264 lines
15 KiB
Python
264 lines
15 KiB
Python
import os
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import math
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from typing import List
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import torch
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import torch.nn as nn
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from diffusers.pipelines.controlnet import MultiControlNetModel
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from .attention_processor import MaskedIPAttnProcessor2_0 as IPAttnProcessor, AttnProcessor2_0 as AttnProcessor, \
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CNAttnProcessor2_0 as CNAttnProcessor
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class MSAdapter(torch.nn.Module):
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def __init__(self, unet,image_proj_model, adapter_modules=None, ckpt_path=None,
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num_tokens=4, text_tokens=77, max_rn=4, num_dummy_tokens=4,
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device="cuda", controlnet=None):
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super().__init__()
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self.unet = unet
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self.image_proj_model = image_proj_model
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self.adapter_modules = adapter_modules
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self.num_tokens = num_tokens
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self.num_dummy_tokens = num_dummy_tokens
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self.text_tokens = text_tokens
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self.max_rn = max_rn
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self.device = device
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self.controlnet = controlnet
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self.cross_attention_dim = self.unet.config.cross_attention_dim
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# set attention processor when inference
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if self.adapter_modules is None:
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self.set_ms_adapter()
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# dummy image tokens
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self.dummy_image_tokens = nn.Parameter(torch.randn(1, self.num_dummy_tokens, self.cross_attention_dim))
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if ckpt_path is not None:
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self.load_from_checkpoint(ckpt_path)
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def load_from_checkpoint(self, ckpt_path: str):
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if os.path.isdir(ckpt_path):
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ckpt_path = os.path.join(ckpt_path, "ms_adapter.bin")
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# Calculate original checksums
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orig_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj_model.parameters()]))
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orig_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.adapter_modules.parameters()]))
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state_dict = torch.load(ckpt_path, map_location="cpu")
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# Load state dict for image_proj_model and adapter_modules when using resampler
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self.image_proj_model.load_state_dict(state_dict["image_proj"], strict=False)
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self.adapter_modules.load_state_dict(state_dict["ms_adapter"], strict=False)
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self.load_state_dict({"dummy_image_tokens": state_dict["dummy_image_tokens"]}, strict=False)
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# Calculate new checksums
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new_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj_model.parameters()]))
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new_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.adapter_modules.parameters()]))
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# Verify if the weights have changed
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assert orig_ip_proj_sum != new_ip_proj_sum, "Weights of image_proj_model did not change!"
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assert orig_adapter_sum != new_adapter_sum, "Weights of adapter_modules did not change!"
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print(f"Successfully loaded weights from checkpoint {ckpt_path}")
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def set_ms_adapter(self, weight_dtype=torch.float16, cache_attention_maps=True):
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# set attention processor
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attn_procs = {}
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for name in self.unet.attn_processors.keys():
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cross_attention_dim = None if name.endswith("attn1.processor") else self.unet.config.cross_attention_dim
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if name.startswith("mid_block"):
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hidden_size = self.unet.config.block_out_channels[-1]
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elif name.startswith("up_blocks"):
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block_id = int(name[len("up_blocks.")])
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hidden_size = list(reversed(self.unet.config.block_out_channels))[block_id]
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elif name.startswith("down_blocks"):
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block_id = int(name[len("down_blocks.")])
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hidden_size = self.unet.config.block_out_channels[block_id]
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if cross_attention_dim is None:
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attn_procs[name] = AttnProcessor()
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else:
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attn_procs[name] = IPAttnProcessor(
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hidden_size=hidden_size,
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cross_attention_dim=cross_attention_dim,
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num_tokens=self.num_tokens,
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text_tokens=self.text_tokens,
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).to(self.device, dtype=weight_dtype)
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self.unet.set_attn_processor(attn_procs)
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self.adapter_modules = torch.nn.ModuleList(self.unet.attn_processors.values())
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if self.controlnet is not None:
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if isinstance(self.controlnet, MultiControlNetModel):
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for controlnet in self.controlnet.nets:
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controlnet.set_attn_processor(
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CNAttnProcessor(text_tokens=self.text_tokens, num_tokens=self.num_tokens))
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else:
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self.controlnet.set_attn_processor(
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CNAttnProcessor(text_tokens=self.text_tokens, num_tokens=self.num_tokens))
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@torch.inference_mode()
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def get_image_embeds(self, processed_images, image_encoder=None, image_proj_type="linear",
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image_encoder_type="clip", weight_dtype=torch.float16,):
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# get image embeds
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#processed_images: [bsz, rn, ...]
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# if use_repo:
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# processed_images = processed_images.view(-1, processed_images.shape[-3], processed_images.shape[-2],
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# processed_images.shape[-1]) # (bsz*rn, ...)
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# #print(processed_images.shape) # torch.Size([1, 3, 224, 224])
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if image_proj_type == "resampler":
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# if use_repo:
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# image_embeds = image_encoder(processed_images.to(self.device, dtype=weight_dtype),
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# output_hidden_states=True).hidden_states[
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# -2] # (bsz*rn, num_tokens, embedding_dim)
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# else:
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image_encoder = image_encoder.encode_image
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processed_images.to(self.device, dtype=weight_dtype)
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#image_embeds = image_encoder(processed_images)["image_embeds"]
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#image_embeds = image_encoder(processed_images)["last_hidden_state"]
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image_embeds=image_encoder(processed_images)["penultimate_hidden_states"]
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# print(image_embeds.shape)
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else:
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image_embeds = image_encoder(
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processed_images.to(self.device, dtype=weight_dtype)).image_embeds # (bsz*rn, embedding_dim)
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return image_embeds # [bsz*rn, ...]
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def set_scale(self, scale, subject_scales):
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for attn_processor in self.pipe.unet.attn_processors.values():
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if isinstance(attn_processor, IPAttnProcessor):
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attn_processor.scale = scale
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attn_processor.subject_scales = subject_scales
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def enable_psuedo_attention_mask(self, mask_threshold=0.5, start_step=5):
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for attn_processor in self.pipe.unet.attn_processors.values():
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if isinstance(attn_processor, IPAttnProcessor):
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attn_processor.mask_threshold = mask_threshold
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attn_processor.start_step = start_step
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attn_processor.use_psuedo_attention_mask = True
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attn_processor.need_text_attention_map = True
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attn_processor.attention_maps = [] # clear attention maps
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def generate(self, pipe, pil_images=None, processed_images=None, prompt=None, negative_prompt=None, scale=1.0,
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num_samples=4, seed=None, guidance_scale=7.5, num_inference_steps=50, image_processor=None,
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image_encoder=None, image_proj_type="linear", image_encoder_type="clip", weight_dtype=torch.float16,
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boxes=None, phrases=None, drop_grounding_tokens=None, phrase_idxes=None,
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eot_idxes=None, height=1024, width=1024, subject_scales=None, mask_threshold=None, start_step=5,
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**kwargs):
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# generate images (validation&inference)
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self.pipe = pipe
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self.set_scale(scale, subject_scales)
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if mask_threshold is not None:
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self.enable_psuedo_attention_mask(mask_threshold, start_step)
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# pil_images: [[xxx, xxx, xxx], [xxx, xxx, xxx], ...]
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bsz = len(pil_images) # only support bsz=1 now
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if processed_images is None:
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# write in this way to promise it can be extended to batch in the future
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processed_images = []
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for pil_image in pil_images:
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processed_image = image_processor(images=pil_image, return_tensors="pt").pixel_values
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processed_images.append(processed_image)
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processed_images = torch.stack(processed_images, dim=0)
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num_prompts = bsz
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if prompt is None:
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prompt = "best quality, high quality"
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if negative_prompt is None:
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negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality" # duplicate
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if not isinstance(prompt, List):
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prompt = [prompt] * num_prompts
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if not isinstance(negative_prompt, List):
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negative_prompt = [negative_prompt] * num_prompts
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cross_attention_kwargs = None
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grounding_kwargs = None
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if boxes is not None:
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boxes = torch.tensor(boxes).to(self.device, weight_dtype)
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if phrases is not None:
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drop_grounding_tokens = drop_grounding_tokens if drop_grounding_tokens is not None else [0] * bsz
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batch_boxes = boxes.view(bsz * boxes.shape[1], -1)
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# write in this way to promise it can be extended to batch in the future
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phrase_input_ids = []
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for phrase in phrases:
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phrase_input_id = pipe.tokenizer(phrase, max_length=pipe.tokenizer.model_max_length,
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padding="max_length", truncation=True,
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return_tensors="pt").input_ids
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phrase_input_ids.append(phrase_input_id)
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phrase_input_ids = torch.stack(phrase_input_ids)
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phrase_input_ids = phrase_input_ids.view(-1, phrase_input_ids.shape[-1])
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phrase_embeds = pipe.text_encoder(phrase_input_ids.to(self.device)).pooler_output
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#print(phrase_embeds.shape,batch_boxes.shape)#torch.Size([1, 768]) torch.Size([1, 4])
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grounding_kwargs = {"boxes": batch_boxes, "phrase_embeds": phrase_embeds,
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"drop_grounding_tokens": drop_grounding_tokens}
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else:
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grounding_kwargs = None
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boxes = torch.repeat_interleave(boxes, repeats=num_samples, dim=0)
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uncond_boxes = torch.zeros_like(boxes)
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boxes = torch.cat([uncond_boxes, boxes], dim=0)
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cross_attention_kwargs = {"boxes": boxes}
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if phrase_idxes is not None:
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phrase_idxes = torch.tensor(phrase_idxes).to(self.device, torch.int)
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eot_idxes = torch.tensor(eot_idxes).to(self.device, torch.int)
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phrase_idxes = torch.repeat_interleave(phrase_idxes, repeats=num_samples, dim=0)
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eot_idxes = torch.repeat_interleave(eot_idxes, repeats=num_samples, dim=0)
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uncond_phrase_idxes = torch.zeros_like(phrase_idxes)
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uncond_eot_idxes = torch.zeros_like(eot_idxes)
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phrase_idxes = torch.cat([uncond_phrase_idxes, phrase_idxes], dim=0)
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eot_idxes = torch.cat([uncond_eot_idxes, eot_idxes], dim=0)
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if cross_attention_kwargs is None:
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cross_attention_kwargs = {"phrase_idxes": phrase_idxes, "eot_idxes": eot_idxes}
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else:
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cross_attention_kwargs["phrase_idxes"] = phrase_idxes
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cross_attention_kwargs["eot_idxes"] = eot_idxes
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with (torch.inference_mode()):
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image_embeds = self.get_image_embeds(processed_images, image_encoder, image_proj_type=image_proj_type,
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image_encoder_type=image_encoder_type, weight_dtype=weight_dtype,)
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#print(image_embeds.shape) #torch.Size([1, 257, 1664])
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image_embeds=image_embeds.clone().detach().to(self.device, weight_dtype)
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image_prompt_embeds = self.image_proj_model(image_embeds, grounding_kwargs=grounding_kwargs)
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del image_embeds
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image_prompt_embeds = image_prompt_embeds.view(bsz, -1, image_prompt_embeds.shape[-2],
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image_prompt_embeds.shape[
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-1]) # (bsz, rn, num_tokens, cross_attention_dim)
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image_prompt_embeds = image_prompt_embeds.view(bsz,
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image_prompt_embeds.shape[-3] * image_prompt_embeds.shape[
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-2],
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image_prompt_embeds.shape[
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-1]) # (bsz, total_num_tokens*rn, cross_attention_dim)
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image_prompt_embeds = torch.cat([self.dummy_image_tokens, image_prompt_embeds], dim=1)
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uncond_image_prompt_embeds = torch.zeros_like(image_prompt_embeds)
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bs_embed, seq_len, _ = image_prompt_embeds.shape
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image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
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image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
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uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
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uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
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prompt_embeds_, negative_prompt_embeds_, pooled_prompt_embeds, negative_pooled_prompt_embeds = pipe.encode_prompt(
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prompt,
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device=self.device,
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num_images_per_prompt=num_samples,
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do_classifier_free_guidance=True,
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negative_prompt=negative_prompt,
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)
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prompt_embeds = torch.cat([prompt_embeds_, image_prompt_embeds], dim=1)
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negative_prompt_embeds = torch.cat([negative_prompt_embeds_, uncond_image_prompt_embeds], dim=1)
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generator = torch.Generator(self.device).manual_seed(seed) if seed is not None else None
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images = pipe(
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prompt_embeds=prompt_embeds,
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negative_prompt_embeds=negative_prompt_embeds,
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pooled_prompt_embeds=pooled_prompt_embeds,
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negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
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guidance_scale=guidance_scale,
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num_inference_steps=num_inference_steps,
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generator=generator,
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cross_attention_kwargs=cross_attention_kwargs,
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height=height,
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width=width,
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**kwargs,
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).images
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return images
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