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from PIL import Image
from typing import Union
from tqdm import tqdm
from einops import rearrange
import numpy as np
from math import prod
from transformers import AutoTokenizer
from ..core.device.npu_compatible_device import get_device_type
from ..diffusion import FlowMatchScheduler
from ..core import ModelConfig, gradient_checkpoint_forward
from ..diffusion.base_pipeline import BasePipeline, PipelineUnit, ControlNetInput
from ..utils.lora.merge import merge_lora
from ..models.anima_dit import AnimaDiT
from ..models.z_image_text_encoder import ZImageTextEncoder
from ..models.wan_video_vae import WanVideoVAE
class AnimaImagePipeline(BasePipeline):
def __init__(self, device=get_device_type(), torch_dtype=torch.bfloat16):
super().__init__(
device=device, torch_dtype=torch_dtype,
height_division_factor=16, width_division_factor=16,
)
self.scheduler = FlowMatchScheduler("Z-Image")
self.text_encoder: ZImageTextEncoder = None
self.dit: AnimaDiT = None
self.vae: WanVideoVAE = None
self.tokenizer: AutoTokenizer = None
self.tokenizer_t5xxl: AutoTokenizer = None
self.in_iteration_models = ("dit",)
self.units = [
AnimaUnit_ShapeChecker(),
AnimaUnit_NoiseInitializer(),
AnimaUnit_InputImageEmbedder(),
AnimaUnit_PromptEmbedder(),
]
self.model_fn = model_fn_anima
self.compilable_models = ["dit"]
@staticmethod
def from_pretrained(
torch_dtype: torch.dtype = torch.bfloat16,
device: Union[str, torch.device] = get_device_type(),
model_configs: list[ModelConfig] = [],
tokenizer_config: ModelConfig = ModelConfig(model_id="Qwen/Qwen3-0.6B", origin_file_pattern="./"),
tokenizer_t5xxl_config: ModelConfig = ModelConfig(model_id="stabilityai/stable-diffusion-3.5-large", origin_file_pattern="tokenizer_3/"),
vram_limit: float = None,
):
# Initialize pipeline
pipe = AnimaImagePipeline(device=device, torch_dtype=torch_dtype)
model_pool = pipe.download_and_load_models(model_configs, vram_limit)
# Fetch models
pipe.text_encoder = model_pool.fetch_model("z_image_text_encoder")
pipe.dit = model_pool.fetch_model("anima_dit")
pipe.vae = model_pool.fetch_model("wan_video_vae")
if tokenizer_config is not None:
tokenizer_config.download_if_necessary()
pipe.tokenizer = AutoTokenizer.from_pretrained(tokenizer_config.path)
if tokenizer_t5xxl_config is not None:
tokenizer_t5xxl_config.download_if_necessary()
pipe.tokenizer_t5xxl = AutoTokenizer.from_pretrained(tokenizer_t5xxl_config.path)
# VRAM Management
pipe.vram_management_enabled = pipe.check_vram_management_state()
return pipe
@torch.no_grad()
def __call__(
self,
# Prompt
prompt: str,
negative_prompt: str = "",
cfg_scale: float = 4.0,
# Image
input_image: Image.Image = None,
denoising_strength: float = 1.0,
# Shape
height: int = 1024,
width: int = 1024,
# Randomness
seed: int = None,
rand_device: str = "cpu",
# Steps
num_inference_steps: int = 30,
sigma_shift: float = None,
# Progress bar
progress_bar_cmd = tqdm,
):
# Scheduler
self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift)
# Parameters
inputs_posi = {
"prompt": prompt,
}
inputs_nega = {
"negative_prompt": negative_prompt,
}
inputs_shared = {
"cfg_scale": cfg_scale,
"input_image": input_image, "denoising_strength": denoising_strength,
"height": height, "width": width,
"seed": seed, "rand_device": rand_device,
"num_inference_steps": num_inference_steps,
}
for unit in self.units:
inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
# Denoise
self.load_models_to_device(self.in_iteration_models)
models = {name: getattr(self, name) for name in self.in_iteration_models}
for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)
noise_pred = self.cfg_guided_model_fn(
self.model_fn, cfg_scale,
inputs_shared, inputs_posi, inputs_nega,
**models, timestep=timestep, progress_id=progress_id
)
inputs_shared["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs_shared)
# Decode
self.load_models_to_device(['vae'])
image = self.vae.decode(inputs_shared["latents"].unsqueeze(2), device=self.device).squeeze(2)
image = self.vae_output_to_image(image)
self.load_models_to_device([])
return image
class AnimaUnit_ShapeChecker(PipelineUnit):
def __init__(self):
super().__init__(
input_params=("height", "width"),
output_params=("height", "width"),
)
def process(self, pipe: AnimaImagePipeline, height, width):
height, width = pipe.check_resize_height_width(height, width)
return {"height": height, "width": width}
class AnimaUnit_NoiseInitializer(PipelineUnit):
def __init__(self):
super().__init__(
input_params=("height", "width", "seed", "rand_device"),
output_params=("noise",),
)
def process(self, pipe: AnimaImagePipeline, height, width, seed, rand_device):
noise = pipe.generate_noise((1, 16, height//8, width//8), seed=seed, rand_device=rand_device, rand_torch_dtype=pipe.torch_dtype)
return {"noise": noise}
class AnimaUnit_InputImageEmbedder(PipelineUnit):
def __init__(self):
super().__init__(
input_params=("input_image", "noise"),
output_params=("latents", "input_latents"),
onload_model_names=("vae",)
)
def process(self, pipe: AnimaImagePipeline, input_image, noise):
if input_image is None:
return {"latents": noise, "input_latents": None}
pipe.load_models_to_device(['vae'])
if isinstance(input_image, list):
input_latents = []
for image in input_image:
image = pipe.preprocess_image(image).to(device=pipe.device, dtype=pipe.torch_dtype)
input_latents.append(pipe.vae.encode(image))
input_latents = torch.concat(input_latents, dim=0)
else:
image = pipe.preprocess_image(input_image).to(device=pipe.device, dtype=pipe.torch_dtype)
input_latents = pipe.vae.encode(image.unsqueeze(2), device=pipe.device).squeeze(2)
if pipe.scheduler.training:
return {"latents": noise, "input_latents": input_latents}
else:
latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0])
return {"latents": latents, "input_latents": input_latents}
class AnimaUnit_PromptEmbedder(PipelineUnit):
def __init__(self):
super().__init__(
seperate_cfg=True,
input_params_posi={"prompt": "prompt"},
input_params_nega={"prompt": "negative_prompt"},
output_params=("prompt_emb",),
onload_model_names=("text_encoder",)
)
def encode_prompt(
self,
pipe: AnimaImagePipeline,
prompt,
device = None,
max_sequence_length: int = 512,
):
if isinstance(prompt, str):
prompt = [prompt]
text_inputs = pipe.tokenizer(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids.to(device)
prompt_masks = text_inputs.attention_mask.to(device).bool()
prompt_embeds = pipe.text_encoder(
input_ids=text_input_ids,
attention_mask=prompt_masks,
output_hidden_states=True,
).hidden_states[-1]
t5xxl_text_inputs = pipe.tokenizer_t5xxl(
prompt,
max_length=max_sequence_length,
truncation=True,
return_tensors="pt",
)
t5xxl_ids = t5xxl_text_inputs.input_ids.to(device)
return prompt_embeds.to(pipe.torch_dtype), t5xxl_ids
def process(self, pipe: AnimaImagePipeline, prompt):
pipe.load_models_to_device(self.onload_model_names)
prompt_embeds, t5xxl_ids = self.encode_prompt(pipe, prompt, pipe.device)
return {"prompt_emb": prompt_embeds, "t5xxl_ids": t5xxl_ids}
def model_fn_anima(
dit: AnimaDiT = None,
latents=None,
timestep=None,
prompt_emb=None,
t5xxl_ids=None,
use_gradient_checkpointing=False,
use_gradient_checkpointing_offload=False,
**kwargs
):
latents = latents.unsqueeze(2)
timestep = timestep / 1000
model_output = dit(
x=latents,
timesteps=timestep,
context=prompt_emb,
t5xxl_ids=t5xxl_ids,
use_gradient_checkpointing=use_gradient_checkpointing,
use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
)
model_output = model_output.squeeze(2)
return model_output
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