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Upload composable_stable_diffusion_pipeline.py

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composable_stable_diffusion_pipeline.py ADDED
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+ """
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+ modified based on https://github.com/nanlliu/diffusers/blob/866af5d9af94568079f76567382e90154d98e2f8/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
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+ """
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+ import inspect
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+ import warnings
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+ from typing import List, Optional, Union
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+
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+ import torch
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+
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+ from tqdm.auto import tqdm
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+ from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
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+
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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.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
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+ from safety_checker import StableDiffusionSafetyChecker
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+
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+
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+ class ComposableStableDiffusionPipeline(DiffusionPipeline):
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+ def __init__(
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+ self,
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+ vae: AutoencoderKL,
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+ text_encoder: CLIPTextModel,
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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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+ ):
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+ super().__init__()
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+ scheduler = scheduler.set_format("pt")
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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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+ unet=unet,
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+ scheduler=scheduler,
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+ safety_checker=safety_checker,
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+ feature_extractor=feature_extractor,
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+ )
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+
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+ @torch.no_grad()
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+ def __call__(
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+ self,
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+ prompt: Union[str, List[str]],
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+ height: Optional[int] = 512,
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+ width: Optional[int] = 512,
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+ num_inference_steps: Optional[int] = 50,
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+ guidance_scale: Optional[float] = 7.5,
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+ eta: Optional[float] = 0.0,
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+ generator: Optional[torch.Generator] = None,
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+ output_type: Optional[str] = "pil",
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+ **kwargs,
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+ ):
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+ if "torch_device" in kwargs:
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+ device = kwargs.pop("torch_device")
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+ warnings.warn(
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+ "`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."
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+ " Consider using `pipe.to(torch_device)` instead."
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+ )
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+
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+ # Set device as before (to be removed in 0.3.0)
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+ if device is None:
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+ device = "cuda" if torch.cuda.is_available() else "cpu"
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+ self.to(device)
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+
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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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+
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+ if height % 8 != 0 or width % 8 != 0:
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+ raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
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+
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+ if '|' in prompt:
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+ prompt = [x.strip() for x in prompt.split('|')]
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+ print(prompt)
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+
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+ # get prompt text embeddings
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+ text_input = 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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+ truncation=True,
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+ return_tensors="pt",
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+ )
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+ text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
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+
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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://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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+ # get unconditional embeddings for classifier free guidance
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+ if do_classifier_free_guidance:
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+ max_length = text_input.input_ids.shape[-1]
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+ uncond_input = self.tokenizer(
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+ [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
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+ )
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+ uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
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+
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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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+
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+ # get the intial random noise
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+ latents = torch.randn(
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+ (batch_size, self.unet.in_channels, height // 8, width // 8),
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+ generator=generator,
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+ device=self.device,
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+ )
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+
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+ # set timesteps
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+ accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
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+ extra_set_kwargs = {}
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+ if accepts_offset:
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+ extra_set_kwargs["offset"] = 1
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+
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+ self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
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+
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+ # if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas
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+ if isinstance(self.scheduler, LMSDiscreteScheduler):
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+ latents = latents * self.scheduler.sigmas[0]
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+
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+ # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
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+ # eta (Ξ·) is only used with the DDIMScheduler, it will be ignored for other schedulers.
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+ # eta corresponds to Ξ· in DDIM paper: https://arxiv.org/abs/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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+
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+ for i, t in tqdm(enumerate(self.scheduler.timesteps)):
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+ # expand the latents if we are doing classifier free guidance
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+ latent_model_input = torch.cat([latents] * text_embeddings.shape[0]) if do_classifier_free_guidance else latents
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+ if isinstance(self.scheduler, LMSDiscreteScheduler):
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+ sigma = self.scheduler.sigmas[i]
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+ latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
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+
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+ # predict the noise residual
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+ noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings)["sample"]
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+
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+ # perform guidance
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+ if do_classifier_free_guidance:
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+ pred_decomp = noise_pred.chunk(text_embeddings.shape[0])
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+ noise_pred_uncond, noise_pred_text = pred_decomp[0], torch.cat(pred_decomp[1:], dim=0).mean(dim=0, keepdim=True)
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+ noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
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+
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+ # compute the previous noisy sample x_t -> x_t-1
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+ if isinstance(self.scheduler, LMSDiscreteScheduler):
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+ latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs)["prev_sample"]
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+ else:
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+ latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)["prev_sample"]
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+
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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)
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+
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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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+
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+ # run safety checker
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+ safety_cheker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(self.device)
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+ image, has_nsfw_concept = self.safety_checker(images=image, clip_input=safety_cheker_input.pixel_values)
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+
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+ if output_type == "pil":
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+ image = self.numpy_to_pil(image)
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+
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+ return {"sample": image, "nsfw_content_detected": has_nsfw_concept}