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import torch
from torch import nn
from torchvision import transforms
from PIL import Image
from diffusers import AutoencoderKL, DDPMScheduler
from transformers import CLIPTextModel, CLIPTokenizer, CLIPImageProcessor
import time
import os
from io import BytesIO
from trainer.models.base_model import BaseModelConfig
from trainer.models.unet_2d_condition_reward import UNet2DConditionModel
from accelerate.logging import get_logger
logger = get_logger(__name__)
@dataclass
class SD15PreferenceModelConfig(BaseModelConfig):
_target_: str = "trainer.models.sd15_preference_model.SD15PreferenceModel"
pretrained_model_name_or_path: str = 'runwayml/stable-diffusion-v1-5'
clip_ckpt_path: str = 'openai/clip-vit-large-patch14/pytorch_model.bin'
vae_path: str = ""
vision_embed_dim: int = 1280
text_embed_dim: int = 768
projection_dim: int = 768
logit_scale_init_value: float = 2.6592
freeze_text_encoder: bool = False
multi_scale: bool = True
multi_scale_cfg: bool = False
guidance_scale: float = 1.0
class SD15PreferenceModel(nn.Module):
def __init__(self, cfg: SD15PreferenceModelConfig):
super().__init__()
# diffusion models
self.tokenizer = CLIPTokenizer.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="tokenizer")
self.text_encoder = CLIPTextModel.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="text_encoder")
if cfg.vae_path != "":
self.vae = AutoencoderKL.from_pretrained(cfg.vae_path)
else:
self.vae = AutoencoderKL.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="vae")
self.scheduler = DDPMScheduler.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="scheduler")
self.unet = UNet2DConditionModel.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="unet")
# self.pipeline = StableDiffusionPipeline.from_pretrained(pretrained_model_name_or_path)
# self.image_processor = CLIPImageProcessor.from_pretrained(pretrained_model_name_or_path)
# global pooling layer
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
self.cfg = cfg
# projection layers
if cfg.multi_scale:
self.visual_projection = nn.Linear(4800, cfg.projection_dim, bias=False)
else:
self.visual_projection = nn.Linear(cfg.vision_embed_dim, cfg.projection_dim, bias=False)
nn.init.normal_(self.visual_projection.weight, std=0.02)
self.text_projection = nn.Linear(cfg.text_embed_dim, cfg.projection_dim, bias=False)
# load text projections from openai/clip-vit-large-patch14
clip_ckpt = torch.load(cfg.clip_ckpt_path)
self.text_projection.weight.data = clip_ckpt['text_projection.weight'].contiguous()
self.logit_scale = nn.Parameter(torch.ones([]) * cfg.logit_scale_init_value)
self.vae.requires_grad_(False)
if cfg.freeze_text_encoder:
self.text_encoder.requires_grad_(False)
self.val_transform = transforms.Compose(
[
transforms.Resize((512, 512), interpolation=transforms.InterpolationMode.BILINEAR),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
self.do_classifier_free_guidance = self.cfg.guidance_scale > 1.0 or self.cfg.guidance_scale < 1.0
if self.do_classifier_free_guidance:
# generate negative prompt ids
self.neg_prompt_ids = self.tokenizer(
[""],
return_tensors="pt",
padding="max_length",
truncation=True,
max_length=self.tokenizer.model_max_length,
).input_ids
def get_text_features(self, text_inputs=None):
if self.do_classifier_free_guidance:
text_inputs = torch.cat([text_inputs, self.neg_prompt_ids.repeat(text_inputs.shape[0], 1).to(text_inputs.device)], dim=0)
outputs = self.text_encoder(text_inputs, return_dict=False)
encoder_hidden_states = outputs[0]
pooled_output = outputs[1]
if self.do_classifier_free_guidance:
pooled_output_text, pooled_output_ucond = pooled_output.chunk(2, dim=0)
text_features = self.text_projection(pooled_output_text)
else:
text_features = self.text_projection(pooled_output)
return encoder_hidden_states, text_features
def get_image_features(self, encoder_hidden_states=None, image_inputs=None, time_cond=None, generator=None):
with torch.no_grad():
latents = self.vae.encode(image_inputs).latent_dist.sample()
# latents = latents.to(dtype=self.unet.dtype)
latents = latents * self.vae.config.scaling_factor
if generator is not None:
noise = torch.randn(latents.size(), generator=generator, dtype=latents.dtype, device=latents.device)
else:
noise = torch.randn_like(latents)
noisy_latents = self.scheduler.add_noise(latents, noise, time_cond)
if self.do_classifier_free_guidance:
noisy_latents = torch.cat([noisy_latents] * 2, dim=0)
time_cond = torch.cat([time_cond] * 2, dim=0)
mid_output, down_block_res_samples = self.unet(noisy_latents, time_cond, encoder_hidden_states=encoder_hidden_states, return_dict=False, use_up_blocks=False)
if self.cfg.multi_scale:
first_stage_output = down_block_res_samples[2] # [320, 64, 64]
second_stage_output = down_block_res_samples[5] # [640, 32, 32]
third_stage_output = down_block_res_samples[8] # [1280, 16, 16]
fourth_stage_output = down_block_res_samples[11] # [1280, 8, 8]
pooled_first_stage_output = self.avg_pool(first_stage_output).squeeze(dim=[2,3])
pooled_second_stage_output = self.avg_pool(second_stage_output).squeeze(dim=[2,3])
pooled_third_stage_output = self.avg_pool(third_stage_output).squeeze(dim=[2,3])
pooled_fourth_stage_output = self.avg_pool(fourth_stage_output).squeeze(dim=[2,3])
pooled_mid_output = self.avg_pool(mid_output).squeeze(dim=[2,3])
if self.do_classifier_free_guidance:
pooled_mid_output_text, pooled_mid_output_ucond = pooled_mid_output.chunk(2, dim=0)
pooled_mid_output = pooled_mid_output_ucond + self.cfg.guidance_scale * (pooled_mid_output_text - pooled_mid_output_ucond)
if self.cfg.multi_scale_cfg:
pooled_first_stage_output_text, pooled_first_stage_output_ucond = pooled_first_stage_output.chunk(2, dim=0)
pooled_first_stage_output = pooled_first_stage_output_ucond + self.cfg.guidance_scale * (pooled_first_stage_output_text - pooled_first_stage_output_ucond)
pooled_second_stage_output_text, pooled_second_stage_output_ucond = pooled_second_stage_output.chunk(2, dim=0)
pooled_second_stage_output = pooled_second_stage_output_ucond + self.cfg.guidance_scale * (pooled_second_stage_output_text - pooled_second_stage_output_ucond)
pooled_third_stage_output_text, pooled_third_stage_output_ucond = pooled_third_stage_output.chunk(2, dim=0)
pooled_third_stage_output = pooled_third_stage_output_ucond + self.cfg.guidance_scale * (pooled_third_stage_output_text - pooled_third_stage_output_ucond)
pooled_fourth_stage_output_text, pooled_fourth_stage_output_ucond = pooled_fourth_stage_output.chunk(2, dim=0)
pooled_fourth_stage_output = pooled_fourth_stage_output_ucond + self.cfg.guidance_scale * (pooled_fourth_stage_output_text - pooled_fourth_stage_output_ucond)
else:
pooled_first_stage_output_text, pooled_first_stage_output_ucond = pooled_first_stage_output.chunk(2, dim=0)
pooled_first_stage_output = pooled_first_stage_output_text
pooled_second_stage_output_text, pooled_second_stage_output_ucond = pooled_second_stage_output.chunk(2, dim=0)
pooled_second_stage_output = pooled_second_stage_output_text
pooled_third_stage_output_text, pooled_third_stage_output_ucond = pooled_third_stage_output.chunk(2, dim=0)
pooled_third_stage_output = pooled_third_stage_output_text
pooled_fourth_stage_output_text, pooled_fourth_stage_output_ucond = pooled_fourth_stage_output.chunk(2, dim=0)
pooled_fourth_stage_output = pooled_fourth_stage_output_text
concat_pooled_output = torch.cat([pooled_first_stage_output, pooled_second_stage_output, pooled_third_stage_output, pooled_fourth_stage_output, pooled_mid_output], dim=-1)
image_features = self.visual_projection(concat_pooled_output)
else:
pooled_mid_output = self.avg_pool(mid_output).squeeze(dim=[2,3])
if self.do_classifier_free_guidance:
pooled_mid_output_text, pooled_mid_output_ucond = pooled_mid_output.chunk(2, dim=0)
pooled_mid_output = pooled_mid_output_ucond + self.cfg.guidance_scale * (pooled_mid_output_text - pooled_mid_output_ucond)
image_features = self.visual_projection(pooled_mid_output)
return image_features
def forward(self, text_inputs, image_inputs, time_cond, generator=None):
n_p = text_inputs.shape[0]
n_i = image_inputs.shape[0]
outputs = ()
encoder_hidden_states, text_features = self.get_text_features(text_inputs)
outputs += text_features,
if n_i == 2 * n_p:
if self.do_classifier_free_guidance:
encoder_hidden_states_text, encoder_hidden_states_ucond = encoder_hidden_states.chunk(2, dim=0)
encoder_hidden_states = torch.cat([encoder_hidden_states_text] * 2 + [encoder_hidden_states_ucond] * 2, dim=0)
else:
encoder_hidden_states = torch.cat([encoder_hidden_states, encoder_hidden_states], dim=0)
image_features = self.get_image_features(encoder_hidden_states, image_inputs, time_cond, generator=generator)
outputs += image_features,
return outputs
def save(self, path):
self.unet.save_pretrained(os.path.join(path, "unet"), safe_serialization=True)
if not self.cfg.freeze_text_encoder:
self.text_encoder.save_pretrained(os.path.join(path, "text_encoder"), safe_serialization=True)
# save others
state_dict = {
'visual_projection': self.visual_projection.state_dict(),
'text_projection': self.text_projection.state_dict(),
'logit_scale': self.logit_scale.data.item()
}
torch.save(state_dict, os.path.join(path, "state_dict.pt"))
logger.info(f"Save model to path {path} successfully")
def load(self, path):
self.unet = self.unet.from_pretrained(os.path.join(path, "unet"))
logger.info(f"Loading unet weights from {os.path.join(path, 'unet')}")
if not self.cfg.freeze_text_encoder:
self.text_encoder = self.text_encoder.from_pretrained(os.path.join(path, "text_encoder"))
logger.info(f"Loading text_encoder weights from {os.path.join(path, 'text_encoder')}")
# load others
state_dict = torch.load(os.path.join(path, "state_dict.pt"))
self.visual_projection.load_state_dict(state_dict['visual_projection'])
self.text_projection.load_state_dict(state_dict['text_projection'])
self.logit_scale.data = torch.tensor(state_dict['logit_scale'])
logger.info(f"Loading projection and logit_scale weights from {os.path.join(path, 'state_dict.pt')}")
def encode_prompt(self, prompt):
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
return text_inputs.input_ids
def preprocess_image(self, images):
if not isinstance(images, list):
images = [images]
image_inputs = []
for image in images:
if isinstance(image, dict):
image = image["bytes"]
if isinstance(image, bytes):
image = Image.open(BytesIO(image))
elif isinstance(image, str):
image = Image.open(image)
image = image.convert("RGB")
image = self.val_transform(image)
image_inputs.append(image)
image_inputs = torch.stack(image_inputs, dim=0)
return image_inputs
def get_preference_scores(self, prompt, images, timesteps, generator=None):
image_inputs = self.preprocess_image(images).to(self.vae.device, dtype=self.vae.dtype)
text_inputs = self.encode_prompt(prompt).to(self.text_encoder.device)
timesteps = torch.tensor([timesteps] * image_inputs.shape[0], dtype=torch.long).to(self.vae.device)
with torch.no_grad():
text_embs, image_embs = self.forward(text_inputs, image_inputs, timesteps, generator=generator)
image_embs = image_embs / torch.norm(image_embs, dim=-1, keepdim=True)
text_embs = text_embs / torch.norm(text_embs, dim=-1, keepdim=True)
scores = self.logit_scale.exp() * (text_embs @ image_embs.T)[0]
probs = torch.softmax(scores, dim=-1)
return scores.cpu().tolist(), probs.cpu().tolist()
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