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weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` input argument. ip_adapter_image (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. ip_adapter_image_embeds (`torch.Tensor`, *optional*): Pre-generated image embeddings for IP-Adapter. Should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not provided, embeddings are computed from the `ip_adapter_image` input argument. 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`):
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Whether or not to return a [`~pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput`] instead of a plain tuple. joint_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`.
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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 256): Maximum sequence length to use with the `prompt`. mu (`float`, *optional*): `mu` value used for `dynamic_shifting`.
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Examples: Returns: [`~pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput`] 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
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# 1. Check inputs. Raise error if not correct self.check_inputs( prompt, prompt_2, prompt_3, height, width, strength, negative_prompt=negative_prompt, negative_prompt_2=negative_prompt_2, negative_prompt_3=negative_prompt_3, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, pooled_prompt_embeds=pooled_prompt_embeds, negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, max_sequence_length=max_sequence_length, ) self._guidance_scale = guidance_scale self._clip_skip = clip_skip self._joint_attention_kwargs = joint_attention_kwargs self._interrupt = False
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# 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device
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( prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds, ) = self.encode_prompt( prompt=prompt, prompt_2=prompt_2, prompt_3=prompt_3, negative_prompt=negative_prompt, negative_prompt_2=negative_prompt_2, negative_prompt_3=negative_prompt_3, do_classifier_free_guidance=self.do_classifier_free_guidance, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, pooled_prompt_embeds=pooled_prompt_embeds, negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, device=device, clip_skip=self.clip_skip, num_images_per_prompt=num_images_per_prompt, max_sequence_length=max_sequence_length, )
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if self.do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
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# 3. Prepare timesteps scheduler_kwargs = {} if self.scheduler.config.get("use_dynamic_shifting", None) and mu is None: image_seq_len = (int(height) // self.vae_scale_factor // self.transformer.config.patch_size) * ( int(width) // self.vae_scale_factor // self.transformer.config.patch_size ) mu = calculate_shift( image_seq_len, self.scheduler.config.get("base_image_seq_len", 256), self.scheduler.config.get("max_image_seq_len", 4096), self.scheduler.config.get("base_shift", 0.5), self.scheduler.config.get("max_shift", 1.16), ) scheduler_kwargs["mu"] = mu elif mu is not None: scheduler_kwargs["mu"] = mu timesteps, num_inference_steps = retrieve_timesteps( self.scheduler, num_inference_steps, device, sigmas=sigmas, **scheduler_kwargs )
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timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) # check that number of inference steps is not < 1 - as this doesn't make sense if num_inference_steps < 1: raise ValueError( f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline" f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline." ) latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
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# create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise is_strength_max = strength == 1.0 # 4. Preprocess mask and image if padding_mask_crop is not None: crops_coords = self.mask_processor.get_crop_region(mask_image, width, height, pad=padding_mask_crop) resize_mode = "fill" else: crops_coords = None resize_mode = "default" original_image = image init_image = self.image_processor.preprocess( image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode ) init_image = init_image.to(dtype=torch.float32) # 5. Prepare latent variables num_channels_latents = self.vae.config.latent_channels num_channels_transformer = self.transformer.config.in_channels return_image_latents = num_channels_transformer == 16
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latents_outputs = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, image=init_image, timestep=latent_timestep, is_strength_max=is_strength_max, return_noise=True, return_image_latents=return_image_latents, ) if return_image_latents: latents, noise, image_latents = latents_outputs else: latents, noise = latents_outputs # 6. Prepare mask latent variables mask_condition = self.mask_processor.preprocess( mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords ) if masked_image_latents is None: masked_image = init_image * (mask_condition < 0.5) else: masked_image = masked_image_latents
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mask, masked_image_latents = self.prepare_mask_latents( mask_condition, masked_image, batch_size, num_images_per_prompt, height, width, prompt_embeds.dtype, device, generator, self.do_classifier_free_guidance, )
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# match the inpainting pipeline and will be updated with input + mask inpainting model later if num_channels_transformer == 33: # default case for runwayml/stable-diffusion-inpainting num_channels_mask = mask.shape[1] num_channels_masked_image = masked_image_latents.shape[1] if ( num_channels_latents + num_channels_mask + num_channels_masked_image != self.transformer.config.in_channels ): raise ValueError( f"Incorrect configuration settings! The config of `pipeline.transformer`: {self.transformer.config} expects" f" {self.transformer.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +" f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
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f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of" " `pipeline.transformer` or your `mask_image` or `image` input." ) elif num_channels_transformer != 16: raise ValueError( f"The transformer {self.transformer.__class__} should have 16 input channels or 33 input channels, not {self.transformer.config.in_channels}." )
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# 7. Prepare image embeddings if (ip_adapter_image is not None and self.is_ip_adapter_active) or ip_adapter_image_embeds is not None: ip_adapter_image_embeds = self.prepare_ip_adapter_image_embeds( ip_adapter_image, ip_adapter_image_embeds, device, batch_size * num_images_per_prompt, self.do_classifier_free_guidance, ) if self.joint_attention_kwargs is None: self._joint_attention_kwargs = {"ip_adapter_image_embeds": ip_adapter_image_embeds} else: self._joint_attention_kwargs.update(ip_adapter_image_embeds=ip_adapter_image_embeds)
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# 8. Denoising loop num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) self._num_timesteps = len(timesteps) with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): if self.interrupt: continue # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timestep = t.expand(latent_model_input.shape[0]) if num_channels_transformer == 33: latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
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noise_pred = self.transformer( hidden_states=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds, pooled_projections=pooled_prompt_embeds, joint_attention_kwargs=self.joint_attention_kwargs, return_dict=False, )[0] # perform guidance if self.do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
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# compute the previous noisy sample x_t -> x_t-1 latents_dtype = latents.dtype latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0] if num_channels_transformer == 16: init_latents_proper = image_latents if self.do_classifier_free_guidance: init_mask, _ = mask.chunk(2) else: init_mask = mask if i < len(timesteps) - 1: noise_timestep = timesteps[i + 1] init_latents_proper = self.scheduler.scale_noise( init_latents_proper, torch.tensor([noise_timestep]), noise ) latents = (1 - init_mask) * init_latents_proper + init_mask * latents
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if latents.dtype != latents_dtype: if torch.backends.mps.is_available(): # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272 latents = latents.to(latents_dtype) if callback_on_step_end is not None: callback_kwargs = {} for k in callback_on_step_end_tensor_inputs: callback_kwargs[k] = locals()[k] callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
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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) negative_pooled_prompt_embeds = callback_outputs.pop( "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds ) mask = callback_outputs.pop("mask", mask) masked_image_latents = callback_outputs.pop("masked_image_latents", masked_image_latents) # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if XLA_AVAILABLE: xm.mark_step()
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if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[ 0 ] else: image = latents do_denormalize = [True] * image.shape[0] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) if padding_mask_crop is not None: image = [self.image_processor.apply_overlay(mask_image, original_image, i, crops_coords) for i in image] # Offload all models self.maybe_free_model_hooks() if not return_dict: return (image,) return StableDiffusion3PipelineOutput(images=image)
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class StableDiffusionXLWatermarker: def __init__(self): self.watermark = WATERMARK_BITS self.encoder = WatermarkEncoder() self.encoder.set_watermark("bits", self.watermark) def apply_watermark(self, images: torch.Tensor): # can't encode images that are smaller than 256 if images.shape[-1] < 256: return images images = (255 * (images / 2 + 0.5)).cpu().permute(0, 2, 3, 1).float().numpy() # Convert RGB to BGR, which is the channel order expected by the watermark encoder. images = images[:, :, :, ::-1] # Add watermark and convert BGR back to RGB images = [self.encoder.encode(image, "dwtDct")[:, :, ::-1] for image in images] images = np.array(images) images = torch.from_numpy(images).permute(0, 3, 1, 2) images = torch.clamp(2 * (images / 255 - 0.5), min=-1.0, max=1.0) return images
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class StableDiffusionXLPipelineOutput(BaseOutput): """ Output class for Stable Diffusion pipelines. Args: images (`List[PIL.Image.Image]` or `np.ndarray`) List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width, num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline. """ images: Union[List[PIL.Image.Image], np.ndarray]
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class FlaxStableDiffusionXLPipelineOutput(BaseOutput): """ Output class for Flax Stable Diffusion XL pipelines. Args: images (`np.ndarray`) Array of shape `(batch_size, height, width, num_channels)` with images from the diffusion pipeline. """ images: np.ndarray
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class FlaxStableDiffusionXLPipeline(FlaxDiffusionPipeline): def __init__( self, text_encoder: FlaxCLIPTextModel, text_encoder_2: FlaxCLIPTextModel, vae: FlaxAutoencoderKL, tokenizer: CLIPTokenizer, tokenizer_2: CLIPTokenizer, unet: FlaxUNet2DConditionModel, scheduler: Union[ FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler ], dtype: jnp.dtype = jnp.float32, ): super().__init__() self.dtype = dtype self.register_modules( vae=vae, text_encoder=text_encoder, text_encoder_2=text_encoder_2, tokenizer=tokenizer, tokenizer_2=tokenizer_2, unet=unet, scheduler=scheduler, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
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def prepare_inputs(self, prompt: Union[str, List[str]]): if not isinstance(prompt, (str, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") # Assume we have the two encoders inputs = [] for tokenizer in [self.tokenizer, self.tokenizer_2]: text_inputs = tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="np", ) inputs.append(text_inputs.input_ids) inputs = jnp.stack(inputs, axis=1) return inputs
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def __call__( self, prompt_ids: jax.Array, params: Union[Dict, FrozenDict], prng_seed: jax.Array, num_inference_steps: int = 50, guidance_scale: Union[float, jax.Array] = 7.5, height: Optional[int] = None, width: Optional[int] = None, latents: jnp.array = None, neg_prompt_ids: jnp.array = None, return_dict: bool = True, output_type: str = None, jit: bool = False, ): # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor if isinstance(guidance_scale, float) and jit: # Convert to a tensor so each device gets a copy. guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0]) guidance_scale = guidance_scale[:, None] return_latents = output_type == "latent"
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if jit: images = _p_generate( self, prompt_ids, params, prng_seed, num_inference_steps, height, width, guidance_scale, latents, neg_prompt_ids, return_latents, ) else: images = self._generate( prompt_ids, params, prng_seed, num_inference_steps, height, width, guidance_scale, latents, neg_prompt_ids, return_latents, ) if not return_dict: return (images,) return FlaxStableDiffusionXLPipelineOutput(images=images) def get_embeddings(self, prompt_ids: jnp.array, params): # We assume we have the two encoders
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# bs, encoder_input, seq_length te_1_inputs = prompt_ids[:, 0, :] te_2_inputs = prompt_ids[:, 1, :] prompt_embeds = self.text_encoder(te_1_inputs, params=params["text_encoder"], output_hidden_states=True) prompt_embeds = prompt_embeds["hidden_states"][-2] prompt_embeds_2_out = self.text_encoder_2( te_2_inputs, params=params["text_encoder_2"], output_hidden_states=True ) prompt_embeds_2 = prompt_embeds_2_out["hidden_states"][-2] text_embeds = prompt_embeds_2_out["text_embeds"] prompt_embeds = jnp.concatenate([prompt_embeds, prompt_embeds_2], axis=-1) return prompt_embeds, text_embeds def _get_add_time_ids(self, original_size, crops_coords_top_left, target_size, bs, dtype): add_time_ids = list(original_size + crops_coords_top_left + target_size) add_time_ids = jnp.array([add_time_ids] * bs, dtype=dtype) return add_time_ids
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def _generate( self, prompt_ids: jnp.array, params: Union[Dict, FrozenDict], prng_seed: jax.Array, num_inference_steps: int, height: int, width: int, guidance_scale: float, latents: Optional[jnp.array] = None, neg_prompt_ids: Optional[jnp.array] = None, return_latents=False, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") # Encode input prompt prompt_embeds, pooled_embeds = self.get_embeddings(prompt_ids, params) # Get unconditional embeddings batch_size = prompt_embeds.shape[0] if neg_prompt_ids is None: neg_prompt_embeds = jnp.zeros_like(prompt_embeds) negative_pooled_embeds = jnp.zeros_like(pooled_embeds) else: neg_prompt_embeds, negative_pooled_embeds = self.get_embeddings(neg_prompt_ids, params)
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add_time_ids = self._get_add_time_ids( (height, width), (0, 0), (height, width), prompt_embeds.shape[0], dtype=prompt_embeds.dtype ) prompt_embeds = jnp.concatenate([neg_prompt_embeds, prompt_embeds], axis=0) # (2, 77, 2048) add_text_embeds = jnp.concatenate([negative_pooled_embeds, pooled_embeds], axis=0) add_time_ids = jnp.concatenate([add_time_ids, add_time_ids], axis=0) # Ensure model output will be `float32` before going into the scheduler guidance_scale = jnp.array([guidance_scale], dtype=jnp.float32)
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# Create random latents latents_shape = ( batch_size, self.unet.config.in_channels, height // self.vae_scale_factor, width // self.vae_scale_factor, ) if latents is None: latents = jax.random.normal(prng_seed, shape=latents_shape, dtype=jnp.float32) else: if latents.shape != latents_shape: raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}") # Prepare scheduler state scheduler_state = self.scheduler.set_timesteps( params["scheduler"], num_inference_steps=num_inference_steps, shape=latents.shape ) # scale the initial noise by the standard deviation required by the scheduler latents = latents * scheduler_state.init_noise_sigma added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
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# Denoising loop def loop_body(step, args): latents, scheduler_state = args # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes latents_input = jnp.concatenate([latents] * 2) t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step] timestep = jnp.broadcast_to(t, latents_input.shape[0]) latents_input = self.scheduler.scale_model_input(scheduler_state, latents_input, t)
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# predict the noise residual noise_pred = self.unet.apply( {"params": params["unet"]}, jnp.array(latents_input), jnp.array(timestep, dtype=jnp.int32), encoder_hidden_states=prompt_embeds, added_cond_kwargs=added_cond_kwargs, ).sample # perform guidance noise_pred_uncond, noise_prediction_text = jnp.split(noise_pred, 2, axis=0) noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple() return latents, scheduler_state
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if DEBUG: # run with python for loop for i in range(num_inference_steps): latents, scheduler_state = loop_body(i, (latents, scheduler_state)) else: latents, _ = jax.lax.fori_loop(0, num_inference_steps, loop_body, (latents, scheduler_state)) if return_latents: return latents # Decode latents latents = 1 / self.vae.config.scaling_factor * latents image = self.vae.apply({"params": params["vae"]}, latents, method=self.vae.decode).sample image = (image / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1) return image
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class StableDiffusionXLInpaintPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, StableDiffusionXLLoraLoaderMixin, FromSingleFileMixin, IPAdapterMixin, ): r""" Pipeline for text-to-image generation using Stable Diffusion XL. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
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The pipeline also inherits the following loading methods: - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
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Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion XL uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. text_encoder_2 ([` CLIPTextModelWithProjection`]): Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection), specifically the [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class
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[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). tokenizer_2 (`CLIPTokenizer`): Second Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. requires_aesthetics_score (`bool`, *optional*, defaults to `"False"`): Whether the `unet` requires a aesthetic_score condition to be passed during inference. Also see the config of `stabilityai/stable-diffusion-xl-refiner-1-0`. force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
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Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of `stabilityai/stable-diffusion-xl-base-1-0`. add_watermarker (`bool`, *optional*): Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to watermark output images. If not defined, it will default to True if the package is installed, otherwise no watermarker will be used. """
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model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae" _optional_components = [ "tokenizer", "tokenizer_2", "text_encoder", "text_encoder_2", "image_encoder", "feature_extractor", ] _callback_tensor_inputs = [ "latents", "prompt_embeds", "add_text_embeds", "add_time_ids", "mask", "masked_image_latents", ]
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def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, text_encoder_2: CLIPTextModelWithProjection, tokenizer: CLIPTokenizer, tokenizer_2: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, image_encoder: CLIPVisionModelWithProjection = None, feature_extractor: CLIPImageProcessor = None, requires_aesthetics_score: bool = False, force_zeros_for_empty_prompt: bool = True, add_watermarker: Optional[bool] = None, ): super().__init__()
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self.register_modules( vae=vae, text_encoder=text_encoder, text_encoder_2=text_encoder_2, tokenizer=tokenizer, tokenizer_2=tokenizer_2, unet=unet, image_encoder=image_encoder, feature_extractor=feature_extractor, scheduler=scheduler, ) self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt) self.register_to_config(requires_aesthetics_score=requires_aesthetics_score) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8 self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.mask_processor = VaeImageProcessor( vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True )
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add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available() if add_watermarker: self.watermark = StableDiffusionXLWatermarker() else: self.watermark = None # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None): dtype = next(self.image_encoder.parameters()).dtype if not isinstance(image, torch.Tensor): image = self.feature_extractor(image, return_tensors="pt").pixel_values
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image = image.to(device=device, dtype=dtype) if output_hidden_states: image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2] image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0) uncond_image_enc_hidden_states = self.image_encoder( torch.zeros_like(image), output_hidden_states=True ).hidden_states[-2] uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave( num_images_per_prompt, dim=0 ) return image_enc_hidden_states, uncond_image_enc_hidden_states else: image_embeds = self.image_encoder(image).image_embeds image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0) uncond_image_embeds = torch.zeros_like(image_embeds) return image_embeds, uncond_image_embeds
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds def prepare_ip_adapter_image_embeds( self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance ): image_embeds = [] if do_classifier_free_guidance: negative_image_embeds = [] if ip_adapter_image_embeds is None: if not isinstance(ip_adapter_image, list): ip_adapter_image = [ip_adapter_image] if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers): raise ValueError( f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters." )
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for single_ip_adapter_image, image_proj_layer in zip( ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers ): output_hidden_state = not isinstance(image_proj_layer, ImageProjection) single_image_embeds, single_negative_image_embeds = self.encode_image( single_ip_adapter_image, device, 1, output_hidden_state ) image_embeds.append(single_image_embeds[None, :]) if do_classifier_free_guidance: negative_image_embeds.append(single_negative_image_embeds[None, :]) else: for single_image_embeds in ip_adapter_image_embeds: if do_classifier_free_guidance: single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2) negative_image_embeds.append(single_negative_image_embeds) image_embeds.append(single_image_embeds)
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ip_adapter_image_embeds = [] for i, single_image_embeds in enumerate(image_embeds): single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0) if do_classifier_free_guidance: single_negative_image_embeds = torch.cat([negative_image_embeds[i]] * num_images_per_prompt, dim=0) single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds], dim=0) single_image_embeds = single_image_embeds.to(device=device) ip_adapter_image_embeds.append(single_image_embeds) return ip_adapter_image_embeds
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# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt def encode_prompt( self, prompt: str, prompt_2: Optional[str] = None, device: Optional[torch.device] = None, num_images_per_prompt: int = 1, do_classifier_free_guidance: bool = True, negative_prompt: Optional[str] = None, negative_prompt_2: Optional[str] = None, prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, pooled_prompt_embeds: Optional[torch.Tensor] = None, negative_pooled_prompt_embeds: Optional[torch.Tensor] = None, lora_scale: Optional[float] = None, clip_skip: Optional[int] = None, ): r""" Encodes the prompt into text encoder hidden states.
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Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded prompt_2 (`str` or `List[str]`, *optional*): The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is used in both text-encoders device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not 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`). negative_prompt_2 (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders prompt_embeds (`torch.Tensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.Tensor`, *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. pooled_prompt_embeds (`torch.Tensor`, *optional*): Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
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If not provided, pooled text embeddings will be generated from `prompt` input argument. negative_pooled_prompt_embeds (`torch.Tensor`, *optional*): Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` input argument. lora_scale (`float`, *optional*): A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. clip_skip (`int`, *optional*): Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings. """ device = device or self._execution_device
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# set lora scale so that monkey patched LoRA # function of text encoder can correctly access it if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin): self._lora_scale = lora_scale # dynamically adjust the LoRA scale if self.text_encoder is not None: if not USE_PEFT_BACKEND: adjust_lora_scale_text_encoder(self.text_encoder, lora_scale) else: scale_lora_layers(self.text_encoder, lora_scale) if self.text_encoder_2 is not None: if not USE_PEFT_BACKEND: adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale) else: scale_lora_layers(self.text_encoder_2, lora_scale) prompt = [prompt] if isinstance(prompt, str) else prompt if prompt is not None: batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0]
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# Define tokenizers and text encoders tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2] text_encoders = ( [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2] ) if prompt_embeds is None: prompt_2 = prompt_2 or prompt prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2 # textual inversion: process multi-vector tokens if necessary prompt_embeds_list = [] prompts = [prompt, prompt_2] for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders): if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, tokenizer)
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text_inputs = tokenizer( prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1]) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {tokenizer.model_max_length} tokens: {removed_text}" )
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prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True) # We are only ALWAYS interested in the pooled output of the final text encoder if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2: pooled_prompt_embeds = prompt_embeds[0] if clip_skip is None: prompt_embeds = prompt_embeds.hidden_states[-2] else: # "2" because SDXL always indexes from the penultimate layer. prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)] prompt_embeds_list.append(prompt_embeds) prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
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# get unconditional embeddings for classifier free guidance zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt: negative_prompt_embeds = torch.zeros_like(prompt_embeds) negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds) elif do_classifier_free_guidance and negative_prompt_embeds is None: negative_prompt = negative_prompt or "" negative_prompt_2 = negative_prompt_2 or negative_prompt # normalize str to list negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt negative_prompt_2 = ( batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2 )
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uncond_tokens: List[str] if prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = [negative_prompt, negative_prompt_2]
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negative_prompt_embeds_list = [] for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders): if isinstance(self, TextualInversionLoaderMixin): negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer) max_length = prompt_embeds.shape[1] uncond_input = tokenizer( negative_prompt, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) negative_prompt_embeds = text_encoder( uncond_input.input_ids.to(device), output_hidden_states=True, )
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# We are only ALWAYS interested in the pooled output of the final text encoder if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2: negative_pooled_prompt_embeds = negative_prompt_embeds[0] negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2] negative_prompt_embeds_list.append(negative_prompt_embeds) negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1) if self.text_encoder_2 is not None: prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) else: prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device)
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bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] if self.text_encoder_2 is not None: negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) else: negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
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pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( bs_embed * num_images_per_prompt, -1 ) if do_classifier_free_guidance: negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( bs_embed * num_images_per_prompt, -1 ) if self.text_encoder is not None: if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: # Retrieve the original scale by scaling back the LoRA layers unscale_lora_layers(self.text_encoder, lora_scale) if self.text_encoder_2 is not None: if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: # Retrieve the original scale by scaling back the LoRA layers unscale_lora_layers(self.text_encoder_2, lora_scale)
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return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta
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# check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, prompt_2, image, mask_image, height, width, strength, callback_steps, output_type, negative_prompt=None, negative_prompt_2=None, prompt_embeds=None, negative_prompt_embeds=None, ip_adapter_image=None, ip_adapter_image_embeds=None, callback_on_step_end_tensor_inputs=None, padding_mask_crop=None, ): if strength < 0 or strength > 1: raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
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if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if callback_on_step_end_tensor_inputs is not None and not all( k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs ): raise ValueError( 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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if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt_2 is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)): raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
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if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) elif negative_prompt_2 is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." )
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if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) if padding_mask_crop is not None: if not isinstance(image, PIL.Image.Image): raise ValueError( f"The image should be a PIL image when inpainting mask crop, but is of type" f" {type(image)}." ) if not isinstance(mask_image, PIL.Image.Image): raise ValueError( f"The mask image should be a PIL image when inpainting mask crop, but is of type" f" {type(mask_image)}." )
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if output_type != "pil": raise ValueError(f"The output type should be PIL when inpainting mask crop, but is" f" {output_type}.")
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if ip_adapter_image is not None and ip_adapter_image_embeds is not None: raise ValueError( "Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined." ) if ip_adapter_image_embeds is not None: if not isinstance(ip_adapter_image_embeds, list): raise ValueError( f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}" ) elif ip_adapter_image_embeds[0].ndim not in [3, 4]: raise ValueError( f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D" )
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def prepare_latents( self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None, image=None, timestep=None, is_strength_max=True, add_noise=True, return_noise=False, return_image_latents=False, ): shape = ( batch_size, num_channels_latents, int(height) // self.vae_scale_factor, int(width) // self.vae_scale_factor, ) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." )
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if (image is None or timestep is None) and not is_strength_max: raise ValueError( "Since strength < 1. initial latents are to be initialised as a combination of Image + Noise." "However, either the image or the noise timestep has not been provided." ) if image.shape[1] == 4: image_latents = image.to(device=device, dtype=dtype) image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1) elif return_image_latents or (latents is None and not is_strength_max): image = image.to(device=device, dtype=dtype) image_latents = self._encode_vae_image(image=image, generator=generator) image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)
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if latents is None and add_noise: noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) # if strength is 1. then initialise the latents to noise, else initial to image + noise latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep) # if pure noise then scale the initial latents by the Scheduler's init sigma latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents elif add_noise: noise = latents.to(device) latents = noise * self.scheduler.init_noise_sigma else: noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) latents = image_latents.to(device) outputs = (latents,) if return_noise: outputs += (noise,) if return_image_latents: outputs += (image_latents,) return outputs
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def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator): dtype = image.dtype if self.vae.config.force_upcast: image = image.float() self.vae.to(dtype=torch.float32) if isinstance(generator, list): image_latents = [ retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i]) for i in range(image.shape[0]) ] image_latents = torch.cat(image_latents, dim=0) else: image_latents = retrieve_latents(self.vae.encode(image), generator=generator) if self.vae.config.force_upcast: self.vae.to(dtype) image_latents = image_latents.to(dtype) image_latents = self.vae.config.scaling_factor * image_latents return image_latents
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def prepare_mask_latents( self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance ): # resize the mask to latents shape as we concatenate the mask to the latents # we do that before converting to dtype to avoid breaking in case we're using cpu_offload # and half precision mask = torch.nn.functional.interpolate( mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor) ) mask = mask.to(device=device, dtype=dtype)
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# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method if mask.shape[0] < batch_size: if not batch_size % mask.shape[0] == 0: raise ValueError( "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to" f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number" " of masks that you pass is divisible by the total requested batch size." ) mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1) mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask if masked_image is not None and masked_image.shape[1] == 4: masked_image_latents = masked_image else: masked_image_latents = None
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if masked_image is not None: if masked_image_latents is None: masked_image = masked_image.to(device=device, dtype=dtype) masked_image_latents = self._encode_vae_image(masked_image, generator=generator) if masked_image_latents.shape[0] < batch_size: if not batch_size % masked_image_latents.shape[0] == 0: raise ValueError( "The passed images and the required batch size don't match. Images are supposed to be duplicated" f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed." " Make sure the number of images that you pass is divisible by the total requested batch size." ) masked_image_latents = masked_image_latents.repeat( batch_size // masked_image_latents.shape[0], 1, 1, 1 )
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masked_image_latents = ( torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents ) # aligning device to prevent device errors when concating it with the latent model input masked_image_latents = masked_image_latents.to(device=device, dtype=dtype) return mask, masked_image_latents # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline.get_timesteps def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None): # get the original timestep using init_timestep if denoising_start is None: init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0)
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timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] if hasattr(self.scheduler, "set_begin_index"): self.scheduler.set_begin_index(t_start * self.scheduler.order) return timesteps, num_inference_steps - t_start else: # Strength is irrelevant if we directly request a timestep to start at; # that is, strength is determined by the denoising_start instead. discrete_timestep_cutoff = int( round( self.scheduler.config.num_train_timesteps - (denoising_start * self.scheduler.config.num_train_timesteps) ) )
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num_inference_steps = (self.scheduler.timesteps < discrete_timestep_cutoff).sum().item() if self.scheduler.order == 2 and num_inference_steps % 2 == 0: # if the scheduler is a 2nd order scheduler we might have to do +1 # because `num_inference_steps` might be even given that every timestep # (except the highest one) is duplicated. If `num_inference_steps` is even it would # mean that we cut the timesteps in the middle of the denoising step # (between 1st and 2nd derivative) which leads to incorrect results. By adding 1 # we ensure that the denoising process always ends after the 2nd derivate step of the scheduler num_inference_steps = num_inference_steps + 1
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# because t_n+1 >= t_n, we slice the timesteps starting from the end t_start = len(self.scheduler.timesteps) - num_inference_steps timesteps = self.scheduler.timesteps[t_start:] if hasattr(self.scheduler, "set_begin_index"): self.scheduler.set_begin_index(t_start) return timesteps, num_inference_steps
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# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline._get_add_time_ids def _get_add_time_ids( self, original_size, crops_coords_top_left, target_size, aesthetic_score, negative_aesthetic_score, negative_original_size, negative_crops_coords_top_left, negative_target_size, dtype, text_encoder_projection_dim=None, ): if self.config.requires_aesthetics_score: add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,)) add_neg_time_ids = list( negative_original_size + negative_crops_coords_top_left + (negative_aesthetic_score,) ) else: add_time_ids = list(original_size + crops_coords_top_left + target_size) add_neg_time_ids = list(negative_original_size + crops_coords_top_left + negative_target_size)
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passed_add_embed_dim = ( self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim ) expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
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if ( expected_add_embed_dim > passed_add_embed_dim and (expected_add_embed_dim - passed_add_embed_dim) == self.unet.config.addition_time_embed_dim ): raise ValueError( f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model." ) elif ( expected_add_embed_dim < passed_add_embed_dim and (passed_add_embed_dim - expected_add_embed_dim) == self.unet.config.addition_time_embed_dim ): raise ValueError(
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f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model." ) elif expected_add_embed_dim != passed_add_embed_dim: raise ValueError( f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`." )
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add_time_ids = torch.tensor([add_time_ids], dtype=dtype) add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype) return add_time_ids, add_neg_time_ids # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae def upcast_vae(self): dtype = self.vae.dtype self.vae.to(dtype=torch.float32) use_torch_2_0_or_xformers = isinstance( self.vae.decoder.mid_block.attentions[0].processor, ( AttnProcessor2_0, XFormersAttnProcessor, ), ) # if xformers or torch_2_0 is used attention block does not need # to be in float32 which can save lots of memory if use_torch_2_0_or_xformers: self.vae.post_quant_conv.to(dtype) self.vae.decoder.conv_in.to(dtype) self.vae.decoder.mid_block.to(dtype)
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# Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding def get_guidance_scale_embedding( self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32 ) -> torch.Tensor: """ See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 Args: w (`torch.Tensor`): Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings. embedding_dim (`int`, *optional*, defaults to 512): Dimension of the embeddings to generate. dtype (`torch.dtype`, *optional*, defaults to `torch.float32`): Data type of the generated embeddings.
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Returns: `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`. """ assert len(w.shape) == 1 w = w * 1000.0 half_dim = embedding_dim // 2 emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb) emb = w.to(dtype)[:, None] * emb[None, :] emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) if embedding_dim % 2 == 1: # zero pad emb = torch.nn.functional.pad(emb, (0, 1)) assert emb.shape == (w.shape[0], embedding_dim) return emb @property def guidance_scale(self): return self._guidance_scale @property def guidance_rescale(self): return self._guidance_rescale @property def clip_skip(self): return self._clip_skip
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# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. @property def do_classifier_free_guidance(self): return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None @property def cross_attention_kwargs(self): return self._cross_attention_kwargs @property def denoising_end(self): return self._denoising_end @property def denoising_start(self): return self._denoising_start @property def num_timesteps(self): return self._num_timesteps @property def interrupt(self): return self._interrupt
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@torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, prompt_2: Optional[Union[str, List[str]]] = None, image: PipelineImageInput = None, mask_image: PipelineImageInput = None, masked_image_latents: torch.Tensor = None, height: Optional[int] = None, width: Optional[int] = None, padding_mask_crop: Optional[int] = None, strength: float = 0.9999, num_inference_steps: int = 50, timesteps: List[int] = None, sigmas: List[float] = None, denoising_start: Optional[float] = None, denoising_end: Optional[float] = None, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, negative_prompt_2: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0,
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.Tensor] = None, prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, pooled_prompt_embeds: Optional[torch.Tensor] = None, negative_pooled_prompt_embeds: Optional[torch.Tensor] = None, ip_adapter_image: Optional[PipelineImageInput] = None, ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None, output_type: Optional[str] = "pil", return_dict: bool = True, cross_attention_kwargs: Optional[Dict[str, Any]] = None, guidance_rescale: float = 0.0, original_size: Tuple[int, int] = None, crops_coords_top_left: Tuple[int, int] = (0, 0), target_size: Tuple[int, int] = None, negative_original_size: Optional[Tuple[int, int]] = None, negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
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negative_target_size: Optional[Tuple[int, int]] = None, aesthetic_score: float = 6.0, negative_aesthetic_score: float = 2.5, clip_skip: Optional[int] = None, callback_on_step_end: Optional[ Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] ] = None, callback_on_step_end_tensor_inputs: List[str] = ["latents"], **kwargs, ): r""" Function invoked when calling the pipeline for generation.
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Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. prompt_2 (`str` or `List[str]`, *optional*): The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is used in both text-encoders image (`PIL.Image.Image`): `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will be masked out with `mask_image` and repainted according to `prompt`. mask_image (`PIL.Image.Image`): `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
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to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. This is set to 1024 by default for the best results. Anything below 512 pixels won't work well for [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and checkpoints that are not specifically fine-tuned on low resolutions. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. This is set to 1024 by default for the best results. Anything below 512 pixels won't work well for
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[stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and checkpoints that are not specifically fine-tuned on low resolutions. padding_mask_crop (`int`, *optional*, defaults to `None`): The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to image and mask_image. If `padding_mask_crop` is not `None`, it will first find a rectangular region with the same aspect ration of the image and contains all masked area, and then expand that area based on `padding_mask_crop`. The image and mask_image will then be cropped based on the expanded area before resizing to the original image size for inpainting. This is useful when the masked area is small while the image is large and contain information irrelevant for inpainting, such as background.
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strength (`float`, *optional*, defaults to 0.9999): Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores the masked portion of the reference `image`. Note that in the case of `denoising_start` being declared as an integer, the value of `strength` will be ignored. 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
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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. denoising_start (`float`, *optional*): When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and
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it is assumed that the passed `image` is a partly denoised image. Note that when this is specified, strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline is integrated into a "Mixture of Denoisers" multi-pipeline setup, as detailed in [**Refining the Image Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output). denoising_end (`float`, *optional*): When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be completed before it is intentionally prematurely terminated. As a result, the returned sample will still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the
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