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smthem
2025-04-05 08:22:39 +08:00
parent 5c3f1945b0
commit 6762cde3eb
66 changed files with 249306 additions and 3468 deletions
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msdiffusion/models/attention_processor.py
+3
View File
@@ -273,10 +273,13 @@ class MaskedIPAttnProcessor2_0(nn.Module):
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
rf_attention_mask = None
#print(boxes, phrase_idxes, hidden_states.shape[1],self.text_tokens, batch_size, attn.heads,
#hidden_states.dtype, hidden_states.device) #one None 2304 77 2 10 torch.float16 cuda:0
custom_attention_masks = self.prepare_attention_mask_qk(boxes, phrase_idxes, hidden_states.shape[1],
self.text_tokens, batch_size, attn.heads,
hidden_states.dtype, hidden_states.device,
use_masked_text_attention=False)
#print(custom_attention_masks)
attention_mask_qk_image, attention_mask_qk_text, dummy_attention_mask = custom_attention_masks
if attention_mask_qk_image is not None:
attention_mask_qk_image = attention_mask_qk_image.view(batch_size, attn.heads, -1, attention_mask_qk_image.shape[-1])
msdiffusion/models/model.py
+12 -8
View File
@@ -62,7 +62,7 @@ class MSAdapter(torch.nn.Module):
def set_ms_adapter(self, weight_dtype=torch.float16, cache_attention_maps=True):
# set attention processor
attn_procs_ = {}
attn_procs = {}
for name in self.unet.attn_processors.keys():
cross_attention_dim = None if name.endswith("attn1.processor") else self.unet.config.cross_attention_dim
if name.startswith("mid_block"):
@@ -74,15 +74,15 @@ class MSAdapter(torch.nn.Module):
block_id = int(name[len("down_blocks.")])
hidden_size = self.unet.config.block_out_channels[block_id]
if cross_attention_dim is None:
attn_procs_[name] = AttnProcessor()
attn_procs[name] = AttnProcessor()
else:
attn_procs_[name] = IPAttnProcessor(
attn_procs[name] = IPAttnProcessor(
hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
num_tokens=self.num_tokens,
text_tokens=self.text_tokens,
).to(self.device, dtype=weight_dtype)
self.unet.set_attn_processor(attn_procs_)
self.unet.set_attn_processor(attn_procs)
self.adapter_modules = torch.nn.ModuleList(self.unet.attn_processors.values())
if self.controlnet is not None:
if isinstance(self.controlnet, MultiControlNetModel):
@@ -109,11 +109,11 @@ class MSAdapter(torch.nn.Module):
output_hidden_states=True).hidden_states[
-2] # (bsz*rn, num_tokens, embedding_dim)
else:
print(processed_images.shape,1233333)
image_encoder = image_encoder.encode_image
processed_images.to("cpu")
image_embeds = image_encoder(processed_images)["penultimate_hidden_states"]
# print(image_embeds.shape)
print(image_embeds.shape,1234567)
else:
image_embeds = image_encoder(
@@ -174,6 +174,7 @@ class MSAdapter(torch.nn.Module):
grounding_kwargs = None
if boxes is not None:
boxes = torch.tensor(boxes).to(self.device, weight_dtype)
print(boxes.shape,123)
if phrases is not None:
drop_grounding_tokens = drop_grounding_tokens if drop_grounding_tokens is not None else [0] * bsz
batch_boxes = boxes.view(bsz * boxes.shape[1], -1).to(self.device)
@@ -187,7 +188,7 @@ class MSAdapter(torch.nn.Module):
phrase_input_ids = torch.stack(phrase_input_ids)
phrase_input_ids = phrase_input_ids.view(-1, phrase_input_ids.shape[-1])
phrase_embeds = pipe.text_encoder(phrase_input_ids.to(self.device)).pooler_output
# print(phrase_embeds.shape,batch_boxes.shape)#torch.Size([1, 768]) torch.Size([1, 4])
print(phrase_embeds.shape,batch_boxes.shape) # torch.Size([2, 768]) torch.Size([2, 4])
grounding_kwargs = {"boxes": batch_boxes, "phrase_embeds": phrase_embeds,
"drop_grounding_tokens": drop_grounding_tokens}
else:
@@ -219,12 +220,13 @@ class MSAdapter(torch.nn.Module):
use_repo=use_repo)
del image_encoder
torch.cuda.empty_cache()
# print(image_embeds.shape) #torch.Size([1, 257, 1664])
# print(image_embeds.shape) #torch.Size([2, 257, 1664])
if not use_repo:
# print(image_embeds.device,self.device,)
image_embeds=image_embeds.clone().detach().to(self.device, weight_dtype)
#print(image_embeds.device)
image_prompt_embeds = self.image_proj_model(image_embeds, grounding_kwargs=grounding_kwargs)
del image_embeds
torch.cuda.empty_cache()
image_prompt_embeds = image_prompt_embeds.view(bsz, -1, image_prompt_embeds.shape[-2],
@@ -236,6 +238,7 @@ class MSAdapter(torch.nn.Module):
image_prompt_embeds.shape[
-1]) # (bsz, total_num_tokens*rn, cross_attention_dim)
image_prompt_embeds = torch.cat([self.dummy_image_tokens, image_prompt_embeds], dim=1)
uncond_image_prompt_embeds = torch.zeros_like(image_prompt_embeds)
bs_embed, seq_len, _ = image_prompt_embeds.shape
image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
@@ -250,6 +253,7 @@ class MSAdapter(torch.nn.Module):
do_classifier_free_guidance=True,
negative_prompt=negative_prompt,
)
prompt_embeds = torch.cat([prompt_embeds_, image_prompt_embeds], dim=1)
negative_prompt_embeds = torch.cat([negative_prompt_embeds_, uncond_image_prompt_embeds], dim=1)
msdiffusion/models/modelWrapper.py
+186
View File
@@ -0,0 +1,186 @@
import os
import math
from typing import List
import torch
import torch.nn as nn
from diffusers.pipelines.controlnet import MultiControlNetModel
from .attention_processor import MaskedIPAttnProcessor2_0 as IPAttnProcessor, AttnProcessor2_0 as AttnProcessor, \
CNAttnProcessor2_0 as CNAttnProcessor
class MSAdapter(torch.nn.Module):
def __init__(self, pipe, image_proj_model, adapter_modules=None, ckpt_path=None,
num_tokens=4, text_tokens=77, max_rn=4, num_dummy_tokens=4,
device="cuda", controlnet=None):
super().__init__()
self.unet = pipe.unet
self.pipe = pipe
self.image_proj_model = image_proj_model
self.adapter_modules = adapter_modules
self.num_tokens = num_tokens
self.num_dummy_tokens = num_dummy_tokens
self.text_tokens = text_tokens
self.max_rn = max_rn
self.device = device
self.controlnet = controlnet
self.cross_attention_dim = self.unet.config.cross_attention_dim
# set attention processor when inference
if self.adapter_modules is None:
self.set_ms_adapter()
# dummy image tokens
self.dummy_image_tokens = nn.Parameter(torch.randn(1, self.num_dummy_tokens, self.cross_attention_dim))
if ckpt_path is not None:
self.load_from_checkpoint(ckpt_path)
def load_from_checkpoint(self, ckpt_path: str):
# Calculate original checksums
orig_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj_model.parameters()]))
orig_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.adapter_modules.parameters()]))
state_dict = torch.load(ckpt_path, map_location="cpu")
# Load state dict for image_proj_model and adapter_modules when using resampler
self.image_proj_model.load_state_dict(state_dict["image_proj"], strict=False)
self.adapter_modules.load_state_dict(state_dict["ms_adapter"], strict=False)
self.load_state_dict({"dummy_image_tokens": state_dict["dummy_image_tokens"]}, strict=False)
# Calculate new checksums
new_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj_model.parameters()]))
new_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.adapter_modules.parameters()]))
# Verify if the weights have changed
assert orig_ip_proj_sum != new_ip_proj_sum, "Weights of image_proj_model did not change!"
assert orig_adapter_sum != new_adapter_sum, "Weights of adapter_modules did not change!"
print(f"Successfully loaded weights from checkpoint {ckpt_path}")
def set_ms_adapter(self, weight_dtype=torch.float16, cache_attention_maps=True):
# set attention processor
attn_procs_ = {}
for name in self.unet.attn_processors.keys():
cross_attention_dim = None if name.endswith("attn1.processor") else self.unet.config.cross_attention_dim
if name.startswith("mid_block"):
hidden_size = self.unet.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(self.unet.config.block_out_channels))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = self.unet.config.block_out_channels[block_id]
if cross_attention_dim is None:
attn_procs_[name] = AttnProcessor()
else:
attn_procs_[name] = IPAttnProcessor(
hidden_size=hidden_size,
cross_attention_dim=cross_attention_dim,
num_tokens=self.num_tokens,
text_tokens=self.text_tokens,
).to(self.device, dtype=weight_dtype)
self.unet.set_attn_processor(attn_procs_)
self.adapter_modules = torch.nn.ModuleList(self.unet.attn_processors.values())
if self.controlnet is not None:
if isinstance(self.controlnet, MultiControlNetModel):
for controlnet in self.controlnet.nets:
controlnet.set_attn_processor(
CNAttnProcessor(text_tokens=self.text_tokens, num_tokens=self.num_tokens))
else:
self.controlnet.set_attn_processor(
CNAttnProcessor(text_tokens=self.text_tokens, num_tokens=self.num_tokens))
@torch.inference_mode()
def get_image_embeds(self, processed_images, image_encoder=None, image_proj_type="linear",
image_encoder_type="clip", weight_dtype=torch.float16, use_repo=None):
# get image embeds
# processed_images: [bsz, rn, ...]
if use_repo:
processed_images = processed_images.view(-1, processed_images.shape[-3], processed_images.shape[-2],
processed_images.shape[-1]) # (bsz*rn, ...)
# print(processed_images.shape) # torch.Size([1, 3, 224, 224])
if image_proj_type == "resampler":
if use_repo:
image_embeds = image_encoder(processed_images.to(self.device, dtype=weight_dtype),
output_hidden_states=True).hidden_states[
-2] # (bsz*rn, num_tokens, embedding_dim)
else:
image_encoder = image_encoder.encode_image
processed_images.to("cpu")
image_embeds = image_encoder(processed_images)["penultimate_hidden_states"]
# print(image_embeds.shape)
else:
image_embeds = image_encoder(
processed_images.to(self.device, dtype=weight_dtype)).image_embeds # (bsz*rn, embedding_dim)
return image_embeds # [bsz*rn, ...]
def set_scale(self, scale, subject_scales):
for attn_processor in self.pipe.unet.attn_processors.values():
if isinstance(attn_processor, IPAttnProcessor):
attn_processor.scale = scale
attn_processor.subject_scales = subject_scales
def enable_psuedo_attention_mask(self, mask_threshold=0.5, start_step=5):
for attn_processor in self.pipe.unet.attn_processors.values():
if isinstance(attn_processor, IPAttnProcessor):
attn_processor.mask_threshold = mask_threshold
attn_processor.start_step = start_step
attn_processor.use_psuedo_attention_mask = True
attn_processor.need_text_attention_map = True
attn_processor.attention_maps = [] # clear attention maps
def generate(self,pipe,image_embeds, prompt_embeds_, negative_prompt_embeds_,grounding_kwargs,cross_attention_kwargs, bsz,height,width,steps,seed,cfg,pooled_prompt_embeds,negative_pooled_prompt_embeds,scale=1.0,
num_samples=4, weight_dtype=torch.float16, subject_scales=None, mask_threshold=None, start_step=5,**kwargs):
self.set_scale(scale, subject_scales)
if mask_threshold is not None:
self.enable_psuedo_attention_mask(mask_threshold, start_step)
with (torch.inference_mode()):
#print(image_embeds.is_cuda,grounding_kwargs),
image_prompt_embeds = self.image_proj_model(image_embeds, grounding_kwargs=grounding_kwargs) #([2, 16, 2048])
image_prompt_embeds = image_prompt_embeds.view(bsz, -1, image_prompt_embeds.shape[-2],
image_prompt_embeds.shape[
-1]) # (bsz, rn, num_tokens, cross_attention_dim)
image_prompt_embeds = image_prompt_embeds.view(bsz,
image_prompt_embeds.shape[-3] * image_prompt_embeds.shape[
-2],
image_prompt_embeds.shape[
-1]) # (bsz, total_num_tokens*rn, cross_attention_dim)
dummy_image_tokens=self.dummy_image_tokens.to(self.device, weight_dtype)
image_prompt_embeds = torch.cat([dummy_image_tokens, image_prompt_embeds], dim=1)
uncond_image_prompt_embeds = torch.zeros_like(image_prompt_embeds)
bs_embed, seq_len, _ = image_prompt_embeds.shape
image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1) #([1, 36, 2048])
uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
prompt_embeds_=prompt_embeds_.to(self.device, weight_dtype)#([1, 77, 2048])
negative_prompt_embeds_=negative_prompt_embeds_.to(self.device, weight_dtype)
generator=torch.Generator(device=self.device).manual_seed(seed)
prompt_embeds = torch.cat([prompt_embeds_, image_prompt_embeds], dim=1)
negative_prompt_embeds = torch.cat([negative_prompt_embeds_, uncond_image_prompt_embeds], dim=1)
samples = pipe(height=height, width=width, num_inference_steps=steps, guidance_scale=cfg,
generator=generator,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
**kwargs
)[0] # torch.Size([1, 4, 64, 64])
return samples
msdiffusion/models/projection.py
+2 -1
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@@ -200,8 +200,9 @@ class Resampler(nn.Module):
boxes = grounding_kwargs["boxes"]
phrase_embeds = grounding_kwargs["phrase_embeds"]
fourier_embeds = self.fourier_embedder(boxes)
#print(phrase_embeds.shape,fourier_embeds.shape)
grounding_embeds = torch.cat((phrase_embeds, fourier_embeds), dim=-1)
drop_grounding_tokens = grounding_kwargs["drop_grounding_tokens"]
num_ref = x.shape[0] // len(drop_grounding_tokens)
drop_grounding_tokens = [item for item in drop_grounding_tokens for _ in range(num_ref)]