790 lines
38 KiB
Python
790 lines
38 KiB
Python
# Copyright 2025 Alpha-VLLM 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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from typing import Any, Callable, Dict, List, Optional, Tuple, Union
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import numpy as np
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import torch
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from transformers import Gemma2PreTrainedModel, GemmaTokenizer, GemmaTokenizerFast
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from ...image_processor import VaeImageProcessor
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from ...loaders import Lumina2LoraLoaderMixin
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from ...models import AutoencoderKL
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from ...models.transformers.transformer_lumina2 import Lumina2Transformer2DModel
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from ...schedulers import FlowMatchEulerDiscreteScheduler
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from ...utils import (
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deprecate,
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is_torch_xla_available,
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logging,
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replace_example_docstring,
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)
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from ...utils.torch_utils import randn_tensor
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from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
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if is_torch_xla_available():
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import torch_xla.core.xla_model as xm
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XLA_AVAILABLE = True
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else:
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XLA_AVAILABLE = False
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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EXAMPLE_DOC_STRING = """
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Examples:
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```py
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>>> import torch
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>>> from diffusers import Lumina2Pipeline
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>>> pipe = Lumina2Pipeline.from_pretrained("Alpha-VLLM/Lumina-Image-2.0", torch_dtype=torch.bfloat16)
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>>> # Enable memory optimizations.
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>>> pipe.enable_model_cpu_offload()
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>>> prompt = "Upper body of a young woman in a Victorian-era outfit with brass goggles and leather straps. Background shows an industrial revolution cityscape with smoky skies and tall, metal structures"
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>>> image = pipe(prompt).images[0]
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```
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"""
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# Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
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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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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
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def retrieve_timesteps(
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scheduler,
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num_inference_steps: Optional[int] = None,
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device: Optional[Union[str, torch.device]] = None,
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timesteps: Optional[List[int]] = None,
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sigmas: Optional[List[float]] = None,
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**kwargs,
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):
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r"""
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Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
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custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
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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`):
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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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accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
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if not accept_sigmas:
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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" sigmas 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 Lumina2Pipeline(DiffusionPipeline, Lumina2LoraLoaderMixin):
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r"""
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Pipeline for text-to-image generation using Lumina-T2I.
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
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library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
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Args:
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vae ([`AutoencoderKL`]):
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Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
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text_encoder ([`Gemma2PreTrainedModel`]):
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Frozen Gemma2 text-encoder.
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tokenizer (`GemmaTokenizer` or `GemmaTokenizerFast`):
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Gemma tokenizer.
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transformer ([`Transformer2DModel`]):
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A text conditioned `Transformer2DModel` to denoise the encoded image latents.
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scheduler ([`SchedulerMixin`]):
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A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
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"""
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_optional_components = []
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_callback_tensor_inputs = ["latents", "prompt_embeds"]
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model_cpu_offload_seq = "text_encoder->transformer->vae"
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def __init__(
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self,
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transformer: Lumina2Transformer2DModel,
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scheduler: FlowMatchEulerDiscreteScheduler,
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vae: AutoencoderKL,
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text_encoder: Gemma2PreTrainedModel,
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tokenizer: Union[GemmaTokenizer, GemmaTokenizerFast],
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):
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super().__init__()
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self.register_modules(
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vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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transformer=transformer,
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scheduler=scheduler,
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)
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self.vae_scale_factor = 8
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self.default_sample_size = (
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self.transformer.config.sample_size
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if hasattr(self, "transformer") and self.transformer is not None
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else 128
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)
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self.default_image_size = self.default_sample_size * self.vae_scale_factor
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self.system_prompt = "You are an assistant designed to generate superior images with the superior degree of image-text alignment based on textual prompts or user prompts."
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self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2)
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if getattr(self, "tokenizer", None) is not None:
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self.tokenizer.padding_side = "right"
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def _get_gemma_prompt_embeds(
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self,
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prompt: Union[str, List[str]],
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device: Optional[torch.device] = None,
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max_sequence_length: int = 256,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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device = device or self._execution_device
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prompt = [prompt] if isinstance(prompt, str) else prompt
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text_inputs = self.tokenizer(
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prompt,
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padding="max_length",
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max_length=max_sequence_length,
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truncation=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids.to(device)
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untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids.to(device)
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if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
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removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
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logger.warning(
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"The following part of your input was truncated because Gemma can only handle sequences up to"
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f" {max_sequence_length} tokens: {removed_text}"
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)
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prompt_attention_mask = text_inputs.attention_mask.to(device)
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prompt_embeds = self.text_encoder(
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text_input_ids, attention_mask=prompt_attention_mask, output_hidden_states=True
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)
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prompt_embeds = prompt_embeds.hidden_states[-2]
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if self.text_encoder is not None:
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dtype = self.text_encoder.dtype
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elif self.transformer is not None:
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dtype = self.transformer.dtype
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else:
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dtype = None
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prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
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_, seq_len, _ = prompt_embeds.shape
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return prompt_embeds, prompt_attention_mask
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# Adapted from diffusers.pipelines.deepfloyd_if.pipeline_if.encode_prompt
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def encode_prompt(
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self,
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prompt: Union[str, List[str]],
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do_classifier_free_guidance: bool = True,
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negative_prompt: Union[str, List[str]] = None,
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num_images_per_prompt: int = 1,
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device: Optional[torch.device] = None,
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prompt_embeds: Optional[torch.Tensor] = None,
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negative_prompt_embeds: Optional[torch.Tensor] = None,
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prompt_attention_mask: Optional[torch.Tensor] = None,
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negative_prompt_attention_mask: Optional[torch.Tensor] = None,
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system_prompt: Optional[str] = None,
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max_sequence_length: int = 256,
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
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r"""
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Encodes the prompt into text encoder hidden states.
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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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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`
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instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). For
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Lumina-T2I, this should be "".
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do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
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whether to use classifier free guidance or not
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num_images_per_prompt (`int`, *optional*, defaults to 1):
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number of images that should be generated per prompt
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device: (`torch.device`, *optional*):
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torch device to place the resulting embeddings on
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prompt_embeds (`torch.Tensor`, *optional*):
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Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
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provided, text embeddings will be generated from `prompt` input argument.
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negative_prompt_embeds (`torch.Tensor`, *optional*):
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Pre-generated negative text embeddings. For Lumina-T2I, it's should be the embeddings of the "" string.
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max_sequence_length (`int`, defaults to `256`):
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Maximum sequence length to use for the prompt.
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"""
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if device is None:
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device = self._execution_device
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prompt = [prompt] if isinstance(prompt, str) else prompt
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if prompt is not None:
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batch_size = len(prompt)
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else:
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batch_size = prompt_embeds.shape[0]
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if system_prompt is None:
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system_prompt = self.system_prompt
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if prompt is not None:
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prompt = [system_prompt + " <Prompt Start> " + p for p in prompt]
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if prompt_embeds is None:
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prompt_embeds, prompt_attention_mask = self._get_gemma_prompt_embeds(
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prompt=prompt,
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device=device,
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max_sequence_length=max_sequence_length,
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)
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batch_size, seq_len, _ = prompt_embeds.shape
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# duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
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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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prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1)
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prompt_attention_mask = prompt_attention_mask.view(batch_size * num_images_per_prompt, -1)
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# Get negative embeddings for classifier free guidance
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if do_classifier_free_guidance and negative_prompt_embeds is None:
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negative_prompt = negative_prompt if negative_prompt is not None else ""
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# Normalize str to list
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negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
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if prompt is not None and type(prompt) is not type(negative_prompt):
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raise TypeError(
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f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
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f" {type(prompt)}."
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)
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elif isinstance(negative_prompt, str):
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negative_prompt = [negative_prompt]
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elif batch_size != len(negative_prompt):
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raise ValueError(
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f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
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f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
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" the batch size of `prompt`."
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)
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negative_prompt_embeds, negative_prompt_attention_mask = self._get_gemma_prompt_embeds(
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prompt=negative_prompt,
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device=device,
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max_sequence_length=max_sequence_length,
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)
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batch_size, seq_len, _ = negative_prompt_embeds.shape
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# duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
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negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
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negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
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negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1)
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negative_prompt_attention_mask = negative_prompt_attention_mask.view(
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batch_size * num_images_per_prompt, -1
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)
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return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
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def prepare_extra_step_kwargs(self, generator, eta):
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# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
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# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
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# eta corresponds to η in DDIM paper: https://huggingface.co/papers/2010.02502
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# and should be between [0, 1]
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accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
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extra_step_kwargs = {}
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if accepts_eta:
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extra_step_kwargs["eta"] = eta
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# check if the scheduler accepts generator
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accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
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if accepts_generator:
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extra_step_kwargs["generator"] = generator
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return extra_step_kwargs
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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,
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prompt_embeds=None,
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negative_prompt_embeds=None,
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prompt_attention_mask=None,
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negative_prompt_attention_mask=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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raise ValueError(
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f"`height` and `width` have to be divisible by {self.vae_scale_factor * 2} but are {height} and {width}."
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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 be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
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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` but is {type(prompt)}")
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if 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 `prompt`: {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 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 prompt_embeds is not None and prompt_attention_mask is None:
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raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.")
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if negative_prompt_embeds is not None and negative_prompt_attention_mask is None:
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raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.")
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if prompt_embeds is not None and negative_prompt_embeds is not None:
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if prompt_embeds.shape != negative_prompt_embeds.shape:
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raise ValueError(
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"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
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f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
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f" {negative_prompt_embeds.shape}."
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)
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if prompt_attention_mask.shape != negative_prompt_attention_mask.shape:
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raise ValueError(
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"`prompt_attention_mask` and `negative_prompt_attention_mask` must have the same shape when passed directly, but"
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f" got: `prompt_attention_mask` {prompt_attention_mask.shape} != `negative_prompt_attention_mask`"
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f" {negative_prompt_attention_mask.shape}."
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)
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if max_sequence_length is not None and max_sequence_length > 512:
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raise ValueError(f"`max_sequence_length` cannot be greater than 512 but is {max_sequence_length}")
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def enable_vae_slicing(self):
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r"""
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Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
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|
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
|
|
"""
|
|
self.vae.enable_slicing()
|
|
|
|
def disable_vae_slicing(self):
|
|
r"""
|
|
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
|
|
computing decoding in one step.
|
|
"""
|
|
self.vae.disable_slicing()
|
|
|
|
def enable_vae_tiling(self):
|
|
r"""
|
|
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
|
|
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
|
|
processing larger images.
|
|
"""
|
|
self.vae.enable_tiling()
|
|
|
|
def disable_vae_tiling(self):
|
|
r"""
|
|
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
|
|
computing decoding in one step.
|
|
"""
|
|
self.vae.disable_tiling()
|
|
|
|
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
|
|
# 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)
|
|
|
|
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."
|
|
)
|
|
|
|
if latents is None:
|
|
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
|
else:
|
|
latents = latents.to(device)
|
|
|
|
return latents
|
|
|
|
@property
|
|
def guidance_scale(self):
|
|
return self._guidance_scale
|
|
|
|
@property
|
|
def attention_kwargs(self):
|
|
return self._attention_kwargs
|
|
|
|
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
|
# of the Imagen paper: https://huggingface.co/papers/2205.11487 . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
@property
|
|
def do_classifier_free_guidance(self):
|
|
return self._guidance_scale > 1
|
|
|
|
@property
|
|
def num_timesteps(self):
|
|
return self._num_timesteps
|
|
|
|
@torch.no_grad()
|
|
@replace_example_docstring(EXAMPLE_DOC_STRING)
|
|
def __call__(
|
|
self,
|
|
prompt: Union[str, List[str]] = None,
|
|
width: Optional[int] = None,
|
|
height: Optional[int] = None,
|
|
num_inference_steps: int = 30,
|
|
guidance_scale: float = 4.0,
|
|
negative_prompt: Union[str, List[str]] = None,
|
|
sigmas: List[float] = None,
|
|
num_images_per_prompt: Optional[int] = 1,
|
|
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
|
latents: Optional[torch.Tensor] = None,
|
|
prompt_embeds: Optional[torch.Tensor] = None,
|
|
negative_prompt_embeds: Optional[torch.Tensor] = None,
|
|
prompt_attention_mask: Optional[torch.Tensor] = None,
|
|
negative_prompt_attention_mask: Optional[torch.Tensor] = None,
|
|
output_type: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
|
|
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
|
|
system_prompt: Optional[str] = None,
|
|
cfg_trunc_ratio: float = 1.0,
|
|
cfg_normalization: bool = True,
|
|
max_sequence_length: int = 256,
|
|
) -> Union[ImagePipelineOutput, Tuple]:
|
|
"""
|
|
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
|
|
less than `1`).
|
|
num_inference_steps (`int`, *optional*, defaults to 30):
|
|
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.
|
|
guidance_scale (`float`, *optional*, defaults to 4.0):
|
|
Guidance scale as defined in [Classifier-Free Diffusion
|
|
Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
|
|
of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
|
|
`guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
|
|
the text `prompt`, usually at the expense of lower image quality.
|
|
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
|
The number of images to generate per prompt.
|
|
height (`int`, *optional*, defaults to self.unet.config.sample_size):
|
|
The height in pixels of the generated image.
|
|
width (`int`, *optional*, defaults to self.unet.config.sample_size):
|
|
The width in pixels of the generated image.
|
|
eta (`float`, *optional*, defaults to 0.0):
|
|
Corresponds to parameter eta (η) in the DDIM paper: https://huggingface.co/papers/2010.02502. Only
|
|
applies to [`schedulers.DDIMScheduler`], will be ignored for others.
|
|
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.Tensor`, *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 ge generated by sampling using the supplied random `generator`.
|
|
prompt_embeds (`torch.Tensor`, *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_attention_mask (`torch.Tensor`, *optional*): Pre-generated attention mask for text embeddings.
|
|
negative_prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated negative text embeddings. For Lumina-T2I this negative prompt should be "". If not
|
|
provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.
|
|
negative_prompt_attention_mask (`torch.Tensor`, *optional*):
|
|
Pre-generated attention mask for negative text embeddings.
|
|
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.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
|
|
attention_kwargs:
|
|
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.
|
|
system_prompt (`str`, *optional*):
|
|
The system prompt to use for the image generation.
|
|
cfg_trunc_ratio (`float`, *optional*, defaults to `1.0`):
|
|
The ratio of the timestep interval to apply normalization-based guidance scale.
|
|
cfg_normalization (`bool`, *optional*, defaults to `True`):
|
|
Whether to apply normalization-based guidance scale.
|
|
max_sequence_length (`int`, defaults to `256`):
|
|
Maximum sequence length to use with the `prompt`.
|
|
|
|
Examples:
|
|
|
|
Returns:
|
|
[`~pipelines.ImagePipelineOutput`] or `tuple`:
|
|
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
|
|
returned where the first element is a list with the generated images
|
|
"""
|
|
height = height or self.default_sample_size * self.vae_scale_factor
|
|
width = width or self.default_sample_size * self.vae_scale_factor
|
|
self._guidance_scale = guidance_scale
|
|
self._attention_kwargs = attention_kwargs
|
|
|
|
# 1. Check inputs. Raise error if not correct
|
|
self.check_inputs(
|
|
prompt,
|
|
height,
|
|
width,
|
|
negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
prompt_attention_mask=prompt_attention_mask,
|
|
negative_prompt_attention_mask=negative_prompt_attention_mask,
|
|
max_sequence_length=max_sequence_length,
|
|
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
|
|
)
|
|
|
|
# 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]
|
|
|
|
device = self._execution_device
|
|
|
|
# 3. Encode input prompt
|
|
(
|
|
prompt_embeds,
|
|
prompt_attention_mask,
|
|
negative_prompt_embeds,
|
|
negative_prompt_attention_mask,
|
|
) = self.encode_prompt(
|
|
prompt,
|
|
self.do_classifier_free_guidance,
|
|
negative_prompt=negative_prompt,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
device=device,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
prompt_attention_mask=prompt_attention_mask,
|
|
negative_prompt_attention_mask=negative_prompt_attention_mask,
|
|
max_sequence_length=max_sequence_length,
|
|
system_prompt=system_prompt,
|
|
)
|
|
|
|
# 4. Prepare latents.
|
|
latent_channels = self.transformer.config.in_channels
|
|
latents = self.prepare_latents(
|
|
batch_size * num_images_per_prompt,
|
|
latent_channels,
|
|
height,
|
|
width,
|
|
prompt_embeds.dtype,
|
|
device,
|
|
generator,
|
|
latents,
|
|
)
|
|
|
|
# 5. Prepare timesteps
|
|
sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
|
|
image_seq_len = latents.shape[1]
|
|
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", 1.15),
|
|
)
|
|
timesteps, num_inference_steps = retrieve_timesteps(
|
|
self.scheduler,
|
|
num_inference_steps,
|
|
device,
|
|
sigmas=sigmas,
|
|
mu=mu,
|
|
)
|
|
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
|
|
self._num_timesteps = len(timesteps)
|
|
|
|
# 6. Denoising loop
|
|
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
|
for i, t in enumerate(timesteps):
|
|
# compute whether apply classifier-free truncation on this timestep
|
|
do_classifier_free_truncation = (i + 1) / num_inference_steps > cfg_trunc_ratio
|
|
# reverse the timestep since Lumina uses t=0 as the noise and t=1 as the image
|
|
current_timestep = 1 - t / self.scheduler.config.num_train_timesteps
|
|
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
|
current_timestep = current_timestep.expand(latents.shape[0])
|
|
|
|
noise_pred_cond = self.transformer(
|
|
hidden_states=latents,
|
|
timestep=current_timestep,
|
|
encoder_hidden_states=prompt_embeds,
|
|
encoder_attention_mask=prompt_attention_mask,
|
|
return_dict=False,
|
|
attention_kwargs=self.attention_kwargs,
|
|
)[0]
|
|
|
|
# perform normalization-based guidance scale on a truncated timestep interval
|
|
if self.do_classifier_free_guidance and not do_classifier_free_truncation:
|
|
noise_pred_uncond = self.transformer(
|
|
hidden_states=latents,
|
|
timestep=current_timestep,
|
|
encoder_hidden_states=negative_prompt_embeds,
|
|
encoder_attention_mask=negative_prompt_attention_mask,
|
|
return_dict=False,
|
|
attention_kwargs=self.attention_kwargs,
|
|
)[0]
|
|
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
|
|
# apply normalization after classifier-free guidance
|
|
if cfg_normalization:
|
|
cond_norm = torch.norm(noise_pred_cond, dim=-1, keepdim=True)
|
|
noise_norm = torch.norm(noise_pred, dim=-1, keepdim=True)
|
|
noise_pred = noise_pred * (cond_norm / noise_norm)
|
|
else:
|
|
noise_pred = noise_pred_cond
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
latents_dtype = latents.dtype
|
|
noise_pred = -noise_pred
|
|
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
|
|
|
|
if latents.dtype != latents_dtype:
|
|
if torch.backends.mps.is_available():
|
|
# some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
|
|
latents = latents.to(latents_dtype)
|
|
|
|
if callback_on_step_end is not None:
|
|
callback_kwargs = {}
|
|
for k in callback_on_step_end_tensor_inputs:
|
|
callback_kwargs[k] = locals()[k]
|
|
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
|
|
|
|
latents = callback_outputs.pop("latents", latents)
|
|
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
|
|
|
|
# call the callback, if provided
|
|
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
|
progress_bar.update()
|
|
|
|
if XLA_AVAILABLE:
|
|
xm.mark_step()
|
|
|
|
if not output_type == "latent":
|
|
latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
|
|
image = self.vae.decode(latents, return_dict=False)[0]
|
|
image = self.image_processor.postprocess(image, output_type=output_type)
|
|
else:
|
|
image = latents
|
|
|
|
# Offload all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return (image,)
|
|
|
|
return ImagePipelineOutput(images=image)
|
|
|
|
|
|
class Lumina2Text2ImgPipeline(Lumina2Pipeline):
|
|
def __init__(
|
|
self,
|
|
transformer: Lumina2Transformer2DModel,
|
|
scheduler: FlowMatchEulerDiscreteScheduler,
|
|
vae: AutoencoderKL,
|
|
text_encoder: Gemma2PreTrainedModel,
|
|
tokenizer: Union[GemmaTokenizer, GemmaTokenizerFast],
|
|
):
|
|
deprecation_message = "`Lumina2Text2ImgPipeline` has been renamed to `Lumina2Pipeline` and will be removed in a future version. Please use `Lumina2Pipeline` instead."
|
|
deprecate("diffusers.pipelines.lumina2.pipeline_lumina2.Lumina2Text2ImgPipeline", "0.34", deprecation_message)
|
|
super().__init__(
|
|
transformer=transformer,
|
|
scheduler=scheduler,
|
|
vae=vae,
|
|
text_encoder=text_encoder,
|
|
tokenizer=tokenizer,
|
|
)
|