684 lines
33 KiB
Python
684 lines
33 KiB
Python
# Copyright 2025 The CogVideoX team, Tsinghua University & ZhipuAI and The HuggingFace Team.
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# 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 AutoTokenizer, GlmModel
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from ...callbacks import MultiPipelineCallbacks, PipelineCallback
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from ...image_processor import VaeImageProcessor
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from ...loaders import CogView4LoraLoaderMixin
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from ...models import AutoencoderKL, CogView4Transformer2DModel
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from ...pipelines.pipeline_utils import DiffusionPipeline
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from ...schedulers import FlowMatchEulerDiscreteScheduler
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from ...utils import is_torch_xla_available, logging, replace_example_docstring
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from ...utils.torch_utils import randn_tensor
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from .pipeline_output import CogView4PipelineOutput
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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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```python
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>>> import torch
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>>> from diffusers import CogView4Pipeline
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>>> pipe = CogView4Pipeline.from_pretrained("THUDM/CogView4-6B", torch_dtype=torch.bfloat16)
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>>> pipe.to("cuda")
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>>> prompt = "A photo of an astronaut riding a horse on mars"
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>>> image = pipe(prompt).images[0]
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>>> image.save("output.png")
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```
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"""
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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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base_shift: float = 0.25,
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max_shift: float = 0.75,
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) -> float:
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m = (image_seq_len / base_seq_len) ** 0.5
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mu = m * max_shift + base_shift
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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: 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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accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
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accepts_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
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if timesteps is not None and sigmas is not None:
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if not accepts_timesteps and not accepts_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" timestep or sigma schedules. Please check whether you are using the correct scheduler."
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)
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scheduler.set_timesteps(timesteps=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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elif timesteps is not None and sigmas is None:
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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 timesteps is None and sigmas is not None:
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if not accepts_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 CogView4Pipeline(DiffusionPipeline, CogView4LoraLoaderMixin):
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r"""
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Pipeline for text-to-image generation using CogView4.
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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 ([`GLMModel`]):
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Frozen text-encoder. CogView4 uses [glm-4-9b-hf](https://huggingface.co/THUDM/glm-4-9b-hf).
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tokenizer (`PreTrainedTokenizer`):
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Tokenizer of class
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[PreTrainedTokenizer](https://huggingface.co/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizer).
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transformer ([`CogView4Transformer2DModel`]):
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A text conditioned `CogView4Transformer2DModel` 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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model_cpu_offload_seq = "text_encoder->transformer->vae"
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_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
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def __init__(
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self,
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tokenizer: AutoTokenizer,
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text_encoder: GlmModel,
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vae: AutoencoderKL,
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transformer: CogView4Transformer2DModel,
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scheduler: FlowMatchEulerDiscreteScheduler,
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):
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super().__init__()
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self.register_modules(
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tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler
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)
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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.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
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def _get_glm_embeds(
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self,
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prompt: Union[str, List[str]] = None,
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max_sequence_length: int = 1024,
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = 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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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="longest", # not use max length
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max_length=max_sequence_length,
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truncation=True,
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add_special_tokens=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
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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 `max_sequence_length` is set to "
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f" {max_sequence_length} tokens: {removed_text}"
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)
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current_length = text_input_ids.shape[1]
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pad_length = (16 - (current_length % 16)) % 16
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if pad_length > 0:
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pad_ids = torch.full(
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(text_input_ids.shape[0], pad_length),
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fill_value=self.tokenizer.pad_token_id,
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dtype=text_input_ids.dtype,
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device=text_input_ids.device,
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)
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text_input_ids = torch.cat([pad_ids, text_input_ids], dim=1)
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prompt_embeds = self.text_encoder(text_input_ids.to(device), output_hidden_states=True).hidden_states[-2]
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prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
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return prompt_embeds
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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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negative_prompt: Optional[Union[str, List[str]]] = None,
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do_classifier_free_guidance: bool = True,
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num_images_per_prompt: int = 1,
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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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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = None,
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max_sequence_length: int = 1024,
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):
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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 or prompts not to guide the image generation. If not defined, one has to pass
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`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
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less than `1`).
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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. 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. Can be used to easily tweak text inputs, *e.g.* prompt
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weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
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argument.
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device: (`torch.device`, *optional*):
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torch device
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dtype: (`torch.dtype`, *optional*):
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torch dtype
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max_sequence_length (`int`, defaults to `1024`):
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Maximum sequence length in encoded prompt. Can be set to other values but may lead to poorer results.
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"""
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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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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 prompt_embeds is None:
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prompt_embeds = self._get_glm_embeds(prompt, max_sequence_length, device, dtype)
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seq_len = prompt_embeds.size(1)
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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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if do_classifier_free_guidance and negative_prompt_embeds is None:
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negative_prompt = negative_prompt or ""
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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 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 = self._get_glm_embeds(negative_prompt, max_sequence_length, device, dtype)
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seq_len = negative_prompt_embeds.size(1)
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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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return prompt_embeds, negative_prompt_embeds
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def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
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if latents is not None:
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return latents.to(device)
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shape = (
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batch_size,
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num_channels_latents,
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int(height) // self.vae_scale_factor,
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int(width) // self.vae_scale_factor,
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)
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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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return latents
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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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callback_on_step_end_tensor_inputs,
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prompt_embeds=None,
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negative_prompt_embeds=None,
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):
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if height % 16 != 0 or width % 16 != 0:
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raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
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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 negative_prompt_embeds is not None:
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if prompt_embeds.shape[0] != negative_prompt_embeds.shape[0]:
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raise ValueError(
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"`prompt_embeds` and `negative_prompt_embeds` must have the same batch size when passed directly, but"
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f" got: `prompt_embeds` {prompt_embeds.shape} and `negative_prompt_embeds`"
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f" {negative_prompt_embeds.shape}."
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)
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if prompt_embeds.shape[-1] != negative_prompt_embeds.shape[-1]:
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raise ValueError(
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"`prompt_embeds` and `negative_prompt_embeds` must have the same dimension when passed directly, but"
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f" got: `prompt_embeds` {prompt_embeds.shape} and `negative_prompt_embeds`"
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f" {negative_prompt_embeds.shape}."
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)
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@property
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def guidance_scale(self):
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return self._guidance_scale
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# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
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# of the Imagen paper: https://huggingface.co/papers/2205.11487 . `guidance_scale = 1`
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# corresponds to doing no classifier free guidance.
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@property
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def do_classifier_free_guidance(self):
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return self._guidance_scale > 1
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@property
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def num_timesteps(self):
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return self._num_timesteps
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@property
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def attention_kwargs(self):
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return self._attention_kwargs
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@property
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def current_timestep(self):
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return self._current_timestep
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@property
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def interrupt(self):
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return self._interrupt
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@torch.no_grad()
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@replace_example_docstring(EXAMPLE_DOC_STRING)
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def __call__(
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self,
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prompt: Optional[Union[str, List[str]]] = None,
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negative_prompt: Optional[Union[str, List[str]]] = None,
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height: Optional[int] = None,
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width: Optional[int] = None,
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num_inference_steps: int = 50,
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timesteps: Optional[List[int]] = None,
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sigmas: Optional[List[float]] = None,
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guidance_scale: float = 5.0,
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num_images_per_prompt: int = 1,
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
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latents: Optional[torch.FloatTensor] = None,
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prompt_embeds: Optional[torch.FloatTensor] = None,
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negative_prompt_embeds: Optional[torch.FloatTensor] = None,
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original_size: Optional[Tuple[int, int]] = None,
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crops_coords_top_left: Tuple[int, int] = (0, 0),
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output_type: str = "pil",
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return_dict: bool = True,
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attention_kwargs: Optional[Dict[str, Any]] = None,
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callback_on_step_end: Optional[
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Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
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] = None,
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callback_on_step_end_tensor_inputs: List[str] = ["latents"],
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max_sequence_length: int = 1024,
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) -> Union[CogView4PipelineOutput, Tuple]:
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"""
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Function invoked when calling the pipeline for generation.
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Args:
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prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
|
|
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`).
|
|
height (`int`, *optional*, defaults to self.transformer.config.sample_size * self.vae_scale_factor):
|
|
The height in pixels of the generated image. If not provided, it is set to 1024.
|
|
width (`int`, *optional*, defaults to self.transformer.config.sample_size * self.vae_scale_factor):
|
|
The width in pixels of the generated image. If not provided it is set to 1024.
|
|
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.
|
|
timesteps (`List[int]`, *optional*):
|
|
Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
|
|
in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
|
|
passed will be used. Must be in descending order.
|
|
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 `5.0`):
|
|
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.
|
|
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 ge 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.
|
|
original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
|
|
If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
|
|
`original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
|
|
explained in section 2.2 of
|
|
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
|
|
crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
|
|
`crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
|
|
`crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
|
|
`crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
|
|
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
|
|
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_xl.StableDiffusionXLPipelineOutput`] instead
|
|
of a plain tuple.
|
|
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 `224`):
|
|
Maximum sequence length in encoded prompt. Can be set to other values but may lead to poorer results.
|
|
|
|
Examples:
|
|
|
|
Returns:
|
|
[`~pipelines.cogview4.pipeline_CogView4.CogView4PipelineOutput`] or `tuple`:
|
|
[`~pipelines.cogview4.pipeline_CogView4.CogView4PipelineOutput`] if `return_dict` is True, otherwise a
|
|
`tuple`. When returning a tuple, the first element is a list with the generated images.
|
|
"""
|
|
|
|
if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
|
|
callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
|
|
|
|
height = height or self.transformer.config.sample_size * self.vae_scale_factor
|
|
width = width or self.transformer.config.sample_size * self.vae_scale_factor
|
|
|
|
original_size = original_size or (height, width)
|
|
target_size = (height, width)
|
|
|
|
# Check inputs. Raise error if not correct
|
|
self.check_inputs(
|
|
prompt,
|
|
height,
|
|
width,
|
|
negative_prompt,
|
|
callback_on_step_end_tensor_inputs,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
)
|
|
self._guidance_scale = guidance_scale
|
|
self._attention_kwargs = attention_kwargs
|
|
self._current_timestep = None
|
|
self._interrupt = False
|
|
|
|
# Default 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
|
|
|
|
# Encode input prompt
|
|
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
|
|
prompt,
|
|
negative_prompt,
|
|
self.do_classifier_free_guidance,
|
|
num_images_per_prompt=num_images_per_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
max_sequence_length=max_sequence_length,
|
|
device=device,
|
|
)
|
|
|
|
# Prepare latents
|
|
latent_channels = self.transformer.config.in_channels
|
|
latents = self.prepare_latents(
|
|
batch_size * num_images_per_prompt,
|
|
latent_channels,
|
|
height,
|
|
width,
|
|
torch.float32,
|
|
device,
|
|
generator,
|
|
latents,
|
|
)
|
|
|
|
# Prepare additional timestep conditions
|
|
original_size = torch.tensor([original_size], dtype=prompt_embeds.dtype, device=device)
|
|
target_size = torch.tensor([target_size], dtype=prompt_embeds.dtype, device=device)
|
|
crops_coords_top_left = torch.tensor([crops_coords_top_left], dtype=prompt_embeds.dtype, device=device)
|
|
|
|
original_size = original_size.repeat(batch_size * num_images_per_prompt, 1)
|
|
target_size = target_size.repeat(batch_size * num_images_per_prompt, 1)
|
|
crops_coords_top_left = crops_coords_top_left.repeat(batch_size * num_images_per_prompt, 1)
|
|
|
|
# Prepare timesteps
|
|
image_seq_len = ((height // self.vae_scale_factor) * (width // self.vae_scale_factor)) // (
|
|
self.transformer.config.patch_size**2
|
|
)
|
|
timesteps = (
|
|
np.linspace(self.scheduler.config.num_train_timesteps, 1.0, num_inference_steps)
|
|
if timesteps is None
|
|
else np.array(timesteps)
|
|
)
|
|
timesteps = timesteps.astype(np.int64).astype(np.float32)
|
|
sigmas = timesteps / self.scheduler.config.num_train_timesteps if sigmas is None else sigmas
|
|
mu = calculate_shift(
|
|
image_seq_len,
|
|
self.scheduler.config.get("base_image_seq_len", 256),
|
|
self.scheduler.config.get("base_shift", 0.25),
|
|
self.scheduler.config.get("max_shift", 0.75),
|
|
)
|
|
timesteps, num_inference_steps = retrieve_timesteps(
|
|
self.scheduler, num_inference_steps, device, timesteps, sigmas, mu=mu
|
|
)
|
|
self._num_timesteps = len(timesteps)
|
|
|
|
# Denoising loop
|
|
transformer_dtype = self.transformer.dtype
|
|
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
|
|
|
|
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
|
for i, t in enumerate(timesteps):
|
|
if self.interrupt:
|
|
continue
|
|
|
|
self._current_timestep = t
|
|
latent_model_input = latents.to(transformer_dtype)
|
|
|
|
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
|
timestep = t.expand(latents.shape[0])
|
|
|
|
noise_pred_cond = self.transformer(
|
|
hidden_states=latent_model_input,
|
|
encoder_hidden_states=prompt_embeds,
|
|
timestep=timestep,
|
|
original_size=original_size,
|
|
target_size=target_size,
|
|
crop_coords=crops_coords_top_left,
|
|
attention_kwargs=attention_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
# perform guidance
|
|
if self.do_classifier_free_guidance:
|
|
noise_pred_uncond = self.transformer(
|
|
hidden_states=latent_model_input,
|
|
encoder_hidden_states=negative_prompt_embeds,
|
|
timestep=timestep,
|
|
original_size=original_size,
|
|
target_size=target_size,
|
|
crop_coords=crops_coords_top_left,
|
|
attention_kwargs=attention_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_cond - noise_pred_uncond)
|
|
else:
|
|
noise_pred = noise_pred_cond
|
|
|
|
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
|
|
|
|
# call the callback, if provided
|
|
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, self.scheduler.sigmas[i], callback_kwargs)
|
|
latents = callback_outputs.pop("latents", latents)
|
|
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
|
|
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
|
|
|
|
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()
|
|
|
|
self._current_timestep = None
|
|
|
|
if not output_type == "latent":
|
|
latents = latents.to(self.vae.dtype) / self.vae.config.scaling_factor
|
|
image = self.vae.decode(latents, return_dict=False, generator=generator)[0]
|
|
else:
|
|
image = latents
|
|
|
|
image = self.image_processor.postprocess(image, output_type=output_type)
|
|
|
|
# Offload all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return (image,)
|
|
|
|
return CogView4PipelineOutput(images=image)
|