File size: 15,195 Bytes
4e2a1b3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 | import math
from typing import Optional, Union
import torch
from PIL import Image
from transformers import Qwen2VLForConditionalGeneration
ImageInput = Union[Image.Image, list[Image.Image], tuple[Image.Image, ...]]
HPSV3_INSTRUCTION = """
You are tasked with evaluating a generated image based on Visual Quality and Text Alignment and give a overall score to estimate the human preference. Please provide a rating from 0 to 10, with 0 being the worst and 10 being the best.
**Visual Quality:**
Evaluate the overall visual quality of the image. The following sub-dimensions should be considered:
- **Reasonableness:** The image should not contain any significant biological or logical errors, such as abnormal body structures or nonsensical environmental setups.
- **Clarity:** Evaluate the sharpness and visibility of the image. The image should be clear and easy to interpret, with no blurring or indistinct areas.
- **Detail Richness:** Consider the level of detail in textures, materials, lighting, and other visual elements (e.g., hair, clothing, shadows).
- **Aesthetic and Creativity:** Assess the artistic aspects of the image, including the color scheme, composition, atmosphere, depth of field, and the overall creative appeal. The scene should convey a sense of harmony and balance.
- **Safety:** The image should not contain harmful or inappropriate content, such as political, violent, or adult material. If such content is present, the image quality and satisfaction score should be the lowest possible.
**Text Alignment:**
Assess how well the image matches the textual prompt across the following sub-dimensions:
- **Subject Relevance** Evaluate how accurately the subject(s) in the image (e.g., person, animal, object) align with the textual description. The subject should match the description in terms of number, appearance, and behavior.
- **Style Relevance:** If the prompt specifies a particular artistic or stylistic style, evaluate how well the image adheres to this style.
- **Contextual Consistency**: Assess whether the background, setting, and surrounding elements in the image logically fit the scenario described in the prompt. The environment should support and enhance the subject without contradictions.
- **Attribute Fidelity**: Check if specific attributes mentioned in the prompt (e.g., colors, clothing, accessories, expressions, actions) are faithfully represented in the image. Minor deviations may be acceptable, but critical attributes should be preserved.
- **Semantic Coherence**: Evaluate whether the overall meaning and intent of the prompt are captured in the image. The generated content should not introduce elements that conflict with or distort the original description.
Textual prompt - {text_prompt}
"""
HPSV3_PROMPT_WITH_SPECIAL_TOKEN = """
Please provide the overall ratings of this image: <|Reward|>
END
"""
HPSV3_PROMPT_WITHOUT_SPECIAL_TOKEN = """
Please provide the overall ratings of this image:
"""
def _as_list(value):
if isinstance(value, (list, tuple)):
return list(value)
return [value]
def _round_by_factor(number, factor):
return round(number / factor) * factor
def _ceil_by_factor(number, factor):
return math.ceil(number / factor) * factor
def _floor_by_factor(number, factor):
return math.floor(number / factor) * factor
def _smart_resize(height, width, factor=28, min_pixels=256 * 28 * 28, max_pixels=256 * 28 * 28):
if max(height, width) / min(height, width) > 200:
raise ValueError(f"Image aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}.")
h_bar = max(factor, _round_by_factor(height, factor))
w_bar = max(factor, _round_by_factor(width, factor))
if h_bar * w_bar > max_pixels:
beta = math.sqrt((height * width) / max_pixels)
h_bar = _floor_by_factor(height / beta, factor)
w_bar = _floor_by_factor(width / beta, factor)
elif h_bar * w_bar < min_pixels:
beta = math.sqrt(min_pixels / (height * width))
h_bar = _ceil_by_factor(height * beta, factor)
w_bar = _ceil_by_factor(width * beta, factor)
return h_bar, w_bar
class HPSv3RewardModelMixin:
def init_reward_head(
self,
output_dim=2,
reward_token="special",
special_token_ids=None,
rm_head_type="ranknet",
rm_head_kwargs=None,
):
self.output_dim = output_dim
self.reward_token = "special" if special_token_ids is not None else reward_token
self.special_token_ids = special_token_ids
hidden_size = getattr(self.config, "hidden_size", None)
if hidden_size is None and hasattr(self.config, "text_config"):
hidden_size = self.config.text_config.hidden_size
if rm_head_type == "ranknet":
rm_head_kwargs = rm_head_kwargs or {}
hidden = rm_head_kwargs.get("hidden_size", 1024)
dropout = rm_head_kwargs.get("dropout", 0.05)
self.rm_head = torch.nn.Sequential(
torch.nn.Linear(hidden_size, hidden),
torch.nn.ReLU(),
torch.nn.Dropout(dropout),
torch.nn.Linear(hidden, 16),
torch.nn.ReLU(),
torch.nn.Linear(16, output_dim),
)
else:
self.rm_head = torch.nn.Linear(hidden_size, output_dim, bias=False)
self.rm_head.to(torch.float32)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[list[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
pixel_values: Optional[torch.Tensor] = None,
pixel_values_videos: Optional[torch.FloatTensor] = None,
image_grid_thw: Optional[torch.LongTensor] = None,
video_grid_thw: Optional[torch.LongTensor] = None,
rope_deltas: Optional[torch.LongTensor] = None,
**kwargs,
):
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
kwargs.pop("logits_to_keep", None)
mm_token_type_ids = kwargs.pop("mm_token_type_ids", None)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
pixel_values=pixel_values,
pixel_values_videos=pixel_values_videos,
image_grid_thw=image_grid_thw,
video_grid_thw=video_grid_thw,
rope_deltas=rope_deltas,
mm_token_type_ids=mm_token_type_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs,
)
hidden_states = outputs.last_hidden_state if hasattr(outputs, "last_hidden_state") else outputs[0]
logits = self.rm_head(hidden_states.to(next(self.rm_head.parameters()).dtype))
batch_size = input_ids.shape[0] if input_ids is not None else inputs_embeds.shape[0]
pad_token_id = getattr(self.config, "pad_token_id", None)
if pad_token_id is None and hasattr(self.config, "text_config"):
pad_token_id = getattr(self.config.text_config, "pad_token_id", None)
if pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if pad_token_id is None:
sequence_lengths = -1
elif input_ids is not None:
sequence_lengths = torch.eq(input_ids, pad_token_id).int().argmax(-1) - 1
sequence_lengths = sequence_lengths % input_ids.shape[-1]
sequence_lengths = sequence_lengths.to(logits.device)
else:
sequence_lengths = -1
if self.reward_token == "last":
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
elif self.reward_token == "mean":
valid_lengths = torch.clamp(sequence_lengths, min=0, max=logits.size(1) - 1)
pooled_logits = torch.stack([logits[i, : valid_lengths[i]].mean(dim=0) for i in range(batch_size)])
elif self.reward_token == "special":
if self.special_token_ids is None:
raise ValueError("HPSv3 reward_token='special' requires special_token_ids.")
special_token_mask = torch.zeros_like(input_ids, dtype=torch.bool)
for special_token_id in self.special_token_ids:
special_token_mask = special_token_mask | (input_ids == special_token_id)
pooled_logits = logits[special_token_mask, ...].view(batch_size, -1)
else:
raise ValueError(f"Invalid HPSv3 reward token mode: {self.reward_token}")
return {"logits": pooled_logits}
class HPSv3Qwen2VLRewardModel(HPSv3RewardModelMixin, Qwen2VLForConditionalGeneration):
def __init__(
self,
config=None,
vocab_size=None,
output_dim=2,
reward_token="special",
special_token_ids=None,
rm_head_type="ranknet",
rm_head_kwargs=None,
):
if config is None:
config = self.default_config(vocab_size or 151658)
elif vocab_size is not None and hasattr(config, "text_config"):
config.text_config.vocab_size = vocab_size
super().__init__(config)
self.init_reward_head(
output_dim=output_dim,
reward_token=reward_token,
special_token_ids=special_token_ids,
rm_head_type=rm_head_type,
rm_head_kwargs=rm_head_kwargs,
)
@staticmethod
def default_config(vocab_size=151658):
from transformers import Qwen2VLConfig
return Qwen2VLConfig(
text_config={
"vocab_size": vocab_size,
"hidden_size": 3584,
"intermediate_size": 18944,
"num_hidden_layers": 28,
"num_attention_heads": 28,
"num_key_value_heads": 4,
"hidden_act": "silu",
"max_position_embeddings": 32768,
"initializer_range": 0.02,
"rms_norm_eps": 1e-6,
"use_cache": True,
"use_sliding_window": False,
"sliding_window": 32768,
"max_window_layers": 28,
"attention_dropout": 0.0,
"rope_parameters": {
"rope_type": "default",
"type": "mrope",
"mrope_section": [16, 24, 24],
"rope_theta": 1000000.0,
},
"bos_token_id": 151643,
"eos_token_id": 151645,
},
vision_config={
"depth": 32,
"embed_dim": 1280,
"hidden_size": 3584,
"mlp_ratio": 4,
"num_heads": 16,
"in_channels": 3,
"patch_size": 14,
"spatial_merge_size": 2,
"temporal_patch_size": 2,
},
image_token_id=151655,
video_token_id=151656,
vision_start_token_id=151652,
vision_end_token_id=151653,
tie_word_embeddings=False,
)
class HPSv3Model(torch.nn.Module):
def __init__(
self,
model,
processor,
use_special_tokens=True,
max_pixels=256 * 28 * 28,
min_pixels=256 * 28 * 28,
score_index=0,
):
super().__init__()
self.model = model
self.processor = processor
self.use_special_tokens = use_special_tokens
self.max_pixels = max_pixels
self.min_pixels = min_pixels
self.score_index = score_index
@property
def device(self):
return next(self.parameters(), torch.tensor([])).device
def _normalize_inputs(self, prompts, images):
images = _as_list(images)
prompts = _as_list(prompts)
if len(prompts) == 1 and len(images) > 1:
prompts = prompts * len(images)
if len(images) == 1 and len(prompts) > 1:
images = images * len(prompts)
if len(prompts) != len(images):
raise ValueError(f"Expected the same number of prompts and images, got {len(prompts)} and {len(images)}.")
return prompts, images
def _prepare_images(self, images):
prepared = []
for image in images:
image = image.convert("RGB")
height, width = image.height, image.width
resized_height, resized_width = _smart_resize(
height,
width,
min_pixels=self.min_pixels,
max_pixels=self.max_pixels,
)
prepared.append(image.resize((resized_width, resized_height), Image.BICUBIC))
return prepared
def _messages(self, prompts, images):
suffix = HPSV3_PROMPT_WITH_SPECIAL_TOKEN if self.use_special_tokens else HPSV3_PROMPT_WITHOUT_SPECIAL_TOKEN
messages = []
for prompt, image in zip(prompts, images):
messages.append(
[
{
"role": "user",
"content": [
{"type": "image", "image": image},
{"type": "text", "text": HPSV3_INSTRUCTION.format(text_prompt=prompt) + suffix},
],
}
]
)
return messages
def _prepare_batch(self, prompts, images):
images = self._prepare_images(images)
messages = self._messages(prompts, images)
text = self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
batch = self.processor(text=text, images=images, padding=True, return_tensors="pt")
return batch.to(self.device)
@torch.no_grad()
def forward(self, prompts: Union[str, list[str]], images):
prompts, images = self._normalize_inputs(prompts, images)
batch = self._prepare_batch(prompts, images)
rewards = self.model(return_dict=True, **batch)["logits"]
if rewards.ndim == 2:
return rewards[:, self.score_index]
return rewards |