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vllm-omni/vllm_omni/diffusion/models/sd3/pipeline_sd3.py
T
Alex BrooksandGao Han ce8a7dfd2d [Bugfix] Fix Dtype Crashes in SD3 (#2526) 
Signed-off-by: Alex Brooks <albrooks@redhat.com>
Co-authored-by: Gao Han <hgaoaf@connect.ust.hk>
2026-05-08 16:05:38 +08:00

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import inspect
import json
import logging
import os
from collections.abc import Iterable
import torch
from diffusers.image_processor import VaeImageProcessor
from diffusers.schedulers.scheduling_flow_match_euler_discrete import (
FlowMatchEulerDiscreteScheduler,
)
from diffusers.utils.torch_utils import randn_tensor
from torch import nn
from transformers import CLIPTextModelWithProjection, CLIPTokenizer, T5EncoderModel, T5Tokenizer
from vllm.model_executor.models.utils import AutoWeightsLoader
from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
from vllm_omni.diffusion.distributed.autoencoders.autoencoder_kl import DistributedAutoencoderKL
from vllm_omni.diffusion.distributed.cfg_parallel import CFGParallelMixin
from vllm_omni.diffusion.distributed.utils import get_local_device
from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader
from vllm_omni.diffusion.model_loader.hub_prefetch import prefetch_subfolders
from vllm_omni.diffusion.models.sd3.sd3_transformer import (
SD3Transformer2DModel,
)
from vllm_omni.diffusion.profiler.diffusion_pipeline_profiler import DiffusionPipelineProfilerMixin
from vllm_omni.diffusion.request import OmniDiffusionRequest
from vllm_omni.model_executor.model_loader.weight_utils import (
download_weights_from_hf_specific,
)
logger = logging.getLogger(__name__)
def get_sd3_image_post_process_func(
od_config: OmniDiffusionConfig,
):
if od_config.output_type == "latent":
return lambda x: x
model_name = od_config.model
if os.path.exists(model_name):
model_path = model_name
else:
model_path = download_weights_from_hf_specific(model_name, None, ["*"])
vae_config_path = os.path.join(model_path, "vae/config.json")
with open(vae_config_path) as f:
vae_config = json.load(f)
vae_scale_factor = 2 ** (len(vae_config["block_out_channels"]) - 1) if "block_out_channels" in vae_config else 8
image_processor = VaeImageProcessor(vae_scale_factor=vae_scale_factor)
def post_process_func(
images: torch.Tensor,
):
return image_processor.postprocess(images)
return post_process_func
def calculate_shift(
image_seq_len,
base_seq_len: int = 256,
max_seq_len: int = 4096,
base_shift: float = 0.5,
max_shift: float = 1.15,
):
m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
b = base_shift - m * base_seq_len
mu = image_seq_len * m + b
return mu
def retrieve_timesteps(
scheduler,
num_inference_steps: int | None = None,
device: str | torch.device | None = None,
timesteps: list[int] | None = None,
sigmas: list[float] | None = None,
**kwargs,
) -> tuple[torch.Tensor, int]:
r"""
Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
Args:
scheduler (`SchedulerMixin`):
The scheduler to get timesteps from.
num_inference_steps (`int`):
The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
must be `None`.
device (`str` or `torch.device`, *optional*):
The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
timesteps (`List[int]`, *optional*):
Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
`num_inference_steps` and `sigmas` must be `None`.
sigmas (`List[float]`, *optional*):
Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
`num_inference_steps` and `timesteps` must be `None`.
Returns:
`Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
second element is the number of inference steps.
"""
if timesteps is not None and sigmas is not None:
raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
if timesteps is not None:
accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
if not accepts_timesteps:
raise ValueError(
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
f" timestep schedules. Please check whether you are using the correct scheduler."
)
scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
timesteps = scheduler.timesteps
num_inference_steps = len(timesteps)
elif sigmas is not None:
accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
if not accept_sigmas:
raise ValueError(
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
f" sigmas schedules. Please check whether you are using the correct scheduler."
)
scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
timesteps = scheduler.timesteps
num_inference_steps = len(timesteps)
else:
scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
timesteps = scheduler.timesteps
return timesteps, num_inference_steps
class StableDiffusion3Pipeline(nn.Module, CFGParallelMixin, DiffusionPipelineProfilerMixin):
def __init__(
self,
*,
od_config: OmniDiffusionConfig,
prefix: str = "",
):
super().__init__()
self.od_config = od_config
self.weights_sources = [
DiffusersPipelineLoader.ComponentSource(
model_or_path=od_config.model,
subfolder="transformer",
revision=None,
prefix="transformer.",
fall_back_to_pt=True,
)
]
self.device = get_local_device()
model = od_config.model
# Check if model is a local path
local_files_only = os.path.exists(model)
# See ``hub_prefetch.py`` for the transformers v5 subfolder race.
# SD3.5 loads six subfolders in a row, each with multi-shard
# safetensors - it is the worst-case fan-out for the race window.
prefetch_subfolders(
model,
[
"scheduler",
"tokenizer",
"tokenizer_2",
"tokenizer_3",
"text_encoder",
"text_encoder_2",
"text_encoder_3",
"vae",
],
local_files_only=local_files_only,
)
# ``transformers`` v5 changed ``from_pretrained``'s default
# ``torch_dtype`` from fp32 to whatever ``config.torch_dtype`` is
# baked into the checkpoint (fp16 for ``stabilityai/stable-
# diffusion-3.5-medium``). Without an explicit override the text
# encoders + VAE end up in fp16 while the rest of the pipeline
# (transformer, ``CombinedTimestepTextProjEmbeddings``,
# ``PatchEmbed``) is materialised in ``od_config.dtype`` (bf16
# by default). The first time latents flow through the
# transformer this surfaces as ``RuntimeError: Input type
# (c10::Half) and bias type (c10::BFloat16) should be the same``
# inside ``SD3Transformer2DModel.pos_embed`` (Buildkite #8418
# ``test_sd3_medium``). Pin every weight-loading
# ``from_pretrained`` to ``od_config.dtype`` so the whole
# pipeline is dtype-consistent. Tokenizers and the scheduler
# don't carry weights and therefore don't take ``torch_dtype``.
dtype = od_config.dtype
self.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
model, subfolder="scheduler", local_files_only=local_files_only
)
self.tokenizer = CLIPTokenizer.from_pretrained(model, subfolder="tokenizer", local_files_only=local_files_only)
self.tokenizer_2 = CLIPTokenizer.from_pretrained(
model, subfolder="tokenizer_2", local_files_only=local_files_only
)
self.tokenizer_3 = T5Tokenizer.from_pretrained(
model, subfolder="tokenizer_3", local_files_only=local_files_only
)
self.text_encoder = CLIPTextModelWithProjection.from_pretrained(
model,
subfolder="text_encoder",
torch_dtype=dtype,
local_files_only=local_files_only,
)
self.text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(
model,
subfolder="text_encoder_2",
torch_dtype=dtype,
local_files_only=local_files_only,
)
self.text_encoder_3 = T5EncoderModel.from_pretrained(
model,
subfolder="text_encoder_3",
torch_dtype=dtype,
local_files_only=local_files_only,
)
self.transformer = SD3Transformer2DModel(od_config=od_config)
self.vae = DistributedAutoencoderKL.from_pretrained(
model,
subfolder="vae",
torch_dtype=dtype,
local_files_only=local_files_only,
).to(self.device)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.tokenizer_max_length = (
self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
)
self.default_sample_size = 128
self.patch_size = 2
self.output_type = self.od_config.output_type
self.setup_diffusion_pipeline_profiler(
enable_diffusion_pipeline_profiler=self.od_config.enable_diffusion_pipeline_profiler
)
def check_inputs(
self,
prompt,
prompt_2,
prompt_3,
height,
width,
negative_prompt=None,
negative_prompt_2=None,
negative_prompt_3=None,
prompt_embeds=None,
negative_prompt_embeds=None,
max_sequence_length=None,
):
if (
height % (self.vae_scale_factor * self.patch_size) != 0
or width % (self.vae_scale_factor * self.patch_size) != 0
):
raise ValueError(
f"`height` and `width` have to be divisible by "
f"{self.vae_scale_factor * self.patch_size} but are "
f"{height} and {width}. You can use height "
f"{height - height % (self.vae_scale_factor * self.patch_size)} "
f"and width {width - width % (self.vae_scale_factor * self.patch_size)}."
)
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt_2 is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt_3 is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt_3`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
elif prompt_3 is not None and (not isinstance(prompt_3, str) and not isinstance(prompt_3, list)):
raise ValueError(f"`prompt_3` has to be of type `str` or `list` but is {type(prompt_3)}")
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
elif negative_prompt_3 is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt_3`: {negative_prompt_3} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
if max_sequence_length is not None and max_sequence_length > 512:
raise ValueError(f"`max_sequence_length` cannot be greater than 512 but is {max_sequence_length}")
def _get_clip_prompt_embeds(
self,
prompt: str | list[str] = "",
num_images_per_prompt: int = 1,
clip_model_index: int = 0,
):
clip_tokenizers = [self.tokenizer, self.tokenizer_2]
clip_text_encoders = [self.text_encoder, self.text_encoder_2]
tokenizer = clip_tokenizers[clip_model_index]
text_encoder = clip_text_encoders[clip_model_index]
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = tokenizer.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {self.tokenizer_max_length} tokens: {removed_text}"
)
prompt_embeds = text_encoder(text_input_ids.to(self.device), output_hidden_states=True)
pooled_prompt_embeds = prompt_embeds[0].to(dtype=self.od_config.dtype, device=self.device)
prompt_embeds = prompt_embeds.hidden_states[-2]
prompt_embeds = prompt_embeds.to(dtype=self.od_config.dtype, device=self.device)
_, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt)
pooled_prompt_embeds = pooled_prompt_embeds.view(batch_size * num_images_per_prompt, -1)
return prompt_embeds, pooled_prompt_embeds
def _get_t5_prompt_embeds(
self,
prompt: str | list[str] = "",
num_images_per_prompt: int = 1,
max_sequence_length: int = 256,
) -> torch.Tensor:
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
if self.text_encoder_3 is None:
return torch.zeros(
(
batch_size,
max_sequence_length,
self.transformer.joint_attention_dim,
),
device=self.device,
dtype=self.od_config.dtype,
)
text_inputs = self.tokenizer_3(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
).to(self.device)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer_3(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer_3.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because `max_sequence_length` is set to "
f" {max_sequence_length} tokens: {removed_text}"
)
prompt_embeds = self.text_encoder_3(text_input_ids.to(self.device))[0]
prompt_embeds = prompt_embeds.to(dtype=self.od_config.dtype, device=self.device)
_, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
return prompt_embeds
def encode_prompt(
self,
prompt: str | list[str],
prompt_2: str | list[str],
prompt_3: str | list[str],
prompt_embeds: torch.Tensor | None = None,
max_sequence_length: int = 256,
num_images_per_prompt: int = 1,
):
r"""
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
used in all text-encoders
prompt_3 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to the `tokenizer_3` and `text_encoder_3`. If not defined, `prompt` is
used in all text-encoders
num_images_per_prompt (`int`):
number of images that should be generated per prompt
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
"""
prompt = [prompt] if isinstance(prompt, str) else prompt
pooled_prompt_embeds = None
if prompt_embeds is None:
prompt_2 = prompt_2 or prompt
prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
prompt_3 = prompt_3 or prompt
prompt_3 = [prompt_3] if isinstance(prompt_3, str) else prompt_3
prompt_embed, pooled_prompt_embed = self._get_clip_prompt_embeds(
prompt=prompt,
num_images_per_prompt=num_images_per_prompt,
clip_model_index=0,
)
prompt_2_embed, pooled_prompt_2_embed = self._get_clip_prompt_embeds(
prompt=prompt_2,
num_images_per_prompt=num_images_per_prompt,
clip_model_index=1,
)
clip_prompt_embeds = torch.cat([prompt_embed, prompt_2_embed], dim=-1)
t5_prompt_embed = self._get_t5_prompt_embeds(
prompt=prompt_3,
num_images_per_prompt=num_images_per_prompt,
max_sequence_length=max_sequence_length,
)
clip_prompt_embeds = torch.nn.functional.pad(
clip_prompt_embeds, (0, t5_prompt_embed.shape[-1] - clip_prompt_embeds.shape[-1])
)
prompt_embeds = torch.cat([clip_prompt_embeds, t5_prompt_embed], dim=-2)
pooled_prompt_embeds = torch.cat([pooled_prompt_embed, pooled_prompt_2_embed], dim=-1)
return prompt_embeds, pooled_prompt_embeds
def prepare_latents(
self,
batch_size,
num_channels_latents,
height,
width,
generator,
latents=None,
) -> torch.Tensor:
if latents is not None:
return latents.to(device=self.device, dtype=self.od_config.dtype)
shape = (
batch_size,
num_channels_latents,
int(height) // self.vae_scale_factor,
int(width) // self.vae_scale_factor,
)
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."
)
latents = randn_tensor(shape, generator=generator, device=self.device, dtype=self.od_config.dtype)
return latents
def prepare_timesteps(self, num_inference_steps, sigmas, image_seq_len):
scheduler_kwargs = {}
if self.scheduler.config.get("use_dynamic_shifting", None):
mu = calculate_shift(
image_seq_len,
self.scheduler.config.get("base_image_seq_len", 256),
self.scheduler.config.get("max_image_seq_len", 4096),
self.scheduler.config.get("base_shift", 0.5),
self.scheduler.config.get("max_shift", 1.16),
)
scheduler_kwargs["mu"] = mu
timesteps, num_inference_steps = retrieve_timesteps(
self.scheduler,
num_inference_steps,
sigmas=sigmas,
**scheduler_kwargs,
)
return timesteps, num_inference_steps
@property
def guidance_scale(self):
return self._guidance_scale
@property
def num_timesteps(self):
return self._num_timesteps
@property
def current_timestep(self):
return self._current_timestep
@property
def interrupt(self):
return self._interrupt
def diffuse(
self,
latents: torch.Tensor,
timesteps: torch.Tensor,
prompt_embeds: torch.Tensor,
pooled_prompt_embeds: torch.Tensor | None,
negative_prompt_embeds: torch.Tensor | None,
negative_pooled_prompt_embeds: torch.Tensor | None,
do_true_cfg: bool,
guidance_scale: float,
cfg_normalize: bool = False,
) -> torch.Tensor:
"""
Diffusion loop with optional classifier-free guidance.
Args:
latents: Noise latents to denoise
timesteps: Diffusion timesteps
prompt_embeds: Positive prompt embeddings
pooled_prompt_embeds: Pooled positive prompt embeddings
negative_prompt_embeds: Negative prompt embeddings
negative_pooled_prompt_embeds: Pooled negative prompt embeddings
do_true_cfg: Whether to apply CFG
guidance_scale: CFG scale factor
cfg_normalize: Whether to normalize CFG output (default: False)
Returns:
Denoised latents
"""
self.scheduler.set_begin_index(0)
for _, t in enumerate(timesteps):
if self.interrupt:
continue
self._current_timestep = t
# Broadcast timestep to match batch size
timestep = t.expand(latents.shape[0]).to(device=self.device, dtype=self.od_config.dtype)
positive_kwargs = {
"hidden_states": latents,
"timestep": timestep,
"encoder_hidden_states": prompt_embeds,
"pooled_projections": pooled_prompt_embeds,
"return_dict": False,
}
if do_true_cfg:
negative_kwargs = {
"hidden_states": latents,
"timestep": timestep,
"encoder_hidden_states": negative_prompt_embeds,
"pooled_projections": negative_pooled_prompt_embeds,
"return_dict": False,
}
else:
negative_kwargs = None
# Predict noise with automatic CFG parallel handling
noise_pred = self.predict_noise_maybe_with_cfg(
do_true_cfg,
guidance_scale,
positive_kwargs,
negative_kwargs,
cfg_normalize,
)
# Compute the previous noisy sample x_t -> x_t-1 with automatic CFG sync
latents = self.scheduler_step_maybe_with_cfg(noise_pred, t, latents, do_true_cfg)
return latents
def forward(
self,
req: OmniDiffusionRequest,
prompt: str | list[str] = "",
prompt_2: str | list[str] = "",
prompt_3: str | list[str] = "",
negative_prompt: str | list[str] = "",
negative_prompt_2: str | list[str] = "",
negative_prompt_3: str | list[str] = "",
height: int | None = None,
width: int | None = None,
num_inference_steps: int = 28,
sigmas: list[float] | None = None,
num_images_per_prompt: int = 1,
generator: torch.Generator | list[torch.Generator] | None = None,
latents: torch.Tensor | None = None,
prompt_embeds: torch.Tensor | None = None,
negative_prompt_embeds: torch.Tensor | None = None,
pooled_prompt_embeds: torch.Tensor | None = None,
negative_pooled_prompt_embeds: torch.Tensor | None = None,
max_sequence_length: int = 256,
) -> DiffusionOutput:
# TODO: In online mode, sometimes it receives [{"negative_prompt": None}, {...}], so cannot use .get("...", "")
# TODO: May be some data formatting operations on the API side. Hack for now.
prompt = [p if isinstance(p, str) else (p.get("prompt") or "") for p in req.prompts] or prompt
negative_prompt = [
"" if isinstance(p, str) else (p.get("negative_prompt") or "") for p in req.prompts
] or negative_prompt
height = req.sampling_params.height or self.default_sample_size * self.vae_scale_factor
width = req.sampling_params.width or self.default_sample_size * self.vae_scale_factor
sigmas = req.sampling_params.sigmas or sigmas
max_sequence_length = req.sampling_params.max_sequence_length or max_sequence_length
num_inference_steps = req.sampling_params.num_inference_steps or num_inference_steps
generator = req.sampling_params.generator or generator
num_images_per_prompt = (
req.sampling_params.num_outputs_per_prompt
if req.sampling_params.num_outputs_per_prompt > 0
else num_images_per_prompt
)
# 1. check inputs
# 2. encode prompts
# 3. prepare latents and timesteps
# 4. diffusion process
# 5. decode latents
# 6. post-process outputs
self.check_inputs(
prompt,
prompt_2,
prompt_3,
height,
width,
negative_prompt=negative_prompt,
negative_prompt_2=negative_prompt_2,
negative_prompt_3=negative_prompt_3,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
max_sequence_length=max_sequence_length,
)
self._guidance_scale = req.sampling_params.guidance_scale
self._current_timestep = None
self._interrupt = False
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
prompt_embeds, pooled_prompt_embeds = self.encode_prompt(
prompt=prompt,
prompt_2=prompt_2,
prompt_3=prompt_3,
prompt_embeds=prompt_embeds,
max_sequence_length=max_sequence_length,
)
do_cfg = self.guidance_scale > 1
if do_cfg:
negative_prompt_embeds, negative_pooled_prompt_embeds = self.encode_prompt(
prompt=negative_prompt,
prompt_2=negative_prompt_2,
prompt_3=negative_prompt_3,
prompt_embeds=negative_prompt_embeds,
max_sequence_length=max_sequence_length,
)
num_channels_latents = self.transformer.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
generator,
latents,
)
timesteps, num_inference_steps = self.prepare_timesteps(num_inference_steps, sigmas, latents.shape[1])
self._num_timesteps = len(timesteps)
# Denoising loop using diffuse method
latents = self.diffuse(
latents=latents,
timesteps=timesteps,
prompt_embeds=prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds if do_cfg else None,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds if do_cfg else None,
do_true_cfg=do_cfg,
guidance_scale=self.guidance_scale,
cfg_normalize=False,
)
self._current_timestep = None
if self.output_type == "latent":
image = latents
else:
# Ensure the latents are the same dtype as the VAE for decode
latents = latents.to(self.vae.dtype)
latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
image = self.vae.decode(latents, return_dict=False)[0]
return DiffusionOutput(
output=image, stage_durations=self.stage_durations if hasattr(self, "stage_durations") else None
)
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
loader = AutoWeightsLoader(self)
return loader.load_weights(weights)
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