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@ -5,8 +5,8 @@ from typing import List, Optional, Union, Callable
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import PIL.Image
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import torch
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from diffusers import StableDiffusionPipeline
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from diffusers.models import AutoencoderKL, UNet2DConditionModel
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from diffusers.pipeline_utils import DiffusionPipeline
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from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
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from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import preprocess
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from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
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@ -26,7 +26,7 @@ class PipelineIntermediateState:
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predicted_original: Optional[torch.Tensor] = None
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class StableDiffusionGeneratorPipeline(DiffusionPipeline):
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class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
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r"""
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Pipeline for text-to-image generation using Stable Diffusion.
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@ -67,10 +67,10 @@ class StableDiffusionGeneratorPipeline(DiffusionPipeline):
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tokenizer: CLIPTokenizer,
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unet: UNet2DConditionModel,
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scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
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safety_checker: StableDiffusionSafetyChecker,
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feature_extractor: CLIPFeatureExtractor,
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safety_checker: Optional[StableDiffusionSafetyChecker],
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feature_extractor: Optional[CLIPFeatureExtractor],
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):
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super().__init__()
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super().__init__(vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor)
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self.register_modules(
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vae=vae,
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@ -88,51 +88,6 @@ class StableDiffusionGeneratorPipeline(DiffusionPipeline):
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)
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self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward)
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def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
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r"""
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Enable sliced attention computation.
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When this option is enabled, the attention module will split the input tensor in slices, to compute attention
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in several steps. This is useful to save some memory in exchange for a small speed decrease.
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Args:
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slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
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When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
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a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
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`attention_head_dim` must be a multiple of `slice_size`.
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"""
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if slice_size == "auto":
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# half the attention head size is usually a good trade-off between
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# speed and memory
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slice_size = self.unet.config.attention_head_dim // 2
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self.unet.set_attention_slice(slice_size)
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def disable_attention_slicing(self):
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r"""
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Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
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back to computing attention in one step.
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"""
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# set slice_size = `None` to disable `attention slicing`
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self.enable_attention_slicing(None)
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def enable_xformers_memory_efficient_attention(self):
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r"""
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Enable memory efficient attention as implemented in xformers.
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When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference
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time. Speed up at training time is not guaranteed.
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Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention
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is used.
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"""
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self.unet.set_use_memory_efficient_attention_xformers(True)
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def disable_xformers_memory_efficient_attention(self):
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r"""
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Disable memory efficient attention as implemented in xformers.
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"""
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self.unet.set_use_memory_efficient_attention_xformers(False)
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def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
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text_embeddings: torch.Tensor, unconditioned_embeddings: torch.Tensor,
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guidance_scale: float,
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@ -195,10 +150,17 @@ class StableDiffusionGeneratorPipeline(DiffusionPipeline):
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# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
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# corresponds to doing no classifier free guidance.
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do_classifier_free_guidance = guidance_scale > 1.0
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text_embeddings, unconditioned_embeddings = self.get_text_embeddings(prompt, opposing_prompt, do_classifier_free_guidance, batch_size)\
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.to(self.unet.device)
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combined_embeddings = self._encode_prompt(prompt, device=self._execution_device, num_images_per_prompt=1,
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do_classifier_free_guidance=do_classifier_free_guidance,
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negative_prompt=opposing_prompt)
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text_embeddings, unconditioned_embeddings = combined_embeddings.chunk(2)
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self.scheduler.set_timesteps(num_inference_steps)
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latents = self.prepare_latents(latents, batch_size, height, width, generator, self.unet.dtype)
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latents = self.prepare_latents(batch_size=batch_size, num_channels_latents=self.unet.in_channels,
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height=height, width=width,
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dtype=self.unet.dtype, device=self._execution_device,
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generator=generator,
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latents=latents)
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yield from self.generate_from_embeddings(latents, text_embeddings, unconditioned_embeddings,
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guidance_scale, run_id=run_id, **extra_step_kwargs)
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@ -248,9 +210,10 @@ class StableDiffusionGeneratorPipeline(DiffusionPipeline):
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# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
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torch.cuda.empty_cache()
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image = self.decode_to_image(latents)
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output = StableDiffusionPipelineOutput(images=image, nsfw_content_detected=[])
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yield self.check_for_safety(output)
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with torch.inference_mode():
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image = self.decode_latents(latents)
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output = StableDiffusionPipelineOutput(images=image, nsfw_content_detected=[])
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yield self.check_for_safety(output, dtype=text_embeddings.dtype)
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@torch.inference_mode()
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def step(self, t: torch.Tensor, latents: torch.Tensor, guidance_scale: float,
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@ -340,46 +303,12 @@ class StableDiffusionGeneratorPipeline(DiffusionPipeline):
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return timesteps
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@torch.inference_mode()
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def check_for_safety(self, output):
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if not getattr(self, 'feature_extractor') or not getattr(self, 'safety_checker'):
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return output
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images = output.images
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safety_checker_output = self.feature_extractor(self.numpy_to_pil(images),
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return_tensors="pt").to(self.device)
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screened_images, has_nsfw_concept = self.safety_checker(
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images=images, clip_input=safety_checker_output.pixel_values)
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def check_for_safety(self, output, dtype):
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with torch.inference_mode():
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screened_images, has_nsfw_concept = self.run_safety_checker(
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output.images, device=self._execution_device, dtype=dtype)
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return StableDiffusionPipelineOutput(screened_images, has_nsfw_concept)
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@torch.inference_mode()
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def decode_to_image(self, latents):
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# scale and decode the image latents with vae
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latents = 1 / 0.18215 * latents
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image = self.vae.decode(latents).sample
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image = (image / 2 + 0.5).clamp(0, 1)
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image = image.cpu().permute(0, 2, 3, 1).numpy()
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return image
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@torch.inference_mode()
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def get_text_embeddings(self,
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prompt: Union[str, List[str]],
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opposing_prompt: Union[str, List[str]],
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do_classifier_free_guidance: bool,
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batch_size: int):
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# get prompt text embeddings
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text_input = self._tokenize(prompt)
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text_embeddings = self.text_encoder(text_input.input_ids)[0]
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# get unconditional embeddings for classifier free guidance
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if do_classifier_free_guidance:
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# opposing prompt defaults to blank caption for everything in the batch
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text_anti_input = self._tokenize(opposing_prompt or [""] * batch_size)
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uncond_embeddings = self.text_encoder(text_anti_input.input_ids)[0]
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else:
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uncond_embeddings = None
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return text_embeddings, uncond_embeddings
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@torch.inference_mode()
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def get_learned_conditioning(self, c: List[List[str]], *, return_tokens=True, fragment_weights=None):
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"""
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@ -406,28 +335,3 @@ class StableDiffusionGeneratorPipeline(DiffusionPipeline):
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def channels(self) -> int:
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"""Compatible with DiffusionWrapper"""
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return self.unet.in_channels
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def prepare_latents(self, latents, batch_size, height, width, generator, dtype):
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# get the initial random noise unless the user supplied it
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# Unlike in other pipelines, latents need to be generated in the target device
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# for 1-to-1 results reproducibility with the CompVis implementation.
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# However this currently doesn't work in `mps`.
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latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8)
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if latents is None:
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latents = torch.randn(
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latents_shape,
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generator=generator,
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device=self.unet.device,
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dtype=dtype
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)
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else:
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if latents.shape != latents_shape:
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raise ValueError(
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f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
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if latents.device != self.unet.device:
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raise ValueError(f"Unexpected latents device, got {latents.device}, "
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f"expected {self.unet.device}")
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# scale the initial noise by the standard deviation required by the scheduler
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latents *= self.scheduler.init_noise_sigma
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return latents
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Kevin Turner