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ERNIE-Image Text-to-Image Pipeline for DiffSynth-Studio.
Architecture: SharedAdaLN DiT + RoPE 3D + Joint Image-Text Attention.
"""
import torch
from typing import Union, Optional
from tqdm import tqdm
from transformers import AutoTokenizer
from ..core.device.npu_compatible_device import get_device_type
from ..diffusion import FlowMatchScheduler
from ..core import ModelConfig
from ..diffusion.base_pipeline import BasePipeline, PipelineUnit
from ..models.ernie_image_text_encoder import ErnieImageTextEncoder
from ..models.ernie_image_dit import ErnieImageDiT
from ..models.flux2_vae import Flux2VAE
class ErnieImagePipeline(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("ERNIE-Image")
self.text_encoder: ErnieImageTextEncoder = None
self.dit: ErnieImageDiT = None
self.vae: Flux2VAE = None
self.tokenizer: AutoTokenizer = None
self.in_iteration_models = ("dit",)
self.units = [
ErnieImageUnit_ShapeChecker(),
ErnieImageUnit_PromptEmbedder(),
ErnieImageUnit_NoiseInitializer(),
ErnieImageUnit_InputImageEmbedder(),
]
self.model_fn = model_fn_ernie_image
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="PaddlePaddle/ERNIE-Image", origin_file_pattern="tokenizer/"),
vram_limit: float = None,
):
pipe = ErnieImagePipeline(device=device, torch_dtype=torch_dtype)
model_pool = pipe.download_and_load_models(model_configs, vram_limit)
pipe.text_encoder = model_pool.fetch_model("ernie_image_text_encoder")
pipe.dit = model_pool.fetch_model("ernie_image_dit")
pipe.vae = model_pool.fetch_model("flux2_vae")
if tokenizer_config is not None:
tokenizer_config.download_if_necessary()
pipe.tokenizer = AutoTokenizer.from_pretrained(tokenizer_config.path)
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,
# Shape
height: int = 1024,
width: int = 1024,
# Randomness
seed: int = None,
rand_device: str = "cuda",
# Steps
num_inference_steps: int = 50,
sigma_shift: float = 3.0,
# Progress bar
progress_bar_cmd=tqdm,
):
# Scheduler
self.scheduler.set_timesteps(num_inference_steps=num_inference_steps, shift=sigma_shift)
# Parameters
inputs_posi = {"prompt": prompt}
inputs_nega = {"negative_prompt": negative_prompt}
inputs_shared = {
"height": height, "width": width, "seed": seed,
"cfg_scale": cfg_scale, "num_inference_steps": num_inference_steps,
"rand_device": rand_device,
}
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'])
latents = inputs_shared["latents"]
image = self.vae.decode(latents)
image = self.vae_output_to_image(image)
self.load_models_to_device([])
return image
class ErnieImageUnit_ShapeChecker(PipelineUnit):
def __init__(self):
super().__init__(
input_params=("height", "width"),
output_params=("height", "width"),
)
def process(self, pipe: ErnieImagePipeline, height, width):
height, width = pipe.check_resize_height_width(height, width)
return {"height": height, "width": width}
class ErnieImageUnit_PromptEmbedder(PipelineUnit):
def __init__(self):
super().__init__(
seperate_cfg=True,
input_params_posi={"prompt": "prompt"},
input_params_nega={"prompt": "negative_prompt"},
output_params=("prompt_embeds", "prompt_embeds_mask"),
onload_model_names=("text_encoder",)
)
def encode_prompt(self, pipe: ErnieImagePipeline, prompt):
if isinstance(prompt, str):
prompt = [prompt]
text_hiddens = []
text_lens_list = []
for p in prompt:
ids = pipe.tokenizer(
p,
add_special_tokens=True,
truncation=True,
padding=False,
)["input_ids"]
if len(ids) == 0:
if pipe.tokenizer.bos_token_id is not None:
ids = [pipe.tokenizer.bos_token_id]
else:
ids = [0]
input_ids = torch.tensor([ids], device=pipe.device)
outputs = pipe.text_encoder(
input_ids=input_ids,
)
# Text encoder returns tuple of (hidden_states_tuple,) where each layer's hidden state is included
all_hidden_states = outputs[0]
hidden = all_hidden_states[-2][0] # [T, H] - second to last layer
text_hiddens.append(hidden)
text_lens_list.append(hidden.shape[0])
# Pad to uniform length
if len(text_hiddens) == 0:
text_in_dim = pipe.text_encoder.config.hidden_size if hasattr(pipe.text_encoder, 'config') else 3072
return {
"prompt_embeds": torch.zeros((0, 0, text_in_dim), device=pipe.device, dtype=pipe.torch_dtype),
"prompt_embeds_mask": torch.zeros((0,), device=pipe.device, dtype=torch.long),
}
normalized = [th.to(pipe.device).to(pipe.torch_dtype) for th in text_hiddens]
text_lens = torch.tensor([t.shape[0] for t in normalized], device=pipe.device, dtype=torch.long)
Tmax = int(text_lens.max().item())
text_in_dim = normalized[0].shape[1]
text_bth = torch.zeros((len(normalized), Tmax, text_in_dim), device=pipe.device, dtype=pipe.torch_dtype)
for i, t in enumerate(normalized):
text_bth[i, :t.shape[0], :] = t
return {"prompt_embeds": text_bth, "prompt_embeds_mask": text_lens}
def process(self, pipe: ErnieImagePipeline, prompt):
pipe.load_models_to_device(self.onload_model_names)
if pipe.text_encoder is not None:
return self.encode_prompt(pipe, prompt)
return {}
class ErnieImageUnit_NoiseInitializer(PipelineUnit):
def __init__(self):
super().__init__(
input_params=("height", "width", "seed", "rand_device"),
output_params=("noise",),
)
def process(self, pipe: ErnieImagePipeline, height, width, seed, rand_device):
latent_h = height // pipe.height_division_factor
latent_w = width // pipe.width_division_factor
latent_channels = pipe.dit.in_channels
# Use pipeline device if rand_device is not specified
if rand_device is None:
rand_device = str(pipe.device)
noise = pipe.generate_noise(
(1, latent_channels, latent_h, latent_w),
seed=seed,
rand_device=rand_device,
rand_torch_dtype=pipe.torch_dtype,
)
return {"noise": noise}
class ErnieImageUnit_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: ErnieImagePipeline, input_image, noise):
if input_image is None:
# T2I path: use noise directly as initial latents
return {"latents": noise, "input_latents": None}
# I2I path: VAE encode input image
pipe.load_models_to_device(['vae'])
image = pipe.preprocess_image(input_image).to(device=pipe.device, dtype=pipe.torch_dtype)
input_latents = pipe.vae.encode(image)
if pipe.scheduler.training:
return {"latents": noise, "input_latents": input_latents}
else:
# In inference mode, add noise to encoded latents
latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0])
return {"latents": latents}
def model_fn_ernie_image(
dit: ErnieImageDiT,
latents=None,
timestep=None,
prompt_embeds=None,
prompt_embeds_mask=None,
use_gradient_checkpointing=False,
use_gradient_checkpointing_offload=False,
**kwargs,
):
output = dit(
hidden_states=latents,
timestep=timestep,
text_bth=prompt_embeds,
text_lens=prompt_embeds_mask,
use_gradient_checkpointing=use_gradient_checkpointing,
use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
)
return output
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