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from contextlib import nullcontext
from typing import List, Optional, Tuple, Union
import torch
from einops import rearrange
from PIL import Image
from tqdm.auto import tqdm
from diffusers import DiffusionPipeline
from diffusers.pipelines.pipeline_utils import ImagePipelineOutput
from .constants import SUPPORTED_IMAGE_SIZES
PromptType = Union[str, List[str]]
def _get_pkv_seq_len(past_key_values) -> int:
"""Get cached sequence length from past_key_values (supports tuple and DynamicCache)."""
if hasattr(past_key_values, "get_seq_length"):
return past_key_values.get_seq_length()
return past_key_values[0][0].shape[2]
class BitDanceDiffusionPipeline(DiffusionPipeline):
model_cpu_offload_seq = "text_encoder->projector->diffusion_head->autoencoder"
def __init__(
self,
tokenizer,
text_encoder,
autoencoder,
diffusion_head,
projector,
supported_image_sizes: Optional[List[List[int]]] = None,
dtype: Optional[torch.dtype] = None,
) -> None:
super().__init__()
self.register_modules(
tokenizer=tokenizer,
text_encoder=text_encoder,
autoencoder=autoencoder,
diffusion_head=diffusion_head,
projector=projector,
)
image_sizes = supported_image_sizes or SUPPORTED_IMAGE_SIZES
self.register_to_config(supported_image_sizes=[list(size) for size in image_sizes])
self.hidden_size = self.text_encoder.config.hidden_size
self.vae_patch_size = self.autoencoder.patch_size
self.parallel_num = int(self.diffusion_head.config.parallel_num)
self.ps = int(self.parallel_num**0.5)
if self.ps * self.ps != self.parallel_num:
raise ValueError(
f"parallel_num must be a perfect square (got {self.parallel_num})."
)
self._build_pos_embed()
@property
def supported_image_sizes(self) -> List[List[int]]:
return [list(size) for size in self.config.supported_image_sizes]
def _execution_device_fallback(self) -> torch.device:
if getattr(self, "_execution_device", None) is not None:
return self._execution_device
return next(self.text_encoder.parameters()).device
def _build_pos_embed(self) -> None:
max_resolution = max(max(size) for size in self.supported_image_sizes)
max_len = max_resolution // self.vae_patch_size
pos_embed_1d = self._get_1d_sincos_pos_embed(self.hidden_size // 2, max_len)
self.pos_embed_1d = pos_embed_1d
@staticmethod
def _get_1d_sincos_pos_embed(dim: int, max_len: int, pe_interpolation: float = 1.0) -> torch.Tensor:
if dim % 2 != 0:
raise ValueError(f"dim must be even, got {dim}")
omega = torch.arange(dim // 2, dtype=torch.float32)
omega /= dim / 2.0
omega = 1.0 / 10000**omega
pos = torch.arange(max_len, dtype=torch.float32) / pe_interpolation
out = torch.einsum("m,d->md", pos, omega)
emb_sin = torch.sin(out)
emb_cos = torch.cos(out)
return torch.cat([emb_sin, emb_cos], dim=1)
def _get_2d_embed(self, h: int, w: int, ps: int = 1) -> torch.Tensor:
emb_v = self.pos_embed_1d[:h]
emb_h = self.pos_embed_1d[:w]
grid_v = emb_v.view(h, 1, self.hidden_size // 2).repeat(1, w, 1)
grid_h = emb_h.view(1, w, self.hidden_size // 2).repeat(h, 1, 1)
pos_embed = torch.cat([grid_h, grid_v], dim=-1)
return rearrange(pos_embed, "(h p1) (w p2) c -> (h w p1 p2) c", p1=ps, p2=ps)
def _encode_prompt_to_embeds(
self,
prompt: str,
image_size: Tuple[int, int],
num_images_per_prompt: int,
guidance_scale: float,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], torch.Tensor]:
device = self._execution_device_fallback()
model = self.text_encoder.model
tokenizer = self.tokenizer
cond_prompt = f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
uncond_prompt = "<|im_start|>assistant\n"
cond_ids = torch.tensor(tokenizer.encode(cond_prompt), device=device, dtype=torch.long)
cond_emb = model.embed_tokens(cond_ids)
uncond_emb = None
if guidance_scale > 1.0:
uncond_ids = torch.tensor(tokenizer.encode(uncond_prompt), device=device, dtype=torch.long)
uncond_emb = model.embed_tokens(uncond_ids)
image_h, image_w = image_size
img_start_id = tokenizer.convert_tokens_to_ids("<|vision_start|>")
res_h_token_id = tokenizer.convert_tokens_to_ids(f"<|res_{image_h // self.vae_patch_size}|>")
res_w_token_id = tokenizer.convert_tokens_to_ids(f"<|res_{image_w // self.vae_patch_size}|>")
img_start_emb = model.embed_tokens(torch.tensor([img_start_id, res_h_token_id, res_w_token_id], device=device))
for i in range(1, self.parallel_num):
query_token_id = tokenizer.convert_tokens_to_ids(f"<|query_{i}|>")
query_token = torch.tensor([query_token_id], device=device, dtype=torch.long)
query_embed = model.embed_tokens(query_token)
img_start_emb = torch.cat([img_start_emb, query_embed], dim=0)
input_embeds_cond = torch.cat([cond_emb, img_start_emb], dim=0).unsqueeze(0).repeat(num_images_per_prompt, 1, 1)
input_embeds_uncond = None
if guidance_scale > 1.0 and uncond_emb is not None:
input_embeds_uncond = torch.cat([uncond_emb, img_start_emb], dim=0).unsqueeze(0).repeat(num_images_per_prompt, 1, 1)
return input_embeds_cond, input_embeds_uncond, img_start_emb
def _decode_tokens_to_image(self, image_latents: torch.Tensor, image_size: Tuple[int, int], ps: int = 1) -> torch.Tensor:
h, w = image_size
image_latents = rearrange(image_latents, "b (h w p1 p2) c -> b c (h p1) (w p2)", h=h // ps, w=w // ps, p1=ps, p2=ps)
return self.autoencoder.decode(image_latents)
@torch.no_grad()
def _generate_single_prompt(
self,
prompt: str,
height: int,
width: int,
num_inference_steps: int,
guidance_scale: float,
num_images_per_prompt: int,
generator: Optional[torch.Generator],
show_progress_bar: bool,
) -> torch.Tensor:
image_size = (height, width)
if list(image_size) not in self.supported_image_sizes:
raise ValueError(
f"image_size {list(image_size)} is not supported. "
f"Please choose from {self.supported_image_sizes}"
)
h, w = height // self.vae_patch_size, width // self.vae_patch_size
max_length = h * w
step_width = self.parallel_num
if max_length % step_width != 0:
raise ValueError(
f"max_length ({max_length}) must be divisible by parallel_num ({step_width})."
)
num_steps = max_length // step_width
device = self._execution_device_fallback()
model = self.text_encoder.model
dtype = next(self.text_encoder.parameters()).dtype
input_embeds_cond, input_embeds_uncond, _ = self._encode_prompt_to_embeds(
prompt=prompt,
image_size=image_size,
num_images_per_prompt=num_images_per_prompt,
guidance_scale=guidance_scale,
)
pos_embed_for_diff = self._get_2d_embed(h, w, ps=self.ps).unsqueeze(0).to(device=device, dtype=dtype)
autocast_ctx = (
torch.amp.autocast("cuda", enabled=True, dtype=torch.bfloat16)
if device.type == "cuda"
else nullcontext()
)
with autocast_ctx:
outputs_c = model(inputs_embeds=input_embeds_cond[:, :-step_width, :], use_cache=True)
pkv_c = outputs_c.past_key_values
bi_attn_mask = torch.ones(
(input_embeds_cond.shape[0], 1, step_width, step_width + _get_pkv_seq_len(pkv_c)),
dtype=torch.bool,
device=device,
)
outputs_c = model(
inputs_embeds=input_embeds_cond[:, -step_width:, :],
past_key_values=pkv_c,
use_cache=True,
attention_mask=bi_attn_mask,
)
pkv_c = outputs_c.past_key_values
hidden_c = outputs_c.last_hidden_state[:, -step_width:]
hidden_u = None
pkv_u = None
if guidance_scale > 1.0 and input_embeds_uncond is not None:
outputs_u = model(inputs_embeds=input_embeds_uncond[:, :-step_width, :], use_cache=True)
pkv_u = outputs_u.past_key_values
bi_attn_mask_u = torch.ones(
(input_embeds_uncond.shape[0], 1, step_width, step_width + _get_pkv_seq_len(pkv_u)),
dtype=torch.bool,
device=device,
)
outputs_u = model(
inputs_embeds=input_embeds_uncond[:, -step_width:, :],
past_key_values=pkv_u,
use_cache=True,
attention_mask=bi_attn_mask_u,
)
pkv_u = outputs_u.past_key_values
hidden_u = outputs_u.last_hidden_state[:, -step_width:]
out_tokens = []
step_iter = range(num_steps)
if show_progress_bar:
step_iter = tqdm(step_iter, total=num_steps, desc="Decoding steps")
for step in step_iter:
if guidance_scale > 1.0 and hidden_u is not None:
h_fused = torch.cat([hidden_c, hidden_u], dim=0)
else:
h_fused = hidden_c
pos_slice = pos_embed_for_diff[:, step * step_width : (step + 1) * step_width, :]
h_fused = h_fused + pos_slice
pred_latents = self.diffusion_head.sample(
h_fused,
num_sampling_steps=num_inference_steps,
cfg=guidance_scale,
generator=generator,
)
curr_tokens = torch.sign(pred_latents)
curr_embeds = self.projector(curr_tokens)
out_tokens.append(curr_tokens[:num_images_per_prompt])
model_input = curr_embeds + pos_slice
bi_attn_mask = torch.ones(
(model_input.shape[0], 1, model_input.shape[1], model_input.shape[1] + _get_pkv_seq_len(pkv_c)),
dtype=torch.bool,
device=device,
)
outputs_c = model(
inputs_embeds=model_input[:num_images_per_prompt],
past_key_values=pkv_c,
use_cache=True,
attention_mask=bi_attn_mask[:num_images_per_prompt],
)
pkv_c = outputs_c.past_key_values
hidden_c = outputs_c.last_hidden_state[:, -step_width:]
if guidance_scale > 1.0 and hidden_u is not None and pkv_u is not None:
bi_attn_mask_u = torch.ones(
(model_input.shape[0], 1, model_input.shape[1], model_input.shape[1] + _get_pkv_seq_len(pkv_u)),
dtype=torch.bool,
device=device,
)
outputs_u = model(
inputs_embeds=model_input[num_images_per_prompt:],
past_key_values=pkv_u,
use_cache=True,
attention_mask=bi_attn_mask_u[num_images_per_prompt:],
)
pkv_u = outputs_u.past_key_values
hidden_u = outputs_u.last_hidden_state[:, -step_width:]
full_output = torch.cat(out_tokens, dim=1)
return self._decode_tokens_to_image(full_output, image_size=(h, w), ps=self.ps)
@torch.no_grad()
def __call__(
self,
prompt: PromptType,
height: int = 1024,
width: int = 1024,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
num_images_per_prompt: int = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
output_type: str = "pil",
return_dict: bool = True,
show_progress_bar: bool = False,
) -> Union[ImagePipelineOutput, Tuple]:
prompts = [prompt] if isinstance(prompt, str) else list(prompt)
if len(prompts) == 0:
raise ValueError("prompt must be a non-empty string or list of strings.")
if isinstance(generator, list) and len(generator) != len(prompts):
raise ValueError("When passing a list of generators, its length must equal len(prompt).")
image_tensors = []
for i, prompt_text in enumerate(prompts):
prompt_generator = generator[i] if isinstance(generator, list) else generator
images = self._generate_single_prompt(
prompt=prompt_text,
height=height,
width=width,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
num_images_per_prompt=num_images_per_prompt,
generator=prompt_generator,
show_progress_bar=show_progress_bar,
)
image_tensors.append(images)
images_pt = torch.cat(image_tensors, dim=0)
images_pt_01 = torch.clamp((images_pt + 1.0) / 2.0, 0.0, 1.0)
if output_type == "pt":
output_images = images_pt_01
elif output_type == "np":
output_images = images_pt_01.permute(0, 2, 3, 1).float().cpu().numpy()
elif output_type == "pil":
images_uint8 = (
torch.clamp(127.5 * images_pt + 128.0, 0, 255)
.permute(0, 2, 3, 1)
.to("cpu", dtype=torch.uint8)
.numpy()
)
output_images = [Image.fromarray(image) for image in images_uint8]
else:
raise ValueError(f"Unsupported output_type={output_type}. Expected 'pil', 'np', or 'pt'.")
if not return_dict:
return (output_images,)
return ImagePipelineOutput(images=output_images)
@torch.no_grad()
def generate(
self,
prompt: str,
height: int = 1024,
width: int = 1024,
num_sampling_steps: int = 50,
guidance_scale: float = 7.5,
num_images: int = 1,
seed: Optional[int] = None,
) -> List[Image.Image]:
generator = None
if seed is not None:
device = self._execution_device_fallback()
generator_device = "cuda" if device.type == "cuda" else "cpu"
generator = torch.Generator(device=generator_device).manual_seed(seed)
output = self(
prompt=prompt,
height=height,
width=width,
num_inference_steps=num_sampling_steps,
guidance_scale=guidance_scale,
num_images_per_prompt=num_images,
generator=generator,
output_type="pil",
return_dict=True,
show_progress_bar=True,
)
return output.images
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