diff --git a/examples/community/README.md b/examples/community/README.md index 4980233075..921160e451 100755 --- a/examples/community/README.md +++ b/examples/community/README.md @@ -39,7 +39,8 @@ If a community doesn't work as expected, please open an issue and ping the autho | CLIP Guided Images Mixing Stable Diffusion Pipeline | Сombine images using usual diffusion models. | [CLIP Guided Images Mixing Using Stable Diffusion](#clip-guided-images-mixing-with-stable-diffusion) | - | [Karachev Denis](https://github.com/TheDenk) | | TensorRT Stable Diffusion Inpainting Pipeline | Accelerates the Stable Diffusion Inpainting Pipeline using TensorRT | [TensorRT Stable Diffusion Inpainting Pipeline](#tensorrt-inpainting-stable-diffusion-pipeline) | - | [Asfiya Baig](https://github.com/asfiyab-nvidia) | | IADB Pipeline | Implementation of [Iterative α-(de)Blending: a Minimalist Deterministic Diffusion Model](https://arxiv.org/abs/2305.03486) | [IADB Pipeline](#iadb-pipeline) | - | [Thomas Chambon](https://github.com/tchambon) -| Zero1to3 Pipeline | Implementation of [Zero-1-to-3: Zero-shot One Image to 3D Object](https://arxiv.org/abs/2303.11328) | [Zero1to3 Pipeline](#Zero1to3-pipeline) | - | [Xin Kong](https://github.com/kxhit) +| Zero1to3 Pipeline | Implementation of [Zero-1-to-3: Zero-shot One Image to 3D Object](https://arxiv.org/abs/2303.11328) | [Zero1to3 Pipeline](#Zero1to3-pipeline) | - | [Xin Kong](https://github.com/kxhit) | +Stable Diffusion XL Long Weighted Prompt Pipeline | A pipeline support unlimited length of prompt and negative prompt, use A1111 style of prompt weighting | [Stable Diffusion XL Long Weighted Prompt Pipeline](#stable-diffusion-xl-long-weighted-prompt-pipeline) | - | [Andrew Zhu](https://xhinker.medium.com/) | To load a custom pipeline you just need to pass the `custom_pipeline` argument to `DiffusionPipeline`, as one of the files in `diffusers/examples/community`. Feel free to send a PR with your own pipelines, we will merge them quickly. @@ -1529,6 +1530,44 @@ CLIP guided stable diffusion images mixing pipline allows to combine two images This approach is using (optional) CoCa model to avoid writing image description. [More code examples](https://github.com/TheDenk/images_mixing) + +### Stable Diffusion XL Long Weighted Prompt Pipeline + +This SDXL pipeline support unlimted length prompt and negative prompt, compatible with A1111 prompt weighted style. + +You can provide both `prompt` and `prompt_2`. if only one prompt is provided, `prompt_2` will be a copy of the provided `prompt`. Here is a sample code to use this pipeline. + +```python +from diffusers import DiffusionPipeline +import torch + +pipe = DiffusionPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0" + , torch_dtype = torch.float16 + , use_safetensors = True + , variant = "fp16" + , custom_pipeline = "lpw_stable_diffusion_xl", +) + +prompt = "photo of a cute (white) cat running on the grass"*20 +prompt2 = "chasing (birds:1.5)"*20 +prompt = f"{prompt},{prompt2}" +neg_prompt = "blur, low quality, carton, animate" + +pipe.to("cuda") +images = pipe( + prompt = prompt + , negative_prompt = neg_prompt +).images[0] + +pipe.to("cpu") +torch.cuda.empty_cache() +images +``` + +In the above code, the `prompt2` is appended to the `prompt`, which is more than 77 tokens. "birds" are showing up in the result. +![Stable Diffusion XL Long Weighted Prompt Pipeline sample](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_long_weighted_prompt.png) + ## Example Images Mixing (with CoCa) ```python import requests diff --git a/examples/community/lpw_stable_diffusion_xl.py b/examples/community/lpw_stable_diffusion_xl.py new file mode 100644 index 0000000000..be7d1d5367 --- /dev/null +++ b/examples/community/lpw_stable_diffusion_xl.py @@ -0,0 +1,1297 @@ +## ---------------------------------------------------------- +# A SDXL pipeline can take unlimited weighted prompt +# +# Author: Andrew Zhu +# Github: https://github.com/xhinker +# Medium: https://medium.com/@xhinker +## ----------------------------------------------------------- + +import inspect +import os +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import torch +from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer + +from diffusers import DiffusionPipeline, StableDiffusionXLPipeline +from diffusers.image_processor import VaeImageProcessor +from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin +from diffusers.models import AutoencoderKL, UNet2DConditionModel +from diffusers.models.attention_processor import ( + AttnProcessor2_0, + LoRAAttnProcessor2_0, + LoRAXFormersAttnProcessor, + XFormersAttnProcessor, +) +from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput +from diffusers.schedulers import KarrasDiffusionSchedulers +from diffusers.utils import ( + is_accelerate_available, + is_accelerate_version, + is_invisible_watermark_available, + logging, + randn_tensor, + replace_example_docstring, +) + + +if is_invisible_watermark_available(): + from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker + + +def parse_prompt_attention(text): + """ + Parses a string with attention tokens and returns a list of pairs: text and its associated weight. + Accepted tokens are: + (abc) - increases attention to abc by a multiplier of 1.1 + (abc:3.12) - increases attention to abc by a multiplier of 3.12 + [abc] - decreases attention to abc by a multiplier of 1.1 + \( - literal character '(' + \[ - literal character '[' + \) - literal character ')' + \] - literal character ']' + \\ - literal character '\' + anything else - just text + + >>> parse_prompt_attention('normal text') + [['normal text', 1.0]] + >>> parse_prompt_attention('an (important) word') + [['an ', 1.0], ['important', 1.1], [' word', 1.0]] + >>> parse_prompt_attention('(unbalanced') + [['unbalanced', 1.1]] + >>> parse_prompt_attention('\(literal\]') + [['(literal]', 1.0]] + >>> parse_prompt_attention('(unnecessary)(parens)') + [['unnecessaryparens', 1.1]] + >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).') + [['a ', 1.0], + ['house', 1.5730000000000004], + [' ', 1.1], + ['on', 1.0], + [' a ', 1.1], + ['hill', 0.55], + [', sun, ', 1.1], + ['sky', 1.4641000000000006], + ['.', 1.1]] + """ + import re + + re_attention = re.compile( + r""" + \\\(|\\\)|\\\[|\\]|\\\\|\\|\(|\[|:([+-]?[.\d]+)\)| + \)|]|[^\\()\[\]:]+|: + """, + re.X, + ) + + re_break = re.compile(r"\s*\bBREAK\b\s*", re.S) + + res = [] + round_brackets = [] + square_brackets = [] + + round_bracket_multiplier = 1.1 + square_bracket_multiplier = 1 / 1.1 + + def multiply_range(start_position, multiplier): + for p in range(start_position, len(res)): + res[p][1] *= multiplier + + for m in re_attention.finditer(text): + text = m.group(0) + weight = m.group(1) + + if text.startswith("\\"): + res.append([text[1:], 1.0]) + elif text == "(": + round_brackets.append(len(res)) + elif text == "[": + square_brackets.append(len(res)) + elif weight is not None and len(round_brackets) > 0: + multiply_range(round_brackets.pop(), float(weight)) + elif text == ")" and len(round_brackets) > 0: + multiply_range(round_brackets.pop(), round_bracket_multiplier) + elif text == "]" and len(square_brackets) > 0: + multiply_range(square_brackets.pop(), square_bracket_multiplier) + else: + parts = re.split(re_break, text) + for i, part in enumerate(parts): + if i > 0: + res.append(["BREAK", -1]) + res.append([part, 1.0]) + + for pos in round_brackets: + multiply_range(pos, round_bracket_multiplier) + + for pos in square_brackets: + multiply_range(pos, square_bracket_multiplier) + + if len(res) == 0: + res = [["", 1.0]] + + # merge runs of identical weights + i = 0 + while i + 1 < len(res): + if res[i][1] == res[i + 1][1]: + res[i][0] += res[i + 1][0] + res.pop(i + 1) + else: + i += 1 + + return res + + +def get_prompts_tokens_with_weights(clip_tokenizer: CLIPTokenizer, prompt: str): + """ + Get prompt token ids and weights, this function works for both prompt and negative prompt + + Args: + pipe (CLIPTokenizer) + A CLIPTokenizer + prompt (str) + A prompt string with weights + + Returns: + text_tokens (list) + A list contains token ids + text_weight (list) + A list contains the correspodent weight of token ids + + Example: + import torch + from transformers import CLIPTokenizer + + clip_tokenizer = CLIPTokenizer.from_pretrained( + "stablediffusionapi/deliberate-v2" + , subfolder = "tokenizer" + , dtype = torch.float16 + ) + + token_id_list, token_weight_list = get_prompts_tokens_with_weights( + clip_tokenizer = clip_tokenizer + ,prompt = "a (red:1.5) cat"*70 + ) + """ + texts_and_weights = parse_prompt_attention(prompt) + text_tokens, text_weights = [], [] + for word, weight in texts_and_weights: + # tokenize and discard the starting and the ending token + token = clip_tokenizer(word, truncation=False).input_ids[1:-1] # so that tokenize whatever length prompt + # the returned token is a 1d list: [320, 1125, 539, 320] + + # merge the new tokens to the all tokens holder: text_tokens + text_tokens = [*text_tokens, *token] + + # each token chunk will come with one weight, like ['red cat', 2.0] + # need to expand weight for each token. + chunk_weights = [weight] * len(token) + + # append the weight back to the weight holder: text_weights + text_weights = [*text_weights, *chunk_weights] + return text_tokens, text_weights + + +def group_tokens_and_weights(token_ids: list, weights: list, pad_last_block=False): + """ + Produce tokens and weights in groups and pad the missing tokens + + Args: + token_ids (list) + The token ids from tokenizer + weights (list) + The weights list from function get_prompts_tokens_with_weights + pad_last_block (bool) + Control if fill the last token list to 75 tokens with eos + Returns: + new_token_ids (2d list) + new_weights (2d list) + + Example: + token_groups,weight_groups = group_tokens_and_weights( + token_ids = token_id_list + , weights = token_weight_list + ) + """ + bos, eos = 49406, 49407 + + # this will be a 2d list + new_token_ids = [] + new_weights = [] + while len(token_ids) >= 75: + # get the first 75 tokens + head_75_tokens = [token_ids.pop(0) for _ in range(75)] + head_75_weights = [weights.pop(0) for _ in range(75)] + + # extract token ids and weights + temp_77_token_ids = [bos] + head_75_tokens + [eos] + temp_77_weights = [1.0] + head_75_weights + [1.0] + + # add 77 token and weights chunk to the holder list + new_token_ids.append(temp_77_token_ids) + new_weights.append(temp_77_weights) + + # padding the left + if len(token_ids) > 0: + padding_len = 75 - len(token_ids) if pad_last_block else 0 + + temp_77_token_ids = [bos] + token_ids + [eos] * padding_len + [eos] + new_token_ids.append(temp_77_token_ids) + + temp_77_weights = [1.0] + weights + [1.0] * padding_len + [1.0] + new_weights.append(temp_77_weights) + + return new_token_ids, new_weights + + +def get_weighted_text_embeddings_sdxl( + pipe: StableDiffusionXLPipeline, + prompt: str = "", + prompt_2: str = None, + neg_prompt: str = "", + neg_prompt_2: str = None, +): + """ + This function can process long prompt with weights, no length limitation + for Stable Diffusion XL + + Args: + pipe (StableDiffusionPipeline) + prompt (str) + prompt_2 (str) + neg_prompt (str) + neg_prompt_2 (str) + Returns: + prompt_embeds (torch.Tensor) + neg_prompt_embeds (torch.Tensor) + """ + if prompt_2: + prompt = f"{prompt} {prompt_2}" + + if neg_prompt_2: + neg_prompt = f"{neg_prompt} {neg_prompt_2}" + + eos = pipe.tokenizer.eos_token_id + + # tokenizer 1 + prompt_tokens, prompt_weights = get_prompts_tokens_with_weights(pipe.tokenizer, prompt) + + neg_prompt_tokens, neg_prompt_weights = get_prompts_tokens_with_weights(pipe.tokenizer, neg_prompt) + + # tokenizer 2 + prompt_tokens_2, prompt_weights_2 = get_prompts_tokens_with_weights(pipe.tokenizer_2, prompt) + + neg_prompt_tokens_2, neg_prompt_weights_2 = get_prompts_tokens_with_weights(pipe.tokenizer_2, neg_prompt) + + # padding the shorter one for prompt set 1 + prompt_token_len = len(prompt_tokens) + neg_prompt_token_len = len(neg_prompt_tokens) + + if prompt_token_len > neg_prompt_token_len: + # padding the neg_prompt with eos token + neg_prompt_tokens = neg_prompt_tokens + [eos] * abs(prompt_token_len - neg_prompt_token_len) + neg_prompt_weights = neg_prompt_weights + [1.0] * abs(prompt_token_len - neg_prompt_token_len) + else: + # padding the prompt + prompt_tokens = prompt_tokens + [eos] * abs(prompt_token_len - neg_prompt_token_len) + prompt_weights = prompt_weights + [1.0] * abs(prompt_token_len - neg_prompt_token_len) + + # padding the shorter one for token set 2 + prompt_token_len_2 = len(prompt_tokens_2) + neg_prompt_token_len_2 = len(neg_prompt_tokens_2) + + if prompt_token_len_2 > neg_prompt_token_len_2: + # padding the neg_prompt with eos token + neg_prompt_tokens_2 = neg_prompt_tokens_2 + [eos] * abs(prompt_token_len_2 - neg_prompt_token_len_2) + neg_prompt_weights_2 = neg_prompt_weights_2 + [1.0] * abs(prompt_token_len_2 - neg_prompt_token_len_2) + else: + # padding the prompt + prompt_tokens_2 = prompt_tokens_2 + [eos] * abs(prompt_token_len_2 - neg_prompt_token_len_2) + prompt_weights_2 = prompt_weights + [1.0] * abs(prompt_token_len_2 - neg_prompt_token_len_2) + + embeds = [] + neg_embeds = [] + + prompt_token_groups, prompt_weight_groups = group_tokens_and_weights(prompt_tokens.copy(), prompt_weights.copy()) + + neg_prompt_token_groups, neg_prompt_weight_groups = group_tokens_and_weights( + neg_prompt_tokens.copy(), neg_prompt_weights.copy() + ) + + prompt_token_groups_2, prompt_weight_groups_2 = group_tokens_and_weights( + prompt_tokens_2.copy(), prompt_weights_2.copy() + ) + + neg_prompt_token_groups_2, neg_prompt_weight_groups_2 = group_tokens_and_weights( + neg_prompt_tokens_2.copy(), neg_prompt_weights_2.copy() + ) + + # get prompt embeddings one by one is not working. + for i in range(len(prompt_token_groups)): + # get positive prompt embeddings with weights + token_tensor = torch.tensor([prompt_token_groups[i]], dtype=torch.long, device=pipe.device) + weight_tensor = torch.tensor(prompt_weight_groups[i], dtype=torch.float16, device=pipe.device) + + token_tensor_2 = torch.tensor([prompt_token_groups_2[i]], dtype=torch.long, device=pipe.device) + + # use first text encoder + prompt_embeds_1 = pipe.text_encoder(token_tensor.to(pipe.device), output_hidden_states=True) + prompt_embeds_1_hidden_states = prompt_embeds_1.hidden_states[-2] + + # use second text encoder + prompt_embeds_2 = pipe.text_encoder_2(token_tensor_2.to(pipe.device), output_hidden_states=True) + prompt_embeds_2_hidden_states = prompt_embeds_2.hidden_states[-2] + pooled_prompt_embeds = prompt_embeds_2[0] + + prompt_embeds_list = [prompt_embeds_1_hidden_states, prompt_embeds_2_hidden_states] + token_embedding = torch.concat(prompt_embeds_list, dim=-1).squeeze(0) + + for j in range(len(weight_tensor)): + if weight_tensor[j] != 1.0: + token_embedding[j] = ( + token_embedding[-1] + (token_embedding[j] - token_embedding[-1]) * weight_tensor[j] + ) + + token_embedding = token_embedding.unsqueeze(0) + embeds.append(token_embedding) + + # get negative prompt embeddings with weights + neg_token_tensor = torch.tensor([neg_prompt_token_groups[i]], dtype=torch.long, device=pipe.device) + neg_token_tensor_2 = torch.tensor([neg_prompt_token_groups_2[i]], dtype=torch.long, device=pipe.device) + neg_weight_tensor = torch.tensor(neg_prompt_weight_groups[i], dtype=torch.float16, device=pipe.device) + + # use first text encoder + neg_prompt_embeds_1 = pipe.text_encoder(neg_token_tensor.to(pipe.device), output_hidden_states=True) + neg_prompt_embeds_1_hidden_states = neg_prompt_embeds_1.hidden_states[-2] + + # use second text encoder + neg_prompt_embeds_2 = pipe.text_encoder_2(neg_token_tensor_2.to(pipe.device), output_hidden_states=True) + neg_prompt_embeds_2_hidden_states = neg_prompt_embeds_2.hidden_states[-2] + negative_pooled_prompt_embeds = neg_prompt_embeds_2[0] + + neg_prompt_embeds_list = [neg_prompt_embeds_1_hidden_states, neg_prompt_embeds_2_hidden_states] + neg_token_embedding = torch.concat(neg_prompt_embeds_list, dim=-1).squeeze(0) + + for z in range(len(neg_weight_tensor)): + if neg_weight_tensor[z] != 1.0: + neg_token_embedding[z] = ( + neg_token_embedding[-1] + (neg_token_embedding[z] - neg_token_embedding[-1]) * neg_weight_tensor[z] + ) + + neg_token_embedding = neg_token_embedding.unsqueeze(0) + neg_embeds.append(neg_token_embedding) + + prompt_embeds = torch.cat(embeds, dim=1) + negative_prompt_embeds = torch.cat(neg_embeds, dim=1) + + return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds + + +# ------------------------------------------------------------------------------------------------------------------------------- +# reuse the backbone code from StableDiffusionXLPipeline +# ------------------------------------------------------------------------------------------------------------------------------- + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + +EXAMPLE_DOC_STRING = """ + Examples: + ```py + from diffusers import DiffusionPipeline + import torch + + pipe = DiffusionPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0" + , torch_dtype = torch.float16 + , use_safetensors = True + , variant = "fp16" + , custom_pipeline = "lpw_stable_diffusion_xl", + ) + + prompt = "a white cat running on the grass"*20 + prompt2 = "play a football"*20 + prompt = f"{prompt},{prompt2}" + neg_prompt = "blur, low quality" + + pipe.to("cuda") + images = pipe( + prompt = prompt + , negative_prompt = neg_prompt + ).images[0] + + pipe.to("cpu") + torch.cuda.empty_cache() + images + ``` +""" + + +# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg +def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): + """ + Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and + Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 + """ + std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True) + std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) + # rescale the results from guidance (fixes overexposure) + noise_pred_rescaled = noise_cfg * (std_text / std_cfg) + # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images + noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg + return noise_cfg + + +class SDXLLongPromptWeightingPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoaderMixin): + 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.) + + In addition the pipeline inherits the following loading methods: + - *LoRA*: [`StableDiffusionXLPipeline.load_lora_weights`] + - *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`] + + as well as the following saving methods: + - *LoRA*: [`loaders.StableDiffusionXLPipeline.save_lora_weights`] + + 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 + [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`]. + """ + + def __init__( + self, + vae: AutoencoderKL, + text_encoder: CLIPTextModel, + text_encoder_2: CLIPTextModelWithProjection, + tokenizer: CLIPTokenizer, + tokenizer_2: CLIPTokenizer, + unet: UNet2DConditionModel, + scheduler: KarrasDiffusionSchedulers, + force_zeros_for_empty_prompt: bool = True, + add_watermarker: Optional[bool] = None, + ): + super().__init__() + + 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.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) + self.default_sample_size = self.unet.config.sample_size + + 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.enable_vae_slicing + def enable_vae_slicing(self): + r""" + Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to + compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. + """ + self.vae.enable_slicing() + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing + def disable_vae_slicing(self): + r""" + Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to + computing decoding in one step. + """ + self.vae.disable_slicing() + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling + def enable_vae_tiling(self): + r""" + Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to + compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow + processing larger images. + """ + self.vae.enable_tiling() + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling + def disable_vae_tiling(self): + r""" + Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to + computing decoding in one step. + """ + self.vae.disable_tiling() + + def enable_model_cpu_offload(self, gpu_id=0): + r""" + Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared + to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` + method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with + `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. + """ + if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): + from accelerate import cpu_offload_with_hook + else: + raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") + + device = torch.device(f"cuda:{gpu_id}") + + if self.device.type != "cpu": + self.to("cpu", silence_dtype_warnings=True) + torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) + + model_sequence = ( + [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2] + ) + model_sequence.extend([self.unet, self.vae]) + + hook = None + for cpu_offloaded_model in model_sequence: + _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook) + + # We'll offload the last model manually. + self.final_offload_hook = hook + + 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.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + pooled_prompt_embeds: Optional[torch.FloatTensor] = None, + negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, + lora_scale: Optional[float] = None, + ): + r""" + Encodes the prompt into text encoder hidden states. + + 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*): + 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.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. + negative_prompt_embeds (`torch.FloatTensor`, *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.FloatTensor`, *optional*): + Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. + If not provided, pooled text embeddings will be generated from `prompt` input argument. + negative_pooled_prompt_embeds (`torch.FloatTensor`, *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. + """ + device = device or self._execution_device + + # 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, LoraLoaderMixin): + self._lora_scale = lora_scale + + 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] + + # 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 + # textual inversion: procecss 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) + + 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}" + ) + + 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 + pooled_prompt_embeds = prompt_embeds[0] + prompt_embeds = prompt_embeds.hidden_states[-2] + + prompt_embeds_list.append(prompt_embeds) + + prompt_embeds = torch.concat(prompt_embeds_list, dim=-1) + + # 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 + + 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 isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt, negative_prompt_2] + 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] + + 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, + ) + # We are only ALWAYS interested in the pooled output of the final text encoder + 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) + + prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) + 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] + negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.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) + + 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 + ) + + 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 + + # 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, + height, + width, + callback_steps, + negative_prompt=None, + negative_prompt_2=None, + prompt_embeds=None, + negative_prompt_embeds=None, + pooled_prompt_embeds=None, + negative_pooled_prompt_embeds=None, + ): + 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 None) or ( + 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 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)}") + 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)}") + + 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." + ) + + 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 prompt_embeds is not None and pooled_prompt_embeds is None: + raise ValueError( + "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`." + ) + + if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None: + raise ValueError( + "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`." + ) + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents + def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): + shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, 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." + ) + + if latents is None: + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + else: + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + def _get_add_time_ids(self, original_size, crops_coords_top_left, target_size, dtype): + add_time_ids = list(original_size + crops_coords_top_left + target_size) + + passed_add_embed_dim = ( + self.unet.config.addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2.config.projection_dim + ) + expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features + + if 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`." + ) + + add_time_ids = torch.tensor([add_time_ids], dtype=dtype) + return add_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, + LoRAXFormersAttnProcessor, + LoRAAttnProcessor2_0, + ), + ) + # 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) + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + prompt: str = None, + prompt_2: Optional[str] = None, + height: Optional[int] = None, + width: Optional[int] = None, + num_inference_steps: int = 50, + denoising_end: Optional[float] = None, + guidance_scale: float = 5.0, + negative_prompt: Optional[str] = None, + negative_prompt_2: Optional[str] = None, + num_images_per_prompt: Optional[int] = 1, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.FloatTensor] = None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + pooled_prompt_embeds: Optional[torch.FloatTensor] = None, + negative_pooled_prompt_embeds: 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, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + guidance_rescale: float = 0.0, + original_size: Optional[Tuple[int, int]] = None, + crops_coords_top_left: Tuple[int, int] = (0, 0), + target_size: Optional[Tuple[int, int]] = None, + ): + r""" + Function invoked when calling the pipeline for generation. + + Args: + prompt (`str`): + The prompt to guide the image generation. If not defined, one has to pass `prompt_embeds`. + instead. + prompt_2 (`str`): + The prompt to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is + used in both text-encoders + height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + The height in pixels of the generated image. + width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + The width in pixels of the generated image. + 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 + expense of slower inference. + 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 as determined by the discrete timesteps selected by the + scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a + "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image + Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output) + guidance_scale (`float`, *optional*, defaults to 5.0): + 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. + negative_prompt (`str`): + The prompt 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`): + The prompt 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 + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + 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. + negative_prompt_embeds (`torch.FloatTensor`, *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.FloatTensor`, *optional*): + Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. + If not provided, pooled text embeddings will be generated from `prompt` input argument. + negative_pooled_prompt_embeds (`torch.FloatTensor`, *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. + 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_xl.StableDiffusionXLPipelineOutput`] 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. + cross_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). + guidance_rescale (`float`, *optional*, defaults to 0.7): + Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are + Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of + [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). + Guidance rescale factor should fix overexposure when using zero terminal SNR. + original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): + If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled. + `original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as + explained in section 2.2 of + [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). + crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): + `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position + `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting + `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of + [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). + target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): + For most cases, `target_size` should be set to the desired height and width of the generated image. If + not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in + section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). + + Examples: + + Returns: + [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`: + [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a + `tuple`. When returning a tuple, the first element is a list with the generated images. + """ + # 0. Default height and width to unet + height = height or self.default_sample_size * self.vae_scale_factor + width = width or self.default_sample_size * self.vae_scale_factor + + original_size = original_size or (height, width) + target_size = target_size or (height, width) + + # 1. Check inputs. Raise error if not correct + self.check_inputs( + prompt, + prompt_2, + height, + width, + callback_steps, + negative_prompt, + negative_prompt_2, + prompt_embeds, + negative_prompt_embeds, + pooled_prompt_embeds, + negative_pooled_prompt_embeds, + ) + + # 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 + + # 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 + (cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None) + # ( + # prompt_embeds, + # negative_prompt_embeds, + # pooled_prompt_embeds, + # negative_pooled_prompt_embeds, + # ) = self.encode_prompt( + # prompt=prompt, + # prompt_2=prompt_2, + # device=device, + # num_images_per_prompt=num_images_per_prompt, + # do_classifier_free_guidance=do_classifier_free_guidance, + # negative_prompt=negative_prompt, + # negative_prompt_2=negative_prompt_2, + # prompt_embeds=prompt_embeds, + # negative_prompt_embeds=negative_prompt_embeds, + # pooled_prompt_embeds=pooled_prompt_embeds, + # negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, + # lora_scale=text_encoder_lora_scale, + # ) + ( + prompt_embeds, + negative_prompt_embeds, + pooled_prompt_embeds, + negative_pooled_prompt_embeds, + ) = get_weighted_text_embeddings_sdxl(pipe=self, prompt=prompt, neg_prompt=negative_prompt) + + # 4. Prepare timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + + timesteps = self.scheduler.timesteps + + # 5. Prepare latent variables + num_channels_latents = self.unet.config.in_channels + latents = self.prepare_latents( + batch_size * num_images_per_prompt, + num_channels_latents, + 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) + + # 7. Prepare added time ids & embeddings + add_text_embeds = pooled_prompt_embeds + add_time_ids = self._get_add_time_ids( + original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype + ) + + if do_classifier_free_guidance: + prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) + add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0) + add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0) + + prompt_embeds = prompt_embeds.to(device) + add_text_embeds = add_text_embeds.to(device) + add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) + + # 8. Denoising loop + num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) + + # 7.1 Apply denoising_end + if denoising_end is not None and type(denoising_end) == float and denoising_end > 0 and denoising_end < 1: + discrete_timestep_cutoff = int( + round( + self.scheduler.config.num_train_timesteps + - (denoising_end * self.scheduler.config.num_train_timesteps) + ) + ) + num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps))) + timesteps = timesteps[:num_inference_steps] + + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + # expand the latents if we are doing classifier free guidance + 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) + + # predict the noise residual + added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} + noise_pred = self.unet( + latent_model_input, + t, + encoder_hidden_states=prompt_embeds, + cross_attention_kwargs=cross_attention_kwargs, + 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) + + if do_classifier_free_guidance and guidance_rescale > 0.0: + # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf + noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale) + + # compute the previous noisy sample x_t -> x_t-1 + 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: + callback(i, t, latents) + + # make sure the VAE is in float32 mode, as it overflows in float16 + if self.vae.dtype == torch.float16 and self.vae.config.force_upcast: + self.upcast_vae() + latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) + + if not output_type == "latent": + image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] + else: + image = latents + return StableDiffusionXLPipelineOutput(images=image) + + # apply watermark if available + if self.watermark is not None: + image = self.watermark.apply_watermark(image) + + image = self.image_processor.postprocess(image, output_type=output_type) + + # Offload last model to CPU + if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: + self.final_offload_hook.offload() + + if not return_dict: + return (image,) + + return StableDiffusionXLPipelineOutput(images=image) + + # Overrride to properly handle the loading and unloading of the additional text encoder. + def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs): + # We could have accessed the unet config from `lora_state_dict()` too. We pass + # it here explicitly to be able to tell that it's coming from an SDXL + # pipeline. + state_dict, network_alphas = self.lora_state_dict( + pretrained_model_name_or_path_or_dict, + unet_config=self.unet.config, + **kwargs, + ) + self.load_lora_into_unet(state_dict, network_alphas=network_alphas, unet=self.unet) + + text_encoder_state_dict = {k: v for k, v in state_dict.items() if "text_encoder." in k} + if len(text_encoder_state_dict) > 0: + self.load_lora_into_text_encoder( + text_encoder_state_dict, + network_alphas=network_alphas, + text_encoder=self.text_encoder, + prefix="text_encoder", + lora_scale=self.lora_scale, + ) + + text_encoder_2_state_dict = {k: v for k, v in state_dict.items() if "text_encoder_2." in k} + if len(text_encoder_2_state_dict) > 0: + self.load_lora_into_text_encoder( + text_encoder_2_state_dict, + network_alphas=network_alphas, + text_encoder=self.text_encoder_2, + prefix="text_encoder_2", + lora_scale=self.lora_scale, + ) + + @classmethod + def save_lora_weights( + self, + save_directory: Union[str, os.PathLike], + unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None, + text_encoder_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None, + text_encoder_2_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None, + is_main_process: bool = True, + weight_name: str = None, + save_function: Callable = None, + safe_serialization: bool = False, + ): + state_dict = {} + + def pack_weights(layers, prefix): + layers_weights = layers.state_dict() if isinstance(layers, torch.nn.Module) else layers + layers_state_dict = {f"{prefix}.{module_name}": param for module_name, param in layers_weights.items()} + return layers_state_dict + + state_dict.update(pack_weights(unet_lora_layers, "unet")) + + if text_encoder_lora_layers and text_encoder_2_lora_layers: + state_dict.update(pack_weights(text_encoder_lora_layers, "text_encoder")) + state_dict.update(pack_weights(text_encoder_2_lora_layers, "text_encoder_2")) + + self.write_lora_layers( + state_dict=state_dict, + save_directory=save_directory, + is_main_process=is_main_process, + weight_name=weight_name, + save_function=save_function, + safe_serialization=safe_serialization, + ) + + def _remove_text_encoder_monkey_patch(self): + self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder) + self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder_2)