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# Tencent HunyuanWorld-1.0 is licensed under TENCENT HUNYUANWORLD-1.0 COMMUNITY LICENSE AGREEMENT
# THIS LICENSE AGREEMENT DOES NOT APPLY IN THE EUROPEAN UNION, UNITED KINGDOM AND SOUTH KOREA AND
# IS EXPRESSLY LIMITED TO THE TERRITORY, AS DEFINED BELOW.
# By clicking to agree or by using, reproducing, modifying, distributing, performing or displaying
# any portion or element of the Tencent HunyuanWorld-1.0 Works, including via any Hosted Service,
# You will be deemed to have recognized and accepted the content of this Agreement,
# which is effective immediately.
# For avoidance of doubts, Tencent HunyuanWorld-1.0 means the 3D generation models
# and their software and algorithms, including trained model weights, parameters (including
# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
# fine-tuning enabling code and other elements of the foregoing made publicly available
# by Tencent at [https://github.com/Tencent-Hunyuan/HunyuanWorld-1.0].
import torch
from transformers import (
CLIPTextModel,
CLIPTokenizer,
T5EncoderModel,
T5TokenizerFast,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.models.autoencoders import AutoencoderKL
from diffusers.models.transformers import FluxTransformer2DModel
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
from diffusers.utils.torch_utils import randn_tensor
from .pipelines import FluxPipeline, FluxFillPipeline
class Text2PanoramaPipelines(FluxPipeline):
@torch.no_grad()
def __call__(self, prompt, **kwargs):
"""Main inpainting call."""
return self._call_shared(prompt=prompt, is_inpainting=False, early_steps=3, **kwargs)
class Image2PanoramaPipelines(FluxFillPipeline):
def __init__(
self,
scheduler: FlowMatchEulerDiscreteScheduler,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
text_encoder_2: T5EncoderModel,
tokenizer_2: T5TokenizerFast,
transformer: FluxTransformer2DModel,
):
# Initilization from FluxFillPipeline
super().__init__(
scheduler=scheduler,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
text_encoder_2=text_encoder_2,
tokenizer_2=tokenizer_2,
transformer=transformer,
)
# change some part of initilization
self.latent_channels = self.vae.config.latent_channels if getattr(
self, "vae", None) else 16
self.mask_processor = VaeImageProcessor(
vae_scale_factor=self.vae_scale_factor * 2,
vae_latent_channels=self.latent_channels,
do_normalize=False,
do_binarize=True,
do_convert_grayscale=True,
)
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(num_inference_steps *
strength, num_inference_steps)
t_start = int(max(num_inference_steps - init_timestep, 0))
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order:]
if hasattr(self.scheduler, "set_begin_index"):
self.scheduler.set_begin_index(t_start * self.scheduler.order)
return timesteps, num_inference_steps - t_start
def prepare_inpainting_latents(
self,
batch_size,
num_channels_latents,
height,
width,
dtype,
device,
generator,
latents=None,
image=None,
is_strength_max=True,
timestep=None,
):
r"""
Prepares the latents for the Image2PanoramaPipelines.
"""
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
# VAE applies 8x compression on images but we must also account for packing which requires
# latent height and width to be divisible by 2.
height = 2 * (int(height) // (self.vae_scale_factor * 2))
width = 2 * (int(width) // (self.vae_scale_factor * 2))
shape = (batch_size, num_channels_latents, height, width)
# Return the latents if they are already provided
if latents is not None:
return latents.to(device=device, dtype=dtype), latent_image_ids
# If no latents are provided, we need to encode the image
image = image.to(device=device, dtype=dtype)
if image.shape[1] != self.latent_channels:
image_latents = self._encode_vae_image(
image=image, generator=generator)
else:
image_latents = image
# Ensure image_latents has the correct shape
if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
# expand init_latents for batch_size
additional_image_per_prompt = batch_size // image_latents.shape[0]
image_latents = torch.cat(
[image_latents] * additional_image_per_prompt, dim=0)
elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:
raise ValueError(
f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
)
else:
image_latents = torch.cat([image_latents], dim=0)
# Add noise to the latents
noise = randn_tensor(shape, generator=generator,
device=device, dtype=dtype)
latents = self.scheduler.scale_noise(image_latents, timestep, noise)
# prepare blended latents
latents = torch.cat(
[latents, latents[:, :, :, :self.blend_extend]], dim=-1)
width_new_blended = latents.shape[-1]
latents = self._pack_latents(
latents, batch_size, num_channels_latents, height, width_new_blended)
# prepare latent image ids
latent_image_ids = self._prepare_latent_image_ids(
batch_size, height // 2, width_new_blended // 2, device, dtype)
return latents, latent_image_ids, width_new_blended
def prepare_blending_latent(
self, latents, height, width, batch_size, num_channels_latents, width_new_blended=None
):
return latents, width_new_blended
def _apply_blending(
self,
latents: torch.Tensor,
height: int,
width_new_blended: int,
num_channels_latents: int,
batch_size: int,
**karwgs,
) -> torch.Tensor:
r"""Apply horizontal blending to latents."""
# Unpack latents for processing
latents_unpack = self._unpack_latents(
latents, height, width_new_blended*self.vae_scale_factor, self.vae_scale_factor
)
# Apply blending
latents_unpack = self.blend_h(latents_unpack, latents_unpack, self.blend_extend)
latent_height = 2 * \
(int(height) // (self.vae_scale_factor * 2))
shifting_extend = karwgs.get("shifting_extend", None)
if shifting_extend is None:
shifting_extend = latents_unpack.size()[-1]//4
latents_unpack = torch.roll(
latents_unpack, shifting_extend, -1)
# Repack latents after blending
latents = self._pack_latents(
latents_unpack, batch_size, num_channels_latents, latent_height, width_new_blended)
return latents
def _apply_blending_mask(
self,
latents: torch.Tensor,
height: int,
width_new_blended: int,
num_channels_latents: int,
batch_size: int,
**kwargs
) -> torch.Tensor:
r"""Apply horizontal blending to mask latents."""
return self._apply_blending(
latents, height, width_new_blended, 80, batch_size, **kwargs
)
def _final_process_latents(
self,
latents: torch.Tensor,
height: int,
width_new_blended: int,
width: int
) -> torch.Tensor:
"""Final processing of latents before decoding."""
# Unpack and crop to target width
latents_unpack = self._unpack_latents(
latents, height, width_new_blended * self.vae_scale_factor, self.vae_scale_factor
)
latents_unpack = self.blend_h(
latents_unpack, latents_unpack, self.blend_extend
)
latents_unpack = latents_unpack[:, :, :, :width // self.vae_scale_factor]
# Repack for final output
return self._pack_latents(
latents_unpack,
latents.shape[0], # batch size
latents.shape[2] // 4, # num_channels_latents
height // self.vae_scale_factor,
width // self.vae_scale_factor
)
@torch.no_grad()
def __call__(self, **kwargs):
"""Main inpainting call."""
return self._call_shared(is_inpainting=True, early_steps=3, blend_extra_chanel=True, **kwargs)
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