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support video-to-video-translation

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Artiprocher
2023-12-21 17:11:58 +08:00
parent f7f4c1038e
commit c1453281df
20 changed files with 1659 additions and 427 deletions
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6
diffsynth/__init__.py Normal file
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@@ -0,0 +1,6 @@
from .data import *
from .models import *
from .prompts import *
from .schedulers import *
from .pipelines import *
from .controlnets import *

2
diffsynth/controlnets/__init__.py Normal file
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@@ -0,0 +1,2 @@
from .controlnet_unit import ControlNetConfigUnit, ControlNetUnit, MultiControlNetManager
from .processors import Annotator

48
diffsynth/controlnets/controlnet_unit.py Normal file
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@@ -0,0 +1,48 @@
import torch
import numpy as np
from .processors import Processor_id
class ControlNetConfigUnit:
def __init__(self, processor_id: Processor_id, model_path, scale=1.0):
self.processor_id = processor_id
self.model_path = model_path
self.scale = scale
class ControlNetUnit:
def __init__(self, processor, model, scale=1.0):
self.processor = processor
self.model = model
self.scale = scale
class MultiControlNetManager:
def __init__(self, controlnet_units=[]):
self.processors = [unit.processor for unit in controlnet_units]
self.models = [unit.model for unit in controlnet_units]
self.scales = [unit.scale for unit in controlnet_units]
def process_image(self, image, return_image=False):
processed_image = [
processor(image)
for processor in self.processors
]
if return_image:
return processed_image
processed_image = torch.concat([
torch.Tensor(np.array(image_, dtype=np.float32) / 255).permute(2, 0, 1).unsqueeze(0)
for image_ in processed_image
], dim=0)
return processed_image
def __call__(self, sample, timestep, encoder_hidden_states, conditionings):
res_stack = None
for conditioning, model, scale in zip(conditionings, self.models, self.scales):
res_stack_ = model(sample, timestep, encoder_hidden_states, conditioning)
res_stack_ = [res * scale for res in res_stack_]
if res_stack is None:
res_stack = res_stack_
else:
res_stack = [i + j for i, j in zip(res_stack, res_stack_)]
return res_stack

50
diffsynth/controlnets/processors.py Normal file
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@@ -0,0 +1,50 @@
from typing_extensions import Literal, TypeAlias
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
from controlnet_aux.processor import (
CannyDetector, MidasDetector, HEDdetector, LineartDetector, LineartAnimeDetector, OpenposeDetector
)
Processor_id: TypeAlias = Literal[
"canny", "depth", "softedge", "lineart", "lineart_anime", "openpose", "tile"
]
class Annotator:
def __init__(self, processor_id: Processor_id, model_path="models/Annotators", detect_resolution=512):
if processor_id == "canny":
self.processor = CannyDetector()
elif processor_id == "depth":
self.processor = MidasDetector.from_pretrained(model_path)
elif processor_id == "softedge":
self.processor = HEDdetector.from_pretrained(model_path)
elif processor_id == "lineart":
self.processor = LineartDetector.from_pretrained(model_path)
elif processor_id == "lineart_anime":
self.processor = LineartAnimeDetector.from_pretrained(model_path)
elif processor_id == "openpose":
self.processor = OpenposeDetector.from_pretrained(model_path)
elif processor_id == "tile":
self.processor = None
else:
raise ValueError(f"Unsupported processor_id: {processor_id}")
self.processor_id = processor_id
self.detect_resolution = detect_resolution
def __call__(self, image):
width, height = image.size
if self.processor_id == "openpose":
kwargs = {
"include_body": True,
"include_hand": True,
"include_face": True
}
else:
kwargs = {}
if self.processor is not None:
image = self.processor(image, detect_resolution=self.detect_resolution, image_resolution=min(width, height), **kwargs)
image = image.resize((width, height))
return image

1
diffsynth/data/__init__.py Normal file
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@@ -0,0 +1 @@
from .video import VideoData, save_video, save_frames

148
diffsynth/data/video.py Normal file
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@@ -0,0 +1,148 @@
import imageio, os
import numpy as np
from PIL import Image
from tqdm import tqdm
class LowMemoryVideo:
def __init__(self, file_name):
self.reader = imageio.get_reader(file_name)
def __len__(self):
return self.reader.count_frames()
def __getitem__(self, item):
return Image.fromarray(np.array(self.reader.get_data(item))).convert("RGB")
def __del__(self):
self.reader.close()
def split_file_name(file_name):
result = []
number = -1
for i in file_name:
if ord(i)>=ord("0") and ord(i)<=ord("9"):
if number == -1:
number = 0
number = number*10 + ord(i) - ord("0")
else:
if number != -1:
result.append(number)
number = -1
result.append(i)
if number != -1:
result.append(number)
result = tuple(result)
return result
def search_for_images(folder):
file_list = [i for i in os.listdir(folder) if i.endswith(".jpg") or i.endswith(".png")]
file_list = [(split_file_name(file_name), file_name) for file_name in file_list]
file_list = [i[1] for i in sorted(file_list)]
file_list = [os.path.join(folder, i) for i in file_list]
return file_list
class LowMemoryImageFolder:
def __init__(self, folder, file_list=None):
if file_list is None:
self.file_list = search_for_images(folder)
else:
self.file_list = [os.path.join(folder, file_name) for file_name in file_list]
def __len__(self):
return len(self.file_list)
def __getitem__(self, item):
return Image.open(self.file_list[item]).convert("RGB")
def __del__(self):
pass
def crop_and_resize(image, height, width):
image = np.array(image)
image_height, image_width, _ = image.shape
if image_height / image_width < height / width:
croped_width = int(image_height / height * width)
left = (image_width - croped_width) // 2
image = image[:, left: left+croped_width]
image = Image.fromarray(image).resize((width, height))
else:
croped_height = int(image_width / width * height)
left = (image_height - croped_height) // 2
image = image[left: left+croped_height, :]
image = Image.fromarray(image).resize((width, height))
return image
class VideoData:
def __init__(self, video_file=None, image_folder=None, height=None, width=None, **kwargs):
if video_file is not None:
self.data_type = "video"
self.data = LowMemoryVideo(video_file, **kwargs)
elif image_folder is not None:
self.data_type = "images"
self.data = LowMemoryImageFolder(image_folder, **kwargs)
else:
raise ValueError("Cannot open video or image folder")
self.length = None
self.set_shape(height, width)
def raw_data(self):
frames = []
for i in range(self.__len__()):
frames.append(self.__getitem__(i))
return frames
def set_length(self, length):
self.length = length
def set_shape(self, height, width):
self.height = height
self.width = width
def __len__(self):
if self.length is None:
return len(self.data)
else:
return self.length
def shape(self):
if self.height is not None and self.width is not None:
return self.height, self.width
else:
height, width, _ = self.__getitem__(0).shape
return height, width
def __getitem__(self, item):
frame = self.data.__getitem__(item)
width, height = frame.size
if self.height is not None and self.width is not None:
if self.height != height or self.width != width:
frame = crop_and_resize(frame, self.height, self.width)
return frame
def __del__(self):
pass
def save_images(self, folder):
os.makedirs(folder, exist_ok=True)
for i in tqdm(range(self.__len__()), desc="Saving images"):
frame = self.__getitem__(i)
frame.save(os.path.join(folder, f"{i}.png"))
def save_video(frames, save_path, fps, quality=9):
writer = imageio.get_writer(save_path, fps=fps, quality=quality)
for frame in tqdm(frames, desc="Saving video"):
frame = np.array(frame)
writer.append_data(frame)
writer.close()
def save_frames(frames, save_path):
os.makedirs(save_path, exist_ok=True)
for i, frame in enumerate(tqdm(frames, desc="Saving images")):
frame.save(os.path.join(save_path, f"{i}.png"))

147
diffsynth/models/__init__.py
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@@ -1,4 +1,4 @@
import torch
import torch, os
from safetensors import safe_open
from .sd_text_encoder import SDTextEncoder
@@ -11,12 +11,18 @@ from .sdxl_unet import SDXLUNet
from .sdxl_vae_decoder import SDXLVAEDecoder
from .sdxl_vae_encoder import SDXLVAEEncoder
from .sd_controlnet import SDControlNet
from .sd_motion import SDMotionModel
class ModelManager:
def __init__(self, torch_type=torch.float16, device="cuda"):
self.torch_type = torch_type
def __init__(self, torch_dtype=torch.float16, device="cuda"):
self.torch_dtype = torch_dtype
self.device = device
self.model = {}
self.model_path = {}
self.textual_inversion_dict = {}
def is_stabe_diffusion_xl(self, state_dict):
param_name = "conditioner.embedders.0.transformer.text_model.embeddings.position_embedding.weight"
@@ -25,7 +31,15 @@ class ModelManager:
def is_stable_diffusion(self, state_dict):
return True
def load_stable_diffusion(self, state_dict, components=None):
def is_controlnet(self, state_dict):
param_name = "control_model.time_embed.0.weight"
return param_name in state_dict
def is_animatediff(self, state_dict):
param_name = "mid_block.motion_modules.0.temporal_transformer.proj_out.weight"
return param_name in state_dict
def load_stable_diffusion(self, state_dict, components=None, file_path=""):
component_dict = {
"text_encoder": SDTextEncoder,
"unet": SDUNet,
@@ -36,11 +50,24 @@ class ModelManager:
if components is None:
components = ["text_encoder", "unet", "vae_decoder", "vae_encoder"]
for component in components:
self.model[component] = component_dict[component]()
self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict))
self.model[component].to(self.torch_type).to(self.device)
if component == "text_encoder":
# Add additional token embeddings to text encoder
token_embeddings = [state_dict["cond_stage_model.transformer.text_model.embeddings.token_embedding.weight"]]
for keyword in self.textual_inversion_dict:
_, embeddings = self.textual_inversion_dict[keyword]
token_embeddings.append(embeddings.to(dtype=token_embeddings[0].dtype))
token_embeddings = torch.concat(token_embeddings, dim=0)
state_dict["cond_stage_model.transformer.text_model.embeddings.token_embedding.weight"] = token_embeddings
self.model[component] = component_dict[component](vocab_size=token_embeddings.shape[0])
self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict))
self.model[component].to(self.torch_dtype).to(self.device)
else:
self.model[component] = component_dict[component]()
self.model[component].load_state_dict(self.model[component].state_dict_converter().from_civitai(state_dict))
self.model[component].to(self.torch_dtype).to(self.device)
self.model_path[component] = file_path
def load_stable_diffusion_xl(self, state_dict, components=None):
def load_stable_diffusion_xl(self, state_dict, components=None, file_path=""):
component_dict = {
"text_encoder": SDXLTextEncoder,
"text_encoder_2": SDXLTextEncoder2,
@@ -60,18 +87,86 @@ class ModelManager:
# I do not know how to solve this problem.
self.model[component].to(torch.float32).to(self.device)
else:
self.model[component].to(self.torch_type).to(self.device)
def load_from_safetensors(self, file_path, components=None):
state_dict = load_state_dict_from_safetensors(file_path)
if self.is_stabe_diffusion_xl(state_dict):
self.load_stable_diffusion_xl(state_dict, components=components)
elif self.is_stable_diffusion(state_dict):
self.load_stable_diffusion(state_dict, components=components)
self.model[component].to(self.torch_dtype).to(self.device)
self.model_path[component] = file_path
def load_controlnet(self, state_dict, file_path=""):
component = "controlnet"
if component not in self.model:
self.model[component] = []
self.model_path[component] = []
model = SDControlNet()
model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
model.to(self.torch_dtype).to(self.device)
self.model[component].append(model)
self.model_path[component].append(file_path)
def load_animatediff(self, state_dict, file_path=""):
component = "motion_modules"
model = SDMotionModel()
model.load_state_dict(model.state_dict_converter().from_civitai(state_dict))
model.to(self.torch_dtype).to(self.device)
self.model[component] = model
self.model_path[component] = file_path
def search_for_embeddings(self, state_dict):
embeddings = []
for k in state_dict:
if isinstance(state_dict[k], torch.Tensor):
embeddings.append(state_dict[k])
elif isinstance(state_dict[k], dict):
embeddings += self.search_for_embeddings(state_dict[k])
return embeddings
def load_textual_inversions(self, folder):
# Store additional tokens here
self.textual_inversion_dict = {}
# Load every textual inversion file
for file_name in os.listdir(folder):
keyword = os.path.splitext(file_name)[0]
state_dict = load_state_dict(os.path.join(folder, file_name))
# Search for embeddings
for embeddings in self.search_for_embeddings(state_dict):
if len(embeddings.shape) == 2 and embeddings.shape[1] == 768:
tokens = [f"{keyword}_{i}" for i in range(embeddings.shape[0])]
self.textual_inversion_dict[keyword] = (tokens, embeddings)
break
def load_model(self, file_path, components=None):
state_dict = load_state_dict(file_path, torch_dtype=self.torch_dtype)
if self.is_animatediff(state_dict):
self.load_animatediff(state_dict, file_path=file_path)
elif self.is_controlnet(state_dict):
self.load_controlnet(state_dict, file_path=file_path)
elif self.is_stabe_diffusion_xl(state_dict):
self.load_stable_diffusion_xl(state_dict, components=components, file_path=file_path)
elif self.is_stable_diffusion(state_dict):
self.load_stable_diffusion(state_dict, components=components, file_path=file_path)
def load_models(self, file_path_list):
for file_path in file_path_list:
self.load_model(file_path)
def to(self, device):
for component in self.model:
self.model[component].to(device)
if isinstance(self.model[component], list):
for model in self.model[component]:
model.to(device)
else:
self.model[component].to(device)
def get_model_with_model_path(self, model_path):
for component in self.model_path:
if isinstance(self.model_path[component], str):
if os.path.samefile(self.model_path[component], model_path):
return self.model[component]
elif isinstance(self.model_path[component], list):
for i, model_path_ in enumerate(self.model_path[component]):
if os.path.samefile(model_path_, model_path):
return self.model[component][i]
raise ValueError(f"Please load model {model_path} before you use it.")
def __getattr__(self, __name):
if __name in self.model:
@@ -80,16 +175,28 @@ class ModelManager:
return super.__getattribute__(__name)
def load_state_dict_from_safetensors(file_path):
def load_state_dict(file_path, torch_dtype=None):
if file_path.endswith(".safetensors"):
return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype)
else:
return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype)
def load_state_dict_from_safetensors(file_path, torch_dtype=None):
state_dict = {}
with safe_open(file_path, framework="pt", device="cpu") as f:
for k in f.keys():
state_dict[k] = f.get_tensor(k)
if torch_dtype is not None:
state_dict[k] = state_dict[k].to(torch_dtype)
return state_dict
def load_state_dict_from_bin(file_path):
return torch.load(file_path, map_location="cpu")
def load_state_dict_from_bin(file_path, torch_dtype=None):
state_dict = torch.load(file_path, map_location="cpu")
if torch_dtype is not None:
state_dict = {i: state_dict[i].to(torch_dtype) for i in state_dict}
return state_dict
def search_parameter(param, state_dict):

40
diffsynth/models/attention.py
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@@ -1,4 +1,15 @@
import torch
from einops import rearrange
def low_version_attention(query, key, value, attn_bias=None):
scale = 1 / query.shape[-1] ** 0.5
query = query * scale
attn = torch.matmul(query, key.transpose(-2, -1))
if attn_bias is not None:
attn = attn + attn_bias
attn = attn.softmax(-1)
return attn @ value
class Attention(torch.nn.Module):
@@ -15,7 +26,7 @@ class Attention(torch.nn.Module):
self.to_v = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv)
self.to_out = torch.nn.Linear(dim_inner, q_dim, bias=bias_out)
def forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None):
def torch_forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None):
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
@@ -36,3 +47,30 @@ class Attention(torch.nn.Module):
hidden_states = self.to_out(hidden_states)
return hidden_states
def xformers_forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None):
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
q = self.to_q(hidden_states)
k = self.to_k(encoder_hidden_states)
v = self.to_v(encoder_hidden_states)
q = rearrange(q, "b f (n d) -> (b n) f d", n=self.num_heads)
k = rearrange(k, "b f (n d) -> (b n) f d", n=self.num_heads)
v = rearrange(v, "b f (n d) -> (b n) f d", n=self.num_heads)
if attn_mask is not None:
hidden_states = low_version_attention(q, k, v, attn_bias=attn_mask)
else:
import xformers.ops as xops
hidden_states = xops.memory_efficient_attention(q, k, v)
hidden_states = rearrange(hidden_states, "(b n) f d -> b f (n d)", n=self.num_heads)
hidden_states = hidden_states.to(q.dtype)
hidden_states = self.to_out(hidden_states)
return hidden_states
def forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None):
return self.torch_forward(hidden_states, encoder_hidden_states=encoder_hidden_states, attn_mask=attn_mask)

566
diffsynth/models/sd_controlnet.py Normal file
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@@ -0,0 +1,566 @@
import torch
from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, PopBlock, DownSampler, UpSampler
class ControlNetConditioningLayer(torch.nn.Module):
def __init__(self, channels = (3, 16, 32, 96, 256, 320)):
super().__init__()
self.blocks = torch.nn.ModuleList([])
self.blocks.append(torch.nn.Conv2d(channels[0], channels[1], kernel_size=3, padding=1))
self.blocks.append(torch.nn.SiLU())
for i in range(1, len(channels) - 2):
self.blocks.append(torch.nn.Conv2d(channels[i], channels[i], kernel_size=3, padding=1))
self.blocks.append(torch.nn.SiLU())
self.blocks.append(torch.nn.Conv2d(channels[i], channels[i+1], kernel_size=3, padding=1, stride=2))
self.blocks.append(torch.nn.SiLU())
self.blocks.append(torch.nn.Conv2d(channels[-2], channels[-1], kernel_size=3, padding=1))
def forward(self, conditioning):
for block in self.blocks:
conditioning = block(conditioning)
return conditioning
class SDControlNet(torch.nn.Module):
def __init__(self, global_pool=False):
super().__init__()
self.time_proj = Timesteps(320)
self.time_embedding = torch.nn.Sequential(
torch.nn.Linear(320, 1280),
torch.nn.SiLU(),
torch.nn.Linear(1280, 1280)
)
self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1)
self.controlnet_conv_in = ControlNetConditioningLayer(channels=(3, 16, 32, 96, 256, 320))
self.blocks = torch.nn.ModuleList([
# CrossAttnDownBlock2D
ResnetBlock(320, 320, 1280),
AttentionBlock(8, 40, 320, 1, 768),
PushBlock(),
ResnetBlock(320, 320, 1280),
AttentionBlock(8, 40, 320, 1, 768),
PushBlock(),
DownSampler(320),
PushBlock(),
# CrossAttnDownBlock2D
ResnetBlock(320, 640, 1280),
AttentionBlock(8, 80, 640, 1, 768),
PushBlock(),
ResnetBlock(640, 640, 1280),
AttentionBlock(8, 80, 640, 1, 768),
PushBlock(),
DownSampler(640),
PushBlock(),
# CrossAttnDownBlock2D
ResnetBlock(640, 1280, 1280),
AttentionBlock(8, 160, 1280, 1, 768),
PushBlock(),
ResnetBlock(1280, 1280, 1280),
AttentionBlock(8, 160, 1280, 1, 768),
PushBlock(),
DownSampler(1280),
PushBlock(),
# DownBlock2D
ResnetBlock(1280, 1280, 1280),
PushBlock(),
ResnetBlock(1280, 1280, 1280),
PushBlock(),
# UNetMidBlock2DCrossAttn
ResnetBlock(1280, 1280, 1280),
AttentionBlock(8, 160, 1280, 1, 768),
ResnetBlock(1280, 1280, 1280),
PushBlock()
])
self.controlnet_blocks = torch.nn.ModuleList([
torch.nn.Conv2d(320, 320, kernel_size=(1, 1)),
torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False),
torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False),
torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False),
torch.nn.Conv2d(640, 640, kernel_size=(1, 1)),
torch.nn.Conv2d(640, 640, kernel_size=(1, 1), bias=False),
torch.nn.Conv2d(640, 640, kernel_size=(1, 1), bias=False),
torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)),
torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False),
torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False),
torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False),
torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False),
torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False),
])
self.global_pool = global_pool
def forward(self, sample, timestep, encoder_hidden_states, conditioning):
# 1. time
time_emb = self.time_proj(timestep[None]).to(sample.dtype)
time_emb = self.time_embedding(time_emb)
time_emb = time_emb.repeat(sample.shape[0], 1)
# 2. pre-process
hidden_states = self.conv_in(sample) + self.controlnet_conv_in(conditioning)
text_emb = encoder_hidden_states
res_stack = [hidden_states]
# 3. blocks
for i, block in enumerate(self.blocks):
hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
# 4. ControlNet blocks
controlnet_res_stack = [block(res) for block, res in zip(self.controlnet_blocks, res_stack)]
# pool
if self.global_pool:
controlnet_res_stack = [res.mean(dim=(2, 3), keepdim=True) for res in controlnet_res_stack]
return controlnet_res_stack
def state_dict_converter(self):
return SDControlNetStateDictConverter()
class SDControlNetStateDictConverter:
def __init__(self):
pass
def from_diffusers(self, state_dict):
# architecture
block_types = [
'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock',
'ResnetBlock', 'AttentionBlock', 'ResnetBlock',
'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'UpSampler',
'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',
'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',
'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock'
]
# controlnet_rename_dict
controlnet_rename_dict = {
"controlnet_cond_embedding.conv_in.weight": "controlnet_conv_in.blocks.0.weight",
"controlnet_cond_embedding.conv_in.bias": "controlnet_conv_in.blocks.0.bias",
"controlnet_cond_embedding.blocks.0.weight": "controlnet_conv_in.blocks.2.weight",
"controlnet_cond_embedding.blocks.0.bias": "controlnet_conv_in.blocks.2.bias",
"controlnet_cond_embedding.blocks.1.weight": "controlnet_conv_in.blocks.4.weight",
"controlnet_cond_embedding.blocks.1.bias": "controlnet_conv_in.blocks.4.bias",
"controlnet_cond_embedding.blocks.2.weight": "controlnet_conv_in.blocks.6.weight",
"controlnet_cond_embedding.blocks.2.bias": "controlnet_conv_in.blocks.6.bias",
"controlnet_cond_embedding.blocks.3.weight": "controlnet_conv_in.blocks.8.weight",
"controlnet_cond_embedding.blocks.3.bias": "controlnet_conv_in.blocks.8.bias",
"controlnet_cond_embedding.blocks.4.weight": "controlnet_conv_in.blocks.10.weight",
"controlnet_cond_embedding.blocks.4.bias": "controlnet_conv_in.blocks.10.bias",
"controlnet_cond_embedding.blocks.5.weight": "controlnet_conv_in.blocks.12.weight",
"controlnet_cond_embedding.blocks.5.bias": "controlnet_conv_in.blocks.12.bias",
"controlnet_cond_embedding.conv_out.weight": "controlnet_conv_in.blocks.14.weight",
"controlnet_cond_embedding.conv_out.bias": "controlnet_conv_in.blocks.14.bias",
}
# Rename each parameter
name_list = sorted([name for name in state_dict])
rename_dict = {}
block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1}
last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""}
for name in name_list:
names = name.split(".")
if names[0] in ["conv_in", "conv_norm_out", "conv_out"]:
pass
elif name in controlnet_rename_dict:
names = controlnet_rename_dict[name].split(".")
elif names[0] == "controlnet_down_blocks":
names[0] = "controlnet_blocks"
elif names[0] == "controlnet_mid_block":
names = ["controlnet_blocks", "12", names[-1]]
elif names[0] in ["time_embedding", "add_embedding"]:
if names[0] == "add_embedding":
names[0] = "add_time_embedding"
names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]]
elif names[0] in ["down_blocks", "mid_block", "up_blocks"]:
if names[0] == "mid_block":
names.insert(1, "0")
block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]]
block_type_with_id = ".".join(names[:4])
if block_type_with_id != last_block_type_with_id[block_type]:
block_id[block_type] += 1
last_block_type_with_id[block_type] = block_type_with_id
while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type:
block_id[block_type] += 1
block_type_with_id = ".".join(names[:4])
names = ["blocks", str(block_id[block_type])] + names[4:]
if "ff" in names:
ff_index = names.index("ff")
component = ".".join(names[ff_index:ff_index+3])
component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component]
names = names[:ff_index] + [component] + names[ff_index+3:]
if "to_out" in names:
names.pop(names.index("to_out") + 1)
else:
raise ValueError(f"Unknown parameters: {name}")
rename_dict[name] = ".".join(names)
# Convert state_dict
state_dict_ = {}
for name, param in state_dict.items():
if ".proj_in." in name or ".proj_out." in name:
param = param.squeeze()
if rename_dict[name] in [
"controlnet_blocks.1.bias", "controlnet_blocks.2.bias", "controlnet_blocks.3.bias", "controlnet_blocks.5.bias", "controlnet_blocks.6.bias",
"controlnet_blocks.8.bias", "controlnet_blocks.9.bias", "controlnet_blocks.10.bias", "controlnet_blocks.11.bias", "controlnet_blocks.12.bias"
]:
continue
state_dict_[rename_dict[name]] = param
return state_dict_
def from_civitai(self, state_dict):
rename_dict = {
"control_model.time_embed.0.weight": "time_embedding.0.weight",
"control_model.time_embed.0.bias": "time_embedding.0.bias",
"control_model.time_embed.2.weight": "time_embedding.2.weight",
"control_model.time_embed.2.bias": "time_embedding.2.bias",
"control_model.input_blocks.0.0.weight": "conv_in.weight",
"control_model.input_blocks.0.0.bias": "conv_in.bias",
"control_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight",
"control_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias",
"control_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight",
"control_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias",
"control_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight",
"control_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias",
"control_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight",
"control_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias",
"control_model.input_blocks.1.0.out_layers.3.weight": "blocks.0.conv2.weight",
"control_model.input_blocks.1.0.out_layers.3.bias": "blocks.0.conv2.bias",
"control_model.input_blocks.1.1.norm.weight": "blocks.1.norm.weight",
"control_model.input_blocks.1.1.norm.bias": "blocks.1.norm.bias",
"control_model.input_blocks.1.1.proj_in.weight": "blocks.1.proj_in.weight",
"control_model.input_blocks.1.1.proj_in.bias": "blocks.1.proj_in.bias",
"control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_q.weight": "blocks.1.transformer_blocks.0.attn1.to_q.weight",
"control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_k.weight": "blocks.1.transformer_blocks.0.attn1.to_k.weight",
"control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_v.weight": "blocks.1.transformer_blocks.0.attn1.to_v.weight",
"control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.1.transformer_blocks.0.attn1.to_out.weight",
"control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.1.transformer_blocks.0.attn1.to_out.bias",
"control_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.1.transformer_blocks.0.act_fn.proj.weight",
"control_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.1.transformer_blocks.0.act_fn.proj.bias",
"control_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.weight": "blocks.1.transformer_blocks.0.ff.weight",
"control_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.bias": "blocks.1.transformer_blocks.0.ff.bias",
"control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_q.weight": "blocks.1.transformer_blocks.0.attn2.to_q.weight",
"control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight": "blocks.1.transformer_blocks.0.attn2.to_k.weight",
"control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_v.weight": "blocks.1.transformer_blocks.0.attn2.to_v.weight",
"control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.1.transformer_blocks.0.attn2.to_out.weight",
"control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.1.transformer_blocks.0.attn2.to_out.bias",
"control_model.input_blocks.1.1.transformer_blocks.0.norm1.weight": "blocks.1.transformer_blocks.0.norm1.weight",
"control_model.input_blocks.1.1.transformer_blocks.0.norm1.bias": "blocks.1.transformer_blocks.0.norm1.bias",
"control_model.input_blocks.1.1.transformer_blocks.0.norm2.weight": "blocks.1.transformer_blocks.0.norm2.weight",
"control_model.input_blocks.1.1.transformer_blocks.0.norm2.bias": "blocks.1.transformer_blocks.0.norm2.bias",
"control_model.input_blocks.1.1.transformer_blocks.0.norm3.weight": "blocks.1.transformer_blocks.0.norm3.weight",
"control_model.input_blocks.1.1.transformer_blocks.0.norm3.bias": "blocks.1.transformer_blocks.0.norm3.bias",
"control_model.input_blocks.1.1.proj_out.weight": "blocks.1.proj_out.weight",
"control_model.input_blocks.1.1.proj_out.bias": "blocks.1.proj_out.bias",
"control_model.input_blocks.2.0.in_layers.0.weight": "blocks.3.norm1.weight",
"control_model.input_blocks.2.0.in_layers.0.bias": "blocks.3.norm1.bias",
"control_model.input_blocks.2.0.in_layers.2.weight": "blocks.3.conv1.weight",
"control_model.input_blocks.2.0.in_layers.2.bias": "blocks.3.conv1.bias",
"control_model.input_blocks.2.0.emb_layers.1.weight": "blocks.3.time_emb_proj.weight",
"control_model.input_blocks.2.0.emb_layers.1.bias": "blocks.3.time_emb_proj.bias",
"control_model.input_blocks.2.0.out_layers.0.weight": "blocks.3.norm2.weight",
"control_model.input_blocks.2.0.out_layers.0.bias": "blocks.3.norm2.bias",
"control_model.input_blocks.2.0.out_layers.3.weight": "blocks.3.conv2.weight",
"control_model.input_blocks.2.0.out_layers.3.bias": "blocks.3.conv2.bias",
"control_model.input_blocks.2.1.norm.weight": "blocks.4.norm.weight",
"control_model.input_blocks.2.1.norm.bias": "blocks.4.norm.bias",
"control_model.input_blocks.2.1.proj_in.weight": "blocks.4.proj_in.weight",
"control_model.input_blocks.2.1.proj_in.bias": "blocks.4.proj_in.bias",
"control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_q.weight": "blocks.4.transformer_blocks.0.attn1.to_q.weight",
"control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_k.weight": "blocks.4.transformer_blocks.0.attn1.to_k.weight",
"control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_v.weight": "blocks.4.transformer_blocks.0.attn1.to_v.weight",
"control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.4.transformer_blocks.0.attn1.to_out.weight",
"control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.4.transformer_blocks.0.attn1.to_out.bias",
"control_model.input_blocks.2.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.4.transformer_blocks.0.act_fn.proj.weight",
"control_model.input_blocks.2.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.4.transformer_blocks.0.act_fn.proj.bias",
"control_model.input_blocks.2.1.transformer_blocks.0.ff.net.2.weight": "blocks.4.transformer_blocks.0.ff.weight",
"control_model.input_blocks.2.1.transformer_blocks.0.ff.net.2.bias": "blocks.4.transformer_blocks.0.ff.bias",
"control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_q.weight": "blocks.4.transformer_blocks.0.attn2.to_q.weight",
"control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight": "blocks.4.transformer_blocks.0.attn2.to_k.weight",
"control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_v.weight": "blocks.4.transformer_blocks.0.attn2.to_v.weight",
"control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.4.transformer_blocks.0.attn2.to_out.weight",
"control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.4.transformer_blocks.0.attn2.to_out.bias",
"control_model.input_blocks.2.1.transformer_blocks.0.norm1.weight": "blocks.4.transformer_blocks.0.norm1.weight",
"control_model.input_blocks.2.1.transformer_blocks.0.norm1.bias": "blocks.4.transformer_blocks.0.norm1.bias",
"control_model.input_blocks.2.1.transformer_blocks.0.norm2.weight": "blocks.4.transformer_blocks.0.norm2.weight",
"control_model.input_blocks.2.1.transformer_blocks.0.norm2.bias": "blocks.4.transformer_blocks.0.norm2.bias",
"control_model.input_blocks.2.1.transformer_blocks.0.norm3.weight": "blocks.4.transformer_blocks.0.norm3.weight",
"control_model.input_blocks.2.1.transformer_blocks.0.norm3.bias": "blocks.4.transformer_blocks.0.norm3.bias",
"control_model.input_blocks.2.1.proj_out.weight": "blocks.4.proj_out.weight",
"control_model.input_blocks.2.1.proj_out.bias": "blocks.4.proj_out.bias",
"control_model.input_blocks.3.0.op.weight": "blocks.6.conv.weight",
"control_model.input_blocks.3.0.op.bias": "blocks.6.conv.bias",
"control_model.input_blocks.4.0.in_layers.0.weight": "blocks.8.norm1.weight",
"control_model.input_blocks.4.0.in_layers.0.bias": "blocks.8.norm1.bias",
"control_model.input_blocks.4.0.in_layers.2.weight": "blocks.8.conv1.weight",
"control_model.input_blocks.4.0.in_layers.2.bias": "blocks.8.conv1.bias",
"control_model.input_blocks.4.0.emb_layers.1.weight": "blocks.8.time_emb_proj.weight",
"control_model.input_blocks.4.0.emb_layers.1.bias": "blocks.8.time_emb_proj.bias",
"control_model.input_blocks.4.0.out_layers.0.weight": "blocks.8.norm2.weight",
"control_model.input_blocks.4.0.out_layers.0.bias": "blocks.8.norm2.bias",
"control_model.input_blocks.4.0.out_layers.3.weight": "blocks.8.conv2.weight",
"control_model.input_blocks.4.0.out_layers.3.bias": "blocks.8.conv2.bias",
"control_model.input_blocks.4.0.skip_connection.weight": "blocks.8.conv_shortcut.weight",
"control_model.input_blocks.4.0.skip_connection.bias": "blocks.8.conv_shortcut.bias",
"control_model.input_blocks.4.1.norm.weight": "blocks.9.norm.weight",
"control_model.input_blocks.4.1.norm.bias": "blocks.9.norm.bias",
"control_model.input_blocks.4.1.proj_in.weight": "blocks.9.proj_in.weight",
"control_model.input_blocks.4.1.proj_in.bias": "blocks.9.proj_in.bias",
"control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_q.weight": "blocks.9.transformer_blocks.0.attn1.to_q.weight",
"control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_k.weight": "blocks.9.transformer_blocks.0.attn1.to_k.weight",
"control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_v.weight": "blocks.9.transformer_blocks.0.attn1.to_v.weight",
"control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.9.transformer_blocks.0.attn1.to_out.weight",
"control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.9.transformer_blocks.0.attn1.to_out.bias",
"control_model.input_blocks.4.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.9.transformer_blocks.0.act_fn.proj.weight",
"control_model.input_blocks.4.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.9.transformer_blocks.0.act_fn.proj.bias",
"control_model.input_blocks.4.1.transformer_blocks.0.ff.net.2.weight": "blocks.9.transformer_blocks.0.ff.weight",
"control_model.input_blocks.4.1.transformer_blocks.0.ff.net.2.bias": "blocks.9.transformer_blocks.0.ff.bias",
"control_model.input_blocks.4.1.transformer_blocks.0.attn2.to_q.weight": "blocks.9.transformer_blocks.0.attn2.to_q.weight",
"control_model.input_blocks.4.1.transformer_blocks.0.attn2.to_k.weight": "blocks.9.transformer_blocks.0.attn2.to_k.weight",
"control_model.input_blocks.4.1.transformer_blocks.0.attn2.to_v.weight": "blocks.9.transformer_blocks.0.attn2.to_v.weight",
"control_model.input_blocks.4.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.9.transformer_blocks.0.attn2.to_out.weight",
"control_model.input_blocks.4.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.9.transformer_blocks.0.attn2.to_out.bias",
"control_model.input_blocks.4.1.transformer_blocks.0.norm1.weight": "blocks.9.transformer_blocks.0.norm1.weight",
"control_model.input_blocks.4.1.transformer_blocks.0.norm1.bias": "blocks.9.transformer_blocks.0.norm1.bias",
"control_model.input_blocks.4.1.transformer_blocks.0.norm2.weight": "blocks.9.transformer_blocks.0.norm2.weight",
"control_model.input_blocks.4.1.transformer_blocks.0.norm2.bias": "blocks.9.transformer_blocks.0.norm2.bias",
"control_model.input_blocks.4.1.transformer_blocks.0.norm3.weight": "blocks.9.transformer_blocks.0.norm3.weight",
"control_model.input_blocks.4.1.transformer_blocks.0.norm3.bias": "blocks.9.transformer_blocks.0.norm3.bias",
"control_model.input_blocks.4.1.proj_out.weight": "blocks.9.proj_out.weight",
"control_model.input_blocks.4.1.proj_out.bias": "blocks.9.proj_out.bias",
"control_model.input_blocks.5.0.in_layers.0.weight": "blocks.11.norm1.weight",
"control_model.input_blocks.5.0.in_layers.0.bias": "blocks.11.norm1.bias",
"control_model.input_blocks.5.0.in_layers.2.weight": "blocks.11.conv1.weight",
"control_model.input_blocks.5.0.in_layers.2.bias": "blocks.11.conv1.bias",
"control_model.input_blocks.5.0.emb_layers.1.weight": "blocks.11.time_emb_proj.weight",
"control_model.input_blocks.5.0.emb_layers.1.bias": "blocks.11.time_emb_proj.bias",
"control_model.input_blocks.5.0.out_layers.0.weight": "blocks.11.norm2.weight",
"control_model.input_blocks.5.0.out_layers.0.bias": "blocks.11.norm2.bias",
"control_model.input_blocks.5.0.out_layers.3.weight": "blocks.11.conv2.weight",
"control_model.input_blocks.5.0.out_layers.3.bias": "blocks.11.conv2.bias",
"control_model.input_blocks.5.1.norm.weight": "blocks.12.norm.weight",
"control_model.input_blocks.5.1.norm.bias": "blocks.12.norm.bias",
"control_model.input_blocks.5.1.proj_in.weight": "blocks.12.proj_in.weight",
"control_model.input_blocks.5.1.proj_in.bias": "blocks.12.proj_in.bias",
"control_model.input_blocks.5.1.transformer_blocks.0.attn1.to_q.weight": "blocks.12.transformer_blocks.0.attn1.to_q.weight",
"control_model.input_blocks.5.1.transformer_blocks.0.attn1.to_k.weight": "blocks.12.transformer_blocks.0.attn1.to_k.weight",
"control_model.input_blocks.5.1.transformer_blocks.0.attn1.to_v.weight": "blocks.12.transformer_blocks.0.attn1.to_v.weight",
"control_model.input_blocks.5.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.12.transformer_blocks.0.attn1.to_out.weight",
"control_model.input_blocks.5.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.12.transformer_blocks.0.attn1.to_out.bias",
"control_model.input_blocks.5.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.12.transformer_blocks.0.act_fn.proj.weight",
"control_model.input_blocks.5.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.12.transformer_blocks.0.act_fn.proj.bias",
"control_model.input_blocks.5.1.transformer_blocks.0.ff.net.2.weight": "blocks.12.transformer_blocks.0.ff.weight",
"control_model.input_blocks.5.1.transformer_blocks.0.ff.net.2.bias": "blocks.12.transformer_blocks.0.ff.bias",
"control_model.input_blocks.5.1.transformer_blocks.0.attn2.to_q.weight": "blocks.12.transformer_blocks.0.attn2.to_q.weight",
"control_model.input_blocks.5.1.transformer_blocks.0.attn2.to_k.weight": "blocks.12.transformer_blocks.0.attn2.to_k.weight",
"control_model.input_blocks.5.1.transformer_blocks.0.attn2.to_v.weight": "blocks.12.transformer_blocks.0.attn2.to_v.weight",
"control_model.input_blocks.5.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.12.transformer_blocks.0.attn2.to_out.weight",
"control_model.input_blocks.5.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.12.transformer_blocks.0.attn2.to_out.bias",
"control_model.input_blocks.5.1.transformer_blocks.0.norm1.weight": "blocks.12.transformer_blocks.0.norm1.weight",
"control_model.input_blocks.5.1.transformer_blocks.0.norm1.bias": "blocks.12.transformer_blocks.0.norm1.bias",
"control_model.input_blocks.5.1.transformer_blocks.0.norm2.weight": "blocks.12.transformer_blocks.0.norm2.weight",
"control_model.input_blocks.5.1.transformer_blocks.0.norm2.bias": "blocks.12.transformer_blocks.0.norm2.bias",
"control_model.input_blocks.5.1.transformer_blocks.0.norm3.weight": "blocks.12.transformer_blocks.0.norm3.weight",
"control_model.input_blocks.5.1.transformer_blocks.0.norm3.bias": "blocks.12.transformer_blocks.0.norm3.bias",
"control_model.input_blocks.5.1.proj_out.weight": "blocks.12.proj_out.weight",
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"control_model.middle_block.2.out_layers.3.bias": "blocks.30.conv2.bias",
"control_model.middle_block_out.0.weight": "controlnet_blocks.12.weight",
"control_model.middle_block_out.0.bias": "controlnet_blocks.7.bias",
}
state_dict_ = {}
for name in state_dict:
if name in rename_dict:
param = state_dict[name]
if ".proj_in." in name or ".proj_out." in name:
param = param.squeeze()
state_dict_[rename_dict[name]] = param
return state_dict_

198
diffsynth/models/sd_motion.py Normal file
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from .sd_unet import SDUNet, Attention, GEGLU
import torch
from einops import rearrange, repeat
class TemporalTransformerBlock(torch.nn.Module):
def __init__(self, dim, num_attention_heads, attention_head_dim, max_position_embeddings=32):
super().__init__()
# 1. Self-Attn
self.pe1 = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, dim))
self.norm1 = torch.nn.LayerNorm(dim, elementwise_affine=True)
self.attn1 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True)
# 2. Cross-Attn
self.pe2 = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, dim))
self.norm2 = torch.nn.LayerNorm(dim, elementwise_affine=True)
self.attn2 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True)
# 3. Feed-forward
self.norm3 = torch.nn.LayerNorm(dim, elementwise_affine=True)
self.act_fn = GEGLU(dim, dim * 4)
self.ff = torch.nn.Linear(dim * 4, dim)
def forward(self, hidden_states, batch_size=1):
# 1. Self-Attention
norm_hidden_states = self.norm1(hidden_states)
norm_hidden_states = rearrange(norm_hidden_states, "(b f) h c -> (b h) f c", b=batch_size)
attn_output = self.attn1(norm_hidden_states + self.pe1[:, :norm_hidden_states.shape[1]])
attn_output = rearrange(attn_output, "(b h) f c -> (b f) h c", b=batch_size)
hidden_states = attn_output + hidden_states
# 2. Cross-Attention
norm_hidden_states = self.norm2(hidden_states)
norm_hidden_states = rearrange(norm_hidden_states, "(b f) h c -> (b h) f c", b=batch_size)
attn_output = self.attn2(norm_hidden_states + self.pe2[:, :norm_hidden_states.shape[1]])
attn_output = rearrange(attn_output, "(b h) f c -> (b f) h c", b=batch_size)
hidden_states = attn_output + hidden_states
# 3. Feed-forward
norm_hidden_states = self.norm3(hidden_states)
ff_output = self.act_fn(norm_hidden_states)
ff_output = self.ff(ff_output)
hidden_states = ff_output + hidden_states
return hidden_states
class TemporalBlock(torch.nn.Module):
def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5):
super().__init__()
inner_dim = num_attention_heads * attention_head_dim
self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True)
self.proj_in = torch.nn.Linear(in_channels, inner_dim)
self.transformer_blocks = torch.nn.ModuleList([
TemporalTransformerBlock(
inner_dim,
num_attention_heads,
attention_head_dim
)
for d in range(num_layers)
])
self.proj_out = torch.nn.Linear(inner_dim, in_channels)
def forward(self, hidden_states, time_emb, text_emb, res_stack, batch_size=1):
batch, _, height, width = hidden_states.shape
residual = hidden_states
hidden_states = self.norm(hidden_states)
inner_dim = hidden_states.shape[1]
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
hidden_states = self.proj_in(hidden_states)
for block in self.transformer_blocks:
hidden_states = block(
hidden_states,
batch_size=batch_size
)
hidden_states = self.proj_out(hidden_states)
hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
hidden_states = hidden_states + residual
return hidden_states, time_emb, text_emb, res_stack
class SDMotionModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.motion_modules = torch.nn.ModuleList([
TemporalBlock(8, 40, 320, eps=1e-6),
TemporalBlock(8, 40, 320, eps=1e-6),
TemporalBlock(8, 80, 640, eps=1e-6),
TemporalBlock(8, 80, 640, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 160, 1280, eps=1e-6),
TemporalBlock(8, 80, 640, eps=1e-6),
TemporalBlock(8, 80, 640, eps=1e-6),
TemporalBlock(8, 80, 640, eps=1e-6),
TemporalBlock(8, 40, 320, eps=1e-6),
TemporalBlock(8, 40, 320, eps=1e-6),
TemporalBlock(8, 40, 320, eps=1e-6),
])
self.call_block_id = {
1: 0,
4: 1,
9: 2,
12: 3,
17: 4,
20: 5,
24: 6,
26: 7,
29: 8,
32: 9,
34: 10,
36: 11,
40: 12,
43: 13,
46: 14,
50: 15,
53: 16,
56: 17,
60: 18,
63: 19,
66: 20
}
def forward(self):
pass
def state_dict_converter(self):
return SDMotionModelStateDictConverter()
class SDMotionModelStateDictConverter:
def __init__(self):
pass
def from_diffusers(self, state_dict):
rename_dict = {
"norm": "norm",
"proj_in": "proj_in",
"transformer_blocks.0.attention_blocks.0.to_q": "transformer_blocks.0.attn1.to_q",
"transformer_blocks.0.attention_blocks.0.to_k": "transformer_blocks.0.attn1.to_k",
"transformer_blocks.0.attention_blocks.0.to_v": "transformer_blocks.0.attn1.to_v",
"transformer_blocks.0.attention_blocks.0.to_out.0": "transformer_blocks.0.attn1.to_out",
"transformer_blocks.0.attention_blocks.0.pos_encoder": "transformer_blocks.0.pe1",
"transformer_blocks.0.attention_blocks.1.to_q": "transformer_blocks.0.attn2.to_q",
"transformer_blocks.0.attention_blocks.1.to_k": "transformer_blocks.0.attn2.to_k",
"transformer_blocks.0.attention_blocks.1.to_v": "transformer_blocks.0.attn2.to_v",
"transformer_blocks.0.attention_blocks.1.to_out.0": "transformer_blocks.0.attn2.to_out",
"transformer_blocks.0.attention_blocks.1.pos_encoder": "transformer_blocks.0.pe2",
"transformer_blocks.0.norms.0": "transformer_blocks.0.norm1",
"transformer_blocks.0.norms.1": "transformer_blocks.0.norm2",
"transformer_blocks.0.ff.net.0.proj": "transformer_blocks.0.act_fn.proj",
"transformer_blocks.0.ff.net.2": "transformer_blocks.0.ff",
"transformer_blocks.0.ff_norm": "transformer_blocks.0.norm3",
"proj_out": "proj_out",
}
name_list = sorted([i for i in state_dict if i.startswith("down_blocks.")])
name_list += sorted([i for i in state_dict if i.startswith("mid_block.")])
name_list += sorted([i for i in state_dict if i.startswith("up_blocks.")])
state_dict_ = {}
last_prefix, module_id = "", -1
for name in name_list:
names = name.split(".")
prefix_index = names.index("temporal_transformer") + 1
prefix = ".".join(names[:prefix_index])
if prefix != last_prefix:
last_prefix = prefix
module_id += 1
middle_name = ".".join(names[prefix_index:-1])
suffix = names[-1]
if "pos_encoder" in names:
rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name]])
else:
rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name], suffix])
state_dict_[rename] = state_dict[name]
return state_dict_
def from_civitai(self, state_dict):
return self.from_diffusers(state_dict)

6
diffsynth/models/sd_unet.py
View File

@@ -279,7 +279,7 @@ class SDUNet(torch.nn.Module):
self.conv_act = torch.nn.SiLU()
self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=3, padding=1)
def forward(self, sample, timestep, encoder_hidden_states, tiled=False, tile_size=64, tile_stride=8, **kwargs):
def forward(self, sample, timestep, encoder_hidden_states, tiled=False, tile_size=64, tile_stride=8, additional_res_stack=None, **kwargs):
# 1. time
time_emb = self.time_proj(timestep[None]).to(sample.dtype)
time_emb = self.time_embedding(time_emb)
@@ -293,6 +293,10 @@ class SDUNet(torch.nn.Module):
# 3. blocks
for i, block in enumerate(self.blocks):
if additional_res_stack is not None and i==31:
hidden_states += additional_res_stack.pop()
res_stack = [res + additional_res for res, additional_res in zip(res_stack, additional_res_stack)]
additional_res_stack = None
if tiled:
hidden_states, time_emb, text_emb, res_stack = self.tiled_inference(
block, hidden_states, time_emb, text_emb, res_stack,

1
diffsynth/pipelines/__init__.py
View File

@@ -1,2 +1,3 @@
from .stable_diffusion import SDPipeline
from .stable_diffusion_xl import SDXLPipeline
from .stable_diffusion_video import SDVideoPipeline

72
diffsynth/pipelines/stable_diffusion.py
View File

@@ -1,4 +1,5 @@
from ..models import ModelManager
from ..models import ModelManager, SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder
from ..controlnets.controlnet_unit import MultiControlNetManager
from ..prompts import SDPrompter
from ..schedulers import EnhancedDDIMScheduler
import torch
@@ -9,9 +10,29 @@ import numpy as np
class SDPipeline(torch.nn.Module):
def __init__(self):
def __init__(self, device="cuda", torch_dtype=torch.float16):
super().__init__()
self.scheduler = EnhancedDDIMScheduler()
self.prompter = SDPrompter()
self.device = device
self.torch_dtype = torch_dtype
# models
self.text_encoder: SDTextEncoder = None
self.unet: SDUNet = None
self.vae_decoder: SDVAEDecoder = None
self.vae_encoder: SDVAEEncoder = None
self.controlnet: MultiControlNetManager = None
def fetch_main_models(self, model_manager: ModelManager):
self.text_encoder = model_manager.text_encoder
self.unet = model_manager.unet
self.vae_decoder = model_manager.vae_decoder
self.vae_encoder = model_manager.vae_encoder
# load textual inversion
self.prompter.load_textual_inversion(model_manager.textual_inversion_dict)
def fetch_controlnet_models(self, controlnet_units=[]):
self.controlnet = MultiControlNetManager(controlnet_units)
def preprocess_image(self, image):
image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0)
@@ -20,13 +41,12 @@ class SDPipeline(torch.nn.Module):
@torch.no_grad()
def __call__(
self,
model_manager: ModelManager,
prompter: SDPrompter,
prompt,
negative_prompt="",
cfg_scale=7.5,
clip_skip=1,
init_image=None,
controlnet_image=None,
denoising_strength=1.0,
height=512,
width=512,
@@ -38,37 +58,59 @@ class SDPipeline(torch.nn.Module):
progress_bar_st=None,
):
# Encode prompts
prompt_emb = prompter.encode_prompt(model_manager.text_encoder, prompt, clip_skip=clip_skip, device=model_manager.device)
negative_prompt_emb = prompter.encode_prompt(model_manager.text_encoder, negative_prompt, clip_skip=clip_skip, device=model_manager.device)
prompt_emb_posi = self.prompter.encode_prompt(self.text_encoder, prompt, clip_skip=clip_skip, device=self.device)
prompt_emb_nega = self.prompter.encode_prompt(self.text_encoder, negative_prompt, clip_skip=clip_skip, device=self.device)
# Prepare scheduler
self.scheduler.set_timesteps(num_inference_steps, denoising_strength)
# Prepare latent tensors
if init_image is not None:
image = self.preprocess_image(init_image).to(device=model_manager.device, dtype=model_manager.torch_type)
latents = model_manager.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
noise = torch.randn((1, 4, height//8, width//8), device=model_manager.device, dtype=model_manager.torch_type)
image = self.preprocess_image(init_image).to(device=self.device, dtype=self.torch_dtype)
latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
noise = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype)
latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0])
else:
latents = torch.randn((1, 4, height//8, width//8), device=model_manager.device, dtype=model_manager.torch_type)
latents = torch.randn((1, 4, height//8, width//8), device=self.device, dtype=self.torch_dtype)
# Prepare ControlNets
if controlnet_image is not None:
controlnet_image = self.controlnet.process_image(controlnet_image).to(device=self.device, dtype=self.torch_dtype)
# Denoise
for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
timestep = torch.IntTensor((timestep,))[0].to(model_manager.device)
timestep = torch.IntTensor((timestep,))[0].to(self.device)
# ControlNet
if controlnet_image is not None:
additional_res_stack_posi = self.controlnet(latents, timestep, prompt_emb_posi, controlnet_image)
additional_res_stack_nega = self.controlnet(latents, timestep, prompt_emb_nega, controlnet_image)
else:
additional_res_stack_posi = None
additional_res_stack_nega = None
# Classifier-free guidance
noise_pred_cond = model_manager.unet(latents, timestep, prompt_emb, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
noise_pred_uncond = model_manager.unet(latents, timestep, negative_prompt_emb, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
noise_pred = noise_pred_uncond + cfg_scale * (noise_pred_cond - noise_pred_uncond)
noise_pred_posi = self.unet(
latents, timestep, prompt_emb_posi,
additional_res_stack=additional_res_stack_posi,
tiled=tiled, tile_size=tile_size, tile_stride=tile_stride
)
noise_pred_nega = self.unet(
latents, timestep, prompt_emb_nega,
additional_res_stack=additional_res_stack_nega,
tiled=tiled, tile_size=tile_size, tile_stride=tile_stride
)
noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
# DDIM
latents = self.scheduler.step(noise_pred, timestep, latents)
# UI
if progress_bar_st is not None:
progress_bar_st.progress(progress_id / len(self.scheduler.timesteps))
# Decode image
image = model_manager.vae_decoder(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
image = self.vae_decoder(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
image = image.cpu().permute(1, 2, 0).numpy()
image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8"))

302
diffsynth/pipelines/stable_diffusion_video.py Normal file
View File

@@ -0,0 +1,302 @@
from ..models import ModelManager, SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder, SDMotionModel
from ..models.sd_unet import PushBlock, PopBlock
from ..controlnets import MultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator
from ..prompts import SDPrompter
from ..schedulers import EnhancedDDIMScheduler
from typing import List
import torch
from tqdm import tqdm
from PIL import Image
import numpy as np
def lets_dance(
unet: SDUNet,
motion_modules: SDMotionModel = None,
controlnet: MultiControlNetManager = None,
sample = None,
timestep = None,
encoder_hidden_states = None,
controlnet_frames = None,
unet_batch_size = 1,
controlnet_batch_size = 1,
device = "cuda",
vram_limit_level = 0,
):
# 1. ControlNet
# This part will be repeated on overlapping frames if animatediff_batch_size > animatediff_stride.
# I leave it here because I intend to do something interesting on the ControlNets.
controlnet_insert_block_id = 30
if controlnet is not None and controlnet_frames is not None:
res_stacks = []
# process controlnet frames with batch
for batch_id in range(0, sample.shape[0], controlnet_batch_size):
batch_id_ = min(batch_id + controlnet_batch_size, sample.shape[0])
res_stack = controlnet(
sample[batch_id: batch_id_],
timestep,
encoder_hidden_states[batch_id: batch_id_],
controlnet_frames[:, batch_id: batch_id_]
)
if vram_limit_level >= 1:
res_stack = [res.cpu() for res in res_stack]
res_stacks.append(res_stack)
# concat the residual
additional_res_stack = []
for i in range(len(res_stacks[0])):
res = torch.concat([res_stack[i] for res_stack in res_stacks], dim=0)
additional_res_stack.append(res)
else:
additional_res_stack = None
# 2. time
time_emb = unet.time_proj(timestep[None]).to(sample.dtype)
time_emb = unet.time_embedding(time_emb)
# 3. pre-process
hidden_states = unet.conv_in(sample)
text_emb = encoder_hidden_states
res_stack = [hidden_states.cpu() if vram_limit_level>=1 else hidden_states]
# 4. blocks
for block_id, block in enumerate(unet.blocks):
# 4.1 UNet
if isinstance(block, PushBlock):
hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
if vram_limit_level>=1:
res_stack[-1] = res_stack[-1].cpu()
elif isinstance(block, PopBlock):
if vram_limit_level>=1:
res_stack[-1] = res_stack[-1].to(device)
hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
else:
hidden_states_input = hidden_states
hidden_states_output = []
for batch_id in range(0, sample.shape[0], unet_batch_size):
batch_id_ = min(batch_id + unet_batch_size, sample.shape[0])
hidden_states, _, _, _ = block(hidden_states_input[batch_id: batch_id_], time_emb, text_emb[batch_id: batch_id_], res_stack)
hidden_states_output.append(hidden_states)
hidden_states = torch.concat(hidden_states_output, dim=0)
# 4.2 AnimateDiff
if motion_modules is not None:
if block_id in motion_modules.call_block_id:
motion_module_id = motion_modules.call_block_id[block_id]
hidden_states, time_emb, text_emb, res_stack = motion_modules.motion_modules[motion_module_id](
hidden_states, time_emb, text_emb, res_stack,
batch_size=1
)
# 4.3 ControlNet
if block_id == controlnet_insert_block_id and additional_res_stack is not None:
hidden_states += additional_res_stack.pop().to(device)
if vram_limit_level>=1:
res_stack = [(res.to(device) + additional_res.to(device)).cpu() for res, additional_res in zip(res_stack, additional_res_stack)]
else:
res_stack = [res + additional_res for res, additional_res in zip(res_stack, additional_res_stack)]
# 5. output
hidden_states = unet.conv_norm_out(hidden_states)
hidden_states = unet.conv_act(hidden_states)
hidden_states = unet.conv_out(hidden_states)
return hidden_states
def lets_dance_with_long_video(
unet: SDUNet,
motion_modules: SDMotionModel = None,
controlnet: MultiControlNetManager = None,
sample = None,
timestep = None,
encoder_hidden_states = None,
controlnet_frames = None,
unet_batch_size = 1,
controlnet_batch_size = 1,
animatediff_batch_size = 16,
animatediff_stride = 8,
device = "cuda",
vram_limit_level = 0,
):
num_frames = sample.shape[0]
hidden_states_output = [(torch.zeros(sample[0].shape, dtype=sample[0].dtype), 0) for i in range(num_frames)]
for batch_id in range(0, num_frames, animatediff_stride):
batch_id_ = min(batch_id + animatediff_batch_size, num_frames)
# process this batch
hidden_states_batch = lets_dance(
unet, motion_modules, controlnet,
sample[batch_id: batch_id_].to(device),
timestep,
encoder_hidden_states[batch_id: batch_id_].to(device),
controlnet_frames[:, batch_id: batch_id_].to(device) if controlnet_frames is not None else None,
unet_batch_size=unet_batch_size, controlnet_batch_size=controlnet_batch_size, device=device, vram_limit_level=vram_limit_level
).cpu()
# update hidden_states
for i, hidden_states_updated in zip(range(batch_id, batch_id_), hidden_states_batch):
bias = max(1 - abs(i - (batch_id + batch_id_ - 1) / 2) / ((batch_id_ - batch_id - 1) / 2), 1e-2)
hidden_states, num = hidden_states_output[i]
hidden_states = hidden_states * (num / (num + bias)) + hidden_states_updated * (bias / (num + bias))
hidden_states_output[i] = (hidden_states, num + 1)
# output
hidden_states = torch.stack([h for h, _ in hidden_states_output])
return hidden_states
class SDVideoPipeline(torch.nn.Module):
def __init__(self, device="cuda", torch_dtype=torch.float16, use_animatediff=True):
super().__init__()
self.scheduler = EnhancedDDIMScheduler(beta_schedule="linear" if use_animatediff else "scaled_linear")
self.prompter = SDPrompter()
self.device = device
self.torch_dtype = torch_dtype
# models
self.text_encoder: SDTextEncoder = None
self.unet: SDUNet = None
self.vae_decoder: SDVAEDecoder = None
self.vae_encoder: SDVAEEncoder = None
self.controlnet: MultiControlNetManager = None
self.motion_modules: SDMotionModel = None
def fetch_main_models(self, model_manager: ModelManager):
self.text_encoder = model_manager.text_encoder
self.unet = model_manager.unet
self.vae_decoder = model_manager.vae_decoder
self.vae_encoder = model_manager.vae_encoder
# load textual inversion
self.prompter.load_textual_inversion(model_manager.textual_inversion_dict)
def fetch_controlnet_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[]):
controlnet_units = []
for config in controlnet_config_units:
controlnet_unit = ControlNetUnit(
Annotator(config.processor_id),
model_manager.get_model_with_model_path(config.model_path),
config.scale
)
controlnet_units.append(controlnet_unit)
self.controlnet = MultiControlNetManager(controlnet_units)
def fetch_motion_modules(self, model_manager: ModelManager):
if "motion_modules" in model_manager.model:
self.motion_modules = model_manager.motion_modules
@staticmethod
def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[]):
pipe = SDVideoPipeline(
device=model_manager.device,
torch_dtype=model_manager.torch_dtype,
use_animatediff="motion_modules" in model_manager.model
)
pipe.fetch_main_models(model_manager)
pipe.fetch_motion_modules(model_manager)
pipe.fetch_controlnet_models(model_manager, controlnet_config_units)
return pipe
def preprocess_image(self, image):
image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0)
return image
def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32):
image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0]
image = image.cpu().permute(1, 2, 0).numpy()
image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8"))
return image
def decode_images(self, latents, tiled=False, tile_size=64, tile_stride=32):
images = [
self.decode_image(latents[frame_id: frame_id+1], tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
for frame_id in range(latents.shape[0])
]
return images
def encode_images(self, processed_images, tiled=False, tile_size=64, tile_stride=32):
latents = []
for image in processed_images:
image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype)
latent = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).cpu()
latents.append(latent)
latents = torch.concat(latents, dim=0)
return latents
@torch.no_grad()
def __call__(
self,
prompt,
negative_prompt="",
cfg_scale=7.5,
clip_skip=1,
num_frames=None,
input_frames=None,
controlnet_frames=None,
denoising_strength=1.0,
height=512,
width=512,
num_inference_steps=20,
vram_limit_level=0,
progress_bar_cmd=tqdm,
progress_bar_st=None,
):
# Encode prompts
prompt_emb_posi = self.prompter.encode_prompt(self.text_encoder, prompt, clip_skip=clip_skip, device=self.device).cpu()
prompt_emb_nega = self.prompter.encode_prompt(self.text_encoder, negative_prompt, clip_skip=clip_skip, device=self.device).cpu()
prompt_emb_posi = prompt_emb_posi.repeat(num_frames, 1, 1)
prompt_emb_nega = prompt_emb_nega.repeat(num_frames, 1, 1)
# Prepare scheduler
self.scheduler.set_timesteps(num_inference_steps, denoising_strength)
# Prepare latent tensors
noise = torch.randn((num_frames, 4, height//8, width//8), device="cpu", dtype=self.torch_dtype)
if input_frames is None:
latents = noise
else:
latents = self.encode_images(input_frames)
latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0])
# Prepare ControlNets
if controlnet_frames is not None:
controlnet_frames = torch.stack([
self.controlnet.process_image(controlnet_frame).to(self.torch_dtype)
for controlnet_frame in progress_bar_cmd(controlnet_frames)
], dim=1)
# Denoise
for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
timestep = torch.IntTensor((timestep,))[0].to(self.device)
# Classifier-free guidance
noise_pred_posi = lets_dance_with_long_video(
self.unet, motion_modules=self.motion_modules, controlnet=self.controlnet,
sample=latents, timestep=timestep, encoder_hidden_states=prompt_emb_posi, controlnet_frames=controlnet_frames,
device=self.device, vram_limit_level=vram_limit_level
)
noise_pred_nega = lets_dance_with_long_video(
self.unet, motion_modules=self.motion_modules, controlnet=self.controlnet,
sample=latents, timestep=timestep, encoder_hidden_states=prompt_emb_nega, controlnet_frames=controlnet_frames,
device=self.device, vram_limit_level=vram_limit_level
)
noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega)
# DDIM
latents = self.scheduler.step(noise_pred, timestep, latents)
# UI
if progress_bar_st is not None:
progress_bar_st.progress(progress_id / len(self.scheduler.timesteps))
# Decode image
output_frames = self.decode_images(latents)
return output_frames

54
diffsynth/prompts/__init__.py
View File

@@ -1,7 +1,6 @@
from transformers import CLIPTokenizer
from ..models import SDTextEncoder, SDXLTextEncoder, SDXLTextEncoder2
from ..models import SDTextEncoder, SDXLTextEncoder, SDXLTextEncoder2, load_state_dict
import torch, os
from safetensors import safe_open
def tokenize_long_prompt(tokenizer, prompt):
@@ -36,49 +35,40 @@ def tokenize_long_prompt(tokenizer, prompt):
return input_ids
def load_textual_inversion(prompt):
# TODO: This module is not enabled now.
textual_inversion_files = os.listdir("models/textual_inversion")
embeddings_768 = []
embeddings_1280 = []
for file_name in textual_inversion_files:
if not file_name.endswith(".safetensors"):
continue
keyword = file_name[:-len(".safetensors")]
if keyword in prompt:
prompt = prompt.replace(keyword, "")
with safe_open(f"models/textual_inversion/{file_name}", framework="pt", device="cpu") as f:
for k in f.keys():
embedding = f.get_tensor(k).to(torch.float32)
if embedding.shape[-1] == 768:
embeddings_768.append(embedding)
elif embedding.shape[-1] == 1280:
embeddings_1280.append(embedding)
if len(embeddings_768)==0:
embeddings_768 = torch.zeros((0, 768))
else:
embeddings_768 = torch.concat(embeddings_768, dim=0)
if len(embeddings_1280)==0:
embeddings_1280 = torch.zeros((0, 1280))
else:
embeddings_1280 = torch.concat(embeddings_1280, dim=0)
return prompt, embeddings_768, embeddings_1280
def search_for_embeddings(state_dict):
embeddings = []
for k in state_dict:
if isinstance(state_dict[k], torch.Tensor):
embeddings.append(state_dict[k])
elif isinstance(state_dict[k], dict):
embeddings += search_for_embeddings(state_dict[k])
return embeddings
class SDPrompter:
def __init__(self, tokenizer_path="configs/stable_diffusion/tokenizer"):
# We use the tokenizer implemented by transformers
self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path)
self.keyword_dict = {}
def encode_prompt(self, text_encoder: SDTextEncoder, prompt, clip_skip=1, device="cuda"):
for keyword in self.keyword_dict:
if keyword in prompt:
prompt = prompt.replace(keyword, self.keyword_dict[keyword])
input_ids = tokenize_long_prompt(self.tokenizer, prompt).to(device)
prompt_emb = text_encoder(input_ids, clip_skip=clip_skip)
prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1))
return prompt_emb
def load_textual_inversion(self, textual_inversion_dict):
self.keyword_dict = {}
additional_tokens = []
for keyword in textual_inversion_dict:
tokens, _ = textual_inversion_dict[keyword]
additional_tokens += tokens
self.keyword_dict[keyword] = " " + " ".join(tokens) + " "
self.tokenizer.add_tokens(additional_tokens)
class SDXLPrompter:

17
diffsynth/schedulers/__init__.py
View File

@@ -3,9 +3,14 @@ import torch, math
class EnhancedDDIMScheduler():
def __init__(self, num_train_timesteps=1000, beta_start=0.00085, beta_end=0.012):
def __init__(self, num_train_timesteps=1000, beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"):
self.num_train_timesteps = num_train_timesteps
betas = torch.square(torch.linspace(math.sqrt(beta_start), math.sqrt(beta_end), num_train_timesteps, dtype=torch.float32))
if beta_schedule == "scaled_linear":
betas = torch.square(torch.linspace(math.sqrt(beta_start), math.sqrt(beta_end), num_train_timesteps, dtype=torch.float32))
elif beta_schedule == "linear":
betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
else:
raise NotImplementedError(f"{beta_schedule} is not implemented")
self.alphas_cumprod = torch.cumprod(1.0 - betas, dim=0).tolist()
self.set_timesteps(10)
@@ -34,14 +39,14 @@ class EnhancedDDIMScheduler():
return prev_sample
def step(self, model_output, timestep, sample):
def step(self, model_output, timestep, sample, to_final=False):
alpha_prod_t = self.alphas_cumprod[timestep]
timestep_id = self.timesteps.index(timestep)
if timestep_id + 1 < len(self.timesteps):
if to_final or timestep_id + 1 >= len(self.timesteps):
alpha_prod_t_prev = 1.0
else:
timestep_prev = self.timesteps[timestep_id + 1]
alpha_prod_t_prev = self.alphas_cumprod[timestep_prev]
else:
alpha_prod_t_prev = 1.0
return self.denoise(model_output, sample, alpha_prod_t, alpha_prod_t_prev)