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July 18, 2023 00:58
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import torch | |
from pipe import StableDiffusionXLPipelineNoWatermark | |
from pipei2i import StableDiffusionXLImg2ImgPipelineNoWatermark | |
from diffusers import DiffusionPipeline | |
from PIL import Image | |
import os | |
import gc | |
import pandas as pd | |
import random, sys | |
import json | |
import signal | |
import time | |
from diffusers.loaders import LoraLoaderMixin | |
from diffusers.models.attention_processor import LoRAAttnProcessor, LoRAAttnProcessor2_0 | |
from diffusers import ( | |
DDIMScheduler, | |
DDIMInverseScheduler, | |
DDIMParallelScheduler, | |
DDPMScheduler, | |
DDPMParallelScheduler, | |
DEISMultistepScheduler, | |
DPMSolverMultistepScheduler, | |
DPMSolverMultistepInverseScheduler, | |
DPMSolverSinglestepScheduler, | |
EulerAncestralDiscreteScheduler, | |
EulerDiscreteScheduler, | |
HeunDiscreteScheduler, | |
IPNDMScheduler, | |
KDPM2AncestralDiscreteScheduler, | |
KDPM2DiscreteScheduler, | |
PNDMScheduler, | |
UniPCMultistepScheduler, | |
) | |
schedulers = { | |
#Based on analysis, these were the lowest ranked 5 samplers: | |
#"DDIM": DDIMScheduler, #Ranked 12 | |
# "DPMSolverSingle": DPMSolverSinglestepScheduler, #Ranked 11 | |
# "PNDM": PNDMScheduler, #Ranked 10 | |
# "DDIMParallel": DDIMParallelScheduler, #Ranked 9 | |
#"DDPM": DDPMScheduler, #Ranked 8 | |
#"DDPMParallel": DDPMParallelScheduler, #6 | |
#"Euler-A-Discrete": EulerAncestralDiscreteScheduler, #7 | |
#"EulerDiscrete": EulerDiscreteScheduler, #4 | |
"HeunDiscrete": HeunDiscreteScheduler, #1/2 | |
#"KDPM2-A-Discrete": KDPM2AncestralDiscreteScheduler, #3 | |
"KDPM2Discrete": KDPM2DiscreteScheduler, #1/2 | |
#"UniPCMulti": UniPCMultistepScheduler, #5 | |
} | |
import sys | |
base_model_path = "./stable-diffusion-xl-base-0.9" | |
refiner_model_path = "./stable-diffusion-xl-refiner-0.9" | |
#lora_model_path = sys.argv[1] or None | |
torch.set_float32_matmul_precision('highest') | |
pipe = StableDiffusionXLPipelineNoWatermark.from_pretrained(base_model_path, torch_dtype=torch.float16, use_safetensors=True, variant="fp16") | |
#lora_state_dict, network_alpha = LoraLoaderMixin.lora_state_dict(lora_model_path) | |
#print(lora_state_dict) | |
#LoraLoaderMixin.load_lora_into_unet(lora_state_dict, network_alpha=network_alpha, unet=pipe.unet) | |
#LoraLoaderMixin.load_lora_into_text_encoder(lora_state_dict, network_alpha=network_alpha, text_encoder=pipe.text_encoder) | |
pipe.enable_xformers_memory_efficient_attention() | |
pipe.enable_vae_tiling() | |
pipe.to("cuda") | |
#pipe.unet = torch.compile(pipe.unet, mode="max-autotune", backend="eager", fullgraph=False) | |
genc = { | |
"num_inference_steps":38, | |
"width":1024, | |
"height":1024, | |
"guidance_scale":6.5, | |
#"guidance_rescale":0.7, | |
} | |
genc2 = { | |
"num_inference_steps":50, | |
#"guidance_scale":5.5, | |
#"guidance_rescale":0.7, | |
} | |
def get_artist_names(): | |
import json | |
data = [] | |
with open('sdxl_best.jsonl', 'r') as f: | |
for line in f: | |
item = json.loads(line) | |
data.append(item) | |
data = [list(x.keys())[0] for x in data] | |
return data | |
artists = get_artist_names() | |
random.shuffle(artists) | |
artists = artists[:4] | |
selected_prompts = [ | |
"Cosmic Shark Octopus Warrior, volcanic explosions in the background, cinematic masterpiece huge scene action, adventure.", | |
"A comedy in three acts, first comes comedy, then comes tragedy, and then the true tragedy occurs.", | |
"A plague doctor examines his potions and wares, preparing for the dark tides and the grim stroke of fate, 4k, 8k, cinematic quality", | |
"The annihilation of time.", | |
] | |
negative = "Deformed, unrealistic, bad quality, grainy, noisy, plastic, hazy, low contrast, watermark, text, writing, signature, artist signature, blurry, pixelated, ugly, malformed, gross" | |
negative_prompt = [negative] * len(selected_prompts) | |
generator = torch.Generator("cuda") | |
def build_prompts(selected, artist_name): | |
prompts = [] | |
for i, s in enumerate(selected): | |
prompt = s + ", art by " + artist_name | |
prompts.append(prompt) | |
return prompts | |
def save_images(target_dir, artist_name, scheduler_name, images): | |
target = os.path.join(target_dir, artist_name) | |
if not os.path.exists(target): | |
os.makedirs(target, exist_ok=True) | |
for i, img in enumerate(images): | |
iname = scheduler_name + ": " + artist_name + " " + str(i) + '.png' | |
img.save(os.path.join(target, iname)) | |
del images | |
def stage1(save=False): | |
outs = [] | |
target_dir = "outputs" | |
os.makedirs(target_dir, exist_ok=True) | |
for scheduler_name, scheduler in schedulers.items(): | |
if pipe.scheduler: | |
del pipe.scheduler | |
pipe.scheduler = scheduler.from_pretrained(base_model_path, subfolder="scheduler") | |
for count, artist_name in enumerate(artists): | |
nseed = random.randint(0, (sys.maxsize/64)) #if seed == -1 else seed | |
generator.manual_seed(nseed) | |
prompts = build_prompts(selected_prompts, artist_name) | |
output_type="latent" | |
if save: | |
output_type="pil" | |
images = pipe(prompt=prompts, negative_prompt=negative_prompt, output_type=output_type, generator=generator, **genc).images | |
if not save: | |
future_params = {"scheduler_name":scheduler_name} | |
outs.append((artist_name, prompts, images, future_params)) | |
else: | |
save_images(target_dir, artist_name, scheduler_name, images) | |
torch.cuda.empty_cache() | |
return outs | |
def stage2(outs): | |
pipe2 = StableDiffusionXLImg2ImgPipelineNoWatermark.from_pretrained(refiner_model_path, torch_dtype=torch.float16, use_safetensors=True, variant="fp16") | |
pipe2.to("cuda") | |
pipe2.enable_xformers_memory_efficient_attention() | |
pipe2.enable_vae_tiling() | |
pipe2.enable_sequential_cpu_offload() | |
pipe2.scheduler = EulerDiscreteScheduler.from_pretrained(refiner_model_path, subfolder="scheduler") | |
target_dir = "outputs" | |
os.makedirs(target_dir, exist_ok=True) | |
for count, (artist_name, prompts, images, future_params) in enumerate(outs): | |
# scheduler_name = future_params["scheduler_name"] | |
# potential_scheduler = schedulers[scheduler_name] | |
# if not pipe2.scheduler or (pipe2.scheduler.__class__ != potential_scheduler): | |
# if pipe2.scheduler: | |
# del pipe2.scheduler | |
# scheduler = potential_scheduler | |
# pipe2.scheduler = scheduler.from_pretrained(refiner_model_path, subfolder="scheduler") | |
# print("Loaded new refiner scheduler: ", pipe2.scheduler.__class__.__name__, potential_scheduler.__name__) | |
scheduler_name = future_params["scheduler_name"] | |
images = pipe2(prompt=prompts, negative_prompt=negative_prompt, image=images, generator=generator, **genc2).images | |
target = os.path.join(target_dir, artist_name) | |
if not os.path.exists(target): | |
os.makedirs(target, exist_ok=True) | |
save_images(target_dir, artist_name, scheduler_name, images) | |
del pipe2 | |
start = time.time() | |
outs = stage1(save=False) | |
torch.cuda.empty_cache() | |
torch.cuda.synchronize() | |
for x in pipe.__module__: | |
del x | |
del pipe | |
gc.collect() | |
torch._dynamo.reset() | |
torch.cuda.empty_cache() | |
torch.cuda.synchronize() | |
outs = sorted(outs, key=lambda x: x[3]["scheduler_name"]) | |
stage2(outs) | |
end = time.time() | |
print(f"Took: {end - start} seconds") |
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# Copyright 2023 The HuggingFace Team. All rights reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
import inspect | |
from typing import Any, Callable, Dict, List, Optional, Tuple, Union | |
import torch | |
import numpy as np | |
import PIL | |
from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer | |
from dataclasses import dataclass | |
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.schedulers import KarrasDiffusionSchedulers | |
from diffusers.utils import ( | |
is_accelerate_available, | |
is_accelerate_version, | |
logging, | |
randn_tensor, | |
replace_example_docstring, | |
) | |
from diffusers.pipelines.pipeline_utils import DiffusionPipeline | |
from diffusers.utils import BaseOutput | |
logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
@dataclass | |
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] | |
EXAMPLE_DOC_STRING = """ | |
Examples: | |
```py | |
>>> import torch | |
>>> from diffusers import StableDiffusionXLPipeline | |
>>> pipe = StableDiffusionXLPipeline.from_pretrained( | |
... "stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16 | |
... ) | |
>>> pipe = pipe.to("cuda") | |
>>> prompt = "a photo of an astronaut riding a horse on mars" | |
>>> image = pipe(prompt).images[0] | |
``` | |
""" | |
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 StableDiffusionXLPipelineNoWatermark(DiffusionPipeline, FromSingleFileMixin, LoraLoaderMixin): | |
r""" | |
Pipeline for text-to-image generation using Stable Diffusion. | |
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: | |
- *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`] | |
- *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`] | |
- *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`] | |
as well as the following saving methods: | |
- *LoRA*: [`loaders.LoraLoaderMixin.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 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. | |
tokenizer (`CLIPTokenizer`): | |
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, | |
): | |
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 | |
# 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 invoked, 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 to save a large amount of memory and to allow the processing of 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 invoked, this method will go back to | |
computing decoding in one step. | |
""" | |
self.vae.disable_tiling() | |
def enable_sequential_cpu_offload(self, gpu_id=0): | |
r""" | |
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, | |
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a | |
`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. | |
Note that offloading happens on a submodule basis. Memory savings are higher than with | |
`enable_model_cpu_offload`, but performance is lower. | |
""" | |
if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): | |
from accelerate import cpu_offload | |
else: | |
raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` 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) | |
for cpu_offloaded_model in [self.unet, self.text_encoder, self.text_encoder_2, self.vae]: | |
cpu_offload(cpu_offloaded_model, device) | |
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 | |
@property | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device | |
def _execution_device(self): | |
r""" | |
Returns the device on which the pipeline's models will be executed. After calling | |
`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module | |
hooks. | |
""" | |
if not hasattr(self.unet, "_hf_hook"): | |
return self.device | |
for module in self.unet.modules(): | |
if ( | |
hasattr(module, "_hf_hook") | |
and hasattr(module._hf_hook, "execution_device") | |
and module._hf_hook.execution_device is not None | |
): | |
return torch.device(module._hf_hook.execution_device) | |
return self.device | |
def encode_prompt( | |
self, | |
prompt, | |
device: Optional[torch.device] = None, | |
num_images_per_prompt: int = 1, | |
do_classifier_free_guidance: bool = True, | |
negative_prompt=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 | |
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`). | |
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: | |
# textual inversion: procecss multi-vector tokens if necessary | |
prompt_embeds_list = [] | |
for tokenizer, text_encoder in zip(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] | |
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) | |
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 "" | |
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] | |
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_embeds_list = [] | |
for tokenizer, text_encoder in zip(tokenizers, text_encoders): | |
# textual inversion: procecss multi-vector tokens if necessary | |
if isinstance(self, TextualInversionLoaderMixin): | |
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, tokenizer) | |
max_length = prompt_embeds.shape[1] | |
uncond_input = tokenizer( | |
uncond_tokens, | |
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] | |
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=text_encoder.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 | |
) | |
# 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 | |
negative_prompt_embeds_list.append(negative_prompt_embeds) | |
negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1) | |
bs_embed = pooled_prompt_embeds.shape[0] | |
pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( | |
bs_embed * num_images_per_prompt, -1 | |
) | |
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, | |
height, | |
width, | |
callback_steps, | |
negative_prompt=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 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)}") | |
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." | |
) | |
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 | |
@torch.no_grad() | |
@replace_example_docstring(EXAMPLE_DOC_STRING) | |
def __call__( | |
self, | |
prompt: Union[str, List[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[Union[str, List[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` or `List[str]`, *optional*): | |
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. | |
instead. | |
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. For instance, if denoising_end is set to | |
0.7 and `num_inference_steps` is fixed at 50, the process will execute only 35 (i.e., 0.7 * 50) | |
denoising steps. As a result, the returned sample will still retain a substantial amount of noise. 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 7.5): | |
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. | |
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_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.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.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.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)): | |
TODO | |
crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): | |
TODO | |
target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): | |
TODO | |
Examples: | |
Returns: | |
[`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] or `tuple`: | |
[`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a | |
`tuple. When returning a tuple, the first element is a list with the generated images, and the second | |
element is a list of `bool`s denoting whether the corresponding generated image likely represents | |
"not-safe-for-work" (nsfw) content, according to the `safety_checker`. | |
""" | |
# 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, | |
height, | |
width, | |
callback_steps, | |
negative_prompt, | |
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 | |
text_encoder_lora_scale = ( | |
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, | |
device, | |
num_images_per_prompt, | |
do_classifier_free_guidance, | |
negative_prompt, | |
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, | |
) | |
# 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: | |
num_inference_steps = int(round(denoising_end * num_inference_steps)) | |
timesteps = timesteps[: num_warmup_steps + self.scheduler.order * 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 | |
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(latents.dtype) | |
self.vae.decoder.conv_in.to(latents.dtype) | |
self.vae.decoder.mid_block.to(latents.dtype) | |
else: | |
latents = latents.float() | |
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) | |
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) |
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# Copyright 2023 The HuggingFace Team. All rights reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
import inspect | |
from typing import Any, Callable, Dict, List, Optional, Tuple, Union | |
import torch | |
import numpy as np | |
import PIL | |
from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer | |
from dataclasses import dataclass | |
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.schedulers import KarrasDiffusionSchedulers | |
from diffusers.utils import ( | |
is_accelerate_available, | |
is_accelerate_version, | |
logging, | |
randn_tensor, | |
replace_example_docstring, | |
) | |
from diffusers.pipeline_utils import DiffusionPipeline | |
from pipe import StableDiffusionXLPipelineOutput | |
logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
EXAMPLE_DOC_STRING = """ | |
Examples: | |
```py | |
>>> import torch | |
>>> from diffusers import StableDiffusionXLImg2ImgPipeline | |
>>> from diffusers.utils import load_image | |
>>> pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained( | |
... "stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16 | |
... ) | |
>>> pipe = pipe.to("cuda") | |
>>> url = "https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/aa_xl/000000009.png" | |
>>> init_image = load_image(url).convert("RGB") | |
>>> prompt = "a photo of an astronaut riding a horse on mars" | |
>>> image = pipe(prompt, image=init_image).images[0] | |
``` | |
""" | |
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 StableDiffusionXLImg2ImgPipelineNoWatermark(DiffusionPipeline, FromSingleFileMixin, LoraLoaderMixin): | |
r""" | |
Pipeline for text-to-image generation using Stable Diffusion. | |
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: | |
- *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`] | |
- *LoRA*: [`loaders.LoraLoaderMixin.load_lora_weights`] | |
- *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`] | |
as well as the following saving methods: | |
- *LoRA*: [`loaders.LoraLoaderMixin.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 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. | |
tokenizer (`CLIPTokenizer`): | |
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`]. | |
""" | |
_optional_components = ["tokenizer", "text_encoder"] | |
def __init__( | |
self, | |
vae: AutoencoderKL, | |
text_encoder: CLIPTextModel, | |
text_encoder_2: CLIPTextModelWithProjection, | |
tokenizer: CLIPTokenizer, | |
tokenizer_2: CLIPTokenizer, | |
unet: UNet2DConditionModel, | |
scheduler: KarrasDiffusionSchedulers, | |
requires_aesthetics_score: bool = False, | |
force_zeros_for_empty_prompt: bool = True, | |
): | |
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.register_to_config(requires_aesthetics_score=requires_aesthetics_score) | |
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) | |
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) | |
# 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 invoked, 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 to save a large amount of memory and to allow the processing of 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 invoked, this method will go back to | |
computing decoding in one step. | |
""" | |
self.vae.disable_tiling() | |
def enable_sequential_cpu_offload(self, gpu_id=0): | |
r""" | |
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, | |
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a | |
`torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called. | |
Note that offloading happens on a submodule basis. Memory savings are higher than with | |
`enable_model_cpu_offload`, but performance is lower. | |
""" | |
if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"): | |
from accelerate import cpu_offload | |
else: | |
raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` 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) | |
for cpu_offloaded_model in [self.unet, self.text_encoder_2, self.vae]: | |
cpu_offload(cpu_offloaded_model, device) | |
if self.text_encoder is not None: | |
cpu_offload(self.text_encoder, device) | |
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 | |
@property | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device | |
def _execution_device(self): | |
r""" | |
Returns the device on which the pipeline's models will be executed. After calling | |
`pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module | |
hooks. | |
""" | |
if not hasattr(self.unet, "_hf_hook"): | |
return self.device | |
for module in self.unet.modules(): | |
if ( | |
hasattr(module, "_hf_hook") | |
and hasattr(module._hf_hook, "execution_device") | |
and module._hf_hook.execution_device is not None | |
): | |
return torch.device(module._hf_hook.execution_device) | |
return self.device | |
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt | |
def encode_prompt( | |
self, | |
prompt, | |
device: Optional[torch.device] = None, | |
num_images_per_prompt: int = 1, | |
do_classifier_free_guidance: bool = True, | |
negative_prompt=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 | |
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`). | |
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: | |
# textual inversion: procecss multi-vector tokens if necessary | |
prompt_embeds_list = [] | |
for tokenizer, text_encoder in zip(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] | |
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) | |
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 "" | |
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] | |
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_embeds_list = [] | |
for tokenizer, text_encoder in zip(tokenizers, text_encoders): | |
# textual inversion: procecss multi-vector tokens if necessary | |
if isinstance(self, TextualInversionLoaderMixin): | |
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, tokenizer) | |
max_length = prompt_embeds.shape[1] | |
uncond_input = tokenizer( | |
uncond_tokens, | |
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] | |
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=text_encoder.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 | |
) | |
# 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 | |
negative_prompt_embeds_list.append(negative_prompt_embeds) | |
negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1) | |
bs_embed = pooled_prompt_embeds.shape[0] | |
pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( | |
bs_embed * num_images_per_prompt, -1 | |
) | |
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, | |
strength, | |
num_inference_steps, | |
callback_steps, | |
negative_prompt=None, | |
prompt_embeds=None, | |
negative_prompt_embeds=None, | |
): | |
if strength < 0 or strength > 1: | |
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}") | |
if num_inference_steps is None: | |
raise ValueError("`num_inference_steps` cannot be None.") | |
elif not isinstance(num_inference_steps, int) or num_inference_steps <= 0: | |
raise ValueError( | |
f"`num_inference_steps` has to be a positive integer but is {num_inference_steps} of type" | |
f" {type(num_inference_steps)}." | |
) | |
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 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)}") | |
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." | |
) | |
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}." | |
) | |
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) | |
else: | |
t_start = int(round(denoising_start * num_inference_steps)) | |
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] | |
return timesteps, num_inference_steps - t_start | |
def prepare_latents( | |
self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None, add_noise=True | |
): | |
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)): | |
raise ValueError( | |
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}" | |
) | |
# Offload text encoder if `enable_model_cpu_offload` was enabled | |
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: | |
self.text_encoder_2.to("cpu") | |
torch.cuda.empty_cache() | |
image = image.to(device=device, dtype=dtype) | |
batch_size = batch_size * num_images_per_prompt | |
if image.shape[1] == 4: | |
init_latents = image | |
else: | |
# make sure the VAE is in float32 mode, as it overflows in float16 | |
image = image.float() | |
self.vae.to(dtype=torch.float32) | |
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." | |
) | |
elif isinstance(generator, list): | |
init_latents = [ | |
self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size) | |
] | |
init_latents = torch.cat(init_latents, dim=0) | |
else: | |
init_latents = self.vae.encode(image).latent_dist.sample(generator) | |
self.vae.to(dtype) | |
init_latents = init_latents.to(dtype) | |
init_latents = self.vae.config.scaling_factor * init_latents | |
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0: | |
# expand init_latents for batch_size | |
additional_image_per_prompt = batch_size // init_latents.shape[0] | |
init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0) | |
elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0: | |
raise ValueError( | |
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts." | |
) | |
else: | |
init_latents = torch.cat([init_latents], dim=0) | |
if add_noise: | |
shape = init_latents.shape | |
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) | |
# get latents | |
init_latents = self.scheduler.add_noise(init_latents, noise, timestep) | |
latents = init_latents | |
return latents | |
def _get_add_time_ids( | |
self, original_size, crops_coords_top_left, target_size, aesthetic_score, negative_aesthetic_score, dtype | |
): | |
if self.config.requires_aesthetics_score: | |
add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,)) | |
add_neg_time_ids = list(original_size + 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(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 | |
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( | |
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`." | |
) | |
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 | |
@torch.no_grad() | |
@replace_example_docstring(EXAMPLE_DOC_STRING) | |
def __call__( | |
self, | |
prompt: Union[str, List[str]] = None, | |
image: Union[ | |
torch.FloatTensor, | |
PIL.Image.Image, | |
np.ndarray, | |
List[torch.FloatTensor], | |
List[PIL.Image.Image], | |
List[np.ndarray], | |
] = None, | |
strength: float = 0.3, | |
num_inference_steps: int = 50, | |
denoising_start: Optional[float] = None, | |
denoising_end: Optional[float] = None, | |
guidance_scale: float = 5.0, | |
negative_prompt: Optional[Union[str, List[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: Tuple[int, int] = None, | |
crops_coords_top_left: Tuple[int, int] = (0, 0), | |
target_size: Tuple[int, int] = None, | |
aesthetic_score: float = 6.0, | |
negative_aesthetic_score: float = 2.5, | |
): | |
r""" | |
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. | |
image (`torch.FloatTensor` or `PIL.Image.Image` or `np.ndarray` or `List[torch.FloatTensor]` or `List[PIL.Image.Image]` or `List[np.ndarray]`): | |
The image(s) to modify with the pipeline. | |
strength (`float`, *optional*, defaults to 0.3): | |
Conceptually, indicates how much to transform 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 `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_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. For example, if `denoising_start` is set to 0.7 and | |
num_inference_steps is fixed at 50, the process will begin only from the 35th (i.e., 0.7 * 50) | |
denoising step. Consequently, the initial part of the denoising process is skipped and it is assumed | |
that the passed `image` is a partly denoised image. 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. For instance, if denoising_end is set to | |
0.7 and `num_inference_steps` is fixed at 50, the process will execute only 35 (i.e., 0.7 * 50) | |
denoising steps. As a result, the returned sample will still retain a substantial amount of noise (ca. | |
30%) and should be denoised by a successor pipeline that has `denoising_start` set to 0.7 so that it | |
only denoised the final 30%. 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 7.5): | |
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. | |
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_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.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.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.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)): | |
TODO | |
crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): | |
TODO | |
target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): | |
TODO | |
aesthetic_score (`float`, *optional*, defaults to 6.0): | |
TODO | |
negative_aesthetic_score (`float`, *optional*, defaults to 2.5): | |
TDOO | |
Examples: | |
Returns: | |
[`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] or `tuple`: | |
[`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a | |
`tuple. When returning a tuple, the first element is a list with the generated images, and the second | |
element is a list of `bool`s denoting whether the corresponding generated image likely represents | |
"not-safe-for-work" (nsfw) content, according to the `safety_checker`. | |
""" | |
# 1. Check inputs. Raise error if not correct | |
self.check_inputs( | |
prompt, | |
strength, | |
num_inference_steps, | |
callback_steps, | |
negative_prompt, | |
prompt_embeds, | |
negative_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 | |
text_encoder_lora_scale = ( | |
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, | |
device, | |
num_images_per_prompt, | |
do_classifier_free_guidance, | |
negative_prompt, | |
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, | |
) | |
# 4. Preprocess image | |
image = self.image_processor.preprocess(image) | |
# 5. Prepare timesteps | |
original_num_steps = num_inference_steps # save for denoising_start/end later | |
self.scheduler.set_timesteps(num_inference_steps, device=device) | |
timesteps, num_inference_steps = self.get_timesteps( | |
num_inference_steps, strength, device, denoising_start=denoising_start | |
) | |
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) | |
add_noise = True if denoising_start is None else False | |
# 6. Prepare latent variables | |
latents = self.prepare_latents( | |
image, | |
latent_timestep, | |
batch_size, | |
num_images_per_prompt, | |
prompt_embeds.dtype, | |
device, | |
generator, | |
add_noise, | |
) | |
# 7. Prepare extra step kwargs. | |
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) | |
height, width = latents.shape[-2:] | |
height = height * self.vae_scale_factor | |
width = width * self.vae_scale_factor | |
original_size = original_size or (height, width) | |
target_size = target_size or (height, width) | |
# 8. Prepare added time ids & embeddings | |
add_text_embeds = pooled_prompt_embeds | |
add_time_ids, add_neg_time_ids = self._get_add_time_ids( | |
original_size, | |
crops_coords_top_left, | |
target_size, | |
aesthetic_score, | |
negative_aesthetic_score, | |
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_neg_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) | |
# 9. Denoising loop | |
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) | |
# 9.1 Apply denoising_end | |
if denoising_end is not None and denoising_start is not None: | |
if denoising_start >= denoising_end: | |
raise ValueError( | |
f"`denoising_end`: {denoising_end} cannot be larger than `denoising_start`: {denoising_start}." | |
) | |
skipped_final_steps = int(round((1 - denoising_end) * original_num_steps)) | |
num_inference_steps = num_inference_steps - skipped_final_steps | |
timesteps = timesteps[: num_warmup_steps + self.scheduler.order * num_inference_steps] | |
elif denoising_end is not None: | |
num_inference_steps = int(round(denoising_end * num_inference_steps)) | |
timesteps = timesteps[: num_warmup_steps + self.scheduler.order * 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 | |
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(latents.dtype) | |
self.vae.decoder.conv_in.to(latents.dtype) | |
self.vae.decoder.mid_block.to(latents.dtype) | |
else: | |
latents = latents.float() | |
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) | |
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) |
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