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from dataclasses import dataclass
from typing import Optional, Union
import torch
from PIL import Image
from tqdm import tqdm
import torchvision.transforms as T
from einops import rearrange
from .hunyuanimage_pipeline import HunyuanImagePipeline, HunyuanImagePipelineConfig
from hyimage.models.model_zoo import (
HUNYUANIMAGE_REFINER_DIT,
HUNYUANIMAGE_REFINER_VAE_16x,
HUNYUANIMAGE_REFINER_TEXT_ENCODER,
)
@dataclass
class HunYuanImageRefinerPipelineConfig(HunyuanImagePipelineConfig):
"""
Configuration class for HunyuanImage refiner pipeline.
Inherits from HunyuanImagePipelineConfig and overrides specific parameters
for the refiner functionality.
"""
default_sampling_steps: int = 4
shift: int = 1
version: str = "v1.0"
cfg_mode: str = ""
@classmethod
def create_default(
cls,
version: str = "v1.0",
use_distilled: bool = False,
**kwargs,
):
dit_config = HUNYUANIMAGE_REFINER_DIT()
vae_config = HUNYUANIMAGE_REFINER_VAE_16x()
text_encoder_config = HUNYUANIMAGE_REFINER_TEXT_ENCODER()
return cls(
dit_config=dit_config,
vae_config=vae_config,
text_encoder_config=text_encoder_config,
reprompt_config=None,
version=version,
**kwargs,
)
class HunYuanImageRefinerPipeline(HunyuanImagePipeline):
"""A refiner pipeline for HunyuanImage that inherits from the main pipeline.
This pipeline refines existing images using the same model architecture
but with different default parameters and an image input.
"""
def __init__(self, config: HunYuanImageRefinerPipelineConfig, **kwargs):
"""Initialize the refiner pipeline.
Args:
config: Refiner-specific configuration
**kwargs: Additional arguments passed to parent class
"""
assert isinstance(config, HunYuanImageRefinerPipelineConfig)
super().__init__(config, **kwargs)
assert self.cfg_distilled
def _condition_aug(self, latents, noise=None, strength=0.25):
"""Apply conditioning augmentation for refiner.
Args:
latents: Input latents tensor
noise: Optional noise tensor, if None will be generated
strength: Augmentation strength factor
Returns:
Augmented latents tensor
"""
if noise is None:
noise = torch.randn_like(latents)
return strength * noise + (1 - strength) * latents
@torch.no_grad()
def __call__(
self,
prompt: str,
negative_prompt: str = "",
width: int = 2048,
height: int = 2048,
use_reprompt: bool = False,
num_inference_steps: Optional[int] = None,
guidance_scale: Optional[float] = None,
shift: int = 4,
seed: Optional[int] = 42,
image: Optional[Image.Image] = None,
**kwargs,
) -> Image.Image:
"""Refine an existing image using text guidance.
Args:
prompt: Text prompt describing the desired refinement
negative_prompt: Negative prompt for guidance
width: Image width
height: Image height
use_reprompt: Whether to use reprompt (ignored for refiner)
num_inference_steps: Number of denoising steps (overrides config if provided)
guidance_scale: Strength of classifier-free guidance (overrides config if provided)
seed: Random seed for reproducibility
image: Image to be refined (required for refiner)
**kwargs: Additional arguments
Returns:
Refined PIL Image
"""
if image is None:
raise ValueError("Image parameter is required for refiner pipeline")
if seed is not None:
generator = torch.Generator(device='cpu').manual_seed(seed)
torch.manual_seed(seed)
else:
generator = None
sampling_steps = (
num_inference_steps
if num_inference_steps is not None
else self.default_sampling_steps
)
guidance_scale = (
guidance_scale if guidance_scale is not None else self.default_guidance_scale
)
shift = shift if shift is not None else self.shift
# Print log about current refinement task
print("=" * 60)
print("🔧 HunyuanImage Refinement Task")
print("-" * 60)
print(f"Prompt: {prompt}")
print(f"Guidance Scale: {guidance_scale}")
print(f"Shift: {self.shift}")
print(f"Seed: {seed}")
print(f"Image Size: {width} x {height}")
print(f"Sampling Steps: {sampling_steps}")
print("=" * 60)
# Encode prompts
pos_text_emb, pos_text_mask = self._encode_text(prompt)
latents = self._prepare_latents(width, height, generator=generator, vae_downsampling_factor=16)
_pil_to_tensor = T.Compose(
[
T.ToTensor(), # convert to tensor and normalize to [0, 1]
T.Normalize([0.5], [0.5]), # transform to [-1, 1]
]
)
image_tensor = (
_pil_to_tensor(image).unsqueeze(0).to("cuda", dtype=self.vae.dtype)
)
image_tensor = image_tensor.unsqueeze(2)
with torch.no_grad():
cond_latents = self.vae.encode(
image_tensor.to(self.device, dtype=self.vae.dtype)
).latent_dist.sample()
# reorg tokens
cond_latents = torch.cat((cond_latents[:, :, :1], cond_latents), dim=2)
cond_latents = rearrange(cond_latents, "b c f h w -> b f c h w")
cond_latents = rearrange(cond_latents, "b (f n) c h w -> b f (n c) h w", n=2)
cond_latents = rearrange(cond_latents, "b f c h w -> b c f h w").contiguous()
if (
hasattr(self.vae.config, "shift_factor")
and self.vae.config.shift_factor
):
cond_latents.sub_(self.vae.config.shift_factor).mul_(
self.vae.config.scaling_factor
)
else:
cond_latents.mul_(self.vae.config.scaling_factor)
# Apply conditioning augmentation
cond_latents = self._condition_aug(cond_latents)
timesteps, sigmas = self.get_timesteps_sigmas(sampling_steps, shift)
text_emb = pos_text_emb
text_mask = pos_text_mask
for i, t in enumerate(tqdm(timesteps, desc="Refining", total=len(timesteps))):
# Concatenate noise latents with condition latents for refiner input
latent_model_input = torch.cat([latents, cond_latents], dim=1)
t_expand = t.repeat(latent_model_input.shape[0])
# Predict noise with guidance
noise_pred = self._denoise_step(
latent_model_input,
t_expand,
text_emb,
text_mask,
None,
None,
guidance_scale,
timesteps_r=None,
)
latents = self.step(latents, noise_pred, sigmas, i)
refined_image = self._decode_latents(latents, reorg_tokens=True)
# Convert to PIL Image
refined_image = (refined_image.squeeze(0).permute(1, 2, 0) * 255).byte().numpy()
pil_image = Image.fromarray(refined_image)
return pil_image
@classmethod
def from_pretrained(
cls,
model_name: str = "hunyuanimage-refiner",
use_distilled: bool = False,
**kwargs,
):
"""Create refiner pipeline from pretrained model.
Args:
model_name: Model name, currently only supports "hunyuanimage-refiner"
use_distilled: Whether to use distilled model (unused for refiner)
**kwargs: Additional configuration options
"""
if model_name == "hunyuanimage-refiner":
version = "v1.0"
else:
raise ValueError(
f"Unsupported refiner model name: {model_name}. Supported names: 'hunyuanimage-refiner'"
)
config = HunYuanImageRefinerPipelineConfig.create_default(
version=version, **kwargs
)
return cls(config=config)
@classmethod
def from_config(cls, config: Union[HunYuanImageRefinerPipelineConfig, HunyuanImagePipelineConfig]):
"""Create refiner pipeline from configuration object.
Args:
config: Configuration object for the pipeline
Returns:
Initialized refiner pipeline instance
"""
return cls(config=config)
# Convenience function for easy access
def RefinerPipeline(
model_name: str = "hunyuanimage-refiner",
**kwargs,
):
"""Factory function to create HunYuanImageRefinerPipeline.
Args:
model_name: Model name, currently only supports "hunyuanimage-refiner"
**kwargs: Additional configuration options
Returns:
Initialized refiner pipeline instance
"""
return HunYuanImageRefinerPipeline.from_pretrained(
model_name, **kwargs
)
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