288 lines
12 KiB
Python
288 lines
12 KiB
Python
import types
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from typing import List, Optional, Tuple, Union
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import torch
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from diffusers.models import PriorTransformer
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from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
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from diffusers.schedulers import UnCLIPScheduler
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from diffusers.utils import logging, randn_tensor
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from transformers import CLIPTextModelWithProjection, CLIPTokenizer
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from transformers.models.clip.modeling_clip import CLIPTextModelOutput
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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def _encode_image(self, image, device, num_images_per_prompt, do_classifier_free_guidance):
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image = image.to(device=device)
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image_embeddings = image # take image as image_embeddings
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image_embeddings = image_embeddings.unsqueeze(1)
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# duplicate image embeddings for each generation per prompt, using mps friendly method
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bs_embed, seq_len, _ = image_embeddings.shape
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image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)
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image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
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if do_classifier_free_guidance:
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uncond_embeddings = torch.zeros_like(image_embeddings)
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# For classifier free guidance, we need to do two forward passes.
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# Here we concatenate the unconditional and text embeddings into a single batch
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# to avoid doing two forward passes
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image_embeddings = torch.cat([uncond_embeddings, image_embeddings])
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return image_embeddings
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class StableUnCLIPPipeline(DiffusionPipeline):
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def __init__(
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self,
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prior: PriorTransformer,
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tokenizer: CLIPTokenizer,
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text_encoder: CLIPTextModelWithProjection,
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prior_scheduler: UnCLIPScheduler,
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decoder_pipe_kwargs: Optional[dict] = None,
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):
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super().__init__()
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decoder_pipe_kwargs = dict(image_encoder=None) if decoder_pipe_kwargs is None else decoder_pipe_kwargs
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decoder_pipe_kwargs["torch_dtype"] = decoder_pipe_kwargs.get("torch_dtype", None) or prior.dtype
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self.decoder_pipe = StableDiffusionImageVariationPipeline.from_pretrained(
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"lambdalabs/sd-image-variations-diffusers", **decoder_pipe_kwargs
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)
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# replace `_encode_image` method
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self.decoder_pipe._encode_image = types.MethodType(_encode_image, self.decoder_pipe)
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self.register_modules(
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prior=prior,
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tokenizer=tokenizer,
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text_encoder=text_encoder,
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prior_scheduler=prior_scheduler,
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)
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def _encode_prompt(
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self,
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prompt,
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device,
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num_images_per_prompt,
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do_classifier_free_guidance,
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text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,
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text_attention_mask: Optional[torch.Tensor] = None,
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):
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if text_model_output is None:
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batch_size = len(prompt) if isinstance(prompt, list) else 1
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# get prompt text embeddings
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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=self.tokenizer.model_max_length,
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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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text_mask = text_inputs.attention_mask.bool().to(device)
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if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
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removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
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logger.warning(
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"The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.tokenizer.model_max_length} tokens: {removed_text}"
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)
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text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
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text_encoder_output = self.text_encoder(text_input_ids.to(device))
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text_embeddings = text_encoder_output.text_embeds
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text_encoder_hidden_states = text_encoder_output.last_hidden_state
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else:
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batch_size = text_model_output[0].shape[0]
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text_embeddings, text_encoder_hidden_states = text_model_output[0], text_model_output[1]
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text_mask = text_attention_mask
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text_embeddings = text_embeddings.repeat_interleave(num_images_per_prompt, dim=0)
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text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
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text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
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if do_classifier_free_guidance:
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uncond_tokens = [""] * batch_size
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uncond_input = self.tokenizer(
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uncond_tokens,
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padding="max_length",
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max_length=self.tokenizer.model_max_length,
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truncation=True,
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return_tensors="pt",
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)
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uncond_text_mask = uncond_input.attention_mask.bool().to(device)
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uncond_embeddings_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))
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uncond_embeddings = uncond_embeddings_text_encoder_output.text_embeds
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uncond_text_encoder_hidden_states = uncond_embeddings_text_encoder_output.last_hidden_state
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# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
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seq_len = uncond_embeddings.shape[1]
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uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt)
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uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len)
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seq_len = uncond_text_encoder_hidden_states.shape[1]
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uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
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uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
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batch_size * num_images_per_prompt, seq_len, -1
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)
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uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
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# done duplicates
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# For classifier free guidance, we need to do two forward passes.
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# Here we concatenate the unconditional and text embeddings into a single batch
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# to avoid doing two forward passes
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text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
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text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
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text_mask = torch.cat([uncond_text_mask, text_mask])
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return text_embeddings, text_encoder_hidden_states, text_mask
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@property
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def _execution_device(self):
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r"""
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Returns the device on which the pipeline's models will be executed. After calling
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`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
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hooks.
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"""
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if self.device != torch.device("meta") or not hasattr(self.prior, "_hf_hook"):
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return self.device
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for module in self.prior.modules():
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if (
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hasattr(module, "_hf_hook")
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and hasattr(module._hf_hook, "execution_device")
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and module._hf_hook.execution_device is not None
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):
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return torch.device(module._hf_hook.execution_device)
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return self.device
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def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
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if latents is None:
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latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
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else:
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if latents.shape != shape:
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raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
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latents = latents.to(device)
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latents = latents * scheduler.init_noise_sigma
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return latents
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def to(self, torch_device: Optional[Union[str, torch.device]] = None):
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self.decoder_pipe.to(torch_device)
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super().to(torch_device)
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@torch.no_grad()
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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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height: Optional[int] = None,
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width: Optional[int] = None,
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num_images_per_prompt: int = 1,
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prior_num_inference_steps: int = 25,
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generator: Optional[torch.Generator] = None,
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prior_latents: Optional[torch.FloatTensor] = None,
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text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,
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text_attention_mask: Optional[torch.Tensor] = None,
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prior_guidance_scale: float = 4.0,
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decoder_guidance_scale: float = 8.0,
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decoder_num_inference_steps: int = 50,
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decoder_num_images_per_prompt: Optional[int] = 1,
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decoder_eta: float = 0.0,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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):
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if prompt is not None:
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if isinstance(prompt, str):
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batch_size = 1
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elif isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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else:
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batch_size = text_model_output[0].shape[0]
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device = self._execution_device
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batch_size = batch_size * num_images_per_prompt
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do_classifier_free_guidance = prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
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text_embeddings, text_encoder_hidden_states, text_mask = self._encode_prompt(
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prompt, device, num_images_per_prompt, do_classifier_free_guidance, text_model_output, text_attention_mask
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)
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# prior
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self.prior_scheduler.set_timesteps(prior_num_inference_steps, device=device)
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prior_timesteps_tensor = self.prior_scheduler.timesteps
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embedding_dim = self.prior.config.embedding_dim
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prior_latents = self.prepare_latents(
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(batch_size, embedding_dim),
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text_embeddings.dtype,
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device,
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generator,
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prior_latents,
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self.prior_scheduler,
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)
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for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):
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# expand the latents if we are doing classifier free guidance
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latent_model_input = torch.cat([prior_latents] * 2) if do_classifier_free_guidance else prior_latents
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predicted_image_embedding = self.prior(
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latent_model_input,
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timestep=t,
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proj_embedding=text_embeddings,
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encoder_hidden_states=text_encoder_hidden_states,
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attention_mask=text_mask,
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).predicted_image_embedding
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if do_classifier_free_guidance:
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predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)
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predicted_image_embedding = predicted_image_embedding_uncond + prior_guidance_scale * (
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predicted_image_embedding_text - predicted_image_embedding_uncond
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)
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if i + 1 == prior_timesteps_tensor.shape[0]:
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prev_timestep = None
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else:
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prev_timestep = prior_timesteps_tensor[i + 1]
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prior_latents = self.prior_scheduler.step(
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predicted_image_embedding,
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timestep=t,
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sample=prior_latents,
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generator=generator,
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prev_timestep=prev_timestep,
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).prev_sample
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prior_latents = self.prior.post_process_latents(prior_latents)
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image_embeddings = prior_latents
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output = self.decoder_pipe(
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image=image_embeddings,
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height=height,
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width=width,
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num_inference_steps=decoder_num_inference_steps,
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guidance_scale=decoder_guidance_scale,
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generator=generator,
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output_type=output_type,
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return_dict=return_dict,
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num_images_per_prompt=decoder_num_images_per_prompt,
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eta=decoder_eta,
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)
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return output
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