
||||
distributed_type: DEEPSPEED
|
||||
downcast_bf16: 'no'
|
||||
enable_cpu_affinity: false
|
||||
machine_rank: 0
|
||||
main_training_function: main
|
||||
mixed_precision: bf16
|
||||
num_machines: 1
|
||||
num_processes: 8
|
||||
rdzv_backend: static
|
||||
same_network: true
|
||||
tpu_env: []
|
||||
tpu_use_cluster: false
|
||||
tpu_use_sudo: false
|
||||
use_cpu: false
|
||||
@@ -13,4 +13,5 @@ accelerate launch --config_file examples/z_image/model_training/full/accelerate_
|
||||
--output_path "./models/train/Z-Image-Turbo_full" \
|
||||
--trainable_models "dit" \
|
||||
--use_gradient_checkpointing \
|
||||
--dataset_num_workers 8
|
||||
--dataset_num_workers 8 \
|
||||
--enable_npu_patch
|
||||
|
||||
@@ -20,12 +20,13 @@ class ZImageTrainingModule(DiffusionTrainingModule):
|
||||
offload_models=None,
|
||||
device="cpu",
|
||||
task="sft",
|
||||
enable_npu_patch=True,
|
||||
):
|
||||
super().__init__()
|
||||
# Load models
|
||||
model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, fp8_models=fp8_models, offload_models=offload_models, device=device)
|
||||
tokenizer_config = ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/") if tokenizer_path is None else ModelConfig(tokenizer_path)
|
||||
self.pipe = ZImagePipeline.from_pretrained(torch_dtype=torch.bfloat16, device=device, model_configs=model_configs, tokenizer_config=tokenizer_config)
|
||||
self.pipe = ZImagePipeline.from_pretrained(torch_dtype=torch.bfloat16, device=device, model_configs=model_configs, tokenizer_config=tokenizer_config, enable_npu_patch=enable_npu_patch)
|
||||
self.pipe = self.split_pipeline_units(task, self.pipe, trainable_models, lora_base_model)
|
||||
|
||||
# Training mode
|
||||
@@ -94,6 +95,7 @@ def z_image_parser():
|
||||
parser = add_general_config(parser)
|
||||
parser = add_image_size_config(parser)
|
||||
parser.add_argument("--tokenizer_path", type=str, default=None, help="Path to tokenizer.")
|
||||
parser.add_argument("--enable_npu_patch", default=False, action="store_true", help="Whether to use npu fused operator patch to improve performance in NPU.")
|
||||
return parser
|
||||
|
||||
|
||||
@@ -136,6 +138,7 @@ if __name__ == "__main__":
|
||||
offload_models=args.offload_models,
|
||||
task=args.task,
|
||||
device=accelerator.device,
|
||||
enable_npu_patch=args.enable_npu_patch
|
||||
)
|
||||
model_logger = ModelLogger(
|
||||
args.output_path,
|
||||
|
||||
Reference in New Issue
Block a user