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Running
on
Zero
| import torch | |
| from diffusers import ImagePipelineOutput, PixArtAlphaPipeline, AutoencoderKL, Transformer2DModel, \ | |
| DPMSolverMultistepScheduler | |
| from diffusers.image_processor import VaeImageProcessor | |
| from diffusers.models.attention import BasicTransformerBlock | |
| from diffusers.models.embeddings import PixArtAlphaTextProjection, PatchEmbed | |
| from diffusers.models.normalization import AdaLayerNormSingle | |
| from diffusers.pipelines.pixart_alpha.pipeline_pixart_alpha import retrieve_timesteps | |
| from typing import Callable, List, Optional, Tuple, Union | |
| from diffusers.utils import deprecate | |
| from torch import nn | |
| from transformers import T5Tokenizer, T5EncoderModel | |
| ASPECT_RATIO_2048_BIN = { | |
| "0.25": [1024.0, 4096.0], | |
| "0.26": [1024.0, 3968.0], | |
| "0.27": [1024.0, 3840.0], | |
| "0.28": [1024.0, 3712.0], | |
| "0.32": [1152.0, 3584.0], | |
| "0.33": [1152.0, 3456.0], | |
| "0.35": [1152.0, 3328.0], | |
| "0.4": [1280.0, 3200.0], | |
| "0.42": [1280.0, 3072.0], | |
| "0.48": [1408.0, 2944.0], | |
| "0.5": [1408.0, 2816.0], | |
| "0.52": [1408.0, 2688.0], | |
| "0.57": [1536.0, 2688.0], | |
| "0.6": [1536.0, 2560.0], | |
| "0.68": [1664.0, 2432.0], | |
| "0.72": [1664.0, 2304.0], | |
| "0.78": [1792.0, 2304.0], | |
| "0.82": [1792.0, 2176.0], | |
| "0.88": [1920.0, 2176.0], | |
| "0.94": [1920.0, 2048.0], | |
| "1.0": [2048.0, 2048.0], | |
| "1.07": [2048.0, 1920.0], | |
| "1.13": [2176.0, 1920.0], | |
| "1.21": [2176.0, 1792.0], | |
| "1.29": [2304.0, 1792.0], | |
| "1.38": [2304.0, 1664.0], | |
| "1.46": [2432.0, 1664.0], | |
| "1.67": [2560.0, 1536.0], | |
| "1.75": [2688.0, 1536.0], | |
| "2.0": [2816.0, 1408.0], | |
| "2.09": [2944.0, 1408.0], | |
| "2.4": [3072.0, 1280.0], | |
| "2.5": [3200.0, 1280.0], | |
| "2.89": [3328.0, 1152.0], | |
| "3.0": [3456.0, 1152.0], | |
| "3.11": [3584.0, 1152.0], | |
| "3.62": [3712.0, 1024.0], | |
| "3.75": [3840.0, 1024.0], | |
| "3.88": [3968.0, 1024.0], | |
| "4.0": [4096.0, 1024.0] | |
| } | |
| ASPECT_RATIO_256_BIN = { | |
| "0.25": [128.0, 512.0], | |
| "0.28": [128.0, 464.0], | |
| "0.32": [144.0, 448.0], | |
| "0.33": [144.0, 432.0], | |
| "0.35": [144.0, 416.0], | |
| "0.4": [160.0, 400.0], | |
| "0.42": [160.0, 384.0], | |
| "0.48": [176.0, 368.0], | |
| "0.5": [176.0, 352.0], | |
| "0.52": [176.0, 336.0], | |
| "0.57": [192.0, 336.0], | |
| "0.6": [192.0, 320.0], | |
| "0.68": [208.0, 304.0], | |
| "0.72": [208.0, 288.0], | |
| "0.78": [224.0, 288.0], | |
| "0.82": [224.0, 272.0], | |
| "0.88": [240.0, 272.0], | |
| "0.94": [240.0, 256.0], | |
| "1.0": [256.0, 256.0], | |
| "1.07": [256.0, 240.0], | |
| "1.13": [272.0, 240.0], | |
| "1.21": [272.0, 224.0], | |
| "1.29": [288.0, 224.0], | |
| "1.38": [288.0, 208.0], | |
| "1.46": [304.0, 208.0], | |
| "1.67": [320.0, 192.0], | |
| "1.75": [336.0, 192.0], | |
| "2.0": [352.0, 176.0], | |
| "2.09": [368.0, 176.0], | |
| "2.4": [384.0, 160.0], | |
| "2.5": [400.0, 160.0], | |
| "3.0": [432.0, 144.0], | |
| "4.0": [512.0, 128.0] | |
| } | |
| ASPECT_RATIO_1024_BIN = { | |
| "0.25": [512.0, 2048.0], | |
| "0.28": [512.0, 1856.0], | |
| "0.32": [576.0, 1792.0], | |
| "0.33": [576.0, 1728.0], | |
| "0.35": [576.0, 1664.0], | |
| "0.4": [640.0, 1600.0], | |
| "0.42": [640.0, 1536.0], | |
| "0.48": [704.0, 1472.0], | |
| "0.5": [704.0, 1408.0], | |
| "0.52": [704.0, 1344.0], | |
| "0.57": [768.0, 1344.0], | |
| "0.6": [768.0, 1280.0], | |
| "0.68": [832.0, 1216.0], | |
| "0.72": [832.0, 1152.0], | |
| "0.78": [896.0, 1152.0], | |
| "0.82": [896.0, 1088.0], | |
| "0.88": [960.0, 1088.0], | |
| "0.94": [960.0, 1024.0], | |
| "1.0": [1024.0, 1024.0], | |
| "1.07": [1024.0, 960.0], | |
| "1.13": [1088.0, 960.0], | |
| "1.21": [1088.0, 896.0], | |
| "1.29": [1152.0, 896.0], | |
| "1.38": [1152.0, 832.0], | |
| "1.46": [1216.0, 832.0], | |
| "1.67": [1280.0, 768.0], | |
| "1.75": [1344.0, 768.0], | |
| "2.0": [1408.0, 704.0], | |
| "2.09": [1472.0, 704.0], | |
| "2.4": [1536.0, 640.0], | |
| "2.5": [1600.0, 640.0], | |
| "3.0": [1728.0, 576.0], | |
| "4.0": [2048.0, 512.0], | |
| } | |
| ASPECT_RATIO_512_BIN = { | |
| "0.25": [256.0, 1024.0], | |
| "0.28": [256.0, 928.0], | |
| "0.32": [288.0, 896.0], | |
| "0.33": [288.0, 864.0], | |
| "0.35": [288.0, 832.0], | |
| "0.4": [320.0, 800.0], | |
| "0.42": [320.0, 768.0], | |
| "0.48": [352.0, 736.0], | |
| "0.5": [352.0, 704.0], | |
| "0.52": [352.0, 672.0], | |
| "0.57": [384.0, 672.0], | |
| "0.6": [384.0, 640.0], | |
| "0.68": [416.0, 608.0], | |
| "0.72": [416.0, 576.0], | |
| "0.78": [448.0, 576.0], | |
| "0.82": [448.0, 544.0], | |
| "0.88": [480.0, 544.0], | |
| "0.94": [480.0, 512.0], | |
| "1.0": [512.0, 512.0], | |
| "1.07": [512.0, 480.0], | |
| "1.13": [544.0, 480.0], | |
| "1.21": [544.0, 448.0], | |
| "1.29": [576.0, 448.0], | |
| "1.38": [576.0, 416.0], | |
| "1.46": [608.0, 416.0], | |
| "1.67": [640.0, 384.0], | |
| "1.75": [672.0, 384.0], | |
| "2.0": [704.0, 352.0], | |
| "2.09": [736.0, 352.0], | |
| "2.4": [768.0, 320.0], | |
| "2.5": [800.0, 320.0], | |
| "3.0": [864.0, 288.0], | |
| "4.0": [1024.0, 256.0], | |
| } | |
| def pipeline_pixart_alpha_call( | |
| self, | |
| prompt: Union[str, List[str]] = None, | |
| negative_prompt: str = "", | |
| num_inference_steps: int = 20, | |
| timesteps: List[int] = None, | |
| guidance_scale: float = 4.5, | |
| num_images_per_prompt: Optional[int] = 1, | |
| height: Optional[int] = None, | |
| width: Optional[int] = None, | |
| eta: float = 0.0, | |
| generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, | |
| latents: Optional[torch.FloatTensor] = None, | |
| prompt_embeds: Optional[torch.FloatTensor] = None, | |
| prompt_attention_mask: Optional[torch.FloatTensor] = None, | |
| negative_prompt_embeds: Optional[torch.FloatTensor] = None, | |
| negative_prompt_attention_mask: Optional[torch.FloatTensor] = None, | |
| output_type: Optional[str] = "pil", | |
| return_dict: bool = True, | |
| callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, | |
| callback_steps: int = 1, | |
| clean_caption: bool = True, | |
| use_resolution_binning: bool = True, | |
| max_sequence_length: int = 120, | |
| **kwargs, | |
| ) -> Union[ImagePipelineOutput, Tuple]: | |
| """ | |
| Function invoked when calling the pipeline for generation. | |
| 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. | |
| 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`). | |
| num_inference_steps (`int`, *optional*, defaults to 100): | |
| The number of denoising steps. More denoising steps usually lead to a higher quality image at the | |
| expense of slower inference. | |
| timesteps (`List[int]`, *optional*): | |
| Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps` | |
| timesteps are used. Must be in descending order. | |
| guidance_scale (`float`, *optional*, defaults to 4.5): | |
| Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). | |
| `guidance_scale` is defined as `w` of equation 2. of [Imagen | |
| Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > | |
| 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, | |
| usually at the expense of lower image quality. | |
| num_images_per_prompt (`int`, *optional*, defaults to 1): | |
| The number of images to generate per prompt. | |
| height (`int`, *optional*, defaults to self.unet.config.sample_size): | |
| The height in pixels of the generated image. | |
| width (`int`, *optional*, defaults to self.unet.config.sample_size): | |
| The width in pixels of the generated image. | |
| eta (`float`, *optional*, defaults to 0.0): | |
| Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to | |
| [`schedulers.DDIMScheduler`], will be ignored for others. | |
| generator (`torch.Generator` or `List[torch.Generator]`, *optional*): | |
| One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) | |
| to make generation deterministic. | |
| latents (`torch.FloatTensor`, *optional*): | |
| Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image | |
| generation. Can be used to tweak the same generation with different prompts. If not provided, a latents | |
| tensor will ge generated by sampling using the supplied random `generator`. | |
| prompt_embeds (`torch.FloatTensor`, *optional*): | |
| Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not | |
| provided, text embeddings will be generated from `prompt` input argument. | |
| prompt_attention_mask (`torch.FloatTensor`, *optional*): Pre-generated attention mask for text embeddings. | |
| negative_prompt_embeds (`torch.FloatTensor`, *optional*): | |
| Pre-generated negative text embeddings. For PixArt-Alpha this negative prompt should be "". If not | |
| provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. | |
| negative_prompt_attention_mask (`torch.FloatTensor`, *optional*): | |
| Pre-generated attention mask for negative text embeddings. | |
| output_type (`str`, *optional*, defaults to `"pil"`): | |
| The output format of the generate image. Choose between | |
| [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. | |
| return_dict (`bool`, *optional*, defaults to `True`): | |
| Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple. | |
| callback (`Callable`, *optional*): | |
| A function that will be called every `callback_steps` steps during inference. The function will be | |
| called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. | |
| callback_steps (`int`, *optional*, defaults to 1): | |
| The frequency at which the `callback` function will be called. If not specified, the callback will be | |
| called at every step. | |
| clean_caption (`bool`, *optional*, defaults to `True`): | |
| Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to | |
| be installed. If the dependencies are not installed, the embeddings will be created from the raw | |
| prompt. | |
| use_resolution_binning (`bool` defaults to `True`): | |
| If set to `True`, the requested height and width are first mapped to the closest resolutions using | |
| `ASPECT_RATIO_1024_BIN`. After the produced latents are decoded into images, they are resized back to | |
| the requested resolution. Useful for generating non-square images. | |
| Examples: | |
| Returns: | |
| [`~pipelines.ImagePipelineOutput`] or `tuple`: | |
| If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is | |
| returned where the first element is a list with the generated images | |
| """ | |
| if "mask_feature" in kwargs: | |
| deprecation_message = "The use of `mask_feature` is deprecated. It is no longer used in any computation and that doesn't affect the end results. It will be removed in a future version." | |
| deprecate("mask_feature", "1.0.0", deprecation_message, standard_warn=False) | |
| # 1. Check inputs. Raise error if not correct | |
| height = height or self.transformer.config.sample_size * self.vae_scale_factor | |
| width = width or self.transformer.config.sample_size * self.vae_scale_factor | |
| if use_resolution_binning: | |
| if self.transformer.config.sample_size == 32: | |
| aspect_ratio_bin = ASPECT_RATIO_256_BIN | |
| elif self.transformer.config.sample_size == 64: | |
| aspect_ratio_bin = ASPECT_RATIO_512_BIN | |
| elif self.transformer.config.sample_size == 128: | |
| aspect_ratio_bin = ASPECT_RATIO_1024_BIN | |
| elif self.transformer.config.sample_size == 256: | |
| aspect_ratio_bin = ASPECT_RATIO_2048_BIN | |
| else: | |
| raise ValueError("Invalid sample size") | |
| orig_height, orig_width = height, width | |
| height, width = self.classify_height_width_bin(height, width, ratios=aspect_ratio_bin) | |
| self.check_inputs( | |
| prompt, | |
| height, | |
| width, | |
| negative_prompt, | |
| callback_steps, | |
| prompt_embeds, | |
| negative_prompt_embeds, | |
| prompt_attention_mask, | |
| negative_prompt_attention_mask, | |
| ) | |
| # 2. Default height and width to transformer | |
| 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 | |
| # 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. | |
| do_classifier_free_guidance = guidance_scale > 1.0 | |
| # 3. Encode input prompt | |
| ( | |
| prompt_embeds, | |
| prompt_attention_mask, | |
| negative_prompt_embeds, | |
| negative_prompt_attention_mask, | |
| ) = self.encode_prompt( | |
| prompt, | |
| do_classifier_free_guidance, | |
| negative_prompt=negative_prompt, | |
| num_images_per_prompt=num_images_per_prompt, | |
| device=device, | |
| prompt_embeds=prompt_embeds, | |
| negative_prompt_embeds=negative_prompt_embeds, | |
| prompt_attention_mask=prompt_attention_mask, | |
| negative_prompt_attention_mask=negative_prompt_attention_mask, | |
| clean_caption=clean_caption, | |
| max_sequence_length=max_sequence_length, | |
| ) | |
| if do_classifier_free_guidance: | |
| prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) | |
| prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0) | |
| # 4. Prepare timesteps | |
| timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps) | |
| # 5. Prepare latents. | |
| latent_channels = self.transformer.config.in_channels | |
| latents = self.prepare_latents( | |
| batch_size * num_images_per_prompt, | |
| latent_channels, | |
| height, | |
| width, | |
| prompt_embeds.dtype, | |
| device, | |
| generator, | |
| latents, | |
| ) | |
| # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline | |
| extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) | |
| # 6.1 Prepare micro-conditions. | |
| added_cond_kwargs = {"resolution": None, "aspect_ratio": None} | |
| if self.transformer.config.sample_size == 128: | |
| resolution = torch.tensor([height, width]).repeat(batch_size * num_images_per_prompt, 1) | |
| aspect_ratio = torch.tensor([float(height / width)]).repeat(batch_size * num_images_per_prompt, 1) | |
| resolution = resolution.to(dtype=prompt_embeds.dtype, device=device) | |
| aspect_ratio = aspect_ratio.to(dtype=prompt_embeds.dtype, device=device) | |
| if do_classifier_free_guidance: | |
| resolution = torch.cat([resolution, resolution], dim=0) | |
| aspect_ratio = torch.cat([aspect_ratio, aspect_ratio], dim=0) | |
| added_cond_kwargs = {"resolution": resolution, "aspect_ratio": aspect_ratio} | |
| # 7. Denoising loop | |
| num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) | |
| with self.progress_bar(total=num_inference_steps) as progress_bar: | |
| for i, t in enumerate(timesteps): | |
| latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents | |
| latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) | |
| current_timestep = t | |
| if not torch.is_tensor(current_timestep): | |
| # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can | |
| # This would be a good case for the `match` statement (Python 3.10+) | |
| is_mps = latent_model_input.device.type == "mps" | |
| if isinstance(current_timestep, float): | |
| dtype = torch.float32 if is_mps else torch.float64 | |
| else: | |
| dtype = torch.int32 if is_mps else torch.int64 | |
| current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device) | |
| elif len(current_timestep.shape) == 0: | |
| current_timestep = current_timestep[None].to(latent_model_input.device) | |
| # broadcast to batch dimension in a way that's compatible with ONNX/Core ML | |
| current_timestep = current_timestep.expand(latent_model_input.shape[0]) | |
| # predict noise model_output | |
| noise_pred = self.transformer( | |
| latent_model_input, | |
| encoder_hidden_states=prompt_embeds, | |
| encoder_attention_mask=prompt_attention_mask, | |
| timestep=current_timestep, | |
| added_cond_kwargs=added_cond_kwargs, | |
| return_dict=False, | |
| )[0] | |
| # perform guidance | |
| if do_classifier_free_guidance: | |
| noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) | |
| noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) | |
| # learned sigma | |
| if self.transformer.config.out_channels // 2 == latent_channels: | |
| noise_pred = noise_pred.chunk(2, dim=1)[0] | |
| else: | |
| noise_pred = noise_pred | |
| # compute previous image: x_t -> x_t-1 | |
| if num_inference_steps == 1: | |
| latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).pred_original_sample | |
| else: | |
| latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] | |
| # 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 callback is not None and i % callback_steps == 0: | |
| step_idx = i // getattr(self.scheduler, "order", 1) | |
| callback(step_idx, t, latents) | |
| if not output_type == "latent": | |
| image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] | |
| if use_resolution_binning: | |
| image = self.resize_and_crop_tensor(image, orig_width, orig_height) | |
| else: | |
| image = latents | |
| if not output_type == "latent": | |
| image = self.image_processor.postprocess(image, output_type=output_type) | |
| # Offload all models | |
| self.maybe_free_model_hooks() | |
| if not return_dict: | |
| return (image,) | |
| return ImagePipelineOutput(images=image) | |
| class PixArtSigmaPipeline(PixArtAlphaPipeline): | |
| r""" | |
| tmp Pipeline for text-to-image generation using PixArt-Sigma. | |
| """ | |
| def __init__( | |
| self, | |
| tokenizer: T5Tokenizer, | |
| text_encoder: T5EncoderModel, | |
| vae: AutoencoderKL, | |
| transformer: Transformer2DModel, | |
| scheduler: DPMSolverMultistepScheduler, | |
| ): | |
| super().__init__(tokenizer, text_encoder, vae, transformer, scheduler) | |
| self.register_modules( | |
| tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler | |
| ) | |
| self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) | |
| self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) | |
| def pixart_sigma_init_patched_inputs(self, norm_type): | |
| assert self.config.sample_size is not None, "Transformer2DModel over patched input must provide sample_size" | |
| self.height = self.config.sample_size | |
| self.width = self.config.sample_size | |
| self.patch_size = self.config.patch_size | |
| interpolation_scale = ( | |
| self.config.interpolation_scale | |
| if self.config.interpolation_scale is not None | |
| else max(self.config.sample_size // 64, 1) | |
| ) | |
| self.pos_embed = PatchEmbed( | |
| height=self.config.sample_size, | |
| width=self.config.sample_size, | |
| patch_size=self.config.patch_size, | |
| in_channels=self.in_channels, | |
| embed_dim=self.inner_dim, | |
| interpolation_scale=interpolation_scale, | |
| ) | |
| self.transformer_blocks = nn.ModuleList( | |
| [ | |
| BasicTransformerBlock( | |
| self.inner_dim, | |
| self.config.num_attention_heads, | |
| self.config.attention_head_dim, | |
| dropout=self.config.dropout, | |
| cross_attention_dim=self.config.cross_attention_dim, | |
| activation_fn=self.config.activation_fn, | |
| num_embeds_ada_norm=self.config.num_embeds_ada_norm, | |
| attention_bias=self.config.attention_bias, | |
| only_cross_attention=self.config.only_cross_attention, | |
| double_self_attention=self.config.double_self_attention, | |
| upcast_attention=self.config.upcast_attention, | |
| norm_type=norm_type, | |
| norm_elementwise_affine=self.config.norm_elementwise_affine, | |
| norm_eps=self.config.norm_eps, | |
| attention_type=self.config.attention_type, | |
| ) | |
| for _ in range(self.config.num_layers) | |
| ] | |
| ) | |
| if self.config.norm_type != "ada_norm_single": | |
| self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6) | |
| self.proj_out_1 = nn.Linear(self.inner_dim, 2 * self.inner_dim) | |
| self.proj_out_2 = nn.Linear( | |
| self.inner_dim, self.config.patch_size * self.config.patch_size * self.out_channels | |
| ) | |
| elif self.config.norm_type == "ada_norm_single": | |
| self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6) | |
| self.scale_shift_table = nn.Parameter(torch.randn(2, self.inner_dim) / self.inner_dim ** 0.5) | |
| self.proj_out = nn.Linear( | |
| self.inner_dim, self.config.patch_size * self.config.patch_size * self.out_channels | |
| ) | |
| # PixArt-Sigma blocks. | |
| self.adaln_single = None | |
| self.use_additional_conditions = False | |
| if self.config.norm_type == "ada_norm_single": | |
| # TODO(Sayak, PVP) clean this, PixArt-Sigma doesn't use additional_conditions anymore | |
| # additional conditions until we find better name | |
| self.adaln_single = AdaLayerNormSingle( | |
| self.inner_dim, use_additional_conditions=self.use_additional_conditions | |
| ) | |
| self.caption_projection = None | |
| if self.caption_channels is not None: | |
| self.caption_projection = PixArtAlphaTextProjection( | |
| in_features=self.caption_channels, hidden_size=self.inner_dim | |
| ) | |