tzhouam
617c6ca55f
- Introduced `from_pretrained_with_prefetch` to handle racy cache scenarios by re-prefetching and retrying on failures. - Updated various model pipelines to utilize the new prefetching mechanism, ensuring robust loading of model components. - Prefetch logic added to multiple models, including HunyuanVideo, LongCatImage, and QwenImage, to mitigate issues with incomplete cache states.
765 lines
32 KiB
Python
765 lines
32 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright 2025 Alibaba Ovis-Image Team and The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import inspect
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import json
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import os
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from collections.abc import Callable, Iterable
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from typing import Any
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import numpy as np
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import torch
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from diffusers.image_processor import VaeImageProcessor
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from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
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from diffusers.schedulers.scheduling_flow_match_euler_discrete import (
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FlowMatchEulerDiscreteScheduler,
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)
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from diffusers.utils.torch_utils import randn_tensor
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from torch import nn
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from transformers import Qwen2TokenizerFast, Qwen3Model
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from vllm.logger import init_logger
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from vllm.model_executor.models.utils import AutoWeightsLoader
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from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
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from vllm_omni.diffusion.distributed.cfg_parallel import CFGParallelMixin
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from vllm_omni.diffusion.distributed.utils import get_local_device
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from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader
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from vllm_omni.diffusion.model_loader.hub_prefetch import from_pretrained_with_prefetch, prefetch_subfolders
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from vllm_omni.diffusion.models.ovis_image.ovis_image_transformer import OvisImageTransformer2DModel
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from vllm_omni.diffusion.profiler.diffusion_pipeline_profiler import DiffusionPipelineProfilerMixin
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from vllm_omni.diffusion.request import OmniDiffusionRequest
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from vllm_omni.model_executor.model_loader.weight_utils import download_weights_from_hf_specific
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logger = init_logger(__name__)
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def get_ovis_image_post_process_func(
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od_config: OmniDiffusionConfig,
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):
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model_name = od_config.model
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if os.path.exists(model_name):
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model_path = model_name
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else:
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model_path = download_weights_from_hf_specific(model_name, None, ["*"])
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vae_config_path = os.path.join(model_path, "vae/config.json")
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with open(vae_config_path) as f:
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vae_config = json.load(f)
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vae_scale_factor = 2 ** (len(vae_config["block_out_channels"]) - 1) if "block_out_channels" in vae_config else 8
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image_processor = VaeImageProcessor(vae_scale_factor=vae_scale_factor * 2)
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def post_process_func(images: torch.Tensor):
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return image_processor.postprocess(images)
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return post_process_func
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def calculate_shift(
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image_seq_len,
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base_seq_len: int = 256,
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max_seq_len: int = 4096,
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base_shift: float = 0.5,
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max_shift: float = 1.15,
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):
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m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
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b = base_shift - m * base_seq_len
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mu = image_seq_len * m + b
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return mu
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def retrieve_timesteps(
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scheduler,
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num_inference_steps: int | None = None,
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device: str | torch.device | None = None,
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timesteps: list[int] | None = None,
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sigmas: list[float] | None = None,
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**kwargs,
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) -> tuple[torch.Tensor, int]:
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r"""
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Calls the scheduler's `set_timesteps` method and retrieves timetemps
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from the scheduler after the call. Handles custom timeteps. Any kwargs will be supplied to `scheduler.set_timeteps`.
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Args:
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scheduler (`SchedulerMixin`):
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The scheduler to get timesteps from.
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num_inference_steps (`int`, *optional*):
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The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
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must be `None`.
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device (`str` or `torch.device`, *optional*):
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The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
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timesteps (`list[int]`, *optional*):
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Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
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`num_inference_steps` and `sigmas` must be `None`.
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sigmas (`list[float]`, *optional*):
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Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
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`num_inference_steps` and `timesteps` must be `None`.
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Returns:
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`Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
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second element is the number of inference steps.
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"""
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if timesteps is not None and sigmas is not None:
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raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
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if timesteps is not None:
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accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
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if not accepts_timesteps:
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raise ValueError(
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f"the current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
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f" timestep schedules. Please check whether you are using the correct scheduler."
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)
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scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
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timesteps = scheduler.timesteps
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num_inference_steps = len(timesteps)
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elif sigmas is not None:
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accepts_timesteps = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
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if not accepts_timesteps:
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raise ValueError(
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f"the current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
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f" sigma schedules. Please check whether you are using the correct scheduler."
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)
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scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
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timesteps = scheduler.timesteps
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num_inference_steps = len(timesteps)
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else:
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scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
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timesteps = scheduler.timesteps
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return timesteps, num_inference_steps
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class OvisImagePipeline(nn.Module, CFGParallelMixin, DiffusionPipelineProfilerMixin):
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def __init__(
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self,
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*,
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od_config: OmniDiffusionConfig,
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prefix: str = "",
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):
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super().__init__()
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self.od_config = od_config
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self.weights_sources = [
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DiffusersPipelineLoader.ComponentSource(
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model_or_path=od_config.model,
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subfolder="transformer",
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revision=None,
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prefix="transformer.",
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fall_back_to_pt=True,
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)
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]
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self._execution_device = get_local_device()
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model = od_config.model
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local_files_only = os.path.exists(model)
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# See ``hub_prefetch.py`` for the transformers v5 multi-worker subfolder
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# race; prefetch the whole component set before any from_pretrained.
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ovis_subfolders = ["scheduler", "text_encoder", "vae", "tokenizer"]
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prefetch_subfolders(model, ovis_subfolders, local_files_only=local_files_only)
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self.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
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model, subfolder="scheduler", local_files_only=local_files_only
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)
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self.text_encoder = from_pretrained_with_prefetch(
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Qwen3Model.from_pretrained,
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model,
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subfolder="text_encoder",
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prefetch_list=ovis_subfolders,
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local_files_only=local_files_only,
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torch_dtype=od_config.dtype,
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)
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self.vae = from_pretrained_with_prefetch(
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AutoencoderKL.from_pretrained,
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model,
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subfolder="vae",
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prefetch_list=ovis_subfolders,
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local_files_only=local_files_only,
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).to(self._execution_device)
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self.tokenizer = Qwen2TokenizerFast.from_pretrained(
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model, subfolder="tokenizer", local_files_only=local_files_only
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)
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self.transformer = OvisImageTransformer2DModel(od_config=od_config)
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self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
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self.tokenizer_max_length = 1024
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self.system_prompt = """Describe the image by detailing the color, quantity, text, shape, size, texture, spatial
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relationships of the objects and background: """
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self.user_prompt_begin_id = 28
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self.tokenizer_max_length = 256 + self.user_prompt_begin_id
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self.default_sample_size = 128
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self.setup_diffusion_pipeline_profiler(
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enable_diffusion_pipeline_profiler=self.od_config.enable_diffusion_pipeline_profiler
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)
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def _get_messages(
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self,
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prompt: str | list[str] = None,
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):
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prompt = [prompt] if isinstance(prompt, str) else prompt
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messages = []
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for each_prompt in prompt:
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message = [
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{
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"role": "user",
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"content": self.system_prompt + each_prompt,
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}
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]
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message = self.tokenizer.apply_chat_template(
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message,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=False,
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)
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messages.append(message)
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return messages
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def _get_ovis_prompt_embeds(
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self,
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prompt: str | list[str] = None,
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num_images_per_prompt: int = 1,
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device: torch.device | None = None,
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dtype: torch.dtype | None = None,
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):
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device = device or self._execution_device
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dtype = dtype or self.text_encoder.dtype
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messages = self._get_messages(prompt)
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batch_size = len(messages)
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tokens = self.tokenizer(
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messages,
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padding="max_length",
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truncation=True,
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max_length=self.tokenizer_max_length,
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return_tensors="pt",
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add_special_tokens=False,
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)
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input_ids = tokens.input_ids.to(device=device)
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attention_mask = tokens.attention_mask.to(device=device)
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outputs = self.text_encoder(
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input_ids=input_ids,
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attention_mask=attention_mask,
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)
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prompt_embeds = outputs.last_hidden_state
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prompt_embeds = prompt_embeds * attention_mask[..., None]
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prompt_embeds = prompt_embeds[:, self.user_prompt_begin_id :, :]
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_, seq_len, _ = prompt_embeds.shape
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prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
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prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
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return prompt_embeds
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def encode_prompt(
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self,
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prompt: str | list[str],
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device: torch.device | None = None,
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num_images_per_prompt: int = 1,
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prompt_embeds: torch.FloatTensor | None = None,
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):
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r"""
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Args:
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prompt (`str` or `list[str]`, *optional*):
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prompt to be encoded
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device: (`torch.device`, *optional*):
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torch.device
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num_images_per_prompt: (`int`):
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number of images that should be generated per prompt
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prompt_embeds: (`torch.FloatTensor`, *optional*):
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Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
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If not provided, text embeddings will be generated from `prompt` input argument.
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"""
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device = device or self._execution_device
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if prompt_embeds is None:
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prompt_embeds = self._get_ovis_prompt_embeds(
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prompt=prompt,
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device=device,
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num_images_per_prompt=num_images_per_prompt,
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)
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dtype = self.text_encoder.dtype if self.text_encoder is not None else self.transformer.dtype
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text_ids = torch.zeros(prompt_embeds.shape[1], 3)
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text_ids[..., 1] = text_ids[..., 1] + torch.arange(prompt_embeds.shape[1])[None, :]
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text_ids[..., 2] = text_ids[..., 2] + torch.arange(prompt_embeds.shape[1])[None, :]
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text_ids = text_ids.to(device=device, dtype=dtype)
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return prompt_embeds, text_ids
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def check_inputs(
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self,
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prompt,
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height,
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width,
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negative_prompt=None,
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prompt_embeds=None,
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negative_prompt_embeds=None,
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callback_on_step_end_tensor_inputs=None,
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max_sequence_length=None,
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):
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if height % (self.vae_scale_factor * 2) != 0 or width % (self.vae_scale_factor * 2) != 0:
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logger.warning(
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f"""`height` and `width` have to be divisible by {self.vae_scale_factor * 2} but are
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{height} and {width}. Dimension will be resized accordingly"""
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)
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# if callback_on_step_end_tensor_inputs is not None and not all(
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# k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
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# ):
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# raise ValueError(
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# f"""`callback_on_step_end_tensor_inputs` has to contain the following keys:
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# {self._callback_tensor_inputs.keys()}"""
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# )
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if prompt is not None and prompt_embeds is not None:
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raise ValueError(
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f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
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" only forward one of the two."
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)
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elif prompt is None and prompt_embeds is None:
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raise ValueError(
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"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
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)
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elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
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raise ValueError(f"`prompt` has to be of type `str` or `list[str]` but is {type(prompt)}")
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if negative_prompt is not None and negative_prompt_embeds is not None:
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raise ValueError(
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f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`: "
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f"{negative_prompt_embeds}. Please make sure to only forward one of the two."
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)
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if max_sequence_length is not None and max_sequence_length > 256:
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raise ValueError(f"`max_sequence_length` has to be less than or equal to 256 but is {max_sequence_length}")
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@staticmethod
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def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
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latent_image_ids = torch.zeros(height, width, 3)
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latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
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latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width)[None, :]
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latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
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latent_image_ids = latent_image_ids.reshape(
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latent_image_id_height * latent_image_id_width, latent_image_id_channels
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)
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return latent_image_ids.to(device=device, dtype=dtype)
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@staticmethod
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def _pack_latents(latents, batch_size, num_channel_latents, height, width):
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latents = latents.view(batch_size, num_channel_latents, height // 2, 2, width // 2, 2)
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latents = latents.permute(0, 2, 4, 1, 3, 5)
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latents = latents.reshape(batch_size, (height // 2) * (width // 2), num_channel_latents * 4)
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return latents
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@staticmethod
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def _unpack_latents(latents, height, width, vae_scale_factor):
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batch_size, num_patches, channels = latents.shape
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# VAE applies 8x compression on images but we must also account for packing which requires
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# latent height and width to be divisible by 2
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height = int(2 * (int(height) // (vae_scale_factor * 2)))
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width = int(2 * (int(width) // (vae_scale_factor * 2)))
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latents = latents.view(batch_size, height // 2, width // 2, channels // 4, 2, 2)
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latents = latents.permute(0, 3, 1, 4, 2, 5)
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latents = latents.reshape(batch_size, channels // (2 * 2), height, width)
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return latents
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def prepare_latents(
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self,
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batch_size,
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num_channel_latents,
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height,
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width,
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dtype,
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device,
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generator,
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latents=None,
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):
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# VAE applies 8x compression on images but we must also account for packing which requires
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# latent height and width to be divisible by 2.
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height = int(2 * (int(height) // (self.vae_scale_factor * 2)))
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width = int(2 * (int(width) // (self.vae_scale_factor * 2)))
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shape = (batch_size, num_channel_latents, height, width)
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if latents is not None:
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latent_image_ids = self._prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype)
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return latents.to(device=device, dtype=dtype), latent_image_ids
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if isinstance(generator, list) and len(generator) != batch_size:
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raise ValueError(
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f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
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f" size of {batch_size}. Make sure the batch size matches the length of the generators."
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)
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latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
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latents = self._pack_latents(latents, batch_size, num_channel_latents, height, width)
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latent_image_ids = self._prepare_latent_image_ids(batch_size, height // 2, width // 2, device, dtype)
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return latents, latent_image_ids
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def prepare_timesteps(self, num_inference_steps, sigmas, image_seq_len):
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sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
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if hasattr(self.scheduler.config, "use_flow_sigmas") and self.scheduler.config.use_flow_sigmas:
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sigmas = None
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mu = calculate_shift(
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image_seq_len,
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self.scheduler.config.get("base_image_seq_len", 256),
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self.scheduler.config.get("max_image_seq_len", 4096),
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self.scheduler.config.get("base_shift", 0.5),
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self.scheduler.config.get("max_shift", 1.15),
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)
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timesteps, num_inference_steps = retrieve_timesteps(
|
|
self.scheduler,
|
|
num_inference_steps,
|
|
self._execution_device,
|
|
sigmas=sigmas,
|
|
mu=mu,
|
|
)
|
|
return timesteps, num_inference_steps
|
|
|
|
def diffuse(
|
|
self,
|
|
latents: torch.Tensor,
|
|
timesteps: torch.Tensor,
|
|
prompt_embeds: torch.Tensor,
|
|
negative_prompt_embeds: torch.Tensor,
|
|
text_ids: torch.Tensor,
|
|
negative_text_ids: torch.Tensor,
|
|
latent_image_ids: torch.Tensor,
|
|
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
|
|
negative_prompt_embeds: Negative prompt embeddings
|
|
text_ids: Position IDs for positive text
|
|
negative_text_ids: Position IDs for negative text
|
|
latent_image_ids: Position IDs for image latents
|
|
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 i, t in enumerate(timesteps):
|
|
if self.interrupt:
|
|
break
|
|
|
|
self._current_timestep = t
|
|
timestep = t.expand(latents.shape[0]).to(latents.dtype)
|
|
|
|
positive_kwargs = {
|
|
"hidden_states": latents,
|
|
"timestep": timestep / 1000,
|
|
"encoder_hidden_states": prompt_embeds,
|
|
"txt_ids": text_ids,
|
|
"img_ids": latent_image_ids,
|
|
"return_dict": False,
|
|
}
|
|
if do_true_cfg:
|
|
negative_kwargs = {
|
|
"hidden_states": latents,
|
|
"timestep": timestep / 1000,
|
|
"encoder_hidden_states": negative_prompt_embeds,
|
|
"txt_ids": negative_text_ids,
|
|
"img_ids": latent_image_ids,
|
|
"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
|
|
|
|
@property
|
|
def guidance_scale(self):
|
|
return self._guidance_scale
|
|
|
|
@property
|
|
def joint_attention_kwargs(self):
|
|
return self._joint_attention_kwargs
|
|
|
|
@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 forward(
|
|
self,
|
|
req: OmniDiffusionRequest,
|
|
prompt: str | list[str] | None = None,
|
|
negative_prompt: str | list[str] | None = None,
|
|
guidance_scale: float = 5.0,
|
|
height: int | None = None,
|
|
width: int | None = None,
|
|
num_inference_steps: int = 50,
|
|
sigmas: list[float] | None = None,
|
|
num_images_per_prompt: int | None = 1,
|
|
generator: torch.Generator | list[torch.Generator] | None = None,
|
|
latents: torch.FloatTensor | None = None,
|
|
prompt_embeds: torch.FloatTensor | None = None,
|
|
negative_prompt_embeds: torch.FloatTensor | None = None,
|
|
output_type: str | None = "pil",
|
|
return_dict: bool = True,
|
|
joint_attention_kwargs: dict[str, Any] | None = None,
|
|
callback_on_step_end: Callable[[int, int, dict], None] | None = None,
|
|
callback_on_step_end_tensor_inputs: list[str] = ["latents"],
|
|
max_sequence_length: int = 256,
|
|
) -> DiffusionOutput:
|
|
r"""
|
|
Function invoked when calling the pipeline for generation.
|
|
|
|
Args:
|
|
prompt (`str` or `list[str]`, *optional*):
|
|
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
|
|
instead.
|
|
negative_prompt (`str` or `list[str]`, *optional*):
|
|
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
|
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
|
|
not greater than `1`).
|
|
guidance_scale (`float`, *optional*, defaults to 1.0):
|
|
True classifier-free guidance (guidance scale) is enabled when `guidance_scale` > 1 and
|
|
`negative_prompt` is provided.
|
|
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
|
The height in pixels of the generated image. This is set to 1024 by default for the best results.
|
|
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
|
The width in pixels of the generated image. This is set to 1024 by default for the best results.
|
|
num_inference_steps (`int`, *optional*, defaults to 50):
|
|
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
|
expense of slower inference.
|
|
sigmas (`list[float]`, *optional*):
|
|
Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
|
|
their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
|
|
will be used.
|
|
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of images to generate per prompt.
|
|
generator (`torch.Generator` or `list[torch.Generator]`, *optional*):
|
|
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
|
|
to make generation deterministic.
|
|
latents (`torch.FloatTensor`, *optional*):
|
|
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
|
|
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
|
tensor will be generated by sampling using the supplied random `generator`.
|
|
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.
|
|
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
|
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
|
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
|
argument.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generate image. Choose between
|
|
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~pipelines.flux.FluxPipelineOutput`] instead of a plain tuple.
|
|
joint_attention_kwargs (`dict`, *optional*):
|
|
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
|
`self.processor` in
|
|
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
|
callback_on_step_end (`Callable`, *optional*):
|
|
A function that calls at the end of each denoising steps during the inference. The function is called
|
|
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
|
|
callback_kwargs: dict)`. `callback_kwargs` will include a list of all tensors as specified by
|
|
`callback_on_step_end_tensor_inputs`.
|
|
callback_on_step_end_tensor_inputs (`list`, *optional*):
|
|
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
|
|
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
|
|
`._callback_tensor_inputs` attribute of your pipeline class.
|
|
max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.
|
|
|
|
Examples:
|
|
|
|
Returns:
|
|
[`~pipelines.ovis_image.OvisImagePipelineOutput`] or `tuple`:
|
|
[`~pipelines.ovis_image.OvisImagePipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When
|
|
returning a tuple, the first element is a list with the generated images.
|
|
"""
|
|
# 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
|
|
if all(isinstance(p, str) or p.get("negative_prompt") is None for p in req.prompts):
|
|
negative_prompt = None
|
|
elif req.prompts:
|
|
negative_prompt = ["" if isinstance(p, str) else (p.get("negative_prompt") or "") for p in req.prompts]
|
|
|
|
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
|
|
num_inference_steps = req.sampling_params.num_inference_steps or num_inference_steps
|
|
sigmas = req.sampling_params.sigmas or sigmas
|
|
guidance_scale = (
|
|
req.sampling_params.guidance_scale if req.sampling_params.guidance_scale is not None else guidance_scale
|
|
)
|
|
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
|
|
)
|
|
|
|
# Steps:
|
|
# 1. Check Inputs
|
|
# 2. encode prompts
|
|
# 4. Prepare latents
|
|
# 5. Prepare timesteps
|
|
# 6. diffusion latents
|
|
# 7. decode latents
|
|
# 8. post process outputs
|
|
|
|
self.check_inputs(
|
|
prompt,
|
|
height,
|
|
width,
|
|
negative_prompt=negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
|
|
max_sequence_length=max_sequence_length,
|
|
)
|
|
|
|
device = self._execution_device
|
|
device = self._execution_device
|
|
self._guidance_scale = guidance_scale
|
|
self._joint_attention_kwargs = joint_attention_kwargs
|
|
self._current_timestep = None
|
|
self._interrupt = False
|
|
|
|
# 2. Define call parameters
|
|
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]
|
|
|
|
do_classifier_free_guidance = guidance_scale > 1.0
|
|
prompt_embeds, text_ids = self.encode_prompt(
|
|
prompt=prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
device=device,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
)
|
|
|
|
negative_text_ids = None
|
|
if do_classifier_free_guidance:
|
|
negative_prompt = negative_prompt if negative_prompt is not None else ""
|
|
negative_prompt_embeds, negative_text_ids = self.encode_prompt(
|
|
prompt=negative_prompt,
|
|
prompt_embeds=negative_prompt_embeds,
|
|
device=device,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
)
|
|
|
|
# 4. Prepare latent variables
|
|
num_channel_latents = self.transformer.in_channels // 4
|
|
latents, latent_image_ids = self.prepare_latents(
|
|
batch_size=batch_size * num_images_per_prompt,
|
|
num_channel_latents=num_channel_latents,
|
|
height=height,
|
|
width=width,
|
|
dtype=prompt_embeds.dtype,
|
|
device=device,
|
|
generator=generator,
|
|
latents=latents,
|
|
)
|
|
|
|
# 5. Prepare timesteps
|
|
|
|
image_seq_len = latents.shape[1]
|
|
timesteps, num_inference_steps = self.prepare_timesteps(num_inference_steps, sigmas, image_seq_len)
|
|
|
|
# num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
|
|
self._num_timesteps = len(timesteps)
|
|
|
|
if self.joint_attention_kwargs is None:
|
|
self._joint_attention_kwargs = {}
|
|
|
|
# 6. Denoising loop using diffuse method
|
|
latents = self.diffuse(
|
|
latents=latents,
|
|
timesteps=timesteps,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds if do_classifier_free_guidance else None,
|
|
text_ids=text_ids,
|
|
negative_text_ids=negative_text_ids if do_classifier_free_guidance else None,
|
|
latent_image_ids=latent_image_ids,
|
|
do_true_cfg=do_classifier_free_guidance,
|
|
guidance_scale=guidance_scale,
|
|
cfg_normalize=False,
|
|
)
|
|
|
|
self._current_timestep = None
|
|
if output_type == "latent":
|
|
image = latents
|
|
else:
|
|
latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
|
|
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)
|